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A Novel Methodology for Extracting and Evaluating Therapeutic Movements in Game-Based Motion Capture Rehabilitation Systems

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JOURNAL OF MEDICAL SYSTEMS 2018年第12期42卷 255-255页

作者： Yang, Zhichao Rafiei, Mohammad H. Hall, Alexis Thomas, Caroline Midtlien, Hali A. Hasselbach, Alexander Adeli, Hojjat Gauthier, Lynne V. Univ Massachusetts Coll Informat & Comp Sci Amherst MA 01003 USA Ohio State Univ Dept Phys Med & Rehabil Columbus OH 43210 USA Ohio State Univ Dept Biomed Informat Columbus OH 43210 USA Ohio State Univ Dept Neurol Columbus OH 43210 USA Ohio State Univ Dept Neurosci Columbus OH 43210 USA Univ Massachusetts Dept Phys Therapy Lowell MA 01854 USA

Virtual rehabilitation yields outcomes that are at least as good as traditional care for improving upper limb function and the capacity to carry out activities of daily living. Due to the advent of low-cost gaming systems and patient preference for game-based therapies, video game technology will likely be increasingly utilized in physical therapy practice in the coming years. Gaming systems that incorporate low-cost motion capture technology often generate large datasets of therapeutic movements performed over the course of rehabilitation. An infrastructure has yet to be established, however, to enable efficient processing of large quantities of movement data that are collected outside of a controlled laboratory setting. In this paper, a methodology is presented for extracting and evaluating therapeutic movements from game-based rehabilitation that occurs in uncontrolled and unmonitored settings. By overcoming these challenges, meaningful kinematic analysis of rehabilitation trajectory within an individual becomes feasible. Moreover, this methodological approach provides a vehicle for analyzing large datasets generated in uncontrolled clinical settings to enable better predictions of rehabilitation potential and dose-response relationships for personalized medicine.

关键词： Stroke Telerehabilitation Serious games Kinect Motion capture Motor rehabilitation Hemiparesis Signal processing clustering algorithm Probability density function

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Missing value estimation for microarray data through cluster analysis

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KNOWLEDGE AND INFORMATION SYSTEMS 2017年第3期52卷 709-750页

作者： Pati, Soumen Kumar Das, Asit Kumar St Thomas Coll Engn & Technol Dept Informat Technol Kolkata India Indian Inst Engn Sci & Technol Dept Comp Sci & Technol Sibpur Howrah India

Microarray datasets with missing values need to impute accurately before analyzing diseases. The proposed method first discretizes the samples and temporarily assigns a value in missing position of a gene by the mean value of all samples in the same class. The frequencies of each gene value in both types of samples for all genes are calculated separately and if the maximum frequency occurs for same expression value in both types, then the whole gene is entered into a subset;otherwise, each portion of the gene of respective sample type (i.e., normal or disease) is entered into two separate subsets. Thus, for each gene expression value, maximum three different clusters of genes are formed. Each gene subset is further partitioned into a stable number of clusters using proposed splitting and merging clustering algorithm that overcomes the weakness of Euclidian distance metric used in high-dimensional space. Finally, similarity between a gene with missing values and centroids of the clusters are measured and the missing values are estimated by corresponding expression values of a centroid having maximum similarity. The method is compared with various statistical, cluster-based and regression-based methods with respect to statistical and biological metrics using microarray datasets to measure its effectiveness.

关键词： DNA Microarray data High-dimensional space clustering algorithm Similarity measurement Missing value imputation

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Two-layer obstacle collision avoidance with machine learning for more energy-efficient unmanned aircraft trajectories

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ROBOTICS AND AUTONOMOUS SYSTEMS 2017年 98卷 158-173页

作者： Choi, Youngjun Jimenez, Hernando Mavris, Dimitri N. Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA Georgia Inst Technol Aerosp Syst Design Lab Atlanta GA 30332 USA

This paper proposes a new two-layer obstacle avoidance algorithm that allows an unmanned aircraft system to avoid multiple obstacles with minimal effort. The algorithm includes a global-path optimization that identifies the number of obstacles resulting from a clustering technique based on obstacle information from an airborne sensor, and specifies a potential threat. A local-path trajectory optimization employs a model predictive control structure based on a multi-phase optimal trajectory resulting from approximated dynamics, vehicle constraints, and the result of the global-path optimization. Numerical flight simulations are conducted with a conventional one-layer obstacle avoidance algorithm and the two-layer obstacle avoidance algorithm. The results of the numerical simulation show that the proposed two-layer optimal obstacle avoidance algorithm generates more energy-efficient avoidance trajectories when an unmanned aircraft meets multiple obstacles. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Obstacle avoidance Optimal trajectory clustering algorithm Model predictive control UAV Path-planning

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Graph analysis of structural brain networks in Alzheimer's disease: beyond small world properties

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BRAIN STRUCTURE & FUNCTION 2017年第2期222卷 923-942页

