• Journal of Electrical Engineering and Technology
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• 제9권3호
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• pp.859-865
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• 2014

Optimization of an electric machine is mainly a nonlinear multi-modal problem. For the optimization of the multi-modal problem, many function calls are required with much consumption of time. To address this problem, this paper proposes a novel hybrid algorithm in which function calls are less than conventional methods. Specifically, the proposed method uses the kriging metamodel and the fill-blank technique to find an approximated solution in a whole problem region. To increase the convergence speed in local peaks, a parallel gradient assisted simplex method is proposed and combined with the kriging meta-model. The correctness and usefulness of the proposed hybrid algorithm is verified through a mathematical test function and applied into the practical optimization as the cogging torque minimization for an interior permanent magnet synchronous machine.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.852-853
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• 2008

This paper proposes an optimal design method for the shape optimization of the permanent magnets (PM) of an in-wheel permanent magnet synchronous motor (PMSM) to reduce the cogging torque considering a total harmonic distortion (THD) and a root mean square (RMS) value of back-EMF. In this method, the Kriging model based on a design of experiment (DOE) is applied to interpolate the objective function in the spaces of design parameters. The optimal design method for the PM of an in-wheel PMSM has to consider multi-variable and multi-objective functions. The developed design method is applied to the optimization for the PM of an in-wheel PMSM.

• Asian Pacific Journal of Cancer Prevention
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• 제14권1호
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• pp.11-13
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• 2013

Background: Esophagus cancer, the third most common gastrointestinal cancer overall, demonstrates high incidence in parts of Iran. The counties of Iran vary in size, shape and population size. The aim of this study was to account for spatial support with Area-to-Area (ATA) Poisson Kriging to increase precision of parameter estimates and yield correct variance and create maps of disease rates. Materials and Methods: This study involved application/ecology methodology, illustrated using esophagus cancer data recorded by the Ministry of Health and Medical Education (in the Non-infectious Diseases Management Center) of Iran. The analysis focused on the 336 counties over the years 2003-2007. ATA was used for estimating the parameters of the map with SpaceStat and ArcGIS9.3 software for analysing the data and drawing maps. Results: Northern counties of Iran have high risk estimation. The ATA Poisson Kriging approach yielded variance increase in large sparsely populated counties. So, central counties had the most prediction variance. Conclusions: The ATAPoisson kriging approach is recommended for estimating parameters of disease mapping since this method accounts for spatial support and patterns in irregular spatial areas. The results demonstrate that the counties in provinces Ardebil, Mazandaran and Kordestan have higher risk than other counties.

• 대한기계학회논문집A
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• 제36권8호
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• pp.873-879
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• 2012

메타모델은 설계 프레임워크 안에서 높은 효율성과 우수한 예측 능력, 타 프로그램과 쉬운 연동성 때문에 공학분야에서 지난 10 년간 최적설계 기법들과 함께 발전해왔다. 메타모델을 구성하기 위해서는 실험계획법, 메타모델링 기법, 검증법과 같은 절차가 요구된다. 검증법은 메타모델의 정확성을 판단하기 때문에 순차적 크리깅 메타모델에서 정확한 크리깅 메타모델을 구성하기 위한 표본점의 개수를 결정한다. 크리깅 메타모델과 같은 보간모델은 표본점에서의 응답을 항상 지나기 때문에 기존 방법으로 메타모델의 정확성을 판단하기 위해서는 추가적인 해석이나 메타모델의 재구성이 요구된다. 본 연구에서는 이러한 추가적인 해석과 메타모델의 재구성을 요구하지 않는 메타모델의 해석적 민감도를 이용하는 민감도 검증법을 제안한다. 14 개의 2 차원 수학예제와 공학예제를 이용하여 이 방법의 타당성을 검증한다.

