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Control and Stability of Large-scale Power system with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

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CSEE Journal of Power and Energy systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

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An optimal container update method for edge-cloud collaboration

An optimal container update method for edge-cloud collaborat...

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作者： Zhang, Haotong Lin, Weiwei Xie, Rong Li, Shenghai Dai, Zhiyan Wang, James Z. School of Software Engineering South China University of Technology Guangzhou China School of Computer Science & Engineering South China University of Technology Guangzhou China Department of New Network Technologies Peng Cheng Laboratory Shenzhen China School of Information Guangdong University of Finance & Economics Guangzhou China School of Computing Clemson University ClemsonSC United States

Emerging computing paradigms provide field-level service responses for users, for example, edge computing, fog computing, and MEC. Edge virtualization technologies represented by Docker can provide a platform-independent, low-resource-consumption operating environment for edge service. The image-pulling time of Docker is a crucial factor affecting the start-up speed of edge services. The layer reuse mechanism of native Docker cannot fully utilize the duplicate data of node local images. In this paper, we propose a chunk reuse mechanism (CRM), which effectively targets node-local duplicate data during container updates and reduces the volume of data transmission required for image building. We orchestrate the CRM process for cloud and remote-cloud nodes to ensure that the resource overhead from container update data preparation and image reconstruction is within an acceptable range. The experimental results show that the CRM proposed in this paper can effectively utilize the node local duplicate data in the synchronous update of containers in multiple nodes, reduce the volume of data transmission, and significantly improve container update efficiency. © 2023 John Wiley & Sons, Ltd.

关键词： Containers

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Privacy-Preserving Electricity Theft Detection Based on Blockchain

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IEEE Transactions on Smart Grid 2023年第5期14卷 4047-4059页

作者： Zhao, Zhiqiang Liu, Yining Zeng, Zhixin Chen, Zhixiong Zhou, Huiyu Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Fujian Key Laboratory of Financial Information Processing The Key Laboratory of Applied Mathematics of Fujian Province University Putian University Putian351100 China University of Leicester School of Computing and Mathematical Sciences LeicesterLE1 7RH United Kingdom

In most electricity theft detection schemes, consumers' power consumption data is directly input into the detection center. Although it is valid in detecting the theft of consumers, the privacy of all consumers is at risk unless the detection center is assumed to be trusted. In fact, it is impractical. Moreover, existing schemes may result in some security problems, such as the collusion attack due to the presence of a trusted third party, and malicious data tampering caused by the system operator (SO) being attacked. Aiming at the problems above, we propose a blockchain-based privacy-preserving electricity theft detection scheme without a third party. Specifically, the proposed scheme uses an improved functional encryption scheme to enable electricity theft detection and load monitoring while preserving consumers' privacy;distributed storage of consumers' data with blockchain to resolve security problems such as data tampering, etc. Meanwhile, we build a long short-term memory network (LSTM) model to perform higher accuracy for electricity theft detection. The proposed scheme is evaluated in a real environment, and the results show that it is more accurate in electricity theft detection within acceptable communication and computational overhead. Our system analysis demonstrates that the proposed scheme can resist various security attacks and preserve consumers' privacy. © 2010-2012 IEEE.

关键词： Long short-term memory

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Multi-Scale Feature Extraction with Supervised Contrastive Learning for Vulnerability Detection 36

Multi-Scale Feature Extraction with Supervised Contrastive L...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Zhao, Yahui Lu, Lu Liang, Zhihong Suo, Siliang School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Pengcheng Laboratory Shenzhen China Electric Power Research Institute CSG Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)1891706594

The capacity of Deep Learning to automatically learn features from source code has facilitated its extensive utilization in detecting software vulnerabilities. However, existing pre-trained models regard code snippets as token sequences, neglecting the inherent structure of the code. The utilization of graph-based code representations is constrained by the limitations of Graph Neural Networks, particularly the difficulty in capturing long-range dependencies, which renders it challenging to learn grammatical and semantic information from complex code graph representations. This paper proposes a novel multi-scale software vulnerability detection method based on supervised contrastive learning. The method integrates local features obtained from code paths with structural features extracted from the Code Property Graph by GraphTrans, thereby achieving a multi-scale feature representation of the source code. Additionally, a supervised contrastive loss function is employed during training in order to fully utilize label information and address the class imbalance problem. The experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods, achieving the highest accuracy, precision, and F1 on the real-world benchmark dataset CodeXGLUE for software vulnerability detection. © 2024 Knowledge systems Institute Graduate school. All rights reserved.

关键词： Contrastive Learning

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Detecting JVM JIT Compiler Bugs via Exploring Two-Dimensional Input Spaces

Detecting JVM JIT Compiler Bugs via Exploring Two-Dimensiona...

