• 수산해양기술연구
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• 제18권1호
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• pp.47-53
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• 1982

본 논문은 비균일대칭성 열Flux인 수직사각 Duct내의 층류조합대류 열전달 효과를 해석하기 위하여 그 유동의 특성 지배 방정식 및 비균일 열Flux의 경계조건을 무차원화 시켜 이를 Galerkin's 방법에 의해 유한요소식으로 정식화하고 이에 대하여 R 하(a) 수 및 압력구배 변수에 대해서 Duct 내의 온도분포, 속도분포 및 Nusselt 수의 값을 계산하였고 온도분포를 열 Flux가 일정 및 없는 경우와 비교하였으며 또 닥트내의 열전달 특성을 R 하(a) 수, 응력구배변수 및 Corner에 따른 변호경향을 조사하였다. 그 결과 1. 본 해석의 경계벽 온도분포 계산치와 유효자료들과의 비교에서 열 Flux가 일정 또는 없는 경우는 그 값이 일치하였다. 2. 닥트내의 온도분포와 Nusselt수의 값은 R 하(a) 수 및 압력구배 변수에 비례하여 증감하였다. 3. Nusselt수는 Corner에서 유속지연에 의한 온도분포의 특성 때문에 그 값이 감소하였으며 최대치는 0.7부근이었다

• 전기학회논문지
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• 제66권5호
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• pp.833-842
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• 2017

In this study, the design methodology of radial basis function neural networks based on incremental K-means clustering is introduced for learning and processing the big data. If there is a lot of dataset to be trained, general clustering may not learn dataset due to the lack of memory capacity. However, the on-line processing of big data could be effectively realized through the parameters operation of recursive least square estimation as well as the sequential operation of incremental clustering algorithm. Radial basis function neural networks consist of condition part, conclusion part and aggregation part. In the condition part, incremental K-means clustering algorithms is used tweights of the conclusion part are given as linear function and parameters are calculated using recursive least squareo get the center points of data and find the fitness using gaussian function as the activation function. Connection s estimation. In the aggregation part, a final output is obtained by center of gravity method. Using machine learning data, performance index are shown and compared with other models. Also, the performance of the incremental K-means clustering based-RBFNNs is carried out by using PSO. This study demonstrates that the proposed model shows the superiority of algorithmic design from the viewpoint of on-line processing for big data.

• Journal of Computing Science and Engineering
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• 제5권2호
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• pp.111-120
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• 2011

Data mining algorithms should exploit new hardware technologies to accelerate computations. Such goal is difficult to achieve in database management system (DBMS) due to its complex internal subsystems and because data mining numeric computations of large data sets are difficult to optimize. This paper explores taking advantage of existing multithreaded capabilities of multicore CPUs as well as caching in RAM memory to efficiently compute summaries of a large data set, a fundamental data mining problem. We introduce parallel algorithms working on multiple threads, which overcome the row aggregation processing bottleneck of accessing secondary storage, while maintaining linear time complexity with respect to data set size. Our proposal is based on a combination of table scans and parallel multithreaded processing among multiple cores in the CPU. We introduce several database-style and hardware-level optimizations: caching row blocks of the input table, managing available RAM memory, interleaving I/O and CPU processing, as well as tuning the number of working threads. We experimentally benchmark our algorithms with large data sets on a DBMS running on a computer with a multicore CPU. We show that our algorithms outperform existing DBMS mechanisms in computing aggregations of multidimensional data summaries, especially as dimensionality grows. Furthermore, we show that local memory allocation (RAM block size) does not have a significant impact when the thread management algorithm distributes the workload among a fixed number of threads. Our proposal is unique in the sense that we do not modify or require access to the DBMS source code, but instead, we extend the DBMS with analytic functionality by developing User-Defined Functions.

• Smart Structures and Systems
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• 제22권1호
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• pp.81-94
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• 2018

