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IEEE International Energy Conference and Exhibition (EnergyCon)

作者： Anatoliy Prystupa Volodymyr Kazymyr Anatolijs Zabaš ta Anatoliy Revko Serhii Stepenko Kateryna Novyk Electrical Engineering Information and Measurement Technologies Department Chernihiv Polytechnic National University Chernihiv Ukraine Information and Computer system Department Chernihiv Polytechnic National University Chernihiv Ukraine Institute of Industrial Electronics and Electrical Engineering Riga Technical University Riga Latvia Electronics Automation Robotics and Mechatronics Department Chernihiv Polytechnic National University Chernihiv Ukraine

This paper presents the results of the investigation of the power supply system of the autonomous monitoring system. In particular, calculations of the system’s daily balance of energy consumption were performed. Also, the question of the influence of the angle of inclination of solar converters on energy generation is considered and the necessity of collecting solar radiation during the whole daylight, not focusing on the maximum amount of energy, is substantiated. This research aimed to develop an algorithm for calculating the power supply system for autonomous monitoring systems. The proposed algorithm takes into account the coordination of diagrams of generation and consumption of energy within the autonomous monitoring station and statistical analysis of information from the NASA website. Experiments have shown the effectiveness of this method. It can be used in future design of power supply systems for autonomous monitoring stations.

关键词： Power measurement Power supplies Statistical analysis NASA Energy measurement Reliability theory Battery charge measurement

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How to respond to emerging threats of cyberspace security via social engineering? A novel paradigm to solve cyberspace security issues

TechRxiv

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TechRxiv 2022年

作者： Lin, Hao Wang, Chun-Dong Gao, Hao-Yu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing Novel Software Technology Ministry of Education Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China College of Data Science and Application Inner Mongolia University of Technology Hohhot China

Cybersecurity researchers always ignore the "people" behind the issues when dealing with cyberspace security issues. The human and technological aspects of cyberspace security must be simultaneously addressed to enable a secure cyber environment. Social engineering is a discipline whose research object is people, and the combination of social engineering and cyberspace security can provide new solution ideas for lots of cyberspace security issues. This paper attempts to answer the question —— "How to solve cyberspace security issues via social engineering?" We briefly review existing frameworks for social engineering attacks and propose a novel, recursive, and iterative framework for social engineering attacks in cyberspace. The existing research achievements of combination of social engineering and cyberspace security were reviewed in the order of cyber personality, hacker psychology, human flesh search, and social engineering defense. To answer this question, we summarized four "guidelines" for using social engineering. Finally, we look ahead, give our thoughts, and discuss answers of the question and future research. It is aimed at getting more researchers of cyberspace security and social science to pay attention to this field. © 2022, CC BY.

关键词： Cybersecurity

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Distributionally Robust Optimization for Computation Offloading in Aerial Access Networks

arXiv

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arXiv 2024年

作者： Jiang, Guanwang Jia, Ziye He, Lijun Dong, Chao Wu, Qihui Han, Zhu Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211106 China National Mobile Communications Research Laboratory Southeast University Jiangsu Nanjing211111 China School of Software Northwestern Polytechnical University Shaanxi Xi’an710072 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States

With the rapid increment of multiple users for data offloading and computation, it is challenging to guarantee the quality of service (QoS) in remote areas. To deal with the challenge, it is promising to combine aerial access networks (AANs) with multi-access edge computing (MEC) equipments to provide computation services with high QoS. However, as for uncertain data sizes of tasks, it is intractable to optimize the offloading decisions and the aerial resources. Hence, in this paper, we consider the AAN to provide MEC services for uncertain tasks. Specifically, we construct the uncertainty sets based on historical data to characterize the possible probability distribution of the uncertain tasks. Then, based on the constructed uncertainty sets, we formulate a distributionally robust optimization problem to minimize the system delay. Next, we relax the problem and reformulate it into a linear programming problem. Accordingly, we design a MEC-based distributionally robust latency optimization algorithm. Finally, simulation results reveal that the proposed algorithm achieves a superior balance between reducing system latency and minimizing energy consumption, as compared to other benchmark mechanisms in the existing literature. © 2024, CC BY.

