Journal Description
Applied Sciences
Applied Sciences
is an international, peer-reviewed, open access journal on all aspects of applied natural sciences published semimonthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Inspec, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Multidisciplinary) / CiteScore - Q1 (General Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.9 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our authors say about Applied Sciences.
- Companion journals for Applied Sciences include: Applied Nano, AppliedChem, Applied Biosciences, Virtual Worlds, Spectroscopy Journal and JETA.
Impact Factor:
2.7 (2022);
5-Year Impact Factor:
2.9 (2022)
Latest Articles
An Ergonomic Risk Assessment System Based on 3D Human Pose Estimation and Collaborative Robot
Appl. Sci. 2024, 14(11), 4823; https://doi.org/10.3390/app14114823 (registering DOI) - 2 Jun 2024
Abstract
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Human pose estimation focuses on methods that allow us to assess ergonomic risk in the workplace and aims to prevent work-related musculoskeletal disorders (WMSDs). The recent increase in the use of Industry 4.0 technologies has allowed advances to be made in machine learning
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Human pose estimation focuses on methods that allow us to assess ergonomic risk in the workplace and aims to prevent work-related musculoskeletal disorders (WMSDs). The recent increase in the use of Industry 4.0 technologies has allowed advances to be made in machine learning (ML) techniques for image processing to enable automated ergonomic risk assessment. In this context, this study aimed to develop a method of calculating joint angles from digital snapshots or videos using computer vision and ML techniques to achieve a more accurate evaluation of ergonomic risk. Starting with an ergonomic analysis, this study explored the use of a semi-supervised training method to detect the skeletons of workers and to estimate the positions and angles of their joints. A criticality index, based on RULA scores and fuzzy rules, is then calculated to evaluate possible corrective actions aimed at reducing WMSDs and improving production capacity using a collaborative robot that supports workers in carrying out critical operations. This method is tested in a real industrial case in which the manual assembly of electrical components is conducted, achieving a reduction in overall ergonomic stress of 13% and an increase in production capacity of 33% during a work shift. The proposed approach can overcome the limitations of recent developments based on computer vision or wearable sensors by performing an assessment with an objective and flexible approach to postural analysis development.
Full article
Open AccessArticle
Analysis of the Effectiveness of Model, Data, and User-Centric Approaches for Chat Application: A Case Study of BlenderBot 2.0
by
Chanjun Park, Jungseob Lee, Suhyune Son, Kinam Park, Jungsun Jang and Heuiseok Lim
Appl. Sci. 2024, 14(11), 4821; https://doi.org/10.3390/app14114821 (registering DOI) - 2 Jun 2024
Abstract
BlenderBot 2.0 represents a significant advancement in open-domain chatbots by incorporating real-time information and retaining user information across multiple sessions through an internet search module. Despite its innovations, there are still areas for improvement. This paper examines BlenderBot 2.0’s limitations and errors from
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BlenderBot 2.0 represents a significant advancement in open-domain chatbots by incorporating real-time information and retaining user information across multiple sessions through an internet search module. Despite its innovations, there are still areas for improvement. This paper examines BlenderBot 2.0’s limitations and errors from three perspectives: model, data, and user interaction. From the data perspective, we highlight the challenges associated with the crowdsourcing process, including unclear guidelines for workers, insufficient measures for filtering hate speech, and the lack of a robust process for verifying the accuracy of internet-sourced information. From the user perspective, we identify nine types of limitations and conduct a thorough investigation into their causes. For each perspective, we propose practical methods for improvement and discuss potential directions for future research. Additionally, we extend our analysis to include perspectives in the era of large language models (LLMs), further broadening our understanding of the challenges and opportunities present in current AI technologies. This multifaceted analysis not only sheds light on BlenderBot 2.0’s current limitations but also charts a path forward for the development of more sophisticated and reliable open-domain chatbots within the broader context of LLM advancements.
Full article
Open AccessArticle
Period-1 Motions and Bifurcations of a 3D Brushless DC Motor System with Voltage Disturbance
by
Bin Chen, Yeyin Xu, Yinghou Jiao and Zhaobo Chen
Appl. Sci. 2024, 14(11), 4820; https://doi.org/10.3390/app14114820 (registering DOI) - 2 Jun 2024
Abstract
Abstract: In this paper, the nonlinear dynamic system of a brushless DC motor with voltage disturbance is studied analytically via a generalized harmonic balance method. A truncated Fourier series with time-varying coefficients is utilized to represent the analytical variations of nonlinear currents
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Abstract: In this paper, the nonlinear dynamic system of a brushless DC motor with voltage disturbance is studied analytically via a generalized harmonic balance method. A truncated Fourier series with time-varying coefficients is utilized to represent the analytical variations of nonlinear currents and voltages within this dynamic system. Bifurcations of periodic currents and voltages are obtained, and their stability is discussed through eigenvalue analysis. The frequency–amplitude characteristics of periodic currents and voltages exhibit complexity in the frequency domain. Comparative illustrations are provided to contrast the analytical solutions with numerical outcomes for periodic currents and voltages. These analytical findings can be effectively employed for controlling the brushless DC motors experiencing voltage disturbances.
