Article
Computer Science, Information Systems
Liulin Yang, Yu Li, Zhi Wei
Summary: This article proposes a method based on multilabel and multiclassification and a fast-multibranch residual network (Fa-Mb-ResNet) for simultaneous fault identification and line selection in distribution networks. The method uses frequency division and time division to learn the features of the time-frequency matrix and employs an improved residual unit structure to enhance learning efficiency. Experimental results show that the proposed method outperforms state-of-the-art methods in fault identification and line selection in distribution networks.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Jae-Guk An, Jin-Uk Song, Yun-Sik Oh
Summary: This paper proposes a fuzzy-based fault section identification (FSI) method that utilizes fault current information measured on feeder remote terminal units (FRTUs) installed at automatic switches. The method provides an intuitive index for decision-making by quantifying the possibility of fault for each candidate section. Additionally, the method improves the FSI time by dynamically generating partial trees using validated fault current information.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Automation & Control Systems
Abdel Karim Abdel Karim, M. Amine Atoui, Virginie Degardin, Pierre Laly, Vincent Cocquempot
Summary: This paper proposes a novel fault detection and isolation (FDI) method for complex embedded wired communication networks. The method is based on power line communication (PLC) transmission systems and uses orthogonal frequency division multiplexing (OFDM) to estimate transmission coefficients. The health indicators and residuals are computed by comparing the online estimated coefficients with the reference coefficients. A methodology for handling complex networks, such as bus networks, is also proposed. The FDI method is validated using real data from a Y-shaped network test bench and simulated data from a more complex network.
Article
Computer Science, Information Systems
Abeer M. Mahmoud, Maha M. A. Lashin, Fadwa Alrowais, Hanen Karamti
Summary: This article proposes two models based on fuzzy logic and genetic algorithms for gearbox and motor fault identification. By analyzing vibration signals, it is possible to avoid the losses and costs caused by breakdowns and increase the lifetime of machine components.
Article
Engineering, Multidisciplinary
Erbao Xu, Yan Li, Lining Peng, Mingshun Yang, Yong Liu
Summary: This study introduces a method for identifying unknown faults using the Box transformer substation as an example, by constructing an IoT framework, using Support Vector Data Description and Particle Swarm Optimization algorithm to achieve timely identification and adaptive updating of unknown faults.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Computer Science, Information Systems
Xiangke Zhang, Zhenming Liu, Yajing Wang, Zhenhai Dou, Guoliang Zhai, Qinqin Wei
Summary: An adaptive residual current detection method based on VMD and DFNN was proposed to improve the detection ability in low-voltage distribution networks. The method achieves high detection accuracy and provides a reference for further research on new adaptive residual current protection devices.
Article
Engineering, Mechanical
Harvey David Rojas, Herbert Enrique Rojas, John Cortes-Romero
Summary: The paper explores alternative fault diagnosis methods based on algebraic identification methods, proposing the use of Extended State Observers (ESO) to estimate time-derivatives related to nonlinear complex terms. Three fault diagnosis schemes focused on residual generation, fault identification, and parameter estimation were presented and compared, aiming to estimate as few parameters as possible for simpler implementation. Experimental results on a twin-rotor aerodynamic system demonstrated the effectiveness and robustness of the proposed strategies.
NONLINEAR DYNAMICS
(2022)
Article
Engineering, Mechanical
Xiaobing Ma, Bingxin Yan, Han Wang, Haitao Liao
Summary: This paper proposes a new prognosis framework for machinery fault diagnosis based on original condition monitoring signals. The framework uses Box-Cox transformation to extract a series of degradation features and utilizes N-BEATS algorithm for time-series prediction. A parameter-based transfer learning method is also introduced to reduce computation complexity. The future original signals of machinery are reconstructed through inverse Box-Cox transformation, and a new failure criterion suitable for decision-level fault prognosis is defined.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Computer Science, Information Systems
V. Sreelekha, A. Prince
Summary: This work presents a fault locator using an Adaptive-Network-based Fuzzy Inference System (ANFIS) for accurately estimating the fault location on a compensated line, along with a backup protection algorithm for identifying faulty lines. The fault locator and identification algorithm utilize the phasors generated by the Phasor Measurement Units (PMUs) placed in the system. Simulation results demonstrate that the fault locator accurately estimates fault locations with a 5% tolerance in various fault conditions.
Article
Engineering, Mechanical
Yongbo Li, Shun Wang, Yang Yang, Zichen Deng
Summary: The paper introduces a method called Symbolic Fuzzy Entropy (SFE) based on symbolic dynamic filtering and fuzzy entropy to extract fault features and eliminate noise, effectively improving calculation efficiency. By extending SFE to multiscale analysis to form MSFE, experimental results demonstrate that MSFE outperforms three other methods in extracting weak fault characteristics.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Biodiversity Conservation
Haibo Chu, Jin Wu, Wenyan Wu, Jiahua Wei
Summary: Daily streamflow forecasting is crucial for ecological processes, stream ecology, and decision-making. This study proposes an integrated modelling approach that combines a dynamic classification method with LSTM models to improve streamflow forecasting considering different flow regimes. The performance of these models is compared to traditional LSTM models using data from 8 stations in different climate regions. Results indicate that the proposed models outperform traditional LSTM models, with the DC-B-LSTM model performing better in arid areas.
