• 한국컴퓨터정보학회논문지
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• 제18권5호
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• pp.147-156
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• 2013

현대 사회의 각 분야는 산업화와 과학기술의 발전으로 빠르게 변화한다. 그러나 빠른 사회 변화의 부작용으로 다양한 문제가 발생하고 있는데, 그 중 범죄는 큰 문제이다. 본 논문은 범죄를 예측하기 위한 모델로 마코프 체인을 적용한 범죄 예측 모델링을 제안한다. 기존의 마코프 체인 모델링은 한 사건의 전체 상태만으로 미래 예측 확률을 구하였으나, 본 논문은 사건 발생 확률 예측을 높이기 위해 전체 상태 예측 확률과 최근 상태 예측 확률로 나누었다. 그리고 전체 상태 예측 확률과 최근 상태 예측 확률의 평균값을 적용하여 미래 예측 확률 모델링으로 구현했다. 데이터는 범죄 발생 건수를 적용하였다. 그 결과 전체 상태만을 대상으로 예측확률을 적용 하였을 때 보다, 전체 상태와 최근상태로 나누어 확률 값을 구한 후, 그 평균값을 예측 확률로 적용하였을 때, 범죄 발생 예측에 근접하다는 결론을 얻었다.

• Journal of Communications and Networks
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• 제15권6호
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• pp.589-600
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• 2013

In this paper, we propose a new feedback scheme called mode selection-based feedback by scheduling probability prediction (SPP-MF) for channel state feedback in OFDMA downlink system. We design the scheme such that it determines the more desirable feedback mode among selective feedback by scheduling probability prediction (SPP-SF) mode and bitmap feedback by scheduling probability prediction (SPP-BF) mode, by calculating and comparing the throughputs of the two modes. In both feedback modes, each user first calculates the scheduling probability of each subchannel (i.e., the probability that a user wins the scheduling competition for a subchannel) and then forms a feedback message based on the scheduling probability. Specifically, in the SPP-SF mode, each user reports the modulation and coding scheme (MCS) levels and indices of its best S subchannels in terms of the scheduling probability. In the SPP-BF mode, each user determines its scheduling probability threshold. Then, it forms a bitmap for the subchannels according to the scheduling probability threshold and sends the bitmap along with the threshold. Numerical results reveal that the proposed SPP-MF scheme achieves significant performance gain over the existing feedback schemes.

• 인터넷정보학회논문지
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• 제19권6호
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• pp.101-112
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• 2018

A lot of manpower and budgets are being used to prevent fires, and only a small portion of the data generated during this process is used for disaster prevention activities. This study develops a prediction model of fire occurrence probability based on data mining in order to more actively use these data for disaster prevention activities. For this purpose, variables for predicting fire occurrence probability of various buildings were selected and data of construction administrative system, national fire information system, and Korea Fire Insurance Association were collected and integrated data set was constructed. After appropriate data cleansing and preprocessing, various data mining methodologies such as artificial neural network, decision trees, SVM, and Naive Bayesian were used to develop a prediction model of the fire occurrence probability of buildings. The most accurate model among the derived models is Linear SVM model which shows 68.42% as experimental data and 63.54% as verification data and it is the best model to predict fire occurrence probability of buildings. As this study develops the prediction model which uses only the set values of the specific ranges, future studies may explore more opportunites to use various setting values not shown in this study.

• Structural Engineering and Mechanics
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• 제45권2호
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• pp.201-209
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• 2013

A new dynamic reliability analysis of structure under repeated random loads is proposed in this paper. The proposed method is developed based on the idea that the probability density of several times random loads can be derived from the probability density of single-time random load. The reliability prediction models of structure based on time responses under several times random loads with and without strength degradation are obtained by using the stress-strength interference theory and probability density evolution method. The resulting differential equations in the prediction models can be solved by using the forward finite difference method. Then, the probability density functions of strength redundancy of the structures can be obtained. Finally, the structural dynamic reliability can be calculated using integral method. The efficiency of the proposed method is demonstrated numerically through a speed reducer. The results have shown that the proposed method is practicable, feasible and gives reasonably accurate prediction.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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• pp.7-16
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• 2003

In this paper, we propose a probabilistic framework to predict the interaction probability of proteins. The notion of domain combination and domain combination pair is newly introduced and the prediction model in the framework takes domain combination pair as a basic unit of protein interactions to overcome the limitations of the conventional domain pair based prediction systems. The framework largely consists of prediction preparation and service stages. In the prediction preparation stage, two appearance pro-bability matrices, which hold information on appearance frequencies of domain combination pairs in the interacting and non-interacting sets of protein pairs, are constructed. Based on the appearance probability matrix, a probability equation is devised. The equation maps a protein pair to a real number in the range of 0 to 1. Two distributions of interacting and non-interacting set of protein pairs are obtained using the equation. In the prediction service stage, the interaction probability of a protein pair is predicted using the distributions and the equation. The validity of the prediction model is evaluated fur the interacting set of protein pairs in Yeast organism and artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in DIP database are used as foaming set of interacting protein pairs, very high sensitivity(86%) and specificity(56%) are achieved within our framework.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2008년도 춘계 학술발표회 초청강연 및 논문집
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• pp.1378-1385
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• 2008

The probability of landslide hazards was predicted to natural terrain around the stone relics of Jinjeon-saji area, which is located in Yangyang, Kangwon Province. As the analysis results of field investigation, laboratory test and geology and geomorphology data, the effect factors of landslides occurrence were evaluated, and then the landslides prediction map was made up by use of prediction model considering the effect factors. The susceptibility of stone relics induced by landslides was investigated as the grading classification of occurrence probability using the landslides prediction map. In the landslides prediction map, the high probability area of landslides over 70% of occurrence probability was 3,489m3, which was 10.1% of total prediction area. If landslides are occurred at the high elevation area, the three stories stone pagoda of Jinjeon-saji (National treasure No.122) and the stone lantern of Jinjeon-saji (Treasure No.439) will be collapsed by debris flow.

