• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.4
• /
• pp.833-839
• /
• 2016

In this paper, we propose a selective feature extraction algorithm between Markov transition probability and co-occurrence probability for an effective image splicing detection. The Features used in our method are composed of the difference values between DCT coefficients in the adjacent blocks and the value of Kullback-Leibler divergence(KLD) is calculated to evaluate the differences between the distribution of original image features and spliced image features. KLD value is an efficient measure for selecting Markov feature or Co-occurrence feature because KLD shows non-similarity of the two distributions. After training the extracted feature vectors using the SVM classifier, we determine whether the presence of the image splicing forgery. To verify our algorithm we used grid search and 6-folds cross-validation. Based on the experimental results it shows that the proposed method has good detection performance with a limited number of features compared to conventional methods.

• The Journal of the Acoustical Society of Korea
• /
• v.39 no.1
• /
• pp.64-68
• /
• 2020

In a conventional speech recognition system using Gaussian Mixture Model-Hidden Markov Model (GMM-HMM), the cepstral feature normalization method based on pole filtering was effective in improving the performance of recognition of short utterances in noisy environments. In this paper, the usefulness of this method for the state-of-the-art speech recognition system using Deep Neural Network (DNN) is examined. Experimental results on AURORA 2 DB show that the cepstral mean and variance normalization based on pole filtering improves the recognition performance of very short utterances compared to that without pole filtering, especially when there is a large mismatch between the training and test conditions.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.6
• /
• pp.597-602
• /
• 2014

We propose hybrid-sensing system for human tracking. This system uses laser scanners and image sensors and is applicable to wide and crowded area such as hallway of university. Concretely, human tracking using laser scanners is at base and image sensors are used for human identification when laser scanners lose persons by occlusion, entering room or going up stairs. We developed the method of human identification for this system. Our method is following: 1. Best-shot images (human images which show human feature clearly) are obtained by the help of human position and direction data obtained by laser scanners. 2. Human identification is conducted by calculating the correlation between the color histograms of best-shot images. It becomes possible to conduct human identification even in crowded scenes by estimating best-shot images. In the experiment in the station, some effectiveness of this method became clear.

• Proceedings of the Technology Innovation Conference
• /
• 2005.02a
• /
• pp.268-286
• /
• 2005

본 연구에서는 OECD 주요 10개국을 대상으로 국가별 정보통신산업의 성장 추이를 각각 분석하고 국별 특성을 비교하는데 목적이 있다. 이를 바탕으로 각국의 정보통신산업이 경기순환 또는 단계별 발전 속성을 지니고 있는지를 파악하고 국가별 공통점과 특이점을 분석하고자 하였다. 방법론적으로 OECD 국가들의 정보통신산업 GDP 추이 및 성장률의 움직임을 국면전환 (regime change) 확산과정으로 묘사함으로써 각 국가별 정보통신산업 발전 양상의 특징 및 국면전환 시점 등을 포착해 내고자 하였다 추세를 갖는 대표적 확산과정인 GBM 모형과 평균회귀 성향을 갖는 대표적 확산과정인 Vasicek 모형에 각각 마코프 국면전환을 도입하여 국가별 정보통신산업 GDP 및 GDP 성장률의 추이에 있어 국면 전환 여부와 독특한 발전 특성을 비교 분석하였다. 실증분석 결과 정보통신산업 GDP의 성장률과 변동성 사이에는 높은 상관관계가 있었으며, 한국, 멕시코 등은 고성장, 고변동성을, 미국, 프랑스, 일본 등은 저성장, 저변동성의 특성을 보이는 것으로 나타났다 또한 한국의 경우 유일하게 성장률과 변동성 모두 국면전환이 일어나는 국가로 나타났다. 장기평균 성장률의 특성에 따라 분류한 결과, 한국, 일본, 미국, 멕시코, 뉴질랜드는 고성장에서 저성장으로의 국면전환, 핀란드와 덴마크는 경기 순환적 국면전환, 노르웨이, 프랑스, 캐나다는 단일 국면으로 분류할 수 있었다. 특히 한국의 경우 평균회귀 속도와 변동성이 타 국가에 비해 높은 특성을 보여주었다. 본 연구는 정보통신산업을 미시적 분석이나 세부 항목별 정량적 분석을 통해서가 아니라 산업의 발전 속성 및 경기 순환 등의 관점에서 분석함으로써 정보통신산업 정책의 수립 및 집행을 거시적 안목 하에 정립할 수 있게 한다는 데 의의를 가진다. 또한 경제변수를 묘사하는데 있어 국면전환 확산과정을 사용함으로써 향후 실물옵션 등을 통한 기술 및 무형자산의 가치평가에 있어 기초자산의 움직임을 보다 정확히 포착해 낼 수 있는 프로세스를 제공하였다는데 또 다른 의의를 갖는다고 하겠다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2002.11b
• /
• pp.762-766
• /
• 2002

