• Title/Summary/Keyword: markov analysis method

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Safety Analysis and Methods in a Railway Signalling System

  • Chang, Kwang-Chi;Lee, Key-Soe;Kim, Jong-Ki
    • KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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    • v.4B no.2
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    • pp.92-99
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    • 2004
  • This paper intends to provide practical safety analysis methods and the criteria for method selections. A careful choice of safety analysis techniques will enhance the efficiency of the safety case process. A couple of recommendations are provided from practical experience.

Investigating the Performance of Bayesian-based Feature Selection and Classification Approach to Social Media Sentiment Analysis (소셜미디어 감성분석을 위한 베이지안 속성 선택과 분류에 대한 연구)

  • Chang Min Kang;Kyun Sun Eo;Kun Chang Lee
    • Information Systems Review
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    • v.24 no.1
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    • pp.1-19
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    • 2022
  • Social media-based communication has become crucial part of our personal and official lives. Therefore, it is no surprise that social media sentiment analysis has emerged an important way of detecting potential customers' sentiment trends for all kinds of companies. However, social media sentiment analysis suffers from huge number of sentiment features obtained in the process of conducting the sentiment analysis. In this sense, this study proposes a novel method by using Bayesian Network. In this model MBFS (Markov Blanket-based Feature Selection) is used to reduce the number of sentiment features. To show the validity of our proposed model, we utilized online review data from Yelp, a famous social media about restaurant, bars, beauty salons evaluation and recommendation. We used a number of benchmarking feature selection methods like correlation-based feature selection, information gain, and gain ratio. A number of machine learning classifiers were also used for our validation tasks, like TAN, NBN, Sons & Spouses BN (Bayesian Network), Augmented Markov Blanket. Furthermore, we conducted Bayesian Network-based what-if analysis to see how the knowledge map between target node and related explanatory nodes could yield meaningful glimpse into what is going on in sentiments underlying the target dataset.

Implementation of Hidden Markov Model based Speech Recognition System for Teaching Autonomous Mobile Robot (자율이동로봇의 명령 교시를 위한 HMM 기반 음성인식시스템의 구현)

  • 조현수;박민규;이민철
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.281-281
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    • 2000
  • This paper presents an implementation of speech recognition system for teaching an autonomous mobile robot. The use of human speech as the teaching method provides more convenient user-interface for the mobile robot. In this study, for easily teaching the mobile robot, a study on the autonomous mobile robot with the function of speech recognition is tried. In speech recognition system, a speech recognition algorithm using HMM(Hidden Markov Model) is presented to recognize Korean word. Filter-bank analysis model is used to extract of features as the spectral analysis method. A recognized word is converted to command for the control of robot navigation.

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Throughput analysis of GBN ARQ scheme under correlated frame losses (상관성을 고려한 GBN ARQ 방식의 throughput 분석)

  • 이종원;김종권;이충웅
    • Journal of the Korean Institute of Telematics and Electronics A
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    • v.32A no.1
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    • pp.26-35
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    • 1995
  • Bo-Back-N ARQ is widely used in packet networks for error and flow control methanisms. This paper analyzes the network throughput under the go-back-N schem. Contrast to other analytic methods which assume independent frame losses or the first order Markov frame analytic methods which assume independent frame losses or the first order Markov frame losses concoptually, the proposed method takes into account the correlation between successive frame losses in a congested node. Computer simulation shows that our method generates more accurate performance results that independent assumption method. We apply the proposed method to analyze the performance of BWM in high speed networs. Our results show that BWM maintains the independence between traffic streams.

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Facial Expression Recognition using 1D Transform Features and Hidden Markov Model

  • Jalal, Ahmad;Kamal, Shaharyar;Kim, Daijin
    • Journal of Electrical Engineering and Technology
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    • v.12 no.4
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    • pp.1657-1662
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    • 2017
  • Facial expression recognition systems using video devices have emerged as an important component of natural human-machine interfaces which contribute to various practical applications such as security systems, behavioral science and clinical practices. In this work, we present a new method to analyze, represent and recognize human facial expressions using a sequence of facial images. Under our proposed facial expression recognition framework, the overall procedure includes: accurate face detection to remove background and noise effects from the raw image sequences and align each image using vertex mask generation. Furthermore, these features are reduced by principal component analysis. Finally, these augmented features are trained and tested using Hidden Markov Model (HMM). The experimental evaluation demonstrated the proposed approach over two public datasets such as Cohn-Kanade and AT&T datasets of facial expression videos that achieved expression recognition results as 96.75% and 96.92%. Besides, the recognition results show the superiority of the proposed approach over the state of the art methods.

Markov Chain Model-Based Trainee Behavior Pattern Analysis for Assessment of Information Security Exercise Courses (정보보안 훈련 시스템의 성취도 평가를 위한 마코브 체인 모델 기반의 학습자 행위 패턴 분석)

  • Lee, Taek;Kim, Do-Hoon;Lee, Myong-Rak;In, Hoh Peter
    • Journal of KIISE:Computing Practices and Letters
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    • v.16 no.12
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    • pp.1264-1268
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    • 2010
  • In this paper, we propose a behavior pattern analysis method for users tasking on hands-on security exercise missions. By analysing and evaluating the observed user behavior data, the proposed method discovers some significant patterns able to contribute mission successes or fails. A Markov chain modeling approach and algorithm is used to automate the whole analysis process. How to apply and understand our proposed method is briefly shown through a case study, "network service configurations for secure web service operation".

