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2023 IEEE International Radar Conference, RADAR 2023

作者： Wang, Chao Jiu, Bo Pu, Wenqiang Li, Kang Zhao, Yu Liu, Hongwei Xidian University National Key Laboratory of Radar Signal Processing Xi'an China The Chinese University of HongKong Shenzhen Research Institude of Big Data Shenzhen China

ISBN: (纸本)9781665482783

Frequency agility is a promising technique for addressing the threat of mainlobe jamming in modern electronic warfare. This paper investigates the confrontation between a subpulse-level frequency agile radar and a smart mainlobe jammer from a game theory perspective. The interaction process between the two sides is modeled as a two-player zero-sum extensive form game, which can capture the multi-stage and highly competitive nature. To obtain the optimal radar frequency agility strategy in the presence of a smart jammer, we introduce the Simultaneous Move Monte Carlo Tree Search (SM-MCTS) algorithm, which improves upon the classic Monte Carlo Tree Search algorithm in both selection and backpropagation steps. Furthermore, we discuss three selection functions that choose actions for the radar and jammer at each game tree node. Finally, simulation results demonstrate the effectiveness of the SM-MCTS algorithm for approximating the Nash equilibrium. © 2023 IEEE.

关键词： Jamming

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Ancient Character Recognition: A Novel Image dataset of Shui Manuscript Characters and Classification Model

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Chinese Journal of Electronics 2023年第1期32卷 64-75页

作者： TANG Minli XIE Shaomin LIU Xiangrong Department of Computer Science and Technology School of Informatics Xiamen University School of Big Data Engineering KaiLi University Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University

Shui manuscripts are part of the national intangible cultural heritage of China. Owing to the particularity of text reading, the level of informatization and intelligence in the protection of Shui manuscript culture is not adequate. To address this issue, this study created Shuishu＿C, the largest image dataset of Shui manuscript characters that has been reported. Furthermore, after extensive experimental validation, we proposed ShuiNet-A,a lightweight artificial neural network model based on the attention mechanism, which combines channel and spatial dimensions to extract key features and finally recognize Shui manuscript characters. The effectiveness and stability of ShuiNet-A were verified through multiple sets of experiments. Our results showed that, on the Shui manuscript dataset with 113 categories, the accuracy of ShuiN et-A was 99.8%, which is 1.5% higher than those of similar studies. The proposed model could contribute to the classification accuracy and protection of ancient Shui manuscript characters.

关键词： Training Speech recognition Artificial neural networks Feature extraction Stability analysis Character recognition Machine translation

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A Model robustness optimization method based on adversarial sample detection 5

A Model robustness optimization method based on adversarial ...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Sun, Jiaze Long, Siyuan Ma, Xianyan Tang, Yanmei Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an710121 China Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781450396899

Deep neural networks are extremely vulnerable due to the existence of adversarial samples. It is a challenging problem to optimize the robustness of the model to protect deep neural networks from the threat of adversarial samples. To improve the model robustness, an integrated detection model of adversarial samples is designed to detect the existence of adversarial samples, which consists of a multi-classification detector and five single-classification detectors to perform double-layer detection of adversarial samples and intercept the adversarial samples finally sent to the image classification model. The detection experiments were conducted on the CIFAR-10 dataset for the adversarial samples generated by five attack algorithms: FGSM, BIM, DeepFool, JSMA, and C&W, and the detection success rate for all types of adversarial samples reached over 98.96%. After that, secondary attack experiments were conducted on the model, and the detection success rate of the model for the second attack adversarial samples reached over 92.36%. It provides an efficient and robust optimization method for deep neural network models in adversarial environments. © 2022 ACM.

关键词： Deep neural networks

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Point Cloud Compression Method Based on Curvature Grading and Inter-point Compression for Industrial Products 15

Point Cloud Compression Method Based on Curvature Grading an...

