• Journal of the Korean Society for information Management
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• v.18 no.3
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• pp.159-178
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• 2001

This paper presents an automatic text summarization model which selects representative sentences from sentence clusters to create a summary. Summary generation experiments were performed on two sets of test documents after learning the optimum environment from a training set. Centroid clustering method turned out to be the most effective in clustering sentences, and sentence weight was found more effective than the similarity value between sentence and cluster centroid vectors in selecting a representative sentence from each cluster. The result of experiments also proves that inverse sentence weight as well as title word weight for terms and location weight for sentences are effective in improving the performance of summarization.

• Korean Journal of Poultry Science
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• v.22 no.1
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• pp.1-6
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• 1995

A total of 514 birds were used to investigate the influence of body weight and shank length at hatch on the body weights at various ages in growing pheasant. Statistical model included the terms of hatch and sex as fixed effects and the two covariates of body weight and shank length at hatch. In this model, the effects of hatch and sex on the body weights at the age of 4, 8, 12, 16 and 20 wk, and the average daily gains from hatch to 8 wk and from 8 to 16 wk of age were highly significant(P<0.01). All the regression coefficients of body weights and average daily gains on the body weight at hatch were also significant(P<0.01). Their estimates were 3.05.7.21. 13.89, 15.18 and 15.33 for the body weights at 4. 8, 12, 16 and 20 wk of age ; 0.111 and 0.142 for the average daily gains from hatch to 8 wk, and from 8 to 16 wk of age, respectively. On the shank length, only the regression coefficients of the body weights at 4 and 8 wk of age and the average daily gains from hatch to 8 wk of age were significant(P<0.01). Results of this study suggest that body weight at hatch do significantly affect the body weights in the growing periods up to' the 20 wk of age, but the shank length at hatch influences the body weights only at early age.1)

• Journal of the Korea Society of Computer and Information
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• v.27 no.4
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• pp.27-36
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• 2022

In this paper, to improve the conditions of the fish processing site, we propose a method to predict the cutting point of fish according to the target weight using AI machine vision. The proposed method performs image-based preprocessing by first photographing the top and front views of the input fish. Then, RANSAC(RANdom SAmple Consensus) is used to extract the fish contour line, and then 3D external information of the fish is obtained using 3D modeling. Next, machine learning is performed on the extracted three-dimensional feature information and measured weight information to generate a neural network model. Subsequently, the fish is cut at the cutting point predicted by the proposed technique, and then the weight of the cut piece is measured. We compared the measured weight with the target weight and evaluated the performance using evaluation methods such as MAE(Mean Absolute Error) and MRE(Mean Relative Error). The obtained results indicate that an average error rate of less than 3% was achieved in comparison to the target weight. The proposed technique is expected to contribute greatly to the development of the fishery industry in the future by being linked to the automation system.

• Aerospace Engineering and Technology
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• v.6 no.1
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• pp.35-44
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• 2007

In this study was made the flutter analysis for the export model of Firefly(Bandi-ho), the small canard aircraft. Stiffness model based on internal load generation finite element model was generated. Mass model based on the weight DB for weight control was generated. Aerodynamic model based on Doublet Lattice Method was generated. Preliminary flutter analysis was made. Based on it, major vibration modes are identified and experimentally obtained via the ground vibration test. The obtained normal mode frequencies were used to correlate the finite element model. Flutter analysis was made again and major flutter mechanisms were summarized. The most important flutter root was identified as a coupled root between rigid body roll mode and anti-symmetric wing pitching mode.

• Journal of radiological science and technology
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• v.43 no.6
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• pp.495-502
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• 2020

In this study, the prevalence of osteoporosis was predicted based on 10 independent variables such as age, weight, and alcohol consumption and 4 tree-based machine-learning models, and the performance of each model was compared. Also the model with the highest performance was used to check the performance by clearing the independent variable, and Area Under Curve(ACU) was utilized to evaluate the performance of the model. The ACU for each model was Decision tree 0.663, Random forest 0.704, GBM 0.702, and XGBoost 0.710 and the importance of the variable was shown in the order of age, weight, and family history. As a result of using XGBoost, the highest performance model and clearing independent variables, the ACU shows the best performance of 0.750 with 7 independent variables. This data suggests that this method be applied to predict osteoporosis, but also other various diseases. In addition, it is expected to be used as basic data for big data research in the health care field.

