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Effects of Adaptive Random Deceleration on Traffic Flow

Journal of Applied Mathematics and Physics 2023年第3期11卷 618-628页

作者： Qi Wang Shu Zhu Liuhua Zhu School of computer Jiangsu University of Science and Technology Zhenjiang China School of Physics and Telecommunication Engineering Yulin Normal University Yulin China Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin China Optoelectronic Information Research Center Yulin Normal University Yulin China

Based on the Nagel-Schreckenberg model, an improved cellular automaton traffic flow model is proposed, in which the random deceleration probability of each vehicle is no longer fixed, but is adaptively adjusted according to the local traffic density in its vision and its current velocity. The numerical simulation results show that the maximum traffic capacity of the improved model under the same parameters is greater than that of the Nagel-Schreckenberg model, and is closer to the measured data. In addition, the traffic flow and vehicle velocity under different meteorological conditions are simulated by using the improved model, and the synchronized flow phenomenon consistent with the actual traffic is reproduced. Meanwhile, the results show that under the same parameters, when the traffic density is equal to 0.3, the traffic flow of the improved model increases by about 11% compared with the original model, and when the traffic density increases to 0.6, the traffic flow increases by about 27%. .

关键词： Traffic Flow Cellular Automaton Adaptive Random Deceleration Synchronized Flow

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Fedsoda: Federated Cross-Assessment and Dynamic Aggregation for Histopathology Segmentation

Fedsoda: Federated Cross-Assessment and Dynamic Aggregation ...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yuan Zhang Yaolei Qi Xiaoming Qi Lotfi Senhadji Yongyue Wei Feng Chen Guanyu Yang Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Inserm LTSI UMR 1099 University of Rennes Public Health and Epidemic Preparedness and Response Center Peking University Department of Biostatistics School of Public Health Nanjing Medical University

Federated learning (FL) for histopathology image segmentation involving multiple medical sites plays a crucial role in advancing the field of accurate disease diagnosis and treatment. However, it is still a task of great challenges due to the sample imbalance across clients and large data heterogeneity from disparate organs, variable segmentation tasks, and diverse distribution. Thus, we propose a novel FL approach for histopathology nuclei and tissue segmentation, FedSODA, via synthetic-driven cross-assessment operation (SO) and dynamic stratified-layer aggregation (DA). Our SO constructs a cross-assessment strategy to connect clients and mitigate the representation bias under sample imbalance. Our DA utilizes layer-wise interaction and dynamic aggregation to diminish heterogeneity and enhance generalization. The effectiveness of our FedSODA has been evaluated on the most extensive histopathology image segmentation dataset from 7 independent datasets. The code is available at https://***/yuanzhang7/FedSODA.

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Hybrid Data-Free Knowledge Distillation

arXiv

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arXiv 2024年

作者： Tang, Jialiang Chen, Shuo Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Center for Advanced Intelligence Project RIKEN Japan Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generation-based methods train student networks by collecting massive real examples and generating synthetic examples, respectively. However, they inevitably become weak in practical scenarios due to the difficulties in gathering or emulating sufficient real-world data. To solve this problem, we propose a novel method called Hybrid Data-Free Distillation (HiDFD), which leverages only a small amount of collected data as well as generates sufficient examples for training student networks. Our HiDFD comprises two primary modules, i.e., the teacher-guided generation and student distillation. The teacher-guided generation module guides a Generative Adversarial Network (GAN) by the teacher network to produce high-quality synthetic examples from very few real-world collected examples. Specifically, we design a feature integration mechanism to prevent the GAN from overfitting and facilitate the reliable representation learning from the teacher network. Meanwhile, we drive a category frequency smoothing technique via the teacher network to balance the generative training of each category. In the student distillation module, we explore a data inflation strategy to properly utilize a blend of real and synthetic data to train the student network via a classifier-sharing-based feature alignment technique. Intensive experiments across multiple benchmarks demonstrate that our HiDFD can achieve state-of-the-art performance using 120 times less collected data than existing methods. Code is available at https://***/tangjialiang97/HiDFD. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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A Shortest Path Query Approach for Encrypted Graphs Based on Padding Dictionary Structure

A Shortest Path Query Approach for Encrypted Graphs Based on...

