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International Journal of control and Automation 2014年第4期7卷 337-344页

作者： Fan, Jianying Wang, An Gao, Yang Yu, Lei School of Measurement-Control and Communication Engineering Harbin University of Science and Technology 150080 Harbin China College of Information and Electronic Technology Jiamusi University 154007 Jiamusi China

In order to solve the key technical problem in the verification process of mechanical and electrical coal mine anemometer, the paper presents digital recognition approach base on the characters of the anemometer image. First of all, collect the mirror images of the anemometer from a plain mirror which in the wind tunnels. And then we process the mirror images with secondary mirroring, grayscaling, denoising and binarization. Followed the location of the digital area can be determined and the incline distortion of the image can be adjusted. The final step is segmenting the single character in the digital character region and identify the corresponding number base on the characteristics of anemometer, which using the LED digital tube to display numbers. The experiments show that this method can accurately identify the numbers on the anemometer and has high recognition rate. The recognition time is able to meet the requirement for the verification of anemometer. Thus the method has high practical value. © 2014 SERSC.

关键词： Coal mines

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Bit-Level Composite Signal Design for Simultaneous Ranging and communication

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China communications 2021年第6期18卷 214-227页

作者： Weigang Chen Yalong He Changcai Han Jinsheng Yang Zhan Xu School of Microelectronics Tianjin UniversityTianjin 300072China Key Laboratory of Modern Measurement and Control Technology Beijing 100192China School of Information and Communication Engineering Beijing Information Science and Technology UniversityBeijing 100192China

As the complexity of space exploration missions augments,how to enhance the overall performance of communication,ranging or other functions has become a challengeable *** the integration of communication and ranging,we present a bit-level composite signal for simultaneous ranging and *** this composite method,through a specially designed mapping scheme using low-weight codewords,the information sequence is converted to a sparse sequence which is then superimposed on the ranging *** ranging,the correlation characteristics of the ranging code component can be maintained to calculate the transmitter-receiver *** communications,the sparse sequence can be extracted without interference by eliminating the ranging code *** results show that the proposed composite signal can support communication and ranging simultaneously with limited sacrifice of ranging performance,and the performance loss of ranging can be controlled and minimized by lowering the density of information sequences using different sparsification encoding methods.

关键词： composite signal spectrum sharing lowweight codeword regenerative PN ranging code

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Image label transfer: Short video labelling by using frame auto-encoder

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The Journal of China Universities of Posts and Telecommunications 2020年第1期27卷 92-99页

作者： Lü Chaohui Huang Yiyang School of Information and Communication Engineering Communication University of ChinaBeijing 100024China Beijing Key Laboratory of Modern Entertainment Technology Communication University of ChinaBeijing 100024China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and TourismBeijing 100024China

Short videos on the Internet have a huge amount, but most of them are unlabeled. In this paper, a rough short video labelling method based on the image classification neural network is proposed. Convolutional auto-encoder is applied to train and learn unlabeled video frames, in order to obtain feature in the specific level. With these features, the video key-frames are extracted by the feature clustering method. These key-frames which represent the video content are put into an image classification network, so that the labels of every video clip can be got. In addition, the different architectures of convolutional auto-encoder are estimated, and a better performance architecture through the experiment result is selected. In the final experiment, the video frame features from the convolutional auto-encoder are compared with those from other extraction methods, where it illustrates remarkable results by the proposed method.

关键词： image feature video labelling convolutional neural network auto-encoder cluster key-frame

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The design of a portable instrument for straightness measurement of large machine tool

The design of a portable instrument for straightness measure...

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2013 International Conference on Mechanical and Electronics engineering, ICMEE 2013

作者： Zhang, Ren Sun, Xue Guo, Xiao Yan Chen, Jie College of Information Engineering Tarim University Xinjiang alar 843300 China College of Measure-control Technology and Communication Engineering Harbin Univ.Sci.Tech Harbin 150080 China

ISBN: (纸本)9783037858103

The paper inctroduces the guide for the design of a portable instrument for straightness measurement of large machine tool. the measuring instrument is designed with two-quadrant photocell which is used as the position sensor, single-chip for the processor is MSP430F427 which have LCD driver circuit, the instrument is charged by lithium battery-powered. Its size is small, no external connection when it is used, data acquisition, processing and error displayed can be done in the part of light target. It have the advantage of convenience, simple and high measurement accuracy when it is used. © (2013) Trans Tech Publications, Switzerland.

