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FPGA-Based Convolution Accelerator and Memristor IP Core for Cooperative Acceleration of the YOLO Network

FPGA-Based Convolution Accelerator and Memristor IP Core for...

引用

Computer Science, Electronic Information Engineering and Intelligent control Technology (CEI), ieee International Conference on

作者： Aolong Tan Yucheng Yang Shukai Duan Lidan Wang College of Artificial Intelligence Southwest University Chongqing China State Key Laboratory of Intelligent Vehicle Safety Technology National & Local Joint Engineering Research Center of Intelligent Transmission and Control Technology China Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips Chongqing China Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University) Ministry of Education Southwest University Chongqing PR China

ISBN: (数字)9798350378542

ISBN: (纸本)9798350378559

Object detection technology is widely used in areas such as autonomous driving, drone reconnaissance, smart surveillance and industrial automation, especially in remote or hazardous scenarios where it can improve efficiency and safety. Therefore, the development of a high-precision, low-power acceleration system integrated with low-power hardware is crucial. This paper proposes a collaborative acceleration scheme for the YOLO network based on an FPGA convolution accelerator and simulated memristors, which successfully implements and accelerates the YOLOv4-Tiny model. First, a reconfigurable single-core accelerator operator is designed for various computational operations in the model. Second, taking advantage of the digital device characteristics of memristors and the hardware resources of the FPGA, a memristor simulation IP core is built to exploit its memory characteristics for convolution operations. Through the synergistic effect of the convolution accelerator and the simulated IP core, a hybrid acceleration of the detection model is achieved. The power consumption of the convolution accelerator is only 2.75W, and in testing, the detection time for a single image is 823ms with an average accuracy of over 0.95, achieving high detection performance. This provides an opportunity to explore and research the efficient acceleration of object detection networks with integrated storage and computation.

关键词： YOLO Performance evaluation Power demand Accuracy Convolution computational modeling Memristors Hardware IP networks Field programmable gate arrays

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Network training method based on Parallel Vision theory for object detection in battlefield environment 1

Network training method based on Parallel Vision theory for ...

引用

1st ieee International Conference on Digital Twins and Parallel intelligence, DTPI 2021

作者： Yuan, Xingsheng Yu, Penglu School of Intelligence Science and Technology National University of Defense Technology Changsha China School of Education for Basic Military National University of Defense Technology Changsha China

ISBN: (纸本)9781665433372

Visual perception and understanding in complex battlefield environments have few data samples, which cannot meet the need of deep network model training in variable and complex environments. In this paper, based on parallel vision theory, deep learning network training for object detection is conducted using artificial virtual battlefield environment. Virtual battlefield environment data are collected for experimental training and validation analysis, and further training tests are conducted in real scene data. The experiments verified that the deep network model training with virtual-real interaction can effectively solve the data deficient problem of object detection in the battlefield. © 2021 ieee.

关键词： Object detection

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Implementation and analysis of dynamic stability for bipedal robotic motion

Implementation and analysis of dynamic stability for bipedal...

引用

ieee symposium Series on computational intelligence (ieee SSCI)

作者： Amos, Matthew Middleton, Richard Biddulph, Alexander Mendes, Alexandre Univ Newcastle Sch Elect Engn & Comp Fac Engn & Built Environm Callaghan NSW Australia

ISBN: (纸本)9781728125473

This work presents the design and simulation of a stable balance and locomotion approach for a bipedal robot. The torque response of a falling body is modelled and a low-pass filter was designed and implemented for the angular position of actuators within the robot's legs. A torque control method is also described, akin to using proportional and derivative control of the angular position of the actuators. Finally, a Zero Moment Point based capture step is described and implemented within simulation. With torque control alone, the result is a stable bipedal recovery from disturbances along the saggital plane of up to 11.25N of force, from a standing pose. In comparison, the previous implementation without dynamic stability leads to the robot falling after a minor disturbance of 2N. When capture step is included in the approach, the robot can recover from disturbances of up to 45N. The codebase is open-source and provides a humanoid robot simulation platform for research teams working in this area.

