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International Conference on Computer and Communications (ICCC)

作者： Xiaoke Zhu Bo Zhao Zhiwei Dong Lingyun Dong Danyang Li School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

This paper introduces the Temporal Transform Network based on Scale Sequences (TTS) for cloth-changing person re-identification in video datasets. The TTS network is designed to capture multi-scale temporal cues within video sequences. It accomplishes this by initially modeling short-term temporal cues between adjacent frames, followed by capturing long-term relationships between non-consecutive frames. In more detail, short-term temporal cues are modeled through parallel inflated convolutions with different time dilation rates, enabling the representation of pedestrian movement and appearance dynamics. Long-term relationships are effectively captured using a temporal self-attention model, mitigating challenges such as occlusion and noise within the video sequence. The TTS network outperforms existing methods across cloth-changing video ReID datasets such as CCVID. For instance, under general settings, our approach exhibits a 1.1% improvement in top-1 accuracy and a corresponding 1.1% increase in mAP compared to the baseline. In cloth-changing settings, we observe a 0.2% enhancement in top-1 accuracy and a notable 1.3% increase in mAP relative to the baseline.

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Oriented Object Detection in Aerial Images with Double Midpoint Vectors

Oriented Object Detection in Aerial Images with Double Midpo...

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International Conference on Computer and Communications (ICCC)

作者： Xiaoke Zhu Qi Hu Xiaopan Chen Caihong Yuan Minghao Li School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

In recent years, oriented object detection technology has made significant progress. Most oriented object detectors represent oriented bounding boxes by adding additional angle information to the horizontal bounding box representation, which leads to serious angle periodicity issues. To address these issues, this paper proposes double midpoint vectors (DMvectors) to represent oriented objects. For any oriented bounding box, we align the center point with the origin in the Cartesian coordinate system, and evenly distribute the midpoints of the box edges across the four quadrants. We use the midpoint of the first and second quadrants, as well as the width and height of the box, to represent the oriented bounding box. DMvectors solve the periodic problem of angles by implicitly including angle information in vectors. To demonstrate the effectiveness of our proposed method, we conducted extensive experiments on two common remote sensing datasets (DOTA and HRSC2016), and our method achieved competitive results.

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical Systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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A Two-stage Selection Hyper-heuristic Algorithm for the Capacitated Vehicle Routing Problem

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IAENG International Journal of Applied Mathematics 2022年第4期52卷 1页

作者： Hou, Yan-e He, Wenwen Wang, Congran Ren, Xinhui Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China

As a classical combinatorial optimization problem, capacitated vehicle routing problem (CVRP) has been continuously studied. However, developing effective approaches for CVRP remains very challengeable. This article presented a two-stage selection hyper-heuristic algorithm for CVRP. The proposed algorithm used the multi-armed bandit algorithm as the high-level selection strategy, which selected a low-level heuristic at each step in the iterative local search framework according to the rewards that it had obtained. For the new neighborhood solution that was found by the selected lowlevel heuristic, a hybrid acceptance strategy was employed to decide whether to accept it. In addition, we also designed a low-level heuristic based on the ruin-and-recreate principle to expand the search space of neighborhood solutions except for the regular neighborhood operators widely used in CVRP. Moreover, the routes of better solutions were also put into a pool of routes. Then a set partitioning procedure model was built based on these routes and then solved by CPLEX to obtain better solution. Computational results on 82 instances demonstrate that the presented algorithm is a satisfactory and competitive approach for CVRP. © 2022, IAENG International Journal of Applied Mathematics. All Rights Reserved.