关键词： Computation offloading

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Artificial Intelligence Based Smart Energy Community Management: A Reinforcement Learning Approach

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CSEE Journal of Power and Energy systems 2019年第1期5卷 1-10页

作者： Suyang Zhou Zijian Hu Wei Gu Meng Jiang Xiao-Ping Zhang School of Electrical Engineering Southeast UniversityNanjingChina Department of Computer Science and Engineering University of Notre DameUSA School of Electronic Electrical and System EngineeringUniversity of BirminghamU.K.

This paper presents a smart energy community management approach which is capable of implementing P2P trading and managing household energy storage systems.A smart residential community concept is proposed consisting of domestic users and a local energy pool,in which users are free to trade with the local energy pool and enjoy cheap renewable energy while avoiding the installation of new energy generation *** local energy pool could harvest surplus energy from users and renewable resources,at the same time it sells energy at a higher price than Feed-in-Tariff(FIT)but lower than the retail *** order to encourage the participation in local energy trading,the electricity price of the energy pool is determined by a real-time demand/supply *** this pricing mechanism,retail price,users and renewable energy could all affect the electricity price which leads to higher consumers’profits and more optimized utilization of renewable *** proposed energy trading process was modeled as a Markov Decision Process(MDP)and a reinforcement learning algorithm was adopted to find the optimal decision in the MDP because of its excellent performance in on-going and model-free *** addition,the fuzzy inference system makes it possible to use Q-learning in continuous state-space problems(Fuzzy Q-learning)considering the infinite possibilities in the energy trading *** evaluate the performance of the proposed demand side management system,a numerical analysis is conducted in a community comparing the electricity costs before and after using the proposed energy management system.

关键词： Artificial intelligence distributed management fuzzy Q-learning microgrid reinforcement learning

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Blockchain-Based Efficient Public Integrity Auditing for Cloud Storage Against Malicious Auditors 16th

Blockchain-Based Efficient Public Integrity Auditing for Clo...

引用

16th International Conference on Information Security and Cryptology, Inscrypt 2020

作者： Li, Shanshan Xu, Chunxiang Zhang, Yuan Yang, Anjia Wen, Xinsheng Chen, Kefei School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China College of Cyber Security Jinan University Guangzhou China Department of Mathematics Hangzhou Normal University Hangzhou China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9783030718510

Public integrity auditing enables a user to delegate a third-party auditor (TPA) to periodically audit the integrity of the outsourced data. Whereas, the security of public auditing schemes relies on the trustworthiness of TPA: once TPA misbehaves, the data integrity auditing would be invalidated. In this paper, we propose a blockchain-based efficient public integrity auditing scheme to resist misbehaved TPA, where the user is required to check the behaviors of TPA in a much longer period compared with that of the data integrity auditing performed by TPA. To free users from heavy computation costs during the checking, our scheme uses two key techniques. We first design a smart contract to (1) enable TPA to record each auditing entry (which records the information about each auditing task) into the blockchain and (2) ensure the validity of each recorded entry. Then, we propose an auditing record chain built on the Ethereum blockchain to link all auditing entries corresponding to the same data in the chronological order. By doing so, the user only needs to check the last auditing entry generated by TPA to verify the trustworthiness of TPA (i.e., whether TPA has correctly performed the prescribed auditing tasks). Compared with existing schemes, where the user has to check multiple entries one by one, our scheme achieves the same security guarantee with constant and low costs in terms of communication and computation. © 2021, Springer Nature Switzerland AG.

