Article
Computer Science, Artificial Intelligence
Yuan Zhong, Guofa Li, Chuanhai Chen, Yan Liu
Summary: Failure mode and effects analysis (FMEA) is an important task in product reliability design as it helps identify weak areas and key components in the design. This paper introduces a new FMEA method that addresses the limitations of existing methods, such as the inability to handle fuzzy information and consider the weight and correlation of risk factors. By integrating Fermatean fuzzy sets (FFS) and Muirhead Mean (MM) operators, the proposed method enhances the expression of fuzzy information and effectively deals with the weight and correlation between influencing factors.
APPLIED SOFT COMPUTING
(2023)
Article
Green & Sustainable Science & Technology
Fahim ul Amin, Qian-Li Dong, Katarzyna Grzybowska, Zahid Ahmed, Bo-Rui Yan
Summary: This article evaluates sustainable supply chain risks using a new fuzzy VIKOR-CRITIC technique and focuses on the logistics industry in Pakistan. The study integrates the fuzzy VIKOR approach with the CRITIC method and establishes thirty criteria divided into four categories. The findings highlight the importance of organizational risks and the minimal influence of environmental hazards.
Article
Computer Science, Artificial Intelligence
Chuanxi Jin, Yan Ran, Genbao Zhang
Summary: This paper proposes a hybrid risk evaluation method that combines picture fuzzy sets, PF-linear programming model, and PF-weighted aggregated sum product assessment method to evaluate product risks and rank failure modes.
Article
Computer Science, Artificial Intelligence
Melike Erdogan, Ihsan Kaya, Ali Karasan, Murat Colak
Summary: This paper proposes a study using a multi-criteria decision-making method to evaluate alternative solutions of autonomous vehicle driving systems, taking into account risk criteria. The results of the study indicate that "Software Specifications" and "Reliability" are the most important main and sub-criteria.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Guo-Niu Zhu, Jin Ma, Jie Hu
Summary: This study proposes a novel fuzzy rough number extended multi-criteria group decision-making strategy to improve traditional FMEA in ranking failure modes. The practical case study validates the advantages of this method in handling uncertainty and subjectivity.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Automation & Control Systems
Qian-Xia Ma, Xiao-Min Zhu, Kai-Yuan Bai, Run-Tong Zhang, Dong-Wei Liu
Summary: This paper presents a new method to improve the performance of the traditional failure mode and effect analysis (FMEA) by using spherical fuzzy sets (SFSs) and spherical fuzzy weight correlation coefficient (SF-WCC). The method includes three stages: capturing risk evaluation information using SFS, determining expert weights objectively using a spherical fuzzy projection model, and prioritizing failure modes using SF-WCC. The experiment demonstrates the applicability and effectiveness of the proposed method, indicating it as a reliable risk assessment technology for practical FMEA problems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Zhi-Chao Wang, Yan Ran, Yifan Chen, Xin Yang, Genbao Zhang
Summary: This paper combines multi-criteria decision making (MCDM) techniques with probabilistic hesitant fuzzy linguistic term sets (PHFLTSs) to implement risk assessment of failure modes, overcoming the deficiencies of conventional methods and providing an effective approach.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Chiranjibe Jana, Amir Mohamadghasemi, Madhumangal Pal, Luis Martinez
Summary: This paper presents an improved version of the interval type-2 fuzzy VIKOR (IT2FVIKOR) method to address the drawbacks of the conventional IT2FVIKOR method. The proposed method introduces more continuous and logical values, resulting in better ranking results for multiple criteria decision making problems.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Automation & Control Systems
Xiang-Kun Zhao, Xiao-Min Zhu, Kai-Yuan Bai, Run-Tong Zhang
Summary: This study proposes a novel FMEA method that utilizes picture fuzzy sets theory to address the challenges in existing methods, such as difficulty in assessment expression and acquisition, imprecision in assessment aggregation, and missing relationships among risk factors. The method simplifies the expert evaluation process through a flexible knowledge acquisition framework, standardizes non-fuzzy values using a strategy-based picture fuzzy conversion method, improves assessment aggregation accuracy with a picture fuzzy evidential reasoning method, and establishes alternative models using picture fuzzy Petri nets to describe the relationships among risk factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Shahzad Faizi, Mubashar Shah, Tabasam Rashid
Summary: This paper presents a multi-criteria group decision-making (MCGDM) method and a modified VIKOR method based on hesitant intuitionistic fuzzy linguistic term sets (HIFLTSs). The study also explores operational laws and aggregation operators for HIFLTSs. The research demonstrates that the proposed methods can accurately describe the fuzziness and uncertainty of experts, and provide effective and reliable ranking results.
Article
Business
Decui Liang, Fangshun Li
Summary: This article proposes a new FMEA model that combines hesitant Pythagorean fuzzy sets and ORESTE method to address the limitations in failure mode assessment and prioritization. The proposed model accurately describes the hesitation and uncertainty of risk assessment information, and provides suitable preventive measures for managers.
