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第34届中国控制与决策会议

作者： YANG Xin WANG Li-Na China Jiliang University College of Mechanical and Electrical Engineering Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University

SLAM technology,which is called simultaneous localization and mapping technology,is to make the robot running in the environment self-locate according to the estimation of its position and posture and the existing map,and at the same time build the map incrementally on this basis,and finally achieve the purpose of building the map and positioning for the whole ***,this paper summarizes the history and development of SLAM ***,it introduces monocular vision SLAM and RGB D SLAM through two kinds of vision SLAM ***,it expounds the content of cooperation between vision technology and single robot,and finds related problems,and looks forward to the future.

关键词： SLAM machine vision single robot cooperation problem

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Understanding and Detecting Privacy Leakage Vulnerabilities in Hyperledger Fabric Chaincodes

Understanding and Detecting Privacy Leakage Vulnerabilities ...

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International Symposium on Software Reliability engineering (ISSRE)

作者： Ziming Chen Yue Li Jianbo Gao Jiashuo Zhang Ke Wang Jianbin Hu Zhi Guan Zhong Chen School of Computer Science Peking University Beijing China College of Computer Science and Technology Taiyuan University of Technology Taiyuan China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing China Peking University Chongqing Research Institute of Big Data Chongqing China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

The application on a blockchain cannot maintain secrecy because its data is replicated across all peers in the network. To remedy this problem, Hyperledger Fabric introduces private data collection (PDC) into its smart contract (i.e. chaincode) to facilitate applications that require privacy. However, recent studies have revealed that PDC is too complex for chaincode developers to fully understand and use correctly, leading to privacy leaks vulnerabilities. In this paper, we present an empirical study on the prevalence of PDC misuse in chaincodes by extracting privacy leakage cases from StackOverflow posts and Hyperledger Fabric repositories on GitHub. Subsequently, we systematically categorize the misuse of PDC into three categories of vulnerabilities resulting in the leakage of private data and provide formal definitions for them. Furthermore, we develop PDChecker, an automated security analysis framework for identifying the privacy and security vulnerabilities in Fabric chaincodes. We evaluated PDChecker on 956 real-world chaincodes applying PDC and found that 67.78% of them contain at least one privacy leakage vulnerability. In addition, PDChecker uncovered 10 zero-day vulnerabilities documented by the China National Vulnerability database.

关键词： Privacy data privacy Distributed ledger databases Smart contracts Fabrics Blockchains Software reliability Security Software development management

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoke Zhu Danyang Li Xiaopan Chen Fumin Qi Fan Zhang Xiao-Yuan Jing School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Department of System Operation National Supercomuting Center in Shenzhen Shenzhen China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Facial image based kinship verification aims to decide whether there exists kinship between the given facial images. In practice, the cross-generation differences will cause adverse effects on kinship verification, which limits the performance. Therefore, how to mine the implied similarity from facial images with large cross-generation divergence is an important problem in kinship verification, which has not yet been well studied. In view of this, we propose a Similarity Mining via Implicit matching pattern LEarning (SMILE) approach for kinship verification. Specifically, SMILE mainly consists of two modules, including a Semi-coupled Multi-pattern Similarity Learning (SMSL) module and a Cross-Generation Feature Normalization (CGFN) module. The SMSL module is designed to learn multiple semi-coupled matching patterns for mining the implicit facial similarity information from different perspectives. The CGFN module aims to reduce the divergence between facial images of parent and child. Extensive experiments demonstrate that the proposed approach outperforms the existing state-of-the-art methods.

关键词： Pattern matching Similarity learning

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Research on Performance Optimization of Web Application System based on JAVA EE 2

Research on Performance Optimization of Web Application Syst...

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2019 2nd International Symposium on Big data and Applied Statistics, ISBDAS 2019

作者： Cai, Zengyu Li, Jingxiao Zhang, Jianwei School of Computer and Communication Engineering Zhengzhou University of Light Industry Henan Key Laboratory of Food Safety Data Intelligence Zhengzhou China Software Engineering College Zhengzhou University of Light Industry Henan Key Laboratory of Food Safety Data Intelligence Zhengzhou China

With the development of informatization, the Web application system is being used by more and more enterprises and institutions. With the increase of the user group, the concurrent visits are increasing, which leads to the poor service quality of Web system. Several high-concurrency solutions existed, but they address only one aspect of the performance problem, and small and medium enterprises need to consider the cost. This paper will analyze the performance factors affecting the Web application system through multiple levels, aiming to build a Web application system with high availability, high concurrency, high expansion and less cost. Based on the factors affecting the performance of the Web application system, the project of the online mall system was optimized, and the service support of the Web application system was adjusted to make it more suitable for the system requirements. Besides, related SQL statements were optimized, Java code layer was optimized, user experience layer was optimized, and performance testing and analysis were completed. © Published under licence by IOP Publishing Ltd.