Full article
(This article belongs to the Section Applied Physics General)
Open AccessArticle
Novel Insights in Soil Mechanics: Integrating Experimental Investigation with Machine Learning for Unconfined Compression Parameter Prediction of Expansive Soil
by
Ammar Alnmr, Haidar Hosamo Hosamo, Chuangxin Lyu, Richard Paul Ray and Mounzer Omran Alzawi
Appl. Sci. 2024, 14(11), 4819; https://doi.org/10.3390/app14114819 (registering DOI) - 2 Jun 2024
Abstract
This paper presents a novel application of machine learning models to clarify the intricate behaviors of expansive soils, focusing on the impact of sand content, saturation level, and dry density. Departing from conventional methods, this research utilizes a data-centric approach, employing a suite
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This paper presents a novel application of machine learning models to clarify the intricate behaviors of expansive soils, focusing on the impact of sand content, saturation level, and dry density. Departing from conventional methods, this research utilizes a data-centric approach, employing a suite of sophisticated machine learning models to predict soil properties with remarkable precision. The inclusion of a 30% sand mixture is identified as a critical threshold for optimizing soil strength and stiffness, a finding that underscores the transformative potential of sand amendment in soil engineering. In a significant advancement, the study benchmarks the predictive power of several models including extreme gradient boosting (XGBoost), gradient boosting regression (GBR), random forest regression (RFR), decision tree regression (DTR), support vector regression (SVR), symbolic regression (SR), and artificial neural networks (ANNs and proposed ANN-GMDH). Symbolic regression equations have been developed to predict the elasticity modulus and unconfined compressive strength of the investigated expansive soil. Despite the complex behaviors of expansive soil, the trained models allow for optimally predicting the values of unconfined compressive parameters. As a result, this paper provides for the first time a reliable and simply applicable approach for estimating the unconfined compressive parameters of expansive soils. The proposed ANN-GMDH model emerges as the pre-eminent model, demonstrating exceptional accuracy with the best metrics. These results not only highlight the ANN’s superior performance but also mark this study as a groundbreaking endeavor in the application of machine learning to soil behavior prediction, setting a new benchmark in the field.
Full article
(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering, 2nd Edition)
Open AccessArticle
Precipitation Simulation and Dynamic Response of a Transmission Line Subject to Wind-Driven Rain during Super Typhoon Lekima
by
Jianping Sun, Mingfeng Huang, Sunce Liao and Wenjuan Lou
Appl. Sci. 2024, 14(11), 4818; https://doi.org/10.3390/app14114818 (registering DOI) - 2 Jun 2024
Abstract
Typhoons bring great damages to transmission line systems located in coastal areas. Strong wind and extreme precipitation are the main sources of damaging effects. Transmission lines suffered from wind-driven rain exhibit more susceptibility to damage due to the coupled effect of wind and
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Typhoons bring great damages to transmission line systems located in coastal areas. Strong wind and extreme precipitation are the main sources of damaging effects. Transmission lines suffered from wind-driven rain exhibit more susceptibility to damage due to the coupled effect of wind and rainwater. This paper presents an integrated numerical simulation framework based on mesoscale WRF model, multiphase CFD model and FEM model to analyze the motions of a transmission line subjected to coupled wind and rain loads during typhoon events. A full-scale transmission line in Zhoushan Island is employed to demonstrate the effectiveness of the proposed framework by simulating typhoon evolution in terms of wind fields and rainfall, solving the coupled wind and rain fields around the conductor and predicting the dynamic responses of the transmission line during Super Typhoon Lekima in 2019. The results show that the horizontal displacements of the transmission line under the joint actions of wind and rain increase approximately 17%–18% compared to those of wind loads only. It is important to consider the coupled effects of wind-driven rain on conductors in the design of transmission lines under typhoon conditions.
