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检索条件"任意字段=International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation"

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Sample-efficient Reinforcement Learning in Robotic Table Tennis

Sample-efficient Reinforcement Learning in Robotic Table Ten...

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IEEE international conference on Robotics and Automation (ICRA)

作者： Jonas Tebbe Lukas Krauch Yapeng Gao Andreas Zell Cognitive Systems Group University of Tuebingen Tuebingen Germany

Reinforcement learning (RL) has achieved some impressive recent successes in various computer games and simulations. Most of these successes are based on having large numbers of episodes from which the agent can learn. In typical robotic applications, however, the number of feasible attempts is very limited. In this paper we present a sample-efficient RL algorithm applied to the example of a table tennis robot. In table tennis every stroke is different, with varying placement, speed and spin. An accurate return therefore has to be found depending on a high-dimensional continuous state space. To make learning in few trials possible the method is embedded into our robot system. In this way we can use a one-step environment. The state space depends on the ball at hitting time (position, velocity, spin) and the action is the racket state (orientation, velocity) at hitting. An actor-critic based deterministic policy gradient algorithm was developed for accelerated learning. Our approach performs competitively both in a simulation and on the real robot in a number of challenging scenarios. Accurate results are obtained without pre-training in under 200 episodes of training. The video presenting our experiments is available at https://***/uRAtdoL6Wpw.

关键词： Training Automation conferences Computational modeling Reinforcement learning Games Robots

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EmuNoC: Hybrid Emulation for Fast and Flexible Network-on-Chip Prototyping on FPGAs

EmuNoC: Hybrid Emulation for Fast and Flexible Network-on-Ch...

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international conference on Field Programmable Logic and Applications

作者： Yee Yang Tan Felix Staudigl Lukas Jünger Anna Drewes Rainer Leupers Jan Moritz Joseph Institute for Communication Technologies and Embedded Systems RWTH Aachen University Germany Institute for Information and Communication Technologies Otto-von-Guericke Universität Magdeburg Germany

Networks-on-Chips (NoCs) recently became widely used, from multi-core CPUs to edge-AI accelerators. Emulation on FPGAs promises to accelerate their RTL modeling compared to slow simulations. However, realistic test stimuli are challenging to generate in hardware for diverse applications. In other words, both a fast and flexible design framework is required. The most promising solution is hybrid emulation, in which parts of the design are simulated in software, and the other parts are emulated in hardware. This paper proposes a novel hybrid emulation framework called EmuNoC. We introduce a clock-synchronization method and software-only packet generation that improves the emulation speed by 36.3 × to 79.3 × over state-of-the-art frameworks while retaining the flexibility of a pure-software interface for stimuli simulation. We also increased the area efficiency to model up to an NoC with 169 routers on a single FPGA, while previous frameworks only achieved 64 routers.

关键词： Emulation Network-on-chip computer architecture Benchmark testing Software Hardware Field programmable gate arrays

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FAuto: An Efficient GMM-HMM FPGA Implementation for Behavior Estimation in Autonomous systems

FAuto: An Efficient GMM-HMM FPGA Implementation for Behavior...

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international Joint conference on Neural Networks (IJCNN)

作者： Junde Li Navyata Gattu Swaroop Ghosh School of Electrical Engineering and Computer Science Pennsylvania State University PA USA

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Driving behavior estimation in car-following scenario based on contextual traffic information is an essential capability for autonomous driving systems. Real-time motion planning based on incomplete environment perception requires complicated probabilistic model for interactions with surrounding objects and road conditions. Hidden Markov Model (HMM) with Gaussian emissions has been used to model driving behaviors for its ability of inferring unobserved states. While the high-dimensional contextual data is continuously processed, the system should be high-performance and power-efficient to make real-time decisions for safe operations. Field Programmable Gate Array (FPGA) is being increasingly used on embedded System-on-Chip (SoC) for mobile applications mainly because of its parallel computation and low-power consumption. This paper implements FAuto: the framework of HMM coupled with GMM algorithm on a Xilinx PYNQ-Z2 board for autonomous systems. We design the hybrid GMM-HMM model in python, and train the model using Next Generation simulation (NGSIM) trajectory data on a CPU platform. The hardware accelerator is designed through Vivado HLS 2018.2, and verified with Jupiter notebook. FAuto achieves 2.59 TOPS/W power efficiency, and 10.39× speedup compared to Python software implementation running on quad-core i7-7500U CPU.

