Research

22 pages, 2595 KiB

Open AccessArticle

Engineering Design and Evaluation of the Process Evaluation Method of Auto Repair Professional Training in Virtual Reality Environment

by Qifeng Xiang, Feiyue Qiu, Jiayue Wang, Jingran Zhang, Junyi Zhu, Lijia Zhu and Guodao Zhang

Appl. Sci. 2022, 12(23), 12200; https://doi.org/10.3390/app122312200 - 29 Nov 2022

Cited by 1 | Viewed by 1320

Abstract

The rapid development of information technology and Internet technology has a far-reaching impact on vocational education. It is possible to accurately and objectively evaluate the training of learners by recording the process data of learners’ realization. The teaching evaluation of traditional vocational skill [...] Read more.

The rapid development of information technology and Internet technology has a far-reaching impact on vocational education. It is possible to accurately and objectively evaluate the training of learners by recording the process data of learners’ realization. The teaching evaluation of traditional vocational skill training requires time, workforce, and educational resources. Due to the limitations of experimental conditions, it is easy to ignore the procedural characteristics of skill training and difficult to implement the procedural evaluation. Based on the above problems, combined with virtual reality and the parts of vocationally skilled auto repair training specialty, using machine learning methods, engineering design of process evaluation method for skilled auto repair training, and takes the secondary vocational auto repair specialty as an example, constructs an evaluation index model based on KSA theoretical model, and evaluates three dimensions: knowledge acquisition, skill mastery, and ability cultivation (knowledge, skill, ability, KSA). The experimental verification of the process evaluator is carried out in the theoretical training evaluation auto repair system (TTE) based on virtual reality. The experimental results can effectively evaluate the practical training of students. The research results of this paper provide a new perspective and reference for the learning evaluation of skill-based training majors. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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15 pages, 2859 KiB

Open AccessArticle

A Partial Multiplicative Dimensional Reduction-Based Reliability Estimation Method for Probabilistic and Non-Probabilistic Hybrid Structural Systems

by Xuyong Chen, Yuanlin Peng, Zhifeng Xu and Qiaoyun Wu

Appl. Sci. 2022, 12(18), 9383; https://doi.org/10.3390/app12189383 - 19 Sep 2022

Viewed by 1141

Abstract

A new reliability estimation method based on partial multiplicative dimensional reduction is proposed for probabilistic and non-probabilistic hybrid structural systems. The proposed method is characterized by decorrelating interval input variables from random input variables using the partial multiplicative dimensional reduction method in conjunction [...] Read more.

A new reliability estimation method based on partial multiplicative dimensional reduction is proposed for probabilistic and non-probabilistic hybrid structural systems. The proposed method is characterized by decorrelating interval input variables from random input variables using the partial multiplicative dimensional reduction method in conjunction with the weakest-link theory. In this method, the failure statistics of the original performance function are equivalent to a statical chain of two elements, in which one of the two elements represents the failures due to random input variables and the other represents the failures due to interval variables. Rather than yielding an estimated interval of failure probability, the proposed method produces a single value for failure probability, which is more meaningful for engineering. In addition, the accuracy, validity, and superiority of the proposed method are demonstrated, and the error-related properties of the proposed method are investigated. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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20 pages, 3684 KiB

Open AccessArticle

Recommendation Algorithm for Multi-Task Learning with Directed Graph Convolutional Networks

by Lifeng Yin, Jianzheng Lu, Guanghai Zheng, Huayue Chen and Wu Deng

Appl. Sci. 2022, 12(18), 8956; https://doi.org/10.3390/app12188956 - 06 Sep 2022

Cited by 1 | Viewed by 1473

Abstract

As an important branch of machine learning, recommendation algorithms have attracted the attention of many experts and scholars. The current recommendation algorithms all more or less have problems such as cold start and single recommended items. In order to overcome these problems and [...] Read more.

As an important branch of machine learning, recommendation algorithms have attracted the attention of many experts and scholars. The current recommendation algorithms all more or less have problems such as cold start and single recommended items. In order to overcome these problems and improve the accuracy of personalized recommendation algorithms, this paper proposes a recommendation for multi-task learning based on directed graph convolutional network (referred to as MTL-DGCNR) and applies it to recommended areas for e-commerce. First, the user’s micro-behavior is constructed and converted into directed graph structure data for model embedding. It can fully consider the embedding of first-order proximity nodes and second-order proximity nodes, which can effectively enhance the transformation ability of features. Secondly, this model adopts the multi-task learning method, and uses knowledge graph embedding to effectively deal with the one-to-many or many-to-many relationship between users and commodities. Finally, it is verified by experiments that MTL-DGCNR has a higher interpretability and accuracy in the field of e-commerce recommendation than other recommendation models. The ranking evaluation experiments, various training methods comparison experiments, and controlling parameter experiments are designed from multiple perspectives to verify the rationality of MTL-DGCNR. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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26 pages, 7932 KiB

Open AccessArticle

A Novel K-Means Clustering Method for Locating Urban Hotspots Based on Hybrid Heuristic Initialization

by Yiping Li, Xiangbing Zhou, Jiangang Gu, Ke Guo and Wu Deng

Appl. Sci. 2022, 12(16), 8047; https://doi.org/10.3390/app12168047 - 11 Aug 2022

Cited by 4 | Viewed by 2343

Abstract

With rapid economic and demographic growth, traffic conditions in medium and large cities are becoming extremely congested. Numerous metropolitan management organizations hope to promote the coordination of traffic and urban development by formulating and improving traffic development strategies. The effectiveness of these solutions [...] Read more.

With rapid economic and demographic growth, traffic conditions in medium and large cities are becoming extremely congested. Numerous metropolitan management organizations hope to promote the coordination of traffic and urban development by formulating and improving traffic development strategies. The effectiveness of these solutions depends largely on an accurate assessment of the distribution of urban hotspots (centers of traffic activity). In recent years, many scholars have employed the K-Means clustering technique to identify urban hotspots, believing it to be efficient. K-means clustering is a sort of iterative clustering analysis. When the data dimensionality is large and the sample size is enormous, the K-Means clustering algorithm is sensitive to the initial clustering centers. To mitigate the problem, a hybrid heuristic “fuzzy system-particle swarm-genetic” algorithm, named FPSO-GAK, is employed to obtain better initial clustering centers for the K-Means clustering algorithm. The clustering results are evaluated and analyzed using three-cluster evaluation indexes (SC, SP and SSE) and two-cluster similarity indexes (CI and CSI). A taxi GPS dataset and a multi-source dataset were employed to test and validate the effectiveness of the proposed algorithm in comparison to the Random Swap clustering algorithm (RS), Genetic K-means algorithm (GAK), Particle Swarm Optimization (PSO) based K-Means, PSO based constraint K-Means, PSO based Weighted K-Means, PSO-GA based K-Means and K-Means++ algorithms. The comparison findings demonstrate that the proposed algorithm can achieve better clustering results, as well as successfully acquire urban hotspots. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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12 pages, 3887 KiB

Open AccessArticle

An Improved Robust Fractal Image Compression Based on M-Estimator

by Penghe Huang, Dongyan Li and Huimin Zhao

Appl. Sci. 2022, 12(15), 7533; https://doi.org/10.3390/app12157533 - 27 Jul 2022

Cited by 2 | Viewed by 1480

Abstract

In this paper, a robust fractal image compression method based on M-estimator is presented. The proposed method applies the M-estimator to the parameter estimation in the fractal encoding procedure using Huber and Tukey’s robust statistics. The M-estimation reduces the influence of the outliers [...] Read more.

