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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hongyang Jiang Qiang Huang Liang Chen Zhi Li Ying Xu Huiyan Sun Yi Chang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun China College of Engineering Key Laboratory of Intelligent ManufacturingTechnology of Ministry of Education Shantou University Shantou China Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province The First Hospital of China Medical University Shenyang China The China-Japan Union Hospital Cancer Systems Biology Center Jilin University Changchun China The China-Japan Union Hospital School of Artificial Intelligence Cancer Systems Biology Center Jilin University Changchun China School of Artificial Intelligence Jilin University Changchun China

Cancer staging, grading and subtyping all represent important problems for precision diagnosis, treatment and mechanistic studies of cancer. The majority of the existing computational methods solve this problem via multi-classification of differential gene-expressions of cancer samples of specific classes (Stages, Grades and subtypes) vs. controls. However, the performance of such classification techniques is generally not satisfactory since the discerning power of differential expression patterns in such classifications is limited. We present here a multi-classification technique, based on co-expression patterns specific to individual subclasses in provided training data as co-expression patterns tend to be more conserved than differential expressions within each subclass. A challenge in implementing this strategy lies in how to effectively derive co-expression patterns in individual samples, which is solved through comparing co-expression patterns within a subclass and those in the subclass plus a new sample. Compared with the state-of-the-art gene expression-based classification methods, our method outperforms them in cancer staging, grading and subtyping of cancer samples from TCGA in almost all the measures used. In addition, the co-expressed genes computationally selected for classifications are biologically meaningful, which will prove important for diagnostic biomarker design, treatment plan selection and possibly mechanistic studies of cancer.

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Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems

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Frontiers of Information Technology & Electronic engineering 2017年第9期18卷 1385-1395页

作者： Xing-chen WU Gui-he QIN Ming-hui SUN He YU Qian-yi XU College of Computer Science and Technology Jilin University Changchun 130012 China MOE Key Laboratory of Symbol Computation and Knowledge Engineering Changchun 130012 China Department of Measurement and Controlling Engineering Changchun University Changchun 130012 China

The introduction ofproportional-integral-dorivative （PID） controllers into cooperative collision avoidance systems （CCASs） has been hindered by difficulties in their optimization and by a lack of study of their effects on vehicle driving stability, comfort, and fuel economy. In this paper, we propose a method to optimize PID controllers using an improved particle swarm optimization （PSO） algorithm, and to bettor manipulate cooperative collision avoidance with other vehicles. First, we use PRESCAN and MATLAB/Simulink to conduct a united simulation, which constructs a CCAS composed of a PID controller, maneuver strategy judging modules, and a path planning module. Then we apply the improved PSO algorithm to optimize the PID controller based on the dynamic vehicle data obtained. Finally, we perform a simulation test of performance before and after the optimization of the PID controller, in which vehicles equipped with a CCAS undertake deceleration driving and steering under the two states of low speed （≤50 km/h） and high speed （≥100 km/h） cruising. The results show that the PID controller optimized using the proposed method can achieve not only the basic functions of a CCAS, but also improvements in vehicle dynamic stability, riding comfort, and fuel economy.

关键词： Cooperative collision avoidance system （CCAS） Improved particle swarm optimization （PSO） PID controller Vehicle comfort Fuel economy

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Intelligent CPSS and its application to health care computing

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Science China(Information Sciences) 2016年第5期59卷 10-12页

作者： Dayou LIU Bo YANG Shang GAO Yungang ZHU Yong LAI College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University

The rapid development of cyber-physical systems(CPS)had a tremendous impact on human behavior and *** human involvement,CPS has naturally evolved into cyberphysical social systems(CPSS)[1].The top five technology brea... 详细信息

关键词： CPSS net

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Design of a Daily Brief Business Report Generator based on Web Scraping with KNN Algorithm

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Journal of Physics: Conference Series 2019年第5期1345卷