关键词： Hidden Markov models Estimation Field programmable gate arrays Computational modeling Markov processes Gaussian mixture model

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Developments in Control Engineering Education: Lab-in-a-Box Project Based Learning

Developments in Control Engineering Education: Lab-in-a-Box ...

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UKACC international conference on Control (CONTROL)

作者： James E. Pickering Department of Mechanical Biomedical and Design Engineering Aston University Birmingham UK

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

The teaching of autonomous vehicle control algorithm design involves the drawing together of multiple topics from both theory and practice. To enable an in-depth knowledge of how the control algorithms of an AV operate, a laboratory-scale approach using ‘lab-in-a-box’ has been developed at Aston University for a project-based module on the Future Vehicle Technologies MSc Course. The aim of the approach is such that students can learn the control engineering fundamentals before moving onto a larger vehicle platform. The adopted approach borrows ideas inspired from model-based design for embedded control. Continuous-time and discrete-time simulation tools are used to design what is in effect a real-time control system. Students are also taught ‘model shop’ skills and computer aided design (CAD), thus allowing the students to build a physical control system to operate in real-time. The focus is on the control of a DC motor, including speed control, temperature control and obstacle avoidance. Initial results presented here are promising, leading to increased levels of student satisfaction and engagement.

关键词： Solid modeling Design automation Education Velocity control DC motors Control engineering education Real-time systems

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VRACK — virtual reality augmented cycling kit: Design and validation

VRACK — virtual reality augmented cycling kit: Design and v...

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IEEE Annual international Symposium Virtual Reality

作者： Richard Ranky Mark Sivak Jeffrey Lewis Venkata Gade Judith E. Deutsch Constantinos Mavroidis Biomedical Mechatronics Laboratory Department of Mechanical & Industrial Engineering Northeastern University Boston MA USA VRehab LLC NJ USA RIVERS (Research in Virtual Environments & Rehabilitation Sciences) Laboratory Department of Rehabilitation and Movement Sciences University of Medicine and Dentistry of New Jersey Newark NJ USA

ISBN: (纸本)9781424462377

In this paper the virtual reality augmented cycling kit (VRACK) a mechatronic rehabilitation system with an interactive virtual environment is presented. It was designed as a modular system that can convert most bicycles in virtual reality (VR) cycles. Novel hardware components embedded with sensors were implemented on a stationary exercise bicycle to monitor physiological and biomechanical parameters of participants while immersing them in a virtual reality simulation providing the user with visual, auditory and haptic feedback. This modular and adaptable system attaches to commercially-available stationary bicycle systems and interfaces with a personal computer for simulation and data acquisition processes. The bicycle system includes novel handle bars based on hydraulic pressure sensors and innovative pedals that monitor lower extremity kinetics and kinematics. Parameters monitored by these systems are communicated to a practitioner's interface screen and can be amplified before entering its virtual environment. The first prototype of the system was successful in demonstrating that a modular mechatronic kit can monitor and record kinetic, kinematic and physiologic parameters of riders.

关键词： Virtual reality Bicycles computerized monitoring Condition monitoring Mechatronics Virtual environment Biomedical monitoring Computational modeling Kinetic theory Kinematics

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embedded Neural Network to Model-based Permanent Magnet Synchronous Motor Diagnostics

Embedded Neural Network to Model-based Permanent Magnet Sync...

