• 지구물리와물리탐사
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• 제22권2호
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• pp.56-61
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• 2019

셀 기반 유한 차분법을 사용하여 P파 속도와 밀도 변화를 고려한 3차원 시간 영역 음향 파동 전파 모델링에서 성능을 향상시킬 수 있는 방법을 살펴보았다. 일반적인 유한 차분법에서는 격자점에 탄성파 속도 또는 밀도와 같은 물성을 할당하고 계산하지만 셀 기반 유한 차분법에서는 이러한 물성을 격자점 사이의 셀에 할당한다. 격자점에서의 차분식 계산을 위해서는 주변 셀의 물성 평균값을 이용하는데 이로 인해 일반적인 유한 차분법에 비해 계산량이 증가하게 된다. 이 연구에서는 이러한 계산량 문제를 개선하기 위해 메모리를 추가로 사용하여 모델링 시간을 30 % 이상 줄일 수 있었다. 또한 밀도가 제한적으로 변화하는 매질에서 셀 기반 유한 차분법과 일반 유한 차분법을 함께 사용하여 모델링 성능을 추가로 향상시킬 수 있었다.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 1998년도 춘계학술대회논문집
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• pp.168-174
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• 1998

In the present study, a method for measuring the relative density by the hardness measurement was proposed for sintered metal powder compacts. It is based on the indentation force equation, by which the relative density is related with the hardness, that was obtained by the finite element analysis of rigid-ball indentation on sintered metal powder compacts. For verifying the method, it was applied to prediction of density distributions in sintered and sintered-and-forged Fe-0.5%C-2%Cu powder compacts.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 1999년도 봄 학술발표회 논문집
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• pp.357-364
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• 1999

The purpose of this study is to analysis the grain distribution and compaction characteristics of structural backfill with reclaimed soil. Five(5) reclaimed soil samples which passed #200 sieve have been used in the test. The study showed that the maximum dry density and the bearing value rate turned out to be becoming smaller when the more the quantity passed #200 sieve, the smaller the soil grain. The maximum dry density value calculated from the compaction md relative density test showed wet method > compaction method > dry method. The correlation coefficient between Rc and Dr based on the grain distribution and the compaction characteristics showed that the maximum dry density value by the wet method is little higher than the compaction method and dry method.

• Journal of the Korean Statistical Society
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• 제27권1호
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• pp.91-100
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• 1998

A method of estimating probability density using regression tools is presented here. It is based on equal-length binning and locally weighted approximate likelihood for bin counts. The method is particularly useful for densities with bounded supports, where it automatically corrects edge effects without using boundary kernels.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3815-3835
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• 2022

Density-based outlier detection is one of the hot issues in data mining. A point is determined as outlier on basis of the density of points near them. The existing density-based detection algorithms have high time complexity, in order to reduce the time complexity, a new outlier detection algorithm DODMD (Density-based Outlier Detection in Multidimensional Datasets) is proposed. Firstly, on the basis of ZH-tree, the concept of micro-cluster is introduced. Each leaf node is regarded as a micro-cluster, and the micro-cluster is calculated to achieve the purpose of batch filtering. In order to obtain n sets of approximate outliers quickly, a greedy method is used to calculate the boundary of LOF and mark the minimum value as LOF_min. Secondly, the outliers can filtered out by LOF_min, the real outliers are calculated, and then the result set is updated to make the boundary closer. Finally, the accuracy and efficiency of DODMD algorithm are verified on real dataset and synthetic dataset respectively.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.260-262
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• 2004

Spatial database system provides many query types and most of them are required frequent disk I/O and much CPU time. k-NN search is to find k-th closest object from the query point and up to now, several k-NN search methods have been proposed. Among these, MINMAX distance method has an aim not to visit unnecessary node by applying pruning technique. But this method access more disk than necessary while pruning unnecessary node. In this paper, we propose new k-NN search algorithm based on density of object. With this method, we predict the radius to be expected to contain k-NN object using density of data set and search those objects within this radius and then adjust radius if failed. Experimental results show that this method outperforms the previous MINMAX distance method. This algorithm visit fewer disks than MINMAX method by the factor of maximum $22\%\;and\;average\;6\%.$

• 한국컴퓨터그래픽스학회논문지
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• 제30권2호
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• pp.1-9
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• 2024