• Geomechanics and Engineering
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• v.30 no.5
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• pp.413-422
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• 2022

TBM is widely used in the construction of various underground projects in the current world, and has the unique advantages that cannot be compared with traditional excavation methods. However, due to the high cost of TBM, the damage is even greater when geological disasters such as collapse occur during excavation. At present, there is still a shortage of research on various types of risk prediction of TBM tunnel, and accurate and reliable risk prediction model is an important theoretical basis for timely risk avoidance during construction. In this paper, a prediction model is proposed to evaluate the risk level of tunnel collapse by establishing a reasonable risk index system, using analytic hierarchy process to determine the index weight, and using the normal cloud model theory. At the same time, the traditional analytic hierarchy process is improved and optimized to ensure the objectivity of the weight values of the indicators in the prediction process, and the qualitative indicators are quantified so that they can directly participate in the process of risk prediction calculation. Through the practical engineering application, the feasibility and accuracy of the method are verified, and further optimization can be analyzed and discussed.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.67-81
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• 2020

The rapid climatic changes being caused by global warming are resulting in abnormal weather conditions worldwide, which in some regions have increased the frequency of landslides. This study was aimed to analyze and compare the landslide susceptibility using the Frequency Ratio (FR), Statistical Index, Weight of Evidence, Certainty Factor, and Index of Entropy (IoE) at Woomyeon Mountain in South Korea. Through the construction of a landslide inventory map, 164 landslide locations in total were found, of which 50 (30%) were reserved to validate the model after 114 (70%) had been chosen at random for model training. The sixteen landslide conditioning factors related to topography, hydrology, pedology, and forestry factors were considered. The results were evaluated and compared using relative operating characteristic curve and the statistical indexes. From the analysis, it was shown that the FR and IoE models were better than the other models. The FR model, with a prediction rate of 0.805, performed slightly better than the IoE model with a prediction rate of 0.798. These models had the same sensitivity values of 0.940. The IoE model gave a specific value of 0.329 and an accuracy value of 0.710, which outperforms the FR model which gave 0.276 and 0.680, respectively, to predict the spatial landslide in the study area. The generated landslide susceptibility maps can be useful for disaster and land use planning.

• Journal of Digital Convergence
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• v.16 no.10
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• pp.79-87
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• 2018

In this study, the evaluation index was developed to design the project evaluation model, and the weight was given through the AHP survey. project was evaluated for the first time by using the designed evaluation model for the field appraisal. As a result, it was revealed that the weight of the propriety index of the participant effect and the project content was the highest among 20 indexes. This study is significant in having built a base on which the evaluation system could be stabilized, by developing the integrative project evaluation model of . However, we have yet to adequately address the evaluator's acceptability and efficacy in evaluating indicators. The validity and continuity of the evaluation model will be secured through this.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1685-1688
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• 2004

I propose a modeling of a mechanism for weight fire uniformity measurement of a tire and a way I interpret a Sampling signal by Loadcell through an analysis, and to measure fire uniformity in this study. Correct a weight fire uniformity measurement was possible through the production of conversion and influence factor of a signal with a basis with the model who was an oscillation mechanics enemy.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2001.11a
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• pp.9-14
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• 2001

부-채널 공격 중에서 가장 핵심이 되는 전력분석 공격은 여러 가지 암호알고리듬이 장착된 스마트 카드 시스템에 대해 공격이 이루어졌으며, 대부분 이 전력분석 공격에 취약한 것으로 알려져 있다. 본 논문에서는 AES로 채택된 Rijndael 알고리듬에 대하여 스마트 카드 구현시 고려되는 전력분석 공격중에서 hamming weight 모델을 이용한 세가지의 DPA 공격을 제시하고 그 대응방안을 설명한다.