• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2619-2621
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• 2002

전력선 통신(Power-Line Communication)은 기본적으로 데이터 전송용이 아닌 전력 전달을 목적으로 설계된 전력선을 매체로 이루어지기 때문에 상당한 잡음과 감쇠 특성을 보이며 또 전력선 topology와 부하의 변화에 따라 전달 함수(transfer function)의 변화도 심하다. 이러한 열악한 채널 특성을 극복하기 위해 전력선 채널에 관한 많은 연구가 진행되고 있으며 그 중에서도 채널 모델링 연구가 활발하게 진행되고 있다. 채널 모델링은, 변복조 방식, 채널 코딩(coding), 커플링(coupling), 필터링(filtering) 등의 적극적인 채널 극복 방안으로서 제시되는 기술을 적용함에 있어서 상당히 중요하다. 본 논문에서는, 채널 모델링 기법으로 제시되고 있는 방식인, 전달 함수 특성과 여러 가지 잡음 특성을 결합한 통계적 모델링 방식[l]을 통해 전력선 채널 모델을 구현하여 실측치와 비교 검토하고, 또 모델링을 통해 얻어지는 파라미터(parameter)를 통해 채널 정보를 효과적으로 Data Base화 할 수 있는 방안에 대해 연구하였으며, 이 Data Base의 활용 방안을 모색하였다.

• Journal of the Korea Society for Simulation
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• v.20 no.2
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• pp.41-48
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• 2011

Naval Gun FCS(Fire Control System) is the most fundamental weapon system in Naval Combat System. Simulationbased verification of FCS is mandatory before sea trial since ballistic solution needs complicated process and uses almost all information produced by own ship sensors. The FCS simulation method is proposed for verification of naval gun FCS and applicable to the FCS design depending on combat system development phase based on available data in each design phase. Verified FCS through proposed simulation method is adapted in real naval combat system and the performance has been proven by sea trial.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2846-2848
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• 1999

본 논문의 목적은 측정된 뇌자도 신호의 잡음제거 및 분석을 목적으로 하는 뇌자도 신호처리 시스템의 개발이다. 뇌자도 신호의 크기는 매우 작고 외부 노이즈 환경에 민감하게 반응하기 때문에 다양한 신호처리 기법을 이용하여 뇌자도 신호의 신뢰성을 높이는 것이 중요하다. 본 논문에서는 40채널 SQUID 시스템을 이용하여 뇌에서 발생하는 자기 신호를 측정하고, 측정된 데이터에 존재하는 노이즈 성분을 선형필터와SQUID 시스템의 레퍼런스 채널을 이용하여 제거하며, 이를 분석하는 뇌자도 신호처리 시스템을 개발하였다. 실제로 청각자극을 이용하여 뇌자도 신호를 측정, 분석 함으로써 개발된 뇌자도 신호처리 시스템의 신뢰성을 확인하였다. 또한 측정한 뇌자도 신호에서 주파수 대역에 따른 뇌자도 신호의 분포를 Map으로 구성하였으며, dipole source의 위치를 표시하였다.

• Journal of the Korean Society for Library and Information Science
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• v.52 no.3
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• pp.289-334
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• 2018

Even though information in many languages other than English is quickly increasing, English is still playing the role of the lingua franca and being accounted for the largest proportion on the web. Therefore, it is necessary to investigate the key features and differences between "information searching behavior using mother tongue as a search language" and "information searching behavior using English as a search language" of users who are non-mother tongue speakers of English to acquire more diverse and abundant information. This study conducted the experiment on the web searching which is applied in concurrent think-aloud method to examine the information searching behavior and the cognitive process in Korean search and English search through the twenty-four undergraduate students at a private university in South Korea. Based on the qualitative data, this study applied the frequency analysis to web search pattern under search language. As a result, it is active, aggressive and independent information searching behavior in Korean search, while information searching behavior in English search is passive, submissive and dependent. In Korean search, the main features are the query formulation by extract and combine the terms from various sources such as users, tasks and system, the search range adjustment in diverse level, the smooth filtering of the item selection in search engine results pages, the exploration and comparison of many items and the browsing of the overall contents of web pages. Whereas, in English search, the main features are the query formulation by the terms principally extracted from task, the search range adjustment in limitative level, the item selection by rely on the relevance between the items such as categories or links, the repetitive exploring on same item, the browsing of partial contents of web pages and the frequent use of language support tools like dictionaries or translators.

• Journal of Korean Society of Transportation
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• v.23 no.4 s.82
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• pp.71-79
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• 2005

Existing arterial link travel time estimation methods relying on either aggregate point-based or individual section-based traffic data have their inherent limitations. This paper demonstrates the utility of data fusion for improving arterial link travel time estimation. If the data describe traffic conditions, an operator wants to know whether the situations are going better or worse. In addition, some traffic information providing strategies require predictions of what would be the values of traffic variables during the next time period. In such situations, it is necessary to use a prediction algorithm in order to extract the average trends in traffic data or make short-term predictions of the control variables. In this research. a multi-step ahead prediction algorithm using Data fusion was developed to predict a link travel time. The algorithm performance were tested in terms of performance measures such as MAE (Mean Absolute Error), MARE(mean absolute relative error), RMSE (Root Mean Square Error), EC(equality coefficient). The performance of the proposed algorithm was superior to the current one-step ahead prediction algorithm.