ECOLOGICAL INDICATORS
(2023)
Article
Automation & Control Systems
Jian Han, Xiuhua Liu, Xinjiang Wei, Shaoxin Sun
Summary: This article discusses fault estimation and fault-tolerant control for nonlinear systems with partially unknown dynamics. A novel adaptive observer is designed to reconstruct system states and deal with faults, while a nonlinear fault-tolerant control technique is proposed using the estimation information obtained by the observer. Three examples are provided to validate the proposed method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Engineering, Multidisciplinary
Haopeng Liang, Jie Cao, Xiaoqiang Zhao
Summary: In industrial systems, the vibration signals of rolling bearings can be complex due to changing operating conditions and environmental noise. This study proposes a multi-scale dynamic adaptive residual network (MSDARN) fault diagnosis method that combines multi-scale learning and attention mechanism to dynamically adjust the weights of different scale convolutional layers. The effectiveness of the proposed method is verified through experiments, showing higher fault classification accuracy compared to other deep learning methods.
Article
Engineering, Environmental
Zhengqing Lin, Zhengwei Hu, Jingchao Peng, Haitao Zhao
Summary: Traditional graph-based dynamic fault detection methods overlook the diversity of dynamic properties of variables in complex chemical processes. To address this issue, a novel neural network structure named DSGNN is proposed, which divides variables into groups based on their dynamic properties and constructs subgraphs for each group. DSGNN utilizes convolution operations to aggregate dynamic information and extracts low-dimensional features using back-propagation technique. Two case studies demonstrate the superiority of DSGNN in multivariate dynamic processes and the Tennessee Eastman process.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2022)
Article
Automation & Control Systems
Rune Gronborg Junker, Rishi Relan, Henrik Madsen
Summary: Despite the simplicity of the Duffing oscillator, it is widely used to describe the rich dynamical behavior of real-world nonlinear systems. This paper proposes a method based on stochastic differential equations (SDEs) for identifying a model of the Duffing oscillator, which captures the underlying physics and handles model uncertainty. The proposed approach is applied to the identification of a forced Duffing oscillator model using benchmark data, and the model performance is compared with existing results from the literature.
Article
Computer Science, Artificial Intelligence
Mahardhika Pratama, Sreenatha G. Anavatti, Meng Joo Er, Edwin David Lughofer
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2015)
Editorial Material
Computer Science, Artificial Intelligence
Mu-Yen Chen, Edwin Lughofer, Ken Sakamura
INFORMATION FUSION
(2015)
Article
Computer Science, Information Systems
Francisco Serdio, Edwin Lughofer, Kurt Pichler, Markus Pichler, Thomas Buchegger, Hajrudin Efendic
INFORMATION SCIENCES
(2015)
Article
Computer Science, Information Systems
Edwin Lughofer, Moamar Sayed-Mouchaweh
INFORMATION SCIENCES
(2015)
Article
Computer Science, Artificial Intelligence
Eva Weigl, Wolfgang Heidl, Edwin Lughofer, Thomas Radauer, Christian Eitzinger
MACHINE VISION AND APPLICATIONS
(2016)
Article
Engineering, Mechanical
Kurt Pichler, Edwin Lughofer, Markus Pichler, Thomas Buchegger, Erich Peter Klement, Matthias Huschenbett
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2016)
Article
Computer Science, Artificial Intelligence
Mahardhika Pratama, Jie Lu, Edwin Lughofer, Guangquan Zhang, Sreenatha Anavatti
Article
Computer Science, Artificial Intelligence
Mahardhika Pratama, Jie Lu, Sreenatha Anavatti, Edwin Lughofer, Chee-Peng Lim
Article
Energy & Fuels
Francisco Serdio Fernandez, Miguel Angel Munoz-Garcia, Susanne Saminger-Platz
Article
Instruments & Instrumentation
Veronika Putz, Sylvia Apostol, Ramesh K. Selvasankar, Thomas Voglhuber-Brunnmaier, Juergen Miethlinger, Bernhard G. Zagar, Thomas Buchegger
TM-TECHNISCHES MESSEN
(2016)
Article
Computer Science, Artificial Intelligence
Francisco Serdio, Edwin Lughofer, Alexandru-Ciprian Zavoianu, Kurt Pichler, Markus Pichler, Thomas Buchegger, Hajrudin Efendic
APPLIED SOFT COMPUTING
(2017)
Proceedings Paper
Engineering, Electrical & Electronic
Edwin Lughofer, Eva Weigl, Wolfgang Heidl, Christian Eitzinger, Thomas Radauer
2015 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2015)
(2015)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)