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An algorithm of sensor optimization in controlled discrete event systems

Journal of computational Information Systems 2014年第20期10卷 8677-8684页

作者： Hao, Shuang Ouyang, Dantong Li, Lin Zhang, Liming Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Sensor optimization is the problem of minimizing sensor activation in a controlled discrete event system. During the evolutionary process, the available resources are supposed to be limited. Therefore, sensors are activated by the agent when it is necessary. Sensor activation policies are the functions that determine which sensors are to be activated. One policy is considered to minimal, if any strictly less activation decided by the agent satisfies the feasibility. In this paper, a new algorithm is proposed to compute the minimal sensor activation policy. The algorithm, based on the operation of Reverse Change and the property of the Label-reached, calculates the minimal solution of sensor activation and achieves a lower complexity of computation relatively.

关键词： Chemical activation

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Author Correction: Altered structural brain asymmetry in autism spectrum disorder in a study of 54 datasets

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Nature communications 2021年第1期12卷 7260页

作者： Merel C Postema Daan van Rooij Evdokia Anagnostou Celso Arango Guillaume Auzias Marlene Behrmann Geraldo Busatto Filho Sara Calderoni Rosa Calvo Eileen Daly Christine Deruelle Adriana Di Martino Ilan Dinstein Fabio Luis S Duran Sarah Durston Christine Ecker Stefan Ehrlich Damien Fair Jennifer Fedor Xin Feng Jackie Fitzgerald Dorothea L Floris Christine M Freitag Louise Gallagher David C Glahn Ilaria Gori Shlomi Haar Liesbeth Hoekstra Neda Jahanshad Maria Jalbrzikowski Joost Janssen Joseph A King Xiang Zhen Kong Luisa Lazaro Jason P Lerch Beatriz Luna Mauricio M Martinho Jane McGrath Sarah E Medland Filippo Muratori Clodagh M Murphy Declan G M Murphy Kirsten O'Hearn Bob Oranje Mara Parellada Olga Puig Alessandra Retico Pedro Rosa Katya Rubia Devon Shook Margot J Taylor Michela Tosetti Gregory L Wallace Fengfeng Zhou Paul M Thompson Simon E Fisher Jan K Buitelaar Clyde Francks Department of Language & Genetics Max Planck Institute for Psycholinguistics Nijmegen The Netherlands. Department of Cognitive Neuroscience Donders Institute for Brain Cognition and Behaviour Donders Centre for Cognitive Neuroimaging Radboud University Medical Centre Nijmegen The Netherlands. Bloorview Research Institute Holland Bloorview Kids Rehabilitation Hospital and Department of Pediatrics University of Toronto Toronto ON USA. Child and Adolescent Psychiatry Department Gregorio Marañón General University Hospital School of Medicine Universidad Complutense IiSGM CIBERSAM Madrid Spain. Institut de Neurosciences de la Timone UMR 7289 Aix Marseille Université CNRS Marseille France. Department of Psychology Carnegie Mellon University Pittsburgh PA USA. Laboratory of Psychiatric Neuroimaging (LIM-21) Departamento e Instituto de Psiquiatria Hospital das Clinicas HCFMUSP Faculdade de Medicina Universidade de Sao Paulo Sao Paulo SP Brazil. IRCCS Stella Maris Foundation viale del Tirreno 331 56128 Pisa Italy. Department of Clinical and Experimental Medicine University of Pisa Pisa Italy. Department of Child and Adolescent Psychiatry and Psychology Hospital Clinic Psychiatry Unit Department of Medicine 2017SGR881 University of Barcelona IDIBAPS CIBERSAM Barcelona Spain. Department of Forensic and Neurodevelopmental Sciences Institute of Psychiatry Psychology & Neuroscience King's College London London UK. Institute for Pediatric Neuroscience NYU Child Study Center New York NY USA. Department of Psychology Ben-Gurion University of the Negev Beer Sheva Israel. Brain Center Rudolf Magnus Department of Psychiatry University Medical Center Utrecht Utrecht The Netherlands. Department of Child and Adolescent Psychiatry Psychosomatics and Psychotherapy University Hospital Goethe University Frankfurt am Main Frankfurt Germany. Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University o

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Selective and incremental fusion for fuzzy and uncertain data based on probabilistic graphical model

Selective and incremental fusion for fuzzy and uncertain dat...