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International Conference on Software Engineering (ICSE)

作者： Haoxiang Jia Ming Wen Zifan Xie Xiaochen Guo Rongxin Wu Maolin Sun Kang Chen Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab. Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System School of Informatics Xiamen University China School of Computer Science and Technology Huazhong University of Science and Technology China

Java Virtual Machine (JVM) is the fundamental software system that supports the interpretation and execution of Java bytecode. To support the surging performance demands for the increasingly complex and large-scale Java programs, Just-In-Time (JIT) compiler was proposed to perform sophisticated runtime optimization. However, this inevitably induces various bugs, which are becoming more pervasive over the decades and can often cause significant consequences. To facilitate the design of effective and efficient testing techniques to detect JIT compiler bugs. This study first performs a preliminary study aiming to understand the characteristics of JIT compiler bugs and the corresponding triggering test cases. Inspired by the empirical findings, we propose JOpFuzzer, a new JVM testing approach with a specific focus on JIT compiler bugs. The main novelty of JOpFuzzer is embodied in three aspects. First, besides generating new seeds, JOpFuzzer also searches for diverse configurations along the new dimension of optimization options. Second, JOpFuzzer learns the correlations between various code features and different optimization options to guide the process of seed mutation and option exploration. Third, it leverages the profile data, which can reveal the program execution information, to guide the fuzzing process. Such nov-elties enable JOpFuzzer to effectively and efficiently explore the two-dimensional input spaces. Extensive evaluation shows that JOpFuzzer outperforms the state-of-the-art approaches in terms of the achieved code coverages. More importantly, it has detected 41 bugs in OpenJDK, and 25 of them have already been confirmed or fixed by the corresponding developers.

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Vulnerability Detection Based on Pre-trained Code Language Model and Convolutional Neural Network 36

Vulnerability Detection Based on Pre-trained Code Language M...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Liao, Tingfeng Lu, Lu Liang, Zhihong Suo, Siliang School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Pengcheng Laboratory Shenzhen China Electric Power Research Institute CSG Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)1891706594

Software vulnerabilities damage the reliability of software systems. Recently, many methods based on deep learning have been proposed for vulnerability detection by learning features from code sequences or various property graphs. However, sequence-based methods frequently fail to consider the structural information of the code, whereas graph-based methods often encounter difficulties in capturing long-distance contextual semantic information. To overcome these limitations, we propose PTCN, a novel vulnerability detection method that integrates Pre-Trained code language model and Convolutional Neural Network (CNN). First, the code tokens sequence and the propagation chains are constructed from the source code in preparation for the subsequent feature extraction. Next, GraphCodeBERT, a graph-based pre-trained code language model, is introduced as a means of learning both the semantic and structural information contained in the code tokens sequence and the propagation chains. Then, a CNN architecture is designed to effectively extract the crucial vulnerability features from the hidden states of the last Transformer layer of GraphCodeBERT for vulnerability detection. The experiments on the public benchmark dataset REVEAL indicate that PTCN outperforms the state-of-the-art methods with respect to the Accuracy and F1-score metrics by 3.41%-15.80% and 22.68%-104.27%, respectively. © 2024 Knowledge systems Institute Graduate school. All rights reserved.

关键词： Convolutional neural networks

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Towards Demystifying Android Adware: Dataset and Payload Location 39

Towards Demystifying Android Adware: Dataset and Payload Loc...

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39th ACM/IEEE International Conference on Automated Software Engineering Workshops, ASEW 2024

作者： Wang, Chao Liu, Tianming Zhao, Yanjie Zhang, Lin Du, Xiaoning Li, Li Wang, Haoyu Monash University Clayton Australia Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China The National Computer Emergency Response Beijing China Beihang University Beijing China

ISBN: (纸本)9798400712494

Adware represents a pervasive threat in the mobile ecosystem, yet its inherent characteristics have been largely overlooked by previous research. This work takes a crucial step towards demystifying Android adware. We present a comprehensive, well-annotated Android adware dataset AdwareZoo, which comprises 15,996 adware samples across 118 distinct adware families collected from both security reports and app repositories, providing a robust foundation for in-depth analysis and future research. We identify adware family payloads by isolating packages from adware samples for VirusTotal rescanning. Our analysis unveils critical insights into the adware ecosystem, highlighting distinctive patterns in family naming conventions and exposing the unexpected classification of widely-used ad networks as adware. We identify diverse payload location strategies, with a notable finding that over 30% of adware families employ payloads beyond conventional Java/Kotlin code. Furthermore, we reveal several evasion techniques utilized by ad-ware, including package name obfuscation and dynamic payload loading. This research not only offers a robust foundation for understanding and combating adware but also highlights the pressing need for increased scrutiny of mobile advertising practices. © 2024 ACM.

