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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Song Yan Tao Li Haotian Zheng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (数字)9781728190044

ISBN: (纸本)9781728190051

The artificial immune danger theory is introduced into the analysis of user behavior characteristics to effectively identify the dormant member users of pharmacies. Take pharmacy members as an example, analyze the behavior characteristics of pharmacy members, select characteristic attributes such as purchasing behavior, medical history, gender and age, payment method, activity level, membership card points, *** the attribute analysis result as the characteristic signal to distinguish between quasi-sleeping member users and normal users, and based on Dendritic Cells Algorithm(DCA) to realize the identification of quasi-sleeping members of *** effectiveness of the algorithm is verified and compared with the real data of pharmacy member users. The results show that the method can effectively detect users who may become sleeping members among pharmacy members, and has a high detection accuracy rate.

关键词： Text analysis Text recognition Predictive models Prediction algorithms Classification algorithms History Immune system

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Semantic segmentation knowledge based MMRF optimal method for fine-grained urban infrastructure classification 5

Semantic segmentation knowledge based MMRF optimal method fo...

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IET International Radar Conference 2020, IET IRC 2020

作者： Dong, Shan Zhuang, Yin Wang, Yupei Chen, He Pang, Long Yang, Zhanxin Long, Teng Engineering Center of Digital Audio and Video Communication University of China Beijing100024 China School of Electronic Engineering and Computer Science Peking University Beijing100087 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781839535635

Automatically understanding pixel-level semantic relation in urban area from very high resolution (VHR) aerial images is the kernel way of urbanization development and planning. However, due to aerial images covering a large urban area with VHR, manually annotated pixel-level multiple instances from urban area is unimaginable. Then, when the low quality pixel-level labelling dataset used for semantic segmentation network supervised learning, these methods cannot provide the fine-grained urban infrastructure classification mapping results. Related to this problem, we proposed the semantic segmentation knowledge (SSK) based multi-scale Markov random field (MMRF) optimal method for fine-grain urban infrastructure classification mapping. First, proposed feature ensemble way is employed to final prediction layer to fuse multiple inputs. These multiple inputs can leverage network to produce the better SSK. Second, the multi-scale wavelet decomposition and SSK are used for modelling of MMRF to produce finer urban classification results. Finally, several experiments based on ISPRS dataset is used for demonstrate that proposed method can produce fine-grained land cover classification results than the state-of-the-art methods. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Semantic Segmentation

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Tumor Gene Selection and Prediction via Supervised Correlation Analysis Based F-Score Method 16th

Tumor Gene Selection and Prediction via Supervised Correlati...

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16th International Conference on Intelligent Computing, ICIC 2020

作者： Cheng, Jia-Jun Li, Bo School of Computer Science of Technology Wuhan University of Science of Technology WuhanHubei430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System WuhanHubei430065 China

ISBN: (纸本)9783030608019

It is imperative to select key genes from tumor gene expressive data for genotype prediction. In this paper, a supervised gene selection method is proposed. Firstly, all the genes are sorted as their descending F-scores, and then supervised correlation analysis is also recommended to reduce the redundancy from those selected genes. At last SVM is introduced to classify those gene subsets. Some experiments are conducted on benchmark tumor gene expressive data sets and results show the performance of the proposed method. © 2020, Springer Nature Switzerland AG.

关键词： Genes

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Reference Point Based Multi-objective Evolutionary Algorithm for DNA Sequence Design 14th

Reference Point Based Multi-objective Evolutionary Algorithm...

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Zhao, Haozhi Xu, Zhiwei Zhang, Kai School of Computer Science and Technology Wuhan University of Science and Technology WuhanHubei430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System WuhanHubei430065 China

ISBN: (纸本)9789811534140

DNA computing is a parallel computing model based on DNA molecules. High-quality DNA sequences can prevent unwanted hybridization errors in the computation process. The design of DNA molecules can be regarded as a multi-objective optimization problem, which needs to satisfy a variety of conflicting DNA encoding constraints and objectives. In this paper, a novel reference point based multi-objective optimization algorithm is proposed for designing reliable DNA sequences. In order to obtain balance Similarity and H-measure objective values, the reference point strategy is adapted to searching for idea solutions. Firstly, every individual should be assigned a rank value by the non-dominated sort algorithm. Secondly, the crowding distance is replaced by the distance to the reference point for each individual. Lastly, the proposed algorithm is compared with some state-of-the-art DNA sequence design algorithms. The experimental results show our algorithm can provide more reliability DNA sequences than existing sequence design techniques. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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Mechanical Properties Prediction for Hot Roll Steel Using Convolutional Neural Network 14th

Mechanical Properties Prediction for Hot Roll Steel Using Co...

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Xu, Hao Xu, Zhiwei Zhang, Kai School of Computer Science and Technology Wuhan University of Science and Technology WuhanHubei430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System WuhanHubei430065 China

ISBN: (纸本)9789811534140

Prediction the mechanical properties is very important in many real-life industry fields. In this paper, we proposed an efficient convolutional neural network (CNN) to predict the mechanical properties of hot roll steel. In this study, 20,000 sets of data are collected from the hot roll factory, where 16,000 sets of data were used for training the CNN model, and 4,000 sets of data were used for testing the performance of the model. Compared with Support Vector Machine (SVM) and Artificial Neural Network (ANN), The experimental results have been demonstrated to provide a competitive and higher prediction accuracy. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Single channel pipelined variable-length FFT processor design

Single channel pipelined variable-length FFT processor desig...

