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6th IEEE International Conference on Data Science in Cyberspace, DSC 2021

作者： Yan, Song Binbin, Wang Xiaohu, Yu Tinghui, Li Shanshan, Zhu Daojing, He Guisong, Yang Sammy, Chan State Grid Shanghai Municipal Electrical Power Research Institute Shanghai China School Of Software Engineering East China Normal University Shanghai China East China Normal University Shanghai China University Of Shanghai For Science And Technology Shanghai China City University Of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781665418157

In recent years, Internet of Things (IoT) devices have received extensive attention and can be seen everywhere in daily life. Attacks against firmware vulnerabilities are becoming more and more frequent. Homology analysis technology is a hot topic in recent years. Firmware vulnerability analysis based on homology analysis can classify the firmware of different versions or even different devices, and detect the vulnerabilities in the firmware by detecting the homology of the open source library. This paper provides a literature review of firmware homology detection. Firstly, the background of firmware vulnerability analysis and the research status of homology detection at home and abroad are briefly introduced. Then, under the source code based homology analysis technology, according to whether it chooses to use the intermediate form to explain the source code and the different intermediate forms, three different homology analysis technologies based on text, token and structure are divided, and the three technologies are compared horizontally. Then, in the non source code based homology analysis technology, the overall process of homology detection based on binary files is introduced, as well as the main technologies such as byte stream comparison and reverse analysis. Finally, various homology analysis methods are analyzed and compared from the aspects of efficiency and accuracy, and the research focus and direction in this field are summarized and prospected. © 2021 IEEE.

关键词： Internet of things

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Generating Frequency-limited Adversarial Examples to Attack Multi-focus Image Fusion Models

Generating Frequency-limited Adversarial Examples to Attack ...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Xin Jin Qian Jiang Peng Liu Xueshuai Gao Puming Wang Shin-Jye Lee Engineering Research Center of Cyberspace Yunnan University Kunming China School of Software Yunnan University Kunming China Guangxi Power Grid Co. Ltd. Nanning China Institute of Technology Management National Chiao Tung University Hsinchu Taiwan

Multi-focus image fusion techniques aim to generate all-in-focus clear images by fusing a set of images with different focused areas. Recently various deep learning based methods have been proposed for image fusion, but the robustness of these models is ignored, while neural networks are vulnerable to adversarial examples. In this work, we proposed a generator based method to attack decision map based multi-focus image fusion models. First, we train a multi-focus image fusion model as the local surrogate model and freeze its weights. Then, adversarial loss and feature separation loss are used to train the attack generator. The two losses maximize the distance of decision maps and feature maps between clean images and adversarial images respectively. Finally, the generated perturbations are limited to certain frequency components using a mask in the discrete cosine transform domain. Experimental results show that the proposed attacks result in serious performance degradation of target models. Besides, we analyze how neural networks recognize focus areas, and find that small multi-focus image fusion models mainly concern high-frequency features and are vulnerable to high frequency noises.

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Reliability Evaluation of Approximate Arithmetic Circuits Based on Signal Probability

Reliability Evaluation of Approximate Arithmetic Circuits Ba...

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International Test Conference in Asia (ITC-Asia)

作者： Zhen Wang Guofa Zhang Jing Ye Jianhui Jiang School of the Computer Science and Technology Shanghai University of Electric Power Shanghai China Chinese Academy of Sciences Institute of Computing Technology Beijing China School of Software Engineering Tongji University Shanghai China

ISBN: (纸本)9781665413350

Recently, approximate arithmetic circuits (AACs) have been widely used in applications with inherent error tolerance. To guarantee the usability of AACs in the approximation applications, it is necessary to analyze AAC reliability. In this paper, we propose two accurate AAC reliability evaluation methods based on signal reliability analysis, where subtraction correlation coefficient and division correlation coefficient are applied respectively to solve the correlation problem caused by fanout reconvergence. The accuracy and scalability of these two methods are verified by randomly selected circuits from the EvoApprox8b library. The experimental results show that, compared with Monte Carlo (MC) simulation, our two proposed methods have average error rates of 0.34% and 0.65% and the run-time costs are 0.012% and 0.019% of MC’s simulation time. Moreover, the proposed methods are superior to two recently reliability analysis based methods in terms of efficiency and accuracy.