Full article
Open AccessReview
How to Make a State of the Art Report—Case Study—Image-Based Road Crack Detection: A Scientometric Literature Review
by
Luxin Fan, SaiHong Tang, Khairol Anuar b. Mohd Ariffin, Mohd Idris Shah b. Ismail and Ruixin Zhao
Appl. Sci. 2024, 14(11), 4817; https://doi.org/10.3390/app14114817 (registering DOI) - 2 Jun 2024
Abstract
Abstract: With the rapid growth in urban construction in Malaysia, road breakage has challenged traditional manual inspection methods. In order to quickly and accurately detect the extent of road breakage, it is crucial to apply automated road crack detection techniques. Researchers have long
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Abstract: With the rapid growth in urban construction in Malaysia, road breakage has challenged traditional manual inspection methods. In order to quickly and accurately detect the extent of road breakage, it is crucial to apply automated road crack detection techniques. Researchers have long studied image-based road crack detection techniques, especially the deep learning methods that have emerged in recent years, leading to breakthrough developments in the field. However, many issues remain in road crack detection methods using deep learning techniques. The field lacks state-of-the-art systematic reviews that can scientifically and effectively analyze existing works, document research trends, summarize outstanding research results, and identify remaining shortcomings. To conduct a systematic review of the relevant literature, a bibliometric analysis and a critical analysis of the papers published in the field were performed. VOSviewer and CiteSpace text mining tools were used to analyze and visualize the bibliometric analysis of some parameters derived from the articles. The history and current status of research in the field by authors from all over the world are elucidated and future trends are analyzed.
Full article
Open AccessArticle
Semantic Segmentation Method for Road Intersection Point Clouds Based on Lightweight LiDAR
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Xiaole Ren, Bin Yu and Yuchen Wang
Appl. Sci. 2024, 14(11), 4816; https://doi.org/10.3390/app14114816 (registering DOI) - 2 Jun 2024
Abstract
Lightweight LiDAR, characterized by its ease of use and cost-effectiveness, offers advantages in road intersection information acquisition. This study used lightweight LiDAR to collect 3D point cloud data from an urban road intersection and propose a semantic segmentation model based on the improved
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Lightweight LiDAR, characterized by its ease of use and cost-effectiveness, offers advantages in road intersection information acquisition. This study used lightweight LiDAR to collect 3D point cloud data from an urban road intersection and propose a semantic segmentation model based on the improved RandLA-Net. Initially, raw data from multiple positions and perspectives were obtained, and complete road intersection point clouds were stitched together using the iterative closest point algorithm for sequential registration. Subsequently, a semantic segmentation method for point clouds based on the improved RandLA-Net was proposed. This method included a spatial information encoding module based on feature similarities and a feature enhancement module based on multi-pooling fusion. This model optimized the feature aggregation capabilities during downsampling with the weighted cross-entropy loss function applied to reduce the impact of input sample scale imbalances. In comparisons of the improved RandLA-Net with PointNet++ and RandLA-Net on the same dataset, our method showed improved segmentation accuracy for various categories. The overall prediction accuracy on two road intersection point cloud test sets was 87.68% and 89.61%, with average F1 scores of 82.76% and 80.61%, respectively. Most notably, the prediction accuracy for road surface areas reached 94.48% and 94.79%. The results show that our model can enrich the spatial feature expression of input data and enhance semantic segmentation performance in road intersection scenarios.
Full article
(This article belongs to the Section Transportation and Future Mobility)
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Open AccessReview
Assessing Table Tennis Technical Proficiency in Individuals with Disabilities: A Scoping Review
by
Pui Wah Kong and Cecilia Man Sze Ma
Appl. Sci. 2024, 14(11), 4815; https://doi.org/10.3390/app14114815 (registering DOI) - 2 Jun 2024
Abstract
Table tennis is a sport that is enjoyed by many, including those with physical and intellectual disabilities. This scoping review summarised the current test protocols for assessing table tennis technical proficiency in individuals with disabilities. Relevant articles were searched through four databases (Scopus,
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Table tennis is a sport that is enjoyed by many, including those with physical and intellectual disabilities. This scoping review summarised the current test protocols for assessing table tennis technical proficiency in individuals with disabilities. Relevant articles were searched through four databases (Scopus, PubMed, SPORTDiscus, and Web of Science) covering three key aspects: disability, table tennis, and technical proficiency. The search resulted in 14 studies included for data extraction, covering physical impairments, intellectual disability, and development coordination disorder. Almost all studies (93%) were conducted on well-trained para-table tennis athletes competing in high-level competitions. There exist protocols to assess service accuracy and stroke accuracy, hand–eye coordination, quality of specific skills and ball control, functional reach, and trunk rotation. The forehand topspin and backhand topspin drives were tested the most. Table tennis robots and video cameras are the common equipment used. Moving forward, future research should develop technical proficiency tests for players across all competency levels. The skill assessment criteria and scoring methods should be standardised and clearly explained. The validity and reliability of tests should be established. Lastly, there is great potential in using artificial intelligence to enhance the assessment of table tennis proficiency in individuals with disabilities.