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Chinese Automation Congress (CAC)

作者： Ming Yang Kaiyang Zhang Chao Mu Xin Wang Yuanlong Liu Heng Zhang Key Laboratory of Computing Power Network and Information Security Shandong Computer Science Center Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Grid Shandong Electric Power Company Jinan China School of Computer Engineering Jiangsu Ocean University Lianyungang China

Hybrid intelligence has emerged as an innovative approach that synergistically combines the strengths of human intelligence and artificial intelligence (AI) to address complex problems. In this paradigm, knowledge graphs play a pivotal role by supplying vital background information and contextual understanding to hybrid intelligence systems. However, due to limitations in local storage capacity, managing large-scale graph data often requires outsourcing to cloud servers. Nevertheless, the use of untrusted cloud servers poses a risk of potential user data leaks. To address these concerns, this paper presents an encryption scheme specifically designed to protect graph data, enabling queries involving intermediate nodes in the shortest path between two nodes within the graph. The proposed scheme ensures the privacy and security of outsourced data by employing a novel method that utilizes padding dictionary structure encryption. This method ensures strict limitations on the cloud server's capabilities, restricting it solely to query operations, it effectively prevents any compromise of user data or unauthorized access to sensitive information, users can securely store and process their graph data on servers, significantly reducing the potential risk of data leakage and unauthorized breaches. To validate the feasibility and effectiveness of the proposed solution, practical experiments were conducted using real-world data.

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Implementation of PRINCE with resource-efficient structures based on FPGAs

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Frontiers of information technology & Electronic Engineering 2021年第11期22卷 1505-1516页

作者： Lang LI Jingya FENG Botao LIU Ying GUO Qiuping LI Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang421002China College of Information Science and Engineering Hunan Normal UniversityChangsha410081China College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China

In this era of pervasive computing, low-resource devices have been deployed in various fields. PRINCE is a lightweight block cipher designed for low latency, and is suitable for pervasive computing applications. In this paper, we propose new circuit structures for PRINCE components by sharing and simplifying logic circuits, to achieve the goal of using a smaller number of logic gates to obtain the same result. Based on the new circuit structures of components and the best sharing among components,we propose three new hardware architectures for PRINCE. The architectures are simulated and synthesized on different programmable gate array devices. The results on Virtex-6 show that compared with existing architectures, the resource consumption of the unrolled, low-cost, and two-cycle architectures is reduced by 73, 119, and 380 slices, respectively. The low-cost architecture costs only 137 slices. The unrolled architecture costs 409 slices and has a throughput of 5.34 Gb/s. To our knowledge, for the hardware implementation of PRINCE, the new low-cost architecture sets new area records, and the new unrolled architecture sets new throughput records. Therefore, the newly proposed architectures are more resource-efficient and suitable for lightweight,latency-critical applications.

关键词： Lightweight block cipher Field-programmable gate array(FPGA) Low-cost PRINCE Embedded security

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SEMI-SUPERVISED SEMANTIC SEGMENTATION VIA ENTROPY MINIMIZATION

SEMI-SUPERVISED SEMANTIC SEGMENTATION VIA ENTROPY MINIMIZATI...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Wu, Jiawei Fan, Haoyi Zhang, Xiaoqing Lin, Shouying Li, Zuoyong College of Mecanical and Electrical Engineering Fujian Agriculture and Forestry University Fuzhou350002 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9781665438643

In this paper, we propose a novel entropy minimization based semi-supervised method for semantic segmentation. Entropy minimization has proven to be an effective semi-supervised method for realizing the cluster assumption, where the decision boundary should lie in low-density regions. Inspired by the existing consistency training semi-supervised segmentation networks with encoder-decoder architecture, we found that there tend to be more large gradient values at the object edges than other positions in the feature map of the encoder, and therefore propose a feature gradient map regularization to enlarge inter-class distance in the feature space for low-entropy of segmentation prediction. Additionally, we introduce an adaptive sharpening scheme with aleatoric uncertainty, and a class consistency constraint regularization, to alleviate the interference of noise with pseudo labels. Extensive experiments on PASCAL VOC, PASCAL-Context, and Leukocyte datasets show that the proposed method achieves state-of-the-art semi-supervised semantic segmentation performance without almost additional calculations and network structures. © 2021 IEEE computer Society. All rights reserved.

关键词： Semantics

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Adaptive Image Steganographic Analysis System Based on Deep Convolutional Neural Network

Adaptive Image Steganographic Analysis System Based on Deep ...

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作者： Ge Jiao College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application

Adaptive steganography emplaces the message into the hard-to-detect noise area or the complex texture area of the image, so the steganography analysis method based on artificial design features needs to design a very complex feature extraction algorithm to detect the steganography image. In view of the advantages of deep learning steganography in automatic extraction of image features and high detection accuracy, an image steganography analysis algorithm is designed by using deep convolutional neural network, and an image steganography analysis system based on deep learning is developed. The system realizes the functions of neural network structure analysis, image steganography and verification, steganography analysis and so on. The system simplifies the operation process of algorithm comparison, reduces the complexity of algorithm performance evaluation, and verifies the feasibility of the proposed algorithm.

