• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.249-258
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• 2004

오늘날 활발하게 이루어지고 있는 유비쿼터스 컴퓨팅 관련 기술 연구는 사용자가 시간과 장소에 구애받지 않고 네트워크에 접근해 다양한 컴퓨터 관련 서비스를 제공 받을 수 있는 방법에 초점을 맞추고 있다. 이 처럼 시간과 공간의 한계를 뛰어 넘은 네트워크로의 자유로운 접근은 일상 생활의 패러다임을 바꾸어 놓게 될 것이다. 유비쿼터스 컴퓨팅 기술을 통해 가장 큰 변화가 일어나는 분야는 일반 가정환경에서 일어나는 인텔리전트 홈 네트워크 (Intelligent Home Network) 라고 할 수 있다. 집에 들어오면, 자동으로 문을 열어주고, 불을 켜주며, 놓쳤던 TV 프로그램을 자동으로 녹화해 놓았다가 원하는 시간에 보여주고, 적당한 시간에 목욕물을 미리 받아준다. 또한 집밖으로 나가기 전, 일기예보에 따라 우산을 챙겨주고, 일정을 확인시켜주며 입고 나갈 옷을 골라줄 수도 있다. 이 모든 일들이 유비쿼터스 컴퓨팅 기술이 가져올 인텔리전트 홈 네트워크의 모습이다. 그러나, 모든 사용자에게 효과적인 서비스를 제공하기 위해서는 홈 네트워크 상의 자원 관리에서 일어날 수 있는 에이전트들간의 자원 접근 권한 충돌을 효율적으로 방지할 수 있는 기술이 필요하다. 유비쿼터스 컴퓨팅 환경에서 자원관리 특성은 점유의 연속성, 자원 사이의 연관성, 그리고 자원과 사용자 사 사이의 연계성의 3 가지 특성을 지니고 있다. 본 논문에서는 유비쿼터스 컴퓨팅 환경에서 일어날 수 있는 자원 충돌 상황을 효율적으로 처리하기 위한 자원 협상 방법을 제안한다. 본 방법은 자원 관리 특성을 바탕으로 시간논리에 기반을 둔 자원 선점과 분배 규칙으로 구성된다.트 시스템은 b-Cart를 기반으로 할 것으로 예측할 수 있다.타났다. 또한, 스네이크의 초기 제어점을 얼굴은 44개, 눈은 16개, 입은 24개로 지정하여 MER추출에 성공한 영상에 대해 스네이크 알고리즘을 수행한 결과, 추출된 영역의 오차율은 각각 2.2%, 2.6%, 2.5%로 나타났다.해서 Template-based reasoning 예를 보인다 본 방법론은 검색노력을 줄이고, 검색에 있어 Feasibility와 Admissibility를 보장한다.매김할 수 있는 중요한 계기가 될 것이다.재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data b

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.391-397
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• 2020

Realistic content such as virtual reality, augmented reality, and mixed reality is emerging as an innovative technology in the education field in that it allows people to safely and efficiently experience dangerous, expensive or impossible situations, such as disaster training or space travel. Recently, as government agencies have supported a lot for producing virtual augmented reality contents about education, various educational contents using virtual augmented reality technology have been developed through the Edutech industry. Many virtual augmented reality-based educational contents are being developed, but mixed reality-based educational contents are very limited which could be more effective for education. This study examines the basic method of producing mixed reality educational contents using Hololens and, on the basis of this, it proposes the method for producing scientific experiment contents. Hololens made it possible to share information in real time without a regular desktop PC, and it is effective for teachers to manage and evaluate students in real time.

