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Efficient Stimuli Generation using Reinforcement Learning in Design Verification

Efficient Stimuli Generation using Reinforcement Learning in...

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International conference on Synthesis, modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD)

作者： Deepak Narayan Gadde thomas Nalapat Aman Kumar Djones Lettnin Wolfgang Kunz Sebastian Simon Infineon Technologies Dresden GmbH & Co. KG Germany Infineon Technologies AG Germany Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau Germany

ISBN: (数字)9798350351927

ISBN: (纸本)9798350351934

the increasing design complexity of system-on-Chips (SoCs) has led to significant verification challenges, particularly in meeting coverage targets within a timely manner. At present, coverage closure is heavily dependent on constrained random and coverage driven verification methodologies where the randomized stimuli are bounded to verify certain scenarios and to reach coverage goals. this process is said to be exhaustive and to consume a lot of project time. In this paper, a novel methodology is proposed to generate efficient stimuli with the help of Reinforcement Learning (RL) to reach the maximum code coverage of the Design Under Verification (DUV). Additionally, an automated framework is created using metamodeling to generate a systemVerilog testbench and an RL environment for any given design. the proposed approach is applied to various designs and the produced results proves that the RL agent provides effective stimuli to achieve code coverage faster in comparison with baseline random simulations. Furthermore, various RL agents and reward schemes are analyzed in our work.

关键词： Analytical models Codes Metamodeling Reinforcement learning system-on-chip Complexity theory Circuit synthesis Integrated circuit modeling

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Motion Modification Method of Musculoskeletal Humanoids by Human Teaching Using Muscle-Based Compensation Control 20

Motion Modification Method of Musculoskeletal Humanoids by H...

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20th IEEE-RAS International conference on Humanoid Robots (HUMANOIDS)

作者： Kawaharazuka, Kento Koga, Yuya Nishiura, Manabu Omura, Yusuke Asano, Yuki Okada, Kei Kawasaki, Koji Inaba, Masayuki Univ Tokyo Grad Sch Informat Sci & Technol Dept Mechanoinformat Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan Toyota Motor Co Ltd Toyota Japan

ISBN: (纸本)9781728193724

While musculoskeletal humanoids have the advantages of various biomimetic structures, it is difficult to accurately control the body, which is challenging to model. Although various learning-based control methods have been developed so far, they cannot completely absorb model errors, and recognition errors are also bound to occur. In this paper, we describe a method to modify the movement of the musculoskeletal humanoid by applying external force during the movement, taking advantage of its flexible body. Considering the fact that the joint angles cannot be measured, and that the external force greatly affects the nonlinear elastic element and not the actuator, the modified motion is reproduced by the proposed muscle-based compensation control. this method is applied to a musculoskeletal humanoid, Musashi, and its effectiveness is confirmed.

关键词： Biological system modeling Force Education Humanoid robots Muscles Elasticity Hardware

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Polynomial approximation of regions of attraction via occupation measures: an application to a biological autonomous system

Polynomial approximation of regions of attraction via occupa...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Pierluigi Francesco De Paola Alessandro Borri Alessia Paglialonga Pasquale Palumbo Fabrizio Dabbene Polytechnic University of Bari Bari Italy Istituto di Elettronica e di Ingegneria Dell’Informazione e Delle Telecomunicazioni (CNR-IEIIT) Italy Istituto di Analisi dei Sistemi ed Informatica (CNR-IASI) Italy Department of Biotechnologies and Biosciences University of Milano-Bicocca Milan Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this work we analyze the problem of computing the regions of attraction of a well-acknowledged model in the framework of diabetes modeling and control, the model by Topp et al. Despite the importance of this pioneering model in the literature of mathematical models of diabetes progression, to the best of our knowledge a clear representation of the regions of attraction of specific target sets under suitable conditions appears to be lacking. We address this problem by means of a method employing moment-sum-of-squares (moment-SOS) hierarchy that we exploit to assess the validity of a general computation of the regions of attraction performed through a Monte Carlo simulation. Addressing the problem of the knowledge of certain region of attraction, this work paves the way to future research aimed at exploiting a polynomial version of diabetes progression models to leverage optimal control problem design via occupation measures and momentSOS hierarchy.

