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Exploring reservoir computing:Implementation via double stochastic nanowire networks

Chinese Physics B 2024年第3期33卷 572-582页

作者：唐健峰夏磊李广隶付军段书凯王丽丹 College of Artificial Intelligence Southwest UniversityChongqing 400715China Brain-inspired Computing & Intelligent Control of Chongqing Key Laboratory Chongqing 400715China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715China Key Laboratory of Luminescence Analysis and Molecular Sensing Ministry of EducationSouthwest UniversityChongqing 400715China State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing 400715China

Neuromorphic computing,inspired by the human brain,uses memristor devices for complex *** studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data *** paper presents a model based on these nanowire networks,with an improved conductance variation *** suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage *** nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction *** experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction *** results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing.

关键词： double-layer stochastic(DS)nanowire network architecture neuromorphic computation nanowire network reservoir computing time series prediction

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Development and Testing of an intelligent Tobacco Leaf Harvesting Robot Based on Machine Vision

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INMATEH - Agricultural engineering 2025年第1期75卷 600-609页

作者： LIN, YuPei YAN, GuoYong WANG, Tao BAI, Tao TANG, Si CHEN, JunJie ZHANG, BaoLin Southwest University College of Artificial Intelligence Chongqing400715 China National & Local Joint Engineering Research Center of Intelligent Transmission and Control Technology Chongqing400715 China Qujing Tobacco Company of Yunnan Province Yunnan 655000 China

The efficiency and quality of tobacco leaf harvesting are crucial for the economic performance of the tobacco industry. To enhance harvesting efficiency, a non-destructive tobacco leaf harvesting robot based on machine vision and robotics technology was developed. Experimental evaluations of key components demonstrated that the biomimetic flexible gripper based on the fin ray effect has good stiffness when the clamping force is 2.5 N, ensuring stable subsequent harvesting and collection of tobacco leaves. The introduction of a 6+1-axis robotic arm significantly expands the working range compared to the original 6-axis design, effectively covering the height of the tobacco stalk. The robotic arm’s speed notably affects harvesting time (P © (2025), American Psychological Association

关键词： Grippers

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Dynamic Memristor based Cyclic Echo State Network for Time Series Prediction 4

Dynamic Memristor based Cyclic Echo State Network for Time S...

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4th International Conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Li, Guangli Xia, Lei Fu, Jun Duan, Shukai Tang, Jianfeng Wang, Lidan Southwest University College of Artificial Intelligence Chongqing China National Local Joint Engineering Research Center of Intelligent Transmission and Control Technology Chongqing China

ISBN: (纸本)9798350394375

Compared with traditional recurrent neural networks, the training cost of echo-state network computing is lower because the neurons in the reservoir are randomly connected. We only need to train the weight of the connection between the reservoir and the output layer. Due to its high accuracy and low training cost for time series forecasting tasks, it is widely used as a time series forecasting model in many fields. It is very important to provide rich reservoir state for echo state network system, which is the key to hardware implementation. In this paper, we propose a cyclic echo state network (DC-ESN) based on dynamic memristors, which generates rich reservoir states through cyclic structure, and can predict complex time series well even with a single memristor. Compared with the traditional echo state network, the size of the reservoir is reduced and the prediction accuracy is greatly improved. Our work can provide an idea for more efficient memristor based echo state network systems in the future to cope with complex time series prediction tasks. © 2024 IEEE.

关键词： Memristors

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Force Compensation control for Electro-Hydraulic Servo System with Pump-Valve Compound Drive via QFT-DTOC

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Chinese Journal of Mechanical engineering 2024年第2期37卷 228-246页

作者： Kaixian Ba Yuan Wang Xiaolong He Chunyu Wang Bin Yu Yaliang Liu Xiangdong Kong Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control Qinhuangdao 066004China School of Mechanical Engineering Yanshan UniversityQinhuangdao 066004China National Engineering Research Center for Local Joint of Advanced Manufacturing Technology and Equipment Yanshan UniversityQinhuangdao 066004China

