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Machine Learning and Optimization Algorithms for Data Analysis and Other...
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Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Advanced Digital and Other Processes".

Deadline for manuscript submissions: closed (1 December 2023) | Viewed by 33628

Special Issue Editors

Dr. Wentao Ma

E-Mail Website
Guest Editor
School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Interests: machine learning; signal processing; information theoretical learning; renewable energy power forecast power; state estimation
Dr. Xinghua Liu

E-Mail Website
Guest Editor
School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Interests: state estimation; machine learning; control theory
Dr. Jiandong Duan

E-Mail Website
Guest Editor
School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Interests: smart grid; machine learning; optimization algorithm for power system
Dr. Siyuan Peng

E-Mail Website
Guest Editor
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
Interests: machine learning; signal processing; information theoretical learning

Special Issue Information

Dear Colleagues,

This is an era of information explosion, and there are a lot of data in many fields, such as medical data, power data, and behavior data generated after browsing goods on e-commerce platforms, etc. How to extract feature information from a large amount of data, conduct data classification and regression analysis, or make reasonable predictions will play an important role in assisting managers to make correct decisions. Machine learning (ML) and optimization methods as an important and effective tool can be used in analyzing and integrating multiple sources of data to solve important engineering problems and designing suitable machine learning and optimization methods that fit the problem well and leverage large datasets is critical for their success in corresponding fields.

This Special Issue will focus on publishing original research works about machine learning and optimization algorithms for data analysis and other engineering applications, including fast and robust optimization solution methods for various ML algorithms, outstanding hyperparameters selection and optimization approaches for ML algorithms, and the applications for data analysis, and other engineering problems(Pattern recognition, regression, and prediction, etc.) in different fields.

Topics of interest for this Special Issue include but are not limited to:

  • Novel and robust ML methods for data analysis
  • Effective optimization solution method for ML algorithms
  • Group intelligence optimization algorithm for parameters selection and optimization of different ML algorithms

Machine learning and optimization methods for other applications in different engineering fields, such as communication, medical care, electric power, finance, etc. 

Dr. Wentao Ma
Dr. Xinghua Liu
Dr. Jiandong Duan
Dr. Siyuan Peng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning algorithm
  • optimization methods
  • selection and optimization of hyperparameter
  • data analysis
  • ML for classification
  • regression and prediction in other engineering areas

Published Papers (19 papers)

