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Joint 17th World Congress of international-Fuzzy-Systems-Association / 9th international conference on soft computing and Intelligent Systems (IFSA-SCIS)

作者： Kim, Eun Kyeong Lee, Hansoo Kim, Sungshin Cho, Hyunhak Pusan Natl Univ Dept Elect & Comp Engn Busan South Korea Pusan Natl Univ Dept Interdisciplinary Cooperat Course Robot Busan South Korea

ISBN: (纸本)9781509049172

A camera has been widely used in practical fields with a diversity of purposes recently. There is a variety purpose of photography: images for memory, medical images for diagnosis, images for object recognition, surveillance images, and so on. In case of images for object recognition, the clarity of images is necessary to analyze the images which are obtained using vision sensors. However, a brightness of the image highly depends on the intensity of illumination in the certain environment. Therefore, we propose a method to solve the problems mentioned above by adjusting brightness automatically by utilizing CIE L*a*b* color space and fuzzy inference system. At first, the proposed method adjusts the brightness of a given image by considering both RGB component and L component of CIE L*a*b* color space. Secondly, the proposed method applies the fuzzy inference system to determine adjustment coefficients of each pixel for adjusting brightness of the image. Through the processes as mentioned above, we can obtain the result which is adjusted its brightness. To verify the proposed method, we compare the result image with two different images, a reference image, and an adjusted image by using offset. It is confirmed that the proposed method can adjust a given image efficiently and automatically.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Linear Regression For pattern recognition

Linear Regression For Pattern Recognition

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international conference on Green computing Communication and Electrical Engineering (ICGCCEE)

作者： Stephen, Priya Jaganathan, Suresh Sri Sivasubramaniya Nadar Coll Engn Dept Comp Sci & Engn Madras Tamil Nadu India

ISBN: (纸本)9781479949816

This paper presents a novel method for pattern recognition problem in terms of linear regression. Normally, patterns from a single-object class lie on a linear subspace. Using this concept, we develop a linear model representing a probe image as a linear combination of class-specific galleries. Linear Regression Classification (LRC) algorithm for pattern recognition belongs to the category of nearest subspace classification. This algorithm is extensively evaluated on several standard digit and English character databases and our own Tamil character database. A comparative study with different databases and methods clearly reflects the efficiency of LRC approach for pattern recognition.

关键词： Databases Linearregression Mathematicalmodel patternrecognition Testing Training Vectors pattern recognition Database Linear Regression Functional training pattern recognition problem Learning Resources Centers Training Cloning Vectors Linear Combination linear models Subspace

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Activity recognition from Accelerometer Data on a Mobile Phone

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10th international Work-conference on Artificial Neural Networks (IWANN 2009)

作者： Brezmes, Tomas Gorricho, Juan-Luis Cotrina, Josep France Telecom R and D Spain Departamento de Telemática UPC Spain

ISBN: (纸本)9783642024801

Real-time monitoring of human movements can be easily envisaged as a useful tool for many purposes and future applications. This paper presents the implementation of a real-time classification system for some basic human movements using a conventional mobile phone equipped with an accelerometer. The aim of this study was to check the present capacity of conventional mobile phones to execute in real-time all the necessary pattern recognition algorithms to classify the corresponding human movements. No server processing data is involved in this approach, so the human monitoring is completely decentralized and only an additional software will be required to remotely report the human monitoring. The feasibility of this approach opens a new range of opportunities to develop new applications at a reasonable low-cost.

关键词： pattern recognition human movement's detection accelerometer

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Using Machine Learning Techniques to Earlier Predict Student's Performance 1

Using Machine Learning Techniques to Earlier Predict Student...

