• International Journal of Ocean System Engineering
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• 제1권2호
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• pp.60-67
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• 2011

This paper presents an in-situ correction method to compensate for the position error of an autonomous underwater vehicle (AUV) near the sea floor. AUVs generally have an inertial navigation system assisted with auxiliary navigational sensors. Since the inertial navigation system shows drift in position without the bottom reflection of a Doppler velocity log, external acoustic positioning systems, such as an ultra short baseline (USBL), are needed to set the position without surfacing the AUV. The main concept of the correction method is as follows: when the AUV arrives near the sea floor, the vehicle moves around horizontally in a circular mode, while the USBL transceiver installed on a surface vessel measures the AUV's position. After acquiring one data set, a least-square curve fitting method is adopted to find the center of the AUV's circular motion, which is transferred to the AUV via an acoustic telemetry modem (ATM). The proposed method is robust for the outlier of USBL, and it is independent of the time delay for the data transfer of the USBL position with the ATM. The proposed method also reduces the intrinsic position error of the USBL, and is applicable to the in-situ calibration as well as the initialization of the AUVs' position. Monte Carlo simulation was conducted to verify the effectiveness of the method.

• 인터넷정보학회논문지
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• 제21권3호
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• pp.113-121
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• 2020

Human Pose Estimation (HPE) which localizes the human body joints becomes a high potential for high-level applications in the field of computer vision. The main challenges of HPE in real-time are occlusion, illumination change and diversity of pose appearance. The single RGB image is fed into HPE framework in order to reduce the computation cost by using depth-independent device such as a common camera, webcam, or phone cam. However, HPE based on the single RGB is not able to solve the above challenges due to inherent characteristics of color or texture. On the other hand, depth information which is fed into HPE framework and detects the human body parts in 3D coordinates can be usefully used to solve the above challenges. However, the depth information-based HPE requires the depth-dependent device which has space constraint and is cost consuming. Especially, the result of depth information-based HPE is less reliable due to the requirement of pose initialization and less stabilization of frame tracking. Therefore, this paper proposes a new method of HPE which is robust in estimating self-occlusion. There are many human parts which can be occluded by other body parts. However, this paper focuses only on head self-occlusion. The new method is a combination of the RGB image-based HPE framework and the depth information-based HPE framework. We evaluated the performance of the proposed method by COCO Object Keypoint Similarity library. By taking an advantage of RGB image-based HPE method and depth information-based HPE method, our HPE method based on RGB-D achieved the mAP of 0.903 and mAR of 0.938. It proved that our method outperforms the RGB-based HPE and the depth-based HPE.

• 한국컴퓨터정보학회논문지
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• 제27권12호
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• pp.209-217
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• 2022

함정 전투관리체계는 다양한 센서, 무장 장비들이 연동 노드를 통해 연결되며 체계내 노드간 효율적인 통신을 위해 DDS(Data Distribution Service) 통신을 활용한다. DDS를 사용하기 위해 체계내 응용소프트웨어 사이에는 DDS토픽을 기본으로 하는 PUB/SUB(Publication/Subscribe)방식으로 통신한다. 이 PUB/SUB방식으로 통신하는 DDS 메시지 구조가 일치하지 않으면 송수신 응용소프트웨어에서 잘못된 명령처리 및 정보전달 등 문제가 발생한다. 이를 개선하기 위해 본 논문에서는 해시트리를 활용한 DDS 메시지 구조 무결성 검증 방법을 제안한다. 제안하는 방법의 전투관리체계에 적용가능성을 확인하기 위해, 제안하는 방법의 메시지 구조 무결성 검증률을 측정하고 전투관리체계의 운용과 개발과정에 미치는 영향을 확인하기 위해 전투관리체계 초기화 시간 비교, 메시지 해시트리의 생성시간 측정을 하였다. 이 시험을 통해 본 논문에서 제안하는 체계 안정성을 위한 메시지 구조 검증 방법이 함정 전투관리체계에 적용 가능함을 확인하였다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제33권8호
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• pp.681-691
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• 2006

유전자 데이타의 클러스터링은 방대한 유전자 정보를 발현 정도에 따라 비슷한 그룹으로 나누어 분석하는 방법으로 유전자의 기능을 분석하는데 사용되어 왔다. 클러스터링의 한 종류인 퍼지 클러스터링은 하나의 샘플이 소속정도에 따라 여러 그룹에 동시에 소속되도록 나누는 방법으로, 하나의 유전자 데이타는 여러가지 유전 정보를 가칠 수 있기 때문에 유전자 발현 데이타의 분석에 보다 적절한 방법이다. 그러나 보통 클러스터링 방법은 초기 값에 민감하고, 지역해에 빠질 수 있는 단점을 갖는다. 이런 단점을 해결하기 위해 본 논문에서는 진화 연산을 이용한 퍼지 클러스터링 방법을 제안한다. 이때, 적합도 평가를 위해서 모든 데이타에 대해 동일한 기준을 적용하는 베이지안 검증방법의 단점을 개선하여, 데이타의 특성 을 고려하여 결정된 적용적 ${\alpha}$-cut 기반 평가방법을 사용한다. SRBCT 데이타와 효모 세포주기 데이타를 이용해 실험을 하고 결과를 분석하여 제안하는 방법의 유용성을 확인하였다.

