检索结果-内蒙古大学图书馆

Manufacturing Technology 2016年第4期16卷 9-10页

作者： Dovica, Miroslav Buša, Ján Spišák, Emil Fabian, Michal Ižol, Peter Fabianová, Jana Faculty of Mechanical Engineering Technical University of Košice Letná 9 Košice040 01 Slovakia Faculty of Electrical Engineering and Informatics Technical University of Košice Letná 9 Košice040 01 Slovakia Faculty of Mining Ecology Process Control and Geotechnology Technical University of Košice Letná 9 Košice040 01 Slovakia

Accuracy of work-pieces of complex free form surfaces is achieved by tolerancing, producing and metrology of workpieces. The tolerance zone is defined by form tolerances, their orientation and location on the work-pieces. Tolerances for complex form surfaces are specified by line profile tolerance or surface profile tolerance. These tolerances control form or combination of size, form, orientation and location. In a machining process the impact of machining parameter settings on the final surface quality will be researched. The influence of toolpaths in connection with the SH (Scallop Height) parameter setting on production accuracy and quality of machined surface will be compared. For geometry verification of the complex form surfaces are coordinate measurements used. The measurement area is modeled with the equations in CATIA V5. The data of machined surface obtained through the contact coordinate measurement are processed using the coordinate system adjustment via the RSS minimization by the Newton method in Matlab/Octave. Calculated values of surface profile deviations at individual machining strategies are used to achieve the required quality of machined surface through optimization of the machining parameters. Copyright © 2016. Published by Manufacturing Technology. All rights reserved.

关键词： Newton-Raphson method

来源：评论

学校读者我要写书评

暂无评论

Vehicle license plate image segmentation system using cellular neural network optimized by adaptive fuzzy and Neuro-Fuzzy algorithms

International Journal of Multimedia and Ubiquitous Engineeri...

引用

International Journal of Multimedia and Ubiquitous engineering 2016年第12期11卷 383-400页

作者： Rahmat, Basuki Joelianto, Endra Ketut Eddy Purnama, I. Purnomo, Mauridhi Hery Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Department of Informatics Engineering Faculty of Industrial Technology Universitas Pembangunan Nasional Veteran Jawa Timur Surabaya60294 Indonesia Instrumentation and Control Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia

Vehicle License Plate Images Segmentation is a substantial stage for developing an Automatic License Plate Recognition (ALPR) system. In this paper, it is considered an efficient segmentation algorithm for extracting vehicle license plate images using Cellular Neural Networks (CNN). The learning CNN templates values are formulated as an optimization problem to achieve the desired performances which can be found by means of Adaptive Fuzzy (AF) algorithm and Neuro-Fuzzy (NF) algorithm techniques. The main objective of the paper is to compare the performances of standard CNN, Adaptive Fuzzy (AF), and Neuro-Fuzzy (NF) on real data of several vehicle license plate images of standard Indonesia License Plates. The results are then compared with ideal vehicle license plate images. Quantitative analysis between ideal vehicle license plate images and segmented vehicle license plate images is presented in terms of Peak signal-to-noise ratio (PSNR), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). From the performance analysis, the CNN template optimized by ANFIS algorithm is more recommended than the standard CNN edge detector or the CNN template optimized by Adaptive Fuzzy algorithm in vehicle license plate image segmentation. It is shown from the calculation that PSNR is 80% better than the standard CNN, and the resulted MSE and RMSE are 70% better than the standard CNN. Whereas the CNN template optimized by Adaptive Fuzzy algorithm achieves the PSNR 90% better than the standard CNN, but it yields the MSE and RMSE 40% worse than the standard CNN. © 2016 SERSC.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Application of the MUSIC transformation for evaluation of acoustic interferences occurring during laboratory measurements of electrical discharges 44

Application of the MUSIC transformation for evaluation of ac...

引用

44th International Congress and Exposition on Noise control engineering, INTER-NOISE 2015

作者： Boczar, Tomasz Zmarzly, Dariusz Frącz, Pawel Department of Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland

Issues undertaken in the paper concern improvement of the acoustic emission (AE) method used in the diagnosis of insulation systems of electric power equipment. When measuring AE signals generated by partial discharges (PD) in systems modeling their basic form, e.g. in paper-oil insulation of power transformers, different sources of acoustic interference signals may occur. The interferences may be generated by sources located inside the investigated insulation systems and also they may be emitted outside, in its vicinity. Acoustic interference signals superpose on the recorded AE waveforms emitted by PD and cause their distortion. This would have a direct impact on the correct evaluation of the measured signals obtained using the AE method. Thus, an analysis of the impact of interfering signals on usefully AE waveforms generated by PD was performed. Results of spectral analysis of recorded interferences, which included estimation of pseudo-spectra calculated using the MUSIC transform are presented in the paper. For the analyzes acoustic signals emitted by several common type interference sources that may occur during measurements under laboratory conditions were used. In the summary, the impact of the analyzed interferences on the achieved measurements results, i.e. AE pulses generated by basic forms of PD, is presented. © 2015 by ASME.