关键词： bipedal walk torque control zero moment point robotics simulation

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Autonomous Vehicle control Using Particle Swarm Optimization in a Mixed control Environment

Autonomous Vehicle Control Using Particle Swarm Optimization...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Wiesner, Na'Shea Sheppard, John Haberman, Brian Montana State Univ Gianforte Sch Comp Bozeman MT 59717 USA Johns Hopkins Univ Appl Phys Lab Laurel MD 20723 USA

ISBN: (纸本)9781728125473

This work examines effective ways of controlling autonomous vehicles on the roadway while human-operated vehicles remain in use. Particle Swarm Optimization is used to control speed, gap, and braking of autonomous vehicles on a merge lane where human-operated vehicles are simulated using the Krauss car-following model. Experiments performed in a simulated environment tested various vehicle densities, ratios of autonomous versus Krauss-operated vehicles, and scenarios where the type of vehicle merging was adjusted. Metrics collected from the simulation include number of merges, collisions, the average merge lane speed, and the average highway or "non-merging" speed. Results show that the autonomous vehicles are able to learn vehicle following and merging techniques to keep merges and speeds maximal, while keeping collisions minimal.

关键词： Autonomous vehicles Krauss car-following model particle swarm optimization vehicle control

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MMAN: Multi-Task and Multi-Scale Attention Network for Concurrently Lower Limbs Segmentation and Landmark Detection

MMAN: Multi-Task and Multi-Scale Attention Network for Concu...

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ieee International symposium on Biomedical Imaging

作者： Lulu Gai Zhi Qiao Lianxi Fan Xianghong Meng Shengru Fang Pei Dong Zhen Qian Beijing United Imaging Research Institute of Intelligent Imaging Beijing China School of Control Science and Engineering Shandong University Jinan China United Imaging Intelligence (Beijing) Co. Ltd Beijing China Tianjin Hospital Tianjin China Tianjin Baodi Hospital Tianjin China

Accurate bone segmentation and anatomical landmark detection are vital tasks for the clinical evaluation and treatment planning for patients with lower limbs X-ray films. To leverage the information between the two tasks and deal with the large-scale images, we propose an efficient end-to-end deep network, i.e., multi-task and multi-scale attention network (MMAN), to concurently segment lower limb bones and localize landmarks from large-scale X-ray films in one stage. The results demonstrate that our MMAN outperforms the other state-of-the-art methods for multi-task learning or single-task landmark detection using two separate stages. Our MMAN method has two main technical contributions. First, the local and global encoders are designed to capture multi-scale inputs and provide shared representations including local image details and global contexts, respectively. Second, a global-local attention module is designed to efficiently leverage the global context and learn task-specific information from shared representations under limited computational costs.

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A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios

A Reinforcement Learning Benchmark for Autonomous Driving in...

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ieee symposium Series on computational intelligence (SSCI)

作者： Yuqi Liu Qichao Zhang Dongbin Zhao The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728190495

In recent years, control under urban intersection scenarios has become an emerging research topic. In such scenarios, the autonomous vehicle confronts complicated situations since it must deal with the interaction with social vehicles timely while obeying the traffic rules. Generally, the autonomous vehicle is supposed to avoid collisions while pursuing better efficiency. The existing work fails to provide a framework that emphasizes the integrity of the scenarios while deploying and testing reinforcement learning(RL) methods. Specifically, we propose a benchmark for training and testing RL-based autonomous driving agents in complex intersection scenarios, which is called RL-CIS. Then, a set of baselines consisting various algorithms are deployed. The test benchmark and baselines provide a fair and comprehensive training and testing platform for the study of RL for autonomous driving in the intersection scenario, advancing RL-based methods for autonomous driving control. The code of our proposed framework can be found at https://***/liuyufi123/ComplexUrbanScenarios.

