• Defense and Technology
• /
• no.9 s.247
• /
• pp.74-79
• /
• 1999

과거는 항공기의 야간임무 수행시 항법장비와 계기에 의존하여 작전을 수행하였다. 이것은 조종사로 하여금 비행 안정감을 주지 못하여 심리적 작전 저해요소로 작용하였다. 따라서, 야간 비행시에도 낮과 유사한 비행조건을 만들어주는 장비 즉, 야시장비체계 개발이 본격화 되었다. 야시장비가 장착된 항공기를 탑승하기 위해서는 야간 투시경(NVG)이 필요하며 항공기 내, 외부 조명의 개조가 전제되어야 한다. 이 글에서는 항공기 야시장비체계 개조의 필요성과 적용실례를 들어 야시장비체계에 대하여 소개하고자 한다.

• Korean Journal of Cognitive Science
• /
• v.12 no.4
• /
• pp.29-39
• /
• 2001

Kaplan(2000) reported that instances were categorized more accurate in the aligned condition than in the non-aligned condition irrespective of similarity between instances[16]. This study investigated wether Kaplan(2000)\\`s results could be explained by stimulus types she used and alignment effects in categorization were due to selective attention to aligned attributes. In Experiment 1. I examined whether attribute alignment produced significant effects on similarity and categorization and aligned attributes were recalled more than non-aligned ones. Results showed that instances were rated more similar and categories were learned more rapidly in the aligned condition than in the non-aligned condition. It can be explained that categories are learned rapidly in the aligned condition because attribute alignment increases within-category similarity. But. the result that aligned attributes were recalled more than non-aliened ones in the attribute recall test implies that alignment effects in categorization can be independent of similarity between instances partially. In Experiment 2. I used equal numbed of attributes defining two categories and instructed subjects to pay their attention to categorization-relevant dimensions only. Results showed that dimension instruction facilitated category learning in the non-aligned condition only but categories were learned more rapidly in the aligned condition than in the non-aliened condition irrespective of instruction types. In conclusion. attribute alignment in categorization may facilitate paying selective attention to categorization-relevant attributes.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.2
• /
• pp.751-758
• /
• 2013

In order to estimate risk of diabetes by analyzing dietary patterns, we examined prevalence of diabetes and impaired fasting glucose among Korean adults who are similar in food frequency to diabetes and impaired fasting glucose. The mean of food frequency of diabetes and impaired fasting glucose was calculated for analyzing the prevalence using the food frequency data from 2007, 2008 and 2009 Korea National Health and Nutrition Examination Survey(KNHANES). Also the most frequent food was estimated for each group by using the mean difference of food frequency. As the results shows that the similar food frequency groups have significant prevalence rate, we expect that this study will contribute to the relations between dietary intakes and prevalence of diabetes.

• Journal of Korean Society for Geospatial Information Science
• /
• v.14 no.4 s.38
• /
• pp.71-77
• /
• 2006

Support Vector Machine (SVM) which has roots in a statistical learning theory is a training algorithm based on structural risk minimization. Generally, SVM algorithm uses the kernel for determining a linearly non-separable boundary and classifying the data. But, classical kernels can not apply to effectively the hyperspectral image classification because it measures similarity using vector's dot-product or euclidian distance. So, This paper proposes the spectral similarity kernel to solve this problem. The spectral similariy kernel that calculate both vector's euclidian and angle distance is a local kernel, it can effectively consider a reflectance property of hyperspectral image. For validating our algorithm, SVM which used polynomial kernel, RBF kernel and proposed kernel was applied to land cover classification in Hyperion image. It appears that SVM classifier using spectral similarity kernel has the most outstanding result in qualitative and spatial estimation.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.6
• /
• pp.663-667
• /
• 2010

A simple approach to semantic document-retrieval is to measure document similarity based on the bag-of-words representation, e.g., cosine similarity between two document vectors. However, such a syntactic method hardly considers the semantic similarity between documents, often producing semantically-unsound search results. We circumvent such a problem by combining supervised machine learning techniques with ontology information based on Markov logic. Specifically, Markov logic networks are learned from similarity-tagged documents with an ontology representing the diverse relationship among words. The learned Markov logic networks, the ontology, and the training documents are applied to the semantic document-retrieval task by inferring similarities between a query document and the training documents. Through experimental evaluation on real world question-answering data, the proposed method has been shown to outperform the simple cosine similarity-based approach in terms of retrieval accuracy.

