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2022 International Conference on Algorithms, Microchips and Network Applications

作者： Han, Daojun Xiang, Hongzhen Xie, Yi Key Laboratory of Big Data Analysis and Processing of Henan Province Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781510653290

Multi depot school bus routing problem (MDSBRP) refers to the school bus routing problem (SBRP) with multiple depots, which effectively reduces the service cost by adjusting the order of school buses from depot to station. In this paper, a heuristic algorithm based on simulated annealing is proposed to solve the multi depot school bus routing problem. The problem considers the constraints of school time window and school bus capacity. Through the operation adjustment of depots and stations, the number and driving distance of school buses can be effectively reduced. The existing benchmark case set and modified test cases are used to test the algorithm. The experimental results show that the use of multi depot mode can effectively reduce the number of school buses and save operating costs, which shows the effectiveness of the algorithm. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Heuristic algorithms

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Fault Estimation for Nonlinear Systems with Disturbance Based on Reinforcement Iterative Learning Strategy 13

Fault Estimation for Nonlinear Systems with Disturbance Base...

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13th IEEE data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Chen, Zhengquan Huang, Ruirui Zhang, Zhiheng Hou, Yandong Henan University School of Computer and Information Engineering Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China School of Artificial Intelligence Henan University Zhengzhou450000 China

ISBN: (纸本)9798350361674

This paper addresses the problem of low fault estimation accuracy in Lipschitz nonlinear systems with external disturbances, presenting an innovative strategy based on a Reinforcement Iterative Learning (RIL) framework. Initially, we conducted precise mathematical modeling, comprehensively considering the impact of Lipschitz non-linearity and external disturbances. Subsequently, an iterative learning fault observer was designed, and the optimization problem for its parameters was reformulated into a reinforcement learning task, clearly defining the states, actions, and reward functions. Employing the Advantage Actor-Critic (A2C) reinforcement learning method, we optimized the gain parameters of the iterative learning fault estimator to approximate non-linearity. Simultaneously, with the increase in iteration count, the neural network weights were continually optimized, achieving accurate fault estimation. Experimental results demonstrate a significant improvement in fault estimation using the proposed method. © 2024 IEEE.

关键词： Adversarial machine learning

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Differentially Private Discrete Multi-Attributed Network Releasing

Differentially Private Discrete Multi-Attributed Network Rel...

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2022 International Conference on Signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Lin, Yinghao Wang, Yuye Zhao, Shihao Qiao, Baojun Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng China Shenzhen Research Institute of Henan University ShenZhen China

ISBN: (纸本)9781510664616

In the area of social network, different attributes have different effects on the structure of network. Most of the existing privacy protection methods for attributed networks ignore the situation which different attributes have different effects on the network structure. They protect the privacy of the attributes indiscriminately. In respect of the issues above, a differentially private discrete multi-attributed network releasing method is proposed. Firstly, a probability model of discrete multi-attributed network is structured and the correlation parameter between multiple attributes and network structure is defined. The factor with different effects of different attributes on network structure is added into the model. Then, the algorithm uses the correlation parameter to establish the partition model of metadata and divides the metadata into different groups. As the group has different network model and attribute between each other, the groups are independence. The differential privacy of discrete multi-attributed network is realized through sanitizing parameters of the model and allocating metadata using exponential mechanism. Finally, experiment on real datasets verifies that the algorithm can satisfy the characteristics of the discrete multi-attributed network. It can also improve the efficiency and data availability. © 2023 SPIE.

关键词： Metadata

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Fusion Attention-ASPP and Context Semantic for Complex Scene Semantic Segmentation

Fusion Attention-ASPP and Context Semantic for Complex Scene...

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2022 International Conference on Signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Lin, YingHao Zhao, ShiHao Cai, Kun Qiao, BaoJun Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng China Shenzhen Research Institute of Henan University ShenZhen China

ISBN: (纸本)9781510664616

Semantic segmentation is widely used in remote sensing data extraction and classification. Existing semantic segmentation networks focus on capturing contextual information in many different ways, simply fusing features at different levels, and ultimately improving the accuracy of semantic segmentation. However, low-level semantic features lack spatial context guidance, and high-level semantic features tend to encode large objects with coarse spatial details, making segmentation results prone to losing fine details. In this paper, we analyze the advantages and disadvantages of different levels of feature maps, and enhance the feature representation from two aspects to solve this problem. On the one hand, inspired by the architectural idea of atrous spatial pyramic pooling (ASPP), we adjust the structure of ASPP module and add the attention module to ASPP, and a new Attention-ASPP(AASPP) module is constructed in this paper. On the other hand, feature information such as boundary contours is enhanced by channel attention modeling, thereby improving local detail representation. Comprehensive experimental results show that our model framework achieves excellent segmentation performance on two public datasets, WHU building dataset and ISPRS Potsdam dataset. © 2023 SPIE.

