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IEEE International Conference on Energy Internet (ICEI)

作者： Yuexi Yang Xiao Zhou Sichao Wang Chunpeng Li Junwei Huang Wenzheng Xu State Key Laboratory of Advanced Power Transmission Technology State Grid Smart Grid Research Institute Co.Ltd. Beijing China Fujian Key Laboratory of Protection and Operation Control for Smart Grid State Grid Fujian Electric Power Research Institute. Fuzhou China National Research Center for Active Distribution Network Technology Beijing Jiaotong University. Beijing China

The grid-connecting control and maximum power point tracking control of photovoltaic inverters have long been the research focus in the field of solar power generation. In this paper, a new coordinated modular multilevel converter (MMC) control strategy based on virtual synchronization and MPPT is proposed for grid-connected inverters in the PV/Battery system. The proposed strategy realizes frequency and voltage droop control with the synchro's inertia and damping characteristics, and improves the power angle stability as well as system voltage stability. Simulation results in MATLAB/Simulink verifies the feasibility and effectiveness of the proposed control strategy.

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Recommender Systems with Condensed Local Differential Privacy 3rd

Recommender Systems with Condensed Local Differential Privac...

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3rd International Conference on Machine Learning for Cyber security, ML4CS 2020

作者： Liu, Ao Yao, Yanqing Cheng, Xianfu State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Key Laboratory of Aerospace Network Security Ministry of Industry and Information Technology School of Cyberspace Science and Technology Beihang University Beijing100191 China Beijing Key Laboratory of Network Technology Beihang University Beijing100191 China

ISBN: (纸本)9783030622220

Recommender systems aim at predicting users’ future behaviors by learning the users’ personal information and historical behaviors. Unfortunately, training by the user’s raw data will inevitably cause the user’s private information to leak. Moreover, exiting privacy-preserving recommendation methods are based on matrix factorization with Local Differential Privacy (LDP). Because LDP performs poorly in small or multi-dimensional data sets, these methods do not work well with sparse or multi-dimensional features. Motivated by these questions, we propose a privacy-preserving recommendation model based on Deep neural networks and Factorization Machines (DeepFM), which can provide users with recommendations under the promise of protecting users’ privacy. In our recommendation model, each user randomly perturbs the calculated gradients to satisfy Condensed Local Differential Privacy (CLDP). The recommender system collects the perturbed gradients to train a recommendation model, which guarantees the safety of users’ private information and has excellent accuracy for the recommendation. Experiments on a real-world dataset show that the recommendation accuracy of our algorithm performs better than the existing methods. © 2020, Springer Nature Switzerland AG.

关键词： Deep neural networks

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Enhanced Practical Byzantine Fault Tolerance for Service Function Chain Deployment: Advancing Big Data Intelligence in Control Systems

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Computers, Materials and Continua 2025年第3期83卷 4393-4409页

作者： Peiying Zhang Yihong Yu Jing Liu Chong Lv Lizhuang Tan Yulin Zhang Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao 266580 China Library of Shanghai Lixin University of Accounting and Finance Shanghai 201209 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan 250014 China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of China Beijing 100081 China Key Laboratory of Intelligent Game Yangtze River Delta Research Institute of NPU Taicang 215400 China Key Laboratory of Education Informatization for Nationalities (Yunnan Normal University) Ministry of Education Kunming 650092 China

As Internet of Things (IoT) technologies continue to evolve at an unprecedented pace, intelligent big data control and information systems have become critical enablers for organizational digital transformation, facilitating data-driven decision making, fostering innovation ecosystems, and maintaining operational stability. In this study, we propose an advanced deployment algorithm for Service Function Chaining (SFC) that leverages an enhanced Practical Byzantine Fault Tolerance (PBFT) mechanism. The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings. By integrating blockchain technology and Deep Reinforcement Learning (DRL), our algorithm not only optimizes resource utilization and quality of service but also ensures robust security during SFC deployment. Specifically, the enhanced PBFT consensus mechanism (VRPBFT) significantly reduces consensus latency and improves Byzantine node detection through the introduction of a Verifiable Random Function (VRF) and a node reputation grading model. Experimental results demonstrate that compared to traditional PBFT, the proposed VRPBFT algorithm reduces consensus latency by approximately 30% and decreases the proportion of Byzantine nodes by 40% after 100 rounds of consensus. Furthermore, the DRL-based SFC deployment algorithm (SDRL) exhibits rapid convergence during training, with improvements in long-term average revenue, request acceptance rate, and revenue/cost ratio of 17%, 14.49%, and 20.35%, respectively, over existing algorithms. Additionally, the CPU resource utilization of the SDRL algorithm reaches up to 42%, which is 27.96% higher than other algorithms. These findings indicate that the proposed algorithm substantially enhances resource utilization efficiency, service quality, and security in SFC deployment.

关键词： Big data intelligent transformation heterogeneous networks service function chain blockchain deep reinforcement learning trusted deployment

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An Improved Privacy-Preserving Stochastic Gradient Descent Algorithm 3rd

An Improved Privacy-Preserving Stochastic Gradient Descent A...

