• 한국수산과학회지
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• 제52권6호
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• pp.719-724
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• 2019

This study is an extension of our previous model for a size-based fish stock assessment. In the previous model, we applied an allometric length-weight relationship (W=α·L^β) to convert lengths of fish to weights, and estimated those parameters α and β, using data about lengths and weights aggregated over years. In this study, we focused on whether consideration of temporal (e.g., year-to-year) variability in those estimates (i.e., ${\hat{\alpha}}$ and ${\hat{\beta}}$) would contributive. After calculating year-specific estimates (i.e., year-specific pairs of ${\hat{\alpha}}$ and ${\hat{\beta}}$) by applying data about lengths and weights separated by year, we evaluated the contribution of those year-specific pairs of ${\hat{\alpha}}$ and ${\hat{\beta}}$ to the performance of the size-based stock assessment model. The model with such year-to-year variability being considered (lower AIC) outperformed that with the variability being ignored (higher AIC). We illustrated this study using data on Korean chub mackerel Scomber japonicus from 2005-2017.

• 한국철도학회논문집
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• 제13권3호
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• pp.327-332
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• 2010

본 논문에서는 다수의 성과지표를 동시에 고려하며 각 역의 운영조건을 반영하고 더불어 화물품목의 중요도를 반영하여 각 화물취급역의 성과를 평가하는 방안을 제안하였다. 본 논문에서는 187개 화물취급역에 대해서 품목별 '발송규모'와 '도착규모'를 성과지표로 사용하고 각 역의 '화물인력'과 '유치가능량'을 운영조건지 표로 사용하며 품목별 중요도를 확신영역 제약조건으로 반영하는 자료포락분석으로 효율성을 평가하였다. 이러한 결과는 철도경영자 또는 철도정책담당자의 경영전략이나 정책의사결정에 유용한 정보로 사용될 수 있을 것으로 기대한다.

• 한국철도학회논문집
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• 제16권2호
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• pp.117-121
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• 2013

철도선로구간의 노후도는 궤도, 노반, 교량, 터널 등 요소시설의 노후도로 결정되며 궤도의 선형 또한 선로구간 노후도에 영향을 준다. 본 논문에서는 계층구조분석(analytic hierarchy process: AHP)과 자료포락분석(data envelopment analysis: DEA)을 활용하여 선로구간의 노후도를 평가하는 방법을 제시한다. AHP는 다수의 전문가들이 평가하는 각 요소시설의 중요도를 체계적으로 파악할 수 있도록 해준다. 요소시설에 대한 중요도는 전문가에 따라 달라질 수 있다. DEA는 선로구간 요소시설의 중요도 가중치 범위를 체계적으로 반영하여 선로구간의 노후도를 평가할 수 있도록 해준다.

• Design & Manufacturing
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• 제13권3호
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• pp.53-58
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• 2019

This paper presents Artificial Neural Network(ANN) method to predict maximum injection pressure of injection molding machine and weights of injection molding products. 5 hidden layers with 10 neurons is used in the ANN. The ANN was conducted with 5 Input parameters and 2 response data. The input parameters, i.e., melt temperature, mold temperature, fill time, packing pressure, and packing time were selected. The combination of the orthogonal array L27 data set and 23 randomly generated data set were applied in order to train and test for ANN. According to the experimental result, error of the ANN for weights was $0.49{\pm}0.23%$. In case of maximum injection pressure, error of the ANN was $1.40{\pm}1.19%$. This value showed that ANN can be successfully predict the injection pressure and the weights of injection molding products.

• 한국수산과학회지
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• 제54권1호
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• pp.80-89
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• 2021

In Korea, marine fish landed at ports are randomly sampled on a periodic basis (e.g., daily or weekly), and body sizes (e.g., lengths and weights) of those sampled fish are measured. The motivation for our study is whether or not such measurements reflect the size distribution, especially the length distribution of fish landed (= a population), because such length measurements are key data for a length-based assessment model. The current sampling method is to sample fish landed at ports by body size group (e.g., very small, small, medium, large, very large), using the sampling weights as the number of boxes by body size group. In this study, we showed that length composition data about fish sampled by the current method did not represent the length frequency distribution of the fish landed, and suggested that an alternative sampling method should be applied of using the sampling weights as the number of fish landed by body size group. We also introduced a method for determining an appropriate sample size.

