• Journal of Internet Computing and Services
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• v.9 no.3
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• pp.1-8
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• 2008

With growing interest in location-based service (LBS), there is increasing necessity for nearest neighbor (NN) search through query while the user is moving. NN search in such a dynamic environment has been performed through the repeated applicaton of the NN method to the search segment, but this increases search cost because of unnecessary redundant calculation. We propose slabbed continuous nearest neighbor (Slabbed_CNN) search, which is a new method that searches CNN in the search segment while moving, Slabbed_CNN reduces calculation costs and provides faster services than existing CNN by reducing the search area and calculation cost of the existing CNN method through reducing the search segment using slabs.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.420-423
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• 2020

스카이라인 질의는 객체의 속성을 기준으로 사용자의 선호에 적합한 대상을 탐색하는 기법이다. 기존 스카이라인 질의는 일괄처리 방식으로 탐색 결과를 반환하지만 대화형 앱이나 모바일 환경과 같이 잦은 위치이동 발생 시 일괄처리 방식으로 스카이라인 질의 결과를 신속하게 받기 어렵다. 최근접 이웃(Nearest Neighbor) 알고리즘은 사용자와 상호 작용이 필요한 대화형 앱에서 실시간으로 선호 객체를 탐색하여 사용자에게 전달함으로써 객체의 반환 속도를 향상시켰다. 그러나 최근접 이웃 알고리즘은 객체 탐색 과정에서 반복적인 비교 연산을 수행하여 불필요한 탐색 시간이 소요된다. 본 논문은 대화형 앱에서 신속한 스카이라인 결과를 산출하고자 연산 대상 객체의 범위를 축소함으로써 최근접 이웃 스카이라인 질의 알고리즘의 성능을 향상시킨 전처리 기법을 제안한다. 데이터 객체는 최대 40,000 개의 실험에서 제안 기법은 최근접 이웃 알고리즘보다 50% 빠른 성능을 나타내어 본 연구의 가용성이 증명되었다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.226-228
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• 2004

최근에 이동객체의 위치정보를 활용한 위치기반서비스(L8S, Location Based Services)에 대한 관심이 증가하고 있다. 전통적으로 정적인 위치정보를 갖는 공간 객체는 GIS(Geographic Information System) 서버에 저장, 관리되었다. 이동객체는 시간에 따라 위치의 변화가 매우 빈번하여 위치 정보가 계속 갱신되기 때문에, 전통적인 GIS 서버로는 관리가 어렵다. 본 논문에서는 기존의 연속적인 최근접 이웃탐색 기법에서 데이터의 처리 순서에 따라 탐색공간과 계산비용이 증가하는 문제점을 슬랩을 사용하여 해결한다. 최근접 이웃의 수직연장선 사이의 공간인 슬랩 내부영역에 대해서만 탐색하도록 하여 탐색영역을 줄이고, 그 내부에 있는 점들에 대해서만 처리하여 계산비용을 줄인다.

• Journal of KIISE:Databases
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• v.36 no.5
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• pp.405-410
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• 2009

This paper proposes an efficient path finding scheme capable of searching the paths to k static objects from a given query point, aiming at both improving the legacy k-nearest neighbor search and making it easily applicable to the road network environment. To the end of improving the speed of finding one-to-many paths, the modified A* obviates the duplicated part of node scans involved in the multiple executions of a one-to-one path finding algorithm. Additionally, the cost to the each object found in this step makes it possible to finalize the k objects according to the network distance from the candidate set as well as to order them by the path cost. Experiment results show that the proposed scheme has the accuracy of around 100% and improves the search speed by $1.3{\sim}3.0$ times of k-nearest neighbor searches, compared with INE, post-Dijkstra, and $na{\ddot{i}}ve$ method.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.336-339
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• 2009

본 논문에서는 최단 경로 탐색 및 거리 계산의 필요성을 가지고 근사 인덱싱 방법의 후처리 부분을 제안한다. 근사 인덱싱 방법이란 오프라인에서 네트워크 공간상의 객체들을 유클리드 공간 상의 절대 좌표로 사상하여 인덱싱한 후, k-최근접 이웃 질의를 처리하는 방법이다. 그러나 기존 연구는 질의 점으로부터 각 정적 객체까지의 경로를 탐색해주지 않을 뿐만 아니라 착오 기각이 발생한다. 따라서 본 논문에서는 질의 점으로부터 k개의 정적 객체까지의 경로를 효과적으로 탐색할 수 있는 방법을 제안한다. 또한, 이 방법을 통하여 착오 기각 역시 완화시킬 수 있는 방법을 제안한다. 실험을 통하여 제안하는 방법이 기존 경로 탐색 기법들에 비해 노드 탐색 횟수 및 실행 성능이 크게 향상시킨 것으로 나타났다.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.6
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• pp.215-218
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• 2014