• Environmental Analysis Health and Toxicology
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• 제29권
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• pp.12.1-12.8
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• 2014

Objectives Cohort studies of associations between air pollution and health have used exposure prediction approaches to estimate individual-level concentrations. A common prediction method used in Korean cohort studies is ordinary kriging. In this study, performance of ordinary kriging models for long-term particulate matter less than or equal to $10{\mu}m$ in diameter ($PM_{10}$) concentrations in seven major Korean cities was investigated with a focus on spatial prediction ability. Methods We obtained hourly $PM_{10}$ data for 2010 at 226 urban-ambient monitoring sites in South Korea and computed annual average $PM_{10}$ concentrations at each site. Given the annual averages, we developed ordinary kriging prediction models for each of the seven major cities and for the entire country by using an exponential covariance reference model and a maximum likelihood estimation method. For model evaluation, cross-validation was performed and mean square error and R-squared ($R^2$) statistics were computed. Results Mean annual average $PM_{10}$ concentrations in the seven major cities ranged between 45.5 and $66.0{\mu}g/m^3$ (standard deviation=2.40 and $9.51{\mu}g/m^3$, respectively). Cross-validated $R^2$ values in Seoul and Busan were 0.31 and 0.23, respectively, whereas the other five cities had $R^2$ values of zero. The national model produced a higher cross-validated $R^2$ (0.36) than those for the city-specific models. Conclusions In general, the ordinary kriging models performed poorly for the seven major cities and the entire country of South Korea, but the model performance was better in the national model. To improve model performance, future studies should examine different prediction approaches that incorporate $PM_{10}$ source characteristics.

• 지구물리와물리탐사
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• 제16권4호
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• pp.257-267
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• 2013

철광산 지역에서 획득한 시추자료와 물리탐사자료를 복합 분석하여 3차원 광체모델링을 수행하였다. 지질조사 및 시추조사 자료를 통해 획득한 5가지 대표 암종에 지수를 부여하였고, 이를 이용하여 광체의 범위를 효율적으로 결정하기 위해 지구통계학적 순차 지표 시뮬레이션(Sequential Indicator Simulation)을 실시하였다. 그리고 전기비저항 탐사 자료와 자기지전류 탐사 자료를 이용한 부가적인 자료를 생성하기 위해 정규크리깅(Ordinary Kriging)과 순차가우스시뮬레이션(Sequential Gaussian Simulation)을 사용하였다. 시추자료에서 획득한 입력변수와 전기비저항자료 간의 상관관계를 분석하여 지구통계학적 복합 분석에 적용하였다. 상관관계 분석 결과, 밀도가 높아질수록 전기비저항이 낮아지는 관계를 확인할 수 있었으며, 이를 통해서 다변량 크리깅 중 하나인 가변적 지역평균 크리깅(Simple Kriging with Local varying means)을 적용하여 지수를 이용한 광체의 모델과 품위 자료를 이용한 품위 분포 모델을 생성하였다. 광체 모델링 결과, 실제 채굴도와 유사한 결과를 확보할 수 있었고, 품위자료에 대한 모델링 결과는 품위별 위치에 따른 변화 정보를 제공하였다.

• 한국측량학회지
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• 제23권1호
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• pp.39-47
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• 2005

본 연구에서는 오늘날 해상측량에서 주로 사용되고 있는 DGPS기법과 음향측심기를 조합하여 취득된 해저지형의 3차원 위치정보를 크리깅(kriging), RBF(radial basis function), 최근린(nearest neighbor) 보간법을 이용하여, 항만공사에서의 호퍼준설량을 산정하였다. 또한, 각각의 보간법에 의해 산정된 호퍼준설량과 실제 준설량을 비교·분석함으로써, 준설량 산정에 있어 DGPS/Echo Sounder 기법의 활용성을 확인할 수 있었다. 그 결과, 크리깅 보간법을 적용한 경우 내용적 차이는 15,364㎥로 약 1.89%의 오차율을 나타내었으며, RBF 보간법과 최근린 보간법을 적용한 경우에는 각각 3.9%, 4.4%의 오차율을 나타내었다. 향후, 항만공사에서의 준설량 산정에 있어서 해저지형의 특성에 따른 적합한 보간법 적용에 관련한 연구가 선행될 경우, 보다 신속하고 정확한 준설량을 산정 할 수 있을 것으로 기대된다.

