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arXiv 2022年

作者： Yue, Pingyue An, Jianping Zhang, Jiankang Ye, Jia Pan, Gaofeng Wang, Shuai Xiao, Pei Hanzo, Lajos The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China The Department of Computing and Informatics Bournemouth University BournemouthBH12 5BB United Kingdom The school of Electrical Engineering Chongqing University Chongqing400044 China The 5GIC & 6GIC Institute for Communication Systems University of Surrey GU2 7XH United Kingdom The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Low Earth Orbit (LEO) satellites undergo a period of rapid development driven by ever-increasing user demands, reduced costs, and technological progress. Since there is a lack of literature on the security and reliability issues of LEO Satellite Communication Systems (SCSs), we aim to fill this knowledge gap. Specifically, we critically appraise the inherent characteristics of LEO SCSs and elaborate on their security and reliability requirements. In light of this, we further discuss their vulnerabilities, including potential security attacks launched against them and reliability risks, followed by outlining the associated lessons learned. Subsequently, we discuss the corresponding security and reliability enhancement solutions, unveil a range of trade-offs, and summarize the lessons gleaned. Furthermore, we shed light on several promising future research directions for enhancing the security and reliability of LEO SCSs, such as integrated sensing and communication, computer vision aided communications, as well as challenges brought about by mega-constellation and commercialization. Finally, we summarize the lessons inferred and crystallize the take-away messages in our design guidelines. Copyright © 2022, The Authors. All rights reserved.

关键词： Satellite communication systems

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Meta-analysis of computational methods for breast cancer classification

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International Journal of Intelligent information and Database Systems 2020年第1期13卷 89-111页

作者： Pham, Tri-Cong Luong, Chi-Mai Doucet, Antoine Hoang, Van-Dung Tran, Diem-Phuc Le, Duc-Hau School of Computer Science and Engineering Thu-yloi University 175 Tay Son Dong Da Hanoi Viet Nam Department of Informatics and Communication Technology University of Science and Technology of Hanoi Hanoi Viet Nam Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Cau Giay Hanoi Viet Nam Institute of Information Technology Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Cau Giay Hanoi Viet Nam Laboratory L3i University of La Rochelle France QuangBinh University Dong Hoi Quang Binh Viet Nam DuyTan University Da Nang Viet Nam Department of Computational Biomedicine Vingroup Big Data Institute No 7 Bang Lang 1 Street Viet Hung Ward Hanoi Viet Nam

Millions of women are suffering from breast cancer pressing burden on their shoulders and the global economy. Meanwhile, general treatment methods are applied without considering personalised health and genetic features. Artificial intelligence appears to be a robust method for breast cancer sub-typing. Most of researches have been implemented on binary classification with limited number of data samples. Multi-classification is much more difficult especially on large number of samples. The study aims to use machine learning to find better ways to subtype breast cancer as well as find new disease causative genes which help facilitate more personalised treatment with limited side effect in the future. This study compares the accuracy of three classification methods in combination with eight feature selection methods on a dataset of 2,682 samples. The study shows that the highest accuracy was 83.96% with the SVM-C005 classifier and percentile feature selection (800 genes). Additionally, our method can predict causative disease genes of breast cancer with four of them known to be associated with breast cancer and 29 promising ones with supporting evidence from the literature. This shows the effectiveness of our research. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Gene expression

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Energy management in electric vehicle using machine learning

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AIP Conference Proceedings 2023年第1期2523卷

作者： T. G. Arul K. Vijay Kumar Usman K. S. Mohideen J. A. M. Rexie L. Chitra. 1Department of MechanicalEngineering St.Mary's Engineering College Hyderabad Telangana India. 2Department of Mechanical Engineering Malla Reddy College of Engineering Hyderabad Telangana India. 3Department of Management Studies Sri Sairam Engineering College Chennai Tamil Nadu India. 4Departmentof Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India. 5Department of Electrical and Electronics Engineering AarupadaiVeedu Institute of Technology Vinayaka Mission's Research Foundation Deemed to be University Paiyanoor Tamil Nadu India.

