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2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI2018)

作者： Zhiying Gao Lijun Sun Zhihua Wei Department of Computer Science and Technology Tongji University Research Center of Big Data and Network Security Tongji University Key Laboratory of Embedded System and Service Computing Tongji University

Nowadays, with the increasing number of protein sequences all over the world, more and more people are paying their attention to predicting protein subcellular location. Since wet experiment is costly and time-consuming, the automatic computational methods are urgent. In this paper, we propose a variant model based on Long Short-Term Memory(LSTM) to predict protein subcellular location. In this model, we employ LSTM to capture long distance dependency features of the sequence data. Moreover, we adjust the loss function of the loss layer to solve multi-label classification problem. Experimental results demonstrate that, compared with the traditional machine learning methods, our method achieves the best performance in various evaluation metrics.

关键词： LSTM multi-label classification protein subcellular localization

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A Study on Influential User Identification in Online Social networks

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Chinese Journal of Electronics 2016年第3期25卷 467-473页

作者： WANG Nan SUN Qindong ZHOU Yadong SHEN Si Shaanxi Key Laboratory of Network Computing and Security Xi'an University of Technology Ministry of Education Key Laboratory For Intelligent Network and Network Security Xi'an Jiaotong University

Influential user evaluation is great importance in many application areas of online social *** order to identify influential users in a more adequate and practical way, we propose a Dynamic regional interaction model(DRI) to evaluate user influence in online social networks. Influential users can be identified by the influence effect on different distance users based on dynamic regional interaction model. We have applied the influential user identification method to Sina Weibo and the experimental results show that compared with the existing methods the proposed method can identify the influence users in a more accuracy and efficiency way.

关键词： Online social networks Influential user identification Dynamic regional interaction model(DRI) User influence evaluation

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An Improved Convolutional Neural network Algorithm for Multi-Label Classification

An Improved Convolutional Neural Network Algorithm for Multi...

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International Conference on Audio, Language and Image Processing, ICALIP

作者： Xinsheng Wang Lijun Sun Zhihua Wei Department of Computer Science and Technology Tongji University Shanghai China Research Center of Big Data and Network Security Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Recent years conventional neural network(CNN) has been applied to different natural language processing(NLP) tasks such as sentence classification, sentence modeling, etc. Some researchers use CNN to do multi-label classification but their work mainly focus on image rather than text. In this paper, we propose an improved CNN via hierarchical dirichlet process(HDP) model to deal with the multi-label classification problem in NLP. We first apply an HDP model to discard some words which are less important semantically. Then we use word embedding methods to transform words to vectors. Finally, we train CNN based on word vectors. Experimental results demonstrate that our method is superior to most traditional multi-label classification methods and TextCNN in terms of performance.

关键词： Feature extraction Training Convolutional neural networks Natural language processing Vocabulary Transforms

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Simulation and Optimization of Temperature Field in Large-Sized MOCVD Reactor by Resistance Heating

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IOP Conference Series: Materials Science and Engineering 2019年第1期538卷

作者： Lili Zhao Zhiming Li Runqiu Guo Lansheng Feng Ligen Lu Xiaoqian Fu Shandong Provincial Key Laboratory of Network based Intelligent Computing School of Information Science and Engineering University of Jinan Jinan Shandong 250022 China State Key Discipline Laboratory of Wide Band-Gap Semiconductor Technology School of Microelectronics Xidian University Xi'an Shanxi 710071 China

The effect of the number of layers and the length of the heat shields on the heating efficiency and temperature distributions in the substrate have been studied by establishing the model of a single-piece 18-inch MOCVD reactor. The results show that the number of layers of the heat shields is directly proportional to the heating efficiency, decreasing the length of the thermal shield can reduce the standard deviation(STD) of the substrate temperature. When the length of the heat shields is 56mm, the coefficient of substrate temperature STD is 21.41 °C and the STD is about 45% lower than the traditional susceptor. An area within substrate radius of 200mm, the coefficient of substrate temperature STD is 2.64 °C and the STD is about 93% lower than the traditional susceptor. The results obtained will provide theoretical basis for developing the heating structure of large-sized MOCVD reactor.

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Revisiting Recent and Current Anomaly Detection based on Machine Learning in Ad-Hoc networks

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Journal of Physics: Conference Series 2019年第1期1288卷

作者： Zhixiao Wang Mingyu Chen Wenyao Yan Wendong Wang Ang Gao Gaoyang Nie Feng Wang Shaobo Yang Xi'an University of Technology Xi'an 710048 China Goettingen University Goettingen 37077 Germany Xi'an Jiaotong University Xi'an 710049 China Shaanxi Provincial Key Laboratory of Network Computing and Security Technology Xi'an 710048 China College of Xi'an Innovation Yan'an University Xi'an 710100 China Yan'an University Yan'an 716000 China National Satellite Meteorological Center China Meteorological Administration Beijing 100081 China

Ad-Hoc network which is one kind of self-organized networks is much more vulnerable than the infrastructural network with the properties of highly changeable linkage, dynamic structure, and wireless connections so that the tradition intrusion detection system (IDS) should be improved to adapt in such network with limited computing resources and open channels. To ensure the security in Ad-Hoc network, the efficient anomaly detection methods should be probed. Over the past years, many studies have implemented anomaly detection methods (intrusion detection techniques) based on machine-learning methods in this field. This article analyzes the existing security problem in Ad-Hoc network, presents the basic theory of intrusion detection for Ad-Hoc network, and reviews the current and recent anomaly detection methods used machine learning techniques in the intrusion detection system.

