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2023 Winter Simulation Conference, WSC 2023

作者： Dengler, Gabriel Bazan, Peter German, Reinhard Lalbakhsh, Pooia Liebmann, Ariel Friedrich-Alexander-Universität Erlangen-Nürnberg Laboratory of Computer Networks and Communication Systems Martensstr. 3 BY Erlangen91058 Germany Monash University Faculty of Information Technology Department of Data Science and Ai ClaytonVIC3168 Australia

ISBN: (纸本)9798350369663

This paper introduces a conversational framework that enhances the usability of smart energy system simulations. This study is centered around OpenAI's Generative Pre-trained Transformer (GPT), a fine-tuned conversational model that allows users to communicate with the system in a natural way. Therefore, users can describe their simulation scenarios in plain language and GPT seamlessly translates these descriptions into Python scripts, used as inputs to the simulation environment, in our case, AnyLogic Simulation Software. Our framework is based on the i7-AnyEnergy core framework to compute distribution flows and relevant statistics. The proposed human-machine interface facilitates and accelerates simulation modeling, as demonstrated through the two scenarios we have provided in this paper. Overall, our conversational framework has the potential to significantly improve the user experience of smart energy system simulation environments. By simplifying the interaction between users and complex simulation models, we enable users to obtain valuable insights rapidly and more easily. © 2023 IEEE.

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Design of Impulsive Feedback Controller for Dosing 32

Design of Impulsive Feedback Controller for Dosing

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32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Medvedev, Alexander Proskurnikov, Anton V. Zhusubaliyev, Zhanybai T. Uppsala University Department of Information Technology UppsalaSE-752 37 Sweden Politecnico di Torino Department of Electronics and Telecommunications Turin10 129 Italy Southwest State University International Scientific Laboratory for Dynamics of Non-Smooth Systems Department of Computer Science Kursk Russia Osh State University Faculty of Mathematics and Information Technology Osh723500 Kyrgyzstan

ISBN: (纸本)9798350395440

This paper proposes a pulse-modulated controller that generates, under stationary conditions, a desired sequence of uniform and equidistant impulsive control actions from continuous measurements of the output of a smooth nonlinear time-invariant positive single-input single-output plant of Wiener structure. The proposed controller is applied to the dosing of the drug atracurium in closed-loop neuromuscular blockade and its performance is studied on a database of patient-specific pharmacokinetic-pharmacodynamic models previously estimated from clinical data. © 2024 IEEE.

关键词： Controllers

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Baseline Results for Selected Nonlinear System Identification Benchmarks

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IFAC-PapersOnLine 2024年第15期58卷 474-479页

作者： Max Champneys Gerben I. Beintema R. Tóth Maarten Schoukens Timothy J. Rogers Dynamics Research Group University of Sheffield Sheffield UK Control System group Eindhoven University of Technology Eindhoven the Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Nonlinear system identification remains an important open challenge across research and academia. Large numbers of novel approaches are seen published each year, each presenting improvements or extensions to existing methods. It is natural, therefore, to consider how one might choose between these competing models. Benchmark datasets provide one clear way to approach this question. However, to make meaningful inference based on benchmark performance it is important to understand how well a new method performs comparatively to results available with well-established methods. This paper presents a set of ten baseline techniques and their relative performances on five popular benchmarks. The aim of this contribution is to stimulate thought and discussion regarding objective comparison of identification methodologies.

关键词： Nonlinear System Identification Benchmarks NARX Nonlinear State-Space

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Exploiting Mode-Switching between Aerial-RIS and Active Radio in UAV-Assisted Wireless Networks 25

Exploiting Mode-Switching between Aerial-RIS and Active Radi...