关键词： Vehicle routing

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A Selection Hyper-heuristic based on Q-learning for School Bus Routing Problem

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IAENG International Journal of Applied Mathematics 2022年第4期52卷 1页

作者： Hou, Yan-e Gu, Wenbo Wang, Chunxiao Yang, Kang Wang, Yujing Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China

School bus routing problem (SBRP) has been studied for decades. Many successful approaches based on heuristics or metaheuristics have been developed for various SBRP problems. However, developing an effective algorithm for SBRP is still a very challenging task. This paper developed a Q-learning-based selection hyper-heuristic to solve basic and open single-school SBRP problems, which both aim to minimize the total travel distance. The proposed algorithm took a Qlearning algorithm as the high-level strategy to select a lowlevel heuristic from a set of low-level heuristics, which are dependent on the problem domain. The selected low-level heuristic was regarded as an action and then executed to improve the current solution. In each stage of the optimization process, the best action with the best cumulative rewards will be chosen to get better results. The presented algorithm was implemented and some experiments were carried out on some Capacitated vehicle routing problem (CVRP) instances and SBRP benchmark instances. Experiment results on two types of instances demonstrate that our proposed hyper-heuristic algorithm is more competition than existing approaches. © 2022, IAENG International Journal of Applied Mathematics. All Rights Reserved.

关键词： Vehicle routing

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Prediction of Hepatocellular Carcinoma Diseases Based on Methylation data and Screening of Hub Genes 2021

Prediction of Hepatocellular Carcinoma Diseases Based on Met...

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2021 5th International Conference on Computer Science and Artificial Intelligence

作者： Yawei Zhang Fangtao Ren Xi Liu Fan Zhang School of computer and information engineering Henan University China Henan Key Laboratory of Big Data Analysis and Processing Henan University China

ISBN: (纸本)9781450384155

DNA methylation is of great significance to the diagnosis, treatment and disease prediction of hepatocellular carcinoma (HCC). The commonly used DNA methylation microarrays have high data dimensions, and different CpG sites detected may map to the same gene. To extract more effective features for HCC disease prediction, this study uses a linear regression model that integrates TCGA database methylation data and gene expression data, based on the DNA methylation microarray data of the GEO database (GSE113017), the corresponding gene expression data was predicted and the differentially expressed genes (3766) with significant differences were screened out, which was used as the feature of the data set. Constructing an Artificial Neural Network (ANN) to train a machine learning model for HCC disease prediction and perform 10-fold cross-validation. The resulting model has an accuracy of 95.1% for HCC disease prediction based on DNA methylation microarray data. Compared with other HCC prediction methods, this method has better performance. Then analyze the differentially expressed genes with protein-protein interaction network (PPI network), and use the top five connected genes in the network as hub genes, namely: GNGT2, GNB4, FPR2, CDC20, NMUR1, which can be used as biomarkers for the diagnosis, treatment and prognosis of HCC.

关键词： DNA Methylation Machine learning Artificial neural networks Hepatocellular carcinoma

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Negative Sample Mining based Deep Feature Learning for Kinship Verification 21

Negative Sample Mining based Deep Feature Learning for Kinsh...

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Proceedings of the 2021 5th International Conference on Video and Image processing

作者： Xiaoke Zhu Changlong Li Xiaopan Chen Minghao Zheng Yunwei Li Jihui Hu Henan Key Laboratory of Big Data Analysis and Processing Henan University China School of Computer and Information Engineering Henan University China

ISBN: (纸本)9781450385893

Kinship verification is an important and challenging task. The aim of it is to find out whether there is a kin relationship between one pair of facial images. However, there are many challenges for kinship verification. One of the most important challenge is the problem of data imbalance. Specifically, if there exists N positive kinship pairs, we can get N(N-1) negative pairs. Obviously, the number of negative kinship pairs is much larger than the number of positive kinship pairs. How to make full use of positive and negative samples for training is a valuable problem. But most of the existing methods just randomly select the same number of negative sample pairs as the positive sample pairs. What is more, different negative pairs contain different discrimination information. Therefore, finding discriminative sample is also an important problem. In this paper, we propose a simple and effective method, named Negative sample Mining based Deep Feature Learning (NMDFL) to solve the above problems. Specifically, different with most existing methods, we sample N positive kinship pairs and T*N negative pairs, where T>1. Then, they are sent to the train net, and their impacts on network updating are adjusted in proportion to the number of positive and negative samples. At the same time, the corresponding weight of each negative sample is dynamically adjusted according to the training results of each time. Experimental results on the KinFaceW-I and KinFaceW-II datasets proves the effectiveness of our method.

