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Proceedings of the 2019 8th International Conference on computing and Pattern Recognition

作者： Wei Li Yaochi Fan Qiaoyong Jiang Qingzheng Xu School of Computer Science and Engineering Xi'an University of Technology and Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China School of Computer Science and Engineering Xi'an University of Technology Xi'an China College of Information and Communication National University of Defense Technology Xi'an China

ISBN: (纸本)9781450376570

Particle swarm optimization (PSO) is an efficient nature-inspired optimization algorithm, which has been widely applied in many engineering fields. The performance of particle swarm optimization (PSO) has been significantly influenced by velocity update strategy. Traditionally, each particle updates its velocity based on its historical best experience and the global best experience, which may make the swarm lose its diversity and lead to premature convergence. To strengthen the performance of PSO, this paper proposes an improved PSO with the velocity-driven strategy (VD-PSO). In VD-PSO algorithm, the particles whose velocities are driven by better velocities focus on exploitation. The historical velocities, such as better velocities, may effectively characterize the landscape information of the optimization problems. In addition, the particles focus on exploration through information exchange amongst velocities. To verify the effectiveness of the proposed algorithm, experiments are conducted with CEC2014 test problems. The experimental results demonstrate the effectiveness of the proposed algorithm for solving the global optimization problems.

关键词： Evolutionary algorithms Velocity-driven strategy Particle swarm optimization Optimization problems

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In-situ data scheduling optimization based on rainbow DQN for IIoT

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Future Generation Computer Systems 2026年 174卷

作者： Peiying Zhang Siyi Li Lizhuang Tan Neeraj Kumar Jian Wang Kai Liu 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 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 Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala 147004 India Department of Computer Science University of Economics and Human Sciences Warszawa 01-043 Poland Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia College of Science China University of Petroleum (East China) Qingdao 266580 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing 100084 China State Key Laboratory of Space Network and Communications Tsinghua University Beijing 100084 China

In industrial systems, in-situ computing refers to a mode of local or proximate data processing within the cloud–edge-terminal architecture. In the Industrial Internet of Things (IIoT) environment, servers are typically deployed near terminal devices to coordinate and manage the scheduling of in-situ data, forming a comprehensive in-situ server system. With the rapid growth of IIoT terminal devices, challenges such as high processing delays and severe demand congestion arise in the in-situ data scheduling process. To address this challenge, this paper proposes a scheduling optimization scheme based on Rainbow DQN. The scheme designs a scheduling buffer and classification strategy to distinguish in-situ data based on storage demand characteristics, thereby optimizing the scheduling process. At the same time, a multi-level storage structure is introduced to store in-situ data classified according to the scheduling strategy. Additionally, the Rainbow DQN algorithm is utilized to assist in the scheduling decision-making of in-situ data retrieval, effectively alleviating system congestion while ensuring certain throughput performance. Extensive simulation experiments show that the Rainbow DQN algorithm achieves a reduction of 60.82% in average waiting delay, 62.94% in average unprocessed demand, and a 58.27% reduction in average completion delay.

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A lithium-ion battery health state assessment and remaining useful life prediction method based on multi slope features

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Journal of Energy Storage 2025年 130卷

作者： Gaige Chen Siqi Zhong Yahong Yang Xiaoshen Zhang Wei Deng Jinsong Liu School of Communications and Information Engineering & School of Artificial Intelligence Xi'an University of Posts and Telecommunications Xi'an Shaanxi 710121 China Shaanxi Union Research Center of University and Enterprise for 5G+ Industrial Internet Communication Terminal Technology Xi'an University of Posts and Telecommunications Xi'an Shaanxi 710121 China Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications Xi'an Shaanxi 710121 China State Key Laboratory of Space Power Sources Shanghai Institute of Space Power-sources Shanghai 200245 China Xi'an Aviation Brake Technology Co. Ltd Xi'an Shaanxi 710065 China Contemporary Amperex Technology Co. Ltd. Ningde Fujian 352107 China

In the field of battery management, accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical to safeguard the safety of batteries and devices. However, current prediction methods are mainly based on sensor data such as battery voltage, current, temperature, time, and other data, and there is a challenge problem with neglecting the intrinsic connection between these data. To address this problem, a lithium-ion battery health state assessment and remaining useful life prediction method based on multi slope features of discharge curves is proposed. First, new slope features of discharge curve are proposed based on the discharge mechanism of lithium-ion batteries; second, slope features are utilized to estimate the state of health (SOH) of the batteries; then, unsupervised clustering is performed on the unlabeled SOH data to construct a health state assessment model; and finally, the RUL prediction model based on the gated recurrent unit with grey wolf optimizer (GWO-GRU) is constructed. The proposed method is validated using the aging data of lithium-ion batteries released by NASA Ames Research Center and the Oxford battery degradation dataset, and compared with other mainstream methods. The experimental results show that the method exhibits high accuracy in RUL prediction, the prediction accuracy is 99 % and 98 % respectively, which is significantly better than traditional methods. Therefore, the method can effectively assess the health state of lithium-ion batteries and accurately predict their RUL, which can provide support for the realization of lithium-ion battery health management and intelligent operation and maintenance.