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Exploiting Meta-Path with Attention Mechanism for Fine-Grained User Location Prediction in LBSNs

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Journal of Physics: Conference Series 2019年第1期1284卷

作者： Zhixiao Wang Wenyao Yan Wendong Wang Ang Gao Lei Yu Shaobo Yang GaoYang Nie School of Computer Science and Engineering Xi'an University of Technology 710048 Xi'an China Telematics Group the University of Goettingen 37077 Goettingen Germany Shaanxi Key Laboratory of Network Computing and Security 710048 Xi'an China School of Electronic and Information Engineering Xi'an Jiaotong University 710048 Xi'an China College of Xi'an Innovation Yan'an University 710100 Xi'an China Yan'an University Yan'an 716000 China National Satellite Meteorological Center China Meteorological Administration Beijing 100081 China

Based on the huge volumes of user check-in data in LBSNs, users' intrinsic mobility patterns can be well explored, which is fundamental for predicting where a user will visit next given his/her historical check-in records. As there are various types of nodes and interactions in LBSNs, they can be treated as Heterogeneous Information network (HIN) where multiple semantic meta-paths can be extracted. Inspired by the recent success of meta-path context based embedding techniques in HIN, in this paper, we design a deep neural network framework leveraging various meta-path contexts for fine-grained user location prediction. Experimental results based on two real-world LBSN datasets demonstrate the best effectiveness of the proposed approach using various evaluation metrics than others.

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A geometric image segmentation method based on a bi-convex, fuzzy, variational principle with teaching-learning optimization

A geometric image segmentation method based on a bi-convex, ...

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作者： Jin, Haiyan Li, Yaning Xing, Bei Wang, Lei Department of Computer Science and Engineering Xi'An University of Technology No. 5 South Jinhua Road Xi'an Shaanxi710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

This paper proposes an efficient, bi-convex, fuzzy, variational (BFV) method with teaching and learning based optimization (TLBO) for geometric image segmentation. Firstly, we adopt a bi-convex, object function to process a geometric image. Then, we introduce TLBO to maximally optimize the length-penalty item, which will be changed under the teaching phase and the learner phase of the TLBO. This makes the length penalty item closer to the target boundary. Therefore, the length-penalty item can be automatically adjusted according to the fitness function, namely the evaluation standards of the image quality. At last, we combine the length-penalty item with the numerical remedy mechanism to achieve better results. Compared with existing methods, simulations show that our method is more effective. © 2016 - IOS Press and the authors. All rights reserved.

关键词： Image segmentation

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Least squares twin SVM decision tree for multi-class classification

Least squares twin SVM decision tree for multi-class classif...

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International Congress on Image and Signal Processing, BioMedical Engineering and Informatics

作者： Qing Yu Lihui Wang Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology China

ISBN: (纸本)9781509037117

In order to solve multi-class classification problem in real world, we improved TSVM in this paper. We combined LSTSVM with partial binary tree to improve classification efficiency. Binary tree hierarchy can solved the inseparable regional issues in OVO-SVM and OVA-SVM classification. Experimental results show that it improved the classification accuracy. It also has better speed-up ratio than the OVO-SVM, OVA-SVM. It also reduced the training time. The time advantage is more obvious, especially the data set is large.

关键词： data mining the least squares support vector machine partial binary tree multi-class classification

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Improved Canopy-FCM algorithm based on MapReduce

Improved Canopy-FCM algorithm based on MapReduce

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International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Qing Yu Zhimin Ding Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology Tianjin China

ISBN: (纸本)9781509037117

In order to solve the problem that FCM algorithm is sensitive to initial clustering center, we use Canopy algorithm to conduct the quick rough clustering. In the meantime, to avoid the blindness of Canopy algorithm, we put forward an improved Canopy-FCM algorithm based on max-min principle. In allusion to the problem that FCM algorithm has high time complexity, this article use the parallel computing frame of MapReduce to design and realize the improved Canopy-FCM algorithm. Experimental result shows: improved Canopy-FCM algorithm based on MapReduce has better clustering quality and running speed than the Canopy-FCM and FCM algorithm based on MapReduce, and the improved Canopy-FCM algorithm based on Hadoop has better speed-up ratio than Canopy-FCM based on Standalone mode.

关键词： Clustering algorithms Algorithm design and analysis Signal processing algorithms Parallel processing Classification algorithms Blindness Data collection

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Smartphone Based Car-Searching System for Large Parking Lot

Smartphone Based Car-Searching System for Large Parking Lot

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IEEE Conference on Industrial Electronics and Applications

作者： Junhuai Li Yang An Rong Fei Huaijun Wang Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Xi'an China

ISBN: (纸本)9781467386456

Find a car in a large parking lot is a challenge in our daily life. In this paper, a novel car-searching approach based on smartphone for a large parking lot is presented. In the new approach, some QR codes are set up in each area which can identify the parking spots. In addition, shortest path algorithm is used to plan the optimal car-searching path. Considering that some parkers with poor sense of direction may lose in the parking lot, a real-time navigation method is proposed for parker to search the car, which is based on the built-in sensors in smartphone and pedometer principle. The new approach is tested in a large indoor parking lot, and the experimental results show the efficiency of the proposed car-searching system.

关键词： Intelligent car-searching system QR code Smartphone Pedometer

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