• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.1
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• pp.33-39
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• 2016

Color image dehazing techniques have been extensively studied, and especially the dark channel prior (DCP)-based method has been widely used. Near infrared (NIR) image based applications are also widespread; however, NIR image-specific dehazing techniques have not attracted great interest. In this paper, the characteristics of NIR images are analyzed and compared with the color images' characteristics. The conventional color image dehazing method is also applied to NIR images to understand its effectiveness on different frequency-band signals. Furthermore, we modify the DCP method considering the characteristics of NIR images and show that our proposed method results in improved dehazed NIR images.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.1
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• pp.26-32
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• 2007

In sequential speaker classification, the lack of prior information about the speakers poses a challenge for model initialization. To address the challenge, a predetermined generic model set, called Sample Speaker Models, was previously proposed. This approach can be useful for accurate speaker modeling without requiring initial speaker data. However, an optimal method for sampling the models from a generic model pool is still required. To solve this problem, the Speaker Quantization method, motivated by vector quantization, is proposed. Experimental results showed that the new approach outperformed the random sampling approach with 25% relative improvement in error rate on switchboard telephone conversations.

• 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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.431-434
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• 2012

시간 흐름에 따라 TV 프로그램 스케줄은 변화하고 스케줄의 변화는 사용자 선호에 영향을 미친다. 이러한 스케줄 변화에 따른 토픽의 흐름이 사용자 선호도에 미치는 영향 외에도, 개성에 따른 선호도의 변화는 개인별 차이가 크다. 본 논문은 사용자 선호도 변화에 적응적으로 대응하면서 시간 변화에도 일정한 관심을 보이는 사용자의 선호도에는 가중치를 더한 모델을 목표로 한다. 따라서 제안 모델은 현재의 시청 데이터를 기준으로 한 사용자별 선호도의 선행 정보(prior)로 이전 시청선호를 두었고, 선호도 변화와 일관성을 고려하여 하나의 시청길이에 대한 선호도뿐만 아니라 여러 시청 길이의 선호도를 결합한 선호도를 구성할 수 있는 확장성 있는 모델을 제시한다. 선호도의 일관성에 대한 가중치 연산에 있어 전체 확률모델의 확률을 향상시키는 연산을 통해 정교성을 더한 모델을 제시한다. 실제 사용자들이 시청한 데이터인 2011 TNMS데이터를 기준으로 제안 모델의 성능을 확인한 결과, 기존의 LDA, MDTM모델 보다 나은 성능을 보임을 확인할 수 있었으며, 1주일 단위 추천결과, 5개 추천 시, 최대 67.9%의 추천 정확도를 확인할 수 있었다.

• Journal of The Korean Association For Science Education
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• v.38 no.5
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• pp.599-610
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• 2018

The purpose of this study is to analyze mechanistic reasoning in Fabre's inquires and to develop mechanistic reasoning model. To analyze the order of the process elements in mechanistic reasoning, 30 chapters were selected in book. Inquiries were analyzed through a framework which is based on Russ et al. (2008). The nine process elements of mechanistic reasoning that was presented in Fabre's inquires were as follows: Describing the Target Phenomenon, Identifying prior Knowledge, Identifying Properties of Objects, Identifying Setup Conditions, Identifying Activities, Conjecturing Entities, Identifying Properties of Entities, Identifying Entities, and Organization of Entities. The order of process elements of mechanistic reasoning was affected by inquiry's subject, types of question, prior knowledge and situation. Three mechanistic reasoning models based on the process elements of mechanistic reasoning were developed: Mechanistic reasoning model for Identifying Entities(MIE), Mechanistic reasoning model for Identifying Activities(MIA), and Mechanistic reasoning model for Identifying Properties of entities (MIP). Science teacher can help students to use the questions of not only "why" but also "How", "If", "What", when students identify entities or generate hypotheses. Also science teacher should be required to understand mechanistic reasoning to give students opportunities to generate diverse hypotheses. If students can't conjecture entities easily, MIA and MIP would be helpful for students.

• Journal of the Korea Computer Industry Society
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• v.4 no.12
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• pp.919-936
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• 2003

Nowadays Electronic Commerce shopping mall is welcomed more and more on the Internet. It is expected that Shopping mall systems come to be various and adaptable to complex requirements according to customers who have these various needs, but just show products list, instead. This thesis suggests various structures of shopping malls showing interface agent model using Case-Based Reasoning one of reasoning method of Artificial Intelligence instead of the method of prior EC shopping mall. 1 constructed case base by making index with shopping mall members and customers' private informations, and pursued difference from prior EC shopping malls by proposing to customers cases of other users' selection of products who have similar private informations with them.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.8
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• pp.1446-1452
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• 1994

In this paper, Weibull distribution is applied to the lifetme distribution of a device. The method of Bayesian estimate used to estimate requiring parameter in order to predict lifetime of device using accelerated lifetime test data, namely failure time of device. The method of Bayesian estimate needs prior information in order to estimate parameter. But this paper proposed the method of parameter estimate without prior information. As stress is temperature, Arrhenius model is applied and the method of linear estimate is applied to predict lifetime of device at the state of normal operation.

• Speech Sciences
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• v.11 no.4
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• pp.75-88
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• 2004

In this paper, we propose a new approach to sequential linear regression adaptation of continuous density hidden Markov models (CDHMMs) based on transformation space model (TSM). The proposed TSM which characterizes the a priori knowledge of the training speakers associated with maximum likelihood linear regression (MLLR) matrix parameters is effectively described in terms of the latent variable models. The TSM provides various sources of information such as the correlation information, the prior distribution, and the prior knowledge of the regression parameters that are very useful for rapid adaptation. The quasi-Bayes (QB) estimation algorithm is formulated to incrementally update the hyperparameters of the TSM and regression matrices simultaneously. Experimental results showed that the proposed TSM approach is better than that of the conventional quasi-Bayes linear regression (QBLR) algorithm for a small amount of adaptation data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.111-114
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• 2003

It has well known that MUSIC and ESPRIT algorithms estimate angle of arrival(AOA) with high resolution by eigenvalue decomposition of the covariance matrix which were obtained from the array antennas. In the case that 2-D large-sized array antenna is required, however, one of the disadvantages of MUSIC and ESPRIT is that they are computationally ineffective, and then they are difficult to implement in real time. To alleviate the computational complexity, several method using neural model have been study. For multiple signals, those methods require huge training data prior to AOA estimation. This paper proposes the algorithm for AOA estimation by interconnected hopfield neural model. Computer simulations show the validity of the proposed algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.3
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• pp.35-42
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• 2007

We describe the problem of reducing the key material in the Even-Mansour cipher without security degradation. Even and Mansour proposed a block cipher based on XORing secret key material just prior to and after applying random oracle permutation P such that $C=k_2\bigoplus P(M\bigoplus k_1)$. Recently, Gentry and Ramzan showed that this scheme in the random permutation oracle can be replaced by the four-round Feistel network construction in the random function oracle and also proved that their scheme is super-pseudorandom. In this paper we reduce the key size from 2n to n, which is the optimal key size of Even-Mansour cipher in the random function oracle model and also give almost the same level of security.