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FPGA-Based Convolution Accelerator and Memristor IP Core for Cooperative Acceleration of the YOLO Network 4

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

引用

4th International Conference on Computer Science, Electronic Information Engineering and Intelligent control Technology, CEI 2024

作者： Tan, Aolong Yang, Yucheng Duan, Shukai Wang, Lidan College of Artificial Intelligence Southwest University Chongqing400715 China State Key Laboratory of Intelligent Vehicle Safety Technology China National Local Joint Engineering Research Center of Intelligent Transmission and Control Technology 400715 China Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips Chongqing400715 China Laboratory of Luminescence Analysis and Molecular Sensing Southwest University Ministry of Education Chongqing400715 China

ISBN: (纸本)9798350378542

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. © 2024 ieee.

关键词： Reconfigurable hardware

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Umbrella Reinforcement Learning - A Tool for Hard Problems

Umbrella Reinforcement Learning - A Tool for Hard Problems

引用

Robotics, automation and Artificial intelligence (RAAI), ieee International Conference on

作者： Egor E. Nuzhin Nikolay V. Brilliantov Applied AI Center Skolkovo Institute of Science and Technology Moscow Russia Department of Mathematics University of Leicester Leicester United Kingdom

ISBN: (数字)9798331520038

ISBN: (纸本)9798331520045

We propose an approach for addressing Reinforcement Learning (RL) problems. It combines the ideas of umbrella sampling, borrowed from Monte Carlo technique of computational physics and chemistry, with optimal control methods, and is realized on the base of neural networks. This results in a powerful algorithm, designed to solve hard RL problems - the problems, with long-time delayed reward, state-traps sticking and a lack of terminal states. It outperforms the prominent algorithms, such as PPO, RND, iLQR and VI, which are among the most efficient for the hard problems. The new algorithm deals with a continuous ensemble of agents and expected return, that includes the ensemble entropy. This results in a quick and efficient search of the optimal policy in terms of “exploration-exploitation trade-off” in the state-action space.

关键词： Monte Carlo methods Chemistry automation Neural networks Optimal control Reinforcement learning Entropy Dynamic programming Artificial intelligence Robots

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Model of Research for Engineering Related Investigations

Model of Research for Engineering Related Investigations

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International symposium on Applied Machine intelligence and Informatics (SAMI)

作者： László Horváth Institute of Applied Mathematics and Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

Integration of all aspects, activities as well as machine and human qualities is one of the main objectives of development in computer and computing related achievements for more than four decades. One of the main subjects of this integration is engineering modeling and by now engineering platforms are capable of model-based integration regarding all objects within and outside of an achievement such as industrial and commercial product or service. This paper is about a novel concept, theory and methodology to realize integration of all activities during given research in an experimental model serving engineering related investigation for high automation in a broad sense. Model mediated research is realized using models with reactive and autonomous characteristics. These models are purposefully complex and include all objects, activities, processes and contextual connections for actual research. Serving full innovation cycle is aimed from the first idea to support achievement which utilizes the related research results. To manage these models, comprehensive engineering modeling platform (EMP) is configured then applied at developing integrated experimental model together with its inside and outside contextual connections. Results in this paper are implemented in the Virtual Research Laboratory (VRL, Obuda University) serving primarily PhD research in applied informatics and the related applied mathematics.

关键词： Technological innovation automation computational modeling Mathematical models Informatics Machine intelligence Context modeling

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An Iterative Mode Merging Method for Multimode Process Monitoring with Transition Mode

An Iterative Mode Merging Method for Multimode Process Monit...