In power system, the energy which is fed to the grid control and management is accomplished using various architectures system. The variation in the control and manage systems are based on the performance and features as well as the overall cost. HEVs were really high increased air and oil emissions control. HEVs of different designs are becoming increasingly common, as they can achieve high fuel savings and mitigate emissions through multiple storage systems. The most powerful approach is the battery-driven *** article examines the management of energy issue with the minimisation of the operating costs of the HEVs for hybrid electric vehicles (HEVs). The energy management strategy and hybrid performance enhancement are the key to ensuring the system functions properly. The battery energy is managed using the proposed GA approach based on reinforcement learning (RL) which is originated from the ML. The results are obtained and verified successfully.

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RETRACTED ARTICLE: An Intelligent and Secure Air Quality Monitoring System Using Neural Network Algorithm and Blockchain

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IETE Journal of Research 2023年第11期69卷 CVII-CXX页

作者： Abu Buker Siddique Rafaqat Kazmi Habib Ullah Khan Sikandar Ali Ali Samad Gulraiz Javaid 1 Department of Software Engineering The Islamia University of Bahawalpur Bahawalpur63100 Pakistan 2 Department of Accounting and Information Systems College of Business and Economics Qatar University Doha 2713 Qatar 3 Department of Information Technology The University of Haripur Haripur22620 Khyber Pakhtunkhwa Pakistan 4 Faculty of Computing Department of Computer Sciences The Islamia University of Bahawalpur Bahawalpur 63100 Pakistan

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”:Abu Buker Siddique, Rafaqat Kazmi, Habib Ullah Khan, Sikandar Ali, Ali Samad & Gulraiz Javaid (2022) An Intelligent and Secure Air Quality Monitoring System Using Neural Network Algorithm and Blockchain, IETE Journal of Research, DOI:10.1080/03772063.2022.2052984Following publication, concerns were raised about the peer review and decision-making processes for this special *** an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal’s peer review standards and *** the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special *** journal has not confirmed if the authors were aware of this compromised peer review *** journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this *** have been informed in our decision-making by our editorial policies and the COPE *** retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as “Retracted”.

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Reconfiguration of spanning trees with many or few leaves 28

Reconfiguration of spanning trees with many or few leaves

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28th Annual European Symposium on Algorithms, ESA 2020

作者： Bousquet, Nicolas Ito, Takehiro Kobayashi, Yusuke Mizuta, Haruka Ouvrard, Paul Suzuki, Akira Wasa, Kunihiro CNRS LIRIS Université de Lyon Université Claude Bernard Lyon 1 France Graduate School of Information Sciences Tohoku University Japan Research Institute for Mathematical Sciences Kyoto University Japan Univ. Bordeaux Bordeaux INP CNRS LaBRI TalenceUMR5800 France Department of Computer Science and Engineering Toyohashi University of Technology Japan

ISBN: (纸本)9783959771627

Let G be a graph and T1,T2 be two spanning trees of G. We say that T1 can be transformed into T2 via an edge flip if there exist two edges e ∈ T1 and f in T2 such that T2 = (T1 \e)∪f. Since spanning trees form a matroid, one can indeed transform a spanning tree into any other via a sequence of edge flips, as observed in [11]. We investigate the problem of determining, given two spanning trees T1,T2 with an additional property Π, if there exists an edge flip transformation from T1 to T2 keeping property Π all along. First we show that determining if there exists a transformation from T1 to T2 such that all the trees of the sequence have at most k (for any fixed k ≥ 3) leaves is PSPACE-complete. We then prove that determining if there exists a transformation from T1 to T2 such that all the trees of the sequence have at least k leaves (where k is part of the input) is PSPACE-complete even restricted to split, bipartite or planar graphs. We complete this result by showing that the problem becomes polynomial for cographs, interval graphs and when k = n-2. © Nicolas Bousquet, Takehiro Ito, Yusuke Kobayashi, Haruka Mizuta, Paul Ouvrard, Akira Suzuki, and Kunihiro Wasa

