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Pattern Synthesis of MIMO Radar Based on Immune Differential Evolution Algorithm

Journal of Physics: Conference Series 2021年第1期1813卷

作者： Ling Jiang Yi Jiang Guangfu Chen Guimin Shi Qi Cheng School of Mathematics and Computer Wuyi University Wuyishan 354300 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyishan 354300 China Big Data Development Administration of Anji County Huzhou 313000 China

In order to optimize the pattern synthesis of multiple input and multiple output (MIMO) radar, immune mechanism is adopted to overcome the premature risk of differential evolution (DE) algorithm, namely immune differential evolution (IDE). Firstly, the modeling of MIMO radar is introduced by encoding the position of array in the binary. Secondly, the immune mechanism is employed to improve the DE. In IDE, two parameters are self-adapted for the mutation operator by immune mechanism to enhance the convergence ability of DE, including scaling factor and crossover rate. Several experiments are conducted to analysis the performance of IDE. The simulation results show that the IDE with variable parameters can get optimal results in MIMO radar with lower the peak side-lobe level (PSLL) and maintain the diversity with stronger convergence ability and shorter calculation speed.

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Detection of Impurity and Bubble Defects in Tire X-Ray Image Based on Improved Extremum Filter and Locally Adaptive-threshold Binaryzation

Detection of Impurity and Bubble Defects in Tire X-Ray Image...

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2018 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018

作者： Zheng, Xiunan Ding, Jianpei Pang, Zengzhi Li, Jinping Shandong Provincial Key Laboratory of Network Based Intelligent Computing China Shandong College University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year China School of Information Science and Engineering University of Jinan China

ISBN: (纸本)9781728105512

The manufacturing technology of all steel radial tire is complex. Some defects will inevitably appear in the tire due to the complex production process. Impurity and bubble are two typical kinds of defects in the tire. In this study, a unified detection of impurity and bubble defects in tire X-ray images is proposed by means of improved extremum filter and improved locally adaptive-threshold binaryzation. Firstly, the tire image is divided into cords and background by extremum filter. Secondly, an improved locally adaptive-threshold binaryzation is used to separate defects from the background. Finally, image denoising and marking are processed. In the experiment, we tested the proposed approach by using 280 tires with various types of defects from a tire factory. For the detection of clear and blur impurities and clear bubbles, the precision of our method can reach 97%, and the recall is 95.7%. It is to be noted that the proposed method can only be used to detect those two defects in sidewall and shoulder of the tire which we refer as the carcass. © 2018 IEEE.

关键词： Radial tires

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DPANet: Depth potentiality-aware gated attention network for RGB-D salient object detection

arXiv

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arXiv 2020年

作者： Chen, Zuyao Cong, Runmin Xu, Qianqian Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100190 China The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong Hong Kong Hong Kong The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

There are two main issues in RGB-D salient object detection: (1) how to effectively integrate the complementarity from the cross-modal RGB-D data;(2) how to prevent the contamination effect from the unreliable depth map. In fact, these two problems are linked and intertwined, but the previous methods tend to focus only on the first problem and ignore the consideration of depth map quality, which may yield the model fall into the sub-optimal state. In this paper, we address these two issues in a holistic model synergistically, and propose a novel network named DPANet to explicitly model the potentiality of the depth map and effectively integrate the cross-modal complementarity. By introducing the depth potentiality perception, the network can perceive the potentiality of depth information in a learning-based manner, and guide the fusion process of two modal data to prevent the contamination occurred. The gated multi-modality attention module in the fusion process exploits the attention mechanism with a gate controller to capture long-range dependencies from a cross-modal perspective. Experimental results compared with 16 state-of-the-art methods on 8 datasets demonstrate the validity of the proposed approach both quantitatively and qualitatively. Copyright © 2020, The Authors. All rights reserved.

关键词： Modal analysis

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Research on network Intrusion Detection Based on Support Vector Machine Optimized with Grasshopper Optimization Algorithm

Research on Network Intrusion Detection Based on Support Vec...