Full article
(This article belongs to the Special Issue Research on Biomechanics, Motor Control and Learning of Human Movements)
Open AccessArticle
An Intelligent Human–Machine Interface Architecture for Long-Term Remote Robot Handling in Fusion Reactor Environments
by
Tamara Benito and Antonio Barrientos
Appl. Sci. 2024, 14(11), 4814; https://doi.org/10.3390/app14114814 (registering DOI) - 2 Jun 2024
Abstract
This paper addresses the intricate challenge posed by remote handling (RH) operations in facilities with operational lifespans surpassing 30 years. The extended RH task horizon necessitates a forward-looking strategy to accommodate the continuous evolution of RH equipment. Confronted with diverse and evolving hardware
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This paper addresses the intricate challenge posed by remote handling (RH) operations in facilities with operational lifespans surpassing 30 years. The extended RH task horizon necessitates a forward-looking strategy to accommodate the continuous evolution of RH equipment. Confronted with diverse and evolving hardware interfaces, a critical requirement emerges for a flexible and adaptive software architecture based on changing situations and past experiences. The paper explores the inherent challenges associated with sustaining and upgrading RH equipment within an extended operational context. In response to this challenge, a groundbreaking, flexible, and maintainable human–machine interface (HMI) architecture named MAMIC is designed, guaranteeing seamless integration with a diverse range of RH equipment developed over the years. Embracing a modular and extensible design, the MAMIC architecture facilitates the effortless incorporation of new equipment without compromising system integrity. Moreover, by adopting this approach, nuclear facilities can proactively steer the evolution of RH equipment, guaranteeing sustained performance and compliance throughout the extended operational lifecycle. The proposed adaptive architecture provides a scalable and future-proof solution, addressing the dynamic landscape of remote handling technology for decades.
Full article
(This article belongs to the Special Issue Human–Artificial Intelligence (AI) Interaction: Latest Advances and Prospects)
Open AccessArticle
Decoupled MPC Power Balancing Strategy for Coupled Inductor Flying Capacitor DC–DC Converter
by
Xin Wei, Kaitao Bi, Genlong Lan, Wei Li and Jin Cui
Appl. Sci. 2024, 14(11), 4813; https://doi.org/10.3390/app14114813 (registering DOI) - 2 Jun 2024
Abstract
Abstract: A decoupled model predictive control (MPC) power balancing strategy for a coupled inductor-based flying capacitor DC–DC converter (FCDC) is a proposed to solve the power imbalance caused by the parameter differences in the coupled inductor. The decoupled mathematical model of coupled inductor
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Abstract: A decoupled model predictive control (MPC) power balancing strategy for a coupled inductor-based flying capacitor DC–DC converter (FCDC) is a proposed to solve the power imbalance caused by the parameter differences in the coupled inductor. The decoupled mathematical model of coupled inductor FCDC is firstly derived by analyzing the converter operation state under various modes. On this basis, the control relationship between inductor current and flying capacitor (FC) voltage is redefined and an MPC power balance strategy based on the inductor current with single-step optimization is proposed. The proposed MPC strategy not only achieves decoupled power balancing control but also solves multi-objective dynamic optimization control of the inductor current and FC voltage, greatly reducing the computation load. A detailed theoretical analysis of the proposed strategy is presented and the balancing performance is effectively verified through the experiments.