关键词： Correlation coefficient Runtime Costs Correlation Monte Carlo methods Scalability Integrated circuit reliability

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Development and Implementation of a Holistic Flexibility Market Architecture

Development and Implementation of a Holistic Flexibility Mar...

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Innovative Smart Grid Technologies (ISGT)

作者： Oliver Kraft Oliver Pohl Ulf Hä ger Kai Heussen Nils Mü ller Zeeshan Afzal Mathias Ekstedt Hossein Farahmand Dmytro Ivanko Ankit Singh Sasiphong Leksawat Andreas Kubis Institute of Energy Systems Energy Efficiency and Energy Economics TU Dortmund University Dortmund Germany Department of Electrical Engineering Technical University of Denmark Kongens Lyngby Denmark Division of Network and System Engineering KTH Royal Institute of Technology Stockholm Sweden Department of Electric Power Engineering Norwegian University of Science and Technology Trondheim Norway Research and Development Department Electrical Energy Division PSI Software AG Dortmund Germany

The demand for increasing flexibility use in power systems is stressed by the changing grid utilization. Making use of largely untapped flexibility potential is possible through novel flexibility markets. Different approaches for these markets are being developed and vary considering their handling of transaction schemes and relation of participating entities. This paper delivers the conceptual development of a holistic system architecture for the realization of an interregional flexibility market, which targets a market based congestion management in the transmission and distribution system through trading between system operators and flexibility providers. The framework combines a market mechanism with the required supplements like appropriate control algorithms for emergency situations, cyber-physical system monitoring and cyber-security assessment. The resulting methods are being implemented and verified in a remote-power-hardware-in-the-loop setup coupling a real world low voltage grid with a geographically distant real time simulation using state of the art control system applications with an integration of the aforementioned architecture components.

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Feature selection based on min-redundancy and max-consistency

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Advances in Computational Intelligence 2021年第1期2卷 1-11页

作者： Guo, Yanting Hu, Meng Tsang, Eric C. C. Chen, Degang Xu, Weihua College of Computer Science and Software Engineering Shenzhen University Shenzhen China Faculty of Information Technology Macau University of Science and Technology Taipa China Department of Mathematics and Physics North China Electric Power University Beijing China College of Artificial Intelligence Southwest University Chongqing China

Feature selection can effectively eliminate irrelevant or redundant features without changing features semantics, so as to improve the performance of learning and reduce the training time. In most of the existing feature selection methods based on rough sets, eliminating the redundant features between features and decisions, and deleting the redundant features between features are performed separately. This will greatly increase the search time of feature subset. To quickly remove redundant features, we define a series of feature evaluation functions that consider both the consistency between features and decisions, and redundancy between features, then propose a novel feature selection method based on min-redundancy and max-consistency. Firstly, we define the consistency of features with respect to decisions and the redundancy between features from neighborhood information granules. Then we propose a combined criterion to measure the importance of features and design a feature selection algorithm based on minimal-redundancy-maximal-consistency (mRMC). Finally, on UCI data sets, mRMC is compared with three other popular feature selection algorithms based on neighborhood idea, from classification accuracy, the number of selected features and running time. The experimental comparison shows that mRMC can quickly delete redundant features and select useful features while ensuring classification accuracy.

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Assessment and Optimization of a Single Flash Geothermal System Recovered by a Trans-Critical Co2 Cycle Using Different Scenarios

SSRN

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SSRN 2023年

作者： Lamagna, Mario Aryanfar, Yashar Hoseinzadeh, Siamak Nastasi, Benedetto Garsia, Davide Astiaso Mohtaram, Soheil Sun, Hong Guang State Key Laboratory of Hydrology-Water Recourses and Hydraulic Engineering International Center for Simulation Software in Engineering and Sciences College of Mechanics and Materials Hohai University Jiangsu Nanjing210098 China Universidad Autónoma de Ciudad Juárez Mexico Department of Planning Design and Technology of Architecture Sapienza University of Rome Rome00196 Italy Department of Earth Sciences and Forecasting Research Center Prevention and Control of Geological Risks China Department of Earth Sciences and Forecasting Research Center Prevention and Control of Geological Risks China School of Energy and Power Engineering University of Shanghai for Science and Technology Shanghai200093 China

