Article
Physics, Multidisciplinary
Zhe Hua, Yancai Xiao, Jiadong Cao
Summary: This study used an improved algorithm to predict misalignment faults in wind turbines, achieving minimal prediction errors and enabling advanced repair measures to reduce losses.
Article
Engineering, Environmental
Bin Chen, Chong Zhou, Yue Liu, Jianhua Liu
Summary: The study utilized grey correlation analysis and least squares support vector machine to predict airport runway icing, achieving an accurate prediction model for ice thickness through sensitivity analysis of multiple parameters and optimization algorithms. Experimental results showed that the model maintained an average relative error within 5%, providing a scientific basis for the deployment of airport deicing equipment.
COLD REGIONS SCIENCE AND TECHNOLOGY
(2022)
Article
Chemistry, Multidisciplinary
Shida Wang, Bo Xu, Zhenhao Zhu, Jing Li, Junyi Lu
Summary: The paper proposes a reliability analysis method based on IPSO-LSSVM for calculating the reliability of concrete gravity dams. The method determines failure modes, creates samples, establishes models, and calculates probabilities to study the reliability of concrete gravity dams.
APPLIED SCIENCES-BASEL
(2022)
Article
Multidisciplinary Sciences
Ahmed Youssef Ali Amer
Summary: This study introduces GLocal-LS-SVM, a novel machine learning algorithm that combines localised and global learning to address challenges associated with decentralised data sources, large datasets, and input-space-related issues. The algorithm employs multiple local LS-SVM models to extract informative support vectors from each local region in the input space, which are then merged to train the global model. Experimental results demonstrate that GLocal-LS-SVM achieves comparable or superior classification performance compared to standard LS-SVM and state-of-the-art models, while also outperforming standard LS-SVM in terms of computational efficiency.
Article
Energy & Fuels
Tongrui Zhang, Ran Li, Yongqin Zhou
Summary: Battery fault diagnosis technology is crucial for the reliable functioning of battery systems. A new online least squares support vector machine method has been introduced in this research, which effectively addresses small and sporadic fault data and improves the accuracy of diagnosis.
Article
Spectroscopy
Masoumeh Valaee, Mahmoud Reza Sohrabi, Fereshteh Motiee
Summary: In this study, two chemometrics methods, PLS and LS-SVM, were used to determine the content of zidovudine (ZDV) and lamivudine (LMV) in synthetic mixtures and anti-HIV pharmaceutical formulation. The results showed that both methods achieved accurate determination of the two components with good recovery rates. The comparison with HPLC as a reference technique demonstrated the reliability of the chemometrics approaches for routine analysis and quality control of the drug.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2023)
Article
Engineering, Mechanical
Tingyu Liu, Peng Zhang, Guo Cui, Xiaodong Yue
Summary: A prediction model based on QPSO-LSSVM was established to improve the accuracy of predicting fracture properties of PVA-CCNS, showing higher accuracy, better convergence, and robustness compared to other models. The proposed model can guide the mix proportion design of CC mixtures.
THEORETICAL AND APPLIED FRACTURE MECHANICS
(2021)
Article
Computer Science, Artificial Intelligence
Barenya Bikash Hazarika, Deepak Gupta
Summary: This paper introduces a new support vector machine (SVM) model and an improved least squares SVM model to address class imbalance learning (CIL) in binary classification problems. The algorithms assign weights to samples based on their class distributions during training to reduce the effects of CIL.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Environmental Sciences
Okan Mert Katipoglu, Metin Sarigol
Summary: With the increasing frequency of floods due to global warming, flood routing models are crucial in predicting floods, preventing loss of life and property, and protecting agricultural lands. This research compares the performance of hybrid machine learning models in flood routing estimation in Ordu, Turkey. The particle swarm optimization least-squares support vector machine technique is found to be the most successful model, with a data partition ratio of Train:70:Test:30. These findings are essential for flood management and taking necessary precautions.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Thermodynamics
Yifei Zhou, Shunli Wang, Yanxin Xie, Tao Zhu, Carlos Fernandez
Summary: A combined data-driven modeling approach based on particle swarm optimization and unscented Kalman filter is proposed to accurately estimate the state of charge of lithium-ion batteries. Experimental results show that the PSO optimized model significantly improves the precision of the least squares support vector machine and the estimation accuracy of the voltage and SOC.
