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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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Mining maximal dynamic spatial co-location patterns

arXiv

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arXiv 2018年

作者： Hu, Xin Wang, Guoyin Duan, Jiangli College of Big Data and Intelligent Engineering Yangtze Normal University Chongqing408100 China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China

A spatial co-location pattern represents a subset of spatial features with instances that are prevalently located together in a geographic space. Although many algorithms for mining spatial co-location patterns have been proposed, the following selected problems remain: 1) these methods miss certain meaningful patterns (e.g., {Ganoderma_lucidumnew, maple_treedead} and {water_hyacinthnew(increase), algaedead(decrease)}) and obtain the wrong conclusion if the instances of two or more features increase/decrease (i.e., new/dead) in the same/approximate proportion, which has no effect on the prevalent patterns;2) because the number of prevalent spatial co-location patterns is quite large, the efficiency of existing methods is low in mining prevalent spatial co-location patterns. Therefore, we first propose the concept of the dynamic spatial co-location pattern that can reflect the dynamic relationships among spatial features. Second, we mine a small number of prevalent maximal dynamic spatial co-location patterns that can derive all prevalent dynamic spatial co-location patterns, which can improve the efficiency of obtaining all prevalent dynamic spatial co-location patterns. Third, we propose an algorithm for mining prevalent maximal dynamic spatial co-location patterns and two pruning strategies. Finally, the effectiveness and efficiency of the proposed method and the pruning strategies are verified by extensive experiments over real/synthetic datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Efficiency

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Prediction of Potential Disease-Associated MicroRNAs Based on Hidden Conditional Random Field

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Journal of Harbin Institute of Technology(New Series) 2018年第1期25卷 57-66页

作者： Maozu Guo Shuang Cheng Chunyu Wang Xiaoyan Liu Yang Liu School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing 100044 China Beijing Key Laboratory for Research on Intelligent Processing Method of Building Big Data( Beijing University of Civil Engineering and Architecture) Beijing 100044China Institute of Materials China Academy of Engineering PhysicsMianyang 621700SichuanChina

MicroRNAs( miRNAs) are reported to be associated with various diseases. The identification of disease-related miRNAs would be beneficial to the disease diagnosis and prognosis. However,in contrast with the widely available expression profiling, the limited knowledge of molecular function restrict the development of previous methods based on network similarity measure. To construct reliable training data,the decision fusion method is used to prioritize the results of existing methods. After that,the performance of decision fusion method is validated. Furthermore,in consideration of the long range dependencies of successive expression values,Hidden Conditional Random Field model( HCRF) is selected and applied to miRNA expression profiling to infer disease-associated miRNAs. The results show that HCRF achieves superior performance and outperforms the previous methods. The results also demonstrate the power of using expression profiling for discovering disease-associated miRNAs.

关键词： expression profiling hidden conditional random field miRNA-disease association network

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Design of People Flow Monitoring System in Public Place based on MD-MCNN

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Journal of Physics: Conference Series 2020年第1期1606卷

作者： Xiong Tianmin Lei Xiaochun Zheng Xingwen Chen Junyan Feng Yizhou School of computer and information security Guilin University of Electronic Technology Guilin 541004 China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin 541004 China Guangxi Cooperative Innovation Center of cloud computing and Big Data Guilin University of Electronic Technology Guilin 541004 China

Due to the limitation of hardware resources, the traditional people flow monitoring system based on computer vision in public places can't meet different crowd-scale scenarios. Therefore, a people flow monitoring system based on MD-MCNN algorithm is designed, which is an application system combining the improved SSD object detection algorithm MNSSD and MCNN density map regression algorithm. In the initial stage, the system uses MNSSD for accurate detection and counting. If the people flow gradually reaches a certain threshold, the system automatically uses MCNN to estimate people flow until the people flow falls below the threshold. Through the experimental verification, the system can realize the people flow statistics of low-density and high-density people in different scenarios, and can be applied on the existing embedded platform. This scheme can be extended to smart cities, smart scenic spots, smart transportation and other fields.