关键词： Informatization

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Low-Delay Modified OLSR Scheme for Flying AdHoc Networks

Low-Delay Modified OLSR Scheme for Flying AdHoc Networks

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Xiran Xiong Weibo Zheng Shulei Wang Yuanzhe Qu Shiqi Chen Yongchun Yuan Wentao Li Yiming Hong Bingyao Cao Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Engineering No.1 Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai China Engineering No.1 Shanghai Institute of Technical Physics Chinese Academy of Sciences University of Chinese Academy of Sciences Shanghai China

We propose a low-delay modified OLSR scheme for FANET. Through hardware testing, compared with the OLSR protocol, EED and RO have decreased by 100 times and 8% respectively, and PDR has increased by 3%.

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

关键词： Protocols Asia Routing Optical fiber communication Hardware Delays Photonics Testing

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Online Secondary Path Modeling Algorithm Without Auxiliary Noise for Narrowband Active Noise Control

SSRN

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SSRN 2023年

作者： Wang, Cong Wu, Ming Guo, Jianfeng Yang, Jun State Key Laboratory of Acoustics China College of Data Science Taiyuan University of Technology Taiyuan030024 China State Key Laboratory of Acoustics Institute of Acoustics Chinese Academy of Sciences Beijing100190 China Key Laboratory of Noise and Vibration Research Institute of Acoustics Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Narrowband active noise control (NANC) systems effectively control narrowband noise. Online secondary path modeling (SPM) is a practical methodfor real-time noise reduction, especially dealing with the secondary path changes. In addition, auxiliary noise for online SPM is often adopted;however,this method increases the residual noise power and deteriorates the noise reduction performance. Therefore, a strategy for online SPM withoutauxiliary noise for NANC systems is proposed in this paper. The proposed algorithm comprises two stages. Stage A models the primary path, and StageB performs the online SPM while simultaneously performing active control. The secondary output signal is used to model the discrete Fourier transform(DFT) coefficients of the secondary path, avoiding the need for an auxiliary noise and significantly reducing the computational complexity. Moreover, thepredicted primary path from Stage A is employed to obtain the pure desired signal of the online SPM. This step destroys the correlation between the primarynoise and the modeling signal, and accelerates the convergence of the algorithm. The simulation results of recorded data show that the proposedalgorithm has fast convergence speed, high calculation efficiency, convenient parameter adjustment, and can effectively reduce noise when dealing withnoise in real environments. © 2023, The Authors. All rights reserved.

关键词： Discrete Fourier transforms

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Patch Triplet Similarity Purification for Guided Real-World Low-Dose CT Image Denoising

arXiv

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arXiv 2025年

作者： Long, Junhao Yang, Fengwei Yan, Juncheng Zhang, Baoping Jin, Chao Yang, Jian Zou, Changliang Xu, Jun School of Statistics and Data Science Nankai University Tianjin300071 China Department of Radiology The First Affiliated Hospital of Xi’an Jiaotong University Xi’an China Shanxi Engineering Research Center of Computational Imaging and Medical Intelligence Xi’an China Xi’an Key Laboratory of Medical Computational Imaging Xi’an China

Image denoising of low-dose computed tomography (LDCT) is an important problem for clinical diagnosis with reduced radiation exposure. Previous methods are mostly trained with pairs of synthetic or misaligned LDCT and normal-dose CT (NDCT) images. However, trained with synthetic noise or misaligned LDCT/NDCT image pairs, the denoising networks would suffer from blurry structure or motion artifacts. Since non-contrast CT (NCCT) images share the content characteristics to the corresponding NDCT images in a three-phase scan, they can potentially provide useful information for real-world LDCT image denoising. To exploit this aspect, in this paper, we propose to incorporate clean NCCT images as useful guidance for the learning of real-world LDCT image denoising networks. To alleviate the issue of spatial misalignment in training data, we design a new Patch Triplet Similarity Purification (PTSP) strategy to select highly similar patch (instead of image) triplets of LDCT, NDCT, and NCCT images for network training. Furthermore, we modify two image denoising transformers of SwinIR and HAT to accommodate the NCCT image guidance, by replacing vanilla self-attention with cross-attention. On our collected clinical dataset, the modified transformers trained with the data selected by our PTSP strategy show better performance than 15 comparison methods on real-world LDCT image denoising. Ablation studies validate the effectiveness of our NCCT image guidance and PTSP strategy. We will publicly release our data and code. Copyright © 2025, The Authors. All rights reserved.

关键词： Image denoising

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Fusion Method of Tobacco Leaf State Recognition Based on Bayesian Algorithm and Xgboost

Fusion Method of Tobacco Leaf State Recognition Based on Bay...

