Article
Engineering, Industrial
Yongjing Wang, Feiying Lan, Jiayi Liu, Jun Huang, Shizhong Su, Chunqian Ji, Duc Truong Pham, Wenjun Xu, Quan Liu, Zude Zhou
Summary: Remanufacturing involves rebuilding a product using a combination of reused, repaired, and new parts, with disassembly as a key and labor-intensive process. Robotic disassembly is an attractive alternative but still requires manual planning due to the complexity of end-of-life products. The proposed method in this paper allows machines to plan disassembly by generating and manipulating specific matrices, providing a flexible and effective approach for dealing with complex mechanical structures.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Review
Engineering, Industrial
S. K. Ong, M. M. L. Chang, A. Y. C. Nee
Summary: Disassembly sequence planning (DSP) is a challenging research area that has attracted significant attention from researchers worldwide. New solutions are continuously proposed to tackle the complexities of disassembling products, with advancements in computing and introduction of new concepts like virtual reality. Survey papers in the past 12 years have focused on product representation models, sequencing algorithms, and methodology validation in DSP research.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Kaipu Wang, Jun Guo, Baigang Du, Yibing Li, Hongtao Tang, Xinyu Li, Liang Gao
Summary: Collaborative optimization of disassembly line balancing and disassembly sequence planning, combined with a partial destructive disassembly mode, is proposed. A mixed integer linear programming model and a multi-objective improved genetic algorithm are developed. The effectiveness of the proposed model and algorithm is verified in both small-scale and real case studies, and significant improvements are observed in terms of reducing stations, improving smoothness, increasing profits, and reducing energy consumption.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Soran Parsa, Mozafar Saadat
Summary: The research proposes a disassembly planning method based on human-robot collaboration, utilizing human flexibility and robot precision to handle complex disassembly tasks and improve process efficiency. Components are targeted based on remanufacturability parameters, and optimization is done using a mathematical model and genetic algorithm.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2021)
Article
Computer Science, Interdisciplinary Applications
Natalia Hartono, F. Javier Ramirez, D. T. Pham
Summary: Remanufacturing is crucial for a circular economy as it helps reduce landfill waste and preserve natural resources, benefiting the environment. This study proposes a model for planning the sequence of steps for robotic disassembly, using the Bees Algorithm to optimize profitability, energy savings, and greenhouse gas emission reductions.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Computer Science, Information Systems
Ritam Sarkar, Debaditya Barman, Nirmalya Chowdhury
Summary: In this study, a domain knowledge based genetic algorithm is proposed for the path planning problem of a mobile robot. By introducing four new operators, the method shows significant improvement in both single and multiple target scenarios, and has been validated in various simulated environments.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Engineering, Chemical
Jiang Liu, Changshu Zhan, Zhiyong Liu, Shuangqing Zheng, Haiyang Wang, Zhou Meng, Ruya Xu
Summary: This paper proposes a method for solving multi-objective DSP problems in an uncertain environment through stochastic planning. The improved peafowl optimization algorithm (IPOA) is used to efficiently search for optimal or near-optimal solutions. By comparing with real-world industrial case and other advanced algorithms, the superiority of IPOA in solving DSP problems is demonstrated.
Article
Computer Science, Software Engineering
Arun Rehal, Dibakar Sen
Summary: This paper presents a scheme for efficient disassembly sequence planning based on part accessibility. It introduces the concept of shell structures for analyzing the accessibility of parts and explores their applications in maintenance planning and end-of-life processing. A grid-based method is proposed for constructing the shell structure. The results demonstrate the effectiveness of the proposed methods in assessing accessibility and updating shell structures.
COMPUTER-AIDED DESIGN
(2023)
Review
Automation & Control Systems
Xiwang Guo, MengChu Zhou, Abdullah Abusorrah, Fahad Alsokhiry, Khaled Sedraoui
Summary: It is crucial to efficiently disassemble obsolete products to avoid environmental pollution and maximize economic benefits. This paper surveys the state of the art of disassembly sequence planning, aiming to provide guidance for researchers and decision makers in optimal planning solutions. The progress in disassembly sequencing planning is discussed in various aspects, stimulating further engagement in research and development in the Industry 4.0 era.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Automation & Control Systems
Jin Xie, Xinyu Li, Liang Gao
Summary: Disassembly sequence planning (DSP) plays a crucial role in increasing efficiency, reducing costs, and minimizing environmental impacts. A modified grey wolf optimizer (MGWO) is proposed to address the NP-hard combinatorial optimization problem of DSP, introducing new operators to ensure feasibility of solutions and achieve effective results in engineering cases.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2021)
Article
Environmental Sciences
Changshu Zhan, Xuesong Zhang, Guangdong Tian, Duc Truong Pham, Mikhail Ivanov, Anatoly Aleksandrov, Chenxi Fu, Junnan Zhang, Zhen Wu
Summary: Due to environmental pollution and resource shortages, the electric vehicle industry has been rapidly developing, leading to increased market demand for batteries. Proper disassembly of end-of-life vehicle batteries is necessary to promote green remanufacturing, reduce environmental pollution, and decrease reliance on natural resources.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Computer Science, Software Engineering
Yunsheng Tian, Jie Xu, Yichen Li, Jieliang Luo, Shinjiro Sueda, Hui Li, Karl D. D. Willis, Wojciech Matusik
Summary: Assembly planning is a challenging problem in modern industrial manufacturing, but this research proposes a novel method to efficiently plan physically plausible assembly motion and sequences in real-world assemblies. The method combines the assembly-by-disassembly principle and physics-based simulation to explore a reduced search space. Experimental results demonstrate that the method achieves a state-of-the-art success rate and high computational efficiency.
ACM TRANSACTIONS ON GRAPHICS
(2022)
Article
Computer Science, Artificial Intelligence
Weibin Qu, Jie Li, Rong Zhang, Shimin Liu, Jinsong Bao
Summary: Increasing numbers of retired lithium-ion batteries for new energy vehicles pose an ecological threat, making research on their disassembly and recycling methods a priority. The Human-Robot Collaboration Disassembly (HRCD) mode replaces single-person and single-machine disassembly to become the standard method for end-of-life lithium-ion battery disassembly. This paper develops an HRCD environment that enhances the flexibility of disassembly operations through virtual and real interaction functions and recommends real-time cooperation strategies.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Engineering, Industrial
Yuanjun Laili, Fei Ye, Yongjing Wang, Lin Zhang
Summary: This paper discusses the use of a probability matrix to represent uncertain interference and proposes a multi-threshold planning scheme to generate optimal disassembly sequence plans. The research demonstrates that this approach is effective in handling disassembly problems under complex end-of-life conditions.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Amal Allagui, Imen Belhadj, Regis Plateaux, Moncef Hammadi, Olivia Penas, Nizar Aifaoui
Summary: The paper presents a new approach based on the Reinforcement Learning algorithm to optimize Disassembly Sequence Planning. The approach aims to optimize five disassembly parameters or goals and is applied to a demonstrative example.
COMPUTERS IN INDUSTRY
(2023)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)