• 전기학회논문지
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• 제62권4호
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• pp.554-561
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• 2013

In this paper, we introduce a modeling and a process simulator developed with the aid of pRBFNNs for activated sludge process in the sewage treatment system. Activated sludge process(ASP) of sewage treatment system facilities is a process that handles biological treatment reaction and is a very complex system with non-linear characteristics. In this paper, we carry out modeling by using essential ASP factors such as water effluent quality, the manipulated value of various pumps, and water inflow quality, and so on. Intelligent algorithms used for constructing process simulator are developed by considering multi-output polynomial radial basis function Neural Networks(pRBFNNs) as well as Fuzzy C-Means clustering and Particle Swarm Optimization. Here, the apexes of the antecedent gaussian functions of fuzzy rules are decided by C-means clustering algorithm and the apexes of the consequent part of fuzzy rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The coefficients of the consequent polynomial of fuzzy rules and performance index are considered by the Least Square Estimation and Mean Squared Error. The descriptions of developed process simulator architecture and ensuing operation method are handled.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2017년도 춘계공동학술대회
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• pp.135-135
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• 2017

Greenhouse have been developed to provide the plants with good environmental conditions for cultivation crop, two major factors of which are the inside air temperature and humidity. The inside temperature are influenced by the heating systems, ventilators and for systems among others, which in turn are geverned by some type of controller. Likewise, humidity environment is the result of complex mass exchanges between the inside air and the several elements of the greenhouse and the outside boundaries. Most of the existing models are based on the energy balance method and heat balance equation for modelling the heat and mass fluxes and generating dynamic elements. However, greenhouse are classified as complex system, and need to make a sophisticated modeling. Furthermore, there is a difficulty in using classical control methods for complex process system due to the process are non linear and multi-output(MIMO) systems. In order to predict the time evolution of conditions in certain greenhouse as a function, we present here to use of recurrent neural networks(RNN) which has been used to implement the direct dynamics of the inside temperature and inside humidity of greenhouse. For the training, we used algorithm of a backpropagation Through Time (BPTT). Because the environmental parameters are shared by all time steps in the network, the gradient at each output depends not only on the calculations of the current time step, but also the previous time steps. The training data was emulated to 13 input variables during March 1 to 7, and the model was tested with database file of March 8. The RMSE of results of the temperature modeling was $0.976^{\circ}C$, and the RMSE of humidity simulation was 4.11%, which will be given to prove the performance of RNN in prediction of the greenhouse environment.

• Journal of Animal Science and Technology
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• 제52권1호
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• pp.51-56
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• 2010

본 연구는 출산 직후 송아지와의 분리에 따른 어미소의 발성음특징을 파악하고자 출산 후 이틀 이내의 홀스타인 암소 16두를 대상으로 실시하였다. 어미소와 송아지 사이의 후각적, 청각적 접촉이 가능한 경우(실험군 1)와 전혀 접촉을 하지 못하게 격리한 경우(실험군 2)로 나누어 오전 1시부터 4시까지와 오후 1시부터 4시하루 6시간 동안 3일에 걸쳐 디지털 녹음기와 지향성 마이크를 이용하여 발성음을 녹음하여 분석하였다. 어미소의 발성음은 특징에 따라 4가지 유형으로 구분되었다. 발성음의 빈도 및 스펙트로그램과 스펙트럼은 유형별로 차이가 있었으며 발성음의 주파수, 강도 및 길이 역시 차이를 보였다. 또한 날짜의 경과에 따라 어미소의 발성음은 급격하게 감소하였다. 본 연구를 통해 어미소와 송아지의 분리 여부에 따라 어미소의 발성음에는 차이가 있는 것으로 나타났으며, 발성음은 어미소와 송아지 사이 유대관계의 형성에 영향을 미칠 수 있는 매우 중요한 요인으로 판단된다.

• 한국항공우주학회지
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• 제36권8호
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• pp.797-806
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• 2008

본 연구에서는 선형 캐스케이드 실험장치의 유로를 캐스케이드 피치의 두배 넓이로 설정하고 두 개의 블레이드만을 설치하였다. 따라서 동일한 실험장치에서 다수개의 블레이드를 설치하는 경우에 비하여 큰 블레이드에서 실험이 가능하도록 하였다. 아울러 두 개의 블레이드 설치에 따른 주기조건의 어려움을 해소하기 위하여 실험장치 내의 작동유체의 배출이나 꼬리판의 조정을 하지 않아도 주기조건이 되도록 하는 실험장치의 벽면을 설계하였다. 이를 위하여 주기조건에서 얻어진 블레이드 표면에서의 마하수와 동일한 결과가 얻어지도록 목적함수를 설정하였으며, 설계변수로는 벽면의 형상변경과 관련이 있는 12개의 변수를 사용하였다. 벽면의 설계는 기울기 기반의 최적화법을 사용하였으며, 내부유동장의 계산은 상용코드인 CFX-11을 사용하였다. 두 결과의 비교에서 벽면의 조정만으로도 동일한 유동특성이 얻어질 수 있음을 확인하였다.