关键词： Deep Feature Learning Image-based Kinship Verification Mining Negative Samples Unbalanced data

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Enhancing Programming Course Quality through Human Posture Recognition and Screen Content Detection

Enhancing Programming Course Quality through Human Posture R...

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Information Science and Education (ICISE-IE), International Conference on

作者： Xiaopan Chen Haiyang Bai Xiaoke Zhu Lingyuan Dong School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

The evaluation of programming course instruction is a key component of educational management. However, relying solely on supervising teachers for assessment poses challenges in comprehensive evaluation and providing feedback on students' classroom learning status. Additionally, a significant amount of monitoring video data from programming courses in Chinese universities remains underutilized. To address these challenges, this paper combines traditional educational management with artificial intelligence and proposes an intelligent algorithm for detecting students' learning status in programming courses. We first establish a dual YOLO network model to analyze students' postures and screen content in programming classroom monitoring videos, thereby identifying their behaviors. Subsequently, we develop a Programming Classroom Student Behavior Recognition System (PCSBS) that integrates data collection, student behavior detection, and result visualization functionalities. Experimental results demonstrate that the system can accurately provide feedback on students' programming course learning status, offering graded results that assist supervising teachers in their classroom instructional evaluation tasks to enhance educational management efficiency.

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Improving Classroom Quality by Analyzing Student Behaviors Through Computer Vision

Improving Classroom Quality by Analyzing Student Behaviors T...

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Information Science and Education (ICISE-IE), International Conference on

作者： Xiaopan Chen Boyuan Li Xiaoke Zhu Tao Zhang School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

This paper aims to enhance the efficiency of teaching and learning by leveraging computer vision technology for automated analysis of student behavior in the classroom. We propose a feature-enhanced method for recognizing student behavior, which integrates an Upsampling module (UPS) and a convolutional block layer (CBL) module into the YOLOv5 model. This innovative approach significantly improves the detection and feature extraction capabilities for student targets, effectively addressing challenges associated with small-sized targets and lowresolution images. Through algorithm optimization, it achieves a balance in sample diversity, enhancing the model's adaptability and generalizability in complex classroom environments. Building upon these technological advancements, we have developed a Classroom Student Posture Perception System (CSP2S). This system integrates functions for data collection, student behavior detection, and result visualization. The CSP2S operates in realtime by capturing and analyzing students’ behavioral dynamics while providing teachers with immediate feedback. Experimental results demonstrate that this system substantially reduces teachers’ workload in student assessment, offering significant potential to improve teaching effectiveness. These robust technical advancements provide strong support for future applications in real educational environments.

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Online Q&A for Machine Learning Courses Based on Formula Recognition

Online Q&A for Machine Learning Courses Based on Formula Rec...

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Information Science and Education (ICISE-IE), International Conference on

作者： Xiaoke Zhu Xiaopan Chen Minghao Li Tianxiang Zhang School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

Mathematical formulas are ubiquitous in courses such as Machine Learning. Understanding these formulas is the primary challenge for studying of students. If a searchable knowledge base can be built for these formulas, it will help students learn. Accurate identification of these formulas is an important step in building and querying the knowledge base. However, mathematical formulas have complex two-dimensional and nested structures, which makes them inconvenient to search like ordinary text. This paper employs Faster R-CNN to detect regions containing mathematical formulas and improves its backbone network, anchor box dimensions, and pre-training network to make it more accurate and efficient in identifing these formulas. Furthermore, in order to enhance the features and meaningful representation of mathematical formulas in the input image, we introduce the distance transformation. Finally, comparative experiments were conducted on the publicly dataset GTDB. Our proposed approach achieved recognition rates of 91.22% and 85.74% for isolated and hybrid formulas detection.

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