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2020 5th International Conference on intelligent Computing and Signal Processing, ICSP 2020

作者： Shen, Siyuan Wang, Lidan Duan, Shukai He, Xin 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 Intelligent Transmission and Control Technology Chongqing400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing400715 China

An intelligent car plate detection method can make the travel more convenient and efficient. However, traditional methods are reasonably effective under the specific circumstances or strong assumptions only, and there are few databases for car plate detection. Therefore, a novel real-time car plate detection method based on improved Yolov3 has been proposed. In order to select the more precise number of candidate anchor boxed and aspect ratio dimensions, the K-Means algorithm is utilized. To solve the short of the available car plate database, a car plate database which has 6668 pictures has been established. As shown in the experimental results, the method which is proposed by this paper is better than original Yolov3. Thanks to the car plate database, the proposed method obtained better results even in the situation of inclination, too bright or too dark, different weather and so on. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Database systems

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Robust correlation filter that combined temporal regularization with spatial weight L1 constraint 4

Robust correlation filter that combined temporal regularizat...

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

作者： He, Xin Duan, Shukai Wang, Lidan 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 parameter optimization of the Object tracking algorithms has achieved excellent performance based on parameter regularization. Adaptive spatial regularization correlation filter can obtain the optimal weight coefficient for a specific target. Inspired by this, we propose a filter combined temporal regularity with spatial L1 regularization (LTASRCF). The time regularizer is used to remember the data of the previous frame to update the current target position. It performs well in the process of occlusion, deformation and motion blur. Meanwhile, L1 regularization of spatial weights reduces the computational complexity of the coefficients, and improves the robustness of the spatial weight matrix. We have conducted comparative experiments on the OTB-2015 dataset, and the proposed LTASRCF model ranks first among the five popular algorithms, which outperforms by 9.2% and 18.9% than KCF, respectively, and reaches real-time performance. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Tracking (position)

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Chinese image caption of Inceptionv4 and double-layer GRUs based on attention mechanism 5

Chinese image caption of Inceptionv4 and double-layer GRUs b...

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2021 5th International Workshop on Advanced Algorithms and control engineering, IWAACE 2021

作者： Pan, Yongbin Wang, Lidan Duan, Shukai Gan, Xiuling Hong, Liangyi 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

In recent years, there has been a wave of research on English image caption at home and abroad. However, due to the particularity of Chinese image caption task, the research on Chinese image caption has not made good progress. In order to solve this problem, a new Chinese image caption model is implemented. Firstly, the AI challenge dataset is enhanced, and then the Chinese text data of the dataset is preprocessed by Chinese word segmentation tool word2vec. Secondly, based on the encoder-decoder framework, the image visual features are extracted by Inceptionv4 network, the attention mechanism is incorporated in the process of feature extraction and the Chinese sentences are generated by double-layer GRUs network. In the process of training, Adam is used to optimize the algorithm. Finally, A GUI interface is designed to better show the experimental effect. Experiments show that the new Chinese image caption model can automatically generate more fluent and more accurate Chinese caption sentences, and the trained model has excellent performance in many evaluation indexes. © Published under licence by IOP Publishing Ltd.

关键词： Network layers

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Rain Removal from images based on Computer Vision and conditional Generative Adversarial Networks 3

Rain Removal from images based on Computer Vision and condit...

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2020 3rd International Conference on Advanced Algorithms and control engineering, ICAACE 2020

作者： Li, Yunfei Wang, Lidan Chen, Taixing Wang, Ziyuan 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 Chongqing 400715 China

In view of the quick development of modern computer technology, more pictures of natural scenes have been collected and analyzed for application in military fields, scientific research, and computer vision fields related to object detection. However, the sharpness of these pictures collected will be affected by rain and snow, which will reduce the visibility of things in the pictures. This will not only affect the viewing, but also affect scientific research fields such as autonomous driving and video surveillance. Therefore, the research on image rain removing is very practical. An improved algorithm model named pixel-GAN is proposed in this article, which based on conditional Generative Adversarial Networks (cGAN). This new method regards the problem of removing rain from specific images as the style conversion problem. What we improved is that a VGG-16 module is added to the original network to extract perceptual loss as a new part of the loss function. We show the excellent rain-removing ability of the improved algorithm and also illustrate the reason why we add the perceptual loss. Compared with other existing methods, there is no need to set a prior model, the training speed is faster and the proposed model finally generates higher quality pictures. © Published under licence by IOP Publishing Ltd.

