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Density-controlled Delaunay triangulation for 2D sea area

computer Aided Drafting,Design and Manufacturing 2016年第1期26卷 47-53页

作者： Xu Mingze Liu Zhe Ma Long Liu Yuchen Zhou Yuanfeng School of Computer Science and Technology Shandong University Key Laboratory of Marine Environment and Ecology (Ocean University of China) Ministry of Education Computing and information system faculty The University of Melbourne

Triangulation is widely used in scientific research, such as finite element mesh generation, surface reconstruction and the reconstruction of the density field data. This paper proposes a new method combining image processing and density-controlled Centroidal Voronoi tessellations to quickly generate a density-controlled constrained Delaunay triangulation lbr 2D sea area. Firstly, preprocess digital images of the sea area and extract the boundary of seawater region by eight-neighbor searching algorithm. Then, 1 use Odd-Even Testing＂ method to check if one random vertex is inside the boundary and insert random vertices into the boundary. Finally, we get the CDT of random vertices by density-controlled CVT-Lloyd method. We also give some comparisons with existing methods, and our method performs better in final restllt of triangulation.

关键词： constrained Delaunay triangulation： density-controlled CVT-Lloyd method Odd-Even testing

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An experimental evaluation of echo state network for colour image segmentation

An experimental evaluation of echo state network for colour ...

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International Joint Conference on Neural Networks

作者： Abdelkerim Souahlia Ammar Belatreche Abdelkader Benyettou Kevin Curran Faculty of Sciences and Technology University of Djelfa Algeria Department of Computer Science and Digital Technologies Northumbria University UK Faculty of Mathematics and Computer Science University of Sciences and Technology Oran Algeria Intelligent System Research Centre University of Ulster UK

ISBN: (纸本)9781509006212

Image segmentation refers to the process of dividing an image into multiple regions which represent meaningful areas. Image segmentation is an essential step for most image analysis tasks such as object recognition and tracking, pattern recognition, content-based image retrieval, etc. In recent years, a large number of image segmentation algorithms have been developed, but achieving accurate segmentation still remains a challenging task. Recently, reservoir computing (RC) has drawn much attention in machine learning as a new model of recurrent neural networks (RNN). Echo State Network (ESN) represents one efficient realization of RC, which is initially designed to facilitate learning in Recurrent Neural Networks. In this paper we investigate the viability of ESN as feature extractor for pixel classification based colour image segmentation. Extensive experiments are conducted on real world colour image datasets and the global ESN reservoir parameters are varied to identify their operating ranges that allow the use of the reservoir nodes internal activations as new pixel features for the colour image segmentation task. A simple feed forward neural network is used to realize the ESN readout function and classify these new features. The experimental results show that the proposed method achieves high performance image segmentation comparing with state-of-the-art techniques. In addition, a set of empirically derived guidelines for setting the reservoir global parameters are proposed.

关键词： Image segmentation Reservoirs Neurons Feature extraction Image color analysis Recurrent neural networks Image edge detection

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Noise Reduction in Breath Sound Files Using Wavelet Transform Based Filter

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IOP Conference Series: Materials Science and Engineering 2017年第1期190卷

作者： M F Syahputra S I G Situmeang R F Rahmat R Budiarto Department of Information Technology Faculty of Computer Science and Information Technology University of Sumatera Utara Medan Indonesia College of Engineering National Chung Cheng University Taiwan Department of Information System College of Computer Science and Information Technology Albaha University Saudi Arabia

The development of science and technology in the field of healthcare increasingly provides convenience in diagnosing respiratory system problem. Recording the breath sounds is one example of these developments. Breath sounds are recorded using a digital stethoscope, and then stored in a file with sound format. This breath sounds will be analyzed by health practitioners to diagnose the symptoms of disease or illness. However, the breath sounds is not free from interference signals. Therefore, noise filter or signal interference reduction system is required so that breath sounds component which contains information signal can be clarified. In this study, we designed a filter called a wavelet transform based filter. The filter that is designed in this study is using Daubechies wavelet with four wavelet transform coefficients. Based on the testing of the ten types of breath sounds data, the data is obtained in the largest SNRdB bronchial for 74.3685 decibels.

