• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.53-56
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• 2010

본 논문은 Shannon의 정리에 따른 채널 용량에 근접한 성능을 보이는 것으로 알려진 터보 복호기 기반의 반복적인 검출과 복호화(Iterative Detection and Decoding) 기법에서 반복적인 복호화를 수행할 시에 제외되었던 리스트 구 복호기(List Sphere Decoder)에서 사전 정보(prior information)을 이용할 수 있도록 하여 수정된 IDD 기법을 제안하였다. 기존의 기법에서는 사후확률(A posteriori probability)을 계산하기 위하여 리스트 구 복호기를 사용하였으나 반복적인 복호화 수행 시에는 사전 정보를 이용하지 않는 특성으로 인하여 제외된다. 만약 잡음(noise) 등의 이유로 검출된 심볼 벡터 목록이 원래의 것과 매우 다른 경우라도 재 검출을 하지 않기 때문에 반복적인 복호화를 수행하더라도 원래의 정보에 근접하기 어렵게 된다. 본 논문에서는 이러한 기존의 기법에서 리스트 구 복호기를 터보 복호기의 Log Likelihood Ratio (LLR) 값을 사전 정보로 이용할 수 있도록 수정된 리스트 구 복호기를 제안하였다. 수정된 리스트 복호기는 반복적인 복호화를 수행 시 이전의 복호화에서 얻은 정보를 이용하여 새로이 검출된 심볼 벡터 목록을 제공하게 된다. 실제의 통신환경과 유사한 모델의 실험을 통해 수정된 IDD 기법이 기존의 IDD로 구성되는 내부 피드백에 RS 복호기 기반의 외부 피드백으로 구성된 형태로 피드백 회수가 증가할수록 기존의 IDD에 비해 성능이 개선됨을 확인하였다.

• Journal of the Korean Society of Hazard Mitigation
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• v.7 no.4
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• pp.31-39
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• 2007

Disaster mythology is "beliefs about human behavior in disaster that have been proven to be wrong through scientifically conducted research studies." Disaster researchers have discovered a series of disaster myths. The results of research survey based on 491 individuals confirm prior research representing widespread belief in disaster mythology such as panic, looting, crime, evacuation and lethargy. This study also indicates that, while high, the percentage of local government officials demonstrating belief in disaster myths was lower than the public except for looting. The probability of believing disaster myths was greater for individuals who were female (panic and evacuation), old (evacuation and lethargy), in their twenties and thirties (looting), and officials with disaster related work experience (panic, looting and crime). The results indicate the importance of understanding public belief to make effective emergency plans.

• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.215-224
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• 2007

The classification of the satellite images is basic part in Remote sensing. In classification of the satellite images, class separability feature is very effective accuracy of the images classified. For improving classification accuracy, It is necessary to study classification methode than analysis of class separability feature deciding classification probability. In this study, IKONOS, SPOT 5, Landsat TM, were resampled to sizes 1m grid. Above images were calculated the class separability prior to the step for classification of pixels. This Study concludes, each image was measured by the rate of class separability, values classified were showed highly about $1,600{\sim}2,000$.

• Journal of Distribution Science
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• v.16 no.5
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• pp.45-59
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• 2018

Purpose - This paper aims to analyze the distinct pattern of clustering of imported automobile distributors and provide evidence for the phenomenon using Korean data. Research design, data, and methodology - In this paper, we use data from Korea Automobile Importers & Distributors Association of 23 foreign automobile brands to evaluate the degree of concentration of showrooms using locational Gini index. We identify possible causes for the high level of clustering from two perspectives; 1) on the distributors' side and 2) on the customers' side. Results - We find a very strong locational concentration of imported automobile showrooms within close vicinity in the major cities and districts in Korea. Locational Gini coefficients are 0.1024 at the national level, 0.1836~0.3763 at city level, and 0.3941~0.4311 at district level on a [0,0.5] scale. Conclusions - Luxury foreign automobile customers tend to shop extensively around multiple brands prior to their ideal model selection. Accordingly, the imported automobile distributors cluster together close to their direct competitors in order to give a good comparison opportunity for the potential customers. This will maximize the probability of the visits of potential customers and lead to successful sales performance.

• Journal of Digital Convergence
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• v.12 no.7
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• pp.273-278
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• 2014

In vocabulary recognition using HMM(Hidden Markov Model) by model for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate. Improve them with a HMM model is proposed for the optimization of the Bayesian methods. In this paper is posterior distribution and prior distribution in recognition Gaussian mixtures model provides a model to optimize of the Bayesian methods vocabulary recognition. The result of applying the proposed method, the recognition rate of 97.9% in vocabulary recognition, respectively.

• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.2
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• pp.252-261
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• 2014

A Bayesian network (BN) based fault diagnosis framework for semiconductor etching equipment is presented. Suggested framework contains data preprocessing, data synchronization, time series modeling, and BN inference, and the established BNs show the cause and effect relationship in the equipment module level. Statistically significant state variable identification (SVID) data of etch equipment are preselected using principal component analysis (PCA) and derivative dynamic time warping (DDTW) is employed for data synchronization. Elman's recurrent neural networks (ERNNs) for individual SVID parameters are constructed, and the predicted errors of ERNNs are then used for assigning prior conditional probability in BN inference of the fault diagnosis. For the demonstration of the proposed methodology, 300 mm etch equipment model is reconstructed in subsystem levels, and several fault diagnosis scenarios are considered. BNs for the equipment fault diagnosis consists of three layers of nodes, such as root cause (RC), module (M), and data parameter (DP), and the constructed BN illustrates how the observed fault is related with possible root causes. Four out of five different types of fault scenarios are successfully diagnosed with the proposed inference methodology.

• The Journal of Korean Academic Society of Nursing Education
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• v.18 no.3
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• pp.522-532
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• 2012

Purpose: The purposes of this study were to develop an elderly care nursing education program and evaluate its effect on knowledge, attitudes and nursing performance of nurses toward older adults. Methods: A quasi-experimental study with a non-equivalent control group pretest-posttest design was used. Participants were recruited from two geriatric hospitals. The sample consisted of an intervention group (n=29) that participated in the educational program and a control group (n=30). The data were collected prior to and 6 weeks after the intervention using self-administered questionnaires. Data analyses utilized $x^2$-test, Fisher's exact probability test, and t-test. Results: After the intervention, knowledge and nursing performance about the elderly care increased significantly in the experimental group compared to the control group. However, there was no statistically significant difference between the groups in their attitudes toward the elderly. Conclusion: Findings indicate that to provide better nursing care to the elderly, nurses need to be able to practically apply the knowledge they have acquired from the elder-care nursing education programs in hands-on situations. To achieve this, nurses should work to change their personal attitudes about the elderly. Also, evidence-based guidelines for elderly nursing care, administrative support of institutions, and multidisciplinary approaches are required.

• The Journal of the Acoustical Society of Korea
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• v.23 no.8
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• pp.590-596
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• 2004

This paper Presents a new self-adaptation algorithm based on maximum a posteriori (MAP) eigenvoice for Korean connected digit recognition. The proposed MAP eigenvoice is developed by introducing a probability density model for the eigenvoice coefficients. The Proposed approach provides a unified framework that incorporates the Prior model into the conventional eigenvoice estimation. In self-adaptation system we use only one adaptation utterance that will be recognized, we use MAP eigenvoice that is most robust adaptation. In series of self-adaptation experiments on the Korean connected digit recognition task. we demonstrate that the performance of the proposed approach is better than that of the conventional eigenvoice algorithm for a small amount of adaptation data.

• Journal of the Korean Society for Railway
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• v.9 no.4 s.35
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• pp.432-440
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• 2006

Prior to the service evaluation, many kinds of its attributes must be identified on the basis of rational decision owing to complexity and ambiguity inherent in logistics service. there are so many evaluation methods but they are not applicable to logistics service the have property of non-additivity and overlapped attributes. Therefore, probability measure can not used to evaluate logistics service but Fuzzy Measure is required. And the method should be easy to calculate it Recently Fuzzy theory has been applied in Various evaluation problem. Fuzzy evaluation based on Fuzzy theory can accommodate fuzziness in judgement with people through introducing Fuzzy Measure. In this paper, Hierarchical Fuzzy Process is applied to evaluate level of logistics service in Korail and the biggest six logistics companies in the korea which is called 3PL Company. Also Fuzzy Relation Equation which is problem identifying evaluation value at these fuzzy evaluation is applied to verify relation between Input and Output data through @-operation. Therefore, we apply this Fuzzy Relation Equation to Hierarchical Fuzzy Process and verify evaluation value which objects of evaluation are able to possess.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.1
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• pp.53-58
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• 2012

With a game-theoretic view, p-persistence slotted ALOHA is structured as a non-cooperative game, in which a Nash equilibrium is sought to provide a value for the probability of attempting to deliver a packet. An expression of Nash equilibrium necessarily includes the number of active outer stations, which is hardly available in many practical applications. In this paper, we thus propose a Bayesian scheme of predicting the number of active outer stations prior to deciding whether to attempt to deliver a packet or not. Despite only requiring the minimal information that an outer station is genetically able to acquire by itself, the Bayesian scheme demonstrates the competitive predicting performance against a method which depends on heavy information.