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2023 CAA symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Lin, Yu Zhang, Cong Zhang, Sihang Wan, Yiming Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Beijing Insitute of Control Engineering Beijing China

ISBN: (纸本)9798350337754

An industrial process typically consists of multiple production modes, and transitions between two modes often cause difficulties for process monitoring. For the transitions between steady-state modes, it is common to ignore or combine them with other modes for modeling. The former is likely to cause false alarms, and the latter reduces the detection rate of the monitoring process. Efficient and accurate demarcation of the transition mode from the steady mode presents an intriguing research topic. To address this issue, this paper presents an innovative iterative mode merging approach for multimode process monitoring with transition mode. The data is segmented according to the proportion of principal components to the total space, and then iterative mode merging (the merging of data of the same mode) is performed according to the reconstruction error of the probabilistic principal component analysis (PPCA) model. A case study using a multiphase flow dataset demonstrates the effectiveness of the proposed method. © 2023 ieee.

关键词： Process monitoring

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Laboratory Studies of Artificial Inertia

Laboratory Studies of Artificial Inertia

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Péter Kádár István Bendiák KVK Dept. of Power Systems Óbuda University Alternative Energy Resources Knowledge Center Budapest Hungary

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

One of the important parameters and at the same time indicator of the operation of the electrical power network is frequency. A sudden excessive change in this can lead to a system collapse, to the black-out. The speed of change (RoCoF) is increasing due to power electronic inverter feed-ins, the share of rotating machine system inertia is decreasing. As an artificial substitute for this, power electronics “injection” is possible. In this article are introduced some features of artificial inertia in laboratory conditions, such as simulating inverter control, the effect of injection on a large energy system, measuring RoCoF and fast power control of household-scale PV inverters.

关键词： Time-frequency analysis Power measurement Rotating machines Power control Energy measurement Inverters Velocity measurement

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Continuous control Method for Lower Limb Rehabilitation Robot Based on Self-learning Strategy

Continuous Control Method for Lower Limb Rehabilitation Robo...

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ieee International Conference on Mechatronics and automation

作者： Yibo Lu Shijie Guo Mengge Wang Lingling Chen School of Artificial Intelligence and Data Science Hebei University of Technology China Intelligent Rehabilitation Device and Detection Technology Engineering Research Centre of the Ministry of Education China School of Mechanical Engineering Hebei University of Technology China School of Electrical Engineering Hebei University of Technology China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Traditional lower limb rehabilitation robots employ discrete control methods, which are unable to decode human intention in real-time. To enhance human-machine interaction capabilities for patients with lower limb motor dysfunction, this study focuses on continuous control decoding of rehabilitation robots based on a self-learning strategy. An online classification model was developed, employing a multimodal brain-computer interface paradigm. The LGBM-LMS classification model was constructed to classify continuous control commands for the robot, converting them into control instructions for continuous control of the lower limb rehabilitation robot via electroencephalogram (EEG) signals. Experimental results indicate that this method reduces computational complexity and model redundancy, with an average recognition rate improvement of 17.82% per group.

关键词： computational modeling Termination of employment Assistive robots Brain modeling Motors Electroencephalography Brain-computer interfaces

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Aero-Engine Remaining Useful Life Prediction Using A Multi-Dimensional Convolutional Feature Fusion Framework

Aero-Engine Remaining Useful Life Prediction Using A Multi-D...

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2023 CAA symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Wang, Liangcheng Wang, Yang Zhang, Hong Wang, Yanwei Zheng, Ying Huazhong University of Science and Technology The Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Wuhan430074 China Wuhan Institute of Technology School of Mechanical & Electrical Engineering Hubei Wuhan430073 China

ISBN: (纸本)9798350337754

Accurate prediction of aero-engine remaining useful life (RUL) is essential for providing reliable maintenance or alarm decisions. The extraction of degraded features has a great impact on the accuracy of RUL prediction. This paper proposes a feature fusion framework that relies on a multi-dimensional convolutional neural network (MD-CNN). First, the data of each working condition is normalized separately to extract degradation features more effectively. Subsequently, the temporal features are extracted using the one-dimensional convolutional neural network (1D-CNN), while the spatial local features are captured through the utilization of the two-dimensional convolutional neural network (2D-CNN). Finally, the long short-term memory network (LSTM) is utilized as the prediction model to establish the mapping relationship from fusion features to RUL. The result shows that the performance in predicting using the proposed method surpasses that of the existing methods. © 2023 ieee.