• 대한조선학회논문집
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• 제51권4호
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• pp.274-282
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• 2014

Time series of the dynamic response of a slender marine structure was predicted using quadratic Volterra series. The wave-structure interaction system was identified using the NARX(Nonlinear Autoregressive with Exogenous Input) technique, and the network parameters were determined through the supervised training with the prepared datasets. The dataset used for the network training was obtained by carrying out the nonlinear finite element analysis on the freely standing riser under random ocean waves of white noise. The nonlinearities involved in the analysis were both large deformation of the structure under consideration and the quadratic term of relative velocity between the water particle and structure in Morison formula. The linear and quadratic frequency response functions of the given system were extracted using the multi-tone harmonic probing method and the time series of response of the structure was predicted using the quadratic Volterra series. In order to check the applicability of the method, the response of structure under the realistic ocean wave environment with given significant wave height and modal period was predicted and compared with the nonlinear time domain simulation results. It turned out that the predicted time series of the response of structure with quadratic Volterra series successfully captures the slowly varying response with reasonably good accuracy. It is expected that the method can be used in predicting the response of the slender offshore structure exposed to the Morison type load without relying on the computationally expensive time domain analysis, especially for the screening purpose.

• Asian-Australasian Journal of Animal Sciences
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• 제31권8호
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• pp.1098-1102
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• 2018

Objective: Temperament can be defined as a type of behavioral tendency that appears in a relatively stable manner in responses to various external stimuli over time. The aim of this study was to estimate genetic parameters for the records of temperament testing that are used to improve the temperament of Jeju crossbred (Jeju${\times}$Thoroughbred) horses. Methods: This study was conducted using 205 horses (101 females and 104 males) produced between 2010 and 2015. The experimental animals were imprinted and tamed according to the Manual for Horse Taming and Evaluation for Therapeutic Riding Horses and evaluated according to the categories for temperament testing (gentleness, patience, aggressiveness, sensitivity, and friendliness) between 15 months and 18 months of age. Each category was scored on a five-point linear scale. Genetic parameters for the test categories were analyzed using a multi-trait mixed model with repeated records. The ASReml program was used to analyze the data. Results: The heritability of gentleness, patience, aggressiveness, sensitivity and friendliness ranged from 0.08 to 0.53. The standard errors of estimated heritability ranged from 0.13 to 0.17. The test categories showed high genetic correlations with each other, ranging from 0.96 to 0.99 and high repeatability, ranging from 0.70 to 0.73. Conclusion: The results of this study showed that the test categories had moderate heritability and high genetic correlations, but additional studies may be necessary to use the results for the improvement programs of the temperament of Jeju crossbred horses.

• 한국지능시스템학회논문지
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• 제15권3호
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• pp.289-293
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• 2005

패턴 분류는 실세계의 객체를 표현한 다양한 형태의 패턴 정보를 추출하여, 이것이 어떤 부류(클래스)인가를 결정하는 것이다. 패턴 분류 기술은 데이터 마이닝, 산업 자동화나 업무자동화를 위한 컴퓨터 응용 소프트웨어 기술로서 현재 다양한 분야에서 활용되고 있다. 패턴 분류 기술의 최대 목표는 분류 성능 향상이며 이것을 위해 지난 40년간 많은 연구자들이 다양한 접근 방법들을 시도해 왔다. 주로 이용되는 단일 분류 방법들로는 패턴들의 확률적 추론에 기반한 베이즈 분류기, 결정 트리, 거리함수를 이용하는 방법, 신경망, 군집화 등이 있으나 대용량 다차원 데이터를 분석하기에는 효율적이지 못하다. 따라서 상호 보완적인 여러 분류기들을 사용해 결합을 통하여 성능 향상에 도움을 주고 있는 다중 분류기 시스템에 대한 연구가 활발하게 진행되고 있다. 본 논문에서는 다중 SVM(Support Vector Machine) 분류기에 관한 기존 연구의 문제점을 지적하고 새로운 모델을 제안한다. SVM을 다중 클래스 분류기로 확장하기 위해 일대다 정책을 기반으로 하여 각각의 SVM 출력값을 비선형 패턴을 갖는 신호로 간주하고 이를 신경망에 학습하여 최종 분류 성능 결과를 결합하는 모델인 BORSE(Bootstrap Resampling SVM by Ensemble)를 제안한다.