关键词： Computer vision

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An Attention-based Convolutional Neural Network for Melanoma Recognition 5

An Attention-based Convolutional Neural Network for Melanoma...

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2021 5th International Workshop on Advanced Algorithms and control engineering, IWAACE 2021

作者： Chen, Qi Wang, Lidan Gan, Xiuling Duan, Shukai Southwest University College of Electronic and Information Engineering Chongqing400715 China Southwest University College of Artificial Intelligence 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

Early automatic and accurate melanoma recognition is an important method to reduce melanoma deaths. Existing methods are less sensitive to the position of the lesion areas. Network training may be affected by the uncorrelated noisy parts. In light of this circumstance, an end-to-end attention-based network AF-CNN for accurate melanoma recognition is proposed in this paper, which is mainly composed of pre-trained VGG19, attention blocks and a classification layer. Instead of treating each part of the input dermoscopy images equally, our AF-CNN model has strong discriminative ability to focus on the lesion areas. The AF-CNN was evaluated on the ISIC2017 dataset and concluded the proposed single model achieves the state-of-the-art result in melanoma recognition task. © Published under licence by IOP Publishing Ltd.

关键词： Dermatology

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GAU-Net: U-Net Based on Global Attention Mechanism for brain tumor segmentation 5

GAU-Net: U-Net Based on Global Attention Mechanism for brain...

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2021 5th International Workshop on Advanced Algorithms and control engineering, IWAACE 2021

作者： Gan, Xiuling Wang, Lidan Chen, Qi Ge, Yongjie Duan, Shukai School 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

Deep learning has shown great advantages in biomedical image segmentation. The classic model U-Net uses a stacked encoding-decoding structure of convolution operations for feature extraction and pixel-level classification. The stacking of convolutional layers can expand the receptive field, but it is still a local operation and cannot capture long-distance dependence. Therefore, in this work, we propose a Global Attention Mechanism that combines channel attention module and spatial attention module and integrates different convolutions in it. Besides, we design a residual module for the traditional up and down sampling blocks. And finally, we combine them with U-Net to propose a new global attention network GAU-Net. We perform experiments on the dataset BraTS2018. Our model has increased the mIoU from 0.65 to 0.75 with only 5.4% of U-Net parameters. At the same time, the inference time is also significantly shortened with relatively good performance. © Published under licence by IOP Publishing Ltd.

关键词： Convolution

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An Image Denoising Method Based on Deep Residual GAN 4

An Image Denoising Method Based on Deep Residual GAN

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

作者： Wang, Ziyuan Wang, Lidan Duan, Shukai Li, Yunfei Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China Schoolof Electronic and Information Engineering Southwest University Chongqing400715 China College of Artificiall Intelligence Southwest University Chongqing400715 China Braininspired 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

As people come into contact with image data more often, high quality and clear images attract more attention. Many methods have been proposed to deal with image noise problem including deep learning (DL). However most of them is lack of capability when customers want more perceptual details of the image without information loss. In this paper, a deep residual network based on generative adversarial (GAN) network was proposed to complete the image denoising mission. Firstly, a generative-adversarial network structure based on residual blocks was designed. Secondly, a refined loss function was given to train the GAN network. The well designed loss function can help the generated image to be very close to the clear counterpart (ground truth) while enhancing more details in colours and brightness. Finally, extensive experiments show that our network is not only convincing for images denoising, but also effective for other image process tasks, such as image defogging, medical CT denoising etc., presenting impressive and competitive effects. © Published under licence by IOP Publishing Ltd.