关键词： Polynomial approximation

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Enhancement of smart grid technology using end-to-end reinforcement learning in deep q-network

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AIP Conference Proceedings 2022年第1期2555卷

作者： Dhruvi Bansal Mitashi Bansal Kusum Tharani Meenu Gupta Bhawna Goyal Ayush Dogra 1Department of Electrical & Electronics Engineering Bharati Vidyapeeth’s college of Engineering New Delhi India 110063 2Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Noida India 201313 3Department of Computer Science and Engineering Chandigarh University Punjab India 140413 4Department of ECE Chandigarh University Mohali India 140413 5Ronin Institute Montclair NJ USA 07043

This paper reviews the complex task problems which are out of reach for a simple machine. So, there is a need for a solution for such a problem, so the solution is Reinforcement Learning with deep Q-Network. Reinforcement learning techniques are now being researched for their applicability in a wide range of situations. Perhaps because of the rising complexity and unpredictability in the generation and distribution sector of power systems, traditional approaches frequently encounter congestion while attempting to handle decision and control issues that are out of reach for a basic machine. Deep Reinforcement Learning (DRL) is one of these data-driven approaches that is considered true Artificial Intelligence (AI). DRL is a hybrid of Deep Learning (DL) and Reinforcement Learning (RL). Our study examines the fundamental concepts, models, methods, and approaches of DRL. It also presents power system applications such as smart grids, energy management, demand response, the electricity market, operational control, and many others. Furthermore, current advancements in DRL, the coupling of RL with other classical techniques, and the prospects and problems of its applications in the power system are explored.

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Convex optimization for non-equilibrium steady states on a hybrid quantum processor

arXiv

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arXiv 2022年

作者： Lau, Jonathan Wei Zhong Lim, Kian Hwee Bharti, Kishor Kwek, Leong-Chuan Vinjanampathy, Sai Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore117543 Singapore Joint Center for Quantum Information and Computer Science Joint Quantum Institute NIST University of Maryland College ParkMD20742 United States 1 Fusionopolis Way #16-16 Connexis Singapore138632 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore National Institute of Education Nanyang Technological University 1 Nanyang Walk 637616 Singapore School of Electrical and Electronic Engineering Block S2.1 50 Nanyang Avenue Singapore639798 Singapore Department of Physics Indian Institute of Technology-Bombay Powai Mumbai400076 India Centre of Excellence in Quantum Information Computation Science and Technology Indian Institute of Technology Bombay Powai Mumbai400076 India

Finding the transient and steady state properties of open quantum systems is a central problem in various fields of quantum technologies. Here, we present a quantum-assisted algorithm to determine the steady states of open system dynamics. By reformulating the problem of finding the fixed point of Lindblad dynamics as a feasibility semidefinite program, we bypass several well known issues with variational quantum approaches to solving for steady states. We demonstrate that our hybrid approach allows us to estimate the steady states of higher dimensional open quantum systems and discuss how our method can find multiple steady states for systems with symmetries. Copyright © 2022, The Authors. All rights reserved.

关键词： Convex optimization

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Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

arXiv

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arXiv 2021年

作者： Al-Quraan, Mohammad Mohjazi, Lina Bariah, Lina Centeno, Anthony Zoha, Ahmed Arshad, Kamran Assaleh, Khaled Muhaidat, Sami Debbah, Mérouane Imran, Muhammad Ali James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom Technology Innovation Institute 9639 Masdar City Abu Dhabi United Arab Emirates College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Center for Cyber-Physical Systems Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France

