Article
Computer Science, Information Systems
Muhammad Aminu, Noor Atinah Ahmad
Summary: By incorporating a locality preserving feature, LPPLSDA enhances the performance of partial least squares discriminant analysis, especially in face recognition tasks. Experimental results consistently show that LPPLSDA outperforms the conventional PLS-DA method on various benchmarked face databases.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Engineering, Multidisciplinary
Weigang Wang, Fengchao Yuan, Zhansheng Liu
Summary: The SDPP algorithm is proposed for machinery fault diagnosis, extracting low-dimensional features from time-frequency representations and utilizing LS-SVM for recognizing machinery working states. Experimental results demonstrate the superiority of SDPP in machinery fault diagnosis and its potential practical applications.
Article
Engineering, Multidisciplinary
Weigang Wang, Fengchao Yuan, Zhansheng Liu
Summary: The SDPP algorithm, developed based on SPP and LLC, is used for machinery fault diagnosis by extracting low-dimensional features from time-frequency representations to recognize the working states of machinery.
Article
Computer Science, Hardware & Architecture
Yan-Lin He, Kun Li, Li-Long Liang, Yuan Xu, Qun-Xiong Zhu
Summary: Fault diagnosis plays a vital role in ensuring the safety of complex industrial processes. Extracting features from high-dimensional data is an effective approach for handling fault data. The proposed MC-DLPP method, which integrates Monte Carlo sampling, improves feature extraction and tackles the issue of small sample size.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Engineering, Electrical & Electronic
Yan-Lin He, Kun Li, Ning Zhang, Yuan Xu, Qun-Xiong Zhu
Summary: The article proposes a fault diagnosis methodology using discrimination locality preserving projections integrated with sparse autoencoder (SAEDLPP) for achieving higher accuracy in fault diagnosis in industrial processes.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Chemistry, Multidisciplinary
Qi Zhang, Shan Lu, Lei Xie, Aiming Chen, Hongye Su
Summary: A concurrent locality-preserving dynamic latent variable (CLDLV) method is proposed to extract the correlation between process variables and quality variables for quality-related dynamic process monitoring. The method uses a low-rank autoregressive model to handle autocorrelation and cross-correlation properties among the data, and integrates neighborhood structure information into a partial least squares model to reveal the essential structure of the data.
Article
Engineering, Chemical
Cuicui Zhang, Jie Dong, Kaixiang Peng
Summary: This study proposes a fault classification method based on nonlinear feature extraction using reconstructed distance-based discriminant locality preserving projection (RD-DLPP). First, a hypersphere model for each class of data is developed to evaluate the discriminatory difficulty of samples. Second, constraints of the correlations between the k-nearest neighbour points of the sample and the hypersphere are introduced to reconstruct new measure metrics. Finally, an improved fault classification model based on RD-DLPP is established for the construction of a highly discriminant subspace, followed by Bayesian decision for sample classification. The feasibility and efficiency of the proposed method are verified through a case study on the Tennessee Eastman process.
CANADIAN JOURNAL OF CHEMICAL ENGINEERING
(2023)
Article
Acoustics
Di Yang, Yong Lv, Rui Yuan, Ke Yang, Hongyu Zhong
Summary: This paper proposes a novel feature fusion method based on EWNAP and OLPP for vibro-acoustic fault diagnosis of rolling bearings. The method includes three steps: feature extraction, alleviating interference of operating condition, and fusion feature. It can accurately recognize fault patterns under various operating conditions and has robustness.
Article
Quantum Science & Technology
Xiaoyun He, Anqi Zhang, Shengmei Zhao
Summary: A quantum algorithm named QLPP is proposed for efficient dimensionality reduction through locality preserving projection. Compared to the classical LPP algorithm, QLPP shows a polynomial speedup when dealing with large-scale data sets.
QUANTUM INFORMATION PROCESSING
(2022)
Article
Automation & Control Systems
Ning Zhang, Yuan Xu, Qun-Xiong Zhu, Yan-Lin He
Summary: This article presents a novel dimensionality reduction algorithm named DPNLP for fault diagnosis. To solve the singular matrix problem, a regularization-based version of DPNLP called RDPNLP is also introduced. Simulation results show that RDPNLP outperforms other related methods in fault diagnosis.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Chemical
Aimin Miao, Zhishang Cheng, Peng Li, Huawei Cui, Sitong Liu, Hong Wu
Summary: A new local fault classification technique called NPDA was developed and applied in industrial processes, demonstrating improved results compared to traditional global modelling methods. By combining NPE and FDA features, a classification model was constructed and effectively used for fault classification.