Full article
(This article belongs to the Special Issue Challenges for Power Electronics Converters, 2nd Edition)
Open AccessArticle
Hot-Spot Stress Analyses of a T-Shaped Tubular Joint Subjected to Uniform, Grooving and Non-uniform Corrosion
by
Lingsu Liu, Yan Dong, Haikun Yang, Minghui Xu, Xin Liu, Lei Zhang and Yordan Garbatov
Appl. Sci. 2024, 14(11), 4812; https://doi.org/10.3390/app14114812 (registering DOI) - 2 Jun 2024
Abstract
The study aims to investigate the impact of uniform, grooving and non-uniform corrosion degradation on the hot-spot stresses of a T-shaped tubular joint using the finite element method. The through-thickness linearization method is employed to estimate the hot-spot stresses, allowing a more reasonable
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The study aims to investigate the impact of uniform, grooving and non-uniform corrosion degradation on the hot-spot stresses of a T-shaped tubular joint using the finite element method. The through-thickness linearization method is employed to estimate the hot-spot stresses, allowing a more reasonable consideration of the effect of grooving corrosion and non-unform corrosion. The grooving corrosion degradation is modelled assuming that the corrosion rate of the weld metal is 1.4 times that of the base metal. The non-uniform corrosion is modelled by moving the nodes around the weld by a random distance along the direction perpendicular to the surface. The random distances are generated based on the surface roughness parameter Ra. The results indicate that the stress concentration factor (SCF) increases with the uniform corrosion depth. The grooving corroded tubular joint results in a higher SCF than those of the corresponding uniformly corroded tubular joint. The non-uniform corrosion can lead to SCF deviations from the SCF of the uniformly corroded tubular joint. The SCF deviation at the critical region follows the normal distribution, and its standard deviation increases with Ra.
Full article
(This article belongs to the Section Marine Science and Engineering)
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Open AccessArticle
Impact Wear Behavior of the Valve Cone Surface after Plasma Alloying Treatment
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Changzeng Luo, Yajun Yao, Dongbo Wei, Muyao Lin, Pingze Zhang and Shengguan Qu
Appl. Sci. 2024, 14(11), 4811; https://doi.org/10.3390/app14114811 (registering DOI) - 2 Jun 2024
Abstract
Valves are prone to wear under harsh environments, such as high temperatures and reciprocating impacts, which has become one of the most severe factors reducing the service life of engines. As a lightweight ceramic, CrN is considered an excellent protective material with high-temperature
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Valves are prone to wear under harsh environments, such as high temperatures and reciprocating impacts, which has become one of the most severe factors reducing the service life of engines. As a lightweight ceramic, CrN is considered an excellent protective material with high-temperature strength and resistance to wear. In this study, a CrN coating was applied onto the valve cone surface via double-layer glow plasma surface metallurgy technology. The formation process, microstructure, phase composition, hardness, and adhesion strength were analyzed in detail. Impact wear tests were conducted on the valve using a bench test device. The SEM and EDS results showed that the CrN coating evolved from an island-like form to a dense, cell-shaped surface structure. The thickness of the coating was approximately 46 μm and could be divided into a deposition layer and a diffusion layer, from the outer to the inner sections. The presence of element gradients within the diffusion layer proved that the coating and substrate were metallurgically bonded. The adhesion strength of the CrN coating measured via scratch method was as high as 72 N. The average Vickers hardness of the valve cone surface increased from 377.1 HV0.5 to 903.1 HV0.5 following the plasma alloying treatment. After 2 million impacts at 12,000 N and 650 °C, adhesive wear emerged as the primary wear mode of the CrN coating, with an average wear depth of 42.93 μm and a wear amount of 23.49 mg. Meanwhile, the valve substrate exhibited a mixed wear mode of adhesive wear and abrasive wear, with an average wear depth of 118.23 μm and a wear amount of 92.66 mg, being 63.7% and 74.6% higher than those of the coating. Thus, the CrN coating showed excellent impact wear resistance, which contributed to the enhancement of the service life of the valve in harsh environments.
Full article
(This article belongs to the Section Surface Sciences and Technology)
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Open AccessArticle
Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control
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Fangjie Zhang, Yanqin Wu, Yifan Wang and Xiaodong Li
Appl. Sci. 2024, 14(11), 4810; https://doi.org/10.3390/app14114810 (registering DOI) - 2 Jun 2024
Abstract
Constrained by the computing power, adaptive active noise control systems often have a low sampling rate. Therefore, reconstruction filters and anti-aliasing filters with fixed parameters are generally adopted to eliminate the mirror noise and aliasing noise, respectively; however, they may boost the group
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Constrained by the computing power, adaptive active noise control systems often have a low sampling rate. Therefore, reconstruction filters and anti-aliasing filters with fixed parameters are generally adopted to eliminate the mirror noise and aliasing noise, respectively; however, they may boost the group delay of the system. A dynamic matching method based on dual sampling rates is proposed to dynamically adjust the parameters of the reconstruction and anti-aliasing filters, according to the characteristics of the primary sound source, for a compromise between high-frequency noise and group delay. In digital high-sampling-rate regions, data that include high-frequency information are analyzed regularly, following which the parameters of the reconstruction filters and those of the anti-aliasing filters are dynamically matched. In digital low-sampling-rate regions, the estimation of the secondary path transfer function is updated. The results of laboratory experiments show that the proposed method not only can suppress the mirror and aliasing noise for primary sound sources with different spectra, but can also effectively reduce the group delay and improve the noise reduction performance of a system.
