Article
Computer Science, Artificial Intelligence
Dengxiu Yu, Ming Yang, Yan-Jun Liu, Zhen Wang, C. L. Philip Chen
Summary: This article investigates the problem of adaptive fuzzy tracking control for uncertain nonlinear systems with multiple actuators and sensors faults. The challenge of designing the control scheme arises from the fact that all states of the system cannot be accurately measured due to the presence of multiple sensor faults. Additionally, the design of the controller is complicated by multiple actuator faults and external disturbance. To address these issues, different adaptive update laws are designed to mitigate the effects of unknown actuator faults, sensor faults, and external disturbance. The actual states are estimated by combining sensor outputs with adaptive parameters, and the unknown nonlinear functions are approximated using a combination of fuzzy logic systems and state estimation. A novel adaptive fuzzy tracking control algorithm is then developed using the backstepping method. The proposed fault-tolerant control algorithm ensures bounded signals of the system despite the occurrence of multiple faults by employing the Lyapunov function. The effectiveness of the novel algorithm is verified by comparing its control performance to that of another algorithm.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Engineering, Industrial
Adel Mottahedi, Farhang Sereshki, Mohammad Ataei, Ali Nouri Qarahasanlou, Abbas Barabadi
Summary: Resilience is a growing concept in managing engineering systems, but estimating system resilience is challenged by lack of historical data and limited information. Current studies use various indices to quantify resilience, but lack detailed examination of influencing factors. This paper aims to develop a practical methodology using expert judgment and fuzzy set theory to effectively model factors influencing resilience, demonstrated with an underground coal mine fan system.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Computer Science, Information Systems
Wenli Shang, Tianyu Gong, Jing Hou, Jiayue Lu, Zhong Cao
Summary: In this paper, an improved expert decision method based on attack tree model is proposed to address the issues of scientificity and reliability in vulnerability quantitative assessment. By introducing the concept of deviation degree and utilizing aggregation techniques, the method solves the problem of insufficient evaluation data and enhances the reliability and scientific validity.
Review
Computer Science, Artificial Intelligence
Aurora Saibene, Michela Assale, Marta Giltri
Summary: Medical expert systems can support physicians and patients, providing instant access to knowledge and advice with great flexibility. These systems come in various forms, from fuzzy logic to wearable solutions. However, there is a common lack of system validation in the current literature.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
You Cao, Zhijie Zhou, Changhua Hu, Wei He, Shuaiwen Tang
Summary: This article systematically summarizes the interpretability characteristics of the belief rule base (BRB) expert system and proposes four interpretability criteria to ensure its interpretability in optimization. An improved optimization algorithm with interpretability constraints derived from the criteria is developed to establish an interpretable BRB. A case study on the health state evaluation of aerospace relay demonstrates the effectiveness of the proposed method.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Mathematics
Manuel Casal-Guisande, Alberto Comesana-Campos, Alejandro Pereira, Jose-Benito Bouza-Rodriguez, Jorge Cerqueiro-Pequeno
Summary: This study proposes a new method for monitoring the work conditions of machining tools by incorporating expert systems and sound-processing techniques. The method can identify undesirable behaviors of the tools and improve the workplace ergonomics.
Article
Automation & Control Systems
Hua Li, Tao Liu, Xing Wu, Qing Chen
Summary: The study introduces an enhanced SVD method E-SVD to address the issues with SVD, achieving superior signal reconstruction and noise reduction effects through the combination of ISVD and IWPT. Additionally, an evaluation indicator is introduced to assess the performance of the results.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Engineering, Electrical & Electronic
Daichao Wang, Yibin Li, Lei Jia, Yan Song, Yanjun Liu
Summary: Bearing faults are a common cause of machine failures, and reliable and rapid diagnosis is crucial. A novel three-stage feature fusion method called attention-based multidimensional concatenated convolutional neural network is proposed for fault diagnosis using vibration and torque signals. The method effectively improves diagnostic accuracy to 99.8% from 96.4% by learning global information and highlighting important features through the attention mechanism.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Computer Science, Theory & Methods
Yingfang Li, Xingxing He, Dan Meng, Keyun Qin
Summary: This paper presents an improved method for estimating the similarity between LR-type fuzzy numbers and compares it with existing methods. The proposed method overcomes the shortcomings of existing methods by considering the shape of LR-type fuzzy numbers.
FUZZY SETS AND SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yuangang Wang, Haoran Liu, Wenjuan Jia, Shuo Guan, Xiaodong Liu, Xiaodong Duan
Summary: This article proposes a deep fuzzy rule-based classification system (DFRBCS) based on improved WM method, combining fuzzy technique and deep learning strategy to balance model's interpretability and prediction accuracy.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
You Cao, Zhijie Zhou, Shuaiwen Tang, Pengyun Ning, Manlin Chen
Summary: This article conducts a comprehensive analysis of the robustness of the Belief rule base (BRB) expert system and proposes a new robustness analysis method. By analyzing the input transformation, matching degree calculation, matching degree normalization, and rule aggregation of BRB, five guidelines for BRB construction are proposed. The effectiveness of the proposed method is verified through the robustness analysis of the BRB expert system for relay health-state evaluation.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Yi Wang, Zhongping Yang, Fei Lin
Summary: This article focuses on analyzing the sensitivity of wireless power transfer (WPT) system characteristics to capacitor errors and proposes design methods to improve detuning tolerance. The study shows that the proposed design reduces the change ratio of output voltage, increases power factor, and reduces the drop in transfer efficiency.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Computer Science, Information Systems
Xiao-Lei Wang, Li-Ying Hao
Summary: This paper investigates the design of event-triggered fault detection observers for T-S fuzzy systems. An improved matching membership function method is proposed to provide design flexibility. By establishing equality constraints, the membership functions of the residual generator can be obtained directly without calculation. New criteria based on linear matrix inequalities are derived to ensure the desired performance of the FD system. The proposed method overcomes the shortcomings of existing results and is verified through an example.
INFORMATION SCIENCES
(2022)
Article
Engineering, Industrial
Chuanhai Chen, Bowen Li, Jinyan Guo, Zhifeng Liu, Baobao Qi, Chunlei Hua
Summary: In this paper, a bearing life prediction method based on the improved FIDES reliability model is proposed. The degradation model of the bearing is established using the nonlinear Wiener process, and the prediction of bearing life is achieved by introducing the transient failure rate function. Experimental results demonstrate the accurate prediction of bearing service life and the superiority of the model.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Mechanical
Libin Wang, Huanqing Wang, Peter Xiaoping Liu
Summary: This paper proposes a fast convergence feedback control algorithm for a class of nonlinear stochastic systems with actuator faults, using a fuzzy logic system to handle uncertainties and developing an adaptive fuzzy controller to address actuator faults. The presented scheme overcomes complexity issues inherent in conventional methods and ensures finite-time bounded signals and convergence of tracking error to a small neighborhood around the origin. Simulation results demonstrate the effectiveness of the proposed method.
NONLINEAR DYNAMICS
(2021)
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)