Automatic Generation Control (AGC) has functionally controlled the interchange power flow in order to suppress the dynamic oscillations of frequency and tie-line power deviations as a perturbation occurs in the interconnected multi-area power system. Furthermore, Flexible AC Transmission Systems (FACTS) can effectively assist AGC to more enhance the dynamic stability of power system. So, Static Synchronous Series Compensator (SSSC), one of the well-known FACTS devices, is here applied to accurately control and regulate the load frequency of multi-area multi-source interconnected power system. The research and efforts made in this regard have caused to introduce the Fractional Order Proportional Integral Derivative (FOPID) based SSSC, to alleviate both the most significant issues in multi-area interconnected power systems i.e., frequency and tie-line power deviations. Due to multi-objective nature of aforementioned problem, suppression of the frequency and tie-line power deviations is formularized in the form of a multi-object problem. Considering the high performance of Multi Objective Bees Algorithm (MOBA) in solution of the non-linear objectives, it has been utilized to appropriately unravel the optimization problem. To verify and validate the dynamic performance of self-defined FOPID-SSSC, it has been thoroughly evaluated in three different multi-area interconnected power systems. Meanwhile, the dynamic performance of FOPID-SSSC has been accurately compared with a conventional controller based SSSC while the power systems are affected by different Step Load Perturbations (SLPs). Eventually, the simulation results of all three power systems have transparently demonstrated the dynamic performance of FOPID-SSSC to significantly suppress the frequency and tie-line power deviations as compared to conventional controller based SSSC.

• Wind and Structures
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• 제26권5호
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• pp.331-341
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• 2018

A numerical study on the flow over a square cylinder in the vicinity of a wall is conducted for different Couette-Poiseuille-based non-uniform flow with the non-dimensional pressure gradient P varying from 0 to 5. The non-dimensional gap ratio L (=$H^{\ast}/a^{\ast}$) is changed from 0.1 to 2, where $H^{\ast}$ is gap height between the cylinder and wall, and $a^{\ast}$ is the cylinder width. The governing equations are solved numerically through finite volume method based on SIMPLE algorithm on a staggered grid system. Both P and L have a substantial influence on the flow structure, time-mean drag coefficient ${\bar{C}}_D$, fluctuating (rms) lift coefficient ($C_L{^{\prime}}$), and Strouhal number St. The changes in P and L leads to four distinct flow regimes (I, II, III and IV). Following the flow structure change, the ${\bar{C}}_D$, $C_L{^{\prime}}$, and St all vary greatly with the change in L and/or P. The ${\bar{C}}_D$ and $C_L{^{\prime}}$ both grow with increasing P and/or L. The St increases with P for a given L, being less sensitive to L for a smaller P (< 2) and more sensitive to L for a larger P (> 2). A strong relationship is observed between the flow regimes and the values of ${\bar{C}}_D$, $C_L{^{\prime}}$ and St. An increase in P affects the pressure distribution more on the top surface than on bottom surface while an increase in L does the opposite.

• 한국항공우주학회지
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• 제43권5호
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• pp.405-412
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• 2015

본 논문은 회전안정성을 갖는 탄도수정탄의 대기속도 추정을 위한 필터 설계에 대하여 설명한다. 대상 시스템은 운용상의 제약(공간, 파워)으로 인하여, 대기속도 측정을 위한 센서를 사용할 수 없다. 따라서 한정된 센서를 이용한 대기속도 추정이 필요하다. 따라서 본 연구에서는 IMU(가속도계, 자이로)에서 측정하는 3축 가속도와 각속도 데이터만 이용하여, 대기속도 추정을 위한 필터를 설계하였다. 대상 시스템의 경우, 넓은 속도, 고도의 운용범위를 커버하기 위한 추정 필터가 필요하므로 본 연구에서는 확장 칼만필터를 설계하여 기존의 연구와의 차별성을 두었다. 확장 칼만필터 설계를 위한 자코비안 행렬은 NRF(No-roll frame)에서의 간략화된 선형모델을 이용하여 구성하였다. 최종적으로 센서 오차와 바람 모델을 포함한 시뮬레이션을 통해 그 성능을 검토하였다. 이때, 시뮬레이션은 설계한 대기속도와 각속도 모델 오차의 영향을 분석하기 위하여 네 가지 경우의 프로세스 공분산 행렬 값에 대한 영향을 분석하였다.

• 한국산학기술학회논문지
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• 제15권9호
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• pp.5446-5451
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• 2014

내비게이션 단말기에 사용되는 전자지도 제작이 수작업으로 이루어지고 있어 오기가 발생할 수 있기 때문에, 본 논문에서는 내비게이션 정보의 요소로 다루어지는 교통 표지판에 대한 오프라인 자동 인식에 대해 제안하였다. 컴퓨터 비전과 패턴 인식 응용 분야로 2차원 얼굴 인식 분야에 널리 활용되고 있는 주성분분석기법(PCA)과 선형판별분석기법(LDA)을 이용하여 교통표지판을 인식하고자 한다. 먼저 PCA를 이용하여 높은 차원의 2차원 이미지 데이터를 저차원의 특징 벡터영역으로 투영을 시킨다. PCA로부터 구해진 저차원의 특징 벡터를 이용하여 LDA로 분산 매트릭스들 간에 최대가 되고 하고, 분산 매트릭스 내에서는 최소가 되도록 하였다. 실제 도로 환경에서 추출된 교통 신호판의 대부분을 제안된 알고리즘에 의해서 특징 벡터를 40개 이상 사용하였을 경우 92.3%이상의 높은 인식률을 보임을 확인하였다.