In this paper, a robust fractal image compression method based on M-estimator is presented. The proposed method applies the M-estimator to the parameter estimation in the fractal encoding procedure using Huber and Tukey’s robust statistics. The M-estimation reduces the influence of the outliers and makes the fractal encoding algorithm robust to the noisy image. Meanwhile, the quadtree partitioning approach has been used in the proposed methods to improve the efficiency of the encoding algorithm, and some unnecessary computations are eliminated in the parameter estimation procedures. The experimental results demonstrate that the proposed method is insensitive to the outliers in the noisy corrupted image. The comparative data shows that the proposed method is superior in both the encoding time and the quality of retrieved images over other robust fractal compression algorithms. The proposed algorithm is useful for multimedia and image archiving, low-cost consumption applications and progressive image transmission of live images, and in reducing computing time for fractal image compression. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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26 pages, 2328 KiB

Open AccessArticle

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

by Lingling Zhang, Yinjun Fu, Yan Wei, Huiling Chen, Chunyu Xia and Zhennao Cai

Appl. Sci. 2022, 12(14), 6907; https://doi.org/10.3390/app12146907 - 07 Jul 2022

Cited by 3 | Viewed by 1672

Abstract

College students are the group with the most entrepreneurial vitality and potential. How to cultivate their entrepreneurial and innovative ability is one of the important and urgent issues facing this current social development. This paper proposes a reliable, intelligent prediction model of entrepreneurial [...] Read more.

College students are the group with the most entrepreneurial vitality and potential. How to cultivate their entrepreneurial and innovative ability is one of the important and urgent issues facing this current social development. This paper proposes a reliable, intelligent prediction model of entrepreneurial intentions, providing theoretical support for guiding college students’ positive entrepreneurial intentions. The model mainly uses the improved crow search algorithm (CSA) to optimize the kernel extreme learning machine (KELM) model with feature selection (FS), namely CSA-KELM-FS, to study entrepreneurial intention. To obtain the best fitting model and key features, the gradient search rule, local escaping operator, and levy flight mutation (GLL) mechanism are introduced to enhance the CSA (GLLCSA), and FS is used to extract the key features. To verify the performance of the proposed GLLCSA, it is compared with eight other state-of-the-art methods. Further, the GLLCSA-KELM-FS model and five other machine learning methods have been used to predict the entrepreneurial intentions of 842 students from the Wenzhou Vocational College in Zhejiang, China, in the past five years. The results show that the proposed model can correctly predict the students’ entrepreneurial intention with an accuracy rate of 93.2% and excellent stability. According to the prediction results of the proposed model, the key factors affecting the student’s entrepreneurial intention are mainly the major studied, campus innovation, entrepreneurship practice experience, and positive personality. Therefore, the proposed GLLCSA-KELM-FS is expected to be an effective tool for predicting students’ entrepreneurial intentions. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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17 pages, 3470 KiB

Open AccessArticle

An Enhanced Artificial Electric Field Algorithm with Sine Cosine Mechanism for Logistics Distribution Vehicle Routing

by Hongyu Zheng, Juan Gao, Juxia Xiong, Guanglei Yao, Hongjiang Cui and Lirong Zhang

Appl. Sci. 2022, 12(12), 6240; https://doi.org/10.3390/app12126240 - 19 Jun 2022

Cited by 8 | Viewed by 1359

Abstract

Aiming at the scheduling problem of logistics distribution vehicles, an enhanced artificial electric field algorithm (SC-AEFA) based on the sine cosine mechanism is proposed. The development of the SC-AEFA was as follows. First, a map grid model for enterprise logistics distribution vehicle path [...] Read more.

Aiming at the scheduling problem of logistics distribution vehicles, an enhanced artificial electric field algorithm (SC-AEFA) based on the sine cosine mechanism is proposed. The development of the SC-AEFA was as follows. First, a map grid model for enterprise logistics distribution vehicle path planning was established. Then, an enhanced artificial electric field algorithm with the sine cosine mechanism was developed to simulate the logistics distribution vehicle scheduling, establish the logistics distribution vehicle movement law model, and plan the logistics distribution vehicle scheduling path. Finally, a distribution business named fresh enterprise A in the Fuzhou Strait Agricultural and Sideline Products Trading Market was selected to test the effectiveness of the method proposed. The theoretical proof and simulation test results show that the SC-AEFA has a good optimization ability and a strong path planning ability for enterprise logistics vehicle scheduling, which can improve the scheduling ability and efficiency of logistics distribution vehicles and save transportation costs. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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17 pages, 2385 KiB

Open AccessArticle

Adaptive Model Output Following Control for a Networked Thermostat

by Hongjun Li, Yingrui Jin, Ping Liu, Jun Yu, Ran Zhao, Xuebin Yue and Shengjun Wen

Appl. Sci. 2022, 12(12), 6084; https://doi.org/10.3390/app12126084 - 15 Jun 2022

Viewed by 1104

Abstract

The model of a networked temperature control system is easily affected by its surrounding environment. Because of that, it is hard to identify an accurate model. This paper proposes an adaptive model output following control based on system identification for a networked thermostat [...] Read more.

The model of a networked temperature control system is easily affected by its surrounding environment. Because of that, it is hard to identify an accurate model. This paper proposes an adaptive model output following control based on system identification for a networked thermostat system. First, the time-varying system model is built via some thermal laws, whose parameters are identified based on the least-squares method (LSM). The time delay is transferred to deterministic by setting the data buffer. The system stability is ensured by a feedback controller. Meanwhile, an adaptive model output following controller with a command generator tracker (CGT) is designed to adjust the forward control input based on system identification. Finally, the effectiveness of the proposed method is illustrated by simulation and experimental results. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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24 pages, 7594 KiB

Open AccessArticle

Multi-Objective Optimization Design of 6-UPS Parallel Mechanism Based on Taguchi Method and Entropy-Weighted Gray Relational Analysis

by Hao Song, Xiaoliang Chen, Shuai Zhang and Liyou Xu

Appl. Sci. 2022, 12(12), 5836; https://doi.org/10.3390/app12125836 - 08 Jun 2022

Cited by 3 | Viewed by 1434

Abstract

Nowadays, parallel mechanisms are widely used in many fields because of their excellent structural performance. In order to improve the comprehensive performance of 6-UPS parallel mechanism, this article proposes a multi-objective optimization design method for parallel mechanism based on the Taguchi method and [...] Read more.

Nowadays, parallel mechanisms are widely used in many fields because of their excellent structural performance. In order to improve the comprehensive performance of 6-UPS parallel mechanism, this article proposes a multi-objective optimization design method for parallel mechanism based on the Taguchi method and entropy-weighted gray relational analysis (EGRA) method. By establishing a parametric model of the 6-UPS parallel mechanism, taking the peak force on the drive pair of the drive branch chain of the mechanism, the minimum value of the projection angle of the body-fixed coordinate system (BCS) relative to the inertial coordinate system (ICS), and the minimum value of the average projected angle of the BCS relative to the ICS as the objective functions, the relationship between the design variables and the objective function is investigated under the condition that the constraints are satisfied. Using the optimization method proposed in this article, the multi-objective optimization problem is transformed into a single-objective optimization problem based on gray relational grade (GRG). Compared with the non-optimized 6-UPS parallel mechanism, the simulation results show that the peak force on the drive pair of the drive branch chain is reduced by 17.73%, and the minimum value of the projected angle and the minimum value of the average projected angle of the BCS relative to the ICS are increased by 27.36% and 36.17%, respectively, which effectively improves the load-bearing capacity and motion range of the 6-UPS parallel mechanism and verifies the reliability of the optimized design method. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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25 pages, 5983 KiB

Open AccessArticle

Fractional-Order PI^λD^μ Controller Using Adaptive Neural Fuzzy Model for Course Control of Underactuated Ships

by Guangyu Li, Baojie Chen, Huayue Chen and Wu Deng

Appl. Sci. 2022, 12(11), 5604; https://doi.org/10.3390/app12115604 - 31 May 2022

Cited by 5 | Viewed by 1249

Abstract

For the uncertainty caused by the time-varying modeling parameters with the sailing speed in the course control of underactuated ships, a novel identification method based on an adaptive neural fuzzy model (ANFM) is proposed to approximate the inverse dynamic characteristics of the ship [...] Read more.

