Article
Automation & Control Systems
Renhe Yao, Hongkai Jiang, Chunxia Yang, Hongxuan Zhu, Ke Zhu
Summary: This paper proposes a multiband weights-induced periodic sparse representation (MwPSR) method for fault diagnosis and estimation. The method utilizes a new indicator to construct weights and enhance faulty impulses, and embeds an improved periodic target vector to enhance the periodicity of the estimated impulses. Detailed simulations and experiments demonstrate that MwPSR achieves periodic sparsity with high accuracy and robustness, making it reliable for incipient bearing fault diagnosis.
Article
Computer Science, Artificial Intelligence
Zuanyu Zhu, Yu Yang, Niaoqing Hu, Zhe Cheng, Junsheng Cheng
Summary: The study proposes a sparse random projection-based hyperdisk classifier model for fault diagnosis of bevel gearbox. The method efficiently screens out core samples, introduces slack variables and a dynamic penalty parameter to improve the boundary of the model, and develops a strategy to handle imbalanced training data, resulting in better performance and efficiency in fault diagnosis.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Engineering, Mechanical
Wei Chu, Tao Liu, Zhenya Wang, Chang Liu, Jun Zhou
Summary: This paper proposes a rolling bearing fault feature extraction method based on sparse coefficient weighting theory and periodic enhancement strategy, which effectively extracts weak periodic shock features with a low signal-to-noise ratio.
MECHANISM AND MACHINE THEORY
(2022)
Article
Acoustics
Chenglin Zuo, Jun Ma, Hao Xiong, Lin Ran
Summary: A novel image denoising strategy is proposed to reduce noise in digital images captured from CMOS/CCD image sensors, utilizing nonlocal self-similarity and local shape adaptation. By incorporating wavelet thresholding and residual image derived from initial estimate, better denoising performance for NLM is achieved. Experimental results show that the proposed method outperforms original NLM and competes with state-of-the-art denoising methods.
SHOCK AND VIBRATION
(2021)
Article
Computer Science, Information Systems
Minh Tuan Pham, Jong-Myon Kim, Cheol Hong Kim
Summary: This paper proposes an intelligent bearing fault condition monitoring and diagnosis method, which converts acoustic emission signals into spectrograms and uses a convolutional neural network for bearing state inference. By utilizing an efficient neural network architecture search space and separating condition monitoring from fault diagnosis, computing resources can be saved while accuracy is improved.
Article
Automation & Control Systems
Chaoyao Wen, Ping Zhou, Wei Dai, Liang Dong, Tianyou Chai
Summary: This paper proposes a novel online sequential sparse robust neural network model for online modeling of imperfect industrial data streams. The model improves the calculation of network output weights and parameter updating through sparse partial least squares regression and online sequential learning strategy, aiming to enhance modeling performance and adapt to the time-variant dynamics of industrial systems. Moreover, Schweppe generalized M-estimation is adopted to enhance the robustness of the model and address the issue of outliers in input and output samples. Experimental results on two industrial systems demonstrate the high estimation accuracy and practicality of the proposed method in industrial processes.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Feng Liu, Junsheng Cheng, Niaoqing Hu, Zhe Cheng, Yu Yang
Summary: Sparse random similarity feature decomposition (SRSFD) method achieves adaptive signal decomposition by solving sparse optimization problem and generating random time-frequency features using windowed sinusoidal function. It has better decomposition performance and noise robustness compared to existing methods.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Biochemical Research Methods
Shinfeng D. Lin, Luming Chen, Wensheng Chen
Summary: This article proposed a method for thermal face recognition under different conditions, using temperature information and Random Forest classifier. Experimental results demonstrated the robustness of the proposed method against various challenges.
BMC BIOINFORMATICS
(2021)
Article
Engineering, Mechanical
Fengping An, Jianrong Wang
Summary: This paper proposes a rolling bearing fault diagnosis algorithm based on the overlapping group sparse model-deep complex convolutional neural network. This algorithm solves the difficulties in extracting the composite fault signal features of rolling bearings and the problem of multi-scale information. The experimental results show that the proposed method can effectively identify rolling bearing faults under constant and changing operating conditions, and it has a higher classification accuracy compared to traditional machine learning methods.
NONLINEAR DYNAMICS
(2022)
Article
Engineering, Multidisciplinary
Jianzhong Zhang, Yongbin Wu, Zheng Xu, Zakiud Din, Hao Chen
Summary: This paper proposes a method for fault diagnosis of high voltage circuit breakers using multi-sensor information fusion. By using wavelet packet decomposition and D-S evidence theory, the accuracy of fault diagnosis can be improved.
Article
Automation & Control Systems
Mengqi Miao, Jianbo Yu
Summary: In this paper, a sparse representation network (SRNet) is developed to extract impulses from collected signals and used for machinery fault recognition. A convolutional sparse graph is introduced in a sparse representation layer to suppress noise and preserve impulsive characteristics of signals, improving the feature extraction capacity of SRNet. A selective residual learning method is also developed to effectively optimize gradient propagation and further enhance the feature learning performance of SRNet. The feature learning and fault classification capacity of SRNet is evaluated on two gearbox cases, demonstrating its effectiveness compared with other deep neural networks.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Chemical
Lijun Wang, Xiangyang Li, Da Xu, Shijuan Ai, Changxin Chen, Donglai Xu, Chaoge Wang
Summary: This paper proposes a bearing fault feature extraction method based on the EEMD algorithm and improved sparse representation. By separating harmonic components, EEMD decomposition, and constructing a learning dictionary, the impact component in the signal can be effectively extracted.
Article
Computer Science, Information Systems
Shicheng Yang, Ying Wen, Lianghua He, MengChu Zhou
Summary: This study proposes a novel method called sparse common feature-based representation (SCFR) to address the issue of undersampled face recognition encountered in IoT applications, providing better performance without the time-consuming training required by deep learning models.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Software Engineering
Qiao Du, Feipeng Da
Summary: The paper introduces a novel approach called block dictionary learning (BDL) which combines sparse representation and convolutional neural networks to address the fewshot face recognition problem. Through local feature extraction and a global-to-local dictionary learning algorithm, BDL demonstrates effectiveness in comparison with other FFR methods on AR and Extended Yale B datasets.
Article
Computer Science, Information Systems
Xiaofan Liu, Jason Centeno, Juan Alvarado, Lizhe Tan
Summary: This paper proposes a novel algorithm for bearing fault diagnosis using sparse wavelet decomposition for feature extraction combined with a multi-scale one dimensional convolutional neural network (1-D CNN). The proposed algorithm achieves a higher classification accuracy compared to other methods.
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)