作者： Gang Lin Yanchun Liang Xiaoyang Fu Guangshun Chen Shaocan Cai Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Department of Computer Science and Technology School of Computer Science Zhuhai 519041 Guangdong China Department of Computer Science and Technology School of Computer Science Zhuhai College of Jilin University Zhuhai 519041 Guangdong China

In order to generate a report for an enterprise where there is neither the API supporting from their existing website systems nor the granted database access rights approval, a daily business report generator system based on web scraping with k nearest neighbor (kNN) classification algorithm is proposed in this paper. It covers the web crawler technology that is to access existing website system and extract business data. The kNN algorithm is applied to identify the verification code on the login page, and the brief daily report generating in a spread-sheet style grid. Compared with some OCR engine for image recognition, the system in Python can automatically generate the brief daily business reports by the kNN algorithm, which is better than some library with default training set on validating the verification code.

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Anti-Collision Warning Algorithm based on Visual Perception in Front of Vehicle

Anti-Collision Warning Algorithm based on Visual Perception ...

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2017 International Conference on Mechanical,Electronic,Control and Automation engineering(MECAE 2017)

作者： Pengcheng Ding Jindong Zhang Yanghao Zhou Hongliang Liu Xiao Wang College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University

ISBN: (纸本)9781510838451

The vehicles' driving safety plays an important role in the transportation safety's development, and it's a necessary requirement in intelligent transportation and intelligent vehicle. The point that is suggested in this paper is that vehicles' collision warning system which is based on computer vision, and the system is built on computer vision, pattern recognition, machine learning and some other artificial intelligent theory and techniques, discerning the vehicles which is in front of your vehicle and measure the security range, warning the possible danger timely to make sure your drive safe. We use the characteristic which is called haar of the samples to train in the classifier to get a cascaded classifier named Boosted, loading the classifier and image of the vehicles marked and calculating the distance and relative speed. In the last, we do a lot of system simulation experiments, verifying the accuracy and the effectiveness of the system from vehicle outline detection results and safe vehicle determination.

关键词： Computer Vision Pattern Recognition Rear-End Collision Warning

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Vehicle Automatic Vertical & Horizontal Parking Trajectory Prediction and Fuzzy Control

Vehicle Automatic Vertical & Horizontal Parking Trajectory P...

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2017 International Conference on Mechanical,Electronic,Control and Automation engineering(MECAE 2017)

作者： Jinqing Li Jindong Zhang Weikai Wang Chengliang Liu Hongyan Guo Kuo Zhang College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University

ISBN: (纸本)9781510838451

Automatic parking system is a non-accurate modeling system, and the fuzzy algorithm is an excellent choice for the non-accurate modeling. Based on the vehicle kinematics modeling and the experience of artificial parking, we establish the horizontal and vertical models of the vehicle automatic trajectory and the control system through fuzzy inference. By fuzzy controller, parking optimal trajectory can control the speed of the vehicle when reversing garage, and at the same time, the fuzzy controller can control the vehicle body posture reverse garage. These two controls work closely together to achieve accurate body trajectory. The accuracy of the controller is verified by the simulation experiment.

关键词： Vertical Parking Horizontal Parking Trajectory Prediction Fuzzy

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Research and Development of Virtual Instruments System Based on Depth Camera

Research and Development of Virtual Instruments System Based...

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The Third International Conference of Pioneering Computer Scientists, Engineers and Educators,ICPCSEE 2017(originally ICYCSEE)

作者： Xiao-li Xu Ming-hui Sun Xin-yue Sun Wei-yu Zhao Xiaoying Sun College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University College of Communication Engineering Jilin University

This paper takes advantage of the depth camera of somatosensory kinect and sensors to implement gesture recognition and design a virtual instrument system. As long as the user waves his arm without the help of other equipments, our system can automatically recognize the hand gesture and make suitable sound. In order to achieve depth camera's detection of hands movement, this paper introduce the depth imaging technology Light Coding and bone tracking technology to obtain the actual position information and hand movement information of the human body. Feet movement detection uses sensor technology, different stampede strength outputs different digital number after AD conversion so that the intensity can be controlled. A series of experiments show that the system has good fluency and practicality and increased the fun of playing instruments.