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Deep Learning for Password Guessing and Password Strength Evaluation, A Survey

Deep Learning for Password Guessing and Password Strength Ev...

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IEEE International Conference on Trust, security and Privacy in computing and Communications (TrustCom)

作者： Tao Zhang Zelei Cheng Yi Qin Qiang Li Lin Shi Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for Nationalities Nanning China Department of Computer and Information Technology Purdue University West Lafayette USA PLA Information Engineering University Zhengzhou China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China Cloud Computing and Big Data Research Institute China Academy of Information and Communications Technology Beijing China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

Text passwords are the most widely used authentication methods and will also be used in the future. Text passwords can be regarded as meaningful strings, and deep learning methods have an advantage of text processing. LSTM, RNN, GAN and other deep learning models have been using in password guessing and password strength measurements. In the paper, we make a survey on state-of-the-art deep learning methods for password guessing and password strength evaluation, including password pattern extraction, candidate password generation and password strength measurement. Compared with traditional methods, neural networks based methods can achieve better results and performance.

关键词： Deep learning Training Transfer learning security Gallium nitride Password Text processing

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DLEDNet: A deep learning-based image encryption and decryption network for internet of medical things

arXiv

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arXiv 2020年

作者： Ding, Yi Wu, Guozheng Chen, Dajiang Zhang, Ning Gong, Linpeng Cao, Mingsheng Qin, Zhiguang Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu Sichuan610054 China Institute of Electronic and Information Engineering of UESTC in Guangdong Dongwan Guangdong China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu Sichuan610054 China National Natural Science Foundation of China Beijing China Department of Computing Science Texas A&M University-Corpus Christi Corpus ChristiTX78412 United States

With the rapid development of Internet of Medical Things (IoMT) technology, many medical imaging equipments are connected to the medical information network to facilitate the process of diagnosing and treating for doctors. As medical image contains sensitive information, it is of importance yet very challenging to encrypt the medical image to safeguard the privacy or security of the patient. In this work, a deep learning based encryption and decryption network (DLEDNet) is proposed to fulfill the process of encrypting and decrypting the medical image. Specifically, in DLEDNet, the Cycle-GAN network is employed as the main learning network to transfer the medical image from its original domain to the target domain. Target domain is regarded as a "Hidden Factors" to guide the learning model to realize the encryption process. The encrypted image is restored to the original (plaintext) image through a reconstruction network to achieve an image decryption process. In order to realize the data mining process direct from the privacy-protected environment, a ROI (Region of interest)-mining-network is proposed to extract the interested object directly from the encrypted image. The proposed DLEDNet is evaluated on the chest X-ray dataset. Extensive experimental results show that the proposed method can achieve a high level of security with a good performance in efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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NoViSec: Novel Virtual network Mapping Framework for Secure Software-Defined networking

NoViSec: Novel Virtual Network Mapping Framework for Secure ...

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IEEE Wireless Communications and networking Conference Workshops (WCNCW)

作者： Haotong Cao Yue Hu Shengchen Wu Jianbo Du Feng Tian Gagangeet Singh Aujla Longxiang Yang College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China Department of Key Accounts China Mobile Group Jiangsu Co. Ltd. Nanjing China Shanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications Xi'an China School of Computing Newcastle University Newcastle upon Tyne U.K

ISBN: (数字)9781728151786

ISBN: (纸本)9781728151793

network softwarization technologies, such as software defined networking (SDN), network virtualization (NV), and network function virtualization (NFV), emerge and have been greatly promoted by their supporters. This softwarization scheme contributes to isolating network control from data panel, and allocating virtualized physical network resources flexibly. However, physical switches and links, supporting the softwarization scheme, may be attacked by malicious attackers. Even if these switches and links are softwarized and virtualized successfully, the problem of mapping various virtual network requests, known as virtual network embedding, is difficult to solve directly. Each customized VN request must be allocated and implemented in the appropriate manner. To deal with these issues, we research the virtual network mapping for secure SDN-enabled networks. Firstly, a hierarchical virtualization architecture for SDN- enabled networks is presented. Then, the security model for different malicious attacks is presented. Thirdly, we propose the novel and secure virtual network mapping framework, labeled as \textit{NoViSec}, for supporting SDN-enabled networks. Preliminary experiment results are plotted and discussed so as to validate the effectiveness of our secure \textit{NoViSec} framework.