Full article
(This article belongs to the Collection Recent Applications of Active and Passive Noise Control)
Open AccessArticle
Enhancing Livestock Detection: An Efficient Model Based on YOLOv8
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Chengwu Fang, Chunmei Li, Peng Yang, Shasha Kong, Yaosheng Han, Xiangjie Huang and Jiajun Niu
Appl. Sci. 2024, 14(11), 4809; https://doi.org/10.3390/app14114809 (registering DOI) - 2 Jun 2024
Abstract
Maintaining a harmonious balance between grassland ecology and local economic development necessitates effective management of livestock resources. Traditional approaches have proven inefficient, highlighting an urgent need for intelligent solutions. Accurate identification of livestock targets is pivotal for precise livestock farming management. However, the
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Maintaining a harmonious balance between grassland ecology and local economic development necessitates effective management of livestock resources. Traditional approaches have proven inefficient, highlighting an urgent need for intelligent solutions. Accurate identification of livestock targets is pivotal for precise livestock farming management. However, the You Only Look Once version 8 (YOLOv8) model exhibits limitations in accuracy when confronted with complex backgrounds and densely clustered targets. To address these challenges, this study proposes an optimized CCS-YOLOv8 (Comprehensive Contextual Sensing YOLOv8) model. First, we curated a comprehensive livestock detection dataset encompassing the Qinghai region. Second, the YOLOv8n model underwent three key enhancements: (1) incorporating a Convolutional Block Attention Module (CBAM) to accentuate salient image information, thereby boosting feature representational power; (2) integrating a Content-Aware ReAssembly of FEatures (CARAFE) operator to mitigate irrelevant interference, improving the integrity and accuracy of feature extraction; and (3) introducing a dedicated small object detection layer to capture finer livestock details, enhancing the recognition of smaller targets. Experimental results on our dataset demonstrate the CCS-YOLOv8 model’s superior performance, achieving 84.1% precision, 82.2% recall, 84.4% [email protected], 60.3% [email protected], 53.6% [email protected]:0.95, and 83.1% F1-score. These metrics reflect substantial improvements of 1.1%, 7.9%, 5.8%, 6.6%, 4.8%, and 4.7%, respectively, over the baseline model. Compared to mainstream object detection models, CCS-YOLOv8 strikes an optimal balance between accuracy and real-time processing capability. Its robustness is further validated on the VisDrone2019 dataset. The CCS-YOLOv8 model enables rapid and accurate identification of livestock age groups and species, effectively overcoming the challenges posed by complex grassland backgrounds and densely clustered targets. It offers a novel strategy for precise livestock population management and overgrazing prevention, aligning seamlessly with the demands of modern precision livestock farming. Moreover, it promotes local environmental conservation and fosters sustainable development within the livestock industry.
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Open AccessArticle
Sideband Vibro-Acoustics Suppression and Numerical Prediction of Permanent Magnet Synchronous Motor Based on Markov Chain Random Carrier Frequency Modulation
by
Yong Chen, Bingxiao Yan, Liming Zhang, Kefu Yao and Xue Jiang
Appl. Sci. 2024, 14(11), 4808; https://doi.org/10.3390/app14114808 (registering DOI) - 2 Jun 2024
Abstract
This paper presents a Markov chain random carrier frequency modulation (MRCFM) technique for suppressing sideband vibro-acoustic responses caused by discontinuous pulse-width modulation (DPWM) in permanent magnet synchronous motors (PMSMs) for new energy vehicles. Firstly, the spectral and order distributions of the sideband current
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This paper presents a Markov chain random carrier frequency modulation (MRCFM) technique for suppressing sideband vibro-acoustic responses caused by discontinuous pulse-width modulation (DPWM) in permanent magnet synchronous motors (PMSMs) for new energy vehicles. Firstly, the spectral and order distributions of the sideband current harmonics and radial electromagnetic forces introduced by DPWM are characterized and identified. Then, the principle and implementation method of three-state Markov chain random number generation are proposed, and particle swarm optimization (PSO) algorithm is chosen to quickly find the key parameters of transition probability and random gain. A Simulink and JMAG multi-physics field co-simulation model is built to simulate and predict the suppression effect of the MRCFM method on the sideband vibro-acoustic response. Finally, a 12-slot-10-pole PMSM test platform is built for experimental testing. The results show that the sideband current harmonics and vibro-acoustic response are effectively suppressed after the optimization of Markov chain algorithm. The constructed multi-physics field co-simulation model can accurately predict the amplitude characteristics of the sideband current harmonics and vibro-acoustic response.