본 논문에서는 밀도 데이터로부터 다양한 벡터장 패턴을 시각화하는 새로운 방법을 제안한다. 이를 위해 물리 기반 시뮬레이션과 기하학적 처리에서 사용되는 이동최소제곱(Moving least squares, MLS)을 이용한다. 하지만 일반적인 MLS는 벡터기반의 제약조건을 통해 고차 보간되기 때문에 밀도의 특성을 고려하지 못한다. 본 논문에서는 입력 데이터에 내포되어 있는 밀도의 특성을 효율적으로 고려하기 위해 몬테카를로 기반의 가중치를 MLS에 통합하여 다양한 형태의 백터장을 표현할 수 있도록 알고리즘을 설계한다. 결과적으로 일반적인 MLS와 발산제약 기반의 MLS 같은 기존 기법으로는 표현하기 힘든 디테일한 벡터장을 실험을 통해 보여준다.

• 한국통신학회논문지
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• 제42권2호
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• pp.453-459
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• 2017

사용자들은 그들의 관심이 관심지점 (POI: Point-of-Interest)과 관련이 있다는 사실을 언급하기 위해 위치 기반 소셜 네트워크에 체크인하거나 그들의 상태를 올리는 경향이 있다. 관심지역 (AOI: Area-of-Interest)을 찾는 기존 연구는 대부분 위치 기반 소셜 네트워크로부터 수집된 공간 태그된 사진과 함께 밀도 기반 군집화 기법을 사용하여 수행되었다. 반면, 본 연구에서는 POI 중심을 포함한 하나의 군집에 해당하는 POI 경계선을 추정하는 데에 초점을 맞춘다. 트위터 사용자들로부터의 공간 태그된 트윗을 사용하여 POI 중심으로부터 도달할 수 있는 적절한 반경을 찾음으로써 POI 경계선을 추정하는 밀도 기반 저복잡도 두 단계 방법을 소개한다. 두 단계 밀도 기반 추정을 통해 선택된 공간 태그의 convex hull로써 POI 경계선을 추정하는데, 각 단계에서 다른 크기의 반경 증가를 가정하여 진행한다. 제안한 방법은 기본 밀도 기반 군집화 방법보다 계산 복잡도 측면에서 우수한 성능을 가짐을 보인다.

• IEIE Transactions on Smart Processing and Computing
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• 제6권3호
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• pp.183-192
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• 2017

k-nearest neighbor (K-NN) is a well-known classification algorithm, being feature space-based on nearest-neighbor training examples in machine learning. However, K-NN, as we know, is a lazy learning method. Therefore, if a K-NN-based system very much depends on a huge amount of history data to achieve an accurate prediction result for a particular task, it gradually faces a processing-time performance-degradation problem. We have noticed that many researchers usually contemplate only classification accuracy. But estimation speed also plays an essential role in real-time prediction systems. To compensate for this weakness, this paper proposes correlation coefficient-based clustering (CCC) aimed at upgrading the performance of K-NN by leveraging processing-time speed and plurality rule-based density (PRD) to improve estimation accuracy. For experiments, we used real datasets (on breast cancer, breast tissue, heart, and the iris) from the University of California, Irvine (UCI) machine learning repository. Moreover, real traffic data collected from Ojana Junction, Route 58, Okinawa, Japan, was also utilized to lay bare the efficiency of this method. By using these datasets, we proved better processing-time performance with the new approach by comparing it with classical K-NN. Besides, via experiments on real-world datasets, we compared the prediction accuracy of our approach with density peaks clustering based on K-NN and principal component analysis (DPC-KNN-PCA).

• Journal of Microbiology and Biotechnology
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• 제22권7호
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• pp.967-974
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• 2012

Cultivation of Spirulina platensis using ammonium salts or wastewater containing ammonium as alternative nitrogen sources is considered as a commercial way to reduce the production cost. In this research, by analyzing the relationship between biomass production and ammonium-N consumption in the fed-batch culture of Spirulina platensis using ammonium bicarbonate as a nitrogen nutrient source, an online adaptive control strategy based on optical density (OD) measurements for controlling ammonium feeding was presented. The ammonium concentration was successfully controlled between the cell growth inhibitory and limiting concentrations using this OD-based feedback feeding method. As a result, the maximum biomass concentration (2.98 g/l), productivity (0.237 g/l d), nitrogen-to-cell conversion factor (7.32 gX/gN), and contents of protein (64.1%) and chlorophyll (13.4mg/g) obtained by using the OD-based feedback feeding method were higher than those using the constant and variable feeding methods. The OD-based feedback feeding method could be recognized as an applicable way to control ammonium feeding and a benefit for Spirulina platensis cultivations.