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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Xinxin Liu Xiaolong Xu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728105499;9781728105482

With the development of positioning technology, sensor-based positioning has become the most commonly used positioning method because it is more low-cost and efficient with the popularity of smart phones. In this paper, we construct a pedestrian real-time offline positioning and moving trajectory tracking system (HERMES) which only relies on the sensors inside smart phones. In HERMES, based on Pedestrian Dead Reckoning (PDR) model, a stable and accurate pedestrian gait recognition is achieved through the rare double sliding window, the pedestrian step length is estimated in real time by a training pedestrian step length model, and the pedestrian real-time course angle is available through orientation sensor inside smart phones. Thus, if given a pedestrian's initial position, the system can implement the real-time offline pedestrian positioning and moving trajectory tracking. Experiments show the pedestrian gait recognition algorithm implemented in this paper has a high accuracy and the positioning effect is satisfactory. The moving trajectory tracked by the HERMES has a small deviation from the experimenter's actual trajectory.

关键词： Acceleration Real-time systems Gait recognition Smart phones Estimation Trajectory tracking Legged locomotion

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RPSBPT: A Route Planning Scheme with Best Profit for Taxi

RPSBPT: A Route Planning Scheme with Best Profit for Taxi

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Yuhua Qiu Xiaolong Xu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728105499;9781728105482

Reasonable route planning for taxi can not only improve quality of customer experience, but also maximize the benefit of taxi drivers. Most current taxi planning schemes are designed to achieve the shortest route or the shortest time, not to achieve the best profit. In this paper, we propose a route planning scheme with best profit for taxi (RPSBPT). First, we define the optimal profit point and the profit per unit time function. Second, we design the workflow of data cleaning, sampling and partitioning for preprocessing the dataset of taxi trajectory. Then, we integrate the DBSCAN algorithm and the K-means algorithm to obtain the optimal profit points. Finally, the simulate anneal Algorithm (SA), the genetic algorithm (GA), and the ant colony optimization algorithm (ACO) are adopted respectively to plan route for taxi. We constructed the taxi route planning prototype system and applied the proposed route planning scheme to the system. Based on the system and the collected taxi trajectory data at the Jinjiang district of the Chengdu city, we performed a series of experiments to compare the performance of three heuristic algorithms, including optimal route length, algorithm stability, total profit and profitability per unit time. Experimental results show that ACO has the best performance.

关键词： Public transportation Planning Trajectory Clustering algorithms Genetic algorithms Urban areas Partitioning algorithms

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Comparison and Analysis on Typical Network Representation Learning Algorithms

Comparison and Analysis on Typical Network Representation Le...

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IEEE International Conference on Progress in Informatics and computing (PIC)

作者： Yang Wang Xiaolong Xu Yun Li Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781538676738;9781538676721

Large-scale complex networks show complex nonlinear relationships among objects, such as the social relationships in the real world, the citation relationship among papers and the interactions among proteins in biology. The analysis of complex network systems make it possible to reveal network structures, information disseminating laws, and communication patterns. Network representation learning (NRL) algorithms focus on mapping the original network structure information to a low-dimensional vector space through a series of operations under the premise of maximally retaining the network structure. In order to analyze current representative NRL algorithms effectively to provide valuable references for other researchers, we built an experimental platform to perform and test the NRL algorithms based on matrix factorization, the NRL algorithms based on shallow neural network and the NRL algorithms based on deep neural network, with datasets on Collaboration Network, Social Network and Citation Network. We implemented a series of comprehensive experiments, based on metrics include precision@k, micro-F1 and macro-F1. Our experiments include network reconstruction, vertex classification, and link prediction, and show readers principles, performances and applications of typical NRL algorithms.

关键词： Neural networks Prediction algorithms Task analysis Matrix decomposition data visualization Blogs Telecommunications

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Modulus-based Matrix Multi-splitting Multi-Parameter Methods for a Class of Weakly Nonlinear Complementarity Problem

Modulus-based Matrix Multi-splitting Multi-Parameter Methods...