Each joint of a hydraulic-driven legged robot adopts a highly integrated hydraulic drive unit(HDU),which features a high power-weight ***,most HDUs are throttling-valve-controlled cylinder systems,which exhibit high energy *** contrast,pump control systems offer a high ***,their response ability is *** fully utilize the advantages of pump and valve control systems,in this study,a new type of pump-valve compound drive system(PCDS)is designed,which can not only effectively reduce the energy loss,but can also ensure the response speed and response accuracy of the HDUs in robot joints to satisfy the performance requirements of ***,considering the force control requirements of energy conservation,high precision,and fast response of the robot joint HDU,a nonlinear mathematical model of the PCDS force control system is first *** addition,pressure-flow nonlinearity,friction nonlinearity,load complexity and variability,and other factors affecting the system are considered,and a novel force control method based on quantitative feedback theory(QFT)and a disturbance torque observer(DTO)is designed,which is denoted as QFT-DTOC *** method improves the control accuracy and robustness of the force control system,reduces the effect of the disturbance torque on the control performance of the servo motor,and improves the overall force control performance of the ***,experimental verification is performed using the PCDS performance test *** experimental results and quantitative data show that the QFT-DTOC proposed herein can significantly improve the force control performance of the *** relevant force control method can be used as a bottom-control method for the hydraulic servo system to provide a foundation for implementing the top-level trajectory planning of the robot.

关键词： Legged robot Pump-valve compound drive system(PCDS) Force compensation control Quantitative feedback theory(QFT) Disturbance torque observer(DTO)

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Planning of a Single Flow Channel in Valve Blocks Based on Additive Manufacturing and the Ant Colony Algorithm

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Chinese Journal of Mechanical engineering 2024年第5期37卷 191-202页

作者： Jin Zhang Ziyang Li Yuying Zhang Yandong Liu Ying Li Xiangdong Kong School of Mechanical Engineering Yanshan UniversityQinhuangdao 066004China Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control Qinhuangdao 066004China National&Local Joint Engineering Research Center for Advanced Manufacturing Technology and Equipment Qinhuangdao 066004China State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou 310027China

As electro-hydrostatic actuator(EHA)technology advances towards lightweight and integration,the demand for enhanced internal flow pathways in hydraulic valve blocks ***,owing to the constraints imposed by traditional manufacturing processes,conventional hydraulic integrated valve blocks fail to satisfy the demands of a more compact channel layout and lower energy ***,the subjectivity in the arrangement of internal passages results in a time-consuming and labor-intensive *** study employed additive manufacturing technology and the ant colony algorithm and B-spline curves for the meticulous design of internal passages within an aviation EHA valve *** layout environment for the valve block passages was established,and path optimization was achieved using the ant colony algorithm,complemented by smoothing using B-spline ***-dimensional modeling was performed using SolidWorks software,revealing a 10.03%reduction in volume for the optimized passages compared with the original *** fluid dynamics(CFD)simulations were performed using Fluent software,demonstrating that the algorithmically optimized passages effectively prevented the occurrence of vortices at right-angled locations,exhibited superior flow characteristics,and concurrently reduced pressure losses by 34.09%-36.36%.The small discrepancy between the experimental and simulation results validated the efficacy of the ant colony algorithm and B-spline curves in optimizing the passage design,offering a viable solution for channel design in additive manufacturing.

关键词： Hydraulic valve block Flow channel B-spline curve Additive manufacturing Ant colony algorithm

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Memristor-based multi-synaptic spiking neuron circuit for spiking neural network

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Chinese Physics B 2022年第4期31卷 225-233页

作者： Wenwu Jiang Jie Li Hongbo Liu Xicong Qian Yuan Ge Lidan Wang Shukai Duan College of Artificial Intelligence Southwest UniversityChongqing 400715China National&Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715China Brain-inspired Computing and Intelligent Control of Chongqing Key Laboratory Chongqing 400715China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715China

Spiking neural networks(SNNs) are widely used in many fields because they work closer to biological ***,due to its computational complexity,many SNNs implementations are limited to computer ***,this paper proposes a multi-synaptic circuit(MSC) based on memristor,which realizes the multi-synapse connection between neurons and the multi-delay transmission of pulse *** synapse circuit participates in the calculation of the network while transmitting the pulse signal,and completes the complex calculations on the software with ***,a new spiking neuron circuit based on the leaky integrate-and-fire(LIF) model is designed in this *** amplitude and width of the pulse emitted by the spiking neuron circuit can be adjusted as *** combination of spiking neuron circuit and MSC forms the multi-synaptic spiking neuron(MSSN).The MSSN was simulated in PSPICE and the expected result was obtained,which verified the feasibility of the ***,a small SNN was designed based on the mathematical model of *** the SNN is trained and optimized,it obtains a good accuracy in the classification of the IRIS-dataset,which verifies the practicability of the design in the network.