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1st international conference of the Indonesian-Association-for-pattern-recognition (INAPR)

作者： Tanuar, Evawaty Heryadi, Yaya Lukas Abbas, Bahtiar Saleh Gaol, Ford Lumban Bina Nusantara Univ Sch Comp Sci Comp Sci Dept Jakarta 11480 Indonesia Bina Nusantara Univ BINUS Grad Program Comp Sci Jakarta 11480 Indonesia Bina Nusantara Univ Dept Comp Sci BINUS Grad Program Comp Sci Jakarta 11480 Indonesia Univ Katolik Indonesia Atma Jaya Fac Engn CERG Jakarta 12930 Indonesia

ISBN: (纸本)9781538694220

Education field is rich of data, and machine learning used in this field are increasing lately. Based on their first semester result, using machine learning techniques, the student's final year result (GPA) can be predicted. The data used in this experiment are from the computer science subjects, 6 subjects, 1 laboratories results and the GPA on their graduation year. The techniques used in this experiment are Generalized Linear Model, Deep Learning and Decision Tree. From this result, what are the important factors that impact on the result can be extracted to help the students prepared themselves earlier.

关键词： Machine Learning Data Mining Student's Performance - GPA soft computing

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Fusion of learning automata theory and granular inference systems: ANLAGIS. Applications to pattern recognition and machine learning

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NEUROcomputing 2011年第8期74卷 1237-1242页

作者： Maravall, Dario de Lope, Javier Univ Politecn Madrid Ctr Automat & Robot CSIC Dept Appl Intelligent Syst Madrid Spain Univ Politecn Madrid Dept Artificial Intelligence Fac Comp Sci Sch Telecommun Engn Madrid Spain

In this paper the fusion of artificial neural networks, granular computing and learning automata theory is proposed and we present as a final result ANLAGIS, an adaptive neuron-like network based on learning automata and granular inference systems. ANLAGIS can be applied to both pattern recognition and learning control problems. Another interesting contribution of this paper is the distinction between presynaptic and post-synaptic learning in artificial neural networks. Tc illustrate the capabilities of ANLAGIS some experiments on knowledge discovery in data mining and machine learning are presented. The main, novel contribution of ANLAGIS is the incorporation of Learning Automata Theory within its structure;the paper includes also a novel learning scheme for stochastic learning automata. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Artificial neural networks Granular computing Learning automata theory pattern recognition Machine learning

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Vibration Fault Diagnosis of Hydraulic Generator Units Based on pattern recognition and Grey Incidence Analysis

Vibration Fault Diagnosis of Hydraulic Generator Units Based...

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2nd international conference on Bio-Inspired computing

作者： An, Xueli Zhou, Jianzhong Liu, Li Fang, Rengcun Xiang, Xiuqiao Peng, Bing Huazhong Univ Sci & Technol Coll Hydroelect & Digitalizat Engn Wuhan 430074 Peoples R China

ISBN: (纸本)9781424441051

The aim of this investigation is to describe a way of grey incidence analysis to achieve vibration fault pattern recognition for hydraulic generator units. In order to recognise the fault, the collected fault data are analyzed and then standard fault pattern sets of typical vibration faults for hydraulic generator units are founded. Fault pattern recognition is performed by calculating the degree of grey incidence between the pending pattern characteristics and the standard fault pattern sets, according to the criterion for maximum degree of incidence. Results are presented which demonstrate the effectiveness of the proposed method and it can be used as an online diagnosis tool for hydraulic generator units.

关键词： Fault detection

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Research of a Ubiquitous-computing Enabled Service pattern in Converged Network

Research of a Ubiquitous-Computing Enabled Service Pattern i...

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international conference on Business, Economics, and Financial Sciences, Management (BEFM 2011)

作者： Yu, Xiao-Yan Lin, Rong-Heng Huang, Peng-Cheng Beijing Univ Posts & Telecommun Inst Network Technol Beijing 100876 Peoples R China

ISBN: (纸本)9783642279652

The service triggering mechanism of current IMS not only leads to heavy load of the S-CSCF entities, but also limits the provision of feasible service which makes use of context information. Thus, a ubiquitous-computing enabled service triggering and executing pattern in converged network is introduced. Via analyzing service pattern of current IMS architecture, a novel mechanism that introduces P2P like service triggering based on terminal side coordination is introduced, which results in the conversion from central service control to the negotiation service triggering pattern based on context information among terminals. In this new service pattern, context aware triggering is supported. Moreover, service can be executed in a distributed way which means service logic can be composed of terminal and server parts. A detail scenario is also discussed in this paper.