• 한국지능시스템학회논문지
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• 제11권3호
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• pp.223-230
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• 2001

자기구성 지도는 주어진 입력에 대해 올바른 출력 값이 제공되지 않는 비교사 방식으로 학습된다. 또한, 반응하는 순서나 위치를 통해 위상이 보존(topology preserving)되는 특성을 가지고 있어 많은 분야에 응용되고 있다. 그러나, 자기 구성지도는 학습이 되기 전에 위상을 미리 고정시켜야 하기 때문에 실제 문제에 적용하기 어렵다는 단점을 가지고 있다. 구조 적응형 자기구성 지도는 자기구성 지도의 고정된 구조 때문에 발생하는 문제를 해결하기 위해 지도의 구조를 학습 중에 적절하게 변경시킨다. 이때, 변화된 구조의 가중치를 어떻게 초기화시킬 것인가 하는 것이 또한 중요한 문제이다. 이 논문에서는 구조 적응형 자기구성 지도 모델에서 유전자 알고리즘을 이용하여 분화된 노드의 가중치를 결정하는 방법을 제안한다. 이 방법은 기존의 구조 적응형 자기구성 지도보다 다소 높은 인식률을 보였고, 숫자 별 인식률 편차를 줄일 수 있었다. 오프라인 필기 숫자 데이터로 실험한 결과, 제안한 방법이 유용함을 알 수 있었다.

• 한국게임학회 논문지
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• 제9권6호
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• pp.163-170
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• 2009

대부분의 온라인 게임서버 엔진에서의 Accept()사용한 Looping방식의 동적인 메모리 할당 방식의 사용은 다수의 클라이언트를 동시에 수용하고 진행해야하는 온라인 게임 상의 로그인 서버엔진에 많은 부하와 함께 병목현상을 유발하게 된다. 따라서 본 논문에서는 온라인상에서의 효율적인 게임서버를 위해 정적인 메모리 할당기법을 사용하여 메모리 단편화를 최소화하고, 클라이언트의 접속 시 세션연결 및 클라이언트 객체의 초기화를 위해 발생하는 부하를 최소화하기 위한 AcceptEx()를 사용한 풀링기법과 정적메모리를 생성하여 동적 할당처럼 포인터로 할당하는 메모리 풀링 기법을 합친 객체 풀링기법을 제안하고, 제안 기법을 적용한 게임엔진을 설계하여 구현한 후, 성능평가를 통해 제안한 기법의 효율성을 보였다.

• 대한의용생체공학회:의공학회지
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• 제26권6호
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• pp.357-363
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• 2005

Active contour models have been extensively used to segment, match, and track objects of interest in computer vision and image processing applications, particularly to locate object boundaries. With conventional methods an object boundary can be extracted by controlling the internal energy and external energy based on energy minimization. However, this still leaves a number of problems, such as initialization and poor convergence in concave regions. In particular, a contour is unable to enter a concave region based on the stretching and bending characteristic of the internal energy. Therefore, this study proposes a method that controls the internal energy by moving the local perpendicular bisector point of each control point on the contour, and determines the object boundary by minimizing the energy relative to the external energy. Convergence at a concave region can then be effectively implemented as regards the feature of interest using the internal energy, plus several objects can be detected using a multi-detection method based on the initial contour. The proposed method is compared with other conventional methods through objective validation and subjective consideration. As a result, it is anticipated that the proposed method can be efficiently applied to the detection of the pulmonary parenchyma region in medical images.

• 산업경영시스템학회지
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• 제40권2호
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• pp.92-98
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• 2017

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.

• 대한원격탐사학회지
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• 제16권4호
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• pp.305-313
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• 2000

A vicarious calibration method was developed for the OSMI sensor calibration. Employing measured aerosol optical thickness by a sunphotometer and a sky radiometer and water leaving radiance by ship measurements as inputs, TOA (top of the atmosphere) radiance at each OSMI band was simulated in conjunction with a radiative transfer model (Rstar5b) by Nakajima and Tanaka (1988). As a case of examining the accuracy of this method, we simulated TOA radiance based on water leaving radiance measured at NASA/MOBY site and aerosol optical thickness estimated nearby at Lanai, and compared simulated results with SeaWiFS-estimated TOA radiances. The difference falls within about $\pm$5%, suggesting that OMSI sensor can be calibrated with the suggested accuracy. In order to apply this method for the OSMI sensor calibration, ground-based sun photometry and ship measurements were carried out off the east coast of Korean peninsula on May 31, 2000. Simulations of TOA radiance by using these measured data as input to the radiative transfer model show that there are substantial differences between simulated and OSMI-estimated radiances. Such a discrepancy appears to be mainly due to the cloud contamination because satellite image indicates optically thin clouds over the experimental area. Nevertheless results suggest that sensor calibration can be achieved within 5% uncertainty range if there are ground-based measurements of aerosol optical thickness, and water leaving radiances under clear-sky and optically thin atmospheric conditions.

• 대기
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• 제17권2호
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• pp.207-216
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• 2007

The Moving-nest Typhoon Model (MTM) was installed on the Korea Meteorological Administration (KMA)'s CRAY X1E in 2006 and started its test operation in August 2006 to provide track and intensity forecasts of tropical cyclones. In this study, feasibility of the MTM forecast is compared with the Global Data Assimilation and Prediction System (GDAPS) of the KMA and the operational typhoon forecast models in the Japan Meteorological Agency (JMA), from the sixth tropical cyclone to the twentieth in 2006. Forecast skills in terms of the storm position error of the two KMA models were comparable, but MTM showed a slightly better ability. While both GDAPS and MTM produced larger errors than JMA models in track forecast, the predicted intensity was much improved by MTM, making it comparable to the JMA's typhoon forecast model. It is believed that the Geophysical Fluid Dynamics Laboratory (GFDL) bogus initialization method in MTM improves the ability to forecast typhoon intensity.