For the uncertainty caused by the time-varying modeling parameters with the sailing speed in the course control of underactuated ships, a novel identification method based on an adaptive neural fuzzy model (ANFM) is proposed to approximate the inverse dynamic characteristics of the ship in this paper. This model adjusts both its own structure and parameters as it learns, and is able to automatically partition the input space, determine the number of membership functions and the number of fuzzy rules. The trained ANFM is used as an inverse controller, in parallel with a fractional-order PI^λD^μ controller for the course control of underactuated ships. Meanwhile, the sine wave curve and the sawtooth wave curve are considered as the input learning samples of ANFM, respectively, and the inverse dynamics simulation experiments of the ship are carried out. Two different ANFM structures are obtained, which are connected in parallel with the fractional-order PI^λD^μ controller respectively to control the course of ship. The simulation results show that the proposed method can effectively overcome the influence of uncertainty of ship modeling parameters, track the desired course quickly and effectively, and has a good control effect. Finally, comparative experiments of four different controllers are carried out, and the results show that the FO PI^λD^μ controller using ANFM has the advantages of small overshoot, short adjustment time, and precise control. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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16 pages, 543 KiB

Open AccessArticle

Anomaly Detection in Log Files Using Selected Natural Language Processing Methods

by Piotr Ryciak, Katarzyna Wasielewska and Artur Janicki

Appl. Sci. 2022, 12(10), 5089; https://doi.org/10.3390/app12105089 - 18 May 2022

Cited by 5 | Viewed by 5748

Abstract

In this article, we address the problem of detecting anomalies in system log files. Computer systems generate huge numbers of events, which are noted in event log files. While most of them report normal actions, an unusual entry may inform about a failure [...] Read more.

In this article, we address the problem of detecting anomalies in system log files. Computer systems generate huge numbers of events, which are noted in event log files. While most of them report normal actions, an unusual entry may inform about a failure or malware infection. A human operator may easily miss such an entry; therefore, anomaly detection methods are used for this purpose. In our work, we used an approach known from the natural language processing (NLP) domain, which operates on so-called embeddings, that is vector representations of words or phrases. We describe an improved version of the LogEvent2Vec algorithm, proposed in 2020. In contrast to the original version, we propose a significant shortening of the analysis window, which both increased the accuracy of anomaly detection and made further analysis of suspicious sequences much easier. We experimented with various binary classifiers, such as decision trees or multilayer perceptrons (MLPs), and the Blue Gene/L dataset. We showed that selecting an optimal classifier (in this case, MLP) and a short log sequence gave very good results. The improved version of the algorithm yielded the best F1-score of 0.997, compared to 0.886 in the original version of the algorithm. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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31 pages, 5324 KiB

Open AccessArticle

Tool for Predicting College Student Career Decisions: An Enhanced Support Vector Machine Framework

by Zhuang Wang, Guoxi Liang and Huiling Chen

Appl. Sci. 2022, 12(9), 4776; https://doi.org/10.3390/app12094776 - 09 May 2022

Cited by 6 | Viewed by 2347

Abstract

The goal of this research is to offer an effective intelligent model for forecasting college students’ career decisions in order to give a useful reference for career decisions and policy formation by relevant departments. The suggested prediction model is mainly based on a [...] Read more.

The goal of this research is to offer an effective intelligent model for forecasting college students’ career decisions in order to give a useful reference for career decisions and policy formation by relevant departments. The suggested prediction model is mainly based on a support vector machine (SVM) that has been modified using an enhanced butterfly optimization approach with a communication mechanism and Gaussian bare-bones mechanism (CBBOA). To get a better set of parameters and feature subsets, first, we added a communication mechanism to BOA to improve its global search capability and balance exploration and exploitation trends. Then, Gaussian bare-bones was added to increase the population diversity of BOA and its ability to jump out of the local optimum. The optimal SVM model (CBBOA-SVM) was then developed to predict the career decisions of college students based on the obtained parameters and feature subsets that are already optimized by CBBOA. In order to verify the effectiveness of CBBOA, we compared it with some advanced algorithms on all benchmark functions of CEC2014. Simulation results demonstrated that the performance of CBBOA is indeed more comprehensive. Meanwhile, comparisons between CBBOA-SVM and other machine learning approaches for career decision prediction were carried out, and the findings demonstrate that the provided CBBOA-SVM has better classification and more stable performance. As a result, it is plausible to conclude that the CBBOA-SVM is capable of being an effective tool for predicting college student career decisions. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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19 pages, 3036 KiB

Open AccessArticle

Performance Prediction of Rolling Bearing Using EEMD and WCDPSO-KELM Methods

by Xiumei Li and Huimin Zhao

Appl. Sci. 2022, 12(9), 4676; https://doi.org/10.3390/app12094676 - 06 May 2022

Cited by 2 | Viewed by 1298

Abstract

Research on bearings performance degradation trend is significant, and can greatly reduce the loss caused by potential faults in the whole life-cycle of rolling bearings. It is also a very important part of Prognostic and Health Management (PHM). This paper proposed a new [...] Read more.

Research on bearings performance degradation trend is significant, and can greatly reduce the loss caused by potential faults in the whole life-cycle of rolling bearings. It is also a very important part of Prognostic and Health Management (PHM). This paper proposed a new performance degradation prediction method based on optimized kernel extreme learning machine (KELM), improved particle swarm optimization (PSO) and Ensemble Empirical Mode Decomposition (EEMD). Firstly, the particle swarm optimization algorithm was improved by adjusting the inertia weight and linear learning factor and introducing a disturbance term, namely WCDPSO. Then, the penalty coefficient and kernel parameters of KELM were optimized by the WCDPSO, and the WCDPSO-KELM model was obtained. Subsequently, the EEMD method was used to extract original features from sample data, and a performance degradation index is selected from the EEMD feature space, which was input into the WCDPSO-KELM model in order to build a bearing performance degradation prediction trend model. Finally, the proposed method was verified by datasets of rolling bearings that were provided by the PRONOSTIA platform. Experimental results confirmed that the proposed method can efficiently predict the performance degradation trend of rolling bearings. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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15 pages, 1196 KiB

Open AccessArticle

Session-Enhanced Graph Neural Network Recommendation Model (SE-GNNRM)

by Lifeng Yin, Pengyu Chen and Guanghai Zheng

Appl. Sci. 2022, 12(9), 4314; https://doi.org/10.3390/app12094314 - 24 Apr 2022

Cited by 1 | Viewed by 1670

Abstract

Session-based recommendation aims to predict anonymous user actions. Many existing session recommendation models do not fully consider the impact of similar sessions on recommendation performance. Graph neural networks can better capture the conversion relationship of items within a session, but some intra-session conversion [...] Read more.

Session-based recommendation aims to predict anonymous user actions. Many existing session recommendation models do not fully consider the impact of similar sessions on recommendation performance. Graph neural networks can better capture the conversion relationship of items within a session, but some intra-session conversion relationships are not conducive to recommendation, which requires model learning more representative session embeddings. To solve these problems, an improved session-enhanced graph neural network recommendation model, namely SE-GNNRM, is proposed in this paper. In our model, the complex transitions relationship of items and more representative item features are captured through graph neural network and self-attention mechanism in the encoding stage. Then, the attention mechanism is employed to combine short-term and long-term preferences to construct a global session graph and capture similar session information by using a graph attention network fused with similarity. In order to prove the effectiveness of the constructed SE-GNNRM model, three public data sets are selected here. The experiment results show that the SE-GNNRM outperforms the existing baseline models and is an effective model for session-based recommendation. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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12 pages, 489 KiB

Open AccessArticle

The Impacts of Medical Resources on Emerging Self-Limiting Infectious Diseases

by Dayong Zhou, Liyan Gao, Qiuhui Pan and Mingfeng He

Appl. Sci. 2022, 12(9), 4255; https://doi.org/10.3390/app12094255 - 22 Apr 2022

Cited by 1 | Viewed by 1513

Abstract

The spread of emerging self-limiting infectious diseases is closely related to medical resources. This paper introduces the concept of safe medical resources, i.e., the minimum medical resources that are needed to prevent the overburden of medical resources, and explores the impacts of medical [...] Read more.