关键词： Ubiquitous computing P2P IMS Service Trigger Contextawareness

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Coverage in biomimetic pattern recognition

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11th international conference on Rough Sets, Fuzzy Sets, Data Mining and Granular computing (RSFDGrC 2007)

作者： Cao, Wenming Zhao, Guoliang Shenzhen Univ Intelligent Informat Proc Key Lab Beijing 518060 Peoples R China Chinese Acad Sci Inst Semicond Beijing 100083 Peoples R China

ISBN: (纸本)9783540725299

Coverage is a kind of method to cover points of same class samples in feature space, which is based on Biomimetic pattern recognition. The mathematical description of coverage is given and the discriminant boundary of coverage is shown. Coverage is tested in face recognition on ORL database. Both the COVERAGE and SVM networks are used for covering. The results show that COVERAGE act better than SVM in generalization, especially for small sample set, which are consonant with the result of the applications of BPR.

关键词： Biomimetics

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SPECKLE NOISE REDUCTION FOR SAR IMAGES USING INTERSCALE MULTIPLICATION AND soft THRESHOLDING

SPECKLE NOISE REDUCTION FOR SAR IMAGES USING INTERSCALE MULT...

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6th international conference on Wavelet Analysis and pattern recognition

作者： Dachasilaruk, Siriporn Naresuan Univ Fac Engn Dept Elect & Comp Engn Phitsanulok 65000 Thailand

ISBN: (纸本)9781424422388

This paper presents a technique for speckle reduction in SAR images by using the interscale multiplication in Mallat wavelet transform (MWT) and stationary wavelet transform (SWT). The edge and non-edge regions can be detected from median of squared amplitude in each scale. Applying this technique, the large wavelet coefficients generated by edge region and the small wavelet coefficients or speckle noises are then suppressed by using soft thresholding in all high frequency hands. The well-known threshold estimation for soft thresholding based on SimpleShrink, NormalShrink, VisuShrink, SureShrink, and BayesShrink. The despeckled image is then obtained by reconstruction from the resulted coefficients. The assessment of the proposed method, the experiment has conducted using SAR images from JERS-1 satellite. The experimental result shows that the proposed method yields noise suppression and preserves the detail feature of the images, as well as perceptual image quality.

关键词： SAR image speckle noise threshold estimation wavelet thresholding edge detection

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Modified 2-D Cepstrum using soft-computing

Modified 2-D Cepstrum using Soft-Computing

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6th international conference on Genetic and Evolutionary computing (ICGEC)

作者： Chaurasiya, Himanshu Amity Univ Elect & Commun Dept Noida India

ISBN: (纸本)9780769547633

We proposed a method which can solve the low speech recognition rate problem under noisy environment. In our system, the method is GAS-based Speech recognition using Two Dimensional cepstrum. Two dimensional cepstrum (TDC) can simultaneously represent several kinds of information contained in the speech waveform: static and dynamic features, as well as global and fine frequency structures. From analysis, an utterance only some TDC coefficients will be selected to form a feature vector. Hence, it has the advantages of soft computation and less storage space. However, it is quite sensitive to background noise. In order to solve this problem, we propose the GAS-based M-TDC method in our system to improve the performance of TDC under noisy condition. From the experiments with five noise types, we found that the GAS-based M-TDC have better recognition results than the TDC under the noisy environments.

关键词： Mel-Frequency Cepstrum (MFC) Genetic algorithms (GAS) Two Dimensional Cepstrum (TDC) Robust speech feature temporal filter

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