关键词： Training Codes Stochastic processes Reinforcement learning Benchmark testing Sampling methods Concrete

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7He monitoring system of aquaculture environment 13

7He monitoring system of aquaculture environment

引用

13th International symposium on computational intelligence and Design, ISCID 2020

作者： Jin, Chunxia Bai, Qiuchan Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian China Faculty of Automation Huaiyin Institute of Technology Huaian China

ISBN: (纸本)9781728184463

According to the needs of the aquaculture industry, this article proposes to design an aquaculture environment monitoring system for collecting temperature, PH, and dissolved oxygen parameters that affect fishery water quality environment, real-time tracking and monitoring, remote transmission and abnormal handling, etc. The system has played an important role in the application of fish breeding industry, providing technical support for the development of the breeding industry, and bringing greater economic profits to farmers. ©2020 ieee.

关键词： Aquaculture

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Robust Model Predictive Longitudinal Position Tracking control for an Autonomous Vehicle Based on Multiple Models

Robust Model Predictive Longitudinal Position Tracking Contr...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Kempf, Andre Herrmann-Wicklmayr, Markus Mueller, Steffen Mercedes Benz AG Res & Dev RD AFM Stuttgart Germany Univ Saarland Chair Syst Modeling & Simulat Saarbrucken Germany Tech Univ Berlin Dept Automot Engn Berlin Germany

ISBN: (纸本)9781728125473

The aim of this work is to control the longitudinal position of an autonomous vehicle with an internal combustion engine. The powertrain has an inherent dead-time characteristic and constraints on physical stales apply since the vehicle is neither able to accelerate arbitrarily strong, nor to drive arbitrarily fast. A model predictive controller (MPC) is able to cope with both of the aforementioned system properties. MPC heavily relies on a model and therefore a strategy on how to obtain multiple linear state space prediction models of the nonlinear system via input/output data system identification from acceleration data is given. The models are identified in different regions of the vehicle dynamics in order to obtain more accurate predictions. The still remaining plant-model mismatch can be expressed as an additive disturbance which can be handled through robust control theory. Therefore modifications to the models for applying robust MPC tracking control theory are described. Then a controller which guarantees robust constraint satisfaction and recursive feasibility is designed. As a next step, modifications to apply the controller on multiple models are discussed. In this context, a model switching strategy is provided and theoretical and computational limitations are pointed out. Lastly, simulation results arc presented and discussed, including computational load when switching between systems.

关键词： controllers

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The evolution and challenges of modern Distributed control Systems 14

The evolution and challenges of modern Distributed Control S...

引用

ieee 14th International symposium on Applied computational intelligence and Informatics (SACI)

作者： Postovei, Danut Adrian Bulac, Constantin Tristiu, Ion Camachi, Balduino Univ Politehn Bucuresti Fac Power Engn Bucharest Romania Univ Politehn Bucuresti Fac Automat Control & Comp Sci Bucharest Romania

ISBN: (纸本)9781728173764

This paper presents the evolution and development trends of the distributed control systems (DCS) implemented in the Electrical Power Systems, highlighting the impact of the modern versus traditional DCS systems. The principles of the modern DCS systems presented in this paper can be found implemented in Romania in the Transmission and Distribution energy sector, implemented by the Romanian Transmission and System Operator CNTEE Transelectrica SA and, as well as the electricity distribution operators like Electrica, EoN and CEZ.

关键词： DCS SCADA HMI RTU ERP Cyber Security Virtualization substation redundancy

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On the Potential of Rocket League for Driving Team AI Development

On the Potential of Rocket League for Driving Team AI Develo...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Verhoeven, Yannick Preuss, Mike Westfalische Wilhelms Univ Munster Dept Informat Syst Munster Germany Leiden Univ Leiden Inst Adv Comp Sci Leiden Netherlands

ISBN: (纸本)9781728125473

Several Games have been used as test cases for new AI developments in the last years, e.g. the games of the Atari Learning Environment, Go, and also StarCraft. As many single player problems are treated successfully now, research is turning more to Team AI problems: AI/human and AI/AI coordinated decision making in mixed cooperative/competitive environments. OpenAI Five and RoboCup and related environments as MuJoCo soccer are pushing into this direction. Rocket League is an interesting alternative as it has quite different game mechanics compared to soccer, and an API with a number of different hots already exists. We summarize the current state, relate it to the situation in the fields that may provide useful approaches as e.g. sports analysis, and argue that it makes sense to do more research on Rocket League hot control and coordination.

关键词： Rocket League multi-agent control Team AI

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