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15th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023

作者： Wang, Lei Huang, Wei Chen, Yiping Chen, Ran Nanjing University of Posts and Telecommunications School of Computer Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China

ISBN: (纸本)9798350308693

The traditional point cloud compression methods fail to meet the compression requirements of complex surface point cloud files in the industrial field. This paper proposes a point cloud compression method suitable for industrial products with defective and complex surfaces, which compresses the defect and non-defect parts separately using different compression approaches. A curvature-based compression method is designed for the defect points, and an inter-point compression method is developed for the non-defect part, achieving compression of both components of the point cloud. The experimental results show the advantages of the proposed compression method through a comparative analysis of time efficiency, compression ratio, and reconstruction accuracy for industrial point cloud compression. © 2023 IEEE.

关键词： Grading

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A Color Image Encryption Scheme Based on Block Compressive Sensing and Chaotic Systems 2

A Color Image Encryption Scheme Based on Block Compressive S...

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2nd International Conference on Applied Statistics, Computational Mathematics, and Software Engineering, ASCMSE 2023

作者： Tian, Junfeng Chen, Keke Key Laboratory of Big Data Analysis and Processing of Henan Province Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Province Engineering Research Center of Spatial Information Processing Henan University Kaifeng475004 China

ISBN: (数字)9781510668195

ISBN: (纸本)9781510668188

The traditional compressive sensing theory does not take full advantage of the differences in image block information features for sampling, and the measurement matrix does not adequately measure the image based on the image information features. To solve these problems, we design a color image encryption scheme based on block compressive perception and chaotic systems. First, the color image is divided into three channels of RGB and into 8*8 blocks respectively. Then, a zigzag transform is applied to each block so that each pixel value is evenly distributed. Each block is sparsified by the discrete cosine transform, and the measurement matrix generated by the Chen chaotic system is sampled to compress the image blocks to CR times the original block size according to different compression ratios (CR). The resulting chunked compressed samples are further distributed across the pixel values of the image using a cross-channel confusion dislocation method. Finally, the size of the pixel values is changed by a dissimilarity operation. The experimental results show that the scheme can balance the security of image information transmission and the integrity of reception, and has good resistance to various attacks. © 2023 SPIE.

关键词： Cryptography

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Lower Bounds of Uniform Stability in Gradient-Based Bilevel Algorithms for Hyperparameter Optimization 38

Lower Bounds of Uniform Stability in Gradient-Based Bilevel ...

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38th Conference on Neural Information processing Systems, NeurIPS 2024

作者： Wang, Rongzhen Zheng, Chenyu Wu, Guoqiang Min, Xu Zhang, Xiaolu Zhou, Jun Li, Chongxuan Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods China School of Software Shandong University China Ant Group China

Gradient-based bilevel programming leverages unrolling differentiation (UD) or implicit function theorem (IFT) to solve hyperparameter optimization (HO) problems, and is proven effective and scalable in practice. To understand the generalization behavior, existing works establish upper bounds on the uniform stability of these algorithms, while their tightness is still unclear. To this end, this paper attempts to establish stability lower bounds for UD-based and IFT-based algorithms. A central technical challenge arises from the dependency of each outer-level update on the concurrent stage of inner optimization in bilevel programming. To address this problem, we introduce lower-bounded expansion properties to characterize the instability in update rules which can serve as general tools for lower-bound analysis. These properties guarantee the hyperparameter divergence at the outer level and the Lipschitz constant of inner output at the inner level in the context of HO. Guided by these insights, we construct a quadratic example that yields tight lower bounds for the UD-based algorithm and meaningful bounds for a representative IFT-based algorithm. Our tight result indicates that uniform stability has reached its limit in stability analysis for the UD-based algorithm. © 2024 Neural information processing systems foundation. All rights reserved.

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Online Q&A for Machine Learning Courses Based on Formula Recognition 4

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

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4th International Conference on Information Science and Education, ICISE-IE 2023

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

ISBN: (纸本)9798350394610

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. © 2023 IEEE.

关键词： Deep learning

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Research on Pedestrian Gender Detection Based on A Lightweight Model 36

Research on Pedestrian Gender Detection Based on A Lightweig...