关键词： Semantic Segmentation

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Semantic Segmentation of Remote Sensing Images Based on Swin Transformer 3

Semantic Segmentation of Remote Sensing Images Based on Swin...

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3rd International Conference on Artificial Intelligence, Big data and Algorithms, CAIBDA 2023

作者： Lin, Yinghao Zhao, Shihao Wang, Yuye Xie, Yi Qiao, Baojun Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng China Shenzhen Research Institute of Henan University Shenzhen China

ISBN: (纸本)9781643684444

With the evolution of Earth observation technology and remote sensing technologies, the amount of data available for high-resolution remote sensing images has exploded, and high-precision image segmentation has become a current research hotspot. Semantic segmentation technology is becoming increasingly important in fields such as urban planning, land use management, and autonomous driving. Large disparities within intraclass and modest differences intraclass are hallmarks of high resolution remote sensing pictures. Traditional image semantic segmentation methods rely on human-computer interaction and have poor generalization ability. When facing remote sensing images with rich types of ground objects and significant differences in target scales, the segmentation accuracy is not high. In this paper, we suggest an UperSwin decoder structure. The decoder includes several Swin transformer blocks and a fusion upsampling module, where the multi head contextual attention module in the Swin transformer block simultaneously uses multi-scale features and upsampling output features from the backbone network. In addition, the fusion upsampling module concatenates the backbone network features with the output features of the Swin transformer block, and then performs upsampling operations, preserving more detailed information. This article evaluates the accuracy and intersection ratio indicators on the Potsdam and Vaihingen datasets, verifying the feasibility and effectiveness of the model. © 2023 The Authors.

关键词： Land use

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A Review of Detection-Related Multiple Object Tracking in Recent Times 26

A Review of Detection-Related Multiple Object Tracking in Re...

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26th International Conference on Advanced Communications Technology, ICACT 2024

作者： Li, Suya Cao, Ying Xie, Xin Henan University School of Computer and Information Engineering Jinming Campus Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Jinming Campus Kaifeng China

ISBN: (纸本)9791188428120

Multi-object tracking (MOT) is garnering more attention due to its widespread application in the area of autonomous driving, human-computer interaction, and intelligent video surveillance. Especially in recent years, MOT has rapidly developed thanks to related technologies such as object detection, which has helped in handling interfering factors such as crowded scene occlusion, small objects, and similar appearances. Among these, Detection-based MOT is the mainstream for accurately forming objects' trajectories. Therefore, according to the analysis of the last three years' research, this paper particularly focuses on discussing the continuous optimization strategies of MOT around the development of object detection at each stage. In addition, this article also introduces the commonly used benchmark datasets and related applications of MOT. © 2024 Global IT Research Institute - GIRI.

关键词： Object detection

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Learning on Less: Constraining Pre-trained Model Learning for Generalizable Diffusion-Generated Image Detection

arXiv

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arXiv 2024年

作者： Chen, Yingjian Zhang, Lei Niu, Yakun Tan, Lei Chen, Pei Henan Key Laboratory of Big Data Analysis and Processing Henan University China

Diffusion Models enable realistic image generation, raising the risk of misinformation and eroding public trust. Currently, detecting images generated by unseen diffusion models remains challenging due to the limited generalization capabilities of existing methods. To address this issue, we rethink the effectiveness of pre-trained models trained on large-scale, real-world images. Our findings indicate that: 1) Pre-trained models can cluster the features of real images effectively. 2) Models with pre-trained weights can approximate an optimal generalization solution at a specific training step, but it is extremely unstable. Based on these facts, we propose a simple yet effective training method called Learning on Less (LoL). LoL utilizes a random masking mechanism to constrain the model’s learning of the unique patterns specific to a certain type of diffusion model, allowing it to focus on less image content. This leverages the inherent strengths of pre-trained weights while enabling a more stable approach to optimal generalization, which results in the extraction of a universal feature that differentiates various diffusion-generated images from real images. Extensive experiments on the GenImage benchmark demonstrate the remarkable generalization capability of our proposed LoL. With just 1% training data, LoL significantly outperforms the current state-of-the-art, achieving a 13.6% improvement in average ACC across images generated by eight different models. Copyright © 2024, The Authors. All rights reserved.