关键词： Image steganographic analysis Feature extraction Deep learning Convolutional neural networks

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A New Image Encryption Strategy Based on Arnold Transformation and Logistic Map

A New Image Encryption Strategy Based on Arnold Transformati...

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作者： Xiyu Sun Zhong Chen College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application

Aiming at the problem of digital image encryption, an image encryption algorithm is proposed. The algorithm is divided into preparation stage and permutation diffusion stage. In the preparation stage, Arnold algorithm is improved and combined with Logistic chaotic map to generate the necessary sequence for the next stage. In the permutation diffusion stage, the final encrypted image is obtained by permutation, positive XOR operation, shift operation, S-box substitution and reverse XOR operation. The experimental results show that the algorithm can effectively resist the common attacks. For the 128 × 128 Lena gray image, the resistance performance number of pixel change rate(NPCR) is 99.6090, unified average changing intensity(UACI) is 33.4316. In addition, the time consumption of the algorithm is about 0.0055 s, and the experiments show that the algorithm has high security and real-time performance.

关键词： Image encryption Arnold transformation Chaotic system

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Research on Wireless Channel Modeling for Complex Water Surface Environments

Research on Wireless Channel Modeling for Complex Water Surf...

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Intelligent Communication and Networking (ICN), International Conference on

作者： Xujie Li Tianzhang Ge Baochao Wei Haotian Li Ning Xu College of Information Science and Engineering Hohai University Nanjing China Jiangsu Health Development Research Center Nanjing China NHC Contraceptives Adverse Reaction Surveillance Center Nanjing China Jiangsu Provincial Medical Key Laboratory of Fertility Protection and Health Technology Assessment Nanjing China College of Computer and Information Hohai University Nanjing China

Accurate and efficient channel modeling has always been a critical foundation for the deployment and application of 5G communication technology. To meet the requirements of continuous and extensive coverage in 5G, higher demands have been placed on wireless channel characteristics across various scenarios. However, the propagation of wireless signals over water may involve more complex reflection and scattering phenomena. Additionally, the presence of vegetation on water surface further complicates the channel modeling. Addressing these challenges, we assess relevant theories and representative signal attenuation models, and propose a channel model for complex water-based scenarios based on the open water channel model. To ascertain the precision of the proposed model, we establish an experimental platform and conduct measurements on the attenuation of signals across a water pool populated with various obstacles. The results demonstrate that the suggested model more accurately corresponds to the distinctive patterns of wireless signal attenuation encountered within complex water surface environments.

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FST-Net: Facial Soft Tissue Landmark Localization on 3dMD Scans Using Feature Fusion and Local Coordinate Regression

FST-Net: Facial Soft Tissue Landmark Localization on 3dMD Sc...

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IEEE International Symposium on Biomedical Imaging

作者： Zhidong He Han Bao Mingzhang Chen Jiasong Wu Luwei Liu Lotfi Senhadji Huazhong Shu Bin Yan Laboratory of Image Science and Technology Southeast University Nanjing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Ministry of Education) Southeast University Nanjing China Department of Orthodontics The Affiliated Stomatological Hospital of Nanjing Medical University Nanjing China Centre de Recherche en Information Biomédicale Sino-Français Rennes France INSERM LTSI-UMR 1099 Univ-Rennes Rennes France

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Landmark localization of facial soft tissue (FST) is a basic step in 3D morphometric analysis of human face. However, there are few studies on landmark localization of 3D scan images based on deep learning. The methods based on 2D images cannot be directly applied due to the non-Euclidean data structure. In this paper, we propose an end-to-end learning framework to automatically localize 28 landmarks on 3dMD scans, called FST-Net. Our method extracts features from texture images and mesh models. New texture mappings for 3dMD scans are generated by projection to fuse texture and structure features. A dual-branch network integrating transformers is applied to predict the landmark heatmaps from coarse to fine. A local coordinate regression module based on probabilistic distance and heatmap predictions is proposed to compute the landmark coordinates. We collect and annotate 297 3dMD face scans from the clinic to evaluate our model. Experiments show that the average localization error of the model is 1.204mm (the clinically acceptable precision range is 1.5 mm), and the correct landmark detection rate equals to 70.89%. Our model outperforms the current state-of-the-art deep learning methods for landmark localization on the mesh model.

关键词： Location awareness Deep learning Heating systems Three-dimensional displays Accuracy Computational modeling Biological tissues

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