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3rd International Conference on Machine Learning for Cyber security, ML4CS 2020

作者： Cheng, Xianfu Yao, Yanqing Liu, Ao State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Key Laboratory of Aerospace Network Security Ministry of Industry and Information Technology School of Cyberspace Science and Technology Beihang University Beijing100191 China Beijing Key Laboratory of Network Technology Beihang University Beijing100191 China

ISBN: (纸本)9783030622220

Deep learning techniques based on neural network have made significant achievements in various fields of Artificial Intelligence. However, model training requires large-scale datasets, these datasets are crowd-sourced and model parameters will contain the encoding of private information, resulting in the risk of privacy leakage. With the trend towards sharing pre-trained models, the risk of stealing training datasets through member inference attack and model inversion attack is further heightened. To tackle this problem, we propose an improved Differential Privacy Stochastic Gradient Descent algorithm, using simulated annealing algorithm and denoising mechanism to optimize the allocation method of privacy loss and improve model accuracy. We also analyze privacy cost under random shuffle data batch processing methods in detail within the framework of Subsampled Rényi Differential Privacy. Compared with existing methods, our experiments show that we can train deep neural networks with non-convex objective function more efficiently with moderate privacy budgets. © 2020, Springer Nature Switzerland AG.

关键词： Simulated annealing

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A Semantic-based Replacement for Event Image Privacy 24

A Semantic-based Replacement for Event Image Privacy

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24th IEEE International Conference on Computational Science and Engineering, CSE 2021

作者： Chen, Zhenfei Tianqing, Zhu Tian, Bing Wang, Yu Ren, Wei China University of Geosciences School of Computer Science Wuhan China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou China China University of Geosciences Guangxi Key Laboratory of Cryptography and Information Security Key Laboratory of Network Assessment Technology CAS School of Computer Science Beijing China

ISBN: (纸本)9781665416603

Thanks to the continuous development of deep learning and the updating of deep neural networks, the accuracy of various computer vision tasks continue improving. On the one hand, the accuracy of image recognition is significantly improved. On the other hand, it also poses a higher challenge of image-based privacy preservation. Although traditional privacy protection methods such as cryptography methods can provide a good privacy protection, they are extremely inconvenient to use and cannot provide good image utility. In order to obtain a balance between image privacy and utility, we propose a privacy-preserving model based on image semantic replacement. We perform semantic replacement or obfuscation to multiple information. Taking the human figures as an example, the information includes faces, scenes, and dressing style. As that information contributes the most to the recognition, we define those items as the privacy of the original image. We replace the event information of the original image, so that the figures in the image can no longer be recognized. With this strategy, the image can still be detected by various detection networks, such as scene detection, which ensures utility. The framework consists of three parts: detection network, scene replacement network, and clothing replacement network. A comprehensive and quantitative experiment set proves the effectiveness of the proposed model. © 2021 IEEE.

关键词： Semantics

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Fairness of Large Music Models: From a Culturally Diverse Perspective

Fairness of Large Music Models: From a Culturally Diverse Pe...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Qinyuan Wang Bruce Gu He Zhang Yunfeng Li School of Music University of Sydney NSW Australia Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center 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 CNPIEC KEXIN Ltd Beijing China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

This paper explores the fairness of large music models in generating culturally diverse musical compositions, emphasizing the need for inclusivity and equity in AI-generated music. By analyzing popular datasets such as the Million Song Dataset and the Lakh MIDI Dataset, we identify a significant underrepresentation of non-Western musical elements. To address this, we adapt existing models to incorporate non-Western scales, rhythms, and instruments. The adapted models demonstrate a substantial improvement in generating culturally diverse music. Using AI-based synthetic listeners, we evaluate the impact of these adaptations on listener perception and engagement. Results indicate that music from the adapted models scores higher in terms of enjoyment, novelty, cultural resonance, and overall engagement compared to the original models. Our findings highlight the importance of cultural diversity in enhancing the user experience and ensuring ethical AI practices. The study also discusses challenges, such as dataset limitations and model adaptation complexity, and suggests directions for future research to further promote fairness and inclusivity in music AI. This work contributes to the development of more culturally aware and representative music generation systems, aiming to enrich the global musical landscape and foster greater appreciation of diverse musical traditions.

关键词： Adaptation models Ethics Instruments Data science Rhythm Data models User experience Resonance Cultural differences Artificial intelligence

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HCF-Net: Hierarchical Context Fusion network for Infrared Small Object Detection

HCF-Net: Hierarchical Context Fusion Network for Infrared Sm...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Shibiao Xu Shuchen Zheng Wenhao Xu Rongtao Xu Changwei Wang Jiguang Zhang Xiaoqiang Teng Ao Li Li Guo School of Artificial Intelligence Beijing University of Posts and Telecommunications CASIA MAIS School of Artificial Intelligence University of Chinese Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science School of Computer Science and Technology Harbin University of Science and Technology

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Infrared small object detection is an important computer vision task involving the recognition and localization of tiny objects in infrared images, which usually contain only a few pixels. However, it encounters difficulties due to the diminutive size of the objects and the generally complex backgrounds in infrared images. In this paper, we propose a deep learning method, HCF-Net, that significantly improves infrared small object detection performance through multiple practical modules. Specifically, it includes the parallelized patch-aware attention (PPA) module, dimension-aware selective integration (DASI) module, and multi-dilated channel refiner (MDCR) module. The PPA module uses a multi-branch feature extraction strategy to capture feature information at different scales and levels. The DASI module enables adaptive channel selection and fusion. The MDCR module captures spatial features of different receptive field ranges through multiple depth-separable convolutional layers. Extensive experimental results on the SIRST infrared single-frame image dataset show that the proposed HCF-Net performs well, surpassing other traditional and deep learning models. Code is available at https://***/zhengshuchen/HCFNet.

