2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)最新文献_第10页

A Heterogeneous Network Access Selection Algorithm Based on Improved CRITIC 基于改进CRITIC的异构网络接入选择算法

2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) Pub Date : 2018-10-01 DOI: 10.1109/IAEAC.2018.8577486

Dong Liu, Damin Zhang, Juanmin Chen, Zhiyuan Pan

引用次数: 0

Application of UKF Alogrithm in Passive Localization and Tracking UKF算法在被动定位与跟踪中的应用

2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) Pub Date : 2018-10-01 DOI: 10.1109/IAEAC.2018.8577916

Li Bingrong, Qu Chang-wen, Zhou Zheng, Guo Linpeng

引用次数: 1

Content-Based Image Retrieval System Via Deep Learning Method 基于内容的深度学习图像检索系统

2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) Pub Date : 2018-10-01 DOI: 10.1109/IAEAC.2018.8577661

Xinyu Tian, Qinghe Zheng, Jianping Xing

{"title":"Content-Based Image Retrieval System Via Deep Learning Method","authors":"Xinyu Tian, Qinghe Zheng, Jianping Xing","doi":"10.1109/IAEAC.2018.8577661","DOIUrl":"https://doi.org/10.1109/IAEAC.2018.8577661","url":null,"abstract":"Faced with the huge image data in the context of big data era, how to effectively manage, describe, and retrieve them has become a hotspot issue in academic circles. In this paper, we propose an end-to-end image retrieval system based on deep convolutional neural network and differential learning method. We first build an image matching dataset based on the gravitational field model, that is to add a similarity score label for each image in the dataset production stage. Then we train the improved deep learning model and verify the effectiveness of the algorithm on three common image matching dataset (i.e., Caltech-101, Holidays and Oxford Paris). Finally, the experimental results show that our improved deep learning model with differential learning method that used for image retrieval system has state-of-the-art image matching performance. The overall retrieval accuracy in Caltech-101, Holidays and Oxford Paris datasets are 88.5%, 94.1 % and 96.2%, respectively. As the number of returned images increases, the image retrieval accuracy of the system decreases slightly and eventually becomes stable at a high value. And the differential learning based retrieval method is superior to many traditional algorithms in terms of image matching accuracy and single image processing speed.","PeriodicalId":6573,"journal":{"name":"2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"18 1","pages":"1257-1261"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79686773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Spiking neural network with synaptic plasticity for recognition 具有突触可塑性识别的脉冲神经网络

2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) Pub Date : 2018-10-01 DOI: 10.1109/IAEAC.2018.8577629

Jing Li, I. Liu, Weixin Gao, Xiaoyan Huang

{"title":"Spiking neural network with synaptic plasticity for recognition","authors":"Jing Li, I. Liu, Weixin Gao, Xiaoyan Huang","doi":"10.1109/IAEAC.2018.8577629","DOIUrl":"https://doi.org/10.1109/IAEAC.2018.8577629","url":null,"abstract":"The spiking neural network referred to the third generation of neural network simulates the mechanisms of neurons and networks in brain. It has the distributed computational mechanism and robust information processing way like the nervous system. This paper describes that a spiking neural network with the synaptic plasticity recognizes the input scenes. The Digital spiking silicon neuron (DSSN), a mathematical structure-based qualitative model, is used to reproduce the various behaviors of neurons. We also designed the synapse model in our spiking neural network to precisely describe the dynamics of the transmitter release and the postsynaptic current generation. There are three layers in our network. The spiking neurons in layer 1 and 2 with special receptive fields perform the edge detection and orientation selection, respectively. The synaptic plasticity is realized in synaptic connections between spiking neurons in layer 2 and the output layer. The changing of connection is based on the Hebbian learning rule which supposes that the time difference of two spikes modifies the value of connection. We evaluated our spiking neural network with the task of image recognition. The spiking neurons in the output layer fire with the high frequency in response to their relevant input scenes. The simulation results show that our spiking neural network can successfully recognize the input scenes learned before. The recognition is robust against various distortions.","PeriodicalId":6573,"journal":{"name":"2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"7 1","pages":"1728-1732"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84690951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Application of an Improved Neural Network Algorithm in Classification of Communication Noise Signals 一种改进神经网络算法在通信噪声信号分类中的应用

2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) Pub Date : 2018-10-01 DOI: 10.1109/IAEAC.2018.8577927

Changren Yu, Lianxing Jia, Yintao Hou

引用次数: 0

Comparison between Conformal Cambered and Rectangular Dielectric Resonator Antennas 共形弧面和矩形介质谐振器天线的比较

2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) Pub Date : 2018-10-01 DOI: 10.1109/IAEAC.2018.8577515

Wenzhe Liu, Boyuan Ma, J. Pan

引用次数: 0

Astronomical Data Application Research Based on MapReduce 基于MapReduce的天文数据应用研究

2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) Pub Date : 2018-10-01 DOI: 10.1109/IAEAC.2018.8577233

Qingfa Cui, Sheng-Chuan Wu

引用次数: 1

Load ratio optimization of Urban Power Grid Considering penetration of electric vehicles 考虑电动汽车渗透的城市电网负荷率优化

2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) Pub Date : 2018-10-01 DOI: 10.1109/IAEAC.2018.8577722

T. Han, Anjia Mao, Shengyu Kuai, Bin Ye

引用次数: 1

An Improved Adaptive ET-PHD Algorithm for Newborn Target Intensity 一种改进的新生儿目标强度自适应ET-PHD算法

2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) Pub Date : 2018-10-01 DOI: 10.1109/IAEAC.2018.8577717

Cong Peng, Wenqiang Ye

引用次数: 4

Cross Polarization in Large-scale Rectangular Waveguide Slot Array Antenna with Parallel Metallic Plates 平行金属板大型矩形波导槽阵天线的交叉极化

2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) Pub Date : 2018-10-01 DOI: 10.1109/IAEAC.2018.8577687

Xue Haoren, J. Pan

引用次数: 0