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19 pages, 4232 KiB  
Article
Multi-Step Prediction of Wind Power Based on Hybrid Model with Improved Variational Mode Decomposition and Sequence-to-Sequence Network
by Wangwang Bai, Mengxue Jin, Wanwei Li, Juan Zhao, Bin Feng, Tuo Xie, Siyao Li and Hui Li
Processes 2024, 12(1), 191; https://doi.org/10.3390/pr12010191 - 15 Jan 2024
Cited by 1 | Viewed by 852
Abstract
Due to the complexity of wind power, traditional prediction models are incapable of fully extracting the hidden features of multidimensional strong fluctuation data, which results in poor multi-step prediction performance. To predict continuous power effectively in the future, an improved wind power multi-step [...] Read more.
Due to the complexity of wind power, traditional prediction models are incapable of fully extracting the hidden features of multidimensional strong fluctuation data, which results in poor multi-step prediction performance. To predict continuous power effectively in the future, an improved wind power multi-step prediction model combining variational mode decomposition (VMD) with sequence-to-sequence (Seq2Seq) is proposed. Firstly, the wind power sequence is smoothed using VMD and the decomposition parameters of VMD are optimized by using the squirrel search algorithm (SSA) to effectively optimize the decomposition effect. Then, the subsequence obtained from decomposition, together with the original wind power data, is reconstructed into multivariate time series features. Finally, a Seq2Seq model is constructed, and convolutional neural networks (CNNs) with bidirectional gate recurrent units (BiGRUs) are used to learn the coupling and timing relationships of the input data and encode them. The gate recurrent unit (GRU) is decoded to achieve continuous power prediction. Based on the actual operating data of a wind farm, a case analysis is conducted. Experimental results show that SSA-VMD can effectively optimize the decomposition effect, and the subsequences obtained with its decomposition are highly accurate when applied to predictions. The Seq2Seq model has better multi-step prediction results than traditional prediction methods, and as the prediction step size increases, the advantages are more obvious. Full article
(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)
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22 pages, 2679 KiB  
Article
Research on Diesel Engine Speed Control Based on Improved Salp Algorithm
by Boshun Zeng, Qianqiao Shen, Guiyong Wang, Yuhua Wang, You Zhao, Shuchao He and Xuan Yu
Processes 2023, 11(11), 3092; https://doi.org/10.3390/pr11113092 - 27 Oct 2023
Viewed by 878
Abstract
To better regulate the speed of diesel engines and optimize the speed overshoot and fast response, a speed control method combining the improved salp swarm algorithm (ISSA) with the proportional integral and differential (PID) controller was proposed. A real-time simulation model for a [...] Read more.
To better regulate the speed of diesel engines and optimize the speed overshoot and fast response, a speed control method combining the improved salp swarm algorithm (ISSA) with the proportional integral and differential (PID) controller was proposed. A real-time simulation model for a high-pressure common rail diesel engine was established. Addressing the challenges of the salp swarm algorithm (SSA), such as uneven population distribution and its tendency to become trapped in local optima, logistic-tent chaotic mapping, adaptive parameters was introduced, as well as adaptive dynamic inertia weights, elite strategy, and a dynamic inverse strategy. These enhancements bolstered the algorithm’s precision and efficiency in both global and local searches. Using the enhanced SSA, the parameters of the PID controller for the diesel engine model was optimized. The results indicated that the ISSA offers superior parameter identification precision, strengthening speed control stability. During sudden changes in speed and load, the overshoot decreased by an average of more than 30.3% and more than 8.6%, respectively. Moreover, the settling time decreased by an average of more than 0.76 s and 1.52 s, respectively, significantly enhancing the quality of diesel engine speed control. Full article
(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)
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21 pages, 12222 KiB  
Article
Disconnector Fault Diagnosis Based on Multi-Granularity Contrast Learning
by Qian Xie, Haiyi Tang, Baize Liu, Hui Li, Zhe Wang and Jian Dang
Processes 2023, 11(10), 2981; https://doi.org/10.3390/pr11102981 - 14 Oct 2023
Viewed by 1009
Abstract
Most disconnector fault diagnosis methods have high accuracy in model training. However, it is a challenging task to maintain high accuracy, a faster diagnosis speed, and less computation in practical situations. In this paper, we propose a multi-granularity contrastive learning (MG-CL) framework. First, [...] Read more.
Most disconnector fault diagnosis methods have high accuracy in model training. However, it is a challenging task to maintain high accuracy, a faster diagnosis speed, and less computation in practical situations. In this paper, we propose a multi-granularity contrastive learning (MG-CL) framework. First, the original disconnector current data are transformed into two different but related classes: strongly enhanced and weakly enhanced data, by using the strong and weak enhancement modules. Second, we propose the coarse-grained contrastive learning module to preliminarily judge the possibility of faults by learning the features of strongly/weakly enhanced data. Finally, in order to further judge the fault causes, we propose a fine-grained contrastive learning module. By comparing the differences in the data, the final fault type was judged. Our proposed MG-CL framework shows higher accuracy and speed compared with the previous model. Full article
(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)
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16 pages, 13051 KiB  