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International Conference on Computer and Communications (ICCC)

作者： Jianyu Wei Jili Zhang Junlin Li Yang Cao Jing Yang Chongjun Huang Wenwei Xu Chenglin Rao Haijiang Jia Banghong Wu Shiying Nong Guangxi Tobacco Industry Co. Ltd Nanning China Guangxi Zhuang Autonomous Region Tobacco Company Baise Branch Baise China school of mechanical engineering Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

The three-stage curing process is the key link to determining the quality of tobacco products, which is affected by the curing process parameters such as heating point and stable temperature time. In this paper, multiple sensors were used to collect baking temperature, humidity, tobacco weight, and tobacco images during baking. Naive Bayes and extreme gradient lifting tree algorithms and data fusion methods were adopted, and a fusion model for baking state recognition was designed. The accurate identification of baking state is realized. The accuracy of state recognition of tobacco curing state by fusion model reached 99.2%. This model can predict the key parameters of baking objectively and accurately, and assist baking personnel to adjust.

关键词： Temperature sensors Temperature distribution Accuracy Soft sensors data integration Humidity Curing Prediction algorithms data models Bayes methods

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BC-QNet: A quantum-infused ELM model for breast cancer diagnosis

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Computers in Biology and Medicine 2024年 175卷 108483-108483页

作者： Bilal, Anas Imran, Azhar Liu, Xiaowen Liu, Xiling Ahmad, Zohaib Shafiq, Muhammad El-Sherbeeny, Ahmed M. Long, Haixia College of Information Science and Technology Hainan Normal University Haikou571158 China Key Laboratory of Data Science and Smart Education Ministry of Education Hainan Normal University Haikou571158 China Department of Creative Technologies Air University Islamabad44000 Pakistan Department of Criminology & Forensic Sciences Technology Lahore Garrison University Lahore Pakistan School of Information Engineering Qujing Normal University Yunnan China Industrial Engineering Department College of Engineering King Saud University P.O. Box 800 Riyadh11421 Saudi Arabia

The timely and accurate diagnosis of breast cancer is pivotal for effective treatment, but current automated mammography classification methods have their constraints. In this study, we introduce an innovative hybrid model that marries the power of the Extreme Learning Machine (ELM) with FuNet transfer learning, harnessing the potential of the MIAS dataset. This novel approach leverages an Enhanced Quantum-Genetic Binary Grey Wolf Optimizer (Q-GBGWO) within the ELM framework, elevating its performance. Our contributions are twofold: firstly, we employ a feature fusion strategy to optimize feature extraction, significantly enhancing breast cancer classification accuracy. The proposed methodological motivation stems from optimizing feature extraction for improved breast cancer classification accuracy. The Q-GBGWO optimizes ELM parameters, demonstrating its efficacy within the ELM classifier. This innovation marks a considerable advancement beyond traditional methods. Through comparative evaluations against various optimization techniques, the exceptional performance of our Q-GBGWO-ELM model becomes evident. The classification accuracy of the model is exceptionally high, with rates of 96.54 % for Normal, 97.24 % for Benign, and 98.01 % for Malignant classes. Additionally, the model demonstrates a high sensitivity with rates of 96.02 % for Normal, 96.54 % for Benign, and 97.75 % for Malignant classes, and it exhibits impressive specificity with rates of 96.69 % for Normal, 97.38 % for Benign, and 98.16 % for Malignant classes. These metrics are reflected in its ability to classify three different types of breast cancer accurately. Our approach highlights the innovative integration of image data, deep feature extraction, and optimized ELM classification, marking a transformative step in advancing early breast cancer detection and enhancing patient outcomes. © 2024 Elsevier Ltd

关键词： Feature extraction

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Lightweight Transformer Network and Self-supervised Task for Kinship Verification

Lightweight Transformer Network and Self-supervised Task for...

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International Conference on Computer and Communications (ICCC)

作者： Xiaoke Zhu Yunwei Li Danyang Li Lingyun Dong Xiaopan Chen 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

Kinship verification is one of the interesting and critical problems in computer vision research, with significant progress in the past decades. Meanwhile, Vision Transformer (VIT) has recently achieved impressive success in many domains, including object detection, image recognition, and semantic segmentation, among others. Most of the previous work on kinship verification are based on convolutional or recurrent neural networks. Compared with the local processing of images like convolutions, transformers can effectively understand and process images globally. However, due to overuse, there are many Transformer layers of fully connected layers, and VIT speed is still an issue. Therefore, in this paper, inspired by the recent success of Transformer models in vision tasks, we propose a Transformer-based kinship verification for training and optimizing kinship verification models. We first train the basic vision transformer (VIT-B) with 12 transformer layers, then we reduce the transformer layers to 6 layers, namely VIT-S (Small Vit) and 4 layers, namely VIT-T (Tiny Vit), to make a tradeoff between optimization accuracy and efficiency. As the first attempt to apply Transformer to the kinship verification task, it provides a feasible strategy for kinship research topics and verifies the effectiveness of the method in terms of the accuracy of the experimental results.

关键词： Training knowledge engineering Recurrent neural networks Semantic segmentation Self-supervised learning Object detection Transformers

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