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作者： Zhu, Yungang Liu, Dayou Li, Yong Wang, Xinhua College of Computer Science and Technology Jilin University No. 2699 Qianjin Street Changchun China State Key Laboratory of Applied Optics Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Mathematics Jilin University Changchun China

Active and dynamic fusion for fuzzy and uncertain data have key challenges such as high complexity and difficult to guarantee accuracy, etc. In order to resolve the challenging issues, in this article a selective and incremental data fusion approach based on probabilistic graphical model is proposed. General Bayesian networks are adopted to represent the relationship among the data and fusion result. It purposively selects the most informative and decision-relevant data for fusion based on Markov Blanket in probabilistic graphical model. Meanwhile we present a special incremental learning method for updating the fusion model to reflect the temporal changes of environment. Theoretical analysis and experimental results all demonstrate the proposed method has higher accuracy and lower time complexity than existing state-of-the-art methods. © 2015 - IOS Press and the authors. All rights reserved.

关键词： Data fusion

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Detect epistasis based on optimization of artificial neural network architecture and parameters

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ICIC Express Letters 2015年第5期9卷 1499-1506页

作者： Zhou, Zhihui Liu, Guixia Su, Lingta Tian, Yuan College of Computer Science and Technology China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University No. 2699 Qianjin Avenue Changchun China

Epistasis or the interaction of single nucleotide polymorphisms (SNPs) at different loci plays a significant role on the mechanisms and pathogenesis of many common complex, multifactorial diseases and may be responsible for the “missing” heredity in genetic epidemiology. A certain complex network of epistatic interactions may contribute to the disease susceptibility. Epistasis detection is a computational and statistical challenge for the absence of prior knowledge of biological data, intensive computational burden and inappropriate statistical tests. Here we investigate the application of a standard artificial neural network (ANN) to detect epistasis for genome-wide association studies (GWAS) for the ability of ANN to model nonlinear data. The pattern-recognition strategy of ANN is utilized in the hope that genotypes of SNP markers will successfully distinguish between cases and controls in a way leading to the ANN being able to classify the SNPs correctly. Using simulated data under a wide range of settings, we show that the proposed ANN can well model gene-gene interactions with good predictive ability. The performance of ANN shows considerable promise and further improvements could usefully be designed towards reducing the computing speed and optimizing implementation of such networks. © 2015, ICIC International.

关键词： Neural networks

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Metallogenic prediction based on GIS through automatic database building and extraction of regional gravity and magnetic structure

Metallogenic prediction based on GIS through automatic datab...

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International Conference on Electronics, InformationTechnology and Intellectualization, EITI 2014

作者： Ye, S.S. Wang, S.C. Ye, Y.X. Zhao, G.X. Teng, F. Institute of Mineral Resources Prediction with Synthetic Information Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China National Engineering Research Center of Geophysics Exploration Instruments Changchun Jilin University Changchun China Tianjin Institute of Geology and Mineral Resources Tianjin China

ISBN: (纸本)9781138027411

The application of intelligent geophysical interpreting, technology of mapping and database building based on GIS have been discussed under the guidance of the theory and the method of the mineral resources prediction of synthetic information in this paper. The automatic extraction of regional gravity and magnetic structures to build databases has been realized. With the prediction of the iron mine as a case, the whole process of the application of synthetic information in minerals’ prediction of GIS technology has been stated. Through the realization of the data process of regional gravity and magnetic data, and of line-ring structure extraction, geophysical spatial information and map databases have been automatically built. Through a combination of geological and geophysical interpretation, the spatial information and map databases of geophysical ore-finding (iron mine) have been built. Through model units, an ore-finding model of comprehensive information has been built. Through geological units, geophysical-geological spatial information and map databases of synthetic information of predictive models have been built. According to the requirement of different mathematical models for mineral prediction of synthetic information for the data models, the spatial information in the synthetic information databases of predictive models has been extracted, and the geological variables have been converted, and then converted mutually into a digital geological multivariate statistical variable matrix. Different statistic prediction models of the multivariate statistical methods have been established to complete the whole process of metallogenic prediction by the computer. © 2015 Taylor & Francis Group, London.