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IET International Radar Conference 2018, IRC 2018

作者： Wang, Jiale Xie, Yizhuang Yang, Chen Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China

In this study, a single channel pipelined variable-length fast Fourier transformation (FFT) processor design is presented. With the development of science and technology, digital signal processing has been widely used in many fields. The most basic algorithm of digital signal processing theory, FFT, is widely used in radar signal processing, remote sensing, image processing, communications and other fields. A high real-time and high-throughput FFT processor design method based on the field-programmable gate array (FPGA) platforms is proposed to meet the needs of the synthetic aperture radar imaging digital signal processing. The authors conducted the ISE synthesis based on the Xilinx FPGA platform using xc6vlx760ff1760-1 as the target chip and the ModelSim simulation, combined with Matlab, to verify the correctness of this design. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： MATLAB

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Dynamic partial reconfiguration scheme for fault-tolerant FFT processor based on FPGA

Dynamic partial reconfiguration scheme for fault-tolerant FF...

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IET International Radar Conference 2018, IRC 2018

作者： Wei, Xin Xie, Yi Z. Xie, Yu Chen, He Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China

The fast Fourier transform FFT processor is an important part of the space real-time signal processing system based on field programmable gate array (FPGA). Since occupying a large amount of logical resources and storage resources, FFT processor is more vulnerable to high-energy particles in space, resulting in single event upset (SEU). This paper presents a novel FPGA scrubbing framework base on dynamic partial reconfiguration technique for a FFT processor to mitigate SEU. The proposed scheme is compared with the blind scrubbing, the reconfiguration time is reduced by 78%. Then, the resource utilisation is 61.5% less than triple modular redundancy scheme. This paper also presents a DPR controller for FFT processor, which is evaluated in terms of hardware resources and reconfiguration time. A comparison to the Xilinx PRC IP shows that multipath delay feedback FFT controller saves 38.6% resources. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Fast Fourier transforms

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Slim-SSD: An Efficient Fast Object Detection Method

Slim-SSD: An Efficient Fast Object Detection Method

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IEEE Conference on Multimedia information processing and Retrieval (MIPR)

作者： Jing Donglin Linbo Tang BaoJun Zhao Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781728142722

ISBN: (纸本)9781728142739

Object detection networks require large storage space and high computational cost, it is difficult to deploy deep neural networks on embedded devices with limited memory and computing resources in actual object detection tasks. In order to solve these challenges, we propose an efficient object detection framework by pruning channels of the feature extraction layer of the network. First, we apply the L1 regularization to the channel scale factor in the BN layer to obtain the object detection network with sparse structure. Then we trim the channel with less information to get the object detection framework. Based on this method, we obtained Slim-SSD with less trainable parameters and test time. Our experiments on benchmarks show that, on the basis of approximate accuracy with the original network, we have reduced the number of model parameters by 3x, and reduced the testing time by 2x. Our compression method helps to deploy complex object detection networks on resource-constrained embedded platform.

关键词： Object detection Feature extraction Neural networks Detectors Acceleration Training Standards

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The real-time Imaging Method for Sliding Spotlight SAR Based on embedded GPU

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Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of technology 2020年第9期40卷 1018-1025页

作者： Hu, Shan-Qing Li, Hui-Xing Li, Bing-Yi Xie, Yi-Zhuang Chen, Liang Chen, He Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Beijing Institute of Radio Measurement Beijing100854 China

For the real-time SAR imaging systems, aiming at the problems of low real-time performance and high power consumption of traditional computing platforms, an implementation method was studied for embedded GPU. In order to make full use of the limited memory in the embedded GPU, a memory partitioning and reconfiguration scheme was proposed. The page-locked memory and zero-copy technology were used to realize the transmission-calculation parallelization. To achieve high real-time performance, large-scale parallelism was realized by using shared memory, registers, et. The result shows that sliding spotlight SAR imaging processing on the TX2 can only take 12.66 s time and consume 15 W power. This method is also applicable to other modes radar processing algorithms, and can provide reference for the future embedded real-time SAR imaging processing. © 2020, Editorial Department of Transaction of Beijing Institute of technology. All right reserved.

关键词： Graphics processing unit

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QR Code Detection with Faster-RCNN Based on FPN 6th

QR Code Detection with Faster-RCNN Based on FPN

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6th International Conference on Artificial Intelligence and Security, ICAIS 2020

作者： Peng, Jinbo Yuan, Song Yuan, Xin School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan430065 China

ISBN: (纸本)9783030578831

Nowadays, the QR code is often used in many popular fields, such as payment and social networking. Therefore, it is particularly important to quickly and accurately detect the position of QR code in real complex scenes. Traditional QR code detection methods mainly use hand-engineered features for detection. However, the QR code photos we take may be blurred due to pixel, distance, and other problems, and may even produce some rotations and deformations because of the complex scenes. Under such circumstances, the traditional QR code detection methods may not be so applicable. Faster-RCNN was originally used for multiple object detection, but we adjusted it slightly and applied it to the detection of QR code. At the same time, we made a small dataset under complex scenes for training Faster-RCNN networks. However, in complex scenes, the size of the QR code vary greatly due to the distance of shooting, so we add an FPN module to the Faster-RCNN to improve the detection performance for small and multi-scale QR code. Experimental results show that our method has achieved good performances in the detection of QR code in complex scenes. © 2020, Springer Nature Switzerland AG.

关键词： Complex networks

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