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第六届IEEE国际电力电子与运动控制会议 (2009 IEEE 6th international Power Electronics and Motion Control conference-ECCE Asia

作者： Junhong Zhang Yunping Zou Youping Fan Huazhong Univ.of Science and Technology/College of Electrical & Electronics Engineering Wuhan430074 Huazhong Univ.of Science and Technology/College of Electrical & Electronics Engineering Wuhan430074 Wuhan Univ./College of Electrical Engineering Inst.of Bulk Grid SecurityWuhan430072China

Effective Fault detection and diagnosis (FDD) is especially important for some special applications, such as Navy ships operating in hostile environments. So far, FDD for nonlinear systems has not been fully explored. This paper makes an effort to fill the gap by extending an existing Monitor architecture and a series of algorithms for FDD of Permanent Magnet Synchronous Motors (PMSM). A fault model is proposed for the stator winding turn-to-turn fault of PMSM. The model provides a good compromise between computational complexity and model accuracy and is versatile for both the healthy and the fault condition. simulation studies demonstrate a good correspondence with both the theoretical analysis and the experimental observations in the paper.

关键词： Neural networks Permanent magnet motors Computational modeling Fault detection Fault diagnosis Marine vehicles Nonlinear systems computer architecture Stator windings Computational complexity

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Accelerating Brain Circuit simulations of Object Recognition with CELL Processors

Accelerating Brain Circuit Simulations of Object Recognition...

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international Workshop on Innovative Architecture for Future Generation High-Performance Processors and systems

作者： Andrew Felch Jayram Moorkanikara Nageswaran Ashok Chandrashekar Jeff Furlong Nikil Dutt Richard Granger Alex Nicolau Alex Veidenbaum Neukom Institute Dartmouth College Hanover NH USA Centre for Embedded Computer Systems University of California Irvine CA USA

ISBN: (纸本)9780769530772;076953077X

Humans outperform computers on many natural tasks including vision. Given the human ability to recognize objects rapidly and almost effortlessly, it is pragmatically sensible to study and attempt to imitate algorithms used by the brain. Analysis of the anatomical structure and physiological operation of brain circuits has led to derivation of novel algorithms that in initial study have successfully addressed issues of known difficulty in visual processing. These algorithms are slow on uni-processor based systems, thwarting attempts to drive real-time robots for behavioral study, but as might be expected of algorithms designed for highly parallel brain architectures, they are intrinsically parallel and lend themselves to efficient implementation across multiple processors. This paper presents an implementation of such parallel algorithms on a CELL processor and further extends it to a low-cost cluster built using the Sony PlayStation 3 (PS3). The paper describes the modeled brain circuitry, derived algorithms, implementation on the PS3, and initial performance evaluation with respect to both speed and visual object recognition efficacy. The results show that a parallel implementation can achieve a 140x performance improvement on a cluster of 3 PS3s, attaining real-time processing delays. More importantly, we show that the improvements scale linearly, or nearly so in practice. These initial findings, while highly promising in their own right, also provide a new platform to enable extended investigation of large scale brain circuit models. Early prototyping of such large scale models has yielded evidence of their efficacy in recognition of time-varying, partially occluded, scale-invariant objects in arbitrary scenes.

关键词： Acceleration Circuit simulation Object recognition Humans Algorithm design and analysis Clustering algorithms Brain modeling Large-scale systems computer vision Anatomical structure

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Intelligent Traffic Signal Control System Based on Improved YOLOv8 and Deep Reinforcement Learning

Intelligent Traffic Signal Control System Based on Improved ...

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Frontiers Technology of Information and computer (ICFTIC), IEEE international conference on

作者： Ziyuan Liu Hongxiang Chen Jiajie Wang Tianqi Deng Mingshuo Liu Guodong Zhao School of computer science and technology Harbin Engineering University Harbin China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