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Wind turbine detection based on high spatial resolution four-band reflectance images 6

Wind turbine detection based on high spatial resolution four...

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6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Deng, Fengying Qiao, Baojun Li, Kaitao Zhao, Limin Chen, Xingfeng Li, Jiaguo Liu, Jun Sun, Yuan Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Aerospace Information Research Institute Chinese Academy of Science Beijing China School of Space Information Space Engineering University Beijing China

ISBN: (纸本)9781510688018

Wind turbine detection is essential for the power industry and regulatory agencies to efficiently and accurately determine their number and spatial distribution. Under complex underlying surface conditions, the detection accuracy of conventional methods is low, especially when dealing with multi-scale wind turbine targets. In addition, the underutilization of the near-infrared (NIR) band limits the capability of optical remote sensing sensors in wind turbine detection. To address the above problems, this paper proposes a wind turbine detection algorithm YOLOv8m-BD based on high spatial resolution four-band reflectance images using YOLOv8m as the baseline model. Firstly, the four-band image is input through channel adaptation modification. Next, the CSPDarknet53 to 2-stage FPN (C2f) module is enhanced by introducing deformable convolution (DCN) to expand the receptive field. Finally, the effectiveness of our design was validated through extensive experiments on a self-built wind turbine dataset. Compared to the YOLOv8m baseline model that only inputs RGB three bands, the improved YOLOv8m-BD achieves an accuracy of 96.4% and a false alarm rate of 2.0%, an increase of 3.4% in accuracy and a decrease of 2.5% in false alarm rate. © 2025 SPIE.

关键词： Proximity sensors

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Research on the Construction of Curriculum Knowledge Graph Based on GMM 3

Research on the Construction of Curriculum Knowledge Graph B...

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2022 3rd International Conference on Computer Information and Big data Applications, CIBDA 2022

作者： Ma, Xiaqing Xu, Tao Wang, Fengsi Xu, Aopeng Zhang, Zixiang College of Computer and Information Engineering Henan University Henan Province Kaifeng City China Henan Key Laboratory of Big Data Analysis and Processing Henan University Henan Province Kaifeng City China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Henan Province Zhengzhou China

ISBN: (纸本)9783800758760

The knowledge hierarchy provided by the curriculum syllabus is the basic principle for the implementation of course teaching and assessment, which usually summarizes knowledge units into a tree-like hierarchical structure in the form of chapters. But it cannot supply the complete picture of knowledge relationships because of the lack of horizontal connections between knowledge units in it. In order to present the characteristics of knowledge units and the many-tomany relationships between them comprehensively and accurately. This paper proposes a method for the construction of course knowledge structures from the massive test question bank with the Gaussian Mixture Clustering model. This method employs the Gaussian Mixture Clustering model to find the knowledge units and their associations from a large number of test questions, then employs the knowledge graph technology to make the course Knowledge Graph, and construct the curriculum knowledge hierarchy. Extensive empirical research focuses on the physics course in eighth grade (first) course in junior high school. Experimental results demonstrate that the constructed curriculum knowledge hierarchy of the physics course is complete and effective. Furthermore, the average coverage of some random final test papers is 89.2%. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Curricula

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Few-Shot Node Classification on Attributed Networks Based on Prototypical Network 19

Few-Shot Node Classification on Attributed Networks Based on...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Zhao, Yaliang Zhang, Guangming Wang, Jinke Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng China

ISBN: (纸本)9798350346558

In today's society, the interactions between various entities form complicated attributed networks. The node classification task as the core task of analyzing attribute networks has gained a considerable amount of attention from researchers. The existing graph neural network-based node classification approaches all demand lots of labeled data, but a high percentage of node classes in practical scenarios contain only restricted labeled data. So their classification effect is not good. In response to this problem, we put forward an improved graph prototypical network (IGPN) to enhance the few-shot node classification's accuracy. In particular, we start by obtaining the embedding representations of nodes on the attributed network through a graph attention network. Then combined with the node's degree information and neighbor information, the importance coefficient of all nodes in the support set is learned. After that, we can obtain the prototype representation of every class. Finally, the node classification can be completed by the distance of the query nodes' representation from all class prototypes. Our method is preferable to the baseline method on three datasets according to a large number of experiments. © 2022 IEEE.

关键词： Large dataset

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