• Proceedings of the KIEE Conference
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• 1999.11d
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• pp.1126-1127
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• 1999

연산자 분리 시영역 모델을 이용하여 광섬유 커플러 ADM(add-drop multiplexer)의 필터링 효과를 분석하였다. 이 모델은 방향성 결합기나 브래그 격자를 포함하는 소자의 해석에 유용하다. 본 논문에서는 비대칭 구조를 고려했으며 결합계수, 코어의 반경, 개구수, 굴절율 변조 등의 파라미터를 이용하여 최적의 필터링 효과를 얻기 위한 구조를 설계하였다.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.109-114
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• 2018

Collaborative filtering has been widely utilized in recommender systems as typical algorithm for outstanding performance. Since it depends on item rating history structurally, The more sparse rating matrix is, the lower its recommendation accuracy is, and sometimes it is totally useless. Variety of hybrid approaches have tried to combine collaborative filtering and content-based method for improving the sparsity issue in rating matrix. In this study, a new method is suggested for the same purpose, but with different perspective, it deals with no-match situation in person-person similarity evaluation. This method is called the transitive similarity model because it is based on relation graph of people, and it compares recommendation accuracy by applying to Movielens open dataset.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.1-8
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• 2020

In recent years, due to the exponential increase in internet data, many fields such as deep learning have developed, but side effects generated as commercial advertisements, such as viral marketing, have been discovered. This not only damages the essence of the internet for sharing high-quality information, but also causes problems that increase users' search times to acquire high-quality information. In this study, we define advertisement as "a text that obscures the essence of information transmission" and we propose a model for filtering information according to that definition. The proposed model consists of advertisement filtering and advertisement filtering performance improvement and is designed to continuously improve performance. We collected data for filtering advertisements and learned document classification using KorBERT. Experiments were conducted to verify the performance of this model. For data combining five topics, accuracy and precision were 89.2% and 84.3%, respectively. High performance was confirmed, even if atypical characteristics of advertisements are considered. This approach is expected to reduce wasted time and fatigue in searching for information, because our model effectively delivers high-quality information to users through a process of determining and filtering advertisement paragraphs.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.2
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• pp.151-158
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• 2004

In this paper, an inverse filtering technique for the digital filter model is proposed. This technique enables us to obtain a stable non-causal m inverse filter by transforming (approximating) it to a causal stable inverse system. In practice, a causal FIR approximation to this inverse filter is proposed. It can be shown that the impulse response of the inverse filter for all-pass systems is simply the mirror image of the impulse response for the system. Specially, due to this symmetric property of the impulse response of all-pass systems, the proposed technique is more useful for all-pass systems than other systems. In order to illustrate the proposed inverse filtering technique, four examples are presented. Two of them are for all-pass filters. The other two examples are for IIR and FIR filters. Also, computer simulations demonstrate that the proposed technique works very well.

• Journal of Korea Multimedia Society
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• v.11 no.9
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• pp.1213-1226
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• 2008

A recommender system supports people in making recommendations finding a set of people who are likely to provide good recommendations for a given person, or deriving recommendations from implicit behavior such as browsing activity, buying patterns, and time on task. We proposed new recommender system which combined SOM(Self-Organizing Map) neural networks with the Collaborative filtering which most recommender systems hat applied First, we segmented user groups according to demographic characteristics and then we trained the SOM with people's preferences as ito inputs. Finally we applied the classic collaborative filtering to the clustering with similarity in which an recommendation seeker belonged to, and therefore we didn't have to apply the collaborative filtering to the whose data set. Experiments were run for EachMovies data set. The results indicated that the predictive accuracy was increased in terms of MAE(Mean-Absolute-Error).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.1
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• pp.55-62
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• 2007

Recent advances in airborne LiDAR technology allow rapid and inexpensive measurements of topography over large areas. The generation of DTM/DEM is essential to numerous applications such as the fields of civil engineering, environment, city planning and flood modeling. The demand for LiDAR data is increasing due to the reduced cost for DTM generation and the increased reliability, precision and completeness. In order to generate DTM, measurements from non-ground features such as building and vegetation have to be classified and removed. In this paper, a segmented morphology filter was developed to detect non-ground LiDAR measurements. First, segments LiDAR point clouds based on the elevation. Secondly classifies those protruding segments into non-ground points. Those non-ground points such as building and vegetation are removed, while ground points are preserved for DTM generation. For experiments, data sets used in Comparison of Filters (ISPRS, 2003) depicting urban and rural areas were selected. The experimental results show that the proposed filter can remove most of the non-ground points effectively with less commission and omission errors.

