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检索条件"机构=National&Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology"

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Modeling and simulation on melt flow and thermal in crystal growth 36

Modeling and simulation on melt flow and thermal in crystal ...

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第36届中国控制会议

作者： Ni Zhang Ding Liu WeiChao Huang National & Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781538629185

A coupled thermal flow crystal growth model based on Lattice Boltzmann Method（LBM） is constructed for the internal melt in a single crystal furnace,according to the characteristic that the internal temperature distribution of the melt is difficult to *** the melt’s characteristic on the free surface boundary,the specular reflection scheme is used on the free *** flow pattern and the temperature of the internal melt under different growth environment are simulated by the proposed *** results show that,in the pure heating environment,the fiercer melt flows,the greater the temperature gradient below the crystal growth interface,and the more uneven the temperature distribution on the free surface;crystal rotation and crucible rotation reduce the temperature gradient below the growth interface,and improve the temperature distribution uniformity of the free *** simulation result is consistent with the experiment result,which proves the correctness of the simulation model and the boundary scheme on the free surface.

关键词： Modeling computer simulation crystal growth thermal-flow Lattice Boltzmann Method

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MC-CapsNet : Low-level Pooling and High-level Fusion of Multi-columns Capsule Network

MC-CapsNet : Low-level Pooling and High-level Fusion of Mult...

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2021 International Conference on Neuromorphic Computing, ICNC 2021

作者： Li, Ke Duan, Shukai Wang, Lidan Wang, Yining Wang, Shu College of Artificial Intelligence Southwest University Brain-inspired Computing and Intelligent Control of Chongqing Key Lab National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing Brain Science Collaborative Innovation Center Chongqing China College of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (纸本)9781665412872

Capsule network is a new type of neural network structure. The biggest feature of capsule network is 'vector in vector out', which replaces the previous 'scalar in scalar out'. The pristine capsule network completely discards the pooling layers and uses a convolutional kernel size of 9 × 9 to increase the perceptual field However, since the original capsule network has only two feature extraction layers, it cannot extract high level abstract features of objects, which are very important for image classification. Moreover, the original capsule network completely discards the use of pooling layers. Based on the above problems, we propose a multi-column capsule network (MC-CapsNet) with low-level pooling and high-level fusion. Unlike the traditional capsule network, MC-CapsNet has the following features: (a)MC-CapsNet consists of two columns of capsule network: the primary column of capsule network extracts high-level impalpable features;the other column of capsule network extracts subordinate shallow features (b)Pooling operation is added to the initial two convolutional layers of the two columns of capsule network (c)Using the high-level theoretical features extracted from the first capsule network to fuse features with the low-level surface layers extracted from the second capsule network to achieve feature complementarily (d)We add the elements of the digital capsule layers in the two-column capsule network to increase the probability of object features being detected by the capsule. The experimental results show that the classification accuracy of MC-CapsNet reaches 87.96% and 94.42% on the CIFAR10 and FashionMNIST datasets. © 2021 IEEE.

关键词： Convolution

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Research on the weak Signal Detection Method Based on Adaptive Vibrational Resonance 35

Research on the weak Signal Detection Method Based on Adapti...

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第35届中国控制会议

作者： JIAO Shang-bin SUN Di LIU Ding HUANG Wei Chao National & Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781509009107

In this paper,the weak signal detection under a stable noise is investigated based on bistable vibrational resonance(VR) which is driven by a high frequency *** the one hand,the energy of the high frequency drive signal is transferred to the low frequency weak signal when VR occurs;on the other hand,the control of stochastic resonance(SR) is achieved based on VR,which transfers more noise energy into useful signal *** addition,considering the requirements of real-time detection,the amplitude and frequency of the high frequency drive signal are optimized by the knowledge-based particle swarm optimization(KPSO),which takes the mean signal-noise-ratio(MSNR) of output as the fitness function,and the property that VR system produces the best resonance effect just when the valid system parameter a(B,Ω) is greater than zero as ***,the parameter compensation is combined to achieve multi-high frequency weak signals detection with a stable ***,the method is applied to the vibration fault diagnosis of a mono-crystalline silicon furnace,and the experiment results show the effectiveness and practicability of the method.

