• Asian-Australasian Journal of Animal Sciences
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• 제29권12호
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• pp.1756-1760
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• 2016

Estimation of feed intake (FI) for individual animals within a pen is needed in situations where more than one animal share a feeder during feeding trials. A partitioning method (PM) was previously published as a model to estimate the individual FI (IFI). Briefly, the IFI of a pig within the pen was calculated by partitioning IFI into IFI for maintenance ($IFI_m$) and IFI for growth. In the PM, $IFI_m$ is determined based on the metabolic body weight (BW), which is calculated using the coefficient of 106 and exponent of 0.75. Two simulation studies were conducted to test the hypothesis that the use of different coefficients and exponents for metabolic BW to calculate $IFI_m$ improves the accuracy of the estimates of IFI for pigs, and that PM is applied to pigs fed in group-housing systems. The accuracy of prediction represented by difference between actual and estimated IFI was compared using PM, ratio (RM), or averaging method (AM). In simulation studies 1 and 2, the PM estimated IFI better than the AM and RM during most of the periods (p<0.05). The use of 0.60 as the exponent and the coefficient of 197 to calculate metabolic BW did not improve the accuracy of the IFI estimates in both simulation studies 1 and 2. The results imply that the use of $197kcal{\times}kg\;BW^{0.60}$ as metabolizable energy for maintenance in PM does not improve the accuracy of IFI estimations compared with the use of $106kcal{\times}kg\;BW^{0.75}$ and that the PM estimates the IFI of pigs with greater accuracy compared with the averaging or ratio methods in group-housing systems.

• Nuclear Engineering and Technology
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• 제54권6호
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• pp.2276-2296
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• 2022

This study validates the applicability of the CUPID code for simulating subcooled wall boiling under high-pressure conditions against number of DEBORA tests. In addition, a new numerical technique in which the interfacial momentum non-drag forces are calculated at the cell faces rather than the center is presented. This method reduced the numerical instability often triggered by calculating these terms at the cell center. Simulation results showed good agreement against the experimental data except for the bubble sizes in the bulk. Thus, a new model to calculate the Sauter mean diameter is proposed. Next, the effect of the relationship between the bubble departure diameter (D_dep) and the nucleation site density (N) on the performance of the Wall Heat Flux Partitioning (WHFP) model is investigated. Three correlations for D_dep and two for N are grouped into six combinations. Results by the different combinations show that despite the significant difference in the calculated D_dep, most combinations reasonably predict vapor distribution and liquid temperature. Analysis of the axial propagations of wall boiling parameters shows that the N term stabilizes the inconsistences in D_dep values by following a behavior reflective of D_dep to keep the total energy balance. Moreover, ratio of the heat flux components vary widely along the flow depending on the combinations. These results suggest that separate validation of D_dep correlations may be insufficient since its performance relies on the accompanying N correlations.

• 대한임베디드공학회논문지
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• 제5권4호
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• pp.206-216
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• 2010

Wireless sensor networks (WSNs) are used to collect various data in environment monitoring applications. A spatial clustering may reduce energy consumption of data collection by partitioning the WSN into a set of spatial clusters with similar sensing data. For each cluster, only a few sensor nodes (samplers) report their sensing data to a base station (BS). The BS may predict the missed data of non-samplers using the spatial correlations between sensor nodes. ASAP is a representative data collection algorithm using the spatial clustering. It periodically reconstructs the entire network into new clusters to accommodate to the change of spatial correlations, which results in high message overhead. In this paper, we propose a new data collection algorithm, name EPDC (Energy-efficient Periodic Data Collection). Unlike ASAP, EPDC identifies a specific cluster consisting of many dissimilar sensor nodes. Then it reconstructs only the cluster into subclusters each of which includes strongly correlated sensor nodes. EPDC also tries to reduce the message overhead by incorporating a judicious probabilistic model transfer method. We evaluate the performance of EPDC and ASAP using a simulation model. The experiment results show that the performance improvement of EPDC is up to 84% compared to ASAP.

• Nuclear Engineering and Technology
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• 제54권2호
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• pp.608-616
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• 2022

Power peaking factors (PPF) is an important parameter for safe and efficient reactor operation. There are several methods to calculate the PPF at TRIGA research reactors such as MCNP and TRIGLAV codes. However, these methods are time-consuming and required high specifications of a computer system. To overcome these limitations, artificial intelligence was introduced for parameter prediction. Previous studies applied the neural network method to predict the PPF, but the publications using the ANFIS method are not well developed yet. In this paper, the prediction of PPF using the ANFIS was conducted. Two input variables, control rod position, and neutron flux were collected while the PPF was calculated using TRIGLAV code as the data output. These input-output datasets were used for ANFIS model generation, training, and testing. In this study, four ANFIS model with two types of input space partitioning methods shows good predictive performances with R² values in the range of 96%-97%, reveals the strong relationship between the predicted and actual PPF values. The RMSE calculated also near zero. From this statistical analysis, it is proven that the ANFIS could predict the PPF accurately and can be used as an alternative method to develop a real-time monitoring system at TRIGA research reactors.

• Communications for Statistical Applications and Methods
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• 제24권5호
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• pp.457-480
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• 2017

We develop a random partition procedure based on a Dirichlet process prior with Laplace distribution. Gibbs sampling of a Laplace mixture of linear mixed regressions with a Dirichlet process is implemented as a random partition model when the number of clusters is unknown. Our approach provides simultaneous partitioning and parameter estimation with the computation of classification probabilities, unlike its counterparts. A full Gibbs-sampling algorithm is developed for an efficient Markov chain Monte Carlo posterior computation. The proposed method is illustrated with simulated data and one real data of the energy efficiency of Tsanas and Xifara (Energy and Buildings, 49, 560-567, 2012).