关键词： memristor multi-synaptic circuit spiking neuron spiking neural network(SNN)

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A spintronic memristive circuit on the optimized RBF-MLP neural network

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Chinese Physics B 2022年第11期31卷 272-283页

作者： Yuan Ge Jie Li Wenwu Jiang Lidan Wang Shukai Duan School of Artificial Intelligence Southwest UniversityChongqing 400715China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715China Brain-inspired Computing and Intelligent Control of Chongqing Key Laboratory Chongqing 400715China National&Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715China

A radial basis function network(RBF)has excellent generalization ability and approximation accuracy when its parameters are set ***,when relying only on traditional methods,it is difficult to obtain optimal network parameters and construct a stable model as *** view of this,a novel radial basis neural network(RBF-MLP)is proposed in this *** connecting two networks to work cooperatively,the RBF’s parameters can be adjusted adaptively by the structure of the multi-layer perceptron(MLP)to realize the effect of the backpropagation updating ***,a genetic algorithm is used to optimize the network’s hidden layer to confirm the optimal neurons(basis function)number *** addition,a memristive circuit model is proposed to realize the neural network’s operation based on the characteristics of spin *** is verified that the network can adaptively construct a network model with outstanding robustness and can stably achieve 98.33%accuracy in the processing of the Modified national Institute of Standards and technology(MNIST)dataset classification *** experimental results show that the method has considerable application value.

关键词： radial basis function network(RBF) genetic algorithm spintronic memristor memristive circuit

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Spatio- Temporal Neural Network with Contrastive Learning for Vehicle Trajectory Prediction

Spatio- Temporal Neural Network with Contrastive Learning fo...

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2023 International Annual Conference on Complex Systems and intelligent Science, CSIS-IAC 2023

作者： Li, Jiufa Duan, Shukai Wang, Lidan Southwest University College of Artificial Intelligence Chongqing China College of Artificial Intelligence Southwest University National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing China

ISBN: (纸本)9798350309003

In the field of autonomous driving vehicles, accurately predicting vehicle trajectories is crucial for ensuring safety and enhancing ride comfort. However, predicting the trajectory of a target vehicle is challenging due to complex interaction dynamics with nearby vehicles in different scenarios. To improve prediction accuracy, we introduce a novel spatiotemporal neural network with contrastive learning capabilities. This method delves deep into potential encodings within interaction scenes from both local and global perspectives and incorporates future trajectory features through a Conditional Variational Autoencoder (CVAE). We rigorously evaluate this approach through ablation experiments using the US-101 and I-80 datasets from the national Highway Traffic Safety Administration's Next Generation Simulation (NGSIM) dataset. The research results demonstrate that our method outperforms the current state-of-the-art techniques in terms of performance. © 2023 IEEE.

关键词： Trajectories

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High-Performance Sensing: Application of FPGA-based Winograd CNN Accelerator in Electronic Nose Systems 5

High-Performance Sensing: Application of FPGA-based Winograd...

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5th International Conference on Artificial Intelligence and Computer engineering, ICAICE 2024

作者： Tan, Aolong Duan, Shukai Chen, Mingzhe Ding, Ke Li, Changqing Wang, Lidan State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing China College of Artificial Intelligence Southwest University Chongqing400715 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 Ministry of Education Southwest University Chongqing400715 China

ISBN: (纸本)9798331528911

Electronic nose technology has been widely used in various scenarios such as industrial, military, food and environmental, and its recognition accelerator is responsible for completing the detection and identification of gases. Therefore, the application of advanced artificial intelligence algorithms to recognition accelerators is a common practice among researchers nowadays. However, neural networks with high recognition rates tend to have complex structures and a large number of parameters, which exacerbates the difficulty of deployment. To address this challenge, this paper first analyses and processes the detected gas data and proposes a hardware-friendly lightweight gas classification network, and then, based on this network, designs an FPGA-based Winograd CNN accelerator through the study of hardware implementation of Convolutional Neural Networks (CNN). The Winograd algorithm is used to reduce the number of multiplications used in the convolutional operation so as to reduce the consumption of hardware computing resources by the accelerator. Finally, the designed gas classification network is deployed on FPGA to complete the implementation of the recognition acceleration engine in the electronic nose system. The detection and recognition experiments on the laboratory gas dataset demonstrated good performance in terms of 99.5% recognition rate and only 2.94W power consumption of the accelerator, respectively. © 2024 IEEE.

关键词： Electronic nose

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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...

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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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