As urban traffic pressure continues to increase, existing traffic systems have achieved some success in improving vehicle throughput. However, they commonly face issues such as insufficient precision in visual recognition models, traffic signal control algorithms that neglect pedestrian needs, and excessive reliance on hardware infrastructure, leading to high deployment complexity. To address these challenges, an intelligent traffic system based on an improved YOLOv8 model and Deep Q-Network (DQN) algorithm is proposed. The system consists of a road dataization module and a traffic signal control module. In the road dataization module, the improved YOLOv8 model incorporates an attention mechanism to efficiently recognize vehicle information and uses OpenCV to process traffic images, enabling accurate data modeling. In the traffic signal control module, the enhanced DQN algorithm dynamically adjusts signal durations to balance the needs of pedestrians and vehicles, thereby improving traffic efficiency. Verified through the SUMO simulation platform, this approach demonstrates significant ad-vantages in optimizing traffic flow and throughput. Furthermore, by leveraging algorithmic data analysis of the environment, the system reduces dependency on hardware infrastructure, significantly lowering deployment complexity and showing strong potential for practical application. Additionally, the design of a visualized front-end interface and an embedded display system enhances the system's usability and integrity, validating its feasibility.

关键词： Visualization Pedestrians Roads Heuristic algorithms Face recognition Throughput Deep reinforcement learning Hardware Data models Optimization

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An Efficient End-to-End Channel Level Pruning Method for Deep Neural Networks Compression

An Efficient End-to-End Channel Level Pruning Method for Dee...

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IEEE international conference on Software Engineering and Service Sciences (ICSESS)

作者： Lei Zeng Shi Chen Sen Zeng Department of Computer Science University of Science and Techonology of China Hefei Anhui Province China Department of Gree Electric Appliances Zhuhai Guangdong Province China

ISBN: (数字)9781728109459

ISBN: (纸本)9781728109466

Deep neural networks (DNNS) have obtained compelling performance among many visual tasks by a significant increase in the computation and memory consumption, which severely impede their applications on resource-constrained systems like smart mobiles or embedded devices. To solve these problems, recent efforts toward compressing DNNS have received increased focus. In this paper, we proposed an effective end-to-end channel pruning approach to compress DNNS. To this end, firstly, we introduce additional auxiliary classifiers to enhance the discriminative power of shallow and intermediate layers. Secondly, we impose Ll-regularization on the scaling factors and shifting factors in batch normalization (BN) layer, and adopt the fast and iterative shrinkage-thresholding algorithm (FISTA) to effectively prune the redundant channels. Finally, by forcing selected factors to zero, we can prune the corresponding unimportant channels safely, thus obtaining a compact model. We empirically reveal the prominent performance of our approach with several state-of-theart DNNS architectures, including VGGNet, and MobileNet, on different datasets. For instance, on cifar10 dataset, the pruned MobileNet achieves 26. 9x reduction in model parameters and 3. 9x reduction in computational operations with only 0.04% increase of classification error.

关键词： Training Neural networks Classification algorithms Acceleration Hardware Iterative methods Computational modeling

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A flexible low-complexity robust THP approach for MISO downlinks with imperfect CSI

A flexible low-complexity robust THP approach for MISO downl...

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IEEE international conference on Communications in China (ICCC)

作者： Yuan Luechao Wang Gaojiang Gerd Ascheid Liu Cang Xing Zuocheng College of Computer National University of Defense Technology Changsha Hunan P.R. China Institute for Communication Technologies and Embedded Systems RWTH Aachen University Aachen German

Tomlinson-Harashima precoding (THP) is an attractive non-linear interference cancellation method for multiple-input multiple-output (MIMO) system with decentralized receivers. Compared to linear methods, it is able to achieve significant performance improvement while introduce negligible computational complexity increase. However, THP is sensitive to inevitable channel state information (CSI) errors at the transmitter in wireless communications. We statistically model the errors caused by channel estimation and feedback delay. By taking this modeling into account, we propose a flexible low-complexity compensation method for THP with imperfect CSI. In contrast to several robust designs optimized for one specific THP implementation, our approach can achieve average robustness for various THP implementations with negligible complexity overhead. Several canonical and emerging preprocessing algorithms, e.g, sorted QR decomposition (SQRD), vertical Bell Laboratories Space Time (V-BLAST) and lattice reduction (LR), are evaluated under a 802.11ac based MIMO OFDM simulation system. The experimental results show that the proposed compensation approach is able to lower the error floors caused by imperfect CSI significantly for various THP implementations.

关键词： Channel estimation Receivers Robustness Precoding Delays Interference Wireless communication

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