关键词： Android malware

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Spatio-Temporal Transformer-Based Reinforcement Learning for Robot Crowd Navigation

Spatio-Temporal Transformer-Based Reinforcement Learning for...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： He, Haodong Fu, Hao Wang, Qiang Zhou, Shuai Liu, Wei Chen, Yang Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan430081 China Eng. Res. Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan430081 China

ISBN: (纸本)9798350325706

Ensuring robots can move safely and adhere to social norms in dynamic human environments is a crucial step towards robot autonomous decision-making. In existing work, double serial separate modules are generally used to capture spatial and temporal interactions, respectively. However, such methods lead to extra difficulties in improving the utilization of spatio-temporal features and reducing the conservatism of navigation policy. In light of this, this paper proposes a spatiotemporal transformer-based policy optimization algorithm to more effectively preserve the human-robot interactions. Specifically, a gated embedding mechanism is introduced to effectively fuses the spatial and temporal representations by integrating both modalities at the feature level. Then Transformer is leveraged to encode the spatio-temporal semantic information, with the hope of finding the optimal navigation policy. Finally, a combination of spatio-temporal Transformer and self-adjusting policy entropy significantly reduce the conservatism of navigation policies. Experimental results demonstrate the priority of the proposed algorithm over the state-of-the-art methods. © 2023 IEEE.

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Graft: Efficient Inference Serving for Hybrid Deep Learning with SLO Guarantees via DNN Re-alignment

arXiv

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arXiv 2023年

作者： Wu, Jing Wang, Lin Jin, Qirui Liu, Fangming The National Engineering Research Center for Big Data Technology and System The Services Computing Technology and System Lab Cluster and Grid Computing Lab in The School of Computer Science and Technology Huazhong University of Science and Technology 1037 Luoyu Road Wuhan430074 China Paderborn University TU Darmstadt Germany Peng Cheng Laboratory Huazhong University of Science and Technology China

Deep neural networks (DNNs) have been widely adopted for various mobile inference tasks, yet their ever-increasing computational demands are hindering their deployment on resource-constrained mobile devices. Hybrid deep learning partitions a DNN into two parts and deploys them across the mobile device and a server, aiming to reduce inference latency or prolong battery life of mobile devices. However, such partitioning produces (non-uniform) DNN fragments which are hard to serve efficiently on the server. This paper presents Graft—an efficient inference serving system for hybrid deep learning with latency service-level objective (SLO) guarantees. Our main insight is to mitigate the non-uniformity by a core concept called DNN re-alignment, allowing multiple heterogeneous DNN fragments to be restructured to share layers. To fully exploit the potential of DNN re-alignment, Graft employs fine-grained GPU resource sharing. Based on that, we propose efficient algorithms for merging, grouping, and re-aligning DNN fragments to maximize request batching opportunities, minimizing resource consumption while guaranteeing the inference latency SLO. We implement a Graft prototype and perform extensive experiments with five types of widely used DNNs and real-world network traces. Our results show that Graft improves resource efficiency by up to 70% compared with the state-of-the-art inference serving systems. © 2023, CC BY-NC-ND.

关键词： Deep neural networks

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TokenMatcher: Diverse Tokens Matching for Unsupervised Visible-Infrared Person Re-Identification 39

TokenMatcher: Diverse Tokens Matching for Unsupervised Visib...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Xiao Liu, Lekai Yang, Bin Ye, Mang Wang, Zheng Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology China School of Computer Science Wuhan University China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China

ISBN: (纸本)157735897X

Unsupervised visible-infrared person re-identification (US-VI-ReID) seeks to match infrared and visible images of the same individual without the use of annotations. Current methods typically derive cross-modal correspondences through a single global feature matching process for generating pseudo labels and learning modality-invariant features. However, this matching approach is hindered by both intra-modality and inter-modality discrepancies, which result in imprecise measurements. As a consequence, the clustering of individuals with single global feature is often incomplete and unreliable, leading to suboptimal performance in cross-modal clustering tasks. To address these challenges and to extract cross-modality discriminative identity information, we propose a TokenMatcher, which encompasses three key components: Diverse Tokens Matching (DTM), Diverse Tokens Neighbor Learning (DTNL), and the Homogeneous Fusion (HF) Module. DTM utilizes multiple class tokens within the visual transformer framework to capture diverse embedding representations, thereby facilitating the integration of fine-grained information essential for reliable cross-modality correspondences. DTNL enhances the intra-modality and inter-modality consistency among diverse tokens by refining neighborhood sets with insights from neighboring tokens and camera information, promoting robust neighborhood learning and fostering discriminative identity information. Additionally, the HF module consolidates clusters of the same identity while effectively separating those of different identities. Extensive experiments conducted on the publicly available SYSU-MM01 and RegDB datasets demonstrate the efficacy of the proposed method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Unsupervised learning

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