This paper describes the emotion recognition algorithm using HMM(Hidden Markov Model) method. The relation between the mechanic system and the human has just been unilateral so far. This is the why people don't want to get familiar with multi-service robots of today. If the function of the emotion recognition is granted to the robot system, the concept of the mechanic part will be changed a lot. Pitch and Energy extracted from the human speech are good and important factors to classify the each emotion (neutral, happy, sad and angry etc.), which are called prosodic features. HMM is the powerful and effective theory among several methods to construct the statistical model with characteristic vector which is made up with the mixture of prosodic features

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2002.11a
• /
• pp.375.2-375
• /
• 2002

This paper describes the emotion recognition algorithm using HMM(Hidden Markov Model) method. The relation between the mechanic system and the human has just been unilateral so far This is the why people don't want to get familiar with multi-service robots. If the function of the emotion recognition is granted to the robot system, the concept of the mechanic part will be changed a lot. (omitted)

• The Korean Journal of Financial Management
• /
• v.8 no.2
• /
• pp.111-129
• /
• 1991

본 논문에서는 재정가격결정모형(裁定價格決定模型)(Arbitrage Pricing Model)을 기초로 우리나라 주식시장에 영향을 주는 거시경제변수가 무엇인가를 찾고자 하였다. 방법론면에서는 과거변수(過去變數)(lagged variables)에 의해서만 기대치를 형성시키는 AIRMA(Autoregressike Integrated with Moving Average) 방법을 이용하기보다는 마코프속성(屬性)(Markov Property)을 갖는 상태공간모형(狀態空間模型) (State Space Model)을 이용하여 보다 합리적인 거시경제 요인의 이노베이션을 하였다. 또한 단순한 요인분석(要因分析)(factor analysis)에 의한 요인추출은 요인의 표본의존성(標本依存性)(Sample dependency)이 심하므로 그룹간 요인분석(inter-battery factor analysis)을 행하여 추정(推定)된 요인(要因)(요인값 : factor score)과 요인수를 결정하여 관련 거시경제변수를 선택한다. 그룹간 요인분석을 위한 그룹을 형성할 때 그룹내에서는 동질성을 그룹간에는 이질성을 최대한 살리는 것이 필요한데, 이를 위해 군집분석(群集分析)(Cluster Analysis)을 사용한 것이 특징이다. 결론적으로 우리나라 주식시장에 영향을 미치는 거시경제요인(巨視經濟要因)으로 단위노동비율, 제조업제품재고지수, 채권프리미엄, 수출물가지수, 정부부문 통화공급, 회사채수익률, 종합주가지수 등 7가지가 있는 것으로 분석되고 있다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.1
• /
• pp.46-51
• /
• 2007

This paper is to present gesture analysis for human-robot interaction. Understanding human emotions through gesture is one of the necessary skills fo the computers to interact intelligently with their human counterparts. Gesture analysis is consisted of several processes such as detecting of hand, extracting feature, and recognizing emotions. For efficient operation we used recognizing a gesture with HMM(Hidden Markov Model). We constructed a large gesture database, with which we verified our method. As a result, our method is successfully included and operated in a mobile system.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.10
• /
• pp.2137-2142
• /
• 2012

Many studies in detection and classification of the targets in the underwater environments have been conducted for military purposes, as well as for non-military purpose. Due to the complicated characteristics of underwater acoustic signal reflecting multipath environments and spatio-temporal varying characteristics, active sonar target classification technique has been considered as a difficult technique. And it has a difficult in collecting actual underwater data. In this paper, we implemented the simulator to synthesize the active target signal, to extract feature and to classify the target in the underwater environment. In target signal synthesis, highlight and three-dimensional model are used and multi-aspect based hidden markov model is used for target classification.

• Convergence Security Journal
• /
• v.14 no.5
• /
• pp.37-47
• /
• 2014

A Trojan malicious code is one of largest malicious codes and has been known as a virus that causes damage to a system as itself. However, it has been changed as a type that picks user information out stealthily through a backdoor method, and worms or viruses, which represent a characteristic of the Trojan malicious code, have recently been increased. Although several modeling methods for analyzing the diffusion characteristics of worms have proposed, it allows a macroscopic analysis only and shows limitations in estimating specific viruses and malicious codes. Thus, in this study an ESP model that can estimate future occurrences of Trojan malicious codes using the previous Trojan data is proposed. It is verified that the estimated value obtained using the proposed model is similar to the existing actual frequency in causes of the comparison between the obtained value and the result obtained by the Markov chain.