Article
Detection of Cotton Seed Damage Based on Improved YOLOv5
by Zhicheng Liu, Long Wang, Zhiyuan Liu, Xufeng Wang, Can Hu and Jianfei Xing
Processes 2023, 11(9), 2682; https://doi.org/10.3390/pr11092682 - 7 Sep 2023
Cited by 3 | Viewed by 1052
Abstract
The quality of cotton seed is of great significance to the production of cotton in the cotton industry. In order to reduce the workload of the manual sorting of cotton seeds and improve the quality of cotton seed sorting, this paper proposed an [...] Read more.
The quality of cotton seed is of great significance to the production of cotton in the cotton industry. In order to reduce the workload of the manual sorting of cotton seeds and improve the quality of cotton seed sorting, this paper proposed an image-detection method of cotton seed damage based on an improved YOLOv5 algorithm. Images of cotton seeds with different degrees of damage were collected in the same environment. Cotton seeds of three different damage degrees, namely, undamaged, slightly damaged, and seriously damaged, were selected as the research objects. Labeling software was used to mark the images of these cotton seeds and the marked images were input into the improved YOLOv5s detection algorithm for appearance-based damage identification. The algorithm added the lightweight upsampling operator CARAFE to the original YOLOv5s detection algorithm and also improved the loss function. The experimental results showed that the mAP_0.5 value of the improved algorithm reached 99.5% and the recall rate reached 99.3% when the uncoated cotton seeds were detected. When detecting coated cotton seeds, the mAP_0.5 value of the improved algorithm reached 99.2% and the recall rate reached 98.9%. Compared with the traditional appearance-based damage detection approach, the improved YOLOv5s proposed in this paper improved the recognition accuracy and processing speed, and exhibited a better adaptability and generalization ability. Therefore, the proposed method can provide a reference for the appearance detection of crop seeds. Full article
(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)
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15 pages, 2833 KiB  
Article
Optimization of Levenberg Marquardt Algorithm Applied to Nonlinear Systems
by Xinyi Huang, Hao Cao and Bingjing Jia
Processes 2023, 11(6), 1794; https://doi.org/10.3390/pr11061794 - 12 Jun 2023
Cited by 6 | Viewed by 2683
Abstract
As science and technology advance, industrial manufacturing processes get more complicated. Back Propagation Neural Network (BPNN) convergence is comparatively slower for processing nonlinear systems. The nonlinear system used in this study to evaluate the optimization of BPNN based on the LM algorithm proved [...] Read more.
As science and technology advance, industrial manufacturing processes get more complicated. Back Propagation Neural Network (BPNN) convergence is comparatively slower for processing nonlinear systems. The nonlinear system used in this study to evaluate the optimization of BPNN based on the LM algorithm proved the algorithm’s efficacy through a MATLAB simulation analysis. This paper examined the application impact of the enhanced approach using the Continuous stirred tank reactor (CSTR) control system as an example. The study’s findings demonstrate that the LM optimization algorithm’s identification error exceeds 10-5. The research’s suggested control approach for reactant concentration CA in CSTR systems provides a better tracking effect and a stronger anti-interference capacity. Compared to the PI control method, the overall control effect is superior. As a result, the optimization model for nonlinear systems has a greatly improved processing accuracy. With some data support for the accuracy study of neural network models and the application of nonlinear systems, the suggested LM-BP optimization algorithm is evidently more appropriate for nonlinear systems. Full article
(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)
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13 pages, 4129 KiB  
Article
Optimal Control of Rural Water Supply Network Based on Intelligent Algorithm
by Bo Wang, Qi Yang, Ruiyang Sun, Zihan Chen and Xiangtian Nie
Processes 2023, 11(4), 1190; https://doi.org/10.3390/pr11041190 - 12 Apr 2023
Viewed by 1175
Abstract
Optimizing Rural Water Supply Network (RWSN) is the basis for improving rural people’s lives and improving people’s health. Currently, the RWSN in China is relatively backward and can no longer meet the needs of the unified management of rural water resources. To optimize [...] Read more.
Optimizing Rural Water Supply Network (RWSN) is the basis for improving rural people’s lives and improving people’s health. Currently, the RWSN in China is relatively backward and can no longer meet the needs of the unified management of rural water resources. To optimize the RWSN, this study innovatively established a Multi-Objective Optimization Mathematical Model (MOMM) of RWSN, combining economic factors and reliability. This experiment first analyzes the characteristics of the RWSN system and then establishes a MOMM of a water supply network. NSGA-II algorithm and LM algorithm are introduced to handle the multi-objective model. The research results show that compared to Web decision tools, the RWSN based on the LM-NSGA-II algorithm can save 5.4% of the total annual cost of water supply pipelines. Therefore, the MOMM of the rural water supply pipeline based on the LM-NSGA-II algorithm has better economy and reliability. The experiment aims to provide certain reference values for the optimal control of RWSN through this study. Full article
(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)
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21 pages, 3115 KiB  
Article
Deep Belief Network with Swarm Spider Optimization Method for Renewable Energy Power Forecasting
by Yuan Wei, Huanchang Zhang, Jiahui Dai, Ruili Zhu, Lihong Qiu, Yuzhuo Dong and Shuai Fang
Processes 2023, 11(4), 1001; https://doi.org/10.3390/pr11041001 - 26 Mar 2023
