Article
Computer Science, Artificial Intelligence
Mingen Gu, Jinde Zheng, Haiyang Pan, Jinyu Tong
Summary: The paper introduces a new matrix classification method RSSMM, which solves the problems associated with traditional SMM by limiting the loss threshold, introducing the generalized forward-backward algorithm, and designing a generalized smooth Ramp loss function, achieving superior results in the classification of roller bearing fault signals.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Haiyang Pan, Li Sheng, Haifeng Xu, Jinyu Tong, Jinde Zheng, Qingyun Liu
Summary: This paper introduces support matrix machine (SMM) as a classical matrix classification technology, and proposes a new method called pinball transfer support matrix machine (PTSMM) to solve the issue of insufficient annotation samples in practical industrial practice. The experimental results show that PTSMM effectively utilizes samples from source and target domains for modeling, and achieves higher diagnostic accuracy compared to SMM and its improved algorithms.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Liming Liu, Maoxiang Chu, Rongfen Gong, Li Zhang
Summary: The improved nonparallel support vector machine (INPSVM) proposed in this article inherits the advantages of nonparallel support vector machine (NPSVM) while also offering incomparable benefits over twin support vector machine (TSVM). INPSVM effectively eliminates noise effects and achieves higher classification accuracy for both linear and nonlinear datasets compared to other algorithms. Experimental results demonstrate the superior efficiency, accuracy, and robustness of INPSVM.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Information Systems
Jinjun Ren, Yuping Wang, Xiyan Deng
Summary: Class imbalance and noisy data pose challenges for constructing good classifiers using SVM. Fuzzy SVMs (FSVMs) address these issues by using fuzzy membership functions and cost-sensitive learning. However, the accuracy of FSVMs is affected by class imbalance. To overcome this, we propose SFFSVM, which incorporates a new fuzzy membership function and adjusts the importance of samples based on the relationship between estimated and optimal hyperplanes. Experimental results show that SFFSVM outperforms other methods on F1, MCC, and AUC-PR metrics.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2023)
Article
Computer Science, Information Systems
Haiyang Pan, Haifeng Xu, Jinde Zheng, Jinyu Tong
Summary: Currently, support vector machine (SVM) has achieved remarkable success and widespread applications. However, when SVM is used for classifying two-dimensional matrix data, the vectorization of these data often leads to the curse of dimensionality and the loss of structural information. Additionally, SVM is highly sensitive to outliers, causing the hyperplane to be influenced by outliers. Therefore, this study introduces a novel classification method called non-parallel bounded support matrix machine (NPBSMM) specifically designed for matrix-form data. By constructting a constraint norm group (CNG) and incorporating it into the objective function, NPBSMM effectively suppresses the negative impact of outliers and achieves better sparsity. By avoiding the matrix inversion operation present in traditional classification methods, NPBSMM is more suitable for solving large-scale data problems. Furthermore, the use of multi-rank left and right projection matrices enables NPBSMM to effectively extract structural information and enhance its data fitting ability. Experimental results on three roller bearing fault datasets demonstrate the superior performance and robustness of the proposed NPBSMM method compared to other typical classification methods.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Haiyang Pan, Jinde Zheng
Summary: The symplectic hyperdisk matrix machine (SHMM) proposed in this study uses symplectic geometry similarity transformation (SGST) to obtain dimensionless feature matrix, constructing multiple hyperdisks to divide different data types and solving the issue of under estimation in roller bearing fault diagnosis.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Zongmin Liu, Yitian Xu
Summary: In this paper, a novel multi-task nonparallel support vector machine (MTNPSVM) is proposed, which effectively avoids matrix inversion operation and takes full advantage of the kernel trick by introducing epsilon-insensitive loss instead of square loss. The alternating direction method of multipliers (ADMM) is employed to improve computational efficiency, and the properties and sensitivity of the model parameters are further explored.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Haiyang Pan, Haifeng Xu, Jinde Zheng
Summary: This article proposes a new matrix classifier called Symplectic Relevance Matrix Machine (SRMM) based on the probability framework and symplectic geometry theory for roller bearing fault diagnosis. Experimental results show that SRMM has good classification performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Weichen Wu, Yitian Xu, Xinying Pang
Summary: A novel two-stage hybrid screening rule based on variational inequality and duality gap is proposed in this paper, which can accelerate the solving process of support vector machines by deleting more redundant samples while maintaining accuracy. This method also embeds shrinking technique into the fast iterative algorithm, further speeding up the solving process.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Haiyang Pan, Haifeng Xu, Jinde Zheng, Jinyu Tong, Jian Cheng