Globally, the use of renewable energy, particularly geothermal energy, is rising quickly. Geothermal cycles' low efficiency has repeatedly shown how crucial it is to recover heat lost during these cycles. This study suggests a combined power generation cycle replicating using the EES software environment that combines a single flash cycle with a trans-critical carbon dioxide cycle. The findings demonstrate that, in comparison to the BASIC single flash cycle, the design characteristics of the proposed system are greatly improved. The suggested strategy is then enhanced by employing the genetic algorithm, the Nelder-Mead Simplex method, and the direct algorithm. Separator pressure, steam turbine outlet pressure, and carbon dioxide turbine inlet pressure are three assumed variable parameters, and exergy efficiency is the target parameter. In the default operating mode, the system exergy efficiency is 32.46%, increasing to 39.21% using the genetic algorithm and 36.16% using the Nelder-Mead method and 38.82% using the direct algorithm. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

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E3DinSAR: 3-D Localization of RFID-Tagged Objects Based on Interference Synthetic Apertures

E3DinSAR: 3-D Localization of RFID-Tagged Objects Based on I...

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作者： Liang, Xiaoxuan Huang, Zhangqin Yang, Shengqi Qiu, Lanxin Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing China Communication Service Center State Grid Zhejiang Electric Power Company Information and Telecommunication Branch Hangzhou31000 China

RFID is one of the indispensable technologies employed to implement the Internet of Things, where it facilitates the identification and awareness of objects in our daily lives by integrating backscatter tags. The interference synthetic aperture radar (InSAR)-based localization scheme provides high precision in 3-D conditions, although it still has some practical problems such as its strict sampling operation and it is also highly time consuming. In this article, we developed E3DinSAR as an optimized InSAR-based 3-D localization approach for pinpointing tags rapidly and easily with high accuracy. E3DinSAR does not use any other infrastructure or reference tags, and only one movable reader with one antenna is sufficient to implement the localization method, where it measures the phase value of the tag at different positions throughout its movement in order to create multiple holographic images. In contrast to the previously proposed InSAR-based technique, we extend the sampling trajectory of the reader to an arbitrary curve by restricting it to several approximate linear apertures, which allows more flexible operations and increase the accuracy. We also employ a rapid computing method to calculate the results for the holographic images by estimating the direction of arrival for the tag. Experiments were performed where E3DinSAR was implemented with commercial off-the-shelf (COTS) RFID products and our systematic evaluation showed that E3DinSAR achieved a mean locating accuracy of 18.4 cm in a 3-D space. © 2014 IEEE.

关键词： Synthetic aperture radar

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Distributed Coordinated Emergency Frequency Control Strategy for Renewables-Rich power Systems Based on Multi-Agent Reinforcement Learning

SSRN

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SSRN 2025年

作者： Sun, Zhenglong Han, Junpeng Chen, Weihan Zhang, Rui Liu, Yong Levron, Yoash Belikov, Juri Department of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Software Science Tallinn University of Technology Akadeemia Tee 15a Tallinn12618 Estonia

With the rapid growth of renewable energy in the power system and the implementation of large-capacity LCC-HVDC projects, the frequency support capability of the grid has been reduced. Simultaneously, the renewables-rich grid faces the risk of high-power shocks due to LCC-HVDC blocking. Existing centralized, single-measure approaches and the model-based emergency frequency control (EFC) methods are facing tremendous challenges. To fill this gap, this paper proposes a frequency emergency control method for renewables-rich grids that coordinates LCC-HVDC emergency power support and load shedding. The coordination of the two stabilization measures is modeled as a decentralized partially observable Markov decision process, and a multi-agent reinforcement learning (MADRL) approach is employed to optimize the power of measures for each stabilization control substation. An MADRL algorithm that integrates the system frequency response model is developed to overcome the problem of inaccurate evaluation of centralized action value in the MADRL algorithm used for EFC. This approach allows for the global optimization of the distributed coordination strategy through centralized training. The effectiveness and adaptability of the proposed method are demonstrated through simulations of the modified IEEE 39-bus system. © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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URIR: Recommendation algorithm of user RNN encoder and item encoder based on knowledge graph

arXiv

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arXiv 2021年

作者： Zhao, Na Long, Zhen Zhao, Zhi-Dan Wang, Jian Key Laboratory in Software Engineering of Yunnan Province School of Software Yunnan University Kunming650504 China Electric Power Research Institute of Yunnan Power Grid Co. Ltd Kunming650200 China Department of Computer Science School of Engineering Shantou University Shantou515063 China Shantou University Shantou515063 China College of Information Engineering and Automation Kunming University of Science and Technology Kunming650217 China

