News Article | November 23, 2016
Dublin, Nov. 23, 2016 (GLOBE NEWSWIRE) -- Research and Markets has announced the addition of the "Virtual Patient Simulation Market Analysis and Trends- Technology, Product - Forecast to 2025" report to their offering. The Global Virtual Patient Simulation Market is poised to grow at a CAGR of around 19.6% over the next decade to reach approximately $1.49 billion by 2025 Some of the prominent trends that the market is witnessing include advanced mannequins imitate human physiology, robot-assisted surgical simulation training gaining demand, technology innovations in patient simulators, and virtual reality medical training is gaining traction. Based on Technology the market is categorized into haptic technology, tracking techniques, modelling techniques, visual techniques, and virtual reality technology. Moreover modelling techniques is further classified into Visual/Graphics Processing Unit (GPU) & Medical Imaging, and visual techniques are segmented into 2-Dimensional Technology, 3-Dimensional Technology and stereo technology. As per Products the market is segmented into dental simulators, intravenous simulators, simulation platforms, healthcare simulation software, serious games, ultrasound simulators, second life, surgical simulators, and other products. By End-User the market is segregated into academics, hospitals, military, and other end users. 4 Virtual Patient Simulation Market, By Technology 4.1 Haptic Technology 4.2 Tracking Techniques Tracking Market Forecast to 2025 (US$ MN) 4.3 Modeling Techniques 4.4 Visual Techniques 4.5 Virtual Reality Technology 5 Virtual Patient Simulation Market, By Product 5.1 Dental Simulators 5.2 Intravenous Simulators 5.3 Simulation Platforms 5.4 Healthcare Simulation Software 5.5 Serious Games 5.6 Ultrasound Simulators 5.7 Second Life 5.8 Surgical Simulators 5.9 Other Products 6 Virtual Patient Simulationr Market, By End User 6.1 Academics 6.2 Hospitals 6.3 Military 6.4 Other End User 7 Virtual Patient Simulation Market, By Geography 8 Key Player Activities 8.1 Acquisitions & Mergers 8.2 Agreements, Partnerships, Collaborations and Joint Ventures 8.3 Product Launch & Expansions 8.4 Other Activities 9 Leading Companies 9.1 Anesoft Corporation 9.2 Bioflight VR 9.3 Coburger Lehrmittelanstalt 9.4 Deepstream VR 9.5 Decision Simulation 9.6 Dassault Systemes 9.7 Firsthand Technology 9.8 Kyoto Kagaku Co., Ltd 9.9 Immersion Medical 9.10 Mentice AB 9.11 Red Llama 9.12 SynDaver Labs 9.13 Medical Realities 9.14 Edwards Lifesciences 9.15 Voxel-Man 9.16 Oculus VR Inc 9.17 Simulab Corporation 9.18 Surgical Science Sweden AB 9.19 Simbionix Corporation 9.20 Simulaids 9.21 CAE Healthcare 9.22 3B Scientific GmbH For more information about this report visit http://www.researchandmarkets.com/research/9smsbs/virtual_patient
Xu Y.,Harbin Institute of Technology |
Zhu Q.,Harbin Institute of Technology |
Fan Z.,Harbin Institute of Technology |
Fan Z.,East China Jiaotong University |
And 2 more authors.
Neurocomputing | Year: 2013
Transformation methods have been widely used in biometrics such as face recognition, gait recognition and palmprint recognition. It seems that conventional transformation methods seem to be "optimal" for training samples but not for every test sample to be classified. The reason is that conventional transformation methods use only the information of training samples to obtain transform axes. For example, if the transformation method is linear discriminant analysis (LDA), then in the new space obtained using the corresponding transformation, the training samples must have the maximum between-class distance and the minimum within-class distance. However, it is hard to guarantee that the transformation also maximizes the between-class distance and minimizes the within-class distance of the test samples in the new space. Another example is that principal component analysis (PCA) can best represent the training samples with the minimum error; however, it is not guaranteed that every test sample can be also represented with the minimum error. In this paper, we propose to improve conventional transformation methods by relating the training phase with the test sample. The proposed method simultaneously uses both the training samples and test sample to obtain an "optimal" representation of the test sample. In other words, the proposed method not only is an improvement to the conventional transformation method but also has the merits of the representation-based classification, which has shown very good performance in various problems. Differing from conventional distance-based classification, the proposed method evaluates only the distances between the test sample and the "closest" training samples and depends on only them to perform classification. Moreover, the proposed method uses the weighted distance to classify the test sample. The weight is set to the representation coefficient of a linear combination of the training samples that can well represent the test sample. © 2013 Elsevier B.V.