关键词： Convolution

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Energy-efficient Hot-rolling Scheduling of High-quality Steel Products

Energy-efficient Hot-rolling Scheduling of High-quality Stee...

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ieee symposium Series on computational intelligence (SSCI)

作者： Ziyan Zhao Zikuo Bian Chenglong Wang Kun Zou Shixin Liu College of Information Science and Engineering Northeastern University Shenyang China Shandong Iron & Steel Group Rizhao Co. Ltd Rizhao China The State Key Laboratory of Synthetical Automation for Process Industries Shenyang China

Steel production involves many energy-intensive processes. Under the goal of carbon peak and carbon neutrality, it is essential to study the steel production scheduling problems aiming at energy saving to realize the green manufacturing of steel production processes. Aiming at the hot rolling process of high-quality steel products, a novel energy-saving production scheduling problem is studied in this paper. Unlike existing research, this paper additionally considers temperature constraints and the optimization of temperature-keeping equipment assignment in producing high-quality steel products. To solve it efficiently, this paper presents an improved simulated annealing algorithm where destruction and construction strategies from iterated greedy algorithms are embedded into it. Experimental results show that the presented algorithm has obvious advantages compared with other competitive peers. Its excellent solution performance means its great application potential.

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Rolling Scheduling Strategy of Electric Vehicles Based on Dynamic Clustering and Variable Horizon 4

Rolling Scheduling Strategy of Electric Vehicles Based on Dy...

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4th International Conference on Artificial intelligence and Electromechanical automation, AIEA 2023

作者： Song, Sijin Liu, Yutian Tan, Zukuang Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Shandong Jinan250061 China Hunan Electric Power Company Limited Economic and Technical Research Institute State Grid Changsha410004 China Hunan Key Laboratory of Energy Internet Supply-demand and Operation Changsha410004 China

ISBN: (纸本)9781510666412

The overall load profile tremendously changes due to the charging of abundant electric vehicles (EVs), which brings challenges to the active power balance of distribution network. To achieve smaller fluctuation of the interactive power between the distribution network and the bulk grid, a rolling scheduling strategy of EVs based on dynamic clustering and variable horizon is proposed in this paper. First, a clustering criterion for EVs is formulated to reduce the computational complexity, and the EVs are re-clustered after each scheduling. Then, the scheduling model considering the minimum variance of comprehensive load profile and the minimum charging cost is established, whose purpose is to optimize the charging and discharging plans of the EV clusters. The scheduling horizon is the variable continuous period, which is between the starting time of the current execution horizon and the departure time of each cluster. Finally, the simulation results of modified ieee 33-bus system verify that, the optimal EV charging and discharging plan can perfectly coordinate with the photovoltaic output profile while maximizing user satisfaction, which significantly reduces the peak-valley difference of the equivalent load. © 2023 SPIE.

关键词： Electric power distribution

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Dual-Level control Strategy for Vehicle Lateral and Longitudinal Path Following Based on Model Predictive control

Dual-Level Control Strategy for Vehicle Lateral and Longitud...

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ieee symposium on Intelligent Vehicle

作者： Shuai Ma Yunwen Xu Dewei Li Yang Yang Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Path tracking is a key problem in autonomous vehicles, but uncertainties in vehicle models and environmental parameters as well as the couple of lateral and longitudinal behaviors pose a major challenge to path following accuracy and efficiency. To solve this problem, a layered vehicle lateral and longitudinal controller based on model predictive control is proposed in this paper. Firstly, a lateral controller based on a synthesis of robust model predictive control is used to reduce the influence of model uncertainty. Then a longitudinal nonlinear model predictive controller is proposed to achieve the coordination between the lateral following error and the longitudinal forward speed of the vehicle. Finally, the bottom PID controller is used to control the throttle and brake of the vehicle. The effectiveness of the proposed method is verified by experiments, which show that it is robust to the uncertain parameters and the computation is relatively small.

关键词： Uncertainty computational modeling Roads Predictive models computational efficiency Brakes Motion control

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