关键词： Data acquisition

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Rate-compatible spatially coupled repeat-accumulate codes via successive extension

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High technology Letters 2019年第2期25卷 224-229页

作者： Liu Yang Wang Bin Zhang Yuzhi Sun Yanjing School of Communication and Information Engineering Xi’an University of Science and TechnologyXi’an 710054P.R.China School of Information and Control Engineering China Univesity of Mining and TechnologyXuzhou 221116P.R.China

A rate-compatible spatially coupled repeat-accumulate (RC-SC-RA) code is proposed. Its protograph is obtained by extending a given (J, K, L) SC-RA coupled chain (denoted as the mother chain) with extra check nodes and parity bit nodes T times. At each time, the extension is realized via coupling the message bits in the same way as that in the mother chain. Rate-compatibility is achieved by adjusting the extension parameters and applying random puncturing technique. Density evolution analysis shows that the iterative decoding thresholds of all the member codes in the proposed RC-SC-RA code family are very close to Shannon limits over the binary erasure channel. Finite length simulation results are consistent with the thresholds well. Moreover, the proposed RC-SC-RA codes perform better than spatially coupled low density parity check (SC-LDPC) codes in decoding performance especially in lower-rate region.

关键词： spatially coupled LDPC codes spatially coupled RA codes rate compatibility density evolution iterative decoding

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Design of a wireless monitoring network for granary temperature and humidity based on Zigbee

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International Journal of u- and e- Service, Science and technology 2014年第2期7卷 77-82页

作者： Dong, Jingwei Li, Huile Liu, Yuan Guo, Yanwen Tang, Guanhua School of Measure-Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China

The granary monitoring systems in used in China are mostly based on wired networks such as RS-485 serial bus, CAN fieldbus, etc. Inevitably these monitoring systems have drawbacks of high installation cost, high failure rate and high maintenance cost. Aiming at these problems, this paper designed a granary temperature and humidity monitoring system based on Zigbee wireless network. The monitoring system consists of sensor modules as Zigbee end devices, a host PC connected to a Zigbee coordinator and some Zigbee routers if necessary. The sensor module is a temperature and humidity testing device using a CC2430 as the controller and a SHT11 as the sensor. All testing results are displayed as figures and curves in real-time on the host PC, and history data are saved in a database file for further analysis. © 2014 SERSC.

关键词： Zigbee

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The research on surface electromyography signal effective feature extraction

The research on surface electromyography signal effective fe...

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6th International Forum on Strategic technology, IFOST 2011

作者： Zhao, Hanqing Xu, Guoqiang College of Measure-Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China Collge of Mechanical Engineering Heilongjiang Institute of Science and Technology Harbin China College of Automation Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781457703966

An important issue in the classification of electromyography signals is the classifier design. For surface electromyography signals in the acquisition process of serious interference by the noise, in particular frequency electrical interference, we use the zero mean of the electromyography signals and the whole cycle of filtering, obtain effective extraction of the electromyography signals characteristics. we compared the whole period of Electromyography signals filtering and f non-whole period filtering for hand acting, obtains the whole period of electromyography signals filtering to suppress interference is a very effective method. The way of electromyography signals feature extraction provides a simple, fast and practical method and lay the foundation for follow-up electromyography signals multi-action recognition. © 2011 IEEE.