关键词： Smart contract

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Efficient Methods for Approximating the Shapley Value for Asset Sharing in Energy Communities

arXiv

引用

arXiv 2022年

作者： Cremers, Sho Robu, Valentin Zhang, Peter Andoni, Merlinda Norbu, Sonam Flynn, David Amsterdam Netherlands Delft Netherlands Systems Power & Energy Research Group University of Glasgow Glasgow United Kingdom Smart System Research Group Heriot-Watt University Edinburgh United Kingdom Department of Electrical and Computer Engineering Princeton University PrincetonNJ United States

With the emergence of energy communities, where a number of prosumers invest in shared generation and storage, the issue of fair allocation of benefits is increasingly important. The Shapley value has attracted increasing interest for redistribution in energy settings - however, computing it exactly is intractable beyond a few dozen prosumers. In this paper, we first conduct a systematic review of the literature on the use of Shapley value in energy-related applications, as well as efforts to compute or approximate it. Next, we formalise the main methods for approximating the Shapley value in community energy settings, and propose a new one, which we call the stratified expected value approximation. To compare the performance of these methods, we design a novel method for exact Shapley value computation, which can be applied to communities of up to several hundred agents by clustering the prosumers into a smaller number of demand profiles. We perform a large-scale experimental comparison of the proposed methods, for communities of up to 200 prosumers, using large-scale, publicly available data from two large-scale energy trials in the UK (UKERC Energy Data Centre, 2017, UK Power Networks Innovation, 2021). Our analysis shows that, as the number of agents in the community increases, the relative difference to the exact Shapley value converges to under 1% for all the approximation methods considered. In particular, for most experimental scenarios, we show that there is no statistical difference between the newly proposed stratified expected value method and the existing state-of-the-art method that uses adaptive sampling (O’Brien et al., 2015), although the cost of computation for large communities is an order of magnitude lower. © 2022, CC BY.

关键词： Digital storage

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A Retina-LGN-V1 Structure-like Spiking Neuron Network for Image Feature Extraction 21

A Retina-LGN-V1 Structure-like Spiking Neuron Network for Im...

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Proceedings of the 2021 5th International Conference on Video and Image Processing

作者： Sheng Ji Qijun Gu Ye Yuan Peng Zhao Tao Fang Hong Huo Xin Niu Department of Automation_School of Electronic Information and Electrical Engineering_Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China College of Computer Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology China

ISBN: (纸本)9781450385893

SNNs have achieved great attention in recent years as they contain neurons more like those in the brain and use spikes to encode and transmit information efficiently among neurons with lower energy consumption. A Retina-LGN-V1 structure-like spiking neuron network (RLVSL-SNN) is proposed in this paper. It is inspired by the structure of mammalian primary visual pathway, and simulates different biological structures of Retina, LGN and V1. Noise reduction circuit and light adaptation circuit are also simulated for enhancing the robustness of its extracted features. RLVSL-SNN is a bio-plausible neuron network as it has firing rates of neurons in each layer that are similar to those of biological experiments. Besides, a full-connected SNN (FC SNN) is implemented following RLVSL-SNN for classification to evaluate the extracted features. The additive spiking timing dependent plasticity (STDP) learning rules and the ANN-to-SNN conversion method are utilized to train RLVSL-SNN and FC SNN, respectively. The experiments on MNIST dataset have verified that RLVSL-SNN is comparable to AlexNet for classification by features from spikes.

关键词： Additive Spiking Timing Dependent Plasticity Learning Primary Visual Pathway Image Classification Spiking Neural Network

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Interest mining model of micro-blog users by using multi-modal semantics and interest decay model 6th

Interest mining model of micro-blog users by using multi-mod...