• 한국농공학회지
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• 제44권7호
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• pp.64-73
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• 2002

This study was performed to evaluate effects of physical characteristics of both watershed and reservoir on nutrient-chlorophyll relationship in Korean reservoirs. Simple linear models were developed with published data in Korea including 415 reservoirs and 11 multi-purpose dams, and physico-chemical parameters of reservoirs and characteristics relationship of models were analyzed. Theoretical residence time in Korean reservoirs was strongly correlated with the ratio of TA/ST (drainage area + surface area / storage volume) in the logarithmic function. As a result of monthly nutrients-chlorophyll-a regression analysis, significant Chl-a-TP relationship appeared during May～July. The high Chl-a yields per total phosphorus appeared during this time (R$\^$2/=0.51, p<0.001, N= 1088). Chlorophyll-a demonstrated much stronger relationship with TP. than TN. Seasonal algal-nutrient coupling were closely related with N:P ratio in the reservoir water, and it was, in turn, dependent on the monsoon climatic condition (precipitation). Based on the results of regression analysis and high N:P ratio, a major limiting factor of algal growth appeared to be phosphorus during this time. Unlikely TA/ST ratio, DA/SA ratio (drainage area f surface area) was likely to influence directly on the nutrient-Chl-a relationship, indicating that if storage volume and inflowing water volume were the same, algal biomass could be developed more in reservoirs with large surface area. Thus, DA/SA ratio seemed to be an important factor to affect the development of algal biomass in Korean reservoirs. With low determination coefficient of TP-Chl-a relationship, our findings indicated not only nutrient (phosphorus) but also other physical factors, such as DA/SA ratio, may affect algal biomass development in Korean reservoirs, where actual residence time appears to be more closely related to reservoir surface area rather than storage volume.

• ETRI Journal
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• 제38권5호
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• pp.1019-1029
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• 2016

The probability hypothesis density (PHD) filter is an effective means to track multiple targets in that it avoids explicit data associations between the measurements and targets. However, the target birth intensity as a prior is assumed to be known before tracking in a traditional target-tracking algorithm; otherwise, the performance of a conventional PHD filter will decline sharply. Aiming at this problem, a novel target birth intensity scheme and an improved measurement-driven scheme are incorporated into the PHD filter. The target birth intensity estimation scheme, composed of both PHD pre-filter technology and a target velocity extent method, is introduced to recursively estimate the target birth intensity by using the latest measurements at each time step. Second, based on the improved measurement-driven scheme, the measurement set at each time step is divided into the survival target measurement set, birth target measurement set, and clutter set, and meanwhile, the survival and birth target measurement sets are used to update the survival and birth targets, respectively. Lastly, a Gaussian mixture implementation of the PHD filter is presented under a linear Gaussian model assumption. The results of numerical experiments demonstrate that the proposed approach can achieve a better performance in tracking systems with an unknown newborn target intensity.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권3호
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• pp.135-144
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• 2004

In this paper, we propose competitive fuzzy polynomial neurons-based advanced Self-Organizing Neural Networks(SONN) architecture for optimal model identification and discuss a comprehensive design methodology supporting its development. The proposed SONN dwells on the ideas of fuzzy rule-based computing and neural networks. And it consists of layers with activation nodes based on fuzzy inference rules and regression polynomial. Each activation node is presented as Fuzzy Polynomial Neuron(FPN) which includes either the simplified or regression polynomial fuzzy inference rules. As the form of the conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership (unction are studied and the number of the premise input variables used in the rules depends on that of the inputs of its node in each layer. We introduce two kinds of SONN architectures, that is, the basic and modified one with both the generic and the advanced type. Here the basic and modified architecture depend on the number of input variables and the order of polynomial in each layer. The number of the layers and the nodes in each layer of the SONN are not predetermined, unlike in the case of the popular multi-layer perceptron structure, but these are generated in a dynamic way. The superiority and effectiveness of the Proposed SONN architecture is demonstrated through two representative numerical examples.