关键词： Virtual instrument Kinect Sensors Depth data processing Bone tracking

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Design and Implementation of Judge System for Old Man Fall Down

Design and Implementation of Judge System for Old Man Fall D...

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2017 International Conference on Mechanical,Electronic,Control and Automation engineering(MECAE 2017)

作者： Chao Sun Jindong Zhang Jiajia Lin Rui Song Xuan Zang Xiaohan Liu College of Software Jilin University College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University

ISBN: (纸本)9781510838451

This paper taking into account the universal use of Android phones, designs and implements judgments and notices the old man falling cell phone App. By calling the phones accelerometer, gyroscope collection campaigns after processing according to human body dynamics model to determine the specific state of the elderly, and get the specific latitude and longitude via GPS;Cell phone judge after the old man falls can send text messages to bound phone number in advance and inform the GPS to obtain location coordinates, after the fall in the elderly will be able to make the necessary relief in a timely manner.

关键词： APP Fall down Dynamic models GPS

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Pancreatic cancer biomarker detection using recursive feature elimination based on Support Vector Machine and large margin distribution machine

Pancreatic cancer biomarker detection using recursive featur...

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International Conference on Systems and Informatics (ICSAI)

作者： Yidan Lv Yan Wang Yongfei Tan Wei Du Keke Liu Hao Wang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Basic Medicine School Jilin University Changchun China Department of Hepatopancreatobiliary Surgery and Second Affiliated Hospital Harbin Medical University Harbin China

Pancreatic cancer acts as one of the leading causes of cancer-connective deaths. Its five-year overall survival rate being reported is about 7.7% from 2006 to 2012 by the National Cancer Institute. One of the main causes for its poor prognosis is because its non-typical symptoms make early diagnosis very challenging. Therefore, a predominant strategy for early accurate detection and prognostication on pancreatic cancer is vital to the whole course of comprehensive therapy. In this research, we proposed a method which combined Recursive Feature Elimination (RFE) method based on Support Vector Machine (SVM) and Large Margin Distribution Machine (LDM) to identify potential biomarkers for pancreatic cancer. In our experiments, we have strengthened the process of RFE to achieve better performance. The dataset GSE15471 we adopted are from GEO database with 39 pairs of pancreatic ductal carcinoma and adjacent control pancreatic tissues. Through experiments, a panel of twelve genes was identified as biomarkers in pancreatic cancer with 91.28% classification accuracy. The universality of the candidate genes was examined on another dataset GSE28735 and the classification accuracy was higher than 80%. In addition, by using the SVM, LDM and BP classifiers, we compared the ordered feature sets generated by our proposed method with T-test, SVM-RFE and LDM-RFE, and the results indicated our proposed method obtained higher average classification accuracy.

关键词： Cancer Support vector machines Feature extraction Tumors Training data Testing

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Optimizing simple tabular reduction with a bitwise representation 25

Optimizing simple tabular reduction with a bitwise represent...

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Wang, Ruiwei Xia, Wei Yap, Roland H.C. Li, Zhanshan School of Software Jilin University Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering Education Ministry China School of Computing National University of Singapore Singapore

Maintaining Generalized Arc Consistency (GAC) during search is considered an efficient way to solve non-binary constraint satisfaction problems. Bit-based representations have been used effectively in Arc Consistency algorithms. We propose STRbit, a GAC algorithm, based on simple tabular reduction (STR) using an efficient bit vector support data structure. STRbit is extended to deal with compression of the underlying constraint with c-tuples. Experimental evaluation show our algorithms are faster than many algorithms (STR2, STR2-C, STR3, STR3-C and MDDc) across a variety of benchmarks except for problems with small tables where complex data structures do not payoff.

关键词： Data structures

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