作者： John, Majnu Ikuta, Toshikazu Ferbinteanu, Janina North Shore LIJ Hlth Syst Feinstein Inst Med Res Manhasset NY USA North Shore LIJ Hlth Syst Zucker Hillside Hosp Psychiat Res Glen Oaks NY USA Hofstra Univ Dept Math Hempstead NY 11550 USA Univ Mississippi Dept Commun Sci & Disorders Oxford MS USA SUNY Hlth Sci Ctr Dept Physiol & Pharmacol Brooklyn NY 11203 USA SUNY Hlth Sci Ctr Dept Neurol Brooklyn NY 11203 USA

Changes in brain connectivity in patients with early Alzheimer's disease (AD) have been investigated using graph analysis. However, these studies were based on small data sets, explored a limited range of network parameters, and did not focus on more restricted sub-networks, where neurodegenerative processes may introduce more prominent alterations. In this study, we constructed structural brain networks out of 87 regions using data from 135 healthy elders and 100 early AD patients selected from the Open Access Series of Imaging Studies (OASIS) database. We evaluated the graph properties of these networks by investigating metrics of network efficiency, small world properties, segregation, product measures of complexity, and entropy. Because degenerative processes take place at different rates in different brain areas, analysis restricted to sub-networks may reveal changes otherwise undetected. Therefore, we first analyzed the graph properties of a network encompassing all brain areas considered together, and then repeated the analysis after dividing the brain areas into two sub-networks constructed by applying a clustering algorithm. At the level of large scale network, the analysis did not reveal differences between AD patients and controls. In contrast, the same analysis performed on the two sub-networks revealed that small worldness diminished with AD only in the sub-network containing the areas of medial temporal lobe known to be heaviest and earliest affected. The second sub-network, which did not present significant AD-induced modifications of 'classical' small world parameters, nonetheless showed a trend towards an increase in small world propensity, a novel metric that unbiasedly quantifies small world structure. Beyond small world properties, complexity and entropy measures indicated that the intricacy of connection patterns and structural diversity decreased in both sub-networks. These results show that neurodegenerative processes impact volumetric network

关键词： Degenerative processes Complex networks clustering algorithm Small world properties Entropy Complexity Alzheimer's disease

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Separation and Measurement Technique Based on Dynamic Frequency-Selection and Frequency Division Applied to Multi Discharge Sources in GIS

Separation and Measurement Technique Based on Dynamic Freque...

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International Conference on Power System Technology (POWERCON)

作者： Chang Wenzhi Bi Jiangang Du Fei Shao Jin Yuan Shua China Elect Power Res Inst Beijing Peoples R China State Grid Corp China Beijing Peoples R China

ISBN: (纸本)9781538664612

Ultra-high frequency (UHF) monitoring technique for partial discharge (PD) is an important technical means to evaluate the insulation deterioration state of GIS equipment. However, huge interference and aliasing of a multiple PD signals in field test effect the sensitivity and reliability of UHF detection. To identify the interference and aliasing of a multiple PD signals more accurately, meanwhile to cover the shortage of amplitude ratio clustering technique which can not identify signals' characteristics in frequency domain, the accuracy and adaptability of clustering algorithm should be further improved. To solve these problems, the separation and measurement technique based on dynamic frequency-selection and frequency division applied to multi discharge sources are proposed. UHF conditioner with high sensitivity and wide dynamic range are designed, whose work modes include wide-band amplification detection and narrowband frequency selection. Accordingly, the interactive dynamic clustering algorithm is proposed. The PD tests in real GIS with five kinds of PD defect and interference models are designed, the real PD tests indicate that the interactive dynamic clustering algorithm is more adaptable in the complex environment of the multiple PD and interference signals.

关键词： GIS Partial Discharge clustering algorithm State monitoring Ultra-high frequency

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Unsupervised Segmentation of Remote Sensing Images using FD Based Texture Analysis Model and ISODATA

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INTERNATIONAL JOURNAL OF AMBIENT COMPUTING AND INTELLIGENCE 2017年第3期8卷 58-75页

作者： Hemalatha, S. Anouncia, S. Margret VIT Univ Sch Comp Sci & Engn Vellore Tamil Nadu India

In this paper, an unsupervised segmentation methodology is proposed for remotely sensed images by using Fractional Differential (FD) based texture analysis model and Iterative Self-Organizing Data Analysis Technique algorithm (ISODATA). Essentially, image segmentation is used to assign unique class labels to different regions of an image. In this work, it is transformed into texture segmentation by signifying each class label as a unique texture class. The FD based texture analysis model is suggested for texture feature extraction from images and ISODATA is used for segmentation. The proposed methodology was first implemented on artificial target images and then on remote sensing images from Google Earth. The results of the proposed methodology are compared with those of the other texture analysis methods such as LBP (Local Binary Pattern) and NBP (Neighbors based Binary Pattern) by visual inspection as well as using classification measures derived from confusion matrix. It is justified that the proposed methodology outperforms LBP and NBP methods.