• Journal of the Korea Convergence Society
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• v.11 no.6
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• pp.7-14
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• 2020

A Battery Monitoring System capable of State-of-Charge(SOC) estimation using the Extended Kalman Filter(EKF) is described in this paper. In order to accurately estimate the SOC of the battery, the battery cells were modeled as the Thevenin equivalent circuit model. The Thevenin model's parameters were measured in experiments. For the Battery Monitoring System, we designed a battery monitoring device that can calculate the SOC estimation using the EKF and a monitoring server that controls multiple battery monitoring devices. We also develop a web-based dashboard for controlling and monitoring batteries. Especially the computation of the monitoring server could be reduced by calculating the battery SOC estimation at each Battery Monitoring Device.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.122-125
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• 2010

사용자와 취향이 비슷한 사용자를 찾고, 이 유사 사용자가 선호한 아이템을 추천하는 협력적 필터링방식은 일반적으로 많이 사용되는 추천 방식이다. 하지만 협력적 필터링 방식은 어떤 상황적 요소도 고려하지 않아 모든 상황에서 동일한 추천 결과를 제시하게 된다. 반면, 상황을 고려한 추천 방식은 다른 상황에서 그 상황에 적합하다고 판단되는 추천 리스트를 보여주는 다양성을 가지지만 개인의 선호를 반영하지 못하는 한계를 가진다. 이에 협력적 필터링 방식과 상황에 따른 추천 방식을 함께 고려하려는 시도가 있다. 본 논문에서는 시간 상황에 따른 음악 추천 시, 전체 상황에서 가장 유사한 사용자를 찾고 이 유사 사용자의 현재 상황에서의 선호 아이템을 추천하는 모델을 제시하고 실험을 통하여 이 모델의 한계와 실용 가능한 상황을 제시한다.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.1
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• pp.21-29
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• 2003

It is very important to reduce the computational time in estimating the parameters of vision control algorithm for robot's position control in real time. Unfortunately, the batch estimation commonly used requires too murk computational time because it is iteration method. So, the batch estimation has difficulty for robot's position control in real time. On the other hand, the Extended Kalman Filtering(EKF) has many advantages to calculate the parameters of vision system in that it is a simple and efficient recursive procedures. Thus, this study is to develop the EKF algorithm for the robot's vision control in real time. The vision system model used in this study involves six parameters to account for the inner(orientation, focal length etc) and outer (the relative location between robot and camera) parameters of camera. Then, EKF has been first applied to estimate these parameters, and then with these estimated parameters, also to estimate the robot's joint angles used for robot's operation. finally, the practicality of vision control scheme based on the EKF has been experimentally verified by performing the robot's position control.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.07a
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• pp.82-84
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• 2011

본 논문에서는 사용자의 개인적 취향에 맞는 음악을 추천할 수 있는 사용자 감성/상황 정보 융합 기반의 협업 필터링의 확장을 이용한 음악추천시스템을 소개한다. 본 논문에서 제안하는 시스템은 확장된 협업 필터링 방식을 사용하여 추천을 해준다. 이를 위해 본 논문에서는 추천의 근거가 되는 감성과 무드를 Thayer 음악 무드 모델을 이용하여 총 12 가지의 감성 정보, 8 cluster 의 무드 정보로 분류했다. 또한 사용자의 상황 정보, 활동 & 날씨 & 시간에 대해서도 분류하였다. 분류된 정보는 음악감상 UI 를 이용하여 사용자 별 감성, 상황 그리고 음원의 무드 정보로 수집이 되었고, 수집된 정보를 기반으로 사용자 감성과 청취 곡 횟수를 퓨전하여 평가치 매트릭스를 만들었으며, 이를 바탕으로 단계적 협업 필터링에 의해 사용자 취향에 맞는 음악을 추천해 주는 방법이다.