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(This article belongs to the Section Acoustics and Vibrations)
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Open AccessArticle
Stability of Running Stride Biomechanical Parameters during Half-Marathon Race
by
Javier Olaya-Cuartero, Basilio Pueo, Lamberto Villalon-Gasch and Jose M. Jimenez-Olmedo
Appl. Sci. 2024, 14(11), 4807; https://doi.org/10.3390/app14114807 (registering DOI) - 2 Jun 2024
Abstract
This study explores the stability of biomechanical parameters of the running stride of male trained athletes during a half-marathon competition. Using a field-based descriptive design, eight male athletes from a local training group were monitored throughout an official half-marathon race under identical conditions,
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This study explores the stability of biomechanical parameters of the running stride of male trained athletes during a half-marathon competition. Using a field-based descriptive design, eight male athletes from a local training group were monitored throughout an official half-marathon race under identical conditions, assessing biomechanical parameters including ground contact time (GCT), leg spring stiffness (LSS), vertical oscillation (VO), and stride length (SL) recorded via the Stryd Summit Power Meter. A repeated measures analysis of variance (RM ANOVA) was conducted to detect significant changes in biomechanical parameters as the race progressed. Results demonstrated minimal changes in all parameters, with no significant differences observed for GCT (F = 0.96, p = 0.38), VO (F = 0.23, p = 0.87), and SL (F = 1.07, p = 0.35), and a small (η2 = 0.004) yet statistically significant difference in LSS (F = 5.52, p = 0.03) between the first and second segments, indicating that athletes were able to maintain stable biomechanical parameters throughout the race. The conclusion highlights the need for personalized training programs tailored to the unique biomechanical adaptations and demands of endurance running.
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(This article belongs to the Special Issue Advances in Sports, Exercise and Health)
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Open AccessArticle
Analysis of Shafting System Vibration Characteristics for Mixed-Flow Hydropower Units Considering Sand Wear on Turbine Blades
by
Wenhua Chen, Shuo Wang, Haifeng Chen, Weiliang Zhang, Jianfeng Ma and Jun Pan
Appl. Sci. 2024, 14(11), 4806; https://doi.org/10.3390/app14114806 (registering DOI) - 2 Jun 2024
Abstract
Addressing the issue of increased shaft-system vibration in high-altitude mixed-flow hydropower generating units due to sand wear on turbine blades, a three-dimensional model of a specific mixed-flow water turbine was constructed. CFD numerical simulations were employed to analyze the fluid exciting force acting
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Addressing the issue of increased shaft-system vibration in high-altitude mixed-flow hydropower generating units due to sand wear on turbine blades, a three-dimensional model of a specific mixed-flow water turbine was constructed. CFD numerical simulations were employed to analyze the fluid exciting force acting on the turbine runner under varying degrees of blade wear. An approximate analytical model was then established for the variation of fluid exciting force in the turbine runner system using the Fourier harmonic analysis method. A multi-degree-of-freedom mathematical model of flexural and inclined coupling vibration of a hydropower unit’s shafting, considering blade wear, was constructed. The nonlinear dynamic model was numerically calculated by the Runge–Kutta method. The vibration responses of the shafting of hydropower units under different wear degrees were obtained by means of a time-domain diagram, frequency-domain diagram, axis-locus diagram, phase-locus diagram, and Poincare mapping. Based on the formula for calculating the wear amount of the blade material, the runner amplitude degradation trajectory model was established, and the pseudo-failure time of turbine blades was determined according to the allowable value of amplitude.