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Plenary speaker

Plenary speaker

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International Conference on Power systems (ICPS)

作者： Anurag Srivastava Mohan Kolhe Alberto Borghetti Rajiv K. Varma Prasad Enjeti S. S. Mani Venkata Sushil K. Soonee Deepakraj M. Divan School of Electrical Engineering & Computer Science Washington State University USA Faculty of Engineering and Science University of Agder Norway Department of Electrical Electronic and Information Engineering “Guglielmo Marconi” University of Bologna Italy Electrical and Computer Engineering Department The University of Western Ontario London ON Canada Electrical and Computer Engineering Department Texas A & M University Texas USA Departmnet of Electrical Engineering University of Washington Seattle WA USA Power System Operation Corp. Ltd. New Delhi India John E Pippin Chair Director of the Center for Distributed Energy Georgia Institute of Technology Atlanta GA

Provides an abstract of the plenary presentation and may include a brief professional biography of the presenter. The complete presentation was not made available for publication as part of the conference proceedings.

关键词： Renewable energy sources Smart grids Electrical engineering Power system stability Patents Education

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systematic Literature Review on Effort Estimation for Open Sources (OSS) Web Application Development

Systematic Literature Review on Effort Estimation for Open S...

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Future Technologies Conference

作者： Tseu Kwan Lee Koh Tieng Wei Abdul Azim Abd. Ghani Department of Software Engineering and Information System Faculty of Computer Science and Information Technology Universiti Putra Malaysia 43400 UPM Serdang Selangor Malaysia

ISBN: (纸本)9781509041725

The development of Web applications has a crucial role as most organizations have their own corporate Web applications to meet the needs of their respective businesses. Different needs create different complexities which represent a new challenge to Web application development. In order to ensure the timely delivery of a project, software providers offering this service choose to use Open Sources (OSS) as an alternative. Since OSS consist of an existing framework that can be implemented directly into the application, how far does this affect the complexity of the effort estimation? A number of research papers have outlined the efforts made to refine the complexity of this field. However, to our best knowledge a systematic overview of the research done on Web application development that involves OSS usage does not appear to exist. Hence, the aim of this paper is to conduct a systematic literature review (SLR) of OSS Web application development. For this purpose, 34 papers from a total of 67 papers were identified and studied. The findings of this study indicate that (a) no research has been carried out on the field mentioned;(b) there is no early effort estimation model for Web projects that involve the usage of OSS. Therefore, this work provides an overview of the field besides identifying future research possibilities.

关键词： Effort estimation Open sources systematic literature review Web application development

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An evaluation of Error Level Analysis in image forensics 5

An evaluation of Error Level Analysis in image forensics

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5th IEEE International Conference on system Engineering and technology, ICSET 2015

作者： Warif, Nor Bakiah Abd Idris, Mohd. Yamani Idna Wahab, Ainuddin Wahid Abdul Salleh, Rosli Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9781467367134

The advancement in digital image tampering has encouraged studies in the image forensics fields. The image tampering can be found over various image formats such as Joint Photographic Experts Group (JPEG). JPEG is the most common format that supported by devices and applications. Therefore, researchers have been studying the implementation of JPEG algorithm in the image forensics. In this paper, the Error Level Analysis (ELA) technique was evaluated with different types of image tampering, including JPEG compression, image splicing, copy-move and image retouching. From the experiment, the ELA showed reliability with JPEG compression, image splicing and image retouching forgery. © 2015 IEEE.

关键词： Image compression

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Knowledge of extraction from trained neural network by using decision tree

Knowledge of extraction from trained neural network by using...

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International Conference on Science in Information technology (ICSITech)

作者： Soleh Ardiansyah Mazlina Abdul Majid Jasni Mohamad Zain Department of Information Systems Kalimantan Institute of Technology Balikpapan Indonesia Faculty of Computer System and Software Engineering Universiti Malaysia Pahang Gambang-Kuantan Malaysia

ISBN: (纸本)9781509017225

Inside the sets of data, hidden knowledge can be acquired by using neural network. These knowledge are described within topology, using activation function and connection weight at hidden neurons and output neurons. Is hardly to be understanding since neural networks act as a black box. The black box problem can be solved by extracting knowledge (rule) from trained neural network. Thus, the aim of this paper is to extract valuable information from trained neural networks using decision. Further, the Levenberg Marquardt algorithm was applied to training 30 networks for each datasets, using learning parameters and basis weights differences. As the number of hidden neurons increase, mean squared error and mean absolute percentage error decrease, and more time they need to deal with the dataset, that is result of investigation from neural network architectures. Decision tree induction generally performs better in knowledge extraction result with accuracy and precision level from 84.07 to 93.17 percent. The extracted rule can be used to explaining the process of the neural network systems and also can be applied in other systems like expert systems.