• Ocean Systems Engineering
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• 제6권3호
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• pp.265-287
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• 2016

The change of the global performance of a turret-moored FPSO (Floating Production Storage Offloading) with DP (Dynamic Positioning) control is simulated, analyzed, and compared for two different internal turret location cases; bow and midship. Both collinear and non-collinear 100-yr GOM (Gulf of Mexico) storm environments and three cases (mooring-only, with DP position control, with DP position+heading control) are considered. The horizontal trajectory, 6DOF (degree of freedom) motions, fairlead mooring and riser tension, and fuel consumptions are compared. The PID (Proportional-Integral-Derivative) controller based on LQR (linear quadratic regulator) theory and the thrust-allocation algorithm which is based on the penalty optimization theory are implemented in the fully-coupled time-domain hull-mooring-riser-DP simulation program. Both in collinear and non-collinear 100-yr WWC (wind-wave-current) environments, the advantage of mid-ship turret is demonstrated by the significant reduction in heave at the turret location due to the minimal coupling with pitch mode, which is beneficial to mooring and riser design. However, in the non-collinear WWC environment, the mid-turret case exhibits unfavorable weathervaning characteristics, which can be reduced by employing DP position and heading controls as demonstrated in the present case studies. The present study also reveals the plausible cause of the failure of mid-turret Gryphon Alpha FPSO in milder environment than its survival condition.

• Asian Pacific Journal of Cancer Prevention
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• 제15권16호
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• pp.6781-6785
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• 2014

Purpose: This study used receiver operating characteristic curves to analyze Surveillance, Epidemiology and End Results (SEER) medulloblastoma (MB) and primitive neuroectodermal tumor (PNET) outcome data. The aim of this study was to identify and optimize predictive outcome models. Materials and Methods: Patients diagnosed from 1973 to 2009 were selected for analysis of socio-economic, staging and treatment factors available in the SEER database for MB and PNET. For the risk modeling, each factor was fitted by a generalized linear model to predict the outcome (brain cancer specific death, yes/no). The area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. A Monte Carlo algorithm was used to estimate the modeling errors. Results: There were 3,702 patients included in this study. The mean follow up time (S.D.) was 73.7 (86.2) months. Some 40% of the patients were female and the mean (S.D.) age was 16.5 (16.6) years. There were more adult MB/PNET patients listed from SEER data than pediatric and young adult patients. Only 12% of patients were staged. The SEER staging has the highest ROC (S.D.) area of 0.55 (0.05) among the factors tested. We simplified the 3-layered risk levels (local, regional, distant) to a simpler non-metastatic (I and II) versus metastatic (III) model. The ROC area (S.D.) of the 2-tiered model was 0.57 (0.04). Conclusions: ROC analysis optimized the most predictive SEER staging model. The high under staging rate may have prevented patients from selecting definitive radiotherapy after surgery.

• 한국멀티미디어학회논문지
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• 제17권3호
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• pp.300-311
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• 2014

환경의 변화에 따라 급속도로 변화하는 생태계에 대한 체계적인 연구를 위해 식물의 정보를 수집 분석하기 위한 연구가 활발하게 진행되고 있다. 특히, 스마트 기기의 카메라를 이용하여 언제 어디서나 사용자가 원하는 식물의 종류를 검색할 수 있는 기술에 대한 관심이 증가하고 있다. 본 논문은 식물 인식 및 생태계 분석을 위해 다양한 식물의 잎을 종류별로 분석할 수 있는 방법에 대해 제안한다. 이를 위해, 카메라부터 입력된 식물 잎 사진의 관심 영역을 GrabCut을 통해 배경과 분리한 후, 형태 기술자 추출 방법인 SIFT(Scale-Invariant Feature Transform), HOG(Histogram of Oriented Gradient)를 이용하여 형태 기술자를 추출하고, 이것을 부호화 기법 및 공간 피라미드 방법을 이용한 분류 특징 벡터를 만든다. SVM(Support Vector Machine)을 통한 식물 잎 분류 및 인식한다. 다양한 식물 잎에 대한 실험 결과를 통해 비슷한 색상이나 형태를 가지고 있더라도 방향성 특징 기술자를 활용한 식물 잎 분류 방법이 매우 효율적임을 알 수 있다.