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IEEE Symposium on Intelligent Vehicle

作者： Lin Zhao Mikael Nybacka Malte Rothhämel Jonas Mårtensson Department of Engineering Mechanics KTH Royal Institute of Technology Stockholm Sweden Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Remote driving plays a vital role in coordinating automated vehicles in challenging situations. Data transmission latency, however, can cause several problems in remote driving. Firstly, it can degrade the performance of remote-controlled vehicles, evident in issues like lane-following deviation and vehicle stability. Additionally, the remote control tower’s driving feedback is affected by delayed vehicle signals, leading to delayed driving experience. To address this, a model-free-based predictor is employed to compensate for the delay in remote driving. This approach does not require any dynamic model of the system and only needs tuning of two parameters to reduce communication delay. This study enhances the previous work by mitigating the amplitude of overshoot around peak points. It leverages the principle of the second-order derivative to predict the signal’s peak time and uses it to address the predictor’s overshoot issue. The effectiveness of the proposed method is validated using real car data from multiple participants in two scenarios, including Slalom and lane-following. Simulation results indicate that the proposed method can reduce prediction error by nearly 25% compared to previous works. Moreover, the solutions in this study are capable of managing not only delays in remote driving vehicles but also in traditional mechanical systems, such as CAN bus delays in conventional cars.

关键词： Intelligent vehicles Simulation Predictive models Stability analysis Delays Automobiles Mechanical systems

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Hot-carrier thermal breakdown and S-type current-voltage characteristics in perforated graphene structures

arXiv

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arXiv 2025年

作者： Ryzhii, V. Tang, C. Ryzhii, M. Shur, M.S. Frontier Research Institute for Interdisciplinary Sciences Tohoku University Sendai980-8578 Japan Research Institute of Electrical Communication Tohoku University Sendai980-8577 Japan School of Computer Science and Engineering University of Aizu Aizu-Wakamatsu965-8580 Japan Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

We investigate the carrier transport characteristics of perforated graphene layer (PGL) composed of arrays of interdigital coplanar graphene microribbons (GMRs) connected by graphene nanoribbon (GNR) bridges. We analyze their operation at room-temperature. Under an applied bias voltage, two-dimensional electron and hole systems (2DES and 2DHS) form in adjacent GMRs. The terminal current in these PGL structures is primarily governed by thermionic transport across the GNR bridges. As electrons and holes traverse the GNRs, they induce heating in the 2DES and 2DHS, creating a positive feedback loop between carrier heating and thermionic emission. This phenomenon, characterized as hot-carrier thermal breakdown, can give rise to S-shaped inter-GMR current-voltage characteristics. These unique transport properties make PGLs promising candidates for fast, voltage-controlled room-temperature switches and electromagnetic radiation detectors. © 2025, CC BY.

关键词： Thermionic emission

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Analyzing the Impact of External Interventions on Water and Energy Demand: Cybersecurity’s Role in Sustainable Development

Analyzing the Impact of External Interventions on Water and ...

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Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Daryoush Tavangar Rizi Mohammad Hassan Nazari Ali Maleki Seyed Hossein Hosseinian Maryam Fani G. B. Gharehpetian Department of Physics & Energy Engineering Amirkabir University of Technology Tehran Iran Smart Control Systems Department Niroo Research Institute (NRI) Tehran Iran School of Electrical Engineering & Computer Science Washington State University Pullman Washington Department of Electrical Engineering Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798350361322

ISBN: (纸本)9798350361339

The nexus of water, food, and energy constitutes a fundamental aspect of sustainable development. Furthermore, the demand for water and energy is becoming more pronounced. Moreover, there is a growing importance placed on studying cyber security within the water-energy nexus. This research investigates the interdependence of these two systems, conceptualizing them as a water-energy nexus and effort has been conducted on optimizing the economic efficiency of electricity and water systems in order to minimize operating expenses. Subsequently, external interventions, such as the simultaneous injection of false data on both water and energy demand, were addressed through an optimization process in GAMS software. The results suggest that the value of the cost objective function remains constant when inaccurate data is introduced, which hinders the accurate estimation of the system by the system operator. in this system, both water and power loads are directly impacted by these interventions. In particular, the power and water load of the system have exhibited changes ranging from 1.5% to 17.99% for electric load and 3.87% to 17.1% for water load, in comparison to the previous state.

关键词： Meters Renewable energy sources Electricity Distance measurement Software computer crime Sustainable development

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Conflict Resolution Strategy in Handover Management for 4G and 5G Networks

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computers, Materials & Continua 2022年第9期72卷 5215-5232页

作者： Abdulraqeb Alhammadi Wan Haslina Hassan Ayman A.El-Saleh Ibraheem Shayea Hafizal Mohamad Yousef Ibrahim Daradkeh Communication Systems and Networks Research Lab Malaysia-Japan International Institute of TechnologyUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia College of Engineering A’sharqiyah University(ASU)Ibra400Oman Department of Electronics and Communication Engineering Faculty of Electrical and Electronics EngineeringIstanbul Technical University(ITU)Istanbul34467Turke Faculty of Engineering and Built Environment Universiti Sains Islam MalaysiaBandar Baru Nilai71800Malaysia Department of Computer Engineering and Networks College of Engineering at Wadi AddawasirPrince Sattam Bin Abdulaziz UniversityAl Kharj11991KSA