关键词： Partial discharges

来源：评论

学校读者我要写书评

暂无评论

Analysis of interferences in the acoustic measurement of partial discharges from electric power transformers 44

Analysis of interferences in the acoustic measurement of par...

引用

44th International Congress and Exposition on Noise control engineering, INTER-NOISE 2015

作者： Zmarzly, Dariusz Boczar, Tomasz Frącz, Pawel Department of Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland

The subject matter of the paper concerns improvement of the acoustic emission (AE) method applied for diagnosis of insulations systems of electric power devices. During measurement of AE signals emitted by partial discharges (PD) occurring in insulation systems of electric apparatus there exist different forms of interference sources. Interferences, which accompany measurements performed under industrial conditions may be generated within the operating system or they may be emitted in its neighborhood. Sources of interference signals may have their origin in physical phenomena of mechanical, electrical or acoustical nature. Such interferences superpose on the registered AE signals causing their distortion. Because this may have a direct impact on the correct evaluation of the measured signals gathered with AE method, analysis of the impact of interfering signals on the AE pulses emitted by PD was made. In the paper there are presented results of analysis in the frequency and time-frequency domain, of acoustic interference signals generated by the fans and pumps used for cooling the transformer oil. © 2015 by ASME.

关键词： Partial discharges

来源：评论

学校读者我要写书评

暂无评论

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

引用

arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

引用

BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

来源：评论

学校读者我要写书评

暂无评论

26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

引用

BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

来源：评论

学校读者我要写书评

暂无评论

Multi-source information fusion model in rule-based Gaussian-shaped fuzzy control inference system incorporating Gaussian density function

Multi-source information fusion model in rule-based Gaussian...

引用

作者： Li, Zairan He, Ting Cao, Luying Wu, Tunhua McCauley, Pamela Balas, Valentina E. Shi, Fuqian Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation Tianjin University Tianjin China Wenzhou Vocational and Technical College Wenzhou China College of Information and Engineering Wenzhou Medical University Wenzhou325035 China Department of Industrial Engineering and Management Systems University of Central Florida OrlandoFL United States Department of Automation and Applied Informatics Aurel Vlaicu University of Arad Romania

An increasing number of applications require the integration of data from various disciplines, which leads to problems with the fusion of multi-source information. In this paper, a special information structure formalized in terms of three indices (the central presentation, population or scale, and density function) is proposed. Single and mixed Gaussian models are used for single source information and their fusion results, and a parameter estimation method is also introduced. Furthermore, fuzzy similarity computing is developed for solving the fuzzy implications under a Mamdani model and a Gaussian-shaped density function. Finally, an improved rule-based Gaussian-shaped fuzzy control inference system is proposed in combination with a nonlinear conjugate gradient and a Takagi-Sugeno (T-S) model, which demonstrated the effectiveness of the proposed method as compared to other fuzzy inference systems. © 2015 - IOS Press and the authors. All rights reserved.

关键词： Fuzzy control

来源：评论

学校读者我要写书评

暂无评论

Filtering and gradient estimation for distance fields by quadratic regression

Periodica polytechnica Electrical engineering and computer s...

引用

Periodica polytechnica electrical engineering and computer science 2015年第4期59卷 175-180页

作者： Szirmay-Kalos, László Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics P.O.B. 91 BudapestH-1521 Hungary

Distance fields show up in many problems of 3D vision and rendering, for example, a volumetric fusion of depth images results in such a field. Distance fields obtained from measured values are inherently noisy, so its filtering is needed before isoparametric surfaces are extracted from them, and robust normal vector estimation also requires a local smoothing since differentiation is especially sensitive to noise. In this paper, we use regression to find a quadratic function that approximates the zero level surface of the distance field, and apply this both for filtering and normal vector estimation. We also present a computationally efficient method that exploits the regular structure of samples, the symmetry and separability of the weighting functions, and thus avoids the solution of larger linear equations, which otherwise would become necessary when regression is generally attacked. The algorithm is a part of a real-time volumetric fusion application running on the Graphics processing Units (GPU).

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

Sensor fusion for prediction of orientation and position from obstacle using multiple IR sensors an approach based on Kalman filter 19

Sensor fusion for prediction of orientation and position fro...

引用

2014 19th International Conference on Applied Electronics, AE 2014

作者： Rahul Sharma, K. Honc, Daniel Dušek, František Department of Process Control Faculty of Electrical Engineering and Informatics University of Pardubice Pardubice Czech Republic

ISBN: (纸本)9788026102762

Kalman filters have gained immense research attention in robotics, throughout the last decades. Among the applications, localization of robots through Kalman filters proved promising results. This paper presents an application of sensor fusion for prediction of orientation and depth to wall/obstacle by fusing the inputs from three IR range finders. The experimental result demonstrates the capability of Kalman filter to predict the parameters precisely, from noisy sensor inputs. The technique find application in determining the position and orientation from wall which will be helpful in obstacle avoidance decision making, automatic parking of automobiles etc. © 2014 University of West Bohemia.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：