The spread of emerging self-limiting infectious diseases is closely related to medical resources. This paper introduces the concept of safe medical resources, i.e., the minimum medical resources that are needed to prevent the overburden of medical resources, and explores the impacts of medical resources on the spread of emerging self-limiting infectious diseases. The results showed that when the isolation rate of hospitalized patients who have mild infections is low, increasing the isolation rate of patients with severe infections requires safe more medical resources. On the contrary, when the isolation rate of hospitalized patients with mild infections is at a high level, increasing the isolation rate of patients with severe infections results in a decrease in safe medical resources. Furthermore, when the isolation rates of patients with mild and severe infections increase simultaneously, safe medical resources decrease gradually. That is to say, when the medical resources are at a low level, it is more necessary to improve the isolation rates of infected individuals so as to avoid the phenomenon of overburdened medical resources and control the spread of emerging infectious diseases. In addition, overwhelmed medical resources increase the number of deaths. Meanwhile, for different emerging self-limiting infectious diseases, as long as the recovery periods are the same, safe medical resources also remain the same. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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14 pages, 3871 KiB

Open AccessArticle

A Novel Image Recognition Method Based on DenseNet and DPRN

by Lifeng Yin, Pujiang Hong, Guanghai Zheng, Huayue Chen and Wu Deng

Appl. Sci. 2022, 12(9), 4232; https://doi.org/10.3390/app12094232 - 22 Apr 2022

Cited by 7 | Viewed by 2163

Abstract

Image recognition is one of the important branches of computer vision, which has important theoretical and practical significance. For the insufficient use of features, the single type of convolution kernel and the incomplete network optimization problems in densely connected networks (DenseNet), a novel [...] Read more.

Image recognition is one of the important branches of computer vision, which has important theoretical and practical significance. For the insufficient use of features, the single type of convolution kernel and the incomplete network optimization problems in densely connected networks (DenseNet), a novel image recognition method based on DenseNet and deep pyramidal residual networks (DPRN) is proposed in this paper. In the proposed method, a new residual unit based on DPRN is designed, and the idea of a pyramid residual unit is introduced, which makes the input greater than the output. Then, a module based on dilated convolution is designed for parallel feature extraction. Finally, the designed module is fused with DenseNet in order to construct the image recognition model. This model not only overcomes some of the existing problems in DenseNet, but also has the same general applicability as DensenNet. The CIFAR10 and CIFAR100 are selected to prove the effectiveness of the proposed method. The experiment results show that the proposed method can effectively reuse features and has obtained accuracy rates of 83.98 and 51.19%, respectively. It is an effective method for dealing with images in different fields. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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18 pages, 6820 KiB

Open AccessArticle

Multi-Objective Profile Design Optimization to Minimize Wear Damage and Surface Fatigue of City Train Wheel

by Chang-Yong Song and Ha-Yong Choi

Appl. Sci. 2022, 12(8), 3940; https://doi.org/10.3390/app12083940 - 13 Apr 2022

Viewed by 1601

Abstract

Wear and fatigue of wheels have a great effect on the maintenance of railway vehicles and running safety. In the case of an urban railway where no rail lubrication system is installed, it is reported that the risk of wheel damage is high [...] Read more.

Wear and fatigue of wheels have a great effect on the maintenance of railway vehicles and running safety. In the case of an urban railway where no rail lubrication system is installed, it is reported that the risk of wheel damage is high in curved sections. In the present study, we intended to present a method of designing a wheel profile of city trains that can minimize wear and fatigue in curved sections, using the multi-objective optimization method. In multi-objective optimization, we explored a wheel profile design that can reduce wear and fatigue of wheels at the same time, while also satisfying the design performance constraints, such as the safety against derailment and contact force between rails and wheels. A multi-body dynamic analysis was conducted for design performance evaluation, and the best wheel profile design was produced utilizing the analysis result. A wheel profile with minimized wear, a wheel profile with minimized surface fatigue, and a wheel profile with both minimized wear and surface fatigue that can improve the performance of city train wheels were presented respectively using a Pareto-optimal Solution, which is the result of multi-objective optimization. The running safety performances such as derailment and lateral force of the optimized wheel profiles showed improved characteristics when compared to those of the initial wheel profile. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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19 pages, 1038 KiB

Open AccessArticle

Automatic Bug Triaging via Deep Reinforcement Learning

by Yong Liu, Xuexin Qi, Jiali Zhang, Hui Li, Xin Ge and Jun Ai

Appl. Sci. 2022, 12(7), 3565; https://doi.org/10.3390/app12073565 - 31 Mar 2022

Cited by 7 | Viewed by 3114

Abstract

Software maintenance and evolution account for approximately 90% of the software development process (e.g., implementation, testing, and maintenance). Bug triaging refers to an activity where developers diagnose, fix, test, and document bug reports during software development and maintenance to improve the speed of [...] Read more.

Software maintenance and evolution account for approximately 90% of the software development process (e.g., implementation, testing, and maintenance). Bug triaging refers to an activity where developers diagnose, fix, test, and document bug reports during software development and maintenance to improve the speed of bug repair and project progress. However, the large number of bug reports submitted daily increases the triaging workload, and open-source software has a long maintenance cycle. Meanwhile, the developer activity is not stable and changes significantly during software development. Hence, we propose a novel bug triaging model known as auto bug triaging via deep reinforcement learning (BT-RL), which comprises two models: a deep multi-semantic feature (DMSF) fusion model and an online dynamic matching (ODM) model. In the DMSF model, we extract relevant information from bug reports to obtain high-quality feature representation. In the ODM model, through bug report analysis and developer activities, we use a strategy based on the reinforcement learning framework, through which we perform training while learning and recommend developers for bug reports. Extensive experiments on open-source datasets show that the BT-RL method outperforms state-of-the-art methods in bug triaging. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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19 pages, 5721 KiB

Open AccessArticle

Fractional-Order Controller for Course-Keeping of Underactuated Surface Vessels Based on Frequency Domain Specification and Improved Particle Swarm Optimization Algorithm

by Guangyu Li, Yanxin Li, Huayue Chen and Wu Deng

Appl. Sci. 2022, 12(6), 3139; https://doi.org/10.3390/app12063139 - 18 Mar 2022

Cited by 73 | Viewed by 3284

Abstract

In this paper, a new fractional-order (FO) PI^λD^µ controller is designed with the desired gain and phase margin for the automatic rudder of underactuated surface vessels (USVs). The integral order λ and the differential order μ are introduced in the [...] Read more.

In this paper, a new fractional-order (FO) PI^λD^µ controller is designed with the desired gain and phase margin for the automatic rudder of underactuated surface vessels (USVs). The integral order λ and the differential order μ are introduced in the controller, and the two additional adjustable factors make the FO PI^λD^µ controller have better accuracy and robustness. Simulations are carried out for comparison with a ship’s digital PID autopilot. The results show that the FO PI^λD^µ controller has the advantages of a small overshoot, short adjustment time, and precise control. Due to the uncertainty of the model parameters of USVs and two extra parameters, it is difficult to compute the parameters of an FO PI^λD^µ controller. Secondly, this paper proposes a novel particle swarm optimization (PSO) algorithm for dynamic adjustment of the FO PI^λD^µ controller parameters. By dynamically changing the learning factor, the particles carefully search in their own neighborhoods at the early stage of the algorithm to prevent them from missing the global optimum and converging on the local optimum, while at the later stage of evolution, the particles converge on the global optimal solution quickly and accurately to speed up PSO convergence. Finally, comparative experiments of four different controllers under different sailing conditions are carried out, and the results show that the FO PI^λD^µ controller based on the IPSO algorithm has the advantages of a small overshoot, short adjustment time, precise control, and strong anti-disturbance control. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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25 pages, 7160 KiB

Open AccessArticle

Reliability Analysis of Military Vehicles Based on Censored Failures Data

by Mateusz Oszczypała, Jarosław Ziółkowski and Jerzy Małachowski

Appl. Sci. 2022, 12(5), 2622; https://doi.org/10.3390/app12052622 - 03 Mar 2022

Cited by 12 | Viewed by 1908

Abstract

The paper proposes a methodology of reliability testing as applied to vehicles used in military transport systems. After estimating the value of the reliability function using the Kaplan–Meier estimator, reliability models were developed and analysed. The neural model, which achieved the value of [...] Read more.

