• Journal of Family Resource Management and Policy Review
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• v.11 no.4
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• pp.1-20
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• 2007

The purpose of this study was to identify the criteria of evaluating a price-comparison website, and to investigate the consumers' perceived importance and satisfaction of each criterion. Also, it compared both the importance and satisfaction levels based on consumers' socio-economic and Internet-usage characteristics. Data for this study came from a questionnaire completed by consumers (n=417), who had used the price-comparison website, and were analyzed through factor analysis, t-test, and ANOVA. The findings of the study were as follows: First, the evaluation criteria of the price-comparison website were categorized into five variety of information, accuracy, convenience, credibility, and the website system. Second, convenience of searching information was seen by consumers as both the most important and most satisfactory criterion. Variety of information was also considered important. For most of the evaluation criteria, the level of consumers' satisfaction was significantly lower than the level of consumers' recognized importance. Third, consumers in their 20s, students, and housewives were less likely to be satisfied by the price-comparison website overall. Older people were less likely to be satisfied with the convenience of the website, and the higher-income group was less likely to be satisfied with the variety of information on hand.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.133-138
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• 2020

This paper introduces a method for improving accuracy using the first convolution layer, which is not used in most modified CNN(: Convolution Neural Networks). In CNN, such as GoogLeNet and DenseNet, the first convolution layer uses only the traditional methods(3×3 convolutional computation, batch normalization, and activation functions), replacing this with RGB-csb. In addition to the results of preceding studies that can improve accuracy by applying RGB values to feature maps, the accuracy is compared with existing CNN using a limited number of images. The method proposed in this paper shows that the smaller the number of images, the greater the learning accuracy deviation, the more unstable, but the higher the accuracy on average compared to the existing CNN. As the number of images increases, the difference in accuracy between the existing CNN and the proposed method decreases, and the proposed method does not seem to have a significant effect.

• Journal of Animal Science and Technology
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• v.62 no.4
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• pp.438-448
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• 2020

This study was performed to increase the accuracy of genomic estimated breeding value (GEBV) predictions for domestic pigs using single-breed and admixed reference populations (single-breed of Berkshire pigs [BS] with cross breed of Korean native pigs and Landrace pigs [CB]). The principal component analysis (PCA), linkage disequilibrium (LD), and genome-wide association study (GWAS) were performed to analyze the population structure prior to genomic prediction. Reference and test population data sets were randomly sampled 10 times each and precision accuracy was analyzed according to the size of the reference population (100, 200, 300, or 400 animals). For the BS population, prediction accuracy was higher for all economically important traits with larger reference population size. Prediction accuracy was ranged from -0.05 to 0.003, for all traits except carcass weight (CWT), when CB was used as the reference population and BS as the test. The accuracy of CB for backfat thickness (BF) and shear force (SF) using admixed population as reference increased with reference population size, while the results for CWT and muscle pH at 24 hours after slaughter (pH) were equivocal with respect to the relationship between accuracy and reference population size, although overall accuracy was similar to that using the BS as the reference.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.508-512
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• 1989

A new control law of a VSCS is illustrated and put into an analytical form. Using the presented control law, a VSCS shows smooth response, low control input and high accuracy in comparison with those by typical control law.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.479-483
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• 1987

The reducers are widely used to reduce output speed and to amplify driving torque of actuator for industrial robots and many industrial units. But the vibration of robot, which is affected by the reducer, becomes a problem for robot which has to move a driven part with high accuracy. This paper compares experimentally the vibration characteristics of the reducer for industrial robot.

• Journal of the Korean Statistical Society
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• v.25 no.2
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• pp.195-203
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• 1996

A bootstrap method is used to correct the apparent downward bias of a naive plug-in bootstrap model selection criterion, which is shown to enjoy a high degree of accuracy. Comparison of bootstrap method with the asymptotic method is made through an illustrative example.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.505-508
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• 2003

본 연구는 현재 진행되어지고 있는 지적도 전산화사업에서 등록되어지고 있는 필지들의 경계점의 좌표를 추출하여 좌표면적을 구하여 공부상 면적과 비교하고자하는 것

• Proceedings of the Korean Society of Veterinary Clinics Conference
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• 2009.04a
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• pp.267-267
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• 2009

• 대한방사선치료학회:학술대회논문집
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• 2000.04a
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• pp.14-14
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• 2000

• KIISE Transactions on Computing Practices
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• v.24 no.1
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• pp.53-57
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• 2018

A poisoning accident is often caused by a situation in which people eat poisonous mushrooms because they cannot distinguish between edible mushrooms and poisonous mushrooms. In this paper, we propose an automatic mushroom recognition system by using the convolutional neural network. We collected 1478 mushroom images of 38 species using image crawling, and used the dataset for learning the convolutional neural network. A comparison experiment using AlexNet, VGGNet, and GoogLeNet was performed using the collected datasets, and a comparison experiment using a class number expansion and a fine-tuning technique for transfer learning were performed. As a result of our experiment, we achieve 82.63% top-1 accuracy and 96.84% top-5 accuracy on test set of our dataset.