关键词： Deep learning Location awareness Convolutional codes Computer vision Adaptation models Image recognition Object detection

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DICOM-Fuzzer: Research on DICOM Vulnerability Mining Based on Fuzzing technology 14th

DICOM-Fuzzer: Research on DICOM Vulnerability Mining Based o...

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14th EAI International Conference on Communications and networking in China, ChinaCom 2019

作者： Wang, Zhiqiang Li, Quanqi Liu, Qian Liu, Biao Zhang, Jianyi Yang, Tao Liu, Qixu Beijing Electronic Science and Technology Institute Beijing China State Information Center Beijing China Key Lab of Information Network Security Ministry of Public Security Shanghai China Key Laboratory of Network Assessment Technology Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030411138

In recent years, the medical equipment and related information systems show the characteristics of mobility, networking, intelligence. At the same time, security incidents caused by medical equipment emerge in an endless stream, which brings a huge threat to the information security of users and causes serious harm. Most medical devices use open source protocol library, which brings great security risks to the digitalization and informatization of medical devices. Therefore, in the face of growing security threats and challenges, it is urgent to study the security of medical equipment. In this paper, the vulnerability mining of DICOM was studied, the most commonly used communication standard for high-performance medical devices, and a vulnerability mining model based on Fuzzing technology was proposed. This model constructed a vulnerability mining environment by simulating PACS system, and implemented a prototype system DICOM-Fuzzer. The system includes initialization, test case generation and other modules, which can complete large-scale automatic testing and exception monitoring. Then, three different versions of the open source library were selected to test the 1000 test cases generated respectively. It was found that when the received file data was greater than 7080 lines, the overflow would occur, resulting in the denial of service of the system. Finally, the security suggestions and repair measures were put forward, and the future research was described. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Medical applications

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Contrastive Learning for Chest X-ray Classification: A Fusion of Topological Data Analysis and ResNet

Contrastive Learning for Chest X-ray Classification: A Fusio...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Hao Ren Zeyu Luo Fengshi Jing Xinyue Zhang Han He Yonghao Yu Dawei Zhao Faculty of Data Science City University of Macau China Minzu University of China Beijing China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center 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

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Lung diseases such as pneumonia and tuberculosis present significant global health challenges, leading to millions of deaths annually. Chest X-ray imaging is crucial for diagnosing these conditions, but interpretation often relies on specialized radiologists, creating barriers in resource-limited areas. This study introduces a novel framework integrating topological data analysis (TDA), deep learning with ResNet-50, and contrastive learning to enhance the accuracy and computational efficiency of chest X-ray analysis. TDA captures global topological structures, while ResNet-50 extracts detailed local features. Contrastive learning then optimizes the feature space, enhancing the model’s discriminative power and generalization capabilities. Comprehensive evaluations on multiple datasets demonstrate the framework’s robustness and effectiveness, showing substantial improvements in diagnostic performance and computational efficiency. These findings suggest that the proposed approach is well-suited for real-time clinical applications, especially in resource-limited settings. Future work will focus on further optimizing the model and exploring its application to other medical imaging modalities, broadening its clinical utility.

关键词： Deep learning Accuracy Computational modeling Contrastive learning Feature extraction Robustness Real-time systems Computational efficiency X-ray imaging Biomedical imaging

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Unsupervised Pixel-level Crack Detection Based on Generative Adversarial network 20

Unsupervised Pixel-level Crack Detection Based on Generative...

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5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020

作者： Duan, Lijuan Geng, Huiling Pang, Junbiao Zeng, Jun Beijing University of Technology Beijing China Beijing Key Laboratory of Trusted Computing Beijing China Natl. Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing China

ISBN: (纸本)9781450377485

Automatic crack detection from pavement images is an import research field. Meanwhile, crack detection is a challenge task: (1) manual labels are subjective because of low contrast between crack and the surrounding pavement and heavy workload;(2) the excessive dependence of supervised deep learning training on labels. To address these problems, we present an unsupervised method for learning mapping to translate crack images to binary images based on generative adversarial network. We introduce the cyclic consistent loss to increase accuracy of crack localization. Eight residual blocks connected convolutional neural network for feature extraction is used as generator and a 5-layer fully convolutional network is used as discriminator. We analyze the proposed framework and provide qualitative and quantitative comparison. The experimental results show that the proposed method achieves a better performance than several existing methods. © 2020 ACM.

关键词： Crack detection

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