• Title/Summary/Keyword: Soft computing

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A Integration of Heterogeneous Sim에ator based on Distributed Computing Environment Using HLA (분산 컴퓨팅 환경에서 HLA를 이용한 이기종 시뮬레이터 통합)

  • Hwang, Jae-Jun;Lee, Kyu-Young;Choi, Jae-Young
    • Proceedings of the Korean Information Science Society Conference
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    • 2005.11a
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    • pp.754-756
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    • 2005
  • 현대는 실제와 같은 환경을 재현할 수 있도록 제작된 시뮬레이터를 사용하여 실제 훈련을 대체하고 있다. 그러나 많은 사용자들은 이미 개발된 이종의 시뮬레이터들을 하나로 묶어 연동할 필요성을 느끼게 되었고, 이에 따라 다양한 형태의 상호작용이 수반되는 시뮬레이션에 대한 요구를 만족하는 대규모 분산가상환경(large-scaled distributed virtual environment)을 개발하려는 움직임이 크게 늘게 되었다. 이러한 요구를 바탕으로 등장한 것들 중 하나가 HLA이다. High Level Architecture (HLA)는 미국방성에서 모델과 시뮬레이션에 공통의 구조와 인터페이스를 제공하기 위해 개발된 통합 구조이다. HLA는 분산 컴퓨팅 환경에서 각각의 시뮬레이터들이 정보를 교환 할 수 있게 해줌으로써 하나의 통합 시뮬레이션 시스템을 구축하게 해준다. HLA는 크게 Object Model Template (OMT)와 Run-Time Infrastructure (RTI)로 구성되어 있으며, 이를 통하여 공통된 구조와 상호 작용 환경을 제공한다. 각각의 시뮬레이터들은 RTI를 통하여 Federation에 참여하고 선별적으로 원하는 정보를 주고받으며 하나의 통합 시뮬레이션을 이루게 된다. 본 논문에서는 분산 컴퓨팅 환경에서 이기종의 3차원 영상 시뮬레이터들에 HLA 인터페이스를 삽입하고 3차원 영상 시뮬레이션에 적합하게 개발된 공통 구조인 FOM 제공하여 하나의 통합 가상훈련 시스템을 구축하였다. 이 시스템은 현재 KA-32 헬기 시뮬레이터 영상 프로그램 제작에 적용되어있다.

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A Study on Probabilistic Response-time Analysis for Real-time Control Systems (실시간 제어시스템의 확률적 응답시간 해석에 관한 연구)

  • Han, Jae-Hyun;Shin, Min-Suk;Hwang, In-Yong;SunWoo, Myoung-Ho
    • Transactions of the Korean Society of Automotive Engineers
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    • v.14 no.3
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    • pp.186-195
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    • 2006
  • In real-time control systems, the traditional timing analysis based on worst-case response-time(WCRT) is too conservative for the firm and soft real-time control systems, which permit the maximum utilization factor greater than one. We suggested a probabilistic analysis method possible to apply the firm and soft real-time control systems under considering dependency relationship between tasks. The proposed technique determines the deadline miss probability(DMP) of each task from computing the average response-time distribution under a fixed-priority scheduling policy. The method improves the predictable ability forthe average performance and the temporal behavior of real-time control systems.

A Study on Real-Time Operating Systems for Architectural Improvement of Naval Combat Systems (함정용 전투체계 아키텍처 개선을 위한 실시간 운영체제 적용방안 연구)

  • Kim, Chum-Su;Chang, Hye-Min;Joo, Jung-Hyun;Lee, Gyoon-Jung
    • Journal of the Korea Institute of Military Science and Technology
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    • v.16 no.3
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    • pp.260-267
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    • 2013
  • A combat system for navy's battleship is a system of systems who supports naval indigenous operations by integrating and inter-operating many different kind of weapon and non-weapon systems, which has characteristics of large-scale complex computing system. This paper considers a characteristics of naval combat system which has been developed by domestic technology and suggests a way to improve future naval combat system in terms of computing architecture by applying commercial real-time operating system technologies. This paper also provides an evaluation criteria for combat system adaptability of real-time operating systems.

Sub-Frame Analysis-based Object Detection for Real-Time Video Surveillance

  • Jang, Bum-Suk;Lee, Sang-Hyun
    • International Journal of Internet, Broadcasting and Communication
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    • v.11 no.4
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    • pp.76-85
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    • 2019
  • We introduce a vision-based object detection method for real-time video surveillance system in low-end edge computing environments. Recently, the accuracy of object detection has been improved due to the performance of approaches based on deep learning algorithm such as Region Convolutional Neural Network(R-CNN) which has two stage for inferencing. On the other hand, one stage detection algorithms such as single-shot detection (SSD) and you only look once (YOLO) have been developed at the expense of some accuracy and can be used for real-time systems. However, high-performance hardware such as General-Purpose computing on Graphics Processing Unit(GPGPU) is required to still achieve excellent object detection performance and speed. To address hardware requirement that is burdensome to low-end edge computing environments, We propose sub-frame analysis method for the object detection. In specific, We divide a whole image frame into smaller ones then inference them on Convolutional Neural Network (CNN) based image detection network, which is much faster than conventional network designed forfull frame image. We reduced its computationalrequirementsignificantly without losing throughput and object detection accuracy with the proposed method.