Summary: In industrial processes, intelligent fault diagnosis is crucial for ensuring the health of mechanical equipment. Support matrix machine (SMM) is a popular method for intelligent monitoring, but it has limitations in eliminating noise and handling outliers. To overcome these limitations, a novel nonparallel classifier called twin robust matrix machine (TRMM) is proposed, which improves fault diagnosis performance and is insensitive to outliers.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Hongmei Ju, Ye Zhao, Yafang Zhang
Summary: In this paper, a new DAG-F-NPSVM model is established based on improvements to the multi-class NPSVM model, addressing sample noises and unrecognized regions through the use of density information and directed acyclic graph theory, leading to improved classification accuracy and decision speed. The statistical significance of this new method is verified through experiments using UCI machine learning standard data sets and statistical tests.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Automation & Control Systems
Haiyang Pan, Li Sheng, Haifeng Xu, Jinde Zheng, Jinyu Tong, Limin Niu
Summary: Currently, matrix-based classification methods are crucial in mechanical fault diagnosis. However, traditional matrix-based classification methods are typically shallow classifiers, making it difficult to extract deep-seated sensitive features from vibration signals. To address this issue, a new deep stacked pinball transfer matrix machine (DSPTMM) is proposed, which utilizes stacking generalization principle to enhance the performance of traditional shallow matrix-based classifiers. DSPTMM includes a pinball transfer module (PTM) that constructs a deep stacking network. The PTM utilizes pinball loss to achieve enhanced noise robustness by obtaining weak predictions from the previous module, modifies the original results through random projection, and uses them as new feature sets in subsequent modules. Experimental results on two different roller bearing datasets demonstrate that DSPTMM can establish accurate models using limited samples.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Haifeng Xu, Haiyang Pan, Jinde Zheng, Qingyun Liu, Jinyu Tong
Summary: A novel classification method called dynamic penalty adaptive matrix machine (DPAMM) is proposed for machinery fault diagnosis. DPAMM combines adaptive low-rank approximation and dynamic penalty to handle imbalanced samples and extract feature information effectively.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Ruiyao Gao, Kai Qi, Hu Yang
Summary: The paper introduces the use of nonconvex loss functions in support vector machine (SVM) to improve robustness against noise. Two new loss functions, CaEN loss and HK loss, are proposed and applied in a fused robust geometric nonparallel SVM (FRGNHSVM). Experimental results show that FRGNHSVM achieves higher prediction accuracy, especially when dealing with label-contaminated datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Ming-Zeng Liu, Yuan-Hai Shao, Chun-Na Li, Wei-Jie Chen
Summary: This paper proposes a robust smooth pinball loss nonparallel support vector machine (SpinNSVM) for binary classification, which defines a smooth pinball loss function and uses a dual coordinate descent algorithm to solve the model, demonstrating good effectiveness and efficiency through numerical experiments.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Fangyu Chen, Yongchang Wei, Hongchang Ji, Gangyan Xu
Summary: This paper introduces a dual-layer network analytical framework for evaluating standard systems in construction safety management and validates its effectiveness through a case study. The research findings suggest that key standards often encompass a wider array of risks, providing suggestions for revising construction standards.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Minghao Li, Qiubing Ren, Mingchao Li, Ting Kong, Heng Li, Huijing Tian, Shiyuan Liu
Summary: This study proposes a method using digital twin technology to construct a collision early warning system for marine piling. The system utilizes a five-dimensional model and four independently maintainable development modules to maximize its effectiveness. The pile positioning algorithm and collision early warning algorithm are capable of providing warnings for complex pile groups.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Seokhyun Ryu, Sungjoo Lee
Summary: This study proposes the use of patent information to develop a robust technology tree and applies it to the furniture manufacturing process. Through methods such as clustering analysis, semantic analysis, and association-rule mining, technological attributes and their relationships are extracted and analyzed. This approach provides meaningful information to improve the understanding of a target technology and supports research and development planning.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Shuai Ma, Kechen Song, Menghui Niu, Hongkun Tian, Yanyan Wang, Yunhui Yan
Summary: This paper proposes a feature-based domain disentanglement and randomization (FDDR) framework to improve the generalization of deep models in unseen datasets. The framework successfully addresses the appearance difference issue between training and test images by decomposing the defect image into domain-invariant structural features and domain-specific style features. It also utilizes randomly generated samples for training to further expand the training sample.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Fang Xu, Tianyu Zhou, Hengxu You, Jing Du
Summary: This study explores the impact of AR-based egocentric perspectives on indoor wayfinding performance. The results reveal that participants using the egocentric perspective demonstrate improved efficiency, reduced cognitive load, and enhanced spatial awareness in indoor navigation tasks.