关键词： Analytical models Monte Carlo methods Computer aided software engineering Autonomous systems Computational modeling Biological system modeling Optimal control Mathematical models Polynomials Diabetes

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RIV-SLAM: Radar-Inertial-Velocity optimization based graph SLAM

RIV-SLAM: Radar-Inertial-Velocity optimization based graph S...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Dong Wang Stefan May Andreas Nuechter Department of Informatics XVII Robotics Julius-Maximilians-University Wuerzburg Wuerzburg Germany Faculty of Electrical Engineering Precision Engineering Information Technology Nuremberg Institute of Technology Georg Simon Ohm Nuremberg Germany

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

4D imaging radars, commonly known as 4D radars, deliver comprehensive point cloud data that encapsulates range, azimuth, elevation, and Doppler velocity information even in harsh environmental conditions, such as rain, snow, smoke, and fog. However, 4D radar data also suffers from high noise and sparsity, which poses great challenges for SLAM applications. this paper presents RIV-SLAM, a complete radar-inertial-velocity optimization-based graph SLAM system designed to exploit the full potential of 4D imaging radar technology. RIV-SLAM consists of four integral components: front-end, loop closure, IMU pre-integration and graph optimization, each optimized to effectively leverage the unique attributes of radar data and tightly coupled with IMU data. this is also the first SLAM system known to us that outputs an optimized ego velocity. this capability ensures reliable ego motion estimation under extreme conditions (e.g., wheel odometry fails). Furthermore, we develop a new ground extraction approach, specifically adapted for the 4D imaging radar, which substantially improves the system’s z-axis accuracy. Comprehensive evaluations of the RIV-SLAM system on a variety of datasets demonstrate its superior performance, significantly surpassing existing state-of-the-art Radar-SLAM frameworks. the code of RIV-SLAM will be released at: RIV-SLAM

关键词： Simultaneous localization and mapping Accuracy Wheels Estimation Radar Radar imaging Doppler radar Doppler effect optimization Global Positioning system

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Joint optimization of Wind Farm Layout Considering Yaw Control 8th

Joint Optimization of Wind Farm Layout Considering Yaw Cont...

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8th International conference on Life system modeling and Simulation, LSMS 2024 and 8th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wang, Yiqun Jiang, Ruolin Chen, Yuanye Fang, Fang School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819602247

As wind farms continue to grow in size, minimizing wake losses between turbines in layout design becomes increasingly important. Current wind farm layout strategies often focus on the performance of individual turbines without considering wake interactions. this study introduces a coordinated control approach that considers wake effects and demonstrates an improved yaw control results to reduce the impact of wake interactions. By implementing cooperative yaw control among turbines and using the NSGA-II algorithm, the study establishes a dual optimization problem of minimizing turbine spacing and maximizing power output, ultimately yielding the optimal layout for wind turbines. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wind turbines

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A Novel Architecture and Algorithm for Prediction of Students Psychological Health based on Big Data 20

A Novel Architecture and Algorithm for Prediction of Student...

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20th IEEE International conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom)

作者： Cheng, Chen Cheng, Xinzhou Zheng, Yuting Cao, Lijuan Han, Yuhui Jia, Yuwei Zhang, Qingqing Zhang, Yi Zhang, Ya'nan Xu, Lexi China United Network Commun Corp Res Inst Beijing Peoples R China

ISBN: (纸本)9781665416580

Psychological health of students has become a widespread social problem, while the management and assessment of college students' psychological health is still stay in passive and manual mode based on the traditional method. In this paper, we design a novel architecture for the prediction of college students' psychological health based on Multi-Source big data including Operation Support system big data, educational data and psychological health questionnaire data. then we propose the Optimized Decision Tree using Multiple-Target Particle Swarm optimization (DT-MTPSO) algorithm. Experiment shows that the proposed algorithm can solve the Multiple-Target problems effectively and has better performance in F1-score than traditional Decision Tree. In addition, the result of the features selection of DT-MTPSO for different targets shows the relationship between the psychological health level and behavioural characteristics of students for different evaluation indicators, providing guidance to the school managers and educational psychologist.

关键词： Multi-Source Big Data Multi-Target Particle Swarm optimization Psychological Health Machine Learning

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Dynamical modeling and Numerical Simulation of Water Discharge Process of Spherical Objects 20

Dynamical Modeling and Numerical Simulation of Water Dischar...

