• Title/Summary/Keyword: Information processing Model

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Feasibility Study of a Distributed and Parallel Environment for Implementing the Standard Version of AAM Model

  • Naoui, Moulkheir;Mahmoudi, Said;Belalem, Ghalem
    • Journal of Information Processing Systems
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    • v.12 no.1
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    • pp.149-168
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    • 2016
  • The Active Appearance Model (AAM) is a class of deformable models, which, in the segmentation process, integrates the priori knowledge on the shape and the texture and deformation of the structures studied. This model in its sequential form is computationally intensive and operates on large data sets. This paper presents another framework to implement the standard version of the AAM model. We suggest a distributed and parallel approach justified by the characteristics of the model and their potentialities. We introduce a schema for the representation of the overall model and we study of operations that can be parallelized. This approach is intended to exploit the benefits build in the area of advanced image processing.

A Time-Constrained Information Processing Model in Ubiquitous Environments

  • Hur, Sun;Lee, Hyun;Shin, Dong-Min;Lee, Won-Suk
    • ETRI Journal
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    • v.29 no.4
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    • pp.489-496
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    • 2007
  • As pervasive computing is widely adopted and reliable networks are becoming more easily accessible, there is a rapidly growing need to develop a mechanism to analyze and evaluate the performance of ubiquitous environments. This paper presents an information processing model which characterizes a ubiquitous environment where a variety of pieces of information need to be exchanged among devices within a system. The proposed model not only provides analytical tools to evaluate the performance of devices, but also makes it possible to identify key factors in designing a ubiquitous environment. For illustrative purposes, a test bed is constructed and the performance of the system is assessed based on the proposed model.

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Mobile Palmprint Segmentation Based on Improved Active Shape Model

  • Gao, Fumeng;Cao, Kuishun;Leng, Lu;Yuan, Yue
    • Journal of Multimedia Information System
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    • v.5 no.4
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    • pp.221-228
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    • 2018
  • Skin-color information is not sufficient for palmprint segmentation in complex scenes, including mobile environments. Traditional active shape model (ASM) combines gray information and shape information, but its performance is not good in complex scenes. An improved ASM method is developed for palmprint segmentation, in which Perux method normalizes the shape of the palm. Then the shape model of the palm is calculated with principal component analysis. Finally, the color likelihood degree is used to replace the gray information for target fitting. The improved ASM method reduces the complexity, while improves the accuracy and robustness.

Side Information Extrapolation Using Motion-aligned Auto Regressive Model for Compressed Sensing based Wyner-Ziv Codec

  • Li, Ran;Gan, Zongliang;Cui, Ziguan;Wu, Minghu;Zhu, Xiuchang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.7 no.2
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    • pp.366-385
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    • 2013
  • In this paper, we propose a compressed sensing (CS) based Wyner-Ziv (WZ) codec using motion-aligned auto regressive model (MAAR) based side information (SI) extrapolation to improve the compression performance of low-delay distributed video coding (DVC). In the CS based WZ codec, the WZ frame is divided into small blocks and CS measurements of each block are acquired at the encoder, and a specific CS reconstruction algorithm is proposed to correct errors in the SI using CS measurements at the decoder. In order to generate high quality SI, a MAAR model is introduced to improve the inaccurate motion field in auto regressive (AR) model, and the Tikhonov regularization on MAAR coefficients and overlapped block based interpolation are performed to reduce block effects and errors from over-fitting. Simulation experiments show that our proposed CS based WZ codec associated with MAAR based SI generation achieves better results compared to other SI extrapolation methods.

Extended CEP Model for Effective Enterprise Systems Service Monitoring

  • Kum, Deuk Kyu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.2
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    • pp.807-825
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    • 2020
  • In recent years, business environments have become more complex; therefore, enterprises must be capable of responding flexibly and agilely. For these purposes, effective enterprise systems service monitoring and early decision making based on the same, emerge as core competency of the enterprise. In addition, enterprise system techniques that filter meaningful data are needed to event processing. However, the existing study related with this is nothing but discovering of service faults by monitoring depending upon API of BPEL engine or middleware, or is nothing but processing of simple events based on low-level events. Accordingly, there would be limitations to provide useful business information. In this study, we present an extended event processing model that enables delivery of more valuable and useful business information through situation detection. Primarily, the event processing architecture in an enterprise system is proposed as a definite approach, and then define an event meta-model suitable for the proposed architecture. Based on the defined model, we propose the syntax and semantics of the elements that make up the event processing language include various and progressive event operators, the rules, complex event pattern, etc. In addition, an event context mechanism is proposed to analyze more delicate events. Finally, the effectiveness and applicability of proposed approach is presented through a case study.