CANADIAN JOURNAL OF CHEMICAL ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Qun-Xiong Zhu, Xin-Wei Wang, Ning Zhang, Yuan Xu, Yan-Lin He
Summary: A novel K-medoids-based synthetic minority oversampling technique (KMS-LPP) is proposed for fault diagnosis in industrial processes. The method generates minority fault samples and reduces the dimensionality of data to enhance fault diagnosis performance.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Computer Science, Artificial Intelligence
Xiaoke Hao, Ruxue Wang, Yingchun Guo, Yunjia Xiao, Ming Yu, Meiling Wang, Weibin Chen, Daoqiang Zhang
Summary: Alzheimer's disease is a neurodegenerative disease that affects thinking and memory. It is important to accurately diagnose and treat the disease in its early stages. This article proposes a framework called multimodal self-paced locality-preserving learning (MSLPL) to preserve the structural relationships of the data and select samples for training.
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Ning Zhang, Yuan Xu, Qun-Xiong Zhu, Yan-Lin He
Summary: This article proposes an improved locality preserving projections method based on the heat-kernel and cosine weight matrix, named HC-LPP, for fault diagnosis. By optimizing the weight matrix, HC-LPP considers both the distance and correlation among samples, and effectively reduces the dimensionality of data while preserving the spatial geometric structure.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Computer Science, Artificial Intelligence
Bolin Wang, Yuanyuan Sun, Yonghe Chu, Zhihao Yang, Hongfei Lin
Summary: In this study, a global-locality preserving projection method is proposed to refine word representation, by re-embedding word vectors to a manifold semantic space. It extracts local and global features of word vectors, discovers latent semantic structure, and obtains a compact word embedding subspace.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Automation & Control Systems
Yan-Lin He, Qiang Hua, Qun-Xiong Zhu, Shan Lu
Summary: This paper proposes an enhanced method of virtual sample generation using manifold features to develop soft sensors using small data. The results from simulations show that the accuracy performance of soft sensors established with small data can be effectively improved by adding the virtual samples generated by this method, which achieves superior accuracy compared to state-of-the-art virtual sample generation methods.
Article
Automation & Control Systems
Yan-Lin He, Lei Chen, Yanlu Gao, Jia-Hui Ma, Yuan Xu, Qun-Xiong Zhu
Summary: In this article, a novel neural network sandwich structure called A-DBLSTM with an improved attention mechanism is proposed for accurately predicting power consumption. Experimental results demonstrate that A-DBLSTM achieves superior performance compared to other advanced methods in terms of prediction accuracy, and the factors that have the greatest impact on prediction performance can be identified by analyzing the heatmap of the attention layer.
Article
Engineering, Electrical & Electronic
Xue Jiang, Yuan Xu, Wei Ke, Yang Zhang, Qun-Xiong Zhu, Yan-Lin He
Summary: This article proposes an imbalanced multifault diagnosis method based on bias weights AdaBoost (BW-AdaBoost). By under-sampling and using adaptive weights to construct weak classifiers, and integrating base classifiers into a multiclassification model through a hierarchical structure, accurate diagnosis of multiple faults is achieved.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Computer Science, Hardware & Architecture
Ning Zhang, Yuan Xu, Qun-Xiong Zhu, Yan-Lin He
Summary: This article proposes an improved locality preserving projections method based on the heat-kernel and cosine weight matrix, named HC-LPP, for fault diagnosis. By optimizing the weight matrix, HC-LPP considers both the distance and correlation among samples, and effectively reduces the dimensionality of data while preserving the spatial geometric structure.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Engineering, Chemical
Qun-Xiong Zhu, Tian-xiang Xu, Yuan Xu, Yan-Lin He
Summary: The proposed method introduces a novel virtual sample generation approach based on conditional generative adversarial networks (CGANs) with a cycle structure (CS-CGAN) to enhance data sets and enrich sample diversity, ultimately improving the performance of soft sensors by effectively generating realistic samples.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2022)
Article
Engineering, Chemical
Xue Jiang, Yuan Xu, Wei Ke, Yang Zhang, Qunxiong Zhu, Yanlin He
Summary: A novel imbalanced fault diagnosis method integrating KLFDA with ANBSVM is proposed in this paper, which solves the problem of normal data being much more than fault data in fault diagnosis. The simulation experiment shows that the proposed method achieves higher diagnostic accuracy and F1 score.