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36th Chinese Control and Decision Conference, CCDC 2024

作者： Yuling, Hu Xinyi, Wang Weiguang, Zou Beijing University Of Civil Engineering And Architecture Institute Of Electrical And Information Engineering China Beijing University Of Civil Engineering And Architecture Beijing Key Laboratory Of Intelligent Processing For Building Big Data Beijing China

ISBN: (纸本)9798350387780

In recent years, a variety of emergency events have been occurring in buildings in countries. For there are some differences between male and female in movement ability and evacuation behavior, gender has a non-negligible impact on evacuation and decision-making of emergency events. Thus, it is of great practical significance to detect the gender of pedestrians at entrances or exits of buildings in real time to grasp the proportion of different gender people in buildings and to formulate emergency warning or evacuation strategies scientifically. In response to the requirement of rapid emergency response, this paper proposes a pedestrian gender detection method based on a lightweight object detection model YOLOX-Tiny. In addition, considering the inaccurate pedestrian gender detection caused by the change of pedestrian posture and camera angle, the attention mechanism CBAM is introduced to enhance the ability of filtering a large amount of redundant information, obtaining key information and acquiring pedestrian gender characteristic information. Through the experiments of self-made pedestrian gender datasets, the results show that the detection accuracy of the improved model is increased by 1.8%, and the detection speed reaches 251FPS, which can meet both speed and accuracy of pedestrian gender detection in emergencies. © 2024 IEEE.

关键词： Object detection

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Deformation monitoring of the skyscrapers by TomoSAR method 2

Deformation monitoring of the skyscrapers by TomoSAR method

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2nd IEEE International Conference on Signal, Information and data processing, ICSIDP 2024

作者： Zhao, Jingwen Chen, Yan Zhang, Li Pudong Branch Shanghai Surveying and Mapping Institute Shanghai China Pudong Branch Key Laboratory of Spatialtemporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources Shanghai China

ISBN: (纸本)9798331515669

Land subsidence not only endangers the safety of houses, roads, infrastructure and underground pipelines but also causes cascading disasters. The total monitoring of urban surface stability is extremely important for monitoring the health of bridges, subways, buildings, etc. Compared with traditional spot measurement, Interferometric Synthetic Aperture Radar (InSAR) has the advantages of all-weather operation, wide coverage, low cost, high temporal and spatial resolution and high precision. In this paper, the tomographic SAR (TomoSAR) is adopted to solve the layover problem, especially for the skyscrapers in the densely distributed area. In the densely distributed Kowloon MTR station case, the elevation, linear deformation rate and thermal dilation of the skyscrapers are successfully estimated by 4D TomoSAR. The results provide support for health monitoring of buildings and the surrounding areas, facilitating early detection of potential risks. © 2024 IEEE.

关键词： Tall buildings

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Research on resource scheduling method based on Kubernetes

Research on resource scheduling method based on Kubernetes

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Haoyang Song Liming Zhou Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

With the continuous development of cloud computing, the demand for cloud services in all walks of life is increasing. The scale of cloud computing services based on container technology is constantly growing, and Kubernetes has been widely used as a mainstream tool for container orchestration. It supports functions such as automatic deployment and service discovery, dramatically simplifies the container deployment process, and improves the availability and reliability of applications in cloud environments. However, the default scheduling strategy of Kubernetes adopts the greedy idea of first-come-first-served when facing multi-pod tasks, and schedules Pods to working nodes one by one. This scheduling scheme will lead to unbalanced cluster load and low resource utilization. To solve this problem, this paper introduces the selection, crossover, and mutation ideas in genetic algorithms into the artificial fish school algorithm, improves the foraging behavior in the artificial fish school algorithm, and constructs a custom scheduling strategy based on the enhanced artificial fish school algorithm. Experiments have shown that compared with the default scheduling strategy of Kubernetes, the customized scheduling strategy in this paper can effectively improve the load balancing of the cluster and improve the utilization of cluster hardware resources in the scenario of multi-pod scheduling.

关键词： Cloud computing Schedules Clustering algorithms Containers Fish Load management Scheduling Hardware Fish schools Genetic algorithms

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