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25th IEEE Wireless Communications and Networking Conference, WCNC 2024

作者： Zhao, Songhan Long, Yusi Gu, Bo Luong, Nguyen Cong Lyu, Bin Gong, Shimin School Of Intelligent Systems Engineering Sun Yat-sen University Shenzhen Campus China Guangdong Provincial Key Laboratory Of Fire Science And Intelligent Emergency Technology China Phenikaa University Faculty Of Computer Science Viet Nam School Of Communications And Information Engineering Nanjing University Of Posts And Telecommunications China

ISBN: (纸本)9798350303582

In this paper, we employ dual-mode unmanned aerial vehicles (UAVs) equipped with both the active radio frequency (RF) module and aerial reconfigurable intelligent surface (ARIS) to assist ground users (GUs) for both the downlink energy transfer and uplink data transmission in a wireless-powered network. To maximize the GUs' minimum throughput, we propose a collaborative mode switching scheme for the dual-mode UAVs to dynamically switch between the active RF and passive ARIS modes according to the time-varying channel conditions. Besides, we jointly optimize the GUs' transmission control, the UAVs' beamforming, and the trajectory planning strategies. This optimization problem is intractable directly due to the non-convexity in both the objective and constraints. We design an iterative algorithm to first decompose the original problem into several subproblems, and then solve each subproblem individually by approximate optimization methods. Numerical results verify that the UAVs' collaborative mode switching along with their trajectories efficiently improves the transmission performance compared to the benchmarks in which both UAVs are operating in one fixed mode. © 2024 IEEE.

关键词： Antennas

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Building an autonomous driving platform based on ROS and its safety analysis

Building an autonomous driving platform based on ROS and its...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Zelin Wei Qiwei Chen Rui Weng National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

In the new wave of technological development, autonomous driving technologies have become the top priority of the innovation of future transportation. Although many technologies are involved in autonomous driving, it is an autonomous mobile robot in essence, mainly including three parts: perception, positioning, and decision-making. Ensuring its security is one key challenge to realize its widespread applications. Therefore, it is necessary to systematically consider security issues of autonomous driving from the sensor level, the autonomous driving system level, and the underlying communication protocol level. In this thesis, we built an automatic driving platform on the ROS system based on the existing hardware infrastructure——SiLaR, a landrover of the Simplexity Lab. Furthermore, we analyzed security issues of each module of SiLaR. Especially in the process of map construction, due to the limitation of the working principle of LiDAR, some specular reflective materials in the operating environment will interfere with the construction. We also used the sensor cross fusion to calibrate the environmental information through the depth camera during the composition process to ensure the accuracy of map construction.

关键词： Technological innovation Software algorithms Transportation Robot sensing systems Solids Hardware Software Safety Security Autonomous vehicles

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Optimal Synthesis of LTI Koopman Models for Nonlinear systems with Inputs

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IFAC-PapersOnLine 2022年第35期55卷 49-54页

作者： Lucian C. Iacob Roland Tóth Maarten Schoukens Control Systems Group Eindhoven University of Technology Eindhoven The Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

A popular technique used to obtain linear representations of nonlinear systems is the so-called Koopman approach, where the nonlinear dynamics are lifted to a (possibly infinite dimensional) linear space through nonlinear functions called observables. In the lifted space, the dynamics are linear and represented by a so-called Koopman operator. While the Koopman theory was originally introduced for autonomous systems, it has been widely used to derive linear time-invariant (LTI) models for nonlinear systems with inputs through various approximation schemes such as the extended dynamics mode decomposition (EDMD). However, recent extensions of the Koopman theory show that the lifting process for such systems results in a linear parameter-varying (LPV) model instead of an LTI form. As LTI Koopman model based control has been successfully used in practice and it is generally temping to use such LTI descriptions of nonlinear systems, due to the simplicity of the associated control tool chain, a systematic approach is needed to synthesise optimal LTI approximations of LPV Koopman models compared to the ad-hoc schemes such as EDMD, which is based on least-squares regression. In this work, we introduce optimal LTI Koopman approximations of exact Koopman models of nonlinear systems with inputs by using ℓ 2 -gain and generalized H 2 norm performance measures. We demonstrate the advantages of the proposed Koopman modelling procedure compared to EDMD.