A Bio-Inspired Modeling of Visual Information Processing for Action Recognition (생체 기반 시각정보처리 동작인식 모델링)

  • Kim, JinOk
    • KIPS Transactions on Software and Data Engineering
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    • v.3 no.8
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    • pp.299-308
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    • 2014
  • Various literatures related computing of information processing have been recently shown the researches inspired from the remarkably excellent human capabilities which recognize and categorize very complex visual patterns such as body motions and facial expressions. Applied from human's outstanding ability of perception, the classification function of visual sequences without context information is specially crucial task for computer vision to understand both the coding and the retrieval of spatio-temporal patterns. This paper presents a biological process based action recognition model of computer vision, which is inspired from visual information processing of human brain for action recognition of visual sequences. Proposed model employs the structure of neural fields of bio-inspired visual perception on detecting motion sequences and discriminating visual patterns in human brain. Experimental results show that proposed recognition model takes not only into account several biological properties of visual information processing, but also is tolerant of time-warping. Furthermore, the model allows robust temporal evolution of classification compared to researches of action recognition. Presented model contributes to implement bio-inspired visual processing system such as intelligent robot agent, etc.

An Exponential Smoothing Adaptive Failure Detector in the Dual Model of Heartbeat and Interaction

  • Yang, Zhiyong;Li, Chunlin;Liu, Yanpei;Liu, Yunchang;Xu, Lijun
    • Journal of Computing Science and Engineering
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    • v.8 no.1
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    • pp.17-24
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    • 2014
  • In this paper, we propose a new implementation of a failure detector. The implementation uses a dual model of heartbeat and interaction. First, the heartbeat model is adopted to shorten the detection time, if the detection process does not receive the heartbeat message in the expected time. The interaction model is then used to check the process further. The expected time is calculated using the exponential smoothing method. Exponential smoothing can be used to estimate the next arrival time not only in the random data, but also in the data of linear trends. It is proven that the new detector in the paper can eventually be a perfect detector.

Study of the information processing model in a way of product design method (제품디자인 방법에서의 정보 처리 모델 연구)

  • 조성근
    • Archives of design research
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    • v.16 no.1
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    • pp.289-296
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    • 2003
  • The thesis is a study of the information model for the information collection and systematization in the way of product design. In the past, the design was made by the designers hands, worked with the material directly, but today's product design, the material diverted to information, can be considered as it is made essentially through information collection and systematization processing. If the product design is considered the information processing, usually it means a qualitative change of the product design information, not a quantitative change of the information theory. A focus of the study is to grope for a way of changing the subject to information, dealing with when the product design intends to purposes, not the material, When a way of the product design was discussed to solve the problems rationally, in the past, if it is considered as quantitative, qualitative and organic methods and modeling as their means based on the process model, [analysis-generalization-estimation], are formal ism, the way of product design as information is that the product direction as a substance should go through the design information processing, making an alternative plan with the information model cycling to natural order. Because success or failure of the product design in the future depends on information as material.

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An Efficient Web Ontology Storage Considering Hierarchical Knowledge for Jena-based Applications

  • Jeong, Dong-Won;Shin, Hee-Young;Baik, Doo-Kwon;Jeong, Young-Sik
    • Journal of Information Processing Systems
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    • v.5 no.1
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    • pp.11-18
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    • 2009
  • As well as providing various APIs for the development of inference engines and storage models, Jena is widely used in the development of systems or tools related with Web ontology management. However, Jena still has several problems with regard to the development of real applications, one of the most important being that its query processing performance is unacceptable. This paper proposes a storage model to improve the query processing performance of the original Jena storage. The proposed storage model semantically classifies OWL elements, and stores an ontology in separately classified tables according to the classification. In particular, the hierarchical knowledge is managed, which can make the processing performance of inferable queries enhanced and stores information. It enhances the query processing performance by using hierarchical knowledge. For this paper an experimental evaluation was conducted, the results of which showed that the proposed storage model provides a improved performance compared with Jena.