关键词： Biological neural networks Neurons Training Forecasting Predictive models Data mining Decision trees

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Evolutionary ensemble learning of fuzzy randomized neural network for posture recognition

Evolutionary ensemble learning of fuzzy randomized neural ne...

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Proceedings of the Biannual World Automation Congress

作者： Takenori Obo Naoyuki Kubota Chu Kiong Loo Dept. of System Design Tokyo Metropolitan University Tokyo Japan Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia

Many works of human posture recognition have been published in the literature. Gesture and posture recognition by using Kinect Sensor is an important technique for many types of application. In this paper, we apply a neuro-fuzzy system for classification of human posture. A recent approach of learning algorithm for Single-layer hidden feedforward neural network (SLFN), known as Randomized Neural Network (RNN), has been attracting attention as a solution of several problems of traditional neural networks. However, the performance of RNN varies depending on the number of hidden neurons. In this paper, ensemble learning methods are therefore developed to deal with the issue, using Fuzzy-RNNs with various sizes of networks. Moreover, we utilize evolutionary algorithm to optimize the structure of ensembles. This paper discusses the effectiveness of the proposed approach, comparing with performance of some ensemble learning methods by using benchmark datasets and real measurement data of human posture.

关键词： Satellites Ionosphere Prediction algorithms Hidden Markov models

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An application program of fingerprint detection using wavelet transform for authentication 3

An application program of fingerprint detection using wavele...

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3rd Management and Innovation technology International Conference, MITiCON 2016

作者： Kwaochai, Aunchisa Pongyupinpanich, Surapong Areekul, Pratya San-Um, Wimol Intelligent Electronic System Research Laboratory Faculty of Engineering Thai-Nichi Institute of Technology Pattanakarn 37 Saunlaung Bangkok10250 Thailand Master Program of Forensic Informatics Ramkhamhang University Bangkapi Bangkok10240 Thailand Department of Mathematics Computer Science Chulachomklao Royal Military Academy Province Nakornnayok26001 Thailand

ISBN: (纸本)9781509041053

This paper presents the application program of fingerprint detection using wavelet transform for authentication. Fingerprints are obtained from the site of crime, old documents and excavated things. This paper proposes a fingerprint recognition technique based on wavelet-based texture pattern recognition method. By collecting incomplete fingerprints of 2 people with 6 images each. The original seven of uncompleted fingerprint images were undetected and only one was detected. After enhancing image quality using wavelet transform method, it was found that those of seven images were completed. The comparison method was a matching technique. This work focuses on the ability of the program and knowledge of image processing by studying algorithms of image processing. In design algorithm process, different technique enhancements were employed as well as the study of different of filters and algorithms based on existing research results from literature. © 2016 IEEE.

关键词： Wavelet transforms

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Genetic Optimized Fuzzy Extreme Learning Machine Ensembles for Affect Classification

Genetic Optimized Fuzzy Extreme Learning Machine Ensembles f...

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International Symposium on Soft Computing and Intelligent systems (SCIS)

作者： Wei Shiung Liew Chu Kiong Loo Takenori Obo Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Graduate School of System Design Tokyo Metropolitan University Hino Tokyo Japan

ISBN: (纸本)9781509026791

This paper presents a method for generating an optimized ensemble of fuzzy extreme learning machines (FELM) using a combination of genetic algorithms with a Bayesian Information Criterion (GA-BIC) fitness function. The operation of the FELM is equivalent to that of a fuzzy inference system, and is used for learning and classifying a given data set. The relative simplicity of the FELM structure enables a large number of FELMs to be generated in a short time. GA-BIC is used to select the minimum number of FELMs while maximizing the effectiveness of the classifier ensemble.

关键词： Artificial neural networks Genetic algorithms Training Neurons Sociology Statistics Genetics

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