Wang X.,Harbin Institute of Technology |
Zhu Q.,Harbin Institute of Technology |
Cui J.,Harbin Institute of Technology |
Wang Y.,Decision Simulation
Optik | Year: 2013
Sparse representation method (SRM) is a state-of-the-art face recognition method. Nevertheless, SRM exploits image samples rather than image features to perform classification. As we know, the proper feature of the image can be more discriminative than the image sample itself. For example, Gabor and local binary pattern (LBP), two kinds of widely used features, have shown excellent discriminative performance in face recognition. Recently a number of experiments have shown that complete local binary pattern (CLBP) obtains a much better result than LBP in recognizing the texture images. With this paper, we propose a novel sparse representation method based on Gabor and CLBP features for face recognition. Our method first extracts the most discriminative features and then uses SRM to perform face recognition. The proposed method is composed of the following steps: the first step is to perform the histogram equalization operation on the image samples. The second step extracts the Gabor and CLBP features from the image samples. The last step uses the sparse representation method based on the combination of Gabor and CLBP features to perform classification. The rationales of our method are as follows: the first step can reduce the adverse effects caused by the variable illuminations. Both of the Gabor and CLBP features not only are very discriminative but also are complementary. A large number of experiments show the superior performance of our method. For the Feret face database, the rate of classification error of our method is 28.8% lower than that of SRM and 14.8% lower than that of LRC. For the ORL face database, the rate of classification error of our method is 9% lower than that of SRM and 9.5% lower than that of LRC. © 2013 Elsevier GmbH.
Lu Y.,Harbin Institute of Technology |
Fang X.,Harbin Institute of Technology |
Xie B.,Decision Simulation
Neural Computing and Applications | Year: 2014
Linear regression uses the least square algorithm to solve the solution of linear regression equation. Linear regression classification (LRC) shows good classification performance on face image data. However, when the axes of linear regression of class-specific samples have intersections, LRC could not well classify the samples that distribute around intersections. Moreover, the LRC could not perform well at the situation of severe lighting variations. This paper proposes a new classification method, kernel linear regression classification (KLRC), based on LRC and the kernel trick. KLRC is a nonlinear extension of LRC and can offset the drawback of LRC. KLRC implicitly maps the data into a high-dimensional kernel space by using the nonlinear mapping determined by a kernel function. Through this mapping, KLRC is able to make the data more linearly separable and can perform well for face recognition with varying lighting. For comparison, we conduct on three standard databases under some evaluation protocols. The proposed methodology not only outperforms LRC but also takes the better performance than typical kernel methods such as kernel linear discriminant analysis and kernel principal component analysis. © 2013 Springer-Verlag London.
Sun H.-X.,Beijing Institute of Technology |
Sun H.-X.,Decision Simulation |
Zhang Q.,Beijing Institute of Technology
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | Year: 2013
In the framework of cooperative games with coalition structure, it studied the problem of profit allocation by introducing lattice structure based on the situation that all the coalitions are not feasible. First, it generalized five properties which Owen value satisfies and then gave the definition of restricted Owen value by two step allocation. It proves that Owen value satisfies some properties, such as additivity, efficiency, symmetric with coalitions, dummy player property and so on. Finally, it gave an example to verify the allocation method in the paper.
Menezes N.N.C.,Decision Simulation
Proceedings - IEEE International Symposium on Distributed Simulation and Real-Time Applications, DS-RT | Year: 2014
The initialization of distributed heterogeneous simulation systems presents challenges regarding the parallelization of object construction and setup. This paper presents a method for parallel initialization of distributed simulation systems that consists of a two phases setup. Object instantiation and setup are split in Config and Post Bind phases to permit fast creation times allowing distribution of initialization tasks among different nodes and removing the ordering requirement between the initialization of interdependent objects. A framework of references is presented to facilitate the use of remote objects in a MPI environment using proxies to access local and remote variables, served by a reference name server built into the simulation engine. © 2014 IEEE.
Van Nieuwenhuyse I.,Catholic University of Leuven |
De Boeck L.,Decision Simulation |
Lambrecht M.,Catholic University of Leuven |
Vandaele N.J.,Catholic University of Leuven
Computers in Industry | Year: 2011
The planning and decision support capabilities of the manufacturing planning and control system, which provides the core of any enterprise resource planning package, can be enhanced substantively by the inclusion of a decision support module as an add-on at the midterm planning level. This module, called advanced resource planning (ARP), provides a parameter-setting process, with the ultimate goal of yielding realistic information about production lead times for scheduling purposes, sales and marketing, strategic and operational decision making, and suppliers and customers. This article illustrates the ARP approach with reports from several real-life implementations by large industrial companies. © 2010 Elsevier B.V. All rights reserved.
Decision Simulation | Date: 2014-12-12
Computer software platforms for a cloud based and mobile based platform designed to enhance and assess decision-making by leveraging the educational value of simulation.
Decision Simulation | Date: 2012-06-05
Web-based decision simulation platform, namely, computer software platform, which is downloaded from a website, for developing and assessing decision-making skills.