关键词： Nonlinear systems Koopman operator Linear Parameter-Varying systems

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CNsum:Automatic Summarization for Chinese News Text

arXiv

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arXiv 2025年

作者： Zhao, Yu Huang, Songping Zhou, Dongsheng Ding, Zhaoyun Wang, Fei Nian, Aixin Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha410000 China National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian116000 China

Obtaining valuable information from massive data efficiently has become our research goal in the era of Big Data. Text summarization technology has been continuously developed to meet this demand. Recent work has also shown that transformer-based pre-trained language models have achieved great success on various tasks in Natural Language Processing (NLP). Aiming at the problem of Chinese news text summary generation and the application of Transformer structure on Chinese, this paper proposes a Chinese news text summarization model (CNsum) based on Transformer structure, and tests it on Chinese datasets such as THUCNews. The results of the conducted experiments show that CNsum achieves better ROUGE score than the baseline models, which verifies the outperformance of the model. © 2025, CC BY.

关键词： Big data

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Sora for foundation robots with parallel intelligence:three world models,three robotic systems

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Frontiers of Information technology & Electronic Engineering 2024年第7期25卷 917-923页

作者： Lili FAN Chao GUO Yonglin TIAN Hui ZHANG Jun ZHANG Fei-Yue WANG School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China School of Electrical Engineering Wuhan UniversityWuhan 430072China

This paper outlines the initial steps and basic framework for developing foundation/infrastructure robots/robotics based on foundation models and parallel intelligence,as well as the potential applications of new art... 详细信息

关键词： artificial foundation intelligence

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Incentivizing Fresh Information Acquisition via Age-Based Reward

IEEE Transactions on Networking

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IEEE Transactions on Networking 2025年第3期33卷 1174-1188页

作者： Zhiyuan Wang Qingkai Meng Shan Zhang Hongbin Luo School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beijing China Institute of Artificial Intelligence Beihang University Beijing China State Key Laboratory of Software Development Environment Beijing China

Many Internet platforms are information-oriented and crowd-based. They collect fresh information of various points of interest (PoIs) relying on users who happen to be nearby the PoIs. The platform will offer rewards to incentivize users and compensate their costs incurred from information acquisition. In practice, a user’s cost is his/her private information, thus both the user cost and its distribution are hidden to the platform, making it challenging to determine the optimal rewarding decision. In this paper, we investigate how the platform dynamically rewards the users, aiming to jointly reduce the age of information (AoI) and the operational expenditure (OpEx). Due to the hidden cost distribution, this is an online non-convex learning problem with bandit feedback. To overcome the challenge, we first design an age-based reward scheme, which decouples the OpEx from the unknown cost distribution and enables the platform to accurately control its OpEx. We then take advantage of the age-based reward scheme and propose an exponentially discretizing and learning (EDAL) policy for platform operation. We prove that the EDAL policy performs asymptotically as well as the optimal decision (derived from the cost distribution). Simulation results show that the age-based reward scheme protects the platform’s OpEx from the influence of the user crowd characteristics, and also verify the asymptotic optimality of the EDAL policy.

关键词： Costs Reviews Soft sensors Pricing Optimization Electronic mail Stochastic processes Simulation Laboratories Internet

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Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 959-973页

作者： Juexiao Zhou Bin Zhang Haoyang Li Longxi Zhou Zhongxiao Li Yongkang Long Wenkai Han Mengran Wang Huanhuan Cui Jingjing Li Wei Chen Xin Gao Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Department of Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Gene Regulation and Systems Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological *** fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico prediction of TSSs solely based on genomic *** of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome ***,we present Dee Re CT-TSS,a deep learningbased method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing *** show that by effectively incorporating these two sources of information,Dee Re CT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell ***,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific ***,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin *** source code for Dee Re CT-TSS is available at https://github.-com/Joshua Chou2018/Dee Re CT-TSS_release and https://***/biocode/tools/BT007316.

关键词： Transcription start site Machine learning Deep learning Meta-learning RNA sequencing

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