关键词： Adaptive vibrational resonance weak signal detection knowledge-based particle swarm optimization

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Fat-tree-based optical networks-on-chip with WDM: Crosstalk noise exploiting

Fat-tree-based optical networks-on-chip with WDM: Crosstalk ...

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作者： Zhang, Jingping Xie, Yiyuan Wang, Shujian Ye, Yichen Liu, Yuzhu Pan, Gaofeng Liu, Yong Zhang, Jiliang Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing National Joint Engineering Laboratory of Intelligent Transmission and Control Technology College of Electronic and Information Engineering Southwest University Chongqing400715 China School of Optoelectronic Information University of Electronic Science and Technology of Chengdu Chengdu611731 China

Wavelength division multiplexing (WDM) technology as an extension of optical networks-on-chip (ONoCs) performance, can be applied to further overcome shortages of low bandwidth and high-power consumption in multiprocessor systems-on-chip. Nevertheless, WDM-based ONoCs (ONoCs_W) can encounter performance degradation and network scalability constraints because of its power loss and intrinsic crosstalk noise, especially the non-linear four-wave mixing crosstalk noise. Here, a formal systematical exploiting for worst-case crosstalk noise and optical signal-to-noise ratio (OSNR) in arbitrary fat-tree-based ONoCs with WDM (FONoCs_W) is proposed. Meanwhile, the worst-case OSNR optical link candidates are identified. To verify the proposed analysis method, FONoCs_W employing an optical turnaround router with WDM (WOTAR) is used for case study. The study results demonstrate that crosstalk noise significantly diminishes OSNR and restricts scalability of FONoCs_W. For instance, when input optical signal power equals 0 dBm in worst case, the average OSNR is −9.47 dB, the mean power of crosstalk noise for eight wavelengths is slightly greater than that of signal when the number of processor cores is 64;when it equals to 128, the average OSNR is −34.37 dB, decreased by 24.90 dB compared with the network with 64 processor cores. © The Institution of engineering and technology 2018

关键词： Network-on-chip

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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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LAENet: Light-weight asymmetric encoder-decoder network for semantic segmentation

LAENet: Light-weight asymmetric encoder-decoder network for ...

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2021 International Conference on Artificial Intelligence and Industrial technology Applications, AIITA 2021

作者： Hong, Liangyi Duan, Shukai Wang, Lidan Pan, Yongbin College of Electronic and Information Engineering Southwest University Chongqing China College of Artificial Intelligence Southwest University Chongqing China Brain-inspired Computing and Intelligent Control Chongqing Key Lab Chongqing China National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing China Chongqing Brain Science Collaborative Innovation Center Chongqing China

Encode-decoder structure is used in deep learning for real-time dense segmentation task. On account of the limitation of calculation burden on mobile devices, we present a light-weight asymmetric encoder-decoder network in this paper, namely LAENet, which quickly and efficiently accomplish the task of real-time semantic segmentation. We employ an asymmetric convolution and group convolution structure combined with dilated convolution and dense connectivity to reduce computation cost and model size, which can guarantee adequate receptive field and enhance the model learning ability in encoder. On the other hand, feature pyramid networks (FPN) structure combine attention mechanism and ECRE block are utilized in the decoder to strike a balance between the network complexity and segmentation performance. Our approach achieves only have 0.84M parameters, and is able to reach 66 FPS in a single GTX 1080Ti GPU. Experiments on Cityscapes datasets demonstrate that superior performance of LAENet is better than the existing segmentation network, in terms of speed and accuracy trade-off without any post-processing. © Published under licence by IOP Publishing Ltd.

关键词： Convolution

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Scheduling optimization of silicon single crystal production process based on adaptive improved particle swarm optimization

Scheduling optimization of silicon single crystal production...