• Asian-Australasian Journal of Animal Sciences
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• 제13권6호
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• pp.729-732
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• 2000

Weight records from birth to calving and calving scores of 407 two-year old heifers and weights of their offspring from birth to one year of age were used to study the effects of sire birth weight on maternal traits of their female progeny. The heifers (G1) were the progeny of 81 sires (G0) and were classified into three classes based on their sires' birth weights (High, Medium and Low). The heifers were from three distinct breed-groups and were mated to bulls with medium birth weights within each breed-group to produce the second generation (G2). The data were analyzed using a covariance model. The female progeny of high birth-weight sires were heavier from birth to calving than those sired by medium and low birth-weight bulls. The effect of sire birth weight on calving difficulty scores of their female progeny was not significant. Grand progeny (G2) of low birth-weight sires were lighter at birth than those from high birth-weight sires (p<0.05) but they did not differ significantly in weaning and yearling weights with the other two Grand progeny groups. The results indicated that using low birth weight sires would not result in an increase in the incidence of dystocia among their female progeny calving at two-year of age and would not have an adverse effect on weaning and yearling weights of their grand progeny.

• 전자공학회논문지
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• 제50권4호
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• pp.3-8
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• 2013

최근 들어 CDMA 기반 시스템에서 고속 데이터 서비스 지원을 위한 수신 성능 개선 방법의 하나로 칩 단위에서 동작하는 선형 최소 평균 제곱 오차(LMMSE: linear minimum mean-squared error) 수신기에 대한 연구가 활발히 진행되고 있다. 본 논문에서는 D-TxAA (dual stream transmit antenna array) 방식을 사용하는 단일 스트림(single stream) HSDPA MIMO 시스템에서 선부호화 계수(precoding weight) 적용이 LMMSE 수신기의 성능에 미치는 영향을 분석하고자 한다. 우선 D-TxAA 기반 $2{\times}2$ 다중입력 다중출력(MIMO: multiple input multiple output) 시스템에서 수신 신호 순간 파워 (instantaneous power)의 총 합이 최대가 되도록 하는 선부호화 계수 결정 방식을 유도할 것이다. 또한 선부호화 계수의 적용 지연 시간과 단말의 이동 속도가 LMMSE 수신기의 성능에 미치는 영향을 분석하고, 컴퓨터 모의실험을 통해 검증하고자 한다.

• 정보관리학회지
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• 제28권1호
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• pp.309-326
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• 2011

기존 메타데이터의 품질 측정 방법은 오류가 발생한 레코드를 단순히 계수하여 그 비율로 품질을 측정하였다. 이러한 한계를 극복하기 위해 메타데이터 요소별로 상대적 중요 정도를 나타내는 가중치를 적용함으로서, 메타데이터 품질을 체계적으로 계량화 하는 측정 방법을 제시하고자 하였다. 구체적인 가중치 부여 방법으로 엔트로피, 이용자 과업, 그리고 이용 통계를 활용하였다. 또한 이들을 결합하여 통합 가중치를 제시하고 실제 서비스 되고 있는 학술지 기사 메타데이터에 적용하였다. 실험 결과, 엔트로피 가중치 방법은 데이터 자체의 특성을 잘 반영하며, 이용자 과업을 적용한 방법은 이용자의 정보요구를 해결하는 필요한 메타데이터 요소를 제시하며, 통합 가중치는 특정 메타데이터 요소의 오류에 영향을 받지 않으면서 균형 잡힌 측정값을 제시하여 계량화 방법에 적합한 것으로 나타났다.

• 산업경영시스템학회지
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• 제29권2호
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• pp.1-6
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• 2006

This study proposes a new approach which combines Data Envelopment Analysis(DEA) and the Analytic Hierarchy Process(AHP) techniques to effectively evaluate Decision Making Units(DMUs). While DEA evaluates a quantitative data set, employs linear programming to obtain input and output weights and ranks the performance of DMUs, AHP evaluates the qualitative data retrieved from expert opinions and other managerial information in specifying weights. The objective of this research is to design a decision support process for managers to incorporate positive aspects of DEA's absolute numerical evaluations and AHP's human preference structure values. It is believed that a pragmatic manager will be more receptive to the results that include subjective opinions incorporated into the evaluation of the efficiency of each DMU efficiency. The WPDEA method provides better discrimination than the DEA method by reducing the number of efficient units.

• 한국정밀공학회지
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• 제10권4호
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• pp.170-179
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• 1993

In this study, self-organized neural network is used to solve the vorrespondence problem of the axial stereo image. Edge points are extracted from a pair of stereo images and then the edge points of rear image are assined to the output nodes of neural network. In the matching process, the two input nodes of neural networks are supplied with the coordi- nates of the edge point selected randomly from the front image. This input data activate optimal output node and its neighbor nodes whose coordinates are thought to be correspondence point for the present input data, and then their weights are allowed to updated. After several iterations of updating, the weights whose coordinates represent rear edge point are converged to the coordinates of the correspondence points in the front image. Because of the feature map properties of self-organized neural network, noise-free and smoothed depth data can be achieved.