Cited by 6 | Viewed by 1546
Abstract
Renewable energy power prediction plays a crucial role in the development of renewable energy generation, and it also faces a challenging issue because of the uncertainty and complex fluctuation caused by environmental and climatic factors. In recent years, deep learning has been increasingly [...] Read more.
Renewable energy power prediction plays a crucial role in the development of renewable energy generation, and it also faces a challenging issue because of the uncertainty and complex fluctuation caused by environmental and climatic factors. In recent years, deep learning has been increasingly applied in the time series prediction of new energy, where Deep Belief Networks (DBN) can perform outstandingly for learning of nonlinear features. In this paper, we employed the DBN as the prediction model to forecast wind power and PV power. A novel metaheuristic optimization algorithm, called swarm spider optimization (SSO), was utilized to optimize the parameters of the DBN so as to improve its performance. The SSO is a novel swarm spider behavior based optimization algorithm, and it can be employed for addressing complex optimization and engineering problems. Considering that the prediction performance of the DBN is affected by the number of the nodes in the hidden layer, the SSO is used to optimize this parameter during the training stage of DBN (called SSO-DBN), which can significantly enhance the DBN prediction performance. Two datasets, including wind power and PV power with their influencing factors, were used to evaluate the forecasting performance of the proposed SSO-DBN. We also compared the proposed model with several well-known methods, and the experiment results demonstrate that the proposed prediction model has better stability and higher prediction accuracy in comparison to other methods. Full article
(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)
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13 pages, 5029 KiB  
Article
Real-Time Structure Generation Based on Data-Driven Using Machine Learning
by Ying Wang, Feifei Shi and Bingbing Chen
Processes 2023, 11(3), 802; https://doi.org/10.3390/pr11030802 - 8 Mar 2023
Cited by 1 | Viewed by 1255
Abstract
Topology optimization results are highly dependent on the given design constraints and boundary conditions. Moreover, small changes in initial design conditions can result in different topological configurations, which makes topology optimization time-consuming in a given design constraint domain and inefficient in structural design. [...] Read more.
Topology optimization results are highly dependent on the given design constraints and boundary conditions. Moreover, small changes in initial design conditions can result in different topological configurations, which makes topology optimization time-consuming in a given design constraint domain and inefficient in structural design. To address this problem, a data-driven real-time topology optimization framework and method coupled with machine learning by using a principal component analysis algorithm combined with a feedforward neural network are developed in this paper. Meanwhile, through the offline training, the mapping relationship between initial design conditions and topology optimization results is obtained. From this mapping, we estimate the optimal topologies for novel loading configurations. Numerical examples display that the online prediction results are consistent with the results of the topology optimization method. Furthermore, the network parameters are calibrated, and accurate structure prediction is achieved based on the algorithm. In addition, this method ensures the accuracy of high-resolution structural prediction on the premise of small samples. Full article
(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)
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11 pages, 1267 KiB  
Article
Mechanism of Water Use Behavior of College Students Based on the Improved TPB Model
by Lan Zhang, Xue Bai, Jialin Liu, Yan Bai and Jinxin Guan
Processes 2023, 11(2), 643; https://doi.org/10.3390/pr11020643 - 20 Feb 2023
Viewed by 1478
Abstract
Colleges and universities are a typical service water consumers in China, i.e., with a dense population, single structure, and regular water use. This means it is crucial to strengthen the management of their water use behavior. In this paper, first of all, the [...] Read more.
Colleges and universities are a typical service water consumers in China, i.e., with a dense population, single structure, and regular water use. This means it is crucial to strengthen the management of their water use behavior. In this paper, first of all, the main water devices and water use behavior of students were elucidated by investigating and analyzing the structure and scenarios of water use in colleges and universities. Then, a model of water use behavior of college students with sociodemographic and environmental characteristics was constructed based on the theory of planned behavior (TPB). By investigating and analyzing the water use behavior, the theoretical judgment of the improved TPB model that “behavior is the result of interaction between individual and environmental characteristics” was proved, which provides a reference for studying the water demand requirements of college students and supports scientific water-use management in colleges, our results also help the exploration of potential water-saving solutions in order to construct water-conservative colleges and universities. Full article
(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)
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22 pages, 1563 KiB  
Article
Workers’ Opinions on Using the Internet of Things to Enhance the Performance of the Olive Oil Industry: A Machine Learning Approach
by Ahmed Alsayat and Hossein Ahmadi
Processes 2023, 11(1), 271; https://doi.org/10.3390/pr11010271 - 14 Jan 2023
Cited by 7 | Viewed by 2649
Abstract