The paper proposes a methodology of reliability testing as applied to vehicles used in military transport systems. After estimating the value of the reliability function using the Kaplan–Meier estimator, reliability models were developed and analysed. The neural model, which achieved the value of the correlation coefficient R exceeding 0.99, was determined to fit the empirical data the best. On the basis of the approximated reliability function of several models, the reliability characteristics of the tested sample of vehicles were determined. Plots of the failure probability density function for all three models had similar courses over a significant part of the function domain. A failure intensity function was also determined, which varied between models. For the exponential and Weibull model, the expected mileage between failures was calculated, which proved impossible for the neural model. The proposed methodology is capable of modelling reliability characteristics based on the observation of an assumed period of the exploitation process of the selected group of military vehicles. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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10 pages, 1354 KiB

Open AccessArticle

Correction and Fitting Civil Aviation Flight Data EGT Based on RPM: Polynomial Least Squares Analysis

by Nongtian Chen, Youchao Sun, Zongpeng Wang and Chong Peng

Appl. Sci. 2022, 12(5), 2545; https://doi.org/10.3390/app12052545 - 28 Feb 2022

Cited by 4 | Viewed by 1550

Abstract

There are different missing flight data due to various reasons in the process of acquisition and storage, especially in general aviation, which cause inconvenience for flight data analysis. Effectively explaining the relationship between flight data parameters and selecting a simple and effective method [...] Read more.

There are different missing flight data due to various reasons in the process of acquisition and storage, especially in general aviation, which cause inconvenience for flight data analysis. Effectively explaining the relationship between flight data parameters and selecting a simple and effective method for fitting and correcting flight data suitable for engineering applications are the main points of the paper. Herein, a convenient and applicable approach of missing data correction and fitting based on the least squares polynomial method is introduced in this work. Firstly, the polynomial fitting model based on the least squares method is used to establish multi-order polynomial by existing flight data since the order of the least squares polynomial has a direct impact on the fitting effect. The order is too high or too small, over-fitting or deviation will occur, resulting in improper data. Therefore, the optimization and selection of the model order are significant for flight data correction and fitting. Because the flight data of the aircraft engine exhaust gas temperature (EGT) are often lost because of the immature detection technology, a series of the multi-order polynomial are established by the relationship of aircraft engine exhaust gas temperature and Revolutions Per Minute (RPM). Case study results confirm the optimal model order is four for the fitting and correction of aircraft engine exhaust temperature, and the least squares polynomial method is applicable and effective for EGT flight data correction and fitting based on RPM data. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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19 pages, 1349 KiB

Open AccessArticle

TD-Based Adaptive Output Feedback Control of Ship Heading with Stochastic Noise and Unknown Actuator Dead-Zone Input

by Yanli Liu, Runzhi Wang, Yuechao Zhao and Dongdong Mu

Appl. Sci. 2022, 12(4), 1985; https://doi.org/10.3390/app12041985 - 14 Feb 2022

Viewed by 1139

Abstract

To meet the demand of ship control, a new heading control tactic is explored using switching theory. Different from the previous results, the stochastic noise and switched control are considered in the Norrbin nonlinear mathematical model concurrently to discuss ship heading control. Then, [...] Read more.

To meet the demand of ship control, a new heading control tactic is explored using switching theory. Different from the previous results, the stochastic noise and switched control are considered in the Norrbin nonlinear mathematical model concurrently to discuss ship heading control. Then, for the resulting systems, the adaptive control issue is addressed, while the dead-zone input is embedded and unknown. By establishing switched state observers for the corresponding subsystems, the conservatism of the common state observer can be reduced greatly, and the analysis can be achieved under the switching signal satisfying the average dwell time (ADT). The “explosion of terms” problem occurring in the backstepping technique is well remedied via an innovative tracking differentiator (TD) technology, which is an innovation in itself. According to the theory proof, under the developed control tactic, the resulting signals are then to be bounded in probability, with the tracking goal being achieved well. The theoretical design result was analyzed, and the corresponding validity is given through simulation experiments. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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19 pages, 6480 KiB

Open AccessArticle

An Intelligent Diagnosis Method for Machine Fault Based on Federated Learning

by Zhinong Li, Zedong Li, Yunlong Li, Junyong Tao, Qinghua Mao and Xuhui Zhang

Appl. Sci. 2021, 11(24), 12117; https://doi.org/10.3390/app112412117 - 20 Dec 2021

Cited by 13 | Viewed by 2869

Abstract

In engineering, the fault data unevenly distribute and difficultly share, which causes that the existing fault diagnosis methods cannot recognize the newly added fault types. An intelligent diagnosis method for machine fault is proposed based on federated learning. Firstly, the local fault diagnosis [...] Read more.

In engineering, the fault data unevenly distribute and difficultly share, which causes that the existing fault diagnosis methods cannot recognize the newly added fault types. An intelligent diagnosis method for machine fault is proposed based on federated learning. Firstly, the local fault diagnosis models diagnosing the existing fault data and the newly added fault data are established by deep convolutional neural network. Then, the weight parameters of local models are fused into global model parameters by federated learning. Finally, the global model parameters are transmitted to each local model. Therefore, each local model update into a global shared model which can recognize the newly added fault types. The proposed method is verified by bearing data. Compared with the traditional model, which can only diagnose the existing fault data but cannot recognize newly added fault types, the federated fault diagnosis model fusing weight parameters can diagnose newly added faults without exchanging the data, and the accuracy is 100%. The proposed method provides an effective method to solve the poor sharing of fault data and poor generalization of fault diagnosis model for mechanical equipment. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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18 pages, 5804 KiB

Open AccessArticle

Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

by Hongjiang Cui, Ying Guan and Wu Deng

Appl. Sci. 2021, 11(23), 11480; https://doi.org/10.3390/app112311480 - 03 Dec 2021

Cited by 13 | Viewed by 1567

Abstract

Aiming at the problems of poor decomposition quality and the extraction effect of a weak signal with strong noise by empirical mode decomposition (EMD), a novel fault diagnosis method based on cascaded adaptive second-order tristable stochastic resonance (CASTSR) and EMD is proposed in [...] Read more.

Aiming at the problems of poor decomposition quality and the extraction effect of a weak signal with strong noise by empirical mode decomposition (EMD), a novel fault diagnosis method based on cascaded adaptive second-order tristable stochastic resonance (CASTSR) and EMD is proposed in this paper. In the proposed method, low-frequency interference components are filtered by using high-pass filtering, and the restriction conditions of stochastic resonance theory are solved by using an ordinary variable-scale method. Then, a chaotic ant colony optimization algorithm with a global optimization ability is employed to adaptively adjust the parameters of the second-order tristable stochastic resonance system to obtain the optimal stochastic resonance, and noise reduction pretreatment technology based on CASTSR is developed to enhance the weak signal characteristics of low frequency. Next, the EMD is employed to decompose the denoising signal and extract the characteristic frequency from the intrinsic mode function (IMF), so as to realize the fault diagnosis of rolling bearings. Finally, the numerical simulation signal and actual bearing fault data are selected to prove the validity of the proposed method. The experiment results indicate that the proposed fault diagnosis method can enhance the decomposition quality of the EMD, effectively extract features of weak signals, and improve the accuracy of fault diagnosis. Therefore, the proposed fault diagnosis method is an effective fault diagnosis method for rotating machinery. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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23 pages, 863 KiB

Open AccessArticle

Tri-Partition Alphabet-Based State Prediction for Multivariate Time-Series

by Zuo-Cheng Wen, Zhi-Heng Zhang, Xiang-Bing Zhou, Jian-Gang Gu, Shao-Peng Shen, Gong-Suo Chen and Wu Deng

Appl. Sci. 2021, 11(23), 11294; https://doi.org/10.3390/app112311294 - 29 Nov 2021

Cited by 1 | Viewed by 1502

Abstract

Recently, predicting multivariate time-series (MTS) has attracted much attention to obtain richer semantics with similar or better performances. In this paper, we propose a tri-partition alphabet-based state (tri-state) prediction method for symbolic MTSs. First, for each variable, the set of all symbols, i.e., [...] Read more.