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2018 International Conference on Mathematics, Modelling, Simulation and Algorithms（MMSA2018）

作者： Chenliang Li Zhaohe Tian School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology

The modulus-based matrix multi-splitting multiparameter methods for solving a class of weakly nonlinear complementarity problem are presented. The weaker convergence conditions are analyzed. The numerical results show... 详细信息

ISBN: (纸本)9781510863064

关键词： weakly nonlinear complementarity problem modulus-based matrix multi-splitting multi-parameter

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Assessing Mental Stress Based on Smartphone Sensing data: An Empirical Study

Assessing Mental Stress Based on Smartphone Sensing Data: An...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Feng Wang Yasha Wang Jiangtao Wang Haoyi Xiong Junfeng Zhao Daqing Zhang Key Laboratory of High Confdence Software Technologies Ministry of Education Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Peking University Information Technology Institute Tianjin Binhai China National Engineering Research Center of Software Engineering Beijing China Big Data Lab Baidu Research Beijing

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Mental stress is a critical factor affecting one's physical and mental well-being. At the early stage, the effect of stress is often underestimated, while it usually leads to serious issue Lateran. Therefore, it is crucial to detect stress before it evolves into severe problems. Traditional stress detection methods are based on either questionnaires or professional devices, which are time-consuming, costly and intrusive. With the popularity of smartphones embedded with a rich set of sensors, which can capture people's context, such as movement, sound, location and so on, it is an alternative way to access people's behavior by smartphones. Through an empirical study, this paper proposes an automatic and non-intrusive stress detection framework based on smartphone sensing data. First, we construct various discriminative features from multi-modality phone sensing data, in which both absolute and relative features are considered to make the model more personalized. Then, to tackle the challenge of label insufficiency, we further develop a co-training based method for stress level classification. Finally, we evaluate our model based on an open dataset, and the experimental results verify its advantages over other baselines.

关键词： Sensors Stress Feature extraction Psychology Mobile handsets data models Biomedical monitoring

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A Reliable Road Segmentation and Edge Extraction for Sparse 3D Lidar data

A Reliable Road Segmentation and Edge Extraction for Sparse ...

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IEEE intelligent Vehicles Symposium

作者： Jianfeng Gu Yuehui Wang Long Chen Zhihao Zhao Zhe Xuanyuan Kai Huang Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education School of Data and Computer Science Sun Yat-sen University

ISBN: (纸本)9781538644539

Precise segmentation of road areas using cheap Lidar is a tough and critical task due to data sparsity problem. With sparse point clouds, reliable perception of environment is difficult due to the lack of available information and loss of object features. This paper presents a new approach to use sparse 3D Lidar data for road segmentation by fusing multiple frames of point cloud. With registration of multiple frames into a same coordinate system, reliable data can be provided for later ground segmentation and edge extraction. The accuracies of extensive experiments on three kinds of roads demonstrate that the proposed approach obtains high precision and reliability.

关键词： Edge detection Road Lidar segmentation Segmentation Coordinate system

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A hybrid algorithm based on ILP and genetic algorithm for time-aware test case prioritization

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Journal of Southeast University(English Edition) 2018年第1期34卷 28-35页

作者： Sun Jiaze Wang Gang School of Computer Science and Technology Xi’anUniversity of Posts and TelecommunicatioosXi’an 710121China Shaanxi Key Laboratory of Network Data Intelligent Processing X’an University of Posts and TelecommunicationsX’an 710121China

To solve the problem of time-awarc test case prioritization,a hybrid algorithm composed of integer linear programming and the genetic algorithm(ILP-GA)is ***,the test case suite which cm maximize the number of covered program entities a d satisfy time constraints is selected by integer linea ***,the individual is encoded according to the cover matrices of entities,and the coverage rate of program entities is used as the fitness function and the genetic algorithm is used to prioritize the selected test *** typical open source projects are selected as benchmark *** and method are selected as program entities,and time constraint percentages a e 25%and 75%.The experimental results show that the ILP-GA convergence has faster speed and better stability than ILP-additional and IP-total in most cases,which contributes to the detection of software defects as early as possible and reduces the software testing costs.