Today’s global food supply chains are highly dispersed and complex. The adoption and effective utilization of information technology are likely to increase the efficiency of companies. Because of the broad variety of sensors that are currently accessible, the possibilities for Internet of Things [...] Read more.
Today’s global food supply chains are highly dispersed and complex. The adoption and effective utilization of information technology are likely to increase the efficiency of companies. Because of the broad variety of sensors that are currently accessible, the possibilities for Internet of Things (IoT) applications in the olive oil industry are almost limitless. Although previous studies have investigated the impact of the IoT on the performance of industries, this issue has yet to be explored in the olive oil industry. In this study we aimed to develop a new model to investigate the factors influencing supply chain improvement in olive oil companies. The model was used to evaluate the relationship between supply chain improvement and olive oil companies’ performance. Demand planning, manufacturing, transportation, customer service, warehousing, and inventory management were the main factors incorporated into the proposed model. Self-organizing map (SOM) clustering and decision trees were employed in the development of the method. The data were collected from respondents with knowledge related to integrating new technologies into the industry. The results demonstrated that IoT implementation in olive oil companies significantly improved their performance. Moreover, it was found that there was a positive relationship between supply chain improvements via IoT implementation in olive oil companies and their performance. Full article
(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)
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14 pages, 2736 KiB  
Article
Deep Learning Based Target Tracking Algorithm Model for Athlete Training Trajectory
by Yue Wang
Processes 2022, 10(12), 2710; https://doi.org/10.3390/pr10122710 - 15 Dec 2022
Cited by 2 | Viewed by 1287
Abstract
The main function of the athlete tracking system is to collect the real-time competition data of the athletes. Deep learning is a research hotspot in the field of image and video. With the rapid development of science and technology, it has not only [...] Read more.
The main function of the athlete tracking system is to collect the real-time competition data of the athletes. Deep learning is a research hotspot in the field of image and video. With the rapid development of science and technology, it has not only made a breakthrough in theory, but also achieved excellent results in practical application. SiamRPN (Siamese Region Proposal Network) is a single target tracking network model based on deep learning, which has high accuracy and fast operation speed. However, in long-term tracking, if the target is completely obscured and out of the sight of SiamRPN, the tracking of the network will be invalid. Considering the difficulty of long-term tracking, the algorithm is improved and tested by introducing channel attention mechanism and local global search strategy into SiamRPN. Experimental results show that this algorithm has higher accuracy and prediction average overlap rate than the original SiamRPN algorithm when performing tracking tasks on long-term tracking sequences. At the same time, the improved algorithm can still achieve good results in the case of target disappearance and other challenging factors. This study provides an important reference for the coaches of deep learning to realize long-term tracking of athletes. Full article
(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)
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19 pages, 9812 KiB  
Article
Concentrated Stream Data Processing for Vegetation Coverage Monitoring and Recommendation against Rock Desertification
by Guanyao Lu
Processes 2022, 10(12), 2628; https://doi.org/10.3390/pr10122628 - 7 Dec 2022
Cited by 1 | Viewed by 1304
Abstract
The vegetation covering regions is confined due to deforestation, mining industries, and environmental factors. The intensified deforestation and industrial development processes impact the vegetation coverage and fail to meet the food demands. Therefore, accurate monitoring of such regions aids in preventing adversary processes [...] Read more.
The vegetation covering regions is confined due to deforestation, mining industries, and environmental factors. The intensified deforestation and industrial development processes impact the vegetation coverage and fail to meet the food demands. Therefore, accurate monitoring of such regions aids in preventing adversary processes and their plant extinction. The monitoring process requires accurate data collection and analysis to identify the root cause that can be due to human/climatic/environmental changes. This article introduces a concentrated stream data processing method (CSDPM) assisted by an extreme learning paradigm. The different causes are analyzed using the extracted features in different learning perceptron layers. In this learning, the accumulated data is analyzed for similar features and trained for the consecutive or lagging input data streams. The monitoring process concluded with the learning output by classifying the plant extinction reason. Therefore, the identified reason is addressed through official policies with new recommendations or alternate vegetation improvements. More specifically, the data concentrated towards deforestation are the fundamental data required for feature matching. The features are initially trained from the existing datasets and previously acquired data from the converted landscapes. This proposed method is analyzed using the metrics analysis rate, analysis time, recommendation rate, and complexity. Full article
(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)
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13 pages, 334 KiB  
Article
Multiple Graph Adaptive Regularized Semi-Supervised Nonnegative Matrix Factorization with Sparse Constraint for Data Representation