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2023)
Article
Environmental Sciences
Haiyan Yang, Qingda Luo, Xiaobo Sun, Zhe Wang
Summary: The study constructed a comprehensive evaluation index system for urban waterlogging prevention and control resilience in the Beijing-Tianjin-Hebei urban agglomeration, and used the fuzzy VIKOR method to evaluate the resilience. The results revealed regional differences and constraints, and identified vulnerable urban areas. The resilience level in most cities increased, but a few cities showed a decrease, indicating the need to strengthen the capacity for managing waterlogging disasters. The spatial difference in urban waterlogging prevention toughness was significant and should be considered in the expansion and construction of waterlogging prevention and control measures.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Engineering, Environmental
Yu Jianxing, Wu Shibo, Chen Haicheng, Yu Yang, Fan Haizhao, Liu Jiahao
Summary: An improved FMEA method based on cloud model and extended VIKOR is proposed in this study to evaluate the risk of submarine pipelines with enhanced reliability. This method combines cloud model theory and a synthetic dynamic weight algorithm, establishes a two-level risk factor hierarchy, and utilizes an integrated weighting method to comprehensively reveal the relative importance of risk factors. The extension of the VIKOR method with the cloud model is used to determine the risk priority of failure modes.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2021)
Article
Computer Science, Information Systems
Arunodaya Raj Mishra, Shyi-Ming Chen, Pratibha Rani
Summary: This paper presents a novel multiattribute decision making (MADM) approach based on Fermatean hesitant fuzzy sets (FHFSs) and the modified VIKOR method. The method defines distance measures of FHFSs and introduces remoteness indices to establish attribute weights. The performance of the proposed method is demonstrated through examples, and its advantages in terms of robustness and flexibility are shown through comparative study.
INFORMATION SCIENCES
(2022)
Article
Green & Sustainable Science & Technology
Yan Tu, Huayi Wang, Xiaoyang Zhou, Wenjing Shen, Benjamin Lev
Summary: This study aims to develop a comprehensive evaluation methodology for assessing the level of regional water resources coordination (RWRC). By establishing a SEE evaluation indicator system and utilizing a multi-criteria decision-making approach, rankings were conducted for six relevant regions in the North China Plain, with managerial suggestions provided to enhance coordination levels.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Operations Research & Management Science
Jia Huang, Hu-Chen Liu, Chun-Yan Duan, Ming-Shun Song
Summary: This paper proposes a new FMEA model to evaluate and prioritize the risk of failure modes by integrating probabilistic linguistic term sets and TODIM method. The probabilistic linguistic term sets handle the ambiguity in risk assessments, while an extended TODIM method determines the priority ranking of failure modes. Additionally, an objective weighting method based on TOPSIS is presented to derive the relative weights of risk factors.
ANNALS OF OPERATIONS RESEARCH
(2022)
Review
Computer Science, Artificial Intelligence
Jian Wang, Qianqian Ma, Hu-Chen Liu
Summary: Meta-evaluation theory and methods were used to evaluate science and technology project review experts, with dozens of criteria identified in two categories. An empirical study was conducted to analyze the relationships among these criteria and propose a method to handle subjective fuzzy data. The proposed model based on IVIF-BWM and MULTIMOORA was applied in a real case study, which demonstrated its accuracy and reliability.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Business
Hu-Chen Liu, Song-Man Wu, Ze-Ling Wang, Xiao-Yang Li
Summary: This paper introduces a novel QFD method integrating EHFLTSs and prospect theory to overcome the limitations of traditional QFD. By using Choquet integral and extended prospect theory, the ranking orders of ECs are determined effectively.
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2021)
Article
Computer Science, Artificial Intelligence
Qiang Li, Qin-Yu Chen, Zheng Liu, Hu-Chen Liu
Summary: This study proposes a new approach for public transport customer satisfaction evaluation based on an extended thermodynamic method with q-rung orthopair fuzzy sets. By utilizing the stepwise weight assessment ratio analysis method to specify the weights of evaluation criteria, the method effectively evaluates the customer satisfaction levels of different rail transit lines in Shanghai.
Article
Computer Science, Artificial Intelligence
Lin Huang, Ling-Xiang Mao, Yao Chen, Hu-Chen Liu
Summary: With the occurrence of various emergency events becoming increasingly frequent, emergency decision making (EDM) has gained significant attention in research. This study proposes a new EDM method that integrates regret theory, evaluation based on distance from average solution (EDAS) method, and 2-tuple spherical linguistic term sets (TSLTSs). The method efficiently ranks and selects the optimal emergency response solution by considering decision makers' uncertain and vague evaluations. The proposed method is applied to a public health emergency in China and is shown to be superior and practical through a comparative analysis with other EDM methods.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Hu-Chen Liu, Xue Luan, MengChu Zhou, Yun Xiong
Summary: The use of a new type of 2-dimensional uncertain linguistic Petri nets (2DULPN) enables better handling of uncertain linguistic knowledge given by domain experts and their judgments' reliability, as well as capturing the interrelationship among propositions.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Chemistry, Multidisciplinary
Yu-Jie Zhu, Wei Guo, Hu-Chen Liu
Summary: This article proposes a new type of DUCG model by integrating PULSs and the EDAS method, which can overcome the shortcomings of the traditional model and provide more accurate representation of expert knowledge, as well as handling conflicting opinions among experts.