Fifth-generation(5G)cellular networks offer high transmission rates in dense urban ***,a massive deployment of small cells will be required to provide wide-area coverage,which leads to an increase in the number of handovers(HOs).Mobility management is an important issue that requires considerable attention in heterogeneous networks,where 5G ultra-dense small cells coexist with current fourth-generation(4G)*** mobility robustness optimization(MRO)and load balancing optimization(LBO)functions have been introduced in the 3GPP standard to address HO problems,non-robust and nonoptimal algorithms for selecting appropriate HO control parameters(HCPs)still exist,and an optimal solution is subjected to compromise between LBO and MRO ***,HO decision algorithms become *** paper proposes a conflict resolution technique to address the contradiction between MRO and LBO *** proposed technique exploits received signal reference power(RSRP),cell load and user speed to adapt HO margin(HM)and time to trigger(TTT).Estimated HM and TTT depend on a weighting function and HO type which is represented by user status during *** proposed technique is validated with other existing algorithms from the *** results demonstrate that the proposed technique outperforms existing algorithms overall performance *** proposed technique reduces the overall average HO ping-pong probability,HO failure rate and interruption time by more than 90%,46%and 58%,respectively,compared with the other schemes overall speed scenarios and simulation time.

关键词： Mobility management handover 5G heterogeneous networks

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“Should I Lead?” Feasibility Study of User Perception on Following-Robot for Gait Assessment

“Should I Lead?” Feasibility Study of User Perception on F...

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International Conference on Advanced robotics (ICAR)

作者： Alessandra Sorrentino Bruno Ferreira Paulo Menezes Jorge Batista Jorge Dias Laura Fiorini Pietro Benvenuti Filippo Cavallo Department of Industrial Engineering University of Florence Florence Italy Dept. of Electrical and Computer Engineering University of Coimbra Institute of Systems and Robotics Coimbra Portugal Khalifa University of Science and Technology Khalifa University Center for Autonomous Robotic Systems Abu Dhabi UAE BioRobotics Institute Scuola Superiore Sant'Anna Pisa Italy

Current techniques of gait assessment rely on wearable sensors, pressure-sensitive walkway systems, and optical motion capture systems. A less invasive and more portable solution could be represented by a mobile robot that follows the user during the gait activity. The main idea behind this work relies on finding the best robot configuration for less invasive gait analysis with high acceptability from the end users. To this aim, two follow-me configurations have been designed: human-leader (i.e. the robot follows the person from behind), and robot-leader (i.e. robot follows the person from the front). We asked 27 young participants to test both modalities and to evaluate their perception of the robot in 5 domains: comfort, expected conformity, safety, trust, and unobtrusiveness. Additionally, we extracted quantitative parameters related to the walking experience from the data recorded by the platform and we analyzed them in tandem with the qualitative results. The results reported that robot-leader configuration tended to be more appreciated in terms of comfort, trust, and safety. On the contrary, the human-leader configuration is perceived as less obtrusive, less invasive, and in line with users' expectations. Considering the gait assessment application, we expect the human-leader configuration to return more promising and accurate results.

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Learning Based Dynamic Cluster Reconfiguration for UAV Mobility Management with 3D Beamforming

arXiv

引用

arXiv 2024年

作者： Meer, Irshad A. Besser, Karl-Ludwig Ozger, Mustafa Schupke, Dominic Poor, H. Vincent Cavdar, Cicek Department of Electrical and Computer Engineering Princeton University United States Division of Communication Systems KTH Royal Institute of Technology Sweden Airbus Central Research and Technology Munich Germany

In modern cell-less wireless networks, mobility management is undergoing a significant transformation, transitioning from single-link handover management to a more adaptable multi-connectivity cluster reconfiguration approach, including often conflicting objectives like energy-efficient power allocation and satisfying varying reliability requirements. In this work, we address the challenge of dynamic clustering and power allocation for unmanned aerial vehicle (UAV) communication in wireless interference networks. Our objective encompasses meeting varying reliability demands, minimizing power consumption, and reducing the frequency of cluster reconfiguration. To achieve these objectives, we introduce a novel approach based on reinforcement learning using a masked soft actor-critic algorithm, specifically tailored for dynamic clustering and power allocation. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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SEPSYN-OLCP: AN ONLINE LEARNING-BASED FRAMEWORK FOR EARLY SEPSIS PREDICTION WITH UNCERTAINTY QUANTIFICATION USING CONFORMAL PREDICTION

arXiv

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arXiv 2025年

作者： Zhou, Anni Beyah, Raheem Kamaleswaran, Rishikesan Xie, Yao School of Electrical and Computer Engineering Georgia Institute of Technology United States School of Medicine Department of Surgery Duke University United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology United States