关键词： Forecasting

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Survey of active learning for networked data

Survey of active learning for networked data

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2015 4th National Conference on Electrical,Electronics and Computer engineering(NCEECE 2015)

作者： Xinlei Wang Bo Yang Jing Huang The College of Computer Science and Technology Jilin University The College of Computer Science and Technology Key laboratory of Symbolic Computation and knowledge EngineeringJilin University

ISBN: (纸本)9781510819085

Classification in networked data is a popular research of complex network. Because of the large scale of networked data and the shortage of training data, active learning, which is an effective classification method for sparse data in machine learning, is often applied to networked data classification problems. Introduced in this paper are classification methods based on active learning for networked data classification problems with basic concepts and algorithms. Finally, according to the existing research, some problems for the future developing and research of networked data classification issues is presented.

关键词： Active Learning Networked Data Classification Machine Learning

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Document compression scheme based on integer data

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Journal of computational Information Systems 2014年第12期10卷 5203-5210页

作者： Liu, Yang Te, Rigen Li, Xiongfei Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education Jilin University Changchun 130012 China

Data compression does not only save space for data storage, but also improve its safety and efficiency during data transport. As any data can be saved in the form of an integer directly or indirectly, it is a meaningful work for us to extend integer data compression technique to the application of any arbitrary data compression. In this paper we propose a new compression algorithm (CNA), which does not need additional data support and data compression and reduction are lossless, for integer data based on Godel numbers. In the meantime, we will test the approach and show the uniqueness of the compression algorithm result and the correctness of the decompression data. By contrastive experimentation, the CNA compression algorithm proves good result for any documents, especially for integer documents. 1553-9105/Copyright © 2014 Binary Information Press.

关键词： Data compression

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A fast rigid registration algorithm for medical images

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Journal of Fiber Bioengineering and Informatics 2014年第3期7卷 409-418页

作者： Feng, Yuncong Li, Xiongfei Zhang, Xiaoli Ding, Tienan Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China Department of Computer Science and Technology Changchun China

Image registration is a vital research branch in medical image processing and analysis. In this paper, we proposed a new framework for rigid medical image registration. It can also be regarded as a pre-processing of non-rigid image registration algorithms. The interest of the algorithm lies in its simplicity and high e±ciency. In the registration algorithm, we firstly segmented the reference image and °oat image into two parts: tissue parts and background parts. Then the centers of the two images were located through performing distance transform on the two segmented tissue images. Finally, we detected the longest radius of the two tissue regions, by which we determined the rotating angle. We tested the registration algorithm on dozens of medical images, and the experimental results show us that the algorithm is competent for medical image registration.

关键词： Image registration

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Using a random forest method for epistasis detection in genome-wide association studies

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Journal of computational Information Systems 2014年第18期10卷 8043-8050页

作者： Zhou, Zhihui Liu, Guixia Su, Lingtao Han, Liang Yan, Lun College of Computer Science and Technology and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Detecting susceptibility genes and gene-gene interactions (epistasis) is an important issue in genetic association analysis and genetic epidemiology. Due to the huge number of single nucleotide polymorphisms (SNPs) and inappropriate statistical tests, epistasis detection is a computational and statistical challenge and becomes a "needle-in-a-haystack" problem. Also some epistasis detection algorithms proposed in lots of literature have demonstrated their successes for small scale data, while most of them cannot be directly applied into genome-wide association studies (GWAS) and the pathogenesis of many common complex human diseases is mysterious. Here we adopted a random forest method incorporating information theory and a powerful statistical test, B-stat to detect epistasis. We conducted sufficient artificial experiments on a wide range of simulated datasets and compared performance of our random forest method with its two competitors, COE and BEAM. Experimental results demonstrated that this method is quite available and time efficiency for the haystack problem. We also presented the results of the application of the method to the WTCCC type 1 diabetes dataset. We reported some previously well known genes as well as some significant SNP interactions.

关键词： Statistical tests

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A survey on virtual network embedding in cloud computing centers

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Open Automation and Control Systems Journal 2014年第1期6卷 414-425页

作者： Wei, Xiaohui Hu, Shoufeng Li, Hongliang Yang, Fan Jin, Yue College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China

Bridging virtualized environments with physical environments, virtual network plays an important role in Cloud Computing infrastructures. How to allocate physical resources for virtual nodes/links to construct virtual network is known as Virtual Network Embedding (VNE) problem. It is a crucial issue that draws wide attention. This paper surveys existing VNE methods and algorithms and how they work under different application scenarios. First, a survey of different VNE objective metrics is presented;then several VNE algorithms are presented and categorized;finally, the performance of different VNE solutions are compared and discussed. © 2010, Bentham Science Publishers B.V. All rights reserved.

关键词： Cloud computing

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