• Journal of Industrial Convergence
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• v.20 no.8
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• pp.77-84
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• 2022

This study aims to present a method for implementing a decision support system that can be used for selecting emerging technologies by applying a machine learning-based automatic classification technique. To conduct the research, the architecture of the entire system was built and detailed research steps were conducted. First, emerging technology candidate items were selected and trend data was automatically generated using a big data system. After defining the conceptual model and pattern classification structure of technological development, an efficient machine learning method was presented through an automatic classification experiment. Finally, the analysis results of the system were interpreted and methods for utilization were derived. In a DTW-kNN-based classification experiment that combines the Dynamic Time Warping(DTW) method and the k-Nearest Neighbors(kNN) classification model proposed in this study, the identification performance was up to 87.7%, and particularly in the 'eventual' section where the trend highly fluctuates, the maximum performance difference was 39.4% points compared to the Euclidean Distance(ED) algorithm. In addition, through the analysis results presented by the system, it was confirmed that this decision support system can be effectively utilized in the process of automatically classifying and filtering by type with a large amount of trend data.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.10
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• pp.447-454
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• 2015

This paper proposes geographical name denoising by machine learning of event detection based on twitter. Recently, the increasing number of smart phone users are leading the growing user of SNS. Especially, the functions of short message (less than 140 words) and follow service make twitter has the power of conveying and diffusing the information more quickly. These characteristics and mobile optimised feature make twitter has fast information conveying speed, which can play a role of conveying disasters or events. Related research used the individuals of twitter user as the sensor of event detection to detect events that occur in reality. This research employed geographical name as the keyword by using the characteristic that an event occurs in a specific place. However, it ignored the denoising of relationship between geographical name and homograph, it became an important factor to lower the accuracy of event detection. In this paper, we used removing and forecasting, these two method to applied denoising technique. First after processing the filtering step by using noise related database building, we have determined the existence of geographical name by using the Naive Bayesian classification. Finally by using the experimental data, we earned the probability value of machine learning. On the basis of forecast technique which is proposed in this paper, the reliability of the need for denoising technique has turned out to be 89.6%.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.373-380
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• 2022

This paper presents a deep learning based group synchronization that supports networked immersive interactions between remote users. The goal of group synchronization is to enable all participants to synchronously interact with others for increasing user presence Most previous methods focus on NTP-based clock synchronization to enhance time accuracy. Moving average filters are used to control media playout time on the synchronization server. As an example, the exponentially weighted moving average(EWMA) would be able to track and estimate accurate playout time if the changes in input data are not significant. However it needs more time to be stable for any given change over time due to codec and system loads or fluctuations in network status. To tackle this problem, this work proposes the Deep Group Synchronization(DeepGroupSync), a group synchronization based on deep learning that models important features from the data. This model consists of two Gated Recurrent Unit(GRU) layers and one fully-connected layer, which predicts an optimal playout time by utilizing the sequential playout delays. The experiments are conducted with an existing method that uses the EWMA and the proposed method that uses the DeepGroupSync. The results show that the proposed method are more robust against unpredictable or rapid network condition changes than the existing method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.29-35
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• 2006

The third generation mobile communications system requiring the reliable multimedia data transmission has provided with the reliable voice, data and video services over the variable propagation environment. However the broadband wireless multiple access technologies cause Inter Symbol Interference(ISI) or Multiple Access Interference(MAI) to degrade the performance of CDMA(Code Division Multiple Access) system. Constant Modulus Algorithm which is frequently used as the adaptive blind equalizers to remove the interfering signal has ill-convergence phenomenon without proper initialization. In this paper, new blind equalization method based on conventional CMA is proposed to improve the channel efficiency, and through computer simulation this is tested over the time varying fading environment of mobile communication system. consequently, new blind equalization method into concatenated Kalman filter with CMA is verified better than conventional CMA through adopting minimum mean square errors and eye-pattern obtained from algorithm are compared.

• Journal of the Korean Geotechnical Society
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• v.27 no.2
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• pp.63-72
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• 2011

The measured ground water behavior by TDR (time domain reflectometer) sensors were analyzed by the data filtering technique such as moving average method and Fourier transform, and the ground water level and unsaturated zone were tried to be determined numerically. At first, the variation of TDR data according to the saturation degree was measured by lab test, which is translated as a function of saturation degree. Then, changes of ground water level and lateral seepage in field conditions were simulated using acrylic pipe, and the measured data were analyzed to make calibration curve. Furthermore, TDR sensors were installed into the in-situ embankment to insure the field application. The saturation degree, unsaturated and dried zones were determined from the measured data.