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Design of intelligent robot Platform based on Multi-sensor Fusion

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Journal of Physics: Conference Series 2020年第1期1606卷

作者： Ya Dajin Lei Xiaochun Li Yue Chen Junyan Huang Rongcun Lan Lin School of computer and information security Guilin University of Electronic Technology Guilin 541004 China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin 541004 China Guangxi Cooperative Innovation Center of cloud computing and Big Data Guilin University of Electronic Technology Guilin 541004 China

The mobile robot adapts to the more complicated indoor and outdoor environments, and can expand its scope of application. In order to reduce the influence of the cumulative error caused by navigation in complex environments, the indoor mobile robot that combines Inertial Measurement Unit (IMU) and encoder fusion is designed and implemented. In view of the limitations of the traditional single lidar scheme, a Multi-sensor Fusion scheme is proposed to achieve indoor map construction, path planning, multi-point navigation and other functions, and a MSIF KartoSLAM (Multi-sensor Information Fusion) algorithm is proposed, which combines the KartoSLAM algorithm and Multi-sensor information to achieve map construction in complex environments. Through comprehensive testing in the indoor environment, the results show that the Multi-sensor Fusion scheme is superior to the traditional single lidar scheme, and can achieve higher accuracy in mapping and navigation. At the same time, the robot platform can also be combined with the Internet of Things technology and integrated into intelligent housing system.

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The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN

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Sensors (Switzerland) 2019年第14期19卷 3145页

作者： Chen, Yuantao Tao, Jiajun Wang, Jin Chen, Xi Yang, Kai School of Computer and Communication Engineering & Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportatio Changsha University of Science and Technology Changsha410114 China School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China Hunan Institute of Scientific and Technical Information Changsha410001 China Electronics & Information School Yangtze University Jingzhou434023 China Technical Quality Department Hunan ZOOMLION Heavy Industry Intelligent Technology Corporation Limited Changsha410005 China

To address the problem of unstable training and poor accuracy in image classification algorithms based on generative adversarial networks (GAN), a novel sensor network structure for classification processing using auxiliary classifier generative adversarial networks (ACGAN) is proposed in this paper. Firstly, the real/fake discrimination of sensor samples in the network has been canceled at the output layer of the discriminative network and only the posterior probability estimation of the sample tag is outputted. Secondly, by regarding the real sensor samples as supervised data and the generative sensor samples as labeled fake data, we have reconstructed the loss function of the generator and discriminator by using the real/fake attributes of sensor samples and the cross-entropy loss function of the label. Thirdly, the pooling and caching method has been introduced into the discriminator to enable more ekective extraction of the classification features. Finally, feature matching has been added to the discriminative network to ensure the diversity of the generative sensor samples. Experimental results have shown that the proposed algorithm (CP-ACGAN) achieves better classification accuracy on the MNIST dataset, CIFAR10 dataset and CIFAR100 dataset than other solutions. Moreover, when compared with the ACGAN and CNN classification algorithms, which have the same deep network structure as CP-ACGAN, the proposed method continues to achieve better classification effects and stability than other main existing sensor solutions. © Sensors (Switzerland) 2019. All Rights Reserved.

关键词： Generative adversarial networks

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Multi-Dimension Training Scheme to Improve the Innovation Capacity of Postgraduate and Undergraduate Students Collaboratively

Multi-Dimension Training Scheme to Improve the Innovation Ca...

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Frontiers in Education (FIE) Conference

作者： Xiaolong Xu Jianhua Shen Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China College of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781538611753;9781538611746

This Innovative Practice Work in Progress Paper presents a multi-dimension training scheme to improve the innovation capacity of postgraduate and undergraduate students collaboratively. The current training of postgraduate and undergraduate education schemes cannot form a mutually beneficial relation, not suitable for the training of innovation capacity on postgraduate and undergraduate students collaboratively. There are various problems that need to be addressed. The main strategy of the proposed scheme is utilizing real research programs to establish an integrated team mixed with both postgraduate and undergraduate students, focusing on stimulating their innovation capacity in scientific research. In the scheme, we designed a multi-level joint training mechanism based on the mixed pedagogy, the collaborative resource pool platform, the multi-role and team-based innovation talent incubation mechanism, and the preparatory postgraduate student selection and training goal achievement evaluation mechanism. We applied the scheme to the training process of postgraduate students from the majors of Software Engineering, Computer Software and Theory, Computer Application, Computer Technology and Electronic Information Engineering, and undergraduate students from the majors of Computer Science and Technology, Information Security and Communication Engineering in Nanjing University of Posts and Telecommunications. The training results approve the feasibility and effectiveness of the scheme.

