• Journal of the Korean Society for Precision Engineering
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• v.1 no.3
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• pp.71-84
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• 1984

This paper examined strain distribution and radius of curvature of the bulge by finite element method and investigated limit polar thickness strain to predict the formability of sheet metal as we substituted effective strain and the radius of curvature obtained by FEM into instability condition equation successively. In experiment, the radius of curvature and limit polar thickness strainwere obtained by Moire method. Also, a concent- ric set of photogrid circles was used to measure the strain of arbitrary point and mild steel was used as material. This results obtained are as follows: 1) The radius of curvature obtained by FEM is in good agreement with the Moire experimental value. 2) The polar thickness strain is getting larger as the inside is approached from the edge. This means that fracture occurred near the ploe. 3) The circumferential strains agree closely with the meridian strains and the polar thickness strain is about twice the circumferential (or meridian) strain. This result agrees with the fact that anisotropy coefficient (R-value) obtained by tensile test is about one. 4) The theoretical results of limit polar thickness strain obtained by authors' method are better agreement with experimental results than other theoretical results. Therefore, we can better predict the formability of sheet metal with authors' method.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1115-1126
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• 2021

Plant disease is one of the most irritating problems for agriculture growers. Thus, timely detection of plant diseases is of high importance to practical value, and corresponding measures can be taken at the early stage of plant diseases. Therefore, numerous researchers have made unremitting efforts in plant disease identification. However, this problem was not solved effectively until the development of artificial intelligence and big data technologies, especially the wide application of deep learning models in different fields. Since the symptoms of plant diseases mainly appear visually on leaves, computer vision and machine learning technologies are effective and rapid methods for identifying various kinds of plant diseases. As one of the fruits with the highest nutritional value, apple production directly affects the quality of life, and it is important to prevent disease intrusion in advance for yield and taste. In this study, an improved deep residual network is proposed for apple leaf disease identification in a novel way, a global residual connection is added to the original residual network, and the local residual connection architecture is optimized. Including that 1,977 apple leaf disease images with three categories that are collected in this study, experimental results show that the proposed method has achieved 98.74% top-1 accuracy on the test set, outperforming the existing state-of-the-art models in apple leaf disease identification tasks, and proving the effectiveness of the proposed method.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1057-1070
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• 2021

Case-related news filtering is crucial in legal text mining and divides news into case-related and case-unrelated categories. Because case-related news originates from various fields and has different writing styles, it is difficult to establish complete filtering rules or keywords for data collection. In addition, the labeled corpus for case-related news is sparse; therefore, to train a high-performance classification model, it is necessary to annotate the corpus. To address this challenge, we propose topic-enhanced positive-unlabeled learning, which selects positive and negative samples guided by topics. Specifically, a topic model based on a variational autoencoder (VAE) is trained to extract topics from unlabeled samples. By using these topics in the iterative process of positive-unlabeled (PU) learning, the accuracy of identifying case-related news can be improved. From the experimental results, it can be observed that the F1 value of our method on the test set is 1.8% higher than that of the PU learning baseline model. In addition, our method is more robust with low initial samples and high iterations, and compared with advanced PU learning baselines such as nnPU and I-PU, we obtain a 1.1% higher F1 value, which indicates that our method can effectively identify case-related news.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3B
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• pp.421-430
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• 2010

Now a days, as a communications network is being broadband, IPTV(Internet Protocol Television) service which provides various two-way TV service is increasing. But as the data which is transmitted between IPTV set-top box and smart card is almost transmitted to set-top box, the illegal user who gets legal authority by approaching to the context of contents illegally using McComac Hack Attack is not prevented perfectly. In this paper, set-top box access security model is proposed which is for the protection from McComac Hack Attack that tries to get permission for access of IPTV service illegally making data line which is connected from smart card to set-top box by using same kind of other set-top box which illegal user uses. The proposed model reports the result of test which tests the user who wants to get permission illegally by registration the information of a condition of smart card which is usable in set-top box in certification server so that it prevents illegal user. Specially, the proposed model strengthen the security about set-top box by adapting public key which is used for establishing neighbor link and inter-certification process though secret value and random number which is created by Pseudo random function.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.617-629
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• 2020

In order to overcome the limitations of the rule-based intrusion detection system due to changes in Internet computing environments, the emergence of new services, and creativity of attackers, network anomaly detection (NAD) using machine learning and deep learning technologies has received much attention. Most of these existing machine learning and deep learning technologies for NAD use supervised learning methods to learn a set of training data set labeled 'normal' and 'attack'. This paper presents the feasibility of the unsupervised learning AutoEncoder(AE) to NAD from data sets collecting of secured network traffic without labeled responses. To verify the performance of the proposed AE mode, we present the experimental results in terms of accuracy, precision, recall, f1-score, and ROC AUC value on the NSL-KDD training and test data sets. In particular, we model a reference AE through the deep analysis of diverse AEs varying hyper-parameters such as the number of layers as well as considering the regularization and denoising effects. The reference model shows the f1-scores 90.4% and 89% of binary classification on the KDDTest+ and KDDTest-21 test data sets based on the threshold of the 82-th percentile of the AE reconstruction error of the training data set.

• Clinical and Experimental Reproductive Medicine
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• v.27 no.2
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• pp.165-172
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• 2000

Objective: In vitro fertilization (IVF) and a prolonging the time of culture may be helpful in establishing a viable pregnancy through a selection effect. Some embryos do not develop beyond the 4-cell stage and some may not develop to the blastocyst stage. We have evaluated the safety of SET and the outcomes of pregnancy. Methods: Sperms were treated with Ham's F-10 supplemented with 10% human follicular fluid (hFF). oocytes or fertilized oocytes were cultured in Dulