• Journal of Advanced Marine Engineering and Technology
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• 제40권1호
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• pp.62-68
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• 2016

실내 위치인식 기술 중 하나인 WiFi Fingerprint는 기존의 WiFi access point(AP)의 거리에 따른 신호 세기를 활용하여 위치를 추정하는 편리함 때문에 많은 연구가 이루어지고 있다. 하지만 이 방식은 Radio map에 저장된 Reference point에 의존하기 때문에 다른 방식에 비해 위치의 분해능이 떨어지고 연산량이 많다. 본 논문에서는 이러한 문제를 해결하기 위하여 WiFi와 BLE를 융합한 Log-Distance Path Loss Model 기반의 Radio map 설계 알고리즘을 제안한다. 제안한 알고리즘은 Log-Distance Path Loss Model이 적용된 변수 값을 추출하여 Radio map을 설계하는 방식이며 Median Filter를 적용하여 오차를 개선하였다. 기존 Fingerprint와 비교하여 실험한 결과, 위치의 정확도는 평균 2.747m에서 2.112m로 0.635m 감소되는 것을 확인하였으며 연산량은 AP 환경에 따라 33%이상 감소하는 것을 확인하였다.

• 전자공학회논문지
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• 제51권8호
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• pp.126-135
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• 2014

오차 신호의 확률분포 사이의 유클리드 거리 (Euclidean distance between error probability density functions, EDEP)는 충격성 잡음 환경의 적응 신호 처리를 위한 성능 지수로 사용되었다. 이 EDEP 알고리듬의 단점 중의 하나로 각 반복 시간마다 수행하는 이중적분에 의해 과다한 계산상의 복잡성이 있다. 이 논문에서는 EDEP 와 그 기울기 계산에서 계산상의 부담을 줄일 수 있는 반복적 추정 방법을 제안하였다. 데이터 블록 크기 N에 대하여, 기존의 추정 방식에 의한 EDEP와 그 기울기 계산량은 $O(N^2)$인 반면, 제안한 방식의 계산량은 O(N)이다. 성능 시험에서 제안한 방식의 EDEP와 그 기울기는 정상상태에서 기존의 블록 처리 방식과 동일한 추정결과를 나타냈다. 이러한 시뮬레이션 결과로부터, 제안한 방식이 실제 적응신호처리 분야에서 효과적인 방식임을 알 수 있다.

• 한국통신학회논문지
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• 제32권4C호
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• pp.462-467
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• 2007

본 논문은 공간다중화 MIMO 시스템인 ML 수신기법의 연산량을 감소시키는 최소거리탐지 알고리즘 (MDSA: Minimum Distance Searching Algorithm)을 제안한다. 선형수신기의 출력값을 기준비트로 설정하여 탐색공간을 줄이고 기준비트와 수신심벌과의 최소거리를 이용하여 최종송신심벌로의 최적경로를 구함으로써 ML의 연산량을 효율적으로 감소시킨다. 제안한 기법의 연산 반복수는 송신안테나 4개, 성상차수 16일 때, ML 방식에 비해 0.21%로 감소되었다. 성능분석 시뮬레이션 결과는 16QAM에서 송신 안테나 2개, 수신안테나 3개 이상일 때 MDS 는 ML과 성능이 거의 동일하였고, QPSK에서 송신 안테나 4개, 수신안테나 6개 이상일 때 MDS의 성능은 ML에 비해 약간 열화됨을 확인 할 수 있었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.625-627
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• 2005

Existent distance measurement that use camera is method that use both Stereo Camera and Monocular Camera, There is shortcoming that method that use Stereo Camera is sensitive in effect of a lot of expenses and environment variables, and method that use Monocular Camera are big computational complexity and error. In this study, reduce expense and error using Monocular Camera and I suggest algorithm that measure distance, Extract features using scale Invariant features Transform(SIFT) for distance measurement, and this measures distance through features matching and geometrical analysis, Proposed method proves measuring distance with wall by geometrical analysis free wall through feature point abstraction and matching.