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Velocity-driven particle swarm optimization 8

Velocity-driven particle swarm optimization

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8th International Conference on computing and Pattern Recognition, ICCPR 2019

作者： Li, Wei Fan, Yaochi Jiang, Qiaoyong Xu, Qingzheng School of Computer Science and Engineering Xi'An University of Technology China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an710048 China School of Computer Science and Engineering Xi'An University of Technology Xi'an710048 China College of Information and Communication National University of Defense Technology Xi'an710106 China

ISBN: (纸本)9781450376570

关键词： Velocity

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Implementation Method Of Remote Assistance Instruction Based On Hardware Secure Encryption

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Journal of Physics: Conference Series 2021年第1期2136卷

作者： Birong Huang Zhansheng Hou Peng Lin Fei Zhou Zhangjiagang Power Supply Branch of State Grid Jiangsu Electric Power Co. LTD JiangsuChina Global Energy Interconnection Research Institute nanjing Jiangsu China State Grid Key Laboratory Of Information & Network Security Nanjing China

As a public service enterprise related to energy security and national economy and people's livelihood, the safe work is related to all walks of life and thousands of households. In power scenarios, the remote cooperative guidance needs to ensure the security of data communication. The implementation method of remote cooperative guidance in power scenarios based on hardware security encryption realizes the functions of terminal registration, user online and offline management, message call, control signaling negotiation, audio and video real-time call, graphic guidance and target tracking and labeling. Configuration TF card encryption at the mobile terminal chip and video server configuration high speed encryption card, WIFI / 4 g wireless networks to establish encryption channel, remote collaborative guidance terminal power scene registration messages, messages, call, and by guiding and structured data security encryption transmission, the unstructured data such as audio stream and video stream is encrypted. It not only realizes the secure encryption of message communication, but also guarantees the high-speed transmission of audio and video streams.

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Research on information security Protection System of Industrial Control System

Research on Information Security Protection System of Indust...

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IEEE Joint International information Technology and Artificial Intelligence Conference (ITAIC)

作者： Zhenwan Zou Yingsa Hou Huiting Yang Bin Wang Ruxia Yang Electric Power Research Institute of State Grid Xinjiang Electric Power Co. Ltd. Urumqi China Maintenance company of State Grid Xinjiang Electric Power Co. Ltd. Urumqi China Global Energy Interconnection Research Institute Co. Ltd. Nanjing Jiangsu China State Grid Key Laboratory of Information & Network Security Nanjing Jiangsu China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

Aiming at the current status and risks of industrial control system information security, a security protection system was proposed from the technical and management levels. At the technical level, it strengthen security protection by setting up firewall protection, isolating engineer stations, conducting system security tests, and deploying network monitoring. At the management level, it improves safety management through measures such as real-time maintenance and updating of existing management systems and safety technology standards. Finally, the feasibility of this protection system was proved by experiments, which has certain reference significance for the research on safety protection of industrial control systems.

关键词： Industrial control information security Safety management Maintenance engineering Real-time systems Standards Monitoring

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Remote Healthcare Monitoring System for Aging Population Based on IoT and Big Data Analysis

Remote Healthcare Monitoring System for Aging Population Bas...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Qiang Liu Songlin Sun Liang Gong Zhongle Wu Mohamed Cheriet Michel Kadoch National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Academy of Broadcasting Planning NRTA China ETS University of Quebec Montreal Canada

ISBN: (数字)9781728157849

ISBN: (纸本)9781728157856

As the population ages, this will put tremendous pressure on pensions and the medical system. Health insurance for the elderly poses a major challenge to the allocation of limited medical resources. The Internet of Things (IoT) is widely regarded as a potential solution to relieve pressure on healthcare systems. With the rapid development of wireless communication technologies such as backscatter communication, Wi-Fi and 5G, in vivo and in vitro medical devices have been greatly developed. In this article, we propose a real-time big data healthcare analysis system based on the combination of shallow neural networks and deep neural networks. The system consists of two parts: the detection of fog layer abnormality based on shallow neural network and the diagnosis of cloud disease based on deep neural network. The system can not only make full use of the real-time nature of the fog layer but also give full play to its powerful computing power based on reducing the computing pressure of the cloud layer.

关键词： Cloud computing Neural networks Sociology Real-time systems Internet of Things Statistics Medical diagnostic imaging

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