• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.783-783
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• 2012

본 연구에서는 낙동강 임하댐 유역을 대상으로 엔트로피 이론(혼합분포 적용)과 관측소의 공간적 분포를 동시에 고려하여 강우관측망을 평가하였다. 일반적으로 혼합분포를 이용하는 강우관측망 평가는 연속분포를 이용하는 경우 비해 강우의 시공간적 간헐성을 고려할 수 있다는 장점이 있다. 아울러 유역의 면적평균강우량을 산정시 강우관측소는 균등하게 설치된 경우가 가장 이상적이며, 이를 최근린 지수(Nearest neighbor index)를 이용하여 강우관측소 간에 공간적 분포를 등급화하였다. 최근린 지수는 임의의 점에 가장 가까운 인접 점들 간의 거리 특성을 이용하는 방법으로 점의 분포를 보다 지리적으로 파악할 수 있다. 본 연구에서는 엔트로피의 최대 정보전달량 및 강우관측소의 등급을 동시에 고려하기 위해 유클리디언 거리를 이용하여 2개의 목적함수를 통합하였으며, 이를 MOGA(Multi Objective Genetic Algorithm)를 이용하여 최적관측망을 선정하였다. 그 결과 MOGA를 이용하여 관측망을 평가한 경우 엔트로피 이론만을 적용했을 때보다 최적관측소가 보다 분산됨을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.947-949
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• 2015

본 논문은 기존의 시간적으로 이웃한 프레임 사이의 움직임을 추정 보상하여 새로운 프레임을 생성하는 프레임률 향상 기법 (frame rate up conversion)을 제안한다. 움직임 추정(Motion Estimation)을 통하여 계산된 움직임 벡터를 이용하여 프레임을 생성하며, 생성된 프레임에서 발생되는 구명 (hole)과 중첩 (overlap) 영역을 처리하는 기법을 제안한다. 특히 k-NN 보간법(k-nearest neighbor interpolation)[3]과 중간값을 적응적으로 활용하여 향상된 화질의 영상을 생성한다. 실험 결과를 통하여 제안하는 기술의 우수성을 입증하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1520-1522
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• 2015

본 논문은 기존의 시간적으로 이웃한 프레임 사이의 움직임을 추정 보상하여 새로운 프레임을 생성하는 프레임률 향상 기법 (frame rate up conversion)을 제안한다. 움직임 추정(Motion Estimation)을 통하여 계산된 움직임 벡터를 이용하여 프레임을 생성하며, 생성된 프레임에서 발생되는 구멍 (hole)과 중첩 (overlap) 영역을 처리하는 기법을 제안한다. 특히 k-NN 보간법(k-nearest neighbor interpolation)[3]과 중간값을 적응적으로 활용하여 향상된 화질의 영상을 생성한다. 실험 결과를 통하여 제안하는 기술의 우수성을 입증하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1410-1411
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• 2012

최근 모바일 사용자의 안전한 위치기반 서비스의 사용을 위한 아웃소싱 데이터베이스에서 객체 및 사용자의 위치 정보를 보호하는 연구가 위치 데이터를 보호하기 위한 연구가 활발히 진행되고 있다. 그러나 기존 연구는 불필요한 객체 정보를 요구하기 때문에, 높은 질의 처리 시간을 지니는 단점을 지닌다. 이러한 문제점을 해결하기 위해, 본 논문에서는 기준 POI를 중심으로 객체의 방향성 정보와 변환된 거리를 이용하여, 사용자와 객체의 정보를 보호하는 k-최근접 질의처리 알고리즘을 제안한다.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.957-966
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• 2019

This paper studies a novel approach to natural gait cycles based gait recognition via kernel Fisher discriminant analysis (KFDA), which can effectively calculate the features from gait sequences and accelerate the recognition process. The proposed approach firstly extracts the gait silhouettes through moving object detection and segmentation from each gait videos. Secondly, gait energy images (GEIs) are calculated for each gait videos, and used as gait features. Thirdly, KFDA method is used to refine the extracted gait features, and low-dimensional feature vectors for each gait videos can be got. The last is the nearest neighbor classifier is applied to classify. The proposed method is evaluated on the CASIA and USF gait databases, and the results show that our proposed algorithm can get better recognition effect than other existing algorithms.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.93-96
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• 2022