Optimum Size Analysis for Dam Rehabilitation Using Reliability Analysis (신뢰성 분석을 통한 기존 댐 재개발의 적정규모 결정의 관한 연구)

  • Kwon, Hyun-Han;Moon, Young-Il;Choi, Byung-Gyu;Yoon, Yong-Nam
    • Journal of Korea Water Resources Association
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    • v.38 no.2
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    • pp.97-110
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    • 2005
  • This paper presents a procedure of evaluation of reservoir capacity for additional water storage for dam rehabilitation. One of the techniques on the extension of rainfall has been developed, and the daily stream flows were simulated by the NWS-PC long-term rainfall-runoff model with the input of the extended daily rainfall which was stochastically generated by the nonhomogeneous markov chain model. We peformed a reliability analysis to Guisan dam about the optimal capacity of dam rehabilitation by using performance criteria that Hashimoto et al. (1982) presented. We estimated that the most suitable water level is approximately 155EL.M. suggested that this method can use supplemental methods to estimate optimum dam scale.

A Study on the Forecasting of the Number of End of Life Vehicles in Korea using Markov Chain (Markov Chain을 이용한 국내 폐차발생량 예측)

  • Lee, Eun-A;Choi, Hoe-Ryeon;Lee, Hong-Chul
    • Journal of Korean Institute of Industrial Engineers
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    • v.38 no.3
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    • pp.208-219
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    • 2012
  • As the number of end-of-life vehicles (ELVs) has kept increasing, the management of ELV has also become one of the academic research focuses and European Union recently adopted the directive on ELVs. For the stakeholders has become a principle agent of dealing with all about ELVs, it is relevant investment decision to set up and to decide high-cost ELVs entity locations and to forecast future ELVs' amount in advance. In this paper, transition probability matrixes between months are made by using Markov Chain and the number of ELVs is predicted with them. This study will perform a great role as a fundamental material in Korea where just started having interests about recycling resources and studies related to the topic. Moreover, the forecasting method developed for this research can be adopted for other enhancements in different but comparable situations.

대학도서관의 복본수 결정기법에 관한 연구

  • 양재한
    • Journal of Korean Library and Information Science Society
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    • v.13
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    • pp.131-166
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    • 1986
  • This study is designed to review the methods of duplicate copies decision making in the academic library. In this thesis, I surveyed queueing & markov model, statistical model, and simulation model. The contents of the study can be summarized as follows: 1) Queueing and markov model is used for one of duplicate copies decision-making methods. This model was suggested by Leimkuler, Morse, and Chen, etc. Leimkuler proposed growth model, storage model, and availability model through using system analysis method. Queueing theory is a n.0, pplied to Leimkuler's availability model. Morse ad Chen a n.0, pplied queueing and markov model to their theory. They used queueing theory for measuring satisfaction level and Markov model for predicting user demand. 2) Another model of duplicate copies decision-making methods is statistical model. This model is suggested by Grant and Sohn, Jung Pyo. Grant suggested a model with a formula to satisfy the user demand more than 95%, Sohn, Jung Pyo suggested a model with two formulars: one for duplicate copies decision-making by using standard deviation and the other for duplicate copies predicting by using coefficient of variation. 3) Simulation model is used for one of duplicate copies decision-making methods. This model is suggested by Buckland and Arms. Buckland considered both loan period and duplicate copies simultaneously in his simulation model. Arms suggested computer-simulation model as one of duplicate copies decision-making methods. These methods can help improve the efficiency of collection development and solve some problems (space, staff, budget, etc, ) of Korean academic libraries today.

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Development of Autonomous Mobile Robot with Speech Teaching Command Recognition System Based on Hidden Markov Model (HMM을 기반으로 한 자율이동로봇의 음성명령 인식시스템의 개발)

  • Cho, Hyeon-Soo;Park, Min-Gyu;Lee, Hyun-Jeong;Lee, Min-Cheol
    • Journal of Institute of Control, Robotics and Systems
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    • v.13 no.8
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    • pp.726-734
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    • 2007
  • Generally, a mobile robot is moved by original input programs. However, it is very hard for a non-expert to change the program generating the moving path of a mobile robot, because he doesn't know almost the teaching command and operating method for driving the robot. Therefore, the teaching method with speech command for a handicapped person without hands or a non-expert without an expert knowledge to generate the path is required gradually. In this study, for easily teaching the moving path of the autonomous mobile robot, the autonomous mobile robot with the function of speech recognition is developed. The use of human voice as the teaching method provides more convenient user-interface for mobile robot. To implement the teaching function, the designed robot system is composed of three separated control modules, which are speech preprocessing module, DC servo motor control module, and main control module. In this study, we design and implement a speaker dependent isolated word recognition system for creating moving path of an autonomous mobile robot in the unknown environment. The system uses word-level Hidden Markov Models(HMM) for designated command vocabularies to control a mobile robot, and it has postprocessing by neural network according to the condition based on confidence score. As the spectral analysis method, we use a filter-bank analysis model to extract of features of the voice. The proposed word recognition system is tested using 33 Korean words for control of the mobile robot navigation, and we also evaluate the performance of navigation of a mobile robot using only voice command.