• Communications for Statistical Applications and Methods
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• 제27권6호
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• pp.589-602
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• 2020

The development of smart grids has enabled the easy collection of a large amount of power data. There are some common patterns that make it useful to cluster power consumption patterns when analyzing s power big data. In this paper, clustering analysis is based on distance functions for time series and clustering algorithms to discover patterns for power consumption data. In clustering, we use 10 distance measures to find the clusters that consider the characteristics of time series data. A simulation study is done to compare the distance measures for clustering. Cluster validity measures are also calculated and compared such as error rate, similarity index, Dunn index and silhouette values. Real power consumption data are used for clustering, with five distance measures whose performances are better than others in the simulation.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.30-33
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• 2002

To obtain good performance of radial basis function (RBF) neural networks, it needs very careful consideration in design. The selection of several parameters such as the number of centers and widths of the radial basis functions must be considered carefully since they critically affect the network's performance. We propose a learning algorithm for growing of complexity of RBF neural networks which is adapted automatically according to the complexity of tasks. The algorithm generates a new basis function based on the errors of network, the percentage of decreasing rate of errors and the nearest distance from input data to the center of hidden unit. The RBF's center is located at the point where the maximum of absolute interference error occurs in the input space. The width is calculated based on the standard deviation of distance between the center and inputs data. The steepest descent method is also applied for adjusting the weights, centers, and widths. To demonstrate the performance of the proposed algorithm, general problem of function estimation is evaluated. The results obtained from the simulation show that the proposed algorithm for RBF neural networks yields good performance in terms of convergence and accuracy compared with those obtained by conventional multilayer feedforward networks.

• 전자공학회논문지B
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• 제32B권5호
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• pp.77-86
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• 1995

In this paper in order to reduce the encoding complexity required in the full search vector quantization(VQ), a new classified vector quantization(CVQ) technique is described employing the minimum-distance classifier. The determination of the optimal subcodebook sizes for each class is an important task in CVQ designs and is not an easy work. Therefore letting the subcodebook sizes be equal. A CVQ technique. Which satisties the optimal CVQ condition approximately, is proposed. The proposed CVQ is a kind of the partial search VQ because it requires a search process within each subcodebook only, and the minimum encoding complexity since the subcodebook sizes are the same in each class. But simulation results reveal while the encoding complexity is only O(N$^{1/2}$) comparing with O(N) of the full-search VQ. A simple systolic array, which has the through-put of k, is also proposed for the implementation of the VQ. Since the operation of the classifier is identical with that of the VQ, the proposed array is applied to both the classifier and the VQ in the proposed CVQ, which shows the usefulness of the proposed CVQ.

• 대한전자공학회논문지TC
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• 제43권3호
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• pp.95-102
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• 2006

본 논문에서는 K-best 기법에서 심볼 검출 시 소요되는 불필요한 연산을 줄이고자 부분 유클리디언 거리(partial Euclidean distance)의 통계적 값으로서 수정된 Fano-like metric 바이어스를 할당하여 기존의 K-best 기법에 적용함으로써 평균 연산량을 감소시킨 KB-Fano 기법을 제안하였다. 또한 KB-Fano 기법에 K-reduction 기법을 연동한 KR-Fano 기법을 제안하여 모의 실험을 통해 K-reduction의 효과로 인한 비트 오차 확률 측면에서 높은 SNR 영역에서의 성능 개선과 함께 추가적인 평균 연산량 감소가 나타나는 것을 확인하였다.

• Journal of information and communication convergence engineering
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• 제12권1호
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• pp.8-13
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• 2014

We consider the sensor selection problem in large sensor networks where the goal is to find the best set of sensors that maximizes application objectives. Since sensor selection typically involves a large number of sensors, a low complexity should be maintained for practical applications. We propose a geometry-based sensor selection algorithm that utilizes only the information of sensor locations. In particular, by observing that sensors clustered together tend to have redundant information, we theorize that the redundancy is inversely proportional to the distance between sensors and seek to minimize this redundancy by searching for a set of sensors with the maximum average distance. To further reduce the computational complexity, we perform an iterative sequential search without losing optimality. We apply the proposed algorithm to an acoustic sensor network for source localization, and demonstrate using simulations that the proposed algorithm yields significant improvements in the localization performance with respect to the randomly generated sets of sensors.

• International Journal of Advanced Culture Technology
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• 제7권4호
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• pp.302-307
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• 2019

In this paper, we propose an efficient method for AR toolkit Vuforia. In order to increase the scan rate when using the 3D object scanner, the scan rate parameters need to be analyzed in terms of the angle and distance. In addition, in order to increase the tracking rate when tracking an object, the tracking rate has to be evaluated according to the position, complexity, and contrast of the object. To this end, we have defined the difference of scan rate according to angle and distance between camera and object when using object scanner and the recognition time according to object's position, complexity and contrast when tracking object.