Natural language processing (NLP) is utilized to understand a natural text. Text analysis systems use natural language algorithms to find the meaning of large amounts of text. Text classification represents a basic task of NLP with a wide range of applications such as topic labeling, sentiment analysis, spam detection, and intent detection. The algorithm can transform user's unstructured thoughts into more structured data. In this work, a text classifier has been developed that uses academic admission and registration texts as input, analyzes its content, and then automatically assigns relevant tags such as admission, graduate school, and registration. In this work, the well-known algorithms support vector machine SVM and K-nearest neighbor (kNN) algorithms are used to develop the above-mentioned classifier. The obtained results showed that the SVM classifier outperformed the kNN classifier with an overall accuracy of 98.9%. in addition, the mean absolute error of SVM was 0.0064 while it was 0.0098 for kNN classifier. Based on the obtained results, the SVM is used to implement the academic text classification in this work.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.247-248
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• 2020

호스트가 숙소 가격을 정할 때, 기존 숙박 플랫폼들이 제공하는 최적화된 가격을 참고하기 위해선 숙소의 유형, 편의 시설 제공 여부 등 많은 단계를 거쳐야하므로 불편하다. 본 논문은 호스트가 보다 편리하게 자신의 숙소에 최적화된 가격을 알 수 있도록 하는 '숙소의 적정 가격 결정을 위한 웹 사이트'를 개발하였다. 이 웹을 통해 호스트는 더 간편하게 자신의 숙소에 대한 적정 가격을 알고 가격 산정 시 참고할 수 있다.

• Journal of Korean Society of Forest Science
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• v.101 no.4
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• pp.547-557
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• 2012

This study purposed to estimate the forest biomass using k-Nearest Neighbor (k-NN) algorithm. Multiple data sources were used for the analysis such as forest type map, field survey data and Landsat TM data. The accuracy of forest biomass was evaluated with the forest stratification, horizontal reference area (HRA) and spatial filtering. Forests were divided into 3 types such as conifers, broadleaved, and Korean pine (Pinus koriansis) forests. The applied radii of HRA were 4 km, 5 km and 10 km, respectively. The estimated biomass and mean bias for conifers forest was 222 t/ha and 1.8 t/ha when the value of k=8, the radius of HRA was 4 km, and $5{\times}5$ modal was filtered. The estimated forest biomass of Korean pine was 245 t/ha when the value of k=8, the radius of HRA was 4km. The estimated mean biomass and mean bias for broadleaved forests were 251 t/ha and -1.6 t/ha, respectively, when the value of k=6, the radius of HRA was 10 km. The estimated total forest biomass by k-NN method was 799,000t and 237 t/ha. The estimated mean biomass by ${\kappa}NN$method was about 1t/ha more than that of filed survey data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.343-364
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• 2021

In the 5G era, the communication networks tend to be ultra-densified, which will improve the accuracy of indoor positioning and further improve the quality of positioning service. In this study, we propose an indoor three-dimensional (3D) dynamic reconstruction fingerprint matching algorithm (DSR-FP) in a 5G ultra-dense network. The first step of the algorithm is to construct a local fingerprint matrix having low-rank characteristics using partial fingerprint data, and then reconstruct the local matrix as a complete fingerprint library using the FPCA reconstruction algorithm. In the second step of the algorithm, a dynamic base station matching strategy is used to screen out the best quality service base stations and multiple sub-optimal service base stations. Then, the fingerprints of the other base station numbers are eliminated from the fingerprint database to simplify the fingerprint database. Finally, the 3D estimated coordinates of the point to be located are obtained through the K-nearest neighbor matching algorithm. The analysis of the simulation results demonstrates that the average relative error between the reconstructed fingerprint database by the DSR-FP algorithm and the original fingerprint database is 1.21%, indicating that the accuracy of the reconstruction fingerprint database is high, and the influence of the location error can be ignored. The positioning error of the DSR-FP algorithm is less than 0.31 m. Furthermore, at the same signal-to-noise ratio, the positioning error of the DSR-FP algorithm is lesser than that of the traditional fingerprint matching algorithm, while its positioning accuracy is higher.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.7 no.2
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• pp.69-74
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• 2013

In this paper, we propose the pattern classification algorithm of recognizing wrist movements based on electromyogram(EMG) to raise the recognition rate. We consider 30 characteristics of EMG signals wirh the root mean square(RMS) and the difference absolute standard deviation value(DASDV) for the extraction of precise features from EMG signals. To get the groups of each wrist movement, we estimated 2-dimension features. On this basis, we divide each group into two parts with mean to compare and promote the recognition rate of pattern classification effectively. For the motion classification based on EMG, the k-nearest neighbor(k-NN) is used. In this paper, the recognition rate is 92.59% and 0.84% higher than the study before.