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2021 China Automation Congress, CAC 2021

作者： Kang, Lu Liu, Ding Wu, Yali Ping, Guozheng School of Automation and Information Engineering Xi'an University of Technology National and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China School of Automation and Information Engineering Xi'an University of Technology National and Local Joint Engineering Research Center of Crystal Growth Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China School of Automation and Information Engineering Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China

ISBN: (纸本)9781665426473

Aiming at the potential safety hazards of silicon single crystal production enterprises in the process of multi-furnace silicon single crystal production, and considering the maximum power load requirements in actual enterprises, a silicon single crystal production process scheduling model with the goal of minimizing the maximum completion time was established. Based on this model, an adaptive improved particle swarm optimization algorithm (Adaptive GPSO) is proposed to solve it. The algorithm retains the individual optimal value and global optimal value of the particle swarm algorithm, and introduces the crossover operation and mutation operation in the genetic algorithm to improve the new individual generation mechanism. At the same time, it performs dynamic iterative calculations on the mutation probability and crossover probability to avoid the algorithm fall into the local optimum, and improve the diversity and convergence of the algorithm. By solving different scales scheduling problems of silicon single crystal production process, the experiments on real-world data shows that the feasibility and effectiveness of the proposed algorithm. © 2021 IEEE

关键词： Genetic algorithms

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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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Multi-Modal Optimization of Stacked Chip Macro-Module Layout Based on Memristor-Inspired Evolutionary Game 4

Multi-Modal Optimization of Stacked Chip Macro-Module Layout...

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4th International Conference on Electronic Information engineering and Computer Communication, EIECC 2024

作者： Qiao, Kaikai Ma, Ben Chen, Mingze Ding, Ke Li, Changqing Duan, Shukai Wang, Lidan Southwest University College of Artificial Intelligence Chongqing China College of Artificial Intelligence Southwest University Chongqing China National Local Joint Engineering Research Center of of Intelligent Transmission and Control Technology Chongqing China Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips Chongqing China Ministry of Education Chongqing China

ISBN: (纸本)9798331534622

As the complexity of chip design continues to increase, the stacking of multiple device layers in a three dimension(3D) architecture has emerged as a promising approach, which aims to optimize the placement and interconnections of large function blocks across the vertically-stacked device layers. Due to the complex interactions between layout and design constraints, traditional deterministic optimization often struggle to navigate its multi-modal search landscape effectively. Thus, we creatively propose the memristive evolutionary game: the diverse metal layers engage in a strategic interplay that determines the ownership hierarchy of chip macro modules and the 2D layout of these chip macro modules within each designated metal layer is orchestrated using evolutionary algorithm, which harness the power of game theoretic evolution to seek the optimal solution, balancing the objectives of minimizing layout area and wiring length. Upon comparison with alternative algorithms, the game evolution algorithm introduced in this paper exhibits superior optimization performance. © 2024 IEEE.

关键词： Memristors

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Ensemble of online sequential extreme learning machine based on cross-validation 4

Ensemble of online sequential extreme learning machine based...

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2020 4th International Workshop on Advanced Algorithms and control engineering, IWAACE 2020

作者： Wei, Liyun Wang, Lidan Li, Yunfei Duan, Shukai College of Electronic and Information Engineering Southwest University Chongqing400715 China College of Artificial Intelligence Southwest University Chongqing400715 China Brain-inspired Computing and Intelligent Control of Chongqing Key Lab Chongqing400715 China National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing400715 China

The online sequential extreme learning machine (OS-ELM) was proposed as an evolution of the Extreme learning machine (ELM) in 2006. The learning algorithm can learn data by fixing or changing the size of the chunk (a block of data). Compared with other online algorithms, it has faster training speed, and the generalization performance is better. The proposed ensemble of neural networks further demonstrates that the use of the neural network sets with multiple consensus schemes has a greater improvement over the stability of a single network. The basic idea of cross-validation is to group the original data sets, part of which is to train as the training set and the other part of which is to verify as the test set, so the algorithm can reduce the over-fitting problem of the data to a certain extent, and extract more valid information in the limited data set. In this paper, a new ensemble of online sequential extreme learning machine based on cross-validation (ENOS-ELM) is proposed. Through application of cross-validation and integration to the training stage, the accuracy of the algorithm in classification is further improved. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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