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Hamiltonian representation of generalized ribosome flow models

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Hamiltonian representation of generalized ribosome flow mode...

European control Conference (ECC)

作者： Mihá ly A. Vá ghy Gá bor Szederké nyi Faculty of Information Technology and Bionics P&#x00E1 zmany P&#x00E9 ter Catholic University Budapest Hungary Systems and Control Laboratory of ELKH Institute for Computer Science and Control (SZTAKI) Budapest Hungary

ISBN: (数字)9783907144077

ISBN: (纸本)9781665497336

In this paper we study a class of compartmental models with bounded capacities, called generalized ribosome flow models and show that they are formally kinetic, i.e. we can assign a chemical reaction network (CRN) to the system based on the compartmental structure which realizes the nonlinear dynamics. We decompose the model into two dual subsystems both having positive linear first integrals representing conservation. Based on the dynamics of the reaction network we construct a port-Hamiltonian representation of the system in the original and also in the reduced state spaces with clear connection between the structure matrices and the compartmental graph topology. We finally demonstrate that the system is non-expansive in the -norm both in the original and in the reduced state spaces. The generality of our approach ensures that the results are valid for a wide class of reaction rate functions used in the CRN representation.

关键词： Network topology Europe Aerospace electronics Chemical reactions Topology Nonlinear dynamical systems Kinetic theory

On-policy Actor-Critic Reinforcement Learning for Multi-UAV Exploration

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arXiv 2024年

作者： Farid, Ali Moltajaei Roshanian, Jafar Mouhoub, Malek Intelligent Control Systems Institute K. N. Toosi University of Technology Daneshgah Street Tehran*** Iran Department of Computer Science University of Regina Wascana Pkwy ReginaS4S0A2 Canada

Unmanned aerial vehicles (UAVs) have become increasingly popular in various fields, including precision agriculture, search and rescue, and remote sensing. However, exploring unknown environments remains a significant challenge. This study aims to address this challenge by utilizing on-policy Reinforcement Learning (RL) with Proximal Policy Optimization (PPO) to explore the two dimensional area of interest with multiple UAVs. The UAVs will avoid collision with obstacles and each other and do the exploration in a distributed manner. The proposed solution includes actor-critic networks using deep convolutional neural networks (CNN) and long short-term memory (LSTM) for identifying the UAVs and areas that have already been covered. Compared to other RL techniques, such as policy gradient (PG) and asynchronous advantage actor-critic (A3C), the simulation results demonstrate the superiority of the proposed PPO approach. Also, the results show that combining LSTM with CNN in critic can improve exploration. Since the proposed exploration has to work in unknown environments, the results showed that the proposed setup can complete the coverage when we have new maps that differ from the trained maps. Finally, we showed how tuning hyper-parameters may affect the overall performance. © 2024, CC BY-NC-SA.

关键词： Convolutional neural networks

Matrix Completion over Finite Fields: Bounds and Belief Propagation Algorithms

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arXiv 2023年

作者： Soleymani, Mahdi Liu, Qiang Mahdavifar, Hessam Balzano, Laura Halıcıoglu Data Science Institute University of California San Diego United States The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China The Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States

We consider the low rank matrix completion problem over finite fields. This problem has been extensively studied in the domain of real/complex numbers, however, to the best of authors' knowledge, there exists merely one efficient algorithm to tackle the problem in the binary field, due to Saunderson et al. [1]. In this paper, we improve upon the theoretical guarantees for the algorithm provided in [1]. Furthermore, we formulate a new graphical model for the matrix completion problem over the finite field of size q, Fq, and present a message passing (MP) based approach to solve this problem. The proposed algorithm is the first one for the considered matrix completion problem over finite fields of arbitrary size. Our proposed method has a significantly lower computational complexity, reducing it from O(n2r+3) in [1] down to O(n2) (where, the underlying matrix has dimension n × n and r denotes its rank), while also improving the performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

Advancing Offshore Operations with Digital Twin technology: A Case Study of FLNG Operations in Offshore Environments

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Advancing Offshore Operations with Digital Twin Technology: ...