by Kexin Zhang, Lingling Li, Jinhong Di, Yi Wang, Xuezhuan Zhao and Ji Zhang
Processes 2022, 10(12), 2623; https://doi.org/10.3390/pr10122623 - 7 Dec 2022
Cited by 1 | Viewed by 1224
Abstract
Multiple graph and semi-supervision techniques have been successfully introduced into the nonnegative matrix factorization (NMF) model for taking full advantage of the manifold structure and priori information of data to capture excellent low-dimensional data representation. However, the existing methods do not consider the [...] Read more.
Multiple graph and semi-supervision techniques have been successfully introduced into the nonnegative matrix factorization (NMF) model for taking full advantage of the manifold structure and priori information of data to capture excellent low-dimensional data representation. However, the existing methods do not consider the sparse constraint, which can enhance the local learning ability and improve the performance in practical applications. To overcome this limitation, a novel NMF-based data representation method, namely, the multiple graph adaptive regularized semi-supervised nonnegative matrix factorization with sparse constraint (MSNMFSC) is developed in this paper for obtaining the sparse and discriminative data representation and increasing the quality of decomposition of NMF. Particularly, based on the standard NMF, the proposed MSNMFSC method combines the multiple graph adaptive regularization, the limited supervised information and the sparse constraint together to learn the more discriminative parts-based data representation. Moreover, the convergence analysis of MSNMFSC is studied. Experiments are conducted on several practical image datasets in clustering tasks, and the clustering results have shown that MSNMFSC achieves better performance than several most related NMF-based methods. Full article
(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)
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21 pages, 6481 KiB  
Article
Adaptive Composite Fault Diagnosis of Rolling Bearings Based on the CLNGO Algorithm
by Sen Yu and Jie Ma
Processes 2022, 10(12), 2532; https://doi.org/10.3390/pr10122532 - 29 Nov 2022
Cited by 3 | Viewed by 1364
Abstract
In this paper, a novel composite fault diagnosis method combining adaptive feature mode decomposition (FMD) and minimum noise amplitude deconvolution (MNAD) is proposed. Firstly, chaos mapping and leader mutation selection strategy were introduced to improve the Northern Goshawk algorithm (NGO), and a chaotic [...] Read more.
In this paper, a novel composite fault diagnosis method combining adaptive feature mode decomposition (FMD) and minimum noise amplitude deconvolution (MNAD) is proposed. Firstly, chaos mapping and leader mutation selection strategy were introduced to improve the Northern Goshawk algorithm (NGO), and a chaotic leadership Northern Goshawk optimization (CLNGO) algorithm was proposed. The advantages of the CLNGO algorithm in convergence accuracy and speed were verified by 12 benchmark functions. Then, a new index called sparse pulse and cyclicstationarity (SPC) is proposed to evaluate signal sparsity. Finally, SPC is used as the fitness function of CLNGO to optimize FMD and MNAD. The optimal decomposition mode n and filter length of FMD, and filter length L and noise ratio ρ of MNAD are selected. The CLNGO-FMD is used to decompose signal into different modes. The signal is reconstructed based on the kurtosis criterion and the CLNGO-MNAD method is used to reduce the noise of the reconstructed signal twice. The experimental results show that the proposed method can achieve the enhancement of weak features and the removal of noise to extract the fault feature frequency adaptively. Compared with EMD, VMD, MOMEDA, MCKD and other methods, the proposed method has better performance in fault feature frequency extraction, and it is effective for the diagnosis of single faults and composite faults. Full article
(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)
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22 pages, 1503 KiB  
Article
Machine Learning with Gradient-Based Optimization of Nuclear Waste Vitrification with Uncertainties and Constraints
by LaGrande Lowell Gunnell, Kyle Manwaring, Xiaonan Lu, Jacob Reynolds, John Vienna and John Hedengren
Processes 2022, 10(11), 2365; https://doi.org/10.3390/pr10112365 - 11 Nov 2022
Cited by 10 | Viewed by 3381
Abstract
Gekko is an optimization suite in Python that solves optimization problems involving mixed-integer, nonlinear, and differential equations. The purpose of this study is to integrate common Machine Learning (ML) algorithms such as Gaussian Process Regression (GPR), support vector regression (SVR), and artificial neural [...] Read more.
Gekko is an optimization suite in Python that solves optimization problems involving mixed-integer, nonlinear, and differential equations. The purpose of this study is to integrate common Machine Learning (ML) algorithms such as Gaussian Process Regression (GPR), support vector regression (SVR), and artificial neural network (ANN) models into Gekko to solve data based optimization problems. Uncertainty quantification (UQ) is used alongside ML for better decision making. These methods include ensemble methods, model-specific methods, conformal predictions, and the delta method. An optimization problem involving nuclear waste vitrification is presented to demonstrate the benefit of ML in this field. ML models are compared against the current partial quadratic mixture (PQM) model in an optimization problem in Gekko. GPR with conformal uncertainty was chosen as the best substitute model as it had a lower mean squared error of 0.0025 compared to 0.018 and more confidently predicted a higher waste loading of 37.5 wt% compared to 34 wt%. The example problem shows that these tools can be used in similar industry settings where easier use and better performance is needed over classical approaches. Future works with these tools include expanding them with other regression models and UQ methods, and exploration into other optimization problems or dynamic control. Full article