Due to a large amount of information, it is difficult for users to find what they are interested in among the many choices. In order to improve users’ experience, recommendation systems have been widely used in music recommendations, movie recommendations, online shopping, and other scenarios. Recently, Knowledge Graph (KG) has been proven to be an effective tool to improve the performance of recommendation systems. However, a huge challenge in applying knowledge graphs for recommendation is how to use knowledge graphs to obtain better user codes and item codes. In response to this problem, this research proposes a user Recurrent Neural Network (RNN) encoder and item encoder recommendation algorithm based on Knowledge Graph (URIR). This study encodes items by capturing high-level neighbor information to generate items’ representation vectors and applies an RNN and items’ representation vectors to encode users to generate users’ representation vectors, and then perform inner product operation on users’ representation vectors and items’ representation vectors to get probabilities of users interaction with items. Numerical experiments on three real-world datasets demonstrate that URIR is superior performance to state-of-the-art algorithms in indicators such as AUC, Precision, Recall, and MRR. This implies that URIR can effectively use knowledge graph to obtain better user codes and item codes, thereby obtaining better recommendation results. © 2021, CC BY.

关键词： Encoding (symbols)

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Integration of Deep Learning and Machine Learning Techniques for Advancing the Detection of Plant Diseases

Engineering Reports

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engineering Reports 2025年第6期7卷

作者： Karim, Abdul Jumani, Touqeer Ahmed Raza, Muhammad Amir Baloch, Shadi Khan Soomro, Nouman Qadeer Masud, Muhammad Saeed, Muhammad Salman Nahas, Mouaaz Patoli, Aamir Ali Department of Electrical Engineering Mehran University of Engineering and Technology Sindh Khairpur Mirs' Pakistan Department of Electrical Engineering and Computer Science College of Engineering A'Sharqiyah University Ibra Oman Department of Mechatronics Engineering Mehran University of Engineering and Technology Jamshoro Pakistan Department of Software Engineering Mehran University of Engineering and Technology Sindh Khairpur Mirs' Pakistan Department of Electrical Engineering College of Engineering University of Business and Technology Jeddah Saudi Arabia Multan Electric Power Company Multan Pakistan Department of Electrical Engineering Umm Al-Qura University Makkah Saudi Arabia Department of Electronics Engineering Mehran University of Engineering and Technology Jamshoro Pakistan

This research proposed a new hybrid system for the detection of diseases in Pepper, Tomatoes, and Potatoes vegetable crops. Vegetable plant disease poses a significant threat to both the quality and quantity of crop yields, leading to enormous economic losses in the agricultural industry. Early detection and prompt measures are essential for the effective control of plant diseases. Extreme Gradient Boosting (XGBoost) and Convolutional Neural Network (CNN) algorithm were employed to detect diseases precisely. CNN derived intricate patterns from images, whereas the XGBoost algorithm is employed for precise categorization. The model is built for Pepper, Tomatoes, and Potatoes diseases and also for plant type classification using the dataset which was captured by mobile phone camera from Kotri and Unerpur, Sindh, Pakistan with accuracy of 93%, 92%, 99%, and 98%, respectively. Detection of new diseases which were not detected earlier is another primary aim of this research. For the detection of the unknown kind of diseases in plants, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm was utilized for clustering the similar captured images and for disease classification;then CNN XGBoost was applied on the newly developed dataset. This study employed a new automated hybrid framework that combines the strengths of CNN and XGBoost to identify familiar and unfamiliar diseases in vegetable crops. © 2025 The Author(s). engineering Reports published by John Wiley & Sons Ltd.

关键词： Deep learning

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