Recently, predicting multivariate time-series (MTS) has attracted much attention to obtain richer semantics with similar or better performances. In this paper, we propose a tri-partition alphabet-based state (tri-state) prediction method for symbolic MTSs. First, for each variable, the set of all symbols, i.e., alphabets, is divided into strong, medium, and weak using two user-specified thresholds. With the tri-partitioned alphabet, the tri-state takes the form of a matrix. One order contains the whole variables. The other is a feature vector that includes the most likely occurring strong, medium, and weak symbols. Second, a tri-partition strategy based on the deviation degree is proposed. We introduce the piecewise and symbolic aggregate approximation techniques to polymerize and discretize the original MTS. This way, the symbol is stronger and has a bigger deviation. Moreover, most popular numerical or symbolic similarity or distance metrics can be combined. Third, we propose an along–across similarity model to obtain the k-nearest matrix neighbors. This model considers the associations among the time stamps and variables simultaneously. Fourth, we design two post-filling strategies to obtain a completed tri-state. The experimental results from the four-domain datasets show that (1) the tri-state has greater recall but lower precision; (2) the two post-filling strategies can slightly improve the recall; and (3) the along–across similarity model composed by the Triangle and Jaccard metrics are first recommended for new datasets. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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25 pages, 8167 KiB

Open AccessArticle

A Study on Anti-Shock Performance of Marine Diesel Engine Based on Multi-Body Dynamics and Elastohydrodynamic Lubrication

by Liang Chen, Dongxin Xue, Xigeng Song, Zhaoqi He and Dongjie Huang

Appl. Sci. 2021, 11(23), 11259; https://doi.org/10.3390/app112311259 - 27 Nov 2021

Cited by 5 | Viewed by 1788

Abstract

Diesel engine anti-shock performance is important for navy ships. The calculation method is a fast and economic way compared to underwater explosion trial in this field. Researchers of diesel engine anti-shock performance mainly use the spring damping model to simulate the main bearings [...] Read more.

Diesel engine anti-shock performance is important for navy ships. The calculation method is a fast and economic way compared to underwater explosion trial in this field. Researchers of diesel engine anti-shock performance mainly use the spring damping model to simulate the main bearings of a diesel engine. The elastohydrodynamic lubrication method has been continuously used in the main bearings of diesel engines in normal working conditions. This research aims at using the elastohydrodynamic lubrication method in the main bearings of the diesel engine in external shock conditions. The main bearing elastohydrodynamic lubrication and diesel engine multi-body dynamics analysis is based on AVL EXCITE Power Unite software. The external shock is equivalent to the interference on the elastohydrodynamic lubrication calculation. Whether the elastohydrodynamic lubrication algorithm can complete the calculation under interference is the key to the study. By adopting a very small calculation step size, a high number of iterations, and increasing the stiffness of the thrust bearing, the elastohydrodynamic lubrication algorithm can be successfully completed under the external impact environment. The calculation results of the accelerations on engine block feet have a similar trend as the experiment results. Diesel engines with and without shock absorbers in external shock conditions are calculated. This calculation model can also be used for diesel engine dynamics calculations and main bearing lubrication calculations under normal working conditions. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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21 pages, 5058 KiB

Open AccessArticle

A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots

by Xiaojuan Ran, Xiangbing Zhou, Mu Lei, Worawit Tepsan and Wu Deng

Appl. Sci. 2021, 11(23), 11202; https://doi.org/10.3390/app112311202 - 25 Nov 2021

Cited by 159 | Viewed by 7447

Abstract

With the development of cities, urban congestion is nearly an unavoidable problem for almost every large-scale city. Road planning is an effective means to alleviate urban congestion, which is a classical non-deterministic polynomial time (NP) hard problem, and has become an important research [...] Read more.

With the development of cities, urban congestion is nearly an unavoidable problem for almost every large-scale city. Road planning is an effective means to alleviate urban congestion, which is a classical non-deterministic polynomial time (NP) hard problem, and has become an important research hotspot in recent years. A K-means clustering algorithm is an iterative clustering analysis algorithm that has been regarded as an effective means to solve urban road planning problems by scholars for the past several decades; however, it is very difficult to determine the number of clusters and sensitively initialize the center cluster. In order to solve these problems, a novel K-means clustering algorithm based on a noise algorithm is developed to capture urban hotspots in this paper. The noise algorithm is employed to randomly enhance the attribution of data points and output results of clustering by adding noise judgment in order to automatically obtain the number of clusters for the given data and initialize the center cluster. Four unsupervised evaluation indexes, namely, DB, PBM, SC, and SSE, are directly used to evaluate and analyze the clustering results, and a nonparametric Wilcoxon statistical analysis method is employed to verify the distribution states and differences between clustering results. Finally, five taxi GPS datasets from Aracaju (Brazil), San Francisco (USA), Rome (Italy), Chongqing (China), and Beijing (China) are selected to test and verify the effectiveness of the proposed noise K-means clustering algorithm by comparing the algorithm with fuzzy C-means, K-means, and K-means plus approaches. The compared experiment results show that the noise algorithm can reasonably obtain the number of clusters and initialize the center cluster, and the proposed noise K-means clustering algorithm demonstrates better clustering performance and accurately obtains clustering results, as well as effectively capturing urban hotspots. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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22 pages, 5670 KiB

Open AccessArticle

A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation

by Xiaoxu Yang, Jie Liu, Yi Liu, Peng Xu, Ling Yu, Lei Zhu, Huayue Chen and Wu Deng

Appl. Sci. 2021, 11(23), 11192; https://doi.org/10.3390/app112311192 - 25 Nov 2021

Cited by 23 | Viewed by 2203

Abstract

Aiming at the problems of the basic sparrow search algorithm (SSA) in terms of slow convergence speed and the ease of falling into the local optimum, the chaotic mapping strategy, adaptive weighting strategy and t-distribution mutation strategy are introduced to develop a novel [...] Read more.

Aiming at the problems of the basic sparrow search algorithm (SSA) in terms of slow convergence speed and the ease of falling into the local optimum, the chaotic mapping strategy, adaptive weighting strategy and t-distribution mutation strategy are introduced to develop a novel adaptive sparrow search algorithm, namely the CWTSSA in this paper. In the proposed CWTSSA, the chaotic mapping strategy is employed to initialize the population in order to enhance the population diversity. The adaptive weighting strategy is applied to balance the capabilities of local mining and global exploration, and improve the convergence speed. An adaptive t-distribution mutation operator is designed, which uses the iteration number t as the degree of freedom parameter of the t-distribution to improve the characteristic of global exploration and local exploration abilities, so as to avoid falling into the local optimum. In order to prove the effectiveness of the CWTSSA, 15 standard test functions and other improved SSAs, differential evolution (DE), particle swarm optimization (PSO), gray wolf optimization (GWO) are selected here. The compared experiment results indicate that the proposed CWTSSA can obtain higher convergence accuracy, faster convergence speed, better diversity and exploration abilities. It provides a new optimization algorithm for solving complex optimization problems. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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24 pages, 2049 KiB

Open AccessArticle

TDGVRPSTW of Fresh Agricultural Products Distribution: Considering Both Economic Cost and Environmental Cost

by Daqing Wu and Chenxiang Wu

Appl. Sci. 2021, 11(22), 10579; https://doi.org/10.3390/app112210579 - 10 Nov 2021

Cited by 6 | Viewed by 1707

Abstract

The time-dependent vehicle routing problem of time windows of fresh agricultural products distribution have been studied by considering both economic cost and environmental cost. A calculation method for road travel time across time periods is designed in this study. A freshness measure function [...] Read more.