New technological advancements in wireless networks have enlarged the number of connected devices. The unprecedented surge of data volume in wireless systems empowered by artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloud-centric machine learning (ML)-based services are implemented by centrally collecting datasets and training models. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost and (ii) threatened data privacy. In this article, we introduce a comprehensive survey of the fundamentals and enabling technologies of federated learning (FL), a newly emerging technique coined to bring ML to the edge of wireless networks. Moreover, an extensive study is presented detailing various applications of FL in wireless networks and highlighting their challenges and limitations. The efficacy of FL is further explored with emerging prospective beyond fifth-generation (B5G) and sixth-generation (6G) communication systems. This survey aims to provide an overview of the state-of-the-art FL applications in key wireless technologies that will serve as a foundation to establish a firm understanding of the topic. Lastly, we offer a road forward for future research directions. 1 Copyright © 2021, The Authors. All rights reserved.

关键词： Wireless networks

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Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling

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Physical Review Materials 2020年第8期4卷 083802-083802页

作者： Shion Takeno Yuhki Tsukada Hitoshi Fukuoka Toshiyuki Koyama Motoki Shiga Masayuki Karasuyama Department of Computer Science Graduate School of Engineering Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya 466-8555 Japan Center for Advanced Intelligence Project RIKEN 1-4-1 Nihonbashi Chuo-ku Tokyo 103-0027 Japan Department of Materials Design Innovation Engineering Graduate School of Engineering Nagoya University Furo-cho Chikusa-ku Nagoya 464-8603 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan Department of Electrical Electronic and Computer Engineering Faculty of Engineering Gifu University 1-1 Yanagido Gifu 501-1193 Japan Center for Materials Research by Information Integration National Institute for Materials Science 1-2-1 Sengen Tsukuba Ibaraki 305-0047 Japan

information regarding precipitate shapes is critical for estimating material parameters. Hence, we considered estimating a region of material parameter space in which a computational model produces precipitates having shapes similar to those observed in the experimental images. This region, called the lower-error region (LER), reflects intrinsic information of the material contained in the precipitate shapes. However, the computational cost of LER estimation can be high because the accurate computation of the model is required many times to better explore parameters. To overcome this difficulty, we used a Gaussian-process-based multifidelity modeling, in which training data can be sampled from multiple computations with different accuracy levels (fidelity). Lower-fidelity samples may have lower accuracy, but the computational cost is lower than that for higher-fidelity samples. Our proposed sampling procedure iteratively determines the most cost-effective pair of a point and a fidelity level for enhancing the accuracy of LER estimation. We demonstrated the efficiency of our method through estimation of the interface energy and lattice mismatch between MgZn2 and α−Mg phases in an Mg-based alloy. The results showed that the sampling cost required to obtain accurate LER estimation could be drastically reduced.

关键词： Coarse graining Machine learning

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Reconfigurable Intelligent Surface (RIS)-aided Vehicular Networks: Their Protocols, Resource Allocation, and Performance

arXiv

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arXiv 2022年

作者： Chen, Yuanbin Wang, Ying Zhang, Jiayi Zhang, Ping Hanzo, Lajos State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Broadband Communication Peng Cheng Laboratory Shenzhen518055 China The Department of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Reconfigurable intelligent surfaces (RISs) assist in paving the way for the evolution of conventional vehicular networks to autonomous driving. Having said that, the 3rd Generation Partnership Project (3GPP) faces numerous open challenges concerning the RIS-aided vehicle-to-everything (V2X) solutions of the near future. To tackle these challenges and to stimulate future research, this article focuses on the prospective transmission design of RIS-aided V2X communications. In particular, two V2X sidelink modes are enhanced by exploiting RISs and their variants, followed by a customized transmission frame structure that partitions the transmission efforts into different phases. Next, effective channel tracking and resource allocation techniques are developed for attaining a high beamforming gain at low overhead and complexity. Finally, promising research topics are highlighted and future 3GPP standardization items are proposed for RIS-aided V2X systems. © 2022, CC BY-NC-ND.

关键词： Vehicle to Everything

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