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IEEE International Conference on intelligent Data Acquisition and Advanced computing Systems

作者： Zhiwei Ye Yiheng Sun Shuang Sun Sikai Zhan Han Yu Quanfeng Yao Key Laboratory of Intelligent Computing and Information Processing Fujian Province University School of computer Science Hubei university of Technology Wuhan China Wuhan FiberHome Technical Services Co. Ltd.

As one of the most important parts of network security, more significance is attached to intrusion detection system (IDS). Numerous techniques including support vector machine (SVM) have been applied to the intrusion detection. However, many methods are utilized to improve the original SVM whose performance is markedly depended on its kernel parameters. Evolutionary algorithms such as genetic algorithm (GA) and particle swarm algorithm (PSO) are also employed to search better kernel parameters while the traditional optimization methods are vulnerable to fall into local minima with slow speed of convergence. In order to improve the precision of SVM in intrusion detection, the support vector machine based on grasshopper optimization algorithm (GOA-SVM) is proposed in the paper. To verify the practicality of the proposed method, several contrast experiments have been carried out using tool of Matlab. The experimental results finally demonstrates the superior performance of the proposed method on intrusion detection.

关键词： Support vector machines Optimization Intrusion detection Kernel Genetic algorithms Particle swarm optimization Artificial neural networks

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Syntactic and Semantic Features Based Relation Extraction in Agriculture Domain 15th

Syntactic and Semantic Features Based Relation Extraction in...

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15th Web information Systems and Applications Conference, WISA 2018

作者： Liu, Zhanghui Chen, Yiyan Dai, Yuanfei Guo, Chenhao Zhang, Zuwen Chen, Xing College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

ISBN: (纸本)9783030029333

Relation extraction plays an important role in many natural language processing tasks, such as knowledge graph and question answering system. Up to the present, many of the former relation methods work directly on the raw word sequence, so it is often subject to a major limitation: the lack of semantic information, which leads to the problem of the wrong category. This paper presents a novel method to extract relation from Chinese agriculture text by incorporating syntactic parsing feature and word embedding feature. This paper uses word embedding to capture the semantic information of the word. On the basis of the traditional method, this paper integrates the dependency parsing, the core predicate and word embedding features, using the naive Bayes model, support vector machine (SVM) and decision tree to build experiment. We use the websites knowledge base to construct a dataset and evaluate our approach. Experimental results show that our proposed method achieves good performance on the agriculture dataset. The dataset and the word vectors trained by Word2Vec are available at Github (https://***/A-MuMu/*** ). © 2018, Springer Nature Switzerland AG.

关键词： Agriculture

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Exploring Multi-loss Function Based Attention Framework for Vehicle Re-Identification

Exploring Multi-loss Function Based Attention Framework for ...

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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Wei Liu Yi Zhang Zhiming Luo Shaozi Li East China Jiaotong University Jiangxi China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yi Chang China Postdoc Center of Information and Communication Engineering Xiamen University China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyishan China Cognitive Science Department Xiamen University Fujian China

Vehicle Re-identification(Re-ID) refers to the task of matching a specific vehicle across multiple non-overlapping cameras. Precise vehicle Re-ID suffers from two main challenges: (1) different vehicle identities with a similar appearance of the same type have a subtle inter-instance discrepancy, (2) one vehicle usually has large intra-instance differences due to viewpoint and illumination variations. To address these challenges, in this paper, we proposed an Attention-based Framework for Vehicle Re-ID named AFVR, which combines the benefits of both the cross-entropy loss function and the triplet loss function. Experimental results on two popular vehicle Re-ID benchmarks demonstrate the effectiveness of our proposed method, compared with the state of the arts.

关键词： Feature extraction Cameras Training Measurement Lighting Visualization Benchmark testing

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Relative influence maximization in competitive social networks

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Science China(information Sciences) 2017年第10期60卷 252-254页

作者： Dingda YANG Xiangwen LIAO Huawei SHEN Xueqi CHENG Guolong CHEN College of Physics and Information Engineering Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University Institute of Computing Technology Chinese Academy of Sciences

In many realistic scenarios,such as political election and viral marketing,two opposite opinions,i.e.,positive opinion and negative opinion,spread simultaneously in the same social networks[1,2].Consequently,to achiev... 详细信息