The time-dependent vehicle routing problem of time windows of fresh agricultural products distribution have been studied by considering both economic cost and environmental cost. A calculation method for road travel time across time periods is designed in this study. A freshness measure function of agricultural products and a measure function of carbon emission rate are employed by considering time-varying vehicle speeds, fuel consumptions, carbon emissions, perishable agricultural products, customers’ time windows, and minimum freshness. A time-dependent green vehicle routing problem with soft time windows (TDGVRPSTW) model is formulated. The object of the TDGVRPSTW model is to minimize the sum of economic cost and environmental cost. According to the characteristics of the model, a new variable neighborhood adaptive genetic algorithm is designed, which integrates the global search ability of the genetic algorithm and the local search ability of the variable neighborhood descent algorithm. Finally, the experimental data show that the proposed approaches effectively avoid traffic congestions, reduce total distribution costs, and promote energy conservation and emission reduction. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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25 pages, 5055 KiB

Open AccessFeature PaperArticle

An Improved Image Filtering Algorithm for Mixed Noise

by Chun He, Ke Guo and Huayue Chen

Appl. Sci. 2021, 11(21), 10358; https://doi.org/10.3390/app112110358 - 04 Nov 2021

Cited by 8 | Viewed by 2135

Abstract

In recent years, image filtering has been a hot research direction in the field of image processing. Experts and scholars have proposed many methods for noise removal in images, and these methods have achieved quite good denoising results. However, most methods are performed [...] Read more.

In recent years, image filtering has been a hot research direction in the field of image processing. Experts and scholars have proposed many methods for noise removal in images, and these methods have achieved quite good denoising results. However, most methods are performed on single noise, such as Gaussian noise, salt and pepper noise, multiplicative noise, and so on. For mixed noise removal, such as salt and pepper noise + Gaussian noise, although some methods are currently available, the denoising effect is not ideal, and there are still many places worthy of improvement and promotion. To solve this problem, this paper proposes a filtering algorithm for mixed noise with salt and pepper + Gaussian noise that combines an improved median filtering algorithm, an improved wavelet threshold denoising algorithm and an improved Non-local Means (NLM) algorithm. The algorithm makes full use of the advantages of the median filter in removing salt and pepper noise and demonstrates the good performance of the wavelet threshold denoising algorithm and NLM algorithm in filtering Gaussian noise. At first, we made improvements to the three algorithms individually, and then combined them according to a certain process to obtain a new method for removing mixed noise. Specifically, we adjusted the size of window of the median filtering algorithm and improved the method of detecting noise points. We improved the threshold function of the wavelet threshold algorithm, analyzed its relevant mathematical characteristics, and finally gave an adaptive threshold. For the NLM algorithm, we improved its Euclidean distance function and the corresponding distance weight function. In order to test the denoising effect of this method, salt and pepper + Gaussian noise with different noise levels were added to the test images, and several state-of-the-art denoising algorithms were selected to compare with our algorithm, including K-Singular Value Decomposition (KSVD), Non-locally Centralized Sparse Representation (NCSR), Structured Overcomplete Sparsifying Transform Model with Block Cosparsity (OCTOBOS), Trilateral Weighted Sparse Coding (TWSC), Block Matching and 3D Filtering (BM3D), and Weighted Nuclear Norm Minimization (WNNM). Experimental results show that our proposed algorithm is about 2–7 dB higher than the above algorithms in Peak Signal-Noise Ratio (PSNR), and also has better performance in Root Mean Square Error (RMSE), Structural Similarity (SSIM), and Feature Similarity (FSIM). In general, our algorithm has better denoising performance, better restoration of image details and edge information, and stronger robustness than the above-mentioned algorithms. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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22 pages, 1515 KiB

Open AccessArticle

BiTTM: A Core Biterms-Based Topic Model for Targeted Analysis

by Jiamiao Wang, Ling Chen, Lei Li and Xindong Wu

Appl. Sci. 2021, 11(21), 10162; https://doi.org/10.3390/app112110162 - 29 Oct 2021

Cited by 1 | Viewed by 1629

Abstract

While most of the existing topic models perform a full analysis on a set of documents to discover all topics, it is noticed recently that in many situations users are interested in fine-grained topics related to some specific aspects only. As a result, [...] Read more.

While most of the existing topic models perform a full analysis on a set of documents to discover all topics, it is noticed recently that in many situations users are interested in fine-grained topics related to some specific aspects only. As a result, targeted analysis (or focused analysis) has been proposed to address this problem. Given a corpus of documents from a broad area, targeted analysis discovers only topics related with user-interested aspects that are expressed by a set of user-provided query keywords. Existing approaches for targeted analysis suffer from problems such as topic loss and topic suppression because of their inherent assumptions and strategies. Moreover, existing approaches are not designed to address computation efficiency, while targeted analysis is supposed to provide responses to user queries as soon as possible. In this paper, we propose a core BiTerms-based Topic Model (BiTTM). By modelling topics from core biterms that are potentially relevant to the target query, on one hand, BiTTM captures the context information across documents to alleviate the problem of topic loss or suppression; on the other hand, our proposed model enables the efficient modelling of topics related to specific aspects. Our experiments on nine real-world datasets demonstrate BiTTM outperforms existing approaches in terms of both effectiveness and efficiency. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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11 pages, 17764 KiB

Open AccessArticle

Layout Design and Die Casting Using CAE Simulation for Household Appliances

by Hong-Kyu Kwon

Appl. Sci. 2021, 11(21), 10128; https://doi.org/10.3390/app112110128 - 28 Oct 2021

Viewed by 2582

Abstract

Due to the development and industrialization of science and technology, aluminum alloys have been developed in various fields. Recently, the government has been pursuing ways to decrease the weight and increase the recyclability of various components in order to conserve resources, energy, and [...] Read more.

Due to the development and industrialization of science and technology, aluminum alloys have been developed in various fields. Recently, the government has been pursuing ways to decrease the weight and increase the recyclability of various components in order to conserve resources, energy, and the environmental. In keeping with this trend, cast iron products are being replaced by aluminum products in the foundry industry by using high-pressure die casting (HPDC). Casting layout design, relies on the experience and knowledge of mold designers in the casting industry, which proves insufficient to respond to the rapidly changing needs of the era and to increasing production costs. Designing and producing casting layouts using CAD/CAM/CAE technology has become a critical issue. Computer-Aided Engineering (CAE) technology is rapidly increasing with the development of computer software and hardware. CAE technology not only predicts defects in mass production but also performs filling or solidification analysis during the mold design stage before production, enabling optimal mold design methods. New technologies that combine the emerging casting processes of filling and solidification analysis using computer simulation with existing technology and practical experience in the field are rapidly increasing in the foundry industry. Based on empirical knowledge, the layout and design of casting products has traditionally progressed through trial and error. The solutions achieved through scientific calculation and analysis using CAE technology can save a great deal of money and time in the building of die-casting molds and in their design and fabrication. In this study, numerical analysis of household appliances (cooking grills) quickly and accurately predicts problems arising from the filling and solidification of the melted metal in the casting process, thereby ensuring the quality of the final cast product. These results can be used to quickly establish a sound casting layout with reduced production costs. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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22 pages, 1271 KiB

Open AccessArticle

Early Robust Design—Its Effect on Parameter and Tolerance Optimization

by Stefan Goetz, Martin Roth and Benjamin Schleich

Appl. Sci. 2021, 11(20), 9407; https://doi.org/10.3390/app11209407 - 11 Oct 2021

Cited by 9 | Viewed by 2378

Abstract

The development of complex products with high quality in dynamic markets requires appropriate robust design and tolerancing workflows supporting the entire product development process. Despite the large number of methods and tools available for designers and tolerance engineers, there are hardly any consistent [...] Read more.