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(This article belongs to the Section Mechanical Engineering)
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Open AccessArticle
A Lightweight Method for Graph Neural Networks Based on Knowledge Distillation and Graph Contrastive Learning
by
Yong Wang and Shuqun Yang
Appl. Sci. 2024, 14(11), 4805; https://doi.org/10.3390/app14114805 (registering DOI) - 2 Jun 2024
Abstract
Graph neural networks (GNNs) are crucial tools for processing non-Euclidean data. However, due to scalability issues caused by the dependency and topology of graph data, deploying GNNs in practical applications is challenging. Some methods aim to address this issue by transferring GNN knowledge
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Graph neural networks (GNNs) are crucial tools for processing non-Euclidean data. However, due to scalability issues caused by the dependency and topology of graph data, deploying GNNs in practical applications is challenging. Some methods aim to address this issue by transferring GNN knowledge to MLPs through knowledge distillation. However, distilled MLPs cannot directly capture graph structure information and rely only on node features, resulting in poor performance and sensitivity to noise. To solve this problem, we propose a lightweight optimization method for GNNs that combines graph contrastive learning and variable-temperature knowledge distillation. First, we use graph contrastive learning to capture graph structural representations, enriching the input information for the MLP. Then, we transfer GNN knowledge to the MLP using variable temperature knowledge distillation. Additionally, we enhance both node content and structural features before inputting them into the MLP, thus improving its performance and stability. Extensive experiments on seven datasets show that the proposed KDGCL model outperforms baseline models in both transductive and inductive settings; in particular, the KDGCL model achieves an average improvement of 1.63% in transductive settings and 0.8% in inductive settings when compared to baseline models. Furthermore, KDGCL maintains parameter efficiency and inference speed, making it competitive in terms of performance.
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(This article belongs to the Special Issue Application, Optimization and Architecture of Deep Learning Neural Network)
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Open AccessArticle
A Study of Cavitation Erosion in Artificial Submerged Water Jets
by
Haonan Li, Jiawang Chen, Jin Guo, Hai Zhu, Yuan Lin and Han Ge
Appl. Sci. 2024, 14(11), 4804; https://doi.org/10.3390/app14114804 (registering DOI) - 2 Jun 2024
Abstract
The artificially submerged cavitation water jet is effectively utilized by ejecting a high-pressure water stream into a low-pressure water stream through concentric nozzles and utilizing the cavitation phenomenon generated by the shear layer formed between the two streams. In this study, we investigated
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The artificially submerged cavitation water jet is effectively utilized by ejecting a high-pressure water stream into a low-pressure water stream through concentric nozzles and utilizing the cavitation phenomenon generated by the shear layer formed between the two streams. In this study, we investigated the cavitation characteristics of artificially submerged cavitation water jets by combining numerical simulations and erosion experiments. The results indicate that an appropriate standoff distance can generate more cavitation clouds on the workpiece surface, and the erosion characteristics of the artificially submerged cavitation water jet are most pronounced at a dimensionless standoff distance of SD = 30. The shear effect formed between the two jets plays a crucial role in generating initial cavitation bubbles within the flow field of the artificially submerged cavitation water jet. Moreover, increasing the convergent angle between the two jets can significantly enhance the cavitation effect between them and lead to a more substantial cavitation effect. Simultaneously, increasing the pressure of the high-pressure inner nozzle also contributes to enhancing the cavitation effect of the artificially submerged cavitation water jet.
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(This article belongs to the Special Issue Fluid Mechanics: From Theories to Applications)
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Pyramid Feature Attention Network for Speech Resampling Detection
by
Xinyu Zhou, Yujin Zhang, Yongqi Wang, Jin Tian and Shaolun Xu
Appl. Sci. 2024, 14(11), 4803; https://doi.org/10.3390/app14114803 (registering DOI) - 1 Jun 2024
Abstract
Speech forgery and tampering, increasingly facilitated by advanced audio editing software, pose significant threats to the integrity and privacy of digital speech avatars. Speech resampling is a post-processing operation of various speech-tampering means, and the forensic detection of speech resampling is of great
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Speech forgery and tampering, increasingly facilitated by advanced audio editing software, pose significant threats to the integrity and privacy of digital speech avatars. Speech resampling is a post-processing operation of various speech-tampering means, and the forensic detection of speech resampling is of great significance. For speech resampling detection, most of the previous works used traditional methods of feature extraction and classification to distinguish original speech from forged speech. In view of the powerful ability of deep learning to extract features, this paper converts the speech signal into a spectrogram with time-frequency characteristics, and uses the feature pyramid network (FPN) with the Squeeze and Excitation (SE) attention mechanism to learn speech resampling features. The proposed method combines the low-level location information and the high-level semantic information, which dramatically improves the detection performance of speech resampling. Experiments were carried out on a resampling corpus made on the basis of the TIMIT dataset. The results indicate that the proposed method significantly improved the detection accuracy of various resampled speech. For the tampered speech with a resampling factor of 0.9, the detection accuracy is increased by nearly 20%. In addition, the robustness test demonstrates that the proposed model has strong resistance to MP3 compression, and the overall performance is better than the existing methods.
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(This article belongs to the Special Issue Deep Learning for Speech, Image and Language Processing)
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