Sepsis is a life-threatening syndrome with high morbidity and mortality in hospitals. Early prediction of sepsis plays a crucial role in facilitating early interventions for septic patients. However, early sepsis prediction systems with uncertainty quantification and adaptive learning are scarce. This paper proposes Sepsyn-OLCP, a novel online learning algorithm for early sepsis prediction by integrating conformal prediction for uncertainty quantification and Bayesian bandits for adaptive decision-making. By combining the robustness of Bayesian models with the statistical uncertainty guarantees of conformal prediction methodologies, this algorithm delivers accurate and trustworthy predictions, addressing the critical need for reliable and adaptive systems in high-stakes healthcare applications such as early sepsis prediction. We evaluate the performance of Sepsyn-OLCP in terms of regret in stochastic bandit setting, the area under the receiver operating characteristic curve (AUROC), and F-measure. Our results show that Sepsyn-OLCP outperforms existing individual models, increasing AUROC of a neural network from 0.64 to 0.73 without retraining and high computational costs. And the model selection policy converges to the optimal strategy in the long run. We propose a novel reinforcement learning-based framework integrated with conformal prediction techniques to provide uncertainty quantification for early sepsis prediction. The proposed methodology delivers accurate and trustworthy predictions, addressing a critical need in high-stakes healthcare applications like early sepsis prediction. © 2025, CC BY-NC-ND.

关键词： Prediction models

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Reinforcement Learning-Based Optimal Multiple Waypoint Navigation

Reinforcement Learning-Based Optimal Multiple Waypoint Navig...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Christos Vlachos Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos Department of Electrical and Computer Engineering University of Patras Greece School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Greece Center of AI & Robotics (CAIR) New York University Abu Dhabi

In this paper, a novel method based on Artificial Potential Field (APF) theory is presented, for optimal motion planning in fully-known, static workspaces, for multiple final goal configurations. Optimization is achieved through a Reinforcement Learning (RL) framework. More specifically, the parameters of the underlying potential field are adjusted through a policy gradient algorithm in order to minimize a cost function. The main novelty of the proposed scheme lies in the method that provides optimal policies for multiple final positions, in contrast to most existing methodologies that consider a single final configuration. An assessment of the optimality of our results is conducted by comparing our novel motion planning scheme against a RRT* method.

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Investigating Role of Renewable Resources Including Wind Turbines in Energy Hub by Considering Simultaneous Demand for Electricity, Heating and Cooling

Investigating Role of Renewable Resources Including Wind Tur...

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Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Daryoush Tavangar Rizi Mohammad Hassan Nazari Ali Maleki Seyed Hossein Hosseinian Maryam Fani Gevork B. Gharehpetian Department of Physics and Energy Engineering Amirkabir University of Technology Tehran Iran Smart Control Systems Department Niroo Research Institute (NRI) Tehran Iran School of Electrical Engineering & Computer Science Washington State University Pullman Washington Department of Electrical Engineering Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798350361322

ISBN: (纸本)9798350361339

As a crucial component of multi-energy systems (MES), the energy hub significantly enhances their performance and reliability. In the energy system, the utilization of renewable energy sources (RES) can presents a significant optimization problem, capable of substantially reducing environmental pollution and lowering energy costs for users. This paper presents an optimal load dispatch model incorporating a collection of wind turbines, aimed at reducing the overall cost of operating an energy hub. It is also proposes a hub structure based on wind, natural gas, and electricity as a combined heat and power system, utilizing converters and energy storage mechanisms to obtain electricity, thermal, and cooling energy. To accomplish this, two situations are executed in the network, aiming to minimize costs by applying a problem-solving approach to the installation of wind turbines at an energy hub. After implementing energy hub management in the optimal mode, the system’s operating costs are reduced by $16 \%$, demonstrating its advantage.

关键词： Renewable energy sources Costs Electricity Wind turbines Problem-solving Trigeneration Optimization

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Fine-Grained Resource Provisioning and Task Scheduling for Heterogeneous Applications in Distributed Green Clouds

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IEEE/CAA Journal of Automatica Sinica 2020年第5期7卷 1380-1393页

作者： Haitao Yuan Meng Chu Zhou Qing Liu Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA Department of Electrical and Computer Engineering Faculty of Engineeringand the Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21589Saudi Arabia IEEE

An increasing number of enterprises have adopted cloud computing to manage their important business applications in distributed green cloud(DGC)systems for low response time and high cost-effectiveness in recent *** scheduling and resource allocation in DGCs have gained more attention in both academia and industry as they are costly to manage because of high energy *** factors in DGCs,e.g.,prices of power grid,and the amount of green energy express strong spatial *** dramatic increase of arriving tasks brings a big challenge to minimize the energy cost of a DGC provider in a market where above factors all possess spatial *** work adopts a G/G/1 queuing system to analyze the performance of servers in *** on it,a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based bees algorithm(SBA)to find SBA can minimize the energy cost of a DGC provider by optimally allocating tasks of heterogeneous applications among multiple DGCs,and specifying the running speed of each server and the number of powered-on servers in each GC while strictly meeting response time limits of tasks of all *** databased experimental results prove that SBA achieves lower energy cost than several benchmark scheduling methods do.

关键词： Bees algorithm data centers distributed green cloud(DGC) energy optimization intelligent optimization simulated annealing task scheduling machine learning

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