关键词： test case prioritization integer linear programming(I LP) genetic algorithm time constraint

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Application of deep learning in the hydraulic equipment fault diagnosis

Application of deep learning in the hydraulic equipment faul...

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2018 IEEE International Conference on Smart Manufacturing, Industrial and Logistics Engineering, SMILE 2018

作者： Jing, Liu Yankai, Liu Shanshan, Chen Zhijie, Wang Haipeng, Ji Engineering School University of Iowa Iowa52246 United States School of Computer Science and Engineering Hebei University of Technology Tianjin300401 China Hebei Province Key Laboratory of Big Data Calculation Tianjin300401 China Research Institute for Energy Equipment Materials Hebei University of Technology Tianjin3004011 China

ISBN: (纸本)9781538631836

In this paper, combined with the advantages of deep learning and the characteristics of big data equipment to put forward the intelligent fault diagnosis of Diamond Model. In this model, firstly, the data is extracted by the Sparse Autoencoder network and completed the feature extraction;and then the extracted feature vector combined with Softmax classifier is used to classify the equipment fault condition;finally the whole model uses the back propagation algorithm to fine tune the whole network parameters to obtain the optimal parameters that minimize the value of the loss function;ultimately, realized the model of adaptive extraction of fault features and fault conditions of intelligent diagnosis. And through simulation analysis, the accuracy rate of the classification of the DM model is verified to reach 81%, which is significantly higher than the shallow classifier and has a good self-learning ability and the level of knowledge learning ability. © 2018 IEEE.

关键词： Failure analysis

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Time-aware conversion prediction

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Frontiers of Computer science 2017年第4期11卷 702-716页

作者： Wendi JI Xiaoling WANG Feida ZHU Shanghai Key Laboratory of Trustworthy Computing Institute for Data Science and Engineering East China Normal University Shanghai 200062 China School of Information Systems Singapore Management University Singapore 188065 Singapore

The importance of product recommendation has been well recognized as a central task in business intelligence for e-commerce websites. Interestingly, what has been less aware of is the fact that different products take different time periods for conversion. The ＂conversion＂ here refers to actu- ally a more general set of pre-defined actions, including for example purchases or registrations in recommendation and advertising systems. The mismatch between the product＇s ac- tual conversion period and the application＇s target conversion period has been the subtle culprit compromising many exist- ing recommendation algorithms. The challenging question： what products should be recom- mended for a given time period to maximize conversion--is what has motivated us in this paper to propose a rank-based time-aware conversion prediction model （rTCP）, which con- siders both recommendation relevance and conversion time. We adopt lifetime models in survival analysis to model the conversion time and personalize the temporal prediction by incorporating context information such as user preference. A novel mixture lifetime model is proposed to further accom- modate the complexity of conversion intervals. Experimental results on two real-world data sets illustrate the high good- ness of fit of our proposed model rTCP and demonstrate its effectiveness in time-aware conversion rate prediction for ad- vertising and product recommendation.

关键词： conversion time survival analysis product rec-ommendation advertising

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Design and Implementation of Carrier Detection System Based on FPGA

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Journal of Physics: Conference Series 2020年第4期1624卷

作者： Xian Zhang Taiguo Qu Yiwen Liu Dong Yin Shaowei Deng School of Computer Science and Engineering Huaihua University Huaihua 418000 Hunan P. R. China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua 418000 Hunan P. R. China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua 418000 Hunan P. R. China

In order to better monitor and manage the signal transmission in the communication system and base station, and carrier detection is more effective. This paper designs a carrier(Carrier frequency) detection system based on FPGA, including system design requirements, FPGA selection, system framework design, carrier detection method design and so on. The performance indexes of the system are tested, such as carrier frequency range, carrier search time, carrier detection number and CPU consumption rate. The system has the advantages of high flexibility and good portability, and has a good application prospect in communication system, base station and other equipment.

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