ADVANCED ENGINEERING INFORMATICS
(2024)
Review
Computer Science, Artificial Intelligence
Yujie Lu, Shuo Wang, Sensen Fan, Jiahui Lu, Peixian Li, Pingbo Tang
Summary: Image-based 3D reconstruction plays a crucial role in civil engineering by bridging the gap between physical objects and as-built models. This study provides a comprehensive summary of the field over the past decade, highlighting its interdisciplinary nature and integration of various technologies such as photogrammetry, 3D point cloud analysis, semantic segmentation, and deep learning. The proposed 3D reconstruction knowledge framework outlines the essential elements, use phases, and reconstruction scales, and identifies eight future research directions. This review is valuable for scholars interested in the current state and future trends of image-based 3D reconstruction in civil engineering, particularly in relation to deep learning methods.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Hang Zhang, Wenhu Wang, Shusheng Zhang, Yajun Zhang, Jingtao Zhou, Zhen Wang, Bo Huang, Rui Huang
Summary: This paper presents a novel framework for segmenting intersecting machining features using deep reinforcement learning. The framework enhances the effectiveness of intersecting machining feature segmentation by leveraging the robust feature representation, decision-making, and automatic learning capabilities of deep reinforcement learning. Experimental results demonstrate that the proposed approach successfully addresses some existing challenges faced by several state-of-the-art methods in intersecting machining feature segmentation.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Chao Zhao, Weiming Shen
Summary: This paper proposes a semantic-discriminative augmentation-driven network for imbalanced domain generalization fault diagnosis, which enhances the model's generalization capabilities through synthesizing reliable samples and optimizing representations.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Ching-Chih Chang, Teng-Wen Chang, Hsin-Yi Huang, Shih-Ting Tsai
Summary: Ideation is the process of generating ideas through exploring visual and semantic stimuli for creative problem-solving. This process often requires changes in user goals and insights. Using pre-designed content and semantic-visual concepts for ideation can introduce uncertainty. An adaptive workflow is proposed in this study that involves extracting and summarizing semantic-visual features, using clusters of adapted information for multi-label classification, and constructing a design exploration model with visualization and exploration.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Zhen Wang, Shusheng Zhang, Hang Zhang, Yajun Zhang, Jiachen Liang, Rui Huang, Bo Huang
Summary: This research proposes a novel approach for machining feature process planning using graph convolutional neural networks. By representing part information with attribute graphs and constructing a learning model, the proposed method achieves higher accuracy and resolves current limitations in machining feature process planning.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Hong-Wei Xu, Wei Qin, Jin-Hua Hu, Yan-Ning Sun, You -Long Lv, Jie Zhang
Summary: Wafer fabrication is a complex manufacturing system, where understanding the correlation between parameters is crucial for identifying the cause of wafer defects. This study proposes a Copula network deconvolution-based framework for separating direct correlations, which involves constructing a complex network correlation diagram and designing a nonlinear correlation metric model. The proposed method enables explainable fault detection by identifying direct correlations.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Yida Hong, Wenqiang Li, Chuanxiao Li, Hai Xiang, Sitong Ling
Summary: An adaptive push method based on feature transfer is proposed to address sparsity and cold start issues in product intelligent design. By constructing a collaborative filtering algorithm model and transforming the rating model, the method successfully alleviates data sparsity and cold start problems.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Hairui Fang, Jialin An, Bo Sun, Dongsheng Chen, Jingyu Bai, Han Liu, Jiawei Xiang, Wenjie Bai, Dong Wang, Siyuan Fan, Chuanfei Hu, Fir Dunkin, Yingjie Wu
Summary: This work proposes a model for real-time fault diagnosis and distance localization on edge computing devices, achieving lightweight design and high accuracy in complex environments. It also demonstrates a high frame rate on edge computing devices, providing a novel solution for industrial practice.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Yujun Jiao, Xukai Zhai, Luyajing Peng, Junkai Liu, Yang Liang, Zhishuai Yin
Summary: This paper proposes a digital twin-based motion forecasting framework that predicts the future trajectories of workers on construction sites, accurately predicting workers' motions in potential risk scenarios.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Ling-Zhe Zhang, Xiang-Dong Huang, Yan-Kai Wang, Jia-Lin Qiao, Shao-Xu Song, Jian-Min Wang
Summary: Time-series DBMSs based on the LSM-tree have been widely applied in various scenarios. The characteristics of time-series data workload pose challenges to efficient queries. To address issues like query latency and inaccurate range, we propose a novel compaction algorithm called Time-Tiered Compaction.
ADVANCED ENGINEERING INFORMATICS
(2024)