An Effective Intention Reading from User Face for Human-Friendly Interface (인간친화형 인터페이스를 위한 사용자 얼굴에서의 효과적인 의도 파악)

  • 김대진;송원경;김종성;변증남
    • Proceedings of the IEEK Conference
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    • 2000.11c
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    • pp.25-28
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    • 2000
  • In this paper, an effective intention reading scheme is proposed for human-friendly interface. Soft computing techniques such as fuzzy logic and artificial neural networks are used for this. And Gabor filter based feature(GG feature) is also proposed to deal with local activity in the human face. It is based on human visual system and Gabor filter based approach is very popular in these days. The proposed scheme is adopted for human-friendly interface for rehabilitation service robotic system KARES II.

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Gestures as a Means of Human-Friendly Communication between Man and Machine

  • Bien, Zeungnam
    • Proceedings of the IEEK Conference
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    • 2000.07a
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    • pp.3-6
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    • 2000
  • In this paper, ‘gesture’ is discussed as a means of human-friendly communication between man and machine. We classify various gestures into two Categories: ‘contact based’ and ‘non-contact based’ Each method is reviewed and some real applications are introduced. Also, key design issues of the method are addressed and some contributions of soft-computing techniques, such as fuzzy logic, artificial neural networks (ANN), rough set theory and evolutionary computation, are discussed.

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Performance Evaluation of Rough Set Classifier (러프 집합 분류기의 성능 평가)

  • 류재홍;임창균
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.10a
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    • pp.232-235
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    • 1998
  • This paper evaluates the performance of a rough set based pattern classifier using the benchmarks in artificial neural nets depository found in internet. The definition of rough set in soft computing paradigm is briefly introduced. next the design of rough set classifier is suggested. Finally benchmark test results are shown the performance of rough set compare to that of ANNs and decision tree.

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Sentiment Analysis to Classify Scams in Crowdfunding

  • shafqat, Wafa;byun, Yung-cheol
    • Soft Computing and Machine Intelligence
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    • v.1 no.1
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    • pp.24-30
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    • 2021
  • The accelerated growth of the internet and the enormous amount of data availability has become the primary reason for machine learning applications for data analysis and, more specifically, pattern recognition and decision making. In this paper, we focused on the crowdfunding site Kickstarter and collected the comments in order to apply neural networks to classify the projects based on the sentiments of backers. The power of customer reviews and sentiment analysis has motivated us to apply this technique in crowdfunding to find timely indications and identify suspicious activities and mitigate the risk of money loss.

An elastic distributed parallel Hadoop system for bigdata platform and distributed inference engines (동적 분산병렬 하둡시스템 및 분산추론기에 응용한 서버가상화 빅데이터 플랫폼)

  • Song, Dong Ho;Shin, Ji Ae;In, Yean Jin;Lee, Wan Gon;Lee, Kang Se
    • Journal of the Korean Data and Information Science Society
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    • v.26 no.5
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    • pp.1129-1139
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    • 2015
  • Inference process generates additional triples from knowledge represented in RDF triples of semantic web technology. Tens of million of triples as an initial big data and the additionally inferred triples become a knowledge base for applications such as QA(question&answer) system. The inference engine requires more computing resources to process the triples generated while inferencing. The additional computing resources supplied by underlying resource pool in cloud computing can shorten the execution time. This paper addresses an algorithm to allocate the number of computing nodes "elastically" at runtime on Hadoop, depending on the size of knowledge data fed. The model proposed in this paper is composed of the layered architecture: the top layer for applications, the middle layer for distributed parallel inference engine to process the triples, and lower layer for elastic Hadoop and server visualization. System algorithms and test data are analyzed and discussed in this paper. The model hast the benefit that rich legacy Hadoop applications can be run faster on this system without any modification.

Application of ANFIS technique on performance of C and L shaped angle shear connectors

  • Sedghi, Yadollah;Zandi, Yousef;Shariati, Mahdi;Ahmadi, Ebrahim;Azar, Vahid Moghimi;Toghroli, Ali;Safa, Maryam;Mohamad, Edy Tonnizam;Khorami, Majid;Wakil, Karzan
    • Smart Structures and Systems
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    • v.22 no.3
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    • pp.335-340
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    • 2018
  • The behavior of concrete slabs in composite beam with C and L shaped angle shear connectors has been studied in this paper. These two types of angle shear connectors' instalment have been commonly utilized. In this study, the finite element (FE) analysis and soft computing method have been used both to present the shear connectors' push out tests and providing data results used later in soft computing method. The current study has been performed to present the aforementioned shear connectors' behavior based on the variable factors aiming the study of diverse factors' effects on C and L shaped angle in shear connectors. ANFIS (Adaptive Neuro Fuzzy Inference System), has been manipulated in providing the effective parameters in shear strength forecasting by providing input-data comprising: height, length, thickness of shear connectors together with concrete strength and the respective slip of shear connectors. ANFIS has been also used to identify the predominant parameters influencing the shear strength forecast in C and L formed angle shear connectors.