Article
Engineering, Multidisciplinary
Jianqun Zhang, Qing Zhang, Xianrong Qin, Yuantao Sun
Summary: This paper proposes a intelligent fault diagnosis methodology for rotating machinery by combining optimized support vector data description and optimized support vector machine. It explores different entropy-based indicators for feature extraction to improve the accuracy of fault detection and fault identification, which is beneficial to practical application.
Article
Computer Science, Artificial Intelligence
Kuiliang Chen, Zhiwei Wang, Xiaowei Gu, Zhanwei Wang
Summary: This study proposes a method based on global density-weighted support vector data description (GDW-SVDD) to detect chiller faults, which can effectively improve fault detection accuracy and reduce false alarm rate. Experimental results show that compared to conventional methods and other methods, this approach has significant advantages in fault detection accuracy.
APPLIED SOFT COMPUTING
(2021)
Article
Behavioral Sciences
Yu-Bu Wang, Liu Yang, Zhi-Xiong Mao
Summary: By examining the differences in implicit attitudes between runners and non-runners, this study aims to understand the reasons for individuals' exercise behaviors. The results showed that runners had more positive implicit attitudes towards exercise compared to non-runners. The study also revealed the underlying mechanisms for these differences, including higher cortical functional connectivity in runners and its impact on affective expectations towards running. Further research should focus on the effects of implicit attitudes on exercise behaviors.
BEHAVIOURAL BRAIN RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Shervin Rahimzadeh Arashloo
Summary: This study extends the support vector data description method to a general p-norm (p = 1) penalty function on slacks, enabling the formulation of a non-linear cost in the primal space. By introducing a dual norm into the objective function, the proposed method provides a controlling mechanism to adjust the intrinsic sparsity/uniformity of the problem for enhanced descriptive capability.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Fahad Sohrab, Jenni Raitoharju, Alexandros Iosifidis, Moncef Gabbouj
Summary: The paper introduces a novel method to project data from multiple modalities into an optimized subspace for one-class classification. By iteratively transforming data from original feature spaces and utilizing information from the class of interest, the method outperforms competing methods on four out of five datasets.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Hong-Jie Xing, Ping -Ping Zhang
Summary: Compared with support vector data description (SVDD), deep SVDD (DSVDD) is more suitable for large-scale data sets by using mapping network instead of kernel mapping. Contrastive DSVDD (CDSVDD) is proposed to improve the performance of DSVDD in handling large-scale data sets and obtaining discriminative features through self-supervised learning manner. CDSVDD efficiently solves the hypersphere collapse problem of DSVDD and achieves better detection performance compared to other methods.
PATTERN RECOGNITION
(2023)
Article
Engineering, Mechanical
Yuna Pan, Daolai Cheng, Tingting Wei, Yuchen Jia
Summary: This paper proposes a method for assessing the performance degradation of rolling bearings based on deep belief network and improved support vector data description. The method extracts features using the normalized amplitude spectrum of training samples and fuses them into a performance indicator using SVDD. Experimental results demonstrate that the proposed method can detect bearing degradation and reflect overall performance degradation.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Biochemical Research Methods
Yi Zou, Hongjie Wu, Xiaoyi Guo, Li Peng, Yijie Ding, Jijun Tang, Fei Guo
Summary: The proposed Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) showed promising results in predicting DNA-binding proteins, with higher efficiency compared to other methods. This demonstrates the potential of this approach for DBP identification.
CURRENT BIOINFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Pei-Yi Hao, Jung-Hsien Chiang, Yu-De Chen
Summary: This paper proposes a novel possibilistic classification algorithm using support vector machines (SVMs) to effectively handle uncertain information and improve classification performance. The algorithm aims at finding a maximal-margin fuzzy hyperplane based on possibility theory and solves a fuzzy mathematical optimization problem. The proposed algorithm retains the advantages of fuzzy set theory and SVM theory, and it is more robust for handling outliers. Experimental results demonstrate the satisfactory generalization accuracy and ability to describe inherent vagueness in the given dataset.
Article
Computer Science, Artificial Intelligence
Arin Chaudhuri, Carol Sadek, Deovrat Kakde, Haoyu Wang, Wenhao Hu, Hansi Jiang, Seunghyun Kong, Yuwei Liao, Sergiy Peredriy
Summary: Support vector data description (SVDD) is a popular anomaly detection technique that requires the use of a Gaussian kernel, with the bandwidth parameter being crucial for optimal performance. This paper introduces a new unsupervised method for selecting the Gaussian kernel bandwidth, utilizing a low-rank representation of the kernel matrix. The new technique is competitive with existing methods for low-dimensional data and excels in handling high-dimensional data.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Fahad Sohrab, Alexandros Iosifidis, Moncef Gabbouj, Jenni Raitoharju
Summary: In this paper, a novel subspace learning framework for one-class classification is proposed, which presents the problem in the form of graph embedding. The framework includes the previously proposed subspace one-class techniques as special cases and provides further insight on optimization goals. It allows for the incorporation of other meaningful optimization goals and offers alternative solutions to the previously used gradient-based technique. Experimental results demonstrate improved performance compared to baselines and recently proposed methods.
PATTERN RECOGNITION
(2023)
Article
Operations Research & Management Science
Mehmet Turkoz, Sangahn Kim
Summary: The study introduces a generalized SVDD procedure that fits multiple spheres around multi-class data, incorporating anomaly observations and utilizing class relationships and prior information. This approach effectively identifies anomalies in multi-class data through various simulation studies and real-life applications.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Mikhail Savastianov, Keyue Smedley
Summary: This article investigates the high-reliability operation of a three-level neutral-point-clamped inverter under various open-circuit and short-circuit fault combinations. It proposes a new postfault operation method that does not require additional hardware, providing a comprehensive solution for single and multiple-device failures and increasing the inverter reliability by 24%. The article classifies postfault modulations and uncovers previously unknown fault scenarios that can be addressed using the proposed control method. It also proposes a new postfault modulation based on space vector modulation with virtual vectors and verifies the feasibility of the control method through simulation and experimentation.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2023)
Article
Automation & Control Systems
Lianlian Zhang, Fei Qiao, Junkai Wang, Xiaodong Zhai
Summary: The proposed framework in this paper uses health degree (HD) to quantitatively describe the health status of equipment. The method first removes redundant features using principal component analysis (PCA), then extracts normal observations using a support vector data description (SVDD) algorithm. Finally, an improved incremental SVDD algorithm (NISVDD) is introduced for online updating of the normal sample set to improve accuracy and computational efficiency.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Information Systems
Sebastian Maldonado, Julio Lopez, Carla Vairetti
Summary: The predictive performance of classification methods relies heavily on the nature of the environment and dataset shift issue. A novel Fuzzy Support Vector Machine strategy is proposed in this paper to improve performance by redefining the loss function and applying aggregation operators to deal with dataset shift. Our methods outperform traditional classifiers in terms of out-of-time prediction using simulated and real-world dataset for credit scoring.
INFORMATION SCIENCES
(2021)