关键词： net RIM Relative influence maximization in competitive social networks Figure

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MLXR: multi-layer obstacle-avoiding X-architecture Steiner tree construction for VLSI routing

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Science China(information Sciences) 2017年第1期60卷 211-213页

作者： Xing HUANG Wenzhong GUO Genggeng LIU Guolong CHEN College of Mathematics and Computer Sciences Fuzhou University Key Laboratory of Network Computing and Intelligent Information Processing (Fujian Province) Fuzhou University

Dear editor,Rectilinear Steiner minimal tree(RSMT)has been widely used in several modern very large scale integration(VLSI)circuit design *** of its importance,it has been fully studied in the past *** addition,the density of modern VLSI chips has increased *** today’s VLSI designs,there are increasingly more obstacles,such as IP blocks and pre-routed nets,these

关键词： VLSI MLXR multi-layer obstacle-avoiding X-architecture Steiner tree construction for VLSI routing tree

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Chinese Lexical Based Sentiment Analysis Framework in Meteorology

Chinese Lexical Based Sentiment Analysis Framework in Meteor...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Yinan Li Fuquan Zhang Yifan Zhu Sifan Zhang Yu Mao Zhendong Niu School of Computer Science and Technology Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Computing and Information University of Pittsburgh Pittsburgh PA USA

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Evaluating the performance of meteorological service not only relies on physical measurements but also requires public feedback. As a significant part of the evaluation, assessing the sentiment of these feedbacks gives an insightful view which can help to establish reliable decision support in policy making and emergency responding. However, climate and weather are always accompanying phenomena in the evolutionary process of human beings, making the climate and weather-related words themselves contain a large number of extend meanings in people's daily expression. These polysemy phenomena have led to a decline in the performance of traditional general sentiment calculations in the vertical field of meteorology. As far as we know, there is no Chinese sentiment calculation tool in the meteorological field, due to the different expressions between different languages. Facing with the above problems and issues, this paper proposes and establishes a sentiment analysis based on lexicon-based framework of Chinese text in the meteorological field. The framework fuses two major sentiment assessment method consists of two approaches, namely machine learning-based and knowledge based one. First, word embedding similarity and word co-occurrence methods are utilized to construct the domain sentiment dictionary which includes 8,796 negative words and 4,751 positive words. Then, by using the extend sentiment dictionary to select theme words from original text as feature, machine learning models are introduced to assess the sentiment tendency. Experiment showed that the performance of the proposed framework was improved up to 85.79% in terms of overall accuracy compared with traditional methods.

关键词： Dictionaries Meteorology Sentiment analysis Feature extraction Machine learning Vocabulary Semantics

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Image Segmentation Using Fuzzy C-means Optimized by Ant Lion Optimization

Image Segmentation Using Fuzzy C-means Optimized by Ant Lion...

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IEEE International Conference on intelligent Data Acquisition and Advanced computing Systems

作者： Can Jin Zhiwei Ye Lingyu Yan Ye Cao Aixin Zhang Lie Ma Xiang Hu Jiwei Hu School of Computer Science Hubei University of Technology Wuhan China Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Wuhan Jingzhi Technology Co. Ltd. Wuhan China Wuhan FiberHome Technical Services Co. Ltd. Wuhan China

Image segmentation is the indispensable part in the field of computer vision. There are tremendous methods for handling this task such as Otsu-thresholding and Fuzzy C-means (FCM). However, the segmentation results of the images are occasionally unsatisfactory due to the presence of noise. In this paper, two kinds of spatial information consisting of the relative position information and the intensity information of the neighborhood pixels in an image are taken into consideration in constructing the objective function in FCM. Moreover, Ant Lion Optimization, one of the recently proposed optimization algorithms is utilized to optimize the relevant index. Bio-inspired ALO has the robust ability to find optimal parameters in search spaces. So the proposed approach to image segmentation based on Fuzzy C-Means (FCM) and Ant Lion Optimization (ALO) may alleviate this problem to a certain degree. A series of experimental validation has been implemented for demonstrating the performance of the proposed approach in the end of the paper.

关键词： Clustering algorithms Image segmentation Optimization Convergence Linear programming Standards Gaussian noise

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