(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)
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14 pages, 4678 KiB  
Article
Application Research of CFD-MOEA/D Optimization Algorithm in Large-Scale Reservoir Flood Control Scheduling
by Hongbo Jiao, Huaibin Wei, Qi Yang and Min Li
Processes 2022, 10(11), 2318; https://doi.org/10.3390/pr10112318 - 7 Nov 2022
Cited by 2 | Viewed by 1262
Abstract
Reservoir flood control has an important impact on flood protection and plays an important role in reducing the loss of people’s lives and property. In order to play an important role in flood control operation of large-scale reservoirs, a control flood dispatching multi-objective [...] Read more.
Reservoir flood control has an important impact on flood protection and plays an important role in reducing the loss of people’s lives and property. In order to play an important role in flood control operation of large-scale reservoirs, a control flood dispatching multi-objective evolutionary algorithm based on decomposition (CFD-MOEA/D) is proposed. The same type of multi-objective optimization algorithm (non-dominated sorting genetic algorithm II (NSGA-II)) is introduced, and CFD-MOEA/D, NSGA-II, and traditional MOEA/D algorithms are compared. The research results show that the CFD-MOEA/D algorithm can obtain the non-dominated solution of the higher water level in the upstream, and the solution obtained by the CFD-MOEA/D algorithm is more sufficient than the NSGA-II algorithm and the MOEA/D algorithm. When analyzing the HV value curve, the uniformity and convergence of the optimal solution obtained by the CFD-MOEA/D algorithm are better than those of the other two algorithms. The optimal dispatching scheme of the CFD-MOEA/D algorithm is compared with the actual dispatching scheme of the reservoir, and it is found that the maximum upstream water level and the final water level obtained by the CFD-MOEA/D algorithm are both kept at approximately 325 m, which is consistent with the actual dispatching scheme. The new feature of the algorithm is that it uses a decomposition method from coarse to fine and improves the hourly scheduling scheme to obtain higher scheduling efficiency. Full article
(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)
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19 pages, 4397 KiB  
Article
Contrast Maximization-Based Feature Tracking for Visual Odometry with an Event Camera
by Xiang Gao, Hanjun Xue and Xinghua Liu
Processes 2022, 10(10), 2081; https://doi.org/10.3390/pr10102081 - 14 Oct 2022
Cited by 1 | Viewed by 1462
Abstract
As a new type of vision sensor, the dynamic and active-pixel vision sensor (DAVIS) outputs image intensity and asynchronous event streams in the same pixel array. We present a novel visual odometry algorithm based on the DAVIS in this paper. The Harris detector [...] Read more.
As a new type of vision sensor, the dynamic and active-pixel vision sensor (DAVIS) outputs image intensity and asynchronous event streams in the same pixel array. We present a novel visual odometry algorithm based on the DAVIS in this paper. The Harris detector and the Canny detector are utilized to extract an initialized tracking template from the image sequence. The spatio-temporal window is selected by determining the life cycle of the asynchronous event streams. The alignment on timestamps is achieved by tracking the motion relationship between the template and events within the window. A contrast maximization algorithm is adopted for the estimation of the optical flow. The IMU data are used to calibrate the position of the templates during the update process that is exploited to estimate camera trajectories via the ICP algorithm. In the end, the proposed visual odometry algorithm is evaluated in several public object tracking scenarios and compared with several other algorithms. The tracking results show that our visual odometry algorithm can achieve better accuracy and lower latency tracking trajectory than other methods. Full article
(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)
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24 pages, 779 KiB  
Article
Distributed Economic Dispatch Control Method with Frequency Regulator for Smart Grid under Time-Varying Directed Topology
by Lianghao Ji, Weiqi Meng, Shasha Yang and Huaqing Li
Processes 2022, 10(9), 1840; https://doi.org/10.3390/pr10091840 - 13 Sep 2022
Viewed by 1094
Abstract
The paper studies a new distributed control method to solve the economic dispatch problem (EDP) under directed topology based on consensus protocol. Electrical equipment is closely related to frequency, and the frequency of each generator varies independently during operation. Therefore, it hinders the [...] Read more.
The paper studies a new distributed control method to solve the economic dispatch problem (EDP) under directed topology based on consensus protocol. Electrical equipment is closely related to frequency, and the frequency of each generator varies independently during operation. Therefore, it hinders the realization of economic dispatch. To solve the problem, we combine a frequency regulator with a consensus protocol, which eliminates the effect of frequency variation on the designed consensus algorithm. Meanwhile, considering the problem of excessive communication cost and low computational efficiency in large-scale power systems, an event-triggered mechanism is introduced into the designed algorithm. Furthermore, in order to overcome the unexpected loss of communication links, the time-varying topology mechanism is employed to develop the distributed economic dispatch (DED) algorithm to improve the robustness. Then, the stability of the above algorithm is proved by graph theory and convergence analysis. Finally, several simulations illustrate that our proposed methods are effective. Full article
(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)
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Review