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제4권10호
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• pp.337-342
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• 2015

이 논문에서는 소수 추출 방법인 Sieve of Eratosthenes 알고리즘을 병렬화하여 실행 시간과 에너지 소모 면에서 개선된 효과를 얻고자 실험을 진행하였다. 성능 개선을 위해 부하 균등화를 정교하게 조절하도록 파이프라인 작업 방식을 도입하였고, 멀티코어 컴퓨터 클러스터에 하이브리드 병렬 프로그래밍 모델을 활용하여 효과를 높였다. 소규모 컴퓨터 클러스터와 저전력 컴퓨터에서 구현, 실험한 결과 이전 방식보다 연산 속도가 향상되었고, 에너지 사용량도 감소함을 확인하였다.

• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2146-2156
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• 2017

When a failure occurs in active distribution system, it will be isolated through the action of circuit breakers and sectionalizing switches. As a result, the network might be divided into several connected components, in which distributed generations could supply power for customers. Aimed at decreasing customer interruption cost, this paper proposes a theoretically optimal island partition model for such connected components, and a simplified but more practical model is also derived. The model aims to calculate a dynamic island partition schedule during the failure recovery time period, instead of a static islanding status. Fluctuation and stochastic characteristics of the renewable distributed generations and loads are considered, and the interruption cost functions of the loads are fitted. To solve the optimization model, a heuristic search algorithm based on the hill climbing method is proposed. The effectiveness of the proposed model and algorithm is evaluated by comparing with an existing static island partitioning model and intelligent algorithms, respectively.

• 전자공학회논문지CI
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• 제46권5호
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• pp.79-85
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• 2009

무선 센서 네트워크에서 고정된 싱크 노드가 데이터를 수집하므로 싱크 노드와 주변 노드의 에너지는 다른 노드에 비해 상당히 많이 소모된다. 보다 큰 센서 네트워크에서 에너지 불균형은 급격하게 전체 센서 네트워크의 동작을 멈추게 한다. 이 논문은 대규모 무선 센서 네트워크의 수명을 늘이기 위해 모바일 싱크를 이용한 효율적인 데이터 수집 방법을 제안한다. 클러스터링을 통해 네트워크를 나누고 모바일 싱크가 각 클러스터를 방문하여 데이터를 수집한다. 모바일 싱크와 클러스터 헤드 사이의 메시지 전달을 통해 에너지 소비 효율은 높이며 모바일 싱크의 단점인 데이터 수집 시간을 최소화할 수 있는 알고리즘을 제안한다. 또한 에너지 소비 및 데이터 수집 시간 측면에서 알고리즘을 분석하고 시뮬레이션을 통해 분석의 타당성을 증명한다.

• 한국정보과학회논문지:정보통신
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• 제37권1호
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• pp.50-56
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• 2010

이 논문은 대규모 무선 센서 네트워크의 전체 운영 시간을 늘이기 위해 모바일 싱크를 이용한 효율적인 데이터 수집 방법을 제안한다. 클러스터링을 통해 센서 네트워크를 나눈 다음 모바일 싱크가 각 클러스터를 방문하여 데이터를 수집한다. 모바일 싱크와 클러스터 헤드 사이의 메시지 전달을 통해 에너지 소비 효율은 높이며 모바일 싱크의 단점인 데이터 수집 시간을 최소화할 수 있는 프로토콜을 제안한다. 네트워크 확장성을 위해 센서 네트워크 구조는 클러스터내에서 싱글 홉 전송보다는 멀티홉 전송을 지원해야 한다. 멀티홉 전송시 발생하는 중간 노드의 과도한 에너지 소비를 개선하기 위해 주행 경로와 연계된 데이터 병합 과정을 제안한다. 실험결과는 제안 모델이 기존 방법들보다 에너지 소비 및 데이터 수집 시간 측면에서 효율적임을 보여준다.

• Geomechanics and Engineering
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• 제33권3호
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• pp.279-289
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• 2023

Brittleness index (BI) is an important property of rocks because it is a good index to predict rockburst. Due to its importance, several empirical and soft computing (SC) models have been proposed in the literature based on the punch penetration test (PPT) results. These models are very important as there is no clear-cut experimental means for measuring BI asides the PPT which is very costly and time consuming to perform. This study used a novel Multivariate Adaptive regression spline (MARS), M5P, and white-box ANN to predict the BI of rocks using the available data in the literature for an improved BI prediction. The rock density, uniaxial compressive strength (σ_c) and tensile strength (σ_t) were used as the input parameters into the models while the BI was the targeted output. The models were implemented in the MATLAB software. The results of the proposed models were compared with those from existing multilinear regression, linear and nonlinear particle swarm optimization (PSO) and genetic algorithm (GA) based models using similar datasets. The coefficient of determination (R²), adjusted R² (Adj R²), root-mean squared error (RMSE) and mean absolute percentage error (MAPE) were the indices used for the comparison. The outcomes of the comparison revealed that the proposed ANN and MARS models performed better than the other models with R² and Adj R² values above 0.9 and least error values while the M5P gave similar performance to those of the existing models. Weight partitioning method was also used to examine the percentage contribution of model predictors to the predicted BI and tensile strength was found to have the highest influence on the predicted BI.