关键词： Feature extraction

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A U-Net-like full convolutional pavement crack segmentation network based on multi-layer feature fusion

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International Journal of Pavement engineering 2025年第1期26卷

作者： Jin Wang Zhigao Zeng Fengxiang Huang R. Simon Sherratt Osama Alfarraj Amr Tolba Jianming Zhang a School of Computer and Communication Engineering Changsha University of Science and Technology Changsha People's Republic of China a School of Computer and Communication Engineering Changsha University of Science and Technology Changsha People's Republic of Chinad Key Laboratory of Safety Control of Bridge Engineering Ministry of Education (Changsha University of Science and Technology) Changsha People's Republic of China b Department of Biomedical Engineering The University of Reading Reading UK c Computer Science Department Community College King Saud University Riyadh Saudi Arabia

ABSTRACTCracks are an important indicator of pavement health, and it is difficult to achieve pixel-level segmentation of small and thin cracks. The existing network often experiences false segmentation and missed segmentation. Accordingly, a novel end-to-end U-Net-like full convolutional crack segmentation network is constructed. First, we propose a multi-layer feature fusion module to aggregate the texture and semantic features at each stage of encoder, so that the network can find smaller and thinner crack. Second, we design a novel residual structure with a pointwise convolution. Each stage of the encoder and decoder incorporates a residual structure to facilitate the fusion of feature maps with different spatial dimensions. It can also prevent the gradient vanish in the network training process. Finally, we utilise the maximum unpooling to restore spatial structure in up-sampling, which exploits the indices of maximum feature value in down-sampling. Therefore, high-frequency information is better preserved to help accurately restore the details of crack edges. To verify the proposed network performance, experiments are carried out on four open datasets, the proposed network can achieve better performance among five classical networks.

关键词： Crack segmentation deep learning semantic segmentation pavement damage detection

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Target tracking based on KCF combining with spatio-temporal context learning

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International Journal of Performability engineering 2018年第2期14卷 386-395页

作者： Wang, Aili Yang, Zhennan Chen, Yushi Iwahori, Yuji Higher Education Key Lab for Measure and Control Technology and Instrumentations of Heilongjiang Harbin University of Science and Technology Harbin150080 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China Dept of Computer Science Chubu University Aichi Japan

Most target tracking is based on a lot of samples training to build the model of the target, which is then carried on the tracking processing. This will need to choose a lot of tracked target samples for learning and training. However, there are all kinds of deformation of the training samples, including variety of light and scale, and so on, causing the long computation time, high computational complexity, and less robustness. The traditional kernel correlation filtering (KCF) tracking is through online learning of the first frame in the target vide. It then uses cyclic matrix to strengthen samples robustness, reducing the complexity of the calculation and time. But, the traditional KCF nuclear is unsatisfactory used for complex scenarios and quick treatment. In this paper, under the framework of the KCF, the target context information is introduced to make the tracking have better robustness and a better effect to deal with complex scenarios. © 2018 Totem Publisher, Inc. All rights reserved.

关键词： Target tracking

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Boosting Unsupervised Monocular Depth Estimation with Auxiliary Semantic Information

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China communications 2021年第6期18卷 228-243页

作者： Hui Ren Nan Gao Jia Li State Key Laboratory of Media Convergence and Communication、Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism(Communication University of China)、Beijing Key Laboratory of Modern Entertainment Technology(Communication University of China)、School of Information and Communication EngineeringCommunication University of China.No.1Dingfuzhuang StreetChaoyang DistrictBeijing100024China

Learning-based multi-task models have been widely used in various scene understanding tasks,and complement each other,i.e.,they allow us to consider prior semantic information to better infer *** boost the unsupervised monocular depth estimation using semantic segmentation as an auxiliary *** address the lack of cross-domain datasets and catastrophic forgetting problems encountered in multi-task training,we utilize existing methodology to obtain redundant segmentation maps to build our cross-domain dataset,which not only provides a new way to conduct multi-task training,but also helps us to evaluate results compared with those of other *** addition,in order to comprehensively use the extracted features of the two tasks in the early perception stage,we use a strategy of sharing weights in the network to fuse cross-domain features,and introduce a novel multi-task loss function to further smooth the depth *** experiments on KITTI and Cityscapes datasets show that our method has achieved state-of-the-art performance in the depth estimation task,as well improved semantic segmentation.

关键词： unsupervised monocular depth estimation semantic segmentation multi-task model

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