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共 2194 条记录，以下是621-630 订阅

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Measures of cosine similarity intended for fuzzy sets, intuitionistic and interval-valued intuitionistic fuzzy sets with application in medical diagnoses

Measures of cosine similarity intended for fuzzy sets, intui...

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international conference on Computing for Sustainable Global Development (INDIACom)

作者： Priti Gupta Pratiksha Tiwari Department of Statistics MaharshiDayanand University Rohtak Haryana India

ISBN: (纸本)9781467394178

Medical diagnosis deals with uncertainty, unpredictability and insufficient information because of the nature of the subject. Fuzzy sets and its various generalizations are tools that can measure uncertainty and vagueness in a better way. In this communication we have derived cosine similarity measures that can determine similarity between two fuzzy sets or between two intuitionistic fuzzy sets or between two interval-valued intuitionistic fuzzy sets. Further, we have applied it to diagnose a disease on the basis of similarity between symptoms and a pattern.

关键词： Fuzzy sets Frequency selective surfaces Diseases pattern recognition Atmospheric measurements conferences Uncertainty

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Neural Network pattern recognition Based Non-intrusive Load Monitoring for a Residential Energy Management System

Neural Network Pattern Recognition Based Non-intrusive Load ...

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international conference on Information Science and Control Engineering (ICISCE)

作者： Chenyi Zhou Song Liu Peng Liu College of Electrical and Electronic Engineering North China Electric Power University Beijing China

ISBN: (纸本)9781509025367

Detailed energy consumption information of household appliance is meaningful for the demand side management (DSM) and home energy conservation. In this paper, a novel non-intrusive load monitoring (NILM) method is proposed for residential energy management systems(REMS). Unlike existing NILM techniques, this method works effectively with very few smart meter measurement parameters obtained at a low sampling rate. A neural network pattern recognition (NNPR) model is utilized in the NILM system for the first time. The proposed method can detect finite-state appliances by changing the number of output neurons. Experimental results indicate that the proposed method provides a very high identification accuracy. Moreover, this method can estimate each appliance detail energy consumption effectively, which is ideal for scheduling the household appliances and participation in the demand respond (DR).

关键词： Home appliances Neurons Energy consumption Artificial neural networks Monitoring Training Biological neural networks

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Neural Network Based Skeleton recognition and Sudoku Solving

Neural Network Based Skeleton Recognition and Sudoku Solving

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international conference on Signal Processing and Integrated Networks (SPIN)

作者： Gaurav Chitranshi Sharad Tiwari RudraPratap Singh Prateek S Sengar Dept. of Electronics and Communication Engineering Amity University Noida India

ISBN: (纸本)9781467391986

This paper presents the development of an Neural Network Based Skeleton recognition and Sudoku Solving. The main objective of this work is to recognize the number and its corresponding position from a Sudoku image and also to solve any valid Sudoku. The recognition system is designed through an artificial neural network model. The neural network uses the mechanism of feedforwardbackpropogation technique where minimizing error is taken into consideration. Using the gradient of the criteria-field helps in weights modification and thus optimizes the system. The Sudoku is then solved using the backtracking algorithm which is a trial and error method. It takes into account one selection at a time from the multiple choices (1-9). This technique can solve any valid Sudoku. The final result is made to display on the original image by using the database. The database consist of template numbered images which is obtained from the segmentation from the Sudoku images. The system is well trained and effective in recognizing the number compared with the traditional template matching.

关键词： Skeleton Signal processing algorithms Neural networks Image recognition Databases pattern recognition Algorithm design and analysis

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Fine pose estimation of known objects in cluttered scene images 3

Fine pose estimation of known objects in cluttered scene ima...

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3rd iapr Asian conference on pattern recognition, ACPR 2015

作者： Banerjee, Sudipto Aggarwal, Sanchit Namboodiri, Anoop M. CVIT International Institute of Information Technology Hyderabad India

ISBN: (纸本)9781479961009

Understanding the precise 3D structure of an environment is one of the fundamental goals of computer vision and is challenging due to a variety of factors such as appearance variation, illumination, pose, noise, occlusion and scene clutter. A generic solution to the problem is ill-posed due to the loss of depth information during imaging. In this paper, we consider a specific but common situation, where the scene contains known objects. Given 3D models of a set of known objects and a cluttered scene image, we try to detect these objects in the image, and align 3D models to their images to find their exact pose. We develop an approach that poses this as a 3D-to-2D alignment problem. We also deal with pose estimation of 3D articulated objects in images. We evaluate our proposed method on BigBird dataset and our own tabletop dataset, and present experimental comparisons with state-of-the-art methods. © 2015 IEEE.

关键词： Object detection

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Depth-Based Hand Pose Segmentation with Hough Random Forest

Depth-Based Hand Pose Segmentation with Hough Random Forest

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international conference on Green Technology and Sustainable Development (GTSD)

作者： Wei-Jiun Tsai Ju-Chin Chen Kawuu W. Lin Department of Computer Science and Information Engineering National Kaohsiung University of Applied Sciences Kaohsiung Taiwan

ISBN: (纸本)9781509036394

With the development of image processing and computer vision technology, using gesture to communicate with the machine will not only appear in scientific move or just a conceptual product. Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. With this, we can have a more convenient life. Therefore, our goal is using image processing algorithm to effectively recognize the correct gesture from camera and present a user-friendly human-machine interface. In recent years, gesture recognition has become a popular and important issue. It can be used for robot control, appliances control, gaming control, etc. We present an algorithm which is able to correctly calculate the accurate finger joint position and then evaluate the gesture. This algorithm is divided into two parts: hand position detection and gesture recognition. In gesture detection, we capture the depth image from depth camera to solve the problem caused by illumination and background. In gesture recognition, we use an object recognizing algorithm to make the difficult evaluating finger movement problem corresponds to an easier voting classify problem. Depth camera helps our classifier avoid the illumination problem from incorrectly recognizing object.