关键词： Training Technological innovation Collaboration Software Telecommunications Business

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An improved Chinese text multi-label classification method based on CNN

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Journal of Physics: Conference Series 2020年第1期1619卷

作者： Yuanxia Xin Zhi Zhang Collage of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan 430065 Hubei China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan 430065 Hubei China

Text multi-label classification technology can accurately and quickly classify text information into related categories or topics, and help people quickly locate the required content in massive information resources, which is of great significance in application. As the traditional classification algorithm is faced with the problems of low classification accuracy due to the low correlation of data labels, unbalanced label data and few short text feature words, this paper firstly performs hierarchical pre-processing on label data to transform multi-label classification into hierarchical text multi-classification. At the same time, an improved multi-label classification algorithm Multi-label Convolutional Neural Networks (ML-CNN) is proposed. Based on the TensorFlow framework, a CNN model is designed and different training models are constructed for each level of label classification. According to the number of classification levels, the output of the upper level label is stitched to the original input tail as the next level of input. Experiments on the description information of 500,000 Chinese products with labels, show that the improved algorithm will significantly improve the classification accuracy and the accuracy of each level can reach more than 88%, which proves the feasibility and effectiveness of the algorithm.

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MedShapeNet - a large-scale dataset of 3D medical shapes for computer vision

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BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK 2025年第1期70卷 71-90页

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Viet Duc Vu Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Maechler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Kuestner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Loeffler, Maximilian T. Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nuernberger, Andreas Pedrosa, Joo Ferreira, Carlos Aresta, Guilherme Cunha, Antonio Campilho, Aurelio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Roehrig, Rainer Hoelzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, Andre Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hoerst, Fabian Brehmer, Alexan Univ Hosp Essen AoR Inst Artificial Intelligence Med IKIM Girardetstr 2 D-45131 Essen Germany Graz Univ Technol Inst Comp Graph & Vis ICG Inffeldgasse 16c A-8010 Graz Austria Comp Algorithms Med Lab Cafe Graz Austria Univ Med Essen AoR Canc Res Ctr Cologne Essen CCCE Hufelandstr 55 D-45147 Essen Germany Univ Med Essen Univ Hosp Essen Ctr Virtual & Extended Real Med ZvRM Hufelandstr 55 D-45147 Essen Germany Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Swiss Fed Inst Technol Lausanne EPFL Comp Vis Lab Lausanne Switzerland Univ Hosp RWTH Aachen Dept Oral & Maxillofacial Surg Aachen Germany Shanghai Jiao Tong Univ Renji Hosp Sch Med Dept Radiol Shanghai Peoples R China AGH Univ Sci & Technol Dept Measurement & Elect Krakow Poland Univ Appl Sci Western Switzerland HES SO Valais Inst Informat Syst Sierre Switzerland Univ Basel Dept Biomed Engn Ctr Med Image Anal & Nav CIAN Allschwil Switzerland Univ Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA ZhejiangLab Res Ctr Connected Healthcare Big Data Hangzhou Zhejiang Peoples R China Graz Univ Technol Inst Mech Graz Austria Justus Liebig Univ Giessen Univ Hosp Giessen Dept Diagnost & Intervent Radiol Giessen Germany Mehran Univ Engn & Technol Dept Mech Engn Jamshoro Sindh Pakistan Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China Univ Hosp Canadian Surg Technol & Adv Robot CSTAR London ON Canada Shanghai Jiao Tong Univ Inst Biomed Mfg & Life Qual Engn Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai Peoples R China Med Univ Graz Dept Cardiac Surg Graz Austria Techn Univ Munchen Univ Klinikum Geschaftsfuhrender Oberarzt Abt Intervent & Diagn Munich Germany Icometrix Leuven Belgium Tech Univ Munich Dept Informat Garching Germany Univ Zurich Dept Quantitat Biomed Zurich Switzerland Tech Univ Dortmund Fac Stat Dortmund Germany Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China Univ Techn Univ Munchen Geschaftsfuhrender Oberarzt Abt Interv

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models). However, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instruments is missing. Methods: We present MedShapeNet to translate data-driven vision algorithms to medical applications and to adapt state-of-the-art vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. We present use cases in classifying brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. Results: By now, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Conclusions: MedShapeNet contains medical shapes from anatomy and surgical instruments and will continue to collect data for benchmarks and applications. The project page is: https://***/.

关键词： 3D medical shapes benchmark anatomy education shapeomics augmented reality virtual reality

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