CANADIAN JOURNAL OF CHEMICAL ENGINEERING
(2023)
Article
Engineering, Chemical
Yuan Xu, Yang Zhao, Wei Ke, Yan-Lin He, Qun-Xiong Zhu, Yang Zhang, Xiaoqian Cheng
Summary: With the development of industrial processes, effective fault diagnosis in complex production processes has gained attention. This paper proposes a multi-fault diagnosis method based on improved SMOTE. The method utilizes an improved SMOTE algorithm based on Mahalanobis distance for oversampling, kernel local Fisher discriminant analysis for feature extraction, and AdaBoost.M2 classifier for constructing the multi-fault diagnosis model. Experimental results demonstrate higher accuracy and F1-Score using the proposed method.
CANADIAN JOURNAL OF CHEMICAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Yuan Xu, Youyuan Chen, Yang Zhang, Qunxiong Zhu, Yanlin He, Hao Sheng
Summary: Multi-object tracking is an important branch of computer vision used for behavior recognition and event analysis. Most research currently focuses on tracking accuracy, with a lack of research on real-time performance. The proposed Bilateral Association Tracking (BAT) framework uses tracklet as the basic node for tracking and introduces a Parzen density based Hierarchical Agglomerative Clustering (P-HAC) algorithm for generating high confidence tracklets. The Dual Appearance Features (DAF) approach considers both spatial and temporal features, improving tracklet association accuracy. BAT outperforms Deepsort in association accuracy and trajectory integrity without obvious efficiency decline, showing significant advantage on computational cost compared to other state-of-the-art trackers while maintaining competitive tracking accuracy. This research aims to promote real-time tracking applications in the future.
IET IMAGE PROCESSING
(2023)
Article
Automation & Control Systems
Qun-Xiong Zhu, Hong-Tao Zhang, Ye Tian, Ning Zhang, Yuan Xu, Yan-Lin He
Summary: With the development of industrialization, the production scale and complexity of process industries are increasing. However, it is challenging to establish accurate and efficient data-driven soft sensor models in process industries due to limited samples and uneven sample distribution. This paper proposes a novel virtual sample generation method based on the co-training of two K-Nearest Neighbor (KNN) models to solve this problem. The method includes identifying sparse regions, generating input features in these regions, predicting outputs, screening qualified virtual samples, and updating the model for improved prediction accuracy.
Article
Automation & Control Systems
Ning Zhang, Yuan Xu, Qun-Xiong Zhu, Yan-Lin He
Summary: Capturing relevant features from high-dimensional process data for fault diagnosis has become a major challenge and research topic. A novel dimensionality reduction approach called BFNDNLP is proposed to effectively extract discriminative features by considering both intraclass and interclass distances. Simulation results demonstrate that the proposed methodology achieves higher diagnosis accuracy compared to other methods.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Hardware & Architecture
Yan-Lin He, Kun Li, Li-Long Liang, Yuan Xu, Qun-Xiong Zhu
Summary: Fault diagnosis plays a vital role in ensuring the safety of complex industrial processes. Extracting features from high-dimensional data is an effective approach for handling fault data. The proposed MC-DLPP method, which integrates Monte Carlo sampling, improves feature extraction and tackles the issue of small sample size.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Engineering, Electrical & Electronic
Qun-Xiong Zhu, Xin-Wei Wang, Ning Zhang, Yuan Xu, Yan-Lin He
Summary: A novel K-medoids-based synthetic minority oversampling technique (KMS-LPP) is proposed for fault diagnosis in industrial processes. The method generates minority fault samples and reduces the dimensionality of data to enhance fault diagnosis performance.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Proceedings Paper
Automation & Control Systems
Qun-Xiong Zhu, Xiao-Lu Song, Ning Zhang, Ye Tian, Yuan Xu, Yan-Lin He
Summary: With the advent of the big data era, data-driven modeling approaches have gained popularity in recent years. However, the limitation of the actual process leads to a scarcity of high-quality data, resulting in the small sample problem. To address this issue, researchers have proposed a method based on singular value decomposition and gradient boosting decision tree, which uses virtual sample generation to expand the sample size.