The development of complex products with high quality in dynamic markets requires appropriate robust design and tolerancing workflows supporting the entire product development process. Despite the large number of methods and tools available for designers and tolerance engineers, there are hardly any consistent approaches that are applicable throughout all development stages. This is mainly due to the break between the primarily qualitative approaches for the concept stage and the quantitative parameter and tolerance design activities in subsequent stages. Motivated by this, this paper bridges the gap between these two different views by contrasting the used terminology and methods. Moreover, it studies the effects of early robust design decisions with a focus on Suh’s Axiomatic Design axioms on later parameter and tolerance optimization. Since most robust design activities in concept design can be ascribed to these axioms, this allows reliable statements about the specific benefits of early robust design decisions on the entire process considering variation in product development for the first time. The presented effects on the optimization of nominal design parameters and their tolerance values are shown by means of a case study based on ski bindings. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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15 pages, 6246 KiB

Open AccessArticle

A Novel Fault Feature Extraction Method for Bearing Rolling Elements Using Optimized Signal Processing Method

by Weihan Li, Yang Li, Ling Yu, Jian Ma, Lei Zhu, Lingfeng Li, Huayue Chen and Wu Deng

Appl. Sci. 2021, 11(19), 9095; https://doi.org/10.3390/app11199095 - 29 Sep 2021

Cited by 2 | Viewed by 1348

Abstract

A rolling element signal has a long transmission path in the acquisition process. The fault feature of the rolling element signal is more difficult to be extracted. Therefore, a novel weak fault feature extraction method using optimized variational mode decomposition with kurtosis mean [...] Read more.

A rolling element signal has a long transmission path in the acquisition process. The fault feature of the rolling element signal is more difficult to be extracted. Therefore, a novel weak fault feature extraction method using optimized variational mode decomposition with kurtosis mean (KMVMD) and maximum correlated kurtosis deconvolution based on power spectrum entropy and grid search (PGMCKD), namely KMVMD-PGMCKD, is proposed. In the proposed KMVMD-PGMCKD method, a VMD with kurtosis mean (KMVMD) is proposed. Then an adaptive parameter selection method based on power spectrum entropy and grid search for MCKD, namely PGMCKD, is proposed to determine the deconvolution period T and filter order L. The complementary advantages of the KMVMD and PGMCKD are integrated to construct a novel weak fault feature extraction model (KMVMD-PGMCKD). Finally, the power spectrum is employed to deal with the obtained signal by KMVMD-PGMCKD to effectively implement feature extraction. Bearing rolling element signals of Case Western Reserve University and actual rolling element data are selected to prove the validity of the KMVMD-PGMCKD. The experiment results show that the KMVMD-PGMCKD can effectively extract the fault features of bearing rolling elements and accurately diagnose weak faults under variable working conditions. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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16 pages, 5245 KiB

Open AccessArticle

Reliability Analysis of C⁴ISR Systems Based on Goal-Oriented Methodology

by Yifan Li, Hong-Zhong Huang and Tingyu Zhang

Appl. Sci. 2021, 11(14), 6335; https://doi.org/10.3390/app11146335 - 08 Jul 2021

Cited by 3 | Viewed by 1807

Abstract

Hard-and-software integrated systems such as command and control systems (C⁴ISR systems) are typical systems that are comprised of both software and hardware, the failures of such devices result from complicated common cause failures and common (or shared) signals that make classical [...] Read more.

Hard-and-software integrated systems such as command and control systems (C⁴ISR systems) are typical systems that are comprised of both software and hardware, the failures of such devices result from complicated common cause failures and common (or shared) signals that make classical reliability analysis methods will be not applicable. To this end, this paper applies the Goal-Oriented (GO) methodology to detailed analyze the reliability of a C⁴ISR system. The reliability as well as the failure probability of the C⁴ISR system, are reached based on the GO model constructed. At the component level, the reliability of units of the C⁴ISR system is computed. Importance analysis of failures of such a system is completed by the qualitative analysis capability of the GO model, by which critical failures of hardware failures like communication module failures and motherboard module failures as well as software failures like network module application software failures and decompression module software failures are ascertained. This method of this paper contributes to the reliability analysis of all hard-and-software integrated systems. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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22 pages, 7001 KiB

Open AccessArticle

A Study on Learning Parameters in Application of Radial Basis Function Neural Network Model to Rotor Blade Design Approximation

by Chang-Yong Song

Appl. Sci. 2021, 11(13), 6133; https://doi.org/10.3390/app11136133 - 01 Jul 2021

Cited by 2 | Viewed by 1973

Abstract

Meta-model sre generally applied to approximate multi-objective optimization, reliability analysis, reliability based design optimization, etc., not only in order to improve the efficiencies of numerical calculation and convergence, but also to facilitate the analysis of design sensitivity. The radial basis function neural network [...] Read more.

Meta-model sre generally applied to approximate multi-objective optimization, reliability analysis, reliability based design optimization, etc., not only in order to improve the efficiencies of numerical calculation and convergence, but also to facilitate the analysis of design sensitivity. The radial basis function neural network (RBFNN) is the meta-model employing hidden layer of radial units and output layer of linear units, and characterized by relatively fast training, generalization and compact type of networks. It is important to minimize some scattered noisy data to approximate the design space to prevent local minima in the gradient based optimization or the reliability analysis using the RBFNN. Since the noisy data must be smoothed out in order for the RBFNN to be applied as the meta-model to any actual structural design problem, the smoothing parameter must be properly determined. This study aims to identify the effect of various learning parameters including the spline smoothing parameter on the RBFNN performance regarding the design approximation. An actual rotor blade design problem was considered to investigate the characteristics of RBFNN approximation with respect to the range of spline smoothing parameter, the number of training data, and the number of hidden layers. In the RBFNN approximation of the rotor blade design, design sensitivity characteristics such as main effects were also evaluated including the performance analysis according to the variation of learning parameters. From the evaluation results of learning parameters in the rotor blade design, it was found that the number of training data had larger influence on the RBFNN meta-model accuracy than the spline smoothing parameter while the number of hidden layers had little effect on the performances of RBFNN meta-model. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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18 pages, 3231 KiB

Open AccessArticle

Meta-Models and Genetic Algorithm Application to Approximate Optimization with Discrete Variables for Fire Resistance Design of A60 Class Bulkhead Penetration Piece

by Woo Chang Park and Chang Yong Song

Appl. Sci. 2021, 11(7), 2972; https://doi.org/10.3390/app11072972 - 26 Mar 2021

Cited by 9 | Viewed by 3460

Abstract

A60 class bulkhead penetration piece is a fire-resistance apparatus installed on bulkhead compartments to protect lives and to prevent flame diffusion in case of fire accident in ships and offshore plants. In this study, approximate optimization with discrete variables was carried out for [...] Read more.

A60 class bulkhead penetration piece is a fire-resistance apparatus installed on bulkhead compartments to protect lives and to prevent flame diffusion in case of fire accident in ships and offshore plants. In this study, approximate optimization with discrete variables was carried out for the fire-resistance design of an A60 class bulkhead penetration piece (A60 BPP) using various meta-models and multi-island genetic algorithms. Transient heat transfer analysis was carried out to evaluate the fire-resistance design of the A60 class bulkhead penetration piece, and we verified the results of the analysis via a fire test. The design of the experiment’s method was applied to generate the meta-models to be used for the approximate optimization, and the verified results of the transient heat transfer analysis were integrated with the design of the experiment’s method. The meta-models used in the approximate optimization were response surface model, Kriging, and radial basis function-based neural network. In the approximate optimization, the bulkhead penetration piece length, diameter, material type, and insulation density were applied to discrete design variables, and constraints that were considered include temperature, productivity, and cost. The approximate optimum design problem based on the meta-model was formulated such that the discrete design variables were determined by minimizing the weight of the A60 class bulkhead penetration piece subject to the limit values of constraints. In the context of approximate accuracy, the solution results from the approximate optimization were compared to actual analysis results. It was concluded that the radial basis function-based neural network, among the meta-models used in the approximate optimization, showed the most accurate optimum design results for the fire-resistance design of the A60 class bulkhead penetration piece. Full article

(This article belongs to the Special Issue Soft Computing Application to Engineering Design)

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