• The Journal of Information Technology
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• v.5 no.3
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• pp.81-96
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• 2002

Recently business reengineering is the most attractive management reforming skill. Many of the Korean firms are paying attention to business reengineering and many of them have initiated it. The major purposes of this study are; (1) to investigate the relationship between each variable and success or failure of business reengineering, and (2) to examine interaction effects of measurement and performation variables. Two hundred eighteen questionaires were used for analysis. In order to throughout studies that I executed earlier. To look for dependence of critical success factors on analysis was performed to examine patterns between measurement and performance variables. In conclusion, the hypothesized relationships in the research model are supported by the empirical findings of this study. Additionally it is possible to establish theoretical reference system on the basis of critical success factors.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.04b
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• pp.1037-1040
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• 2001

최근 웹을 교육 훈련 분야와 교수 업무 지원 분야에서 활용함으로써, 업무의 효율화를 기하고 학생들에게 필요한 정보와 질 높은 서비스를 제공하는 수단으로 이용하고 있다. 이러한 요구에 따라 웹 상에서 복잡한 학교 업무의 효과적인 관리와 학습 자료 및 업무 자료를 제공할 수 있는 교수 지원 시스템이 필요하다. 본 논문에서는 교수 지원 도메인 분석을 통해 개발 시스템 모델을 제시하고, 효율적인 XML 문서를 지원하는 방법을 제시하고 교수자의 다양한 요구사항을 융통성 있게 수용하고자 한다. 교수 지원 시스템중 학습지도안 작성을 위한 시스템의 표준화된 양식을 제공하기 위해 지도안의 항목들을 분석, 식별하고 프로토타이핑 시스템을 설계하고 이 시스템에 적용할 표준화된 DTD를 작성하고 XML 문서로 표현, 수정함으로써 웹 상에서 쉽게 지도안을 작성 할 수 있도록 한다. 본 논문에서 제안하는 교수 지원 시스템은 교수들이 교수 학습활동에 전념할 수 있도록 교육여건을 개선하고 나아가 교육정보 자료의 효과적인 관리 및 활용을 목적으로 한다.

• The Journal of the Acoustical Society of Korea
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• v.20 no.4
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• pp.35-41
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• 2001

This paper describes Linear Discriminant Analysis and common vector extraction for speech recognition. Voice signal contains psychological and physiological properties of the speaker as well as dialect differences, acoustical environment effects, and phase differences. For these reasons, the same word spelled out by different speakers can be very different heard. This property of speech signal make it very difficult to extract common properties in the same speech class (word or phoneme). Linear algebra method like BT (Karhunen-Loeve Transformation) is generally used for common properties extraction In the speech signals, but common vector extraction which is suggested by M. Bilginer et at. is used in this paper. The method of M. Bilginer et al. extracts the optimized common vector from the speech signals used for training. And it has 100％ recognition accuracy in the trained data which is used for common vector extraction. In spite of these characteristics, the method has some drawback-we cannot use numbers of speech signal for training and the discriminant information among common vectors is not defined. This paper suggests advanced method which can reduce error rate by maximizing the discriminant information among common vectors. And novel method to normalize the size of common vector also added. The result shows improved performance of algorithm and better recognition accuracy of 2％ than conventional method.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.293-299
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• 2018

The weather has a lot of influence on the cultivation of crops. Weather information on agricultural crop cultivation areas is indispensable for efficient cultivation and management of agricultural crops. Despite the high demand for agricultural weather, research on this is in short supply. In this research, we deal with the production method of agricultural weather in Jeollanam-do, which is the main production area of onions through GloSea5 and deep learning. A deep neural network model using the sliding window method was used and utilized to train daily weather prediction for predicting the agricultural weather. RMSE and MAE are used for evaluating the accuracy of the model. The accuracy improves as the learning period increases, so we compare the prediction performance according to the learning period and the prediction period. As a result of the analysis, although the learning period and the prediction period are similar, there was a limit to reflect the trend according to the seasonal change. a modified deep layer neural network model was presented, that applying the difference between the predicted value and the observed value to the next day predicted value.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.16 no.1
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• pp.7-17
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• 2008