关键词： Training Computer vision Vegetation conferences pattern recognition Pose estimation Real-time systems

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Reducing Complexity of Diagnostic Message pattern Specification and recognition on In-Bound Data Using Semantic Techniques

Reducing Complexity of Diagnostic Message Pattern Specificat...

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IEEE international conference on Cyber Security and Cloud Computing (CSCloud)

作者： Gilbert Alipui Lixin Tao Keke Gai Ning Jiang Pace University New York NY US Henan University Kaifeng Henan CN

ISBN: (纸本)9781509009473

Different companies in the same line of business can have similar computer systems with built-in diagnostic routines, and the ability to regularly send error-driven or event-driven environmental diagnostic messages in XML back to the system manufacturer. The system manufacturer typically uses these to determine faults in the system. The outcome of this troubleshooting can also assist end-users and clients in solving problems, and provide the production team valuable information that can be used to improve future versions of the product. A Company merger could lead to the same team processing diagnostic messages from similar but different products, in different syntax, leading to complexity explosion of specifying and maintaining diagnostic message pattern specification and recognition for many different syntaxes. This research reduces the above complexity by extending ISO Schematron, the industry standard language for XML semantic constraints specification and validation, with conceptual rules. Pace University Knowledge Graphs are used to describe the concepts or classes relevant to the diagnostic messages of a system, and the new conceptual Schematron rules are introduced to specify diagnostic patterns on these concepts. Such conceptual diagnostic patterns are then converted automatically into concrete Schematron rules based on the syntax of the specific diagnostic messages. A complete prototype was designed and implemented to validate this new methodology.

关键词： XML Complexity theory Semantics Ontologies OWL Companies Electronic mail

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Single image depth estimation using joint local-global features

Single image depth estimation using joint local-global featu...

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international conference on pattern recognition

作者： H. Mohaghegh N. Karimi S.M.R. Soroushmehr S. Samavi K. Najarian Department of Electrical and Computer Engineering Isfahan University of Technology Iran Michigan Center for Integrative Research in Critical Care University of Michigan Ann Arbor USA Department of Emergency Medicine University of Michigan Ann Arbor USA Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor USA

ISBN: (纸本)9781509048489

Inferring scene depth from a single monocular image is an essential component in several computer vision applications such as 3D modeling and robotics. This process is an ill-posed problem. To tackle this challenging problem, previous efforts have been focusing on exploiting only global or local depth aware properties. We propose a model that incorporates both of them to obtain significantly more accurate depth estimates than using either global or local properties alone. Specifically, we formulate single image depth estimation as a K nearest neighbor search problem at both image level and patch level. At each level, a set of rich depth aware features, describing monocular depth cues, is employed in a nearest-neighbor regression model. By comparing the results with and without patch based fusion, the importance of our joint local-global framework becomes clear. The experimental results also demonstrate superior performance compared with existing data-driven approaches in both quantitative and qualitative analyses with a significantly simpler algorithm than others.

关键词： Three-dimensional displays Estimation Correlation Training Databases Solid modeling Two dimensional displays

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Design of speech recognition system based on LD3320 chip

Design of speech recognition system based on LD3320 chip

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2016 3rd international conference on Materials Engineering,Manufacturing Technology and Control(ICMEMTC 2016)

作者： Lei Wang School of Jianqiao University

Speech recognition technology is a key to voice control technology, through speech recognition enable computer can read the speech command vocabulary, and can be through voice control realize control on all kinds of home appliances in the home, the system combines ld3320 chip ICRoute company, voice control system design, completed the software and hardware system, realized the non- specific human speech recognition, through tests the average recognition rate reached 90%, and the system is easy to build, with good practical value.

关键词： Speech recognition Embedded technology pattern matching

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pattern recognition applications and methods: Third international conference, ICPRAM 2014 Angers, France, march 6–8, 2014 revised selected papers 3rd

Pattern recognition applications and methods: Third internat...

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3rd international conference on pattern recognition Applications and Methods, ICPRAM 2014

作者： Fred, Ana De Marsico, Maria Tabbone, Antoine Instituto de Telecomunicações Lisboa Portugal Computer Science Sapienza University of Rome Rome Italy LORIA-UMR 7503 Vandoevre-lès-Nancy France

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Unsupervised Feature Selection Algorithm Based on Sparse Representation

Unsupervised Feature Selection Algorithm Based on Sparse Rep...

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international conference on Systems and Informatics

作者： Guoqing Cui Jie Yang Masoumeh Zareapoor Jiechen Wang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Complex Network and Control Lab Shanghai Jiao Tong University

ISBN: (纸本)9781509055227

Feature selection, as a preprocessing step to machine learning, plays a pivotal role in removing irrelevant data, reducing dimensionality and improving performance evaluations. Recent years, sparse representation has become a useful tool for both supervised and unsupervised feature selection. So far, most of these algorithms still have many problems such as large computation load, performance with poor stability. Thus, this paper proposes a new unsupervised feature selection algorithm via sparse representation (UFSSR), with respect to efficiency and effectiveness. Firstly, this paper reconstructs part of data matrix via sparse representation, which makes the proposed algorithm be robust and independent of domain knowledge. Then, to reduce the reconstruction error, a new feature evaluation function is given to rank all features. Theoretical analysis and experiments compared with many popular algorithms on a set of datasets demonstrate the improvements brought by UFSSR.

关键词： Unsupervised feature selection Sparse representation Feature evaluation function Computation load Stability

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