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20th IEEE International conference on Mechatronics and Automation, ICMA 2023

作者： Zhong, Lin Dong, Jiawei Liu, Jiabin Wang, Wei Wu, Riyue Wang, Shuqi Zhang, Tao Guangdong University of Technology State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangzhou China Guangdong University of Technology School of Electromechanical Engineering Guangzhou China Harbin Institute of Technology State Key Laboratory of Robotics and System Harbin China

ISBN: (纸本)9798350320831

the process of an object leaping out of the water is very similar to the jumping process of an aquatic animal during prey or avoid predators. the study of the process of an object leaping out of the water is beneficial to the design of robots jumping out of water. In this work, the free jump of a sphere out of the water is successfully simulated based on the Computational Fluid Dynamics (CFD) software Fluent, with User-Defined Functions to control the motion of the sphere, the volume of Fluid model, dynamic meshing, and Detached Eddy Simulation. the velocity of the sphere at the water-air interface and the height of the sphere leaping out from the water surface obtained from the numerical simulation are compared with experimental results. the maximum error between the experimental and the simulated results is 2.28% in the water-discharge velocity and 2.65% in the leaping height from the water surface. In addition, this paper model the dynamics of the sphere during the water discharge process. the paper also analyses the free deformation of water surface, the water film rupture process on the surface of the sphere, the variation of fluid drag on the sphere, and the variation of the fluid velocity field during the free ascent of the sphere after acquiring an initial velocity of 2.81 m/s. © 2023 IEEE.

关键词： Computer software

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Bayesian optimization for Efficient Tuning of Visual Servo and Computed Torque Controllers in a Reinforcement Learning Scenario 20

Bayesian Optimization for Efficient Tuning of Visual Servo a...

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20th International conference on Advanced Robotics (ICAR)

作者： Ribeiro, Eduardo G. Mendes, Raul Q. Terra, Marco H. Grassi Jr, Valdir Univ Sao Paulo Sao Carlos Sch Engn Dept Elect & Comp Engn Sao Carlos Brazil

ISBN: (纸本)9781665436847

Although the search for optimal parameters is a central concern for the design stage of control systems, this adjustment is generally not optimized in the design of visual servo controllers. However, for a classic position-based visual servo controller, the choice of the proportional gain that multiplies the computed error may directly affect the system's performance, and may even lead to instability. On the other hand, adjusting such a parameter can be a time-consuming and hard-working task. thus, in this work, we propose to automate the search for the linear and angular gains of a visual servo controller through Bayesian optimization. We simulate the environment in Matlab with a Kinova GEN3 7DOF robot in a reinforcement learning scenario, in which the projected cost function is evaluated directly on the robot. We demonstrate that Bayesian optimization is capable of finding the visual servo controller gains, as well as the robot internal controller gains, with up to 13 and 14 times fewer iterations when compared to an on-police actor-critic model-free algorithm and the genetic algorithm respectively. Furthermore, we show that the obtained controller performs better considering different control performance parameters and in qualitative evaluations regarding the Cartesian and image spaces.

关键词： Reinforcement learning

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optimization for regional examination centers design considering multiservice collaboration*

Optimization for regional examination centers design conside...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Tianjiao Sun Yewen Deng Liping Zhou Global Logistics Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Medical examinations play a crucial role in medical diagnosis and treatment. With the advancement of internet-based healthcare services and the implementation of a hierarchical medical system, it becomes imperative to redesign the regional medical examination center to meet patients’ diverse service requirements. In this paper, we study the problem of redesigning the examination centers considering the heterogeneity of patient examinations. A mixed-integer programming model is established to determine the location and capacity of new facilities and the expansion of existing facilities to minimize costs associated with travel and round-trips. the commercial solver Gurobi is used to solve the model. Finally, numerical experiments are conducted using real data from the Pudong New Area of Shanghai to demonstrate the effectiveness of the proposed approach. the experiment results indicate that our method can make patients who need multiple examinations visit the hospital only once.

关键词： Costs Computer aided software engineering Sensitivity analysis Hospitals Programming Numerical models Resource management Medical diagnosis Medical diagnostic imaging optimization

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Noncooperative Control of Energy Communities through Learning-based Response Dynamics

Noncooperative Control of Energy Communities through Learnin...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Saba Askari Noghani Paolo Scarabaggio Raffaele Carli Mariagrazia Dotoli Department of Electrical and Information Engineering The Polytechnic of Bari Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

With the growing availability of data, learning-based distributed energy management is emerging as a viable and efficient alternative to traditional model-based schemes. In this context, we propose a novel game-theoretic learning-based method for the distributed control of energy communities. In particular, we consider a community that includes several prosumers equipped with a renewable energy source and an energy storage system. the scheduling of energy activities of all prosumers is formulated as a noncooperative game. Nevertheless, unlike the state-of-the-art, where an optimization problem is typically defined to model the behavior of each prosumer, we approximate each prosumer response strategy using a neural network. We propose a distributed algorithm based on the well-known Banach-Picard iteration to efficiently seek for an equilibrium of the game. Lastly, the convergence and effectiveness of the proposed approach are validated through numerical simulations under different realistic scenarios.

关键词： Renewable energy sources Neural networks Games Pricing Numerical simulation Numerical models Distributed algorithms optimization Convergence Energy storage

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