• The Journal of the Acoustical Society of Korea
• /
• v.15 no.1
• /
• pp.54-60
• /
• 1996

In most pattern recognition and image understanding applications, images are degraded by noise and other distortions. Therefore, it is more relevant to decide how similar two objects are rather than to decide whether the two are exactly the same. In this paper, we propose a method for recognizing degraded symbols using a distance measure between two graphs representing the symbols. a symbol is represented as a graph consisting of nodes and edges based on the run graph concept. The graph is then transformed into a reference model graph with production rule containing the embedding transform. The symbols are recognized by using the distance measure which is estimated by using the number of production rules used and the structural homomorphism between a transformed graph and a model graph. the proposed approach is applies to the recognition of non-note musical symbols and the result are given.

• The KIPS Transactions:PartD
• /
• v.11D no.3
• /
• pp.717-730
• /
• 2004

Recently. many researches on the personalization of a web-site have been actively made. The web personalization predicts the sets of the most interesting URLs for each user through data mining approaches such as clustering techniques. Most existing methods using clustering techniques represented the web transactions as bit vectors that represent whether users visit a certain WRL or not to cluster web transactions. The similarity of the web transactions was decided according to the match degree of bit vectors. However, since the existing methods consider only whether users visit a certain URL or not, users' interestingness on the URL is excluded from clustering web transactions. That is, it is possible that the web transactions with different visit proposes or inclinations are classified into the same group. In this paper. we propose an enhanced transaction modeling with interestingness weight to solve such problems and a new similarity measuring method that exploits the proposed transaction modeling. It is shown through performance evaluation that our similarity measuring method improves the accuracy of the web transaction clustering over the existing method.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.7
• /
• pp.83-90
• /
• 2010

In vocabulary recognition system has reduce recognition rate unrecognized error cause of similar phoneme recognition and due to provided inaccurate vocabulary. Input of inaccurate vocabulary by feature extraction case of recognition by appear result of unrecognized or similar phoneme recognized. Also can't feature extraction properly when phoneme recognition is similar phoneme recognition. In this paper propose vocabulary recognition post-process error correction system using phoneme likelihood based on phoneme feature. Phoneme likelihood is monophone training phoneme data by find out using MFCC and LPC feature extraction method. Similar phoneme is induced able to recognition of accurate phoneme due to inaccurate vocabulary provided unrecognized reduced error rate. Find out error correction using phoneme likelihood and confidence when vocabulary recognition perform error correction for error proved vocabulary. System performance comparison as a result of recognition improve represent MFCC 7.5%, LPC 5.3% by system using error pattern and system using semantic.

• The Journal of the Acoustical Society of Korea
• /
• v.32 no.1
• /
• pp.79-85
• /
• 2013

Feature extraction of CASA system uses time continuity and channel similarity and makes correlogram of auditory elements for the use. In case of using feature extraction with cross correlation coefficient for channel similarity, it has much computational complexity in order to display correlation quantitatively. Therefore, this paper suggests feature extraction method using non-parametric correlation coefficient in order to reduce computational complexity when extracting the feature and tests to segregate target speech by CASA system. As a result of measuring SNR (Signal to Noise Ratio) for the performance evaluation of target speech segregation, the proposed method shows a slight improvement of 0.14 dB on average over the conventional method.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.7
• /
• pp.1517-1524
• /
• 2011

In this paper, a new document summarization method, which uses the semantic features and the pseudo relevance feedback (PRF) by using WordNet, is introduced to extract meaningful sentences relevant to a user query. The proposed method can improve the quality of document summaries because the inherent semantic of the documents are well reflected by the semantic feature from NMF. In addition, it uses the PRF by the semantic features and WordNet to reduce the semantic gap between the high level user's requirement and the low level vector representation. The experimental results demonstrate that the proposed method achieves better performance that the other methods.