• 전기학회논문지
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• 제62권1호
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• pp.140-142
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• 2013

Cognitive radio (CR) has been recently proposed to dynamically access unused-spectrum. Since the spectrum availability for opportunistic access is determined by spectrum sensing, sensing control is identified as one of the most crucial issues of cognitive radio networks. Out-of-band sensing to find an available channels to sense. Sensing is also required in case of spectrum hand-off. Sensing process needs to be done very fast in order to enhance the quality of service (QoS) of the CR nodes, and transmission not to be cut for longer time. During the sensing, the PU(primary user) detection probability condition should be satisfied. We adopt a channel prediction method to find target channels. Proposed prediction method combines chaotic global method and chaotic local method for channel idle probability prediction. Global method focus on channel history information length and order number of prediction model. Local method focus on local prediction trend. Through making simulation, Proposed method can find an available channel with very high probability, total sensing time is minimized, detection probability of PU's are satisfied.

• 한국유체기계학회 논문집
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• 제16권2호
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• pp.42-47
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• 2013

Ship stability prediction is very complex in reality. In this paper, risk based approach is applied to predict the probability of a certified ship, which is effected by the forces of sea especially the wave loading. Safety assessment and risk analysis process are also applied for the probabilistic prediction of ship stability. The survival probability of ships encountering with different waves at sea is calculated by the existed statistics data and risk based models. Finally, ship capsizing probability is calculated according to single degree of freedom(SDF) rolling differential equation and basin erosion theory of nonlinear dynamics. Calculation results show that the survival probabilities of ship excited by the forces of the seas, especially in the beam seas status, can be predicted by the risk based method.

• 대한금속재료학회지
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• 제46권9호
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• pp.554-562
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• 2008

The distribution and prediction interval for the misorientation angle of grain boundary at which $Cr_2N$ was precipitated during heating at $900^{\circ}C$ for $10^4$ sec were newly estimated, and followed by the estimation of mathematical and median rank methods. The probability density function of the misorientation angle can be estimated by a statistical analysis. And then the ($1-{\alpha}$)100% prediction interval of misorientation angle obtained by the estimated probability density function. If the estimated probability density function was symmetric then a prediction interval for the misorientation angle could be derived by the estimated probability density function. In the case of non-symmetric probability density function, the prediction interval could be obtained from the cumulative distribution function of the estimated probability density function. In this paper, 95, 99 and 99.73% prediction interval obtained by probability density function method and cumulative distribution function method and compared with the former results by median rank regression or mathematical method.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제10권4호
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• pp.299-308
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• 2004

최근 단백질 및 도메인과 관련된 방대한 양의 데이타들이 인터넷상에 공표되고 축적됨에 따라, 단백질간의 상호작용에 대한 예측 시스템의 필요성이 제기되고 있다. 본 논문에서는 이러한 데이타를 이용하여 계산적으로 도메인 조합 쌍에 기반하여 단백질의 상호작용 확률을 예측하는 새로운 단백질 상호작용 예측 시스템을 제안한다. 제안된 예측 시스템에서는 기존의 도메인 쌍(domain pair)의 제약성을 극복하기 위하여 도메인 조합(domain combination)과 도메인 조합 쌍(domain combination pair)의 개념이 새롭게 도입하였다. 그리고 도메인 조합 쌍(domain combination pair 또는 dc-pair)을 단백질 상호작용의 기본 단위로 간주하고 예측을 시도한다. 예측 시스템은 크게 예측 준비 과정과 서비스 과정으로 구성되어 있다. 예측 준비 과정에서는 상호작용이 있는 것으로 알려진 단백질 쌍 집합과 상호작용이 없는 것으로 추정되는 단백질 도메인 쌍 집합으로부터 각각 도메인 조합 정보와 그 출현 빈도를 추출한다. 추출된 정보들은 출현 확률 배열(Appearance Probability Matrix 또는 AP matrix)로 불리는 배열 구조에 저장된다. 논문에서는 출현 확률 배열에 기반을 두어, 단백질-단백질 상호작용을 예측하는 확률식 PIP(Primary Interaction Probability)를 고안하고, 고안된 확률식을 이용하여, 상호작용이 있는 것으로 알려진 단백질 쌍 집합과 상호작용이 없는 것으로 추정되는 단백질 도메인 쌍 집합의 확률 값 분포를 생성시킨다. 예측서비스 과정에서는 예측 준비 과정에서 얻어진 분포와 확률식을 이용하여 임의의 단백질 쌍의 상호작용 확률을 계산한다. 예측 모델의 유효성은 효모(yeast)에서 상호작용이 있는 것으로 보고된 단백질 쌍 집합과 상호작용이 없는 것으로 추정되는 단백질 쌍 집합을 이용하여 검증하였다. DIP(Database of Inter-acting Proteins)의 상호작용이 있는 것으로 알려진 효모 단백질 쌍 집합의 80%를 학습 집단으로 사용했을 때, 86%의 sensitivity와 56%의 specificity를 나타내어, 도메인을 기반으로 한 기존의 예측 시스템에 비해서 우월한 예측 정확도를 보여주었다. 이와 같은 예측 정확도의 개선은 본 예측 시스템이 상호작용의 기본 단위로 dc-pair를 채택한 점과 분류를 위하여 새롭게 고안하여 사용한 PIP식이 유효했던 것으로 판단된다.