关键词： Image denoising

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Optimization of ultrasonic-assisted freezing of Penaeus chinensis by response surface methodology

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Food Quality and Safety 2021年第2期5卷 143-151页

作者： Yubin Ying Yingchun Xiang Jialin Liu Xin Chen Lingping Hu Yujin Li Yaqin Hu College of Biosystems Engineering and Food Science National-Local Joint Engineering Laboratory of Intelligent Food Technology and EquipmentZhejiang Key Laboratory for Agro-Food ProcessingIntegrated Research Base of Southern Fruit and Vegetable Preservation TechnologyZhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality ControlNingbo Research InstituteFuli Institute of Food ScienceZhejiang UniversityHangzhouChina College of Food Science and Technology Hainan Tropical Ocean UniversitySanyaChina College of Food Science and Engineering Ocean University of ChinaQingdaoChina

Objectives:Optimization of ultrasonic-assisted freezing of Penaeus chinensis by response surface methodology was studied in order to(1)obtain frozen Penaeus chinensis of high quality and(2)provide practical guidance for the application of ultrasonic-assisted freezing in Penaeus *** and Methods:Three independent and major variables were selected,including initial ultrasonic temperature(℃),ultrasonic power(W)and ultrasonic time(s on/2 s off).On the basis of one-factor experiments,17 groups of experiments were established by response surface methodology according to Box-Behnken *** multiple regression analysis the experimental data were fitted into a second-order polynomial equation,which was tested by proper statistical ***:The optimal ultrasonic conditions were as follows:initial ultrasonic temperature 0℃,ultrasonic power 180 W,ultrasonic time 5 son/2 s *** the optimization conditions,the time of passing through maximum ice crystal generation zone was 105.500 s,which was very close to the predictive passage time of 101.541 ***:Initial ultrasonic temperature,ultrasonic time and ultrasonic power played an important role in the process of ultrasonic-assisted freezing of Penaeus *** surface methodology was used to optimize the three factors in ultrasonic-assisted freezing,which could greatly shorten the time of passing through the maximum ice crystal generation zone and maintain the tissue structure of Penaeus chinensis well.

关键词： Ultrasonic-assisted freezing Penaeus chinensis response surface methodology time of passing through the maximum ice crystal generation zone optimization

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Dynamic Memristor based Cyclic Echo State Network for Time Series Prediction

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

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Neural Networks, Information and Communication engineering (NNICE), International Conference on

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

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.

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开式泵控非对称缸负载容腔独立控制系统（英文）

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Journal of Zhejiang University-Science A(Applied Physics & engineering) 2018年第3期19卷 203-210页

作者： Jing YAO Pei WANG Xiao-ming CAO Zhuo WANG The Laboratory of Heavy Machinery Fluid Power Transmission and Control in Hebei Yanshan University Qinhuangdao 066004 China National and Local Joint Engineering Center for Advanced Forging Press Forming Technology and Equipment Faculty of Engineering Yanshan University Qinhuangdao 066004 China Yanshan University Qinhuangdao 066004 China

目的:为减少能量排放和提升节能效果,并解决非对称缸系统的流量不对称问题,本文对开式泵控非对称缸负载容腔独立控制系统的控制特性及能耗特性进行了深入的研究,以期为其实际应用提供理论支撑。创新点:1.提出开式泵控非对称缸负载容腔... 详细信息

目的:为减少能量排放和提升节能效果,并解决非对称缸系统的流量不对称问题,本文对开式泵控非对称缸负载容腔独立控制系统的控制特性及能耗特性进行了深入的研究,以期为其实际应用提供理论支撑。创新点:1.提出开式泵控非对称缸负载容腔独立控制系统,建立其能量传输模型;2.以压机为对象进行实验研究,采用位置压力负载容腔独立控制方法,获得其能耗与控制特性。方法:1.介绍开式泵控非对称缸负载容腔独立控制系统的构型;2.通过理论推导,建立能量传输模型,得到具有能量回收功能的系统;3.通过实验研究和分析,验证所提系统和方法的有效性。结论:1.基于能量传输模型得到的系统具有较好的节能特性;2.开式泵控非对称缸负载容腔独立控制系统增加了系统控制自由度,验证了负载容腔独立控制方法的有效性;3.开式泵控非对称缸负载容腔独立控制系统采用无杆腔位置控制和有杆腔压力控制组合的控制方法;随着有杆腔压力的提高,在不增加系统能耗的前提下该方法改善了系统的位置控制精度。

关键词：泵控系统非对称缸能耗位置压力组合控制负载容腔独立控制

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