INTERNATIONAL JOURNAL OF GREEN ENERGY
(2023)
Article
Engineering, Electrical & Electronic
Yun Ke, Chong Yao, Enzhe Song, Quan Dong, Liping Yang
Summary: The paper proposes an intelligent fault diagnosis method for common rail injectors based on CHDE and IGOA-LSSVM, which can accurately and quickly identify the fault status of common rail injectors and perform well in pattern recognition.
DIGITAL SIGNAL PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Hongmei Ju, Huan Yi
Summary: The paper introduces the importance of Least Squares Support Vector Machine (LSSVM) in solving classification problems, and proposes an improved fuzzy sparse multi-class LSSVM (IF-S-M-LSSVM) for multi-class classification problems. By using a non-iterative sparse algorithm and adding a fuzzy membership degree, the new model shows advantages in terms of training speed and classification accuracy.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoxi Zhao, Saiji Fu, Yingjie Tian, Kun Zhao
Summary: The QTLS method proposed in this paper combines QTSELF with LSSVM, imposes different penalties on samples based on their locations, and enhances model robustness. Its generalization capacity is investigated using Rademacher complexity theory, and extensive experiments confirm its effectiveness.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Automation & Control Systems
Jiao Zhu, Sugen Chen, Yufei Liu, Cong Hu
Summary: This study proposes a novel energy-based structural least squares twin support vector clustering algorithm (ESLSTWSVC), which improves clustering performance and efficiency by introducing within-class covariance matrix and solving system of linear equations.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Engineering, Geological
Wenping Gong, Shan Tian, Lei Wang, Zhibin Li, Huiming Tang, Tianzheng Li, Liang Zhang
Summary: This paper presents a new method for interval prediction of landslide displacement, integrating dual-output least squares support vector machine and particle swarm optimization algorithms. Case studies demonstrate that the proposed method has the best overall performance compared with other existing methods and provides accurate and reliable results.
Article
Engineering, Industrial
Yudi Fernando, Ming-Lang Tseng, Ika Sari Wahyuni-Td, Ana Beatriz Lopes de Sousa Jabbour, Charbel Jose Chiappetta Jabbour, Cyril Foropon
Summary: This study investigates the direct and indirect effects of information system security practices on the relationship between cyber supply chain risk management and supply chain performance. The findings suggest that operations and governance have significant effects on supply chain performance, while systems integration does not have a significant impact.
JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING
(2023)
Article
Environmental Sciences
Mohammed Hammam Mohammed Al-Madani, Yudi Fernando, Ming-Lang Tseng, Ahmed Zainul Abideen
Summary: This study uses social network analysis to identify current and future research trends in sustainable bioenergy production. The findings reveal four domains in palm oil biodiesel production for sustainable energy management. A framework based on these domains is proposed to guide the future of sustainable bioenergy production. The results indicate potential growth in research topics such as sustainable bioenergy, palm oil biodiesel, energy management, and carbon emissions reduction.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Green & Sustainable Science & Technology
Wanxing Sheng, Rui Li, Tao Yan, Ming-Lang Tseng, Jiale Lou, Lingling Li
Summary: This study proposes a hybrid dynamic economics emissions dispatch (HDEED) model for a distributed power system containing thermal generating units, wind farms and photovoltaic plants. The model considers power balance constraint, transmission loss constraint, output capacity of each generation unit, slope constraint of the power system, and establishes operating cost objective function, pollutant emission objective function, and satisfaction weight coefficient. An improved COOT optimization algorithm with a chaotic initialization strategy is presented, and the results show that it outperforms other algorithms in terms of reducing operating cost and pollutant emission targets.
Article
Management
Yudi Fernando, Amirulhusni Suhaini, Ming-Lang Tseng, Ahmed Zainul Abideen, Muhammad Shabir Shaharudin
Summary: This study contributes to a better understanding of smart warehouse systems and design by analyzing the reasons for warehouse underperformance, introducing smart warehouse enablers in Industry 4.0, and constructing a smart warehouse design from various aspects. The findings show that inventory mismanagement and communication hurdles are key factors causing warehouse underperformance. The insights from this study are valuable for extending the literature and designing smart warehouses to enhance business competitiveness.
INTERNATIONAL JOURNAL OF LOGISTICS-RESEARCH AND APPLICATIONS
(2023)
Article
Management
Tat-Dat Bui, Viqi Ardaniah, Qinghua Zhu, Mohammad Iranmanesh, Ming-Lang Tseng
Summary: This study contributes to data-driven zero-carbon transition in the industrial and manufacturing sectors. It identifies the important attributes for successful transition and highlights the performance gaps in different regions. The results emphasize the significance of energy system provisions, low-carbon transition assessment, and climate change resilience in directing zero-carbon transition studies.