引用

6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Jia, Dongbai Qu, Zhaowei Wang, Xiaoru Li, Fu Zhang, Luhan Yang, Kai Beijing Key Laboratory of Network System and Network Culture Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Portland States University PortlandOR97207-0751 United States

ISBN: (纸本)9789811581007

In order to meet the personalized needs of users and provide better recommendations, how to analyse the user interest accurately has become the focus of research currently. Due to the short content of micro-blog data, the sufficient semantic information it is difficult to get, which leads to the difficulty in accurately mining user interest. Traditional methods mainly use social relations to mine user interest, solving the problem of sparse data to a certain extent. But for users with single social relations, there is a cold start problem, which makes it unable to establish an effective user interest model. In addition, user interest will change over time which results in deviations when using traditional feature extraction methods. In order to solve the problems, we present an interest mining model of micro-blog users by using multi-modal semantics and interest decay model. It builds a connection among semantic relations in multidimensional features. It can solve the data-sparse problem, as well as the cold start problem. The fusion of multiple data semantic represents user interest features more comprehensively. To solve the problem of user interest migration, we propose an interest decay model to assist mining user interest better. In this paper, experiments are carried out on the dataset of 2, 938 user information extracted from micro-blog. The experimental results show that the method proposed in this paper significantly improves the accuracy of user interest mining compared with the existing methods. © Springer Nature Singapore Pte Ltd 2020.

关键词： Modal analysis

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Rank-Based Filter Pruning for Real-Time Deep Uav Trackers

SSRN

引用

SSRN 2022年

作者： Wang, Xucheng Zeng, Dan Zhao, Qijun Li, Shuiwang Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin541001 China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518000 China Sichuan University Chengdu610000 China

UAV tracking is an emerging task and has wide potential applications in such as agriculture, navigation, entertainment and public security. However, the limitations of computing resources, battery capacity, and maximum load of UAV hinder the deployment of DL-based tracking algorithms on UAV. In contrast to deep learning trackers, discriminative correlation filters (DCF)-based trackers stand out in the UAV tracking community because of their high efficiency. However, their precision is usually much lower than trackers based on deep learning. Model compression is a promising way to narrow the gap (i.e., effciency, precision) between DCF- and deep learning- based trackers, which has not caught much attention in UAV tracking community. In this paper, We propose the P-SiamFC++ tracker, which is the first to use rank-based filter pruning to compress the SiamFC++ model, achieving a remarkable balance between efficiency and precision. Our method is general and may encourage further studies on UAV tracking with model compression in the future. Extensive experiments on four UAV benchmarks, including UAV123@10fps, DTB70, UAVDT and Vistrone2018, show that P-SiamFC++ tracker significantly outperforms state-of-the-art UAV tracking methods. © 2022, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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PScalpel: A Machine Learning-based Guider for Protein Phase-Separating Behaviour Alteration 39

PScalpel: A Machine Learning-based Guider for Protein Phase-...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Jia Zhu, Liyan Wang, Zhe Zhang, Chenqiu Wu, Yaoxing Cui, Jun Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China MOE Key Laboratory of Gene Function and Regulation Guangdong Province Key Laboratory of Pharmaceutical Functional Genes State Key Laboratory of Biocontrol School of Life Sciences Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Pharmaceutical Functional Genes First Affiliated Hospital of Sun Yat-sen University School of Life Sciences Sun Yat-sen University Guangzhou China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)157735897X

Missense mutations could affect the Liquid-Liquid Phase Separation (LLPS) propensity of proteins and lead to aberrant phase-separating behaviours, which are recently found to be associated with many diseases including Alzheimer’s and cancer. However, the regulatory role of mutations in LLPS remains unclear due to challenges in accurately characterizing the LLPS ability of mutants, including the high similarity in features, lack of labeled data, and vast amounts of data involved. To bridge this gap and facilitate the discovery of therapeutic strategies, we propose the first machine learning-based guider for protein phase-separating behaviour alteration, PScalpel. PScalpel leverages both structural information and an auxiliary tasks-based graph contrastive learning framework to distinguish the mutants’ LLPS ability, and incorporates a genetic algorithms-based recommendation method to identify mutants with desired LLPS properties. Comprehensive computational and biological experiments validate the effectiveness of PScalpel as a versatile tool for guiding alterations in protein phase separation behavior. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Genetic algorithms

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