IEEE Conference on Cybernetics and Intelligent Systems

作者： Siqi Zhang Xiaoling Liang Ruihang Ji Wanyue Jiang Shuzhi Sam Ge University of Toronto and National University of Singapore National University of Singapore Toronto Canada Electrical and Computer Engineering Department National University of Singapore Singapore Institute for Future and Shandong Key Laboratory of Industrial Control Technology Qingdao University Qingdao China

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

This paper delineates a meticulous case study centered on the deployment of digital twin technology in Unity3D to augment operations in offshore environments, with a particular emphasis on Floating Liquefied Natural Gas operations. It encompasses the integration of real-time meteorological data, the application of a Proportional-Integral-Derivative controller for refined vessel maneuvering, and the incorporation of an advanced voice navigation interface. The primary objective of this research is to substantially elevate the controllability, predictability, and operational efficacy within the realm of offshore asset management. The findings of this study significantly contribute to the enhancement of strategic decision-making frameworks and propel forward-looking innovations in the offshore sector, culminating in heightened operational efficiency and fortified risk management strategies.

关键词： Technological innovation PI control Navigation Real-time systems Digital twins Liquefied natural gas Asset management

Learning of Nash Equilibria in Risk-Averse Games

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arXiv 2024年

作者： Wang, Zifan Shen, Yi Zavlanos, Michael M. Johansson, Karl H. Division of Decision and Control Systems School of Electrical Enginnering and Computer Science KTH Royal Institute of Technology Digital Futures StockholmSE-10044 Sweden The Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States

This paper considers risk-averse learning in convex games involving multiple agents that aim to minimize their individual risk of incurring significantly high costs. Specifically, the agents adopt the conditional value at risk (CVaR) as a risk measure with possibly different risk levels. To solve this problem, we propose a first-order risk-averse leaning algorithm, in which the CVaR gradient estimate depends on an estimate of the Value at Risk (VaR) value combined with the gradient of the stochastic cost function. Although estimation of the CVaR gradients using finitely many samples is generally biased, we show that the accumulated error of the CVaR gradient estimates is bounded with high probability. Moreover, assuming that the risk-averse game is strongly monotone, we show that the proposed algorithm converges to the risk-averse Nash equilibrium. We present numerical experiments on a Cournot game example to illustrate the performance of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

Two-Stage DEA Model with Shared Inputs for Measuring Efficiency of the Governance in Energy Transition

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Two-Stage DEA Model with Shared Inputs for Measuring Efficie...

International Conference on Application of Information and Communication Technologies (AICT)

作者： Svetlana V. Ratner Andrey V. Lychev Economic Dynamics and Innovation Management Laboratory Institute of Control Sciences Russian Academy of Sciences National University of Science and Technology "MISIS" Moscow Russia College of Information Technologies and Computer Sciences National University of Science and Technology "MISIS" Moscow Russia

This study applies two-stage Data Envelopment Analysis (DEA) model with shared inputs to the problem of assessment of results of government support of research, development, and demonstration projects in energy sector in 23 countries in 2010–2018. The performance of governments is evaluated in terms of a positive impact on the growth of the energy efficiency of the national economy and the reduction of its carbon intensity. As intermediate results (outputs of the first and inputs of the second stage), patents in the field of energy are considered. The study reveals most and less efficient countries and indicates the target parameters for improvements in energy efficiency in less efficient countries as policy goals.

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Research on Power Scientific Innovation technology Empowered by AI4S Based on AI Middle Platform

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Research on Power Scientific Innovation Technology Empowered...

Digital Society and Intelligent Systems (DSInS), International Conference on

作者： Yinghui Xu Kehe Wu Baoliang Zhang Leyan Dai Chengyuan Li Hongbin Xie Yuan Jiang Yuxi Zhang Digitisation Department China Electric Power Research Institute Beijing China North China Electric Power University SCHOOL OF CONTROL AND COMPUTER ENGINEERING Beijing China China University of Mining and Technology School of mechanical and electronic engineering Beijing China

ISBN: (数字)9798331528829

ISBN: (纸本)9798331528836

With rising global energy consumption and the importance of environmental preservation, the implementation of green and low-carbon energy transformation has become a global consensus. China's “dual carbon” strategic goal has sped up the transformation process. In this area, artificial intelligence (AI) has demonstrated significant potential for enhancing system operational efficiency, resource allocation, energy demand forecasting, and fault detection. However, power systems are facing massive and complex data problems, which urgently requires an effective and centralized data processing and analysis platform to help it. To address the above challenges, this paper proposes a stage integrating multiple AI technologies-AI middle platform, with the goal of providing a unified and shared technology service platform for the power system, and promoting power scientific research and innovation. Furthermore, this paper discusses the integration strategy of AI middle platform and AI for Science (AI4S), and proposes the use of AI middle platform and AI4S in the agile development process of power AI applications, with the goal of accelerating scientific research and expedition and promoting the deep integration of technological innovation and application. This paper not only provides theoretical support for the intelligent transformation of the power system, but it also outlines a viable path and mechanism for its practical implementation.