LOSA는 Line Operations Safety Audit(항공사 운항안전 감사)의 약자이며 기존의 적발 위주의 기존 Line Audit제도와 달리 조종사의 자발적 참여와 철저한 비밀을 유지하며, 처벌 금지 약속을 통하여 참여자가 평소 습관대로 비행할 수 있게 한다. 훈련된 감사관이 이를 소정의 절차서에 의거 조종석에서 관찰하여 실제의 안전취약 및 위협요소, Error를 포착해서 수집하고 텍사스대학 인적요인 연구소에서 분석하여 최종보고서를 작성하여 제도를 개선하는 안전프로그램이다. 제도와 방안을 개선하는 신개념의 선진 운항감사제도로서 안전관리시스템의 대표적인 비행안전 프로그램으로 통상 3${\sim}$4년을 주기로 실시한다. ICAO, IATA, FAA 및 IFALPA 실행 권고사항으로 현재 약30여개의 항공사가 실시하였다. LOSA는 2009년1월부터 ICAO부속서 6에 의거하여 항공사에서 실행해야 되는 SMS(안전관리 시스템)의 가장 효율적인 Hazard 식별 및 위험 관리도구 중의 하나이다. 본 논문에서는 안전관리시스템의 효과적 도구인 LOSA를 설명하고 항공사내 실행방법을 소개하는데 있다.

• Journal of Korean Society of Forest Science
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• v.101 no.3
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• pp.374-380
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• 2012

The k-Nearest Neighbor (kNN) technique is popularly applied to assess forest resources at the county level and to provide its spatial information by combining large area forest inventory data and remote sensing data. In this study, two approaches such as distance-weighting and stratification of training dataset, were compared to improve kNN-based forest growing stock estimates. When compared with five distance weights (0 to 2 by 0.5), the accuracy of kNN-based estimates was very similar ranged ${\pm}0.6m^3/ha$ in mean deviation. The training dataset were stratified by horizontal reference area (HRA) and forest cover type, which were applied by separately and combined. Even though the accuracy of estimates by combining forest cover type and HRA- 100 km was slightly improved, that by forest cover type was more efficient with sufficient number of training data. The mean of forest growing stock based kNN with HRA-100 and stratification by forest cover type when k=7 were somewhat underestimated ($5m^3/ha$) compared to statistical yearbook of forestry at 2011.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.221-232
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• 2023

This paper presents a novel convolutional neural network (CNN)-based approach for predicting bolt clamping force in the early bolt loosening state of bolted structures. The approach entails tightening eight bolts with different clamping forces and generating frequency responses, which are then used to create a similarity map. This map quantifies the magnitude and shape similarity between the frequency responses and the initial model in a fully fastened state. Krylov subspace-based model order reduction is employed to efficiently handle the large amount of frequency response data. The CNN model incorporates a regression output layer to predict the clamping forces of the bolts. Its performance is evaluated by training the network by using various amounts of training data and convolutional layers. The input data for the model are derived from the magnitude and shape similarity map obtained from the frequency responses. The results demonstrate the diagnostic potential and effectiveness of the proposed approach in detecting early bolt loosening. Accurate bolt clamping force predictions in the early loosening state can thus be achieved by utilizing the frequency response data and CNN model. The findings afford valuable insights into the application of CNNs for assessing the integrity of bolted structures.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.969-974
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• 2005

TPM program, which is a methodology for improving the management results through the management innovation activity of a company, has been widely introduced in the field of a service industry as well as a manufacturing industry. The main purpose of this study is to present the theoretical model by the relationship between active factors of TPM program and management results for a productivity increase, and to investigate the direct and indirect effects on the management of a company through a parametric study. A questionnaire survey of 300 companies that presently utilize the TPM program has been conducted. In order to verify the validity and the reliability of the contents of the questionnaire survey, a confirmatory factor analysis has been done. A frequency analysis has also been performed to examine the characteristics of the respondent. The factor analysis and the frequency analysis were done by using SAS Ver. 8.2, and the verification of a research model was done by using LISREL Ver. 8.52. The active factors of TPM in the research model consist of 'an independent preservation', 'an individual improvement', 'a planned reservation' and 'an education & training'. Among those active factors, the individual improvement and the education & training significantly influence on the facility efficiency and the organization and personnel management. The organization and personnel management has a greater influence on the management results. Therefore, the education and training for employes is most important for the improvement of the management results through TPM program, and the individual improvement activity is also important.