In hashing-based methods for approximate nearest neighbors(ANN) search, by mapping data points to k-bit binary codes, nearest neighbors are searched in a binary embedding space. In this paper, we present a hashing method using a PCA-based clustering method, Principal Direction Divisive Partitioning(PDDP). PDDP is a clustering method which repeatedly partitions the cluster with the largest variance into two clusters by using the first principal direction. The proposed hashing method utilizes the first principal direction as a projective direction for binary coding. Experimental results demonstrate that the proposed method is competitive compared with other hashing methods.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.357-360
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• 2022

본 논문에서는 대용량 데이터에서 빠르게 주변 데이터를 접근하기 위한 자료구조인 최근접 이웃 탐색(Nearest neighbor search, NNS) 문제를 빠르게 풀 수 있는 바이토닉 정렬(Bitonic sort) 기반 해시 테이블을 GPU기반에서 설계하는 방법과 이를 통해 입자 기반 물리 시뮬레이션을 고속화할 수 있는 방법에 대해 살펴본다. 본 논문에서는 CUDA 아키텍처를 이용하여 해시 테이블을 설계하였으며, 계산양이 가장 큰 데이터 정렬부분을 최적화함으로써 NVIDIA에서 제공하는 CUDA 해시 테이블보다 빠른 결과를 얻을 수 있으며, 이 자료구조를 입자 기반 시뮬레이션에 통합함으로써 고성능 시뮬레이션을 쉽게 제작할 수 있다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1518-1524
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• 2014

In this paper, a new method of spatial query, which is called Surround Search (SuSe) is suggested. This method makes it possible to search for the closest spatial object of interest to the user from a query point. SuSe is differentiated from the existing spatial object query schemes, because it locates the closest spatial object of interest around the query point. While SuSe searches the surroundings, the spatial object is saved on an R-tree, and MINDIST, the distance between the query location and objects, is measured by considering an angle that the existing spatial object query methods have not previously considered. The angle between targeted-search objects is found from a query point that is hidden behind another object in order to distinguish hidden objects from them. The distinct feature of this proposed scheme is that it can search the faraway or hidden objects, in contrast to the existing method. SuSe is able to search for spatial objects more precisely, and users can be confident that this scheme will have superior performance to its predecessor.

• Journal of The Korean Association For Science Education
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• v.38 no.2
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• pp.219-234
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• 2018

In this study, we explored the possibility of automating the process of analyzing elements of scientific argument in the context of a Korean classroom. To gather training data, we collected 990 sentences from science education journals that illustrate the results of coding elements of argumentation according to Toulmin's argumentation structure framework. We extracted 483 sentences as a test data set from the transcription of students' discourse in scientific argumentation activities. The words and morphemes of each argument were analyzed using the Python 'KoNLPy' package and the 'Kkma' module for Korean Natural Language Processing. After constructing the 'argument-morpheme:class' matrix for 1,473 sentences, five machine learning techniques were applied to generate predictive models relating each sentences to the element of argument with which it corresponded. The accuracy of the predictive models was investigated by comparing them with the results of pre-coding by researchers and confirming the degree of agreement. The predictive model generated by the k-nearest neighbor algorithm (KNN) demonstrated the highest degree of agreement [54.04% (${\kappa}=0.22$)] when machine learning was performed with the consideration of morpheme of each sentence. The predictive model generated by the KNN exhibited higher agreement [55.07% (${\kappa}=0.24$)] when the coding results of the previous sentence were added to the prediction process. In addition, the results indicated importance of considering context of discourse by reflecting the codes of previous sentences to the analysis. The results have significance in that, it showed the possibility of automating the analysis of students' argumentation activities in Korean language by applying machine learning.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.10
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• pp.63-70
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• 2018

Biomedical signal measurement technology using images has been developed, and researches on respiration signal measurement technology for maintaining life have been continuously carried out. The existing technology measured respiratory signals through a thermal imaging camera that measures heat emitted from a person's body. In addition, research was conducted to measure respiration rate by analyzing human chest movement in real time. However, the image processing using the infrared thermal image may be difficult to detect the respiratory organ due to the external environmental factors (temperature change, noise, etc.), and thus the accuracy of the measurement of the respiration rate is low.In this study, the images were acquired using visible light and infrared thermal camera to enhance the area of the respiratory tract. Then, based on the two images, features of the respiratory tract region are extracted through processes such as face recognition and image matching. The pattern of the respiratory signal is classified through the k-nearest neighbor classifier, which is one of the statistical classification methods. The respiration rate was calculated according to the characteristics of the classified patterns and the possibility of breathing rate measurement was verified by analyzing the measured respiration rate with the actual respiration rate.