• Structural Engineering and Mechanics
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• 제74권2호
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• pp.157-173
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• 2020

In the finite element modelling of long-span cable-stayed bridges, there are a lot of uncertainties brought about by the complex structural configuration, material behaviour, boundary conditions, structural connections, etc. In order to reduce the discrepancies between the theoretical finite element model and the actual static and dynamic behaviour, updating is indispensable after establishment of the finite element model to provide a reliable baseline version for further analysis. Traditional sensitivity-based updating methods cannot support updating based on static and dynamic measurement data at the same time. The finite element model is required in every optimization iteration which limits the efficiency greatly. A convenient but accurate Kriging surrogate model for updating of the finite element model of cable-stayed bridge is proposed. First, a simple cable-stayed bridge is used to verify the method and the updating results of Kriging model are compared with those using the response surface model. Results show that Kriging model has higher accuracy than the response surface model. Then the method is utilized to update the model of a long-span cable-stayed bridge in Hong Kong. The natural frequencies are extracted using various methods from the ambient data collected by the Wind and Structural Health Monitoring System installed on the bridge. The maximum deflection records at two specific locations in the load test form the updating objective function. Finally, the fatigue lives of the structure at two cross sections are calculated with the finite element models before and after updating considering the mean stress effect. Results are compared with those calculated from the strain gauge data for verification.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2005년도 추계학술대회 논문집
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• pp.289-295
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• 2005

LMTT(Linear Motor-based Transfer Technology)는 항만 자동화를 위한 수평 이송 시스템이며, 셔틀카(shuttle car)와 격자구조의 레일에 부착된 스테이터 모듈(stator module)로 구성된 PMLSM(Permanent Magnetic Linear Synchronous Motor)에 의해 구동된다. 본 논문에서는 LMTT시스템에서 컨테이너 운반을 담당하는 셔틀카(shuttle car)를 구성하는 부품인 이동체(mover)의 경량화를 위하여 직교배열표 및 크리깅 방법을 이용하여 최적설계를 수행한다. 설계변수로는 가로빔, 세로빔, 휠빔의 두께를 제한조건 함수로는 안전율이 고려된 응력을 넘지 않도록 설정하였다. 목적함수로는 중량을 설정하였다. 본 연구에서 제시된 방법으로 구한 최적해는 크리깅 내삽법(Kriging interpolation)으로 알려진 DACE(Design and Analysis of Computer Experiments) 모델을 엑셀(Excel)로 수식화하고 구했으며, GENESIS를 이용하여 민감도기반 최적설계로 구해진 최적해와 비교 및 검토하였다.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.91-102
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• 2022

With the evolution of wireless sensor network (WSN) technology, the routing policy has foremost importance in the Internet of Things (IoT). A systematic routing policy is one of the primary mechanics to make certain the precise and robust transmission of wireless sensor networks in an energy-efficient manner. In an IoT environment, WSN is utilized for controlling services concerning data like, data gathering, sensing and transmission. With the advantages of IoT potentialities, the traditional routing in a WSN are augmented with decision-making in an energy efficient manner to concur finer optimization. In this paper, we study how to combine IoT-based deep learning classifier with routing called, Kriging Regressive Deep Belief Neural Learning (KR-DBNL) to propose an efficient data packet routing to cope with scalability issues and therefore ensure robust data packet transmission. The KR-DBNL method includes four layers, namely input layer, two hidden layers and one output layer for performing data transmission between source and destination sensor node. Initially, the KR-DBNL method acquires the patient data from different location. Followed by which, the input layer transmits sensor nodes to first hidden layer where analysis of energy consumption, bandwidth consumption and light intensity are made using kriging regression function to perform classification. According to classified results, sensor nodes are classified into higher performance and lower performance sensor nodes. The higher performance sensor nodes are then transmitted to second hidden layer. Here high performance sensor nodes neighbouring sensor with higher signal strength and frequency are selected and sent to the output layer where the actual data packet transmission is performed. Experimental evaluation is carried out on factors such as energy consumption, packet delivery ratio, packet loss rate and end-to-end delay with respect to number of patient data packets and sensor nodes.