INTERNATIONAL JOURNAL OF LOGISTICS-RESEARCH AND APPLICATIONS
(2023)
Article
Engineering, Industrial
Morteza Ghobakhloo, Mohammad Iranmanesh, Ming-Lang Tseng, Andrius Grybauskas, Alessandro Stefanini, Azlan Amran
Summary: This study addresses the concept of Industry 5.0 and aims to fill the knowledge gaps regarding its definition, scope, and technological components. By conducting a content-centric literature review, the study develops an architectural design for Industry 5.0 and highlights its potential solutions to socio-economic and environmental issues. The study emphasizes the importance of stakeholder involvement and integration for effective governance within this framework.
JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING
(2023)
Article
Engineering, Industrial
Yu Ren, Kuo-Jui Wu, Ming K. Lim, Ming-Lang Tseng
Summary: This study investigates technology transfer in achieving a circular economy model under resource-based view, considering public expectations and expert guidance. High-tech firms aim to apply technology transfer to achieve circular economy principles while facing resource constraints and meeting public expectations. However, there is often a discrepancy between public expectations and expert guidance.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2023)
Article
Environmental Sciences
Yeneneh Tamirat Negash, Liria Salome Calahorrano Sarmiento, Shuan-Wei Tseng, Ming K. Lim, Ming-Lang Tseng
Summary: This study develops a set of measures to address the interrelationship among circular waste-based bioeconomy (CWBE) attributes and uses the fuzzy Delphi method to obtain a valid set of attributes. A fuzzy decision-making trial and evaluation is applied to address the attribute relationships and determine the driving criteria of CWBE development.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Environmental Sciences
Yeneneh Tamirat Negash, Abdiqani Muse Hassan, Ming-Lang Tseng, Mohd Helmi Ali, Ming K. Lim
Summary: This study develops a hierarchical framework that assesses the strategic effectiveness of waste management in the construction industry. Through the fuzzy Delphi method, a valid set of 28 sustainable waste management attributes are identified. These attributes are divided into various elements and constructed into a six-level hierarchical framework using fuzzy interpretive structural modeling. The top aspects for assessing strategic effectiveness in this framework are waste management operational strategy, construction site waste management performance, and the mutual coordination level.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Environmental Sciences
Liang Wang, Xianyi Long, Kuo-Jui Wu, Ming-Lang Tseng, Yue Cao
Summary: China's construction industry is dealing with carbon emissions dilemma and needs to adjust environmental regulations. Many studies neglect the relationship between environmental regulations, technological innovation, and carbon emissions. This study bridges institutional theory to integrate practices in the construction industry. The findings can guide policymakers in reevaluating policy adequacy.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Xiaolin Wang, Liyi Zhan, Yong Zhang, Teng Fei, Ming-Lang Tseng
Summary: This study proposes an environmental cold chain logistics distribution center location model to reduce transportation costs and carbon emissions. It also introduces a hybrid arithmetic whale optimization algorithm to overcome the limitations of the conventional algorithm.
COMPUTERS & INDUSTRIAL ENGINEERING
(2024)
Article
Engineering, Environmental
Kuo-Jui Wu, Hailing Qiu, Caiyan Huang, Anthony S. F. Chiu, Ming-Lang Tseng
Summary: Government resource allocation practices for achieving carbon neutrality should be guided by dynamic system theory to identify potential dynamics. Carbon intensity control dynamics have a significant influence on other dynamics.
RESOURCES CONSERVATION AND RECYCLING
(2024)
Article
Computer Science, Interdisciplinary Applications
Maosheng Yang, Juan Li, Lei Feng, Shih-Chih Chen, Ming-Lang Tseng
Summary: This research provides preliminary and yet important findings on how service robot anthropomorphism is most likely positively associated with consumer usage intention, i.e. the positive influence mechanism of service robot anthropomorphism on consumer usage intention.
INDUSTRIAL MANAGEMENT & DATA SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ling-Ling Li, Xing-Da Fan, Kuo-Jui Wu, Kanchana Sethanan, Ming-Lang Tseng
Summary: This study constructs a distributed generation (DG) multi-objective hierarchical optimal planning model and proposes a solution method based on an improved beluga whale optimization algorithm (IBWO). The study considers the uncertainties in DG output power and the demand response on the load side to determine the optimal location and capacity of DG access to the distribution network. The results show significant reductions in the annual comprehensive cost, total voltage deviation, and power loss of the system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)