Jump to: Research

29 pages, 3973 KiB  
Review
A Review on Data-Driven Quality Prediction in the Production Process with Machine Learning for Industry 4.0
by Abdul Quadir Md, Keshav Jha, Sabireen Haneef, Arun Kumar Sivaraman and Kong Fah Tee
Processes 2022, 10(10), 1966; https://doi.org/10.3390/pr10101966 - 29 Sep 2022
Cited by 15 | Viewed by 4009
Abstract
The quality-control process in manufacturing must ensure the product is free of defects and performs according to the customer’s expectations. Maintaining the quality of a firm’s products at the highest level is very important for keeping an edge over the competition. To maintain [...] Read more.
The quality-control process in manufacturing must ensure the product is free of defects and performs according to the customer’s expectations. Maintaining the quality of a firm’s products at the highest level is very important for keeping an edge over the competition. To maintain and enhance the quality of their products, manufacturers invest a lot of resources in quality control and quality assurance. During the assembly line, parts will arrive at a constant interval for assembly. The quality criteria must first be met before the parts are sent to the assembly line where the parts and subparts are assembled to get the final product. Once the product has been assembled, it is again inspected and tested before it is delivered to the customer. Because manufacturers are mostly focused on visual quality inspection, there can be bottlenecks before and after assembly. The manufacturer may suffer a loss if the assembly line is slowed down by this bottleneck. To improve quality, state-of-the-art sensors are being used to replace visual inspections and machine learning is used to help determine which part will fail. Using machine learning techniques, a review of quality assessment in various production processes is presented, along with a summary of the four industrial revolutions that have occurred in manufacturing, highlighting the need to detect anomalies in assembly lines, the need to detect the features of the assembly line, the use of machine learning algorithms in manufacturing, the research challenges, the computing paradigms, and the use of state-of-the-art sensors in Industry 4.0. Full article
(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)
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