关键词： Technological innovation Energy consumption Accuracy Power industry Regulation Power systems Time factors Resource management Artificial intelligence Sustainable development

When Circular Economy Meets the Smart City Ecosystem: Defining the Smart and Circular City

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arXiv 2024年

作者： Mylonas, Georgios Kalogeras, Athanasios Petersen, Sobah Abbas Muñoz, Luis Chatzigiannakis, Ioannis Industrial Systems Institute Athena Research Center Patras Greece Dpt. of Computer Science Norwegian University of Science and Technology Trondheim Norway Dpt. of Communications Engineering Universidad de Cantabria Santander Spain Dpt. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

Smart cities have been a very active research area in the past 20 years, while continuously adapting to new technological advancements and keeping up with the times regarding sustainability and climate change. In this context, there have been numerous proposals to expand the scope of smart cities, focusing on resilience and sustainability, among other aspects, resulting in terms like smart sustainable cities. At the same time, there is an ongoing discussion regarding the degree in which smart cities put people at their centre. In this work, we argue toward expanding the current smart city definition by integrating the circular economy as one of its central pillars and adopting the term smart (and) circular city. We discuss the ways a smart and circular city encompasses both sustainability and smartness in an integral manner, while also being well-positioned to foster novel business activity and models and helping to place citizens at the heart of the smart city. In this sense, we also argue that previous research in smart cities and technologies, such as those related to Industry 4.0, can serve as a cornerstone to implement circular economy activities within cities, at a scale that exceeds current activities that are based on more conventional approaches. We also outline current open challenges in this domain and research questions that still need to be addressed. Copyright © 2024, The Authors. All rights reserved.

关键词： Circular economy

Dual defense:Combining preemptive exclusion of members and knowledge distillation to mitigate membership inference attacks

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Journal of Information and Intelligence 2025年第1期3卷 68-90页

作者： Jun Niu Peng Liu Chunhui Huang Yangming Zhang Moxuan Zeng Kuo Shen Yangzhong Wang Suyu An Yulong Shen Xiaohong Jiang Jianfeng Ma He Wang Gaofei Wu Anmin Fu Chunjie Cao Xiaoyan Zhu Yuqing Zhang School of Computer Science and Technology Xidian UniversityXi'an 710071China Information Sciences and Technology Pennsylvania State UniversityPA 16802USA School of Cyberspace Security Hainan UniversityHaikou 570228China School of Hangzhou Institute of Technology Xidian UniversityHangzhou 311231China School of Systems Information Science Future University of HakodateHakodate 041-8655Japan School of Cyber Engineering Xidian UniversityXi'an 710071China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China School of Telecommunications Engineering Xidian UniversityXi'an 710071China School of Computer&Control Engineering University of Chinese Academy of SciencesBeijing 101408China

Membership inference(MI)attacks threaten user privacy through determining if a given data example has been used to train a target *** MI defenses protect the membership privacy through preemptive exclusion of members techniques and knowledge ***,using either of these two defenses alone,the defense effect can still offers an unsatisfactory trade-off between membership privacy and *** that the defense method that directly combines these two defenses is still very limited(e.g.,the test accuracy of the target model is decreased by about 40%(in our experiments)),in this work,we propose a dual defense(DD)method that includes the preemptive exclusion of high-risk member samples module and the knowledge distillation module,which thwarts the access of the resulting models to the private training data twice to mitigate MI *** defense method can be divided into two steps:the preemptive exclusion of high-risk member samples(Step 1)and the knowledge distillation to obtain the protected student model(Step 2).We propose three types of exclusions:existing MI attacks-based exclusions,sample distances of members and nonmembers-based exclusions,and mutual information value-based exclusions,to preemptively exclude the high-risk member *** the knowledge distillation phase,we add ground-truth labeled data to the reference dataset to decrease the protected student model's dependency on soft labels,aiming to maintain or improve its test *** evaluation shows that DD significantly outperforms state-of-the-art defenses and offers a better privacy-utility *** example,DD achieves∼100%test accuracy improvement over the distillation for membership privacy(DMP)defense for ResNet50 trained on *** simultaneously achieves the reductions in the attack effectiveness(e.g.,the TPR@0.01%FPR of enhanced MI attacks decreased by 2.10%on the ImageNet dataset,the membership advantage(MA)of risk score-based attacks d

关键词： Machine learning Membership inference defenses Preemptive exclusion Knowledge distillation