关键词： Classification Accuracy clustering algorithm Confusion Matrix Fractional Derivatives ISODATA Remotely Sensed Images Texture Analysis Unsupervised Segmentation

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A framework for habitat monitoring and climate change modelling: construction and validation of the Environmental Stratification of Estonia

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REGIONAL ENVIRONMENTAL CHANGE 2017年第2期17卷 335-349页

作者： Villoslada, Miguel Bunce, Robert G. H. Sepp, Kalev Jongman, Rob H. G. Metzger, Marc J. Kull, Tiiu Raet, Janar Kuusemets, Valdo Kull, Ain Leito, Aivar Estonian Univ Life Sci Kreutzwaldi 5Room 2C18 EE-51014 Tartu Estonia Wageningen UR Alterra Droevendaalsesteeg 3 NL-6708 PB Wageningen Netherlands Univ Edinburgh Sch GeoSci Drummond St Edinburgh EH8 9XP Midlothian Scotland Univ Tartu Vanemuise 46 EE-51014 Tartu Estonia

Environmental stratifications provide the framework for efficient surveillance and monitoring of biodiversity and ecological resources, as well as modelling exercises. An obstacle for agricultural landscape monitoring in Estonia has been the lack of a framework for the objective selection of monitoring sites. This paper describes the construction and testing of the Environmental Stratification of Estonia (ESE). Principal components analysis was used to select the variables that capture the most amount of variation. Seven climate variables and topography were selected and subsequently subjected to the ISODATA clustering routine in order to produce relatively homogeneous environmental strata. The ESE contains eight strata, which have been described in terms of soil, land cover and climatic parameters. In order to assess the reliability of the stratification procedure for the selection of monitoring sites, the ESE was compared with the previous map of Landscape Regions of Estonia and correlated with five environmental data sets. All correlations were significant. The stratification has therefore already been used to extend the current series of samples in agricultural landscapes into a more statistically robust series of monitoring sites. The potential for applying climate change scenarios to assess the shifts in the strata and associated ecological impacts is also examined.

关键词： Climate Geomorphology clustering algorithm Monitoring Stratified random sampling

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Computer Graphics and Graphic Image Processing Technology and Application Analysis in the New Period

Computer Graphics and Graphic Image Processing Technology an...

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2018 4th International Conference on Mechatronic Systems and Materials Application(ICMSMA 2018)

作者： Xiaohong Zhang Zhiqiong Liang Jiangxi University of Engineering

Computer graphics uses computers to study graphic representations,process images,and display generated disciplines,realizing the combination of imagery and real-life graphics in people's subjective *** decades of development,various kinds of computer graphics and image processing software have come into being,providing people with a lot of convenience for subjective processing and operation,and have been widely used in many *** deepening of the industrialized development process has prompted the rise of the computer core graphic image processing technology *** technology has emerged at the end of the last *** the in-depth development of the industrialization process in the world,its great vitality and vitality continue to show and stand out.

关键词： Computer graphics Graphic image processing technology Image analysis clustering algorithm Application

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Cluster Analysis of Container Station Based on Container Application Data

Cluster Analysis of Container Station Based on Container App...

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2nd International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)

作者： Shilin Li Lingxi Zhu Jun Liu Qingying Lai Xu Wang School of Beijing Jiaotong University China Railway Container Transportation

With the increase of the number of railway container manufacturers, the average maintenance cost of each factory's container is uneven. In order to evaluate the quality of the container, it is necessary to do cluster analysis of railway container stations, thus classify the container by the different stations the containers go through. This paper is based on the data of container application for nearly 10 years and constructs the index system of station cluster analysis. Through the cargo ticket and maintenance information of the container, the index data of the station is obtained. Then compares the different results of each clustering algorithm and selects the best clustering algorithm. Finally analyze the data characteristics and the reason of grouping results.

关键词： Railway Transportation Data Mining Analysis of Stations clustering algorithm

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Data-Driven Agile Software Development Collaborative Tools Based on Cluster Computing

Data-Driven Agile Software Development Collaborative Tools B...

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2018 4th International Conference on Mechatronic Systems and Materials Application(ICMSMA 2018)

作者： Feng Zhang School of Medical Information Engineering Taishan Medical University

The emergence of the software crisis in the mid-1960 s has forced people to reconsider the standards,methods,and technologies of software *** 2018,47 software experts gathered together to outline the values and principles that allowed the software development team to work quickly and respond to *** application modes such as its own content have greatly promoted the rapid development of network multimedia *** are no longer satisfied with browsing text and still pictures on web pages,and expect to be able to obtain more rich multimedia applications such as online audio and video browsing and *** this article,we analyze cluster computing and apply it to collaborative tools for data-driven agile software development.

关键词： Cluster computing clustering algorithm Data drive Agile software development Collaborative model

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