Proceedings Paper
Computer Science, Artificial Intelligence
Qun-Xiong Zhu, Ning Zhang, Yan-Lin He, Yuan Xu
Summary: With the introduction of a new synthetic minority over-sampling technique (SMOTE) that considers the correlation of samples, integrated with locality sensitive discriminant analysis (LSDA) and LightGBM fault diagnosis methodology (CSMOTE-LSDA-LightGBM), this article aims to solve difficulties faced in fault diagnosis due to high-dimensional and imbalanced data in the process industry. The proposed methodology includes SMOTE for resampling, LSDA for dimensionality reduction, and LightGBM for fault classification. Simulation results show improved accuracy compared to imbalanced data and traditional methods, indicating the applicability of CSMOTE-LSDA-LightGBM for fault diagnosis.
2022 8TH INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT'22)
(2022)
Article
Engineering, Electrical & Electronic
Ye Tian, Yuan Xu, Qun-Xiong Zhu, Yan-Lin He
Summary: This article proposes a novel SISAE-GRU model for accurate soft sensing of dynamic processes. The experimental results show that the proposed model outperforms other methods with lower RMSE.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Automation & Control Systems
Carmen Bisogni, Lucia Cimmino, Michele Nappi, Toni Pannese, Chiara Pero
Summary: This paper presents a gait-based emotion recognition method that does not rely on facial cues, achieving competitive performance on small and unbalanced datasets. The proposed approach utilizes advanced deep learning architecture and achieves high recognition and accuracy rates.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Soung Sub Lee
Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jian Wang, Xiuying Zhan, Yuping Yan, Guosheng Zhao
Summary: This paper proposes a method of user recruitment and adaptation degree improvement via community collaboration to solve the task allocation problem in sparse mobile crowdsensing. By matching social relationships and perception task characteristics, the entire perceptual map can be accurately inferred.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen
Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li
Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou
Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kemal Ucak, Gulay Oke Gunel
Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dexuan Zou, Mengdi Li, Haibin Ouyang
Summary: In this study, a photovoltaic thermal collector is integrated into a combined cooling, heating, and power system to reduce primary energy consumption, operation cost, and carbon dioxide emission. By applying a novel genetic algorithm and constraint handling approach, it is found that the CCHP scenarios with PV/T are more efficient and achieve the lowest energy consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Abhinav Pandey, Litton Bhandari, Vidit Gaur
Summary: This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zahra Ramezanpoor, Adel Ghazikhani, Ghasem Sadeghi Bajestani
Summary: Time series analysis is a method used to analyze phenomena with temporal measurements. Visibility graphs are a technique for representing and analyzing time series, particularly when dealing with rotations in the polar plane. This research proposes a visibility graph algorithm that efficiently handles biological time series with rotation in the polar plane. Experimental results demonstrate the effectiveness of the proposed algorithm in both synthetic and real world time series.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
ChunLi Li, Qintai Hu, Shuping Zhao, Jigang Wu, Jianbin Xiong
Summary: Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial. However, the nonlinear and non-stationary vibration signals generated in harsh environments pose challenges in distinguishing fault signals from normal ones. This paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model to address these challenges. The effectiveness of the proposed method in fault diagnosis is demonstrated through validation and comparative analysis with a published method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Fatemeh Chahkoutahi, Mehdi Khashei
Summary: The classification rate is the most important factor in selecting an appropriate classification approach. In this paper, the influence of different cost/loss functions on the classification rate of different classifiers is compared, and empirical results show that cost/loss functions significantly affect the classification rate.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jicong Duan, Xibei Yang, Shang Gao, Hualong Yu
Summary: The study proposes a novel partition-based imbalanced multi-label learning algorithm, MLHC, which divides the original label space into disconnected subspaces using hierarchical clustering. It successfully tackles the class imbalance problem in multi-label data and outperforms other class imbalance multi-label learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Review
Automation & Control Systems
Qing Qin, Yuanyuan Chen
Summary: This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)