APPLIED SCIENCES-BASEL
(2022)
Article
Green & Sustainable Science & Technology
Hua Shi, Ling-Xiang Mao, Ke Li, Xiang-Hu Wang, Hu-Chen Liu
Summary: A new approach to prioritize engineering characteristics in Quality Function Deployment (QFD) is proposed based on double hierarchy hesitant linguistic term sets and the ORESTE method. This approach overcomes the deficiencies of traditional QFD in handling uncertain assessments and prioritizing engineering characteristics. A case study is presented to illustrate its feasibility and practicability.
Article
Business
Chun-Yan Duan, Xu-Qi Chen, Hua Shi, Hu-Chen Liu
Summary: Failure mode and effects analysis (FMEA) is a tool used to improve the reliability and safety of a system, process, or service. However, traditional FMEA has limitations that affect its effectiveness. This article proposes a new FMEA model using double hierarchy hesitant fuzzy linguistic term sets and k-means clustering to evaluate and cluster the risk of failure modes. A case study is presented to demonstrate the effectiveness and usefulness of this approach.
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2022)
Review
Computer Science, Artificial Intelligence
Ya-Xuan Yu, Hua-Ping Gong, Hu-Chen Liu, Xun Mou
Summary: Fuzzy Petri nets (FPNs) are a powerful modeling tool for expert systems. This paper conducts a bibliometric analysis of FPN studies to understand the developments, focus areas, and research trends in this field. The analysis reveals that the publication on FPNs has increased rapidly since 2018. The influential authors, emerging research trends, and hot research topics are identified, providing important reference for scholars and practitioners in understanding the research status and future research agenda of FPNs.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Engineering, Industrial
Hu -Chen Liu, Jing-Hui Wang, Ling Zhang, Qi-Zhen Zhang
Summary: Human reliability analysis is a method used to enhance the safety and reliability of complex socio-technical systems. Current studies often involve small groups of experts, which is insufficient for addressing increasingly complex problems. This article proposes a large group SLIM model that considers experts' noncooperative behaviors and social relations, and effectively calculates the human error probabilities of tasks.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Automation & Control Systems
Hua Shi, Jing-Hui Wang, Ling Zhang, Hu-Chen Liu
Summary: Because human errors are the main cause of accidents in safety-critical industries, human reliability analysis (HRA) is crucial for improving the reliability of complex engineering systems. However, assessing human errors is challenging due to uncertainty in state evaluation information, multiple common performance conditions (CPCs), and their correlations. This paper proposes a new cognitive reliability and error analysis method (CREAM) that integrates linguistic D numbers and the decision-making trial and evaluation laboratory-based analytic network process (DANP) method to quantitatively analyze human errors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Zheng Liu, Xun Mou, Hu-Chen Liu, Ling Zhang
Summary: This article presents a new FMEA approach that integrates probabilistic linguistic preference relations (PLPRs) and gained and lost dominance score (GLDS) method. The PLPRs are used to describe risk evaluations of experts through pairwise comparison of failure modes. An extended GLDS method is introduced to consider both group and individual risk attitudes and determine the risk ranking of failure modes. Furthermore, a two-step optimization model is proposed to determine the weights of risk factors when their weighing information is unknown.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Ya-Juan Han, Miao-Miao Cao, Hu-Chen Liu
Summary: This paper proposes a new hybrid QFD approach for determining the priority of engineering characteristics in order to satisfy customer requirements. It utilizes multi-granular unbalanced linguistic term sets to describe vague relational evaluations and employs an opinion evolution social network consensus reaching model to derive consensual relational evaluations. The combined compromise solution method is adopted to determine the priority orders of engineering characteristics, taking into account conflicting customer requirements.
APPLIED SOFT COMPUTING
(2023)
Article
Psychology, Multidisciplinary
Wei Guo, Xin-Rong Chen, Hu-Chen Liu
Summary: Research shows that Easterners are more risk intolerant but more willing to accept ambiguous conditions in the gain domain compared to Westerners. Surprisingly, Easterners and Westerners have similar attitudes towards risk and ambiguity in the loss domain. Cultural differences between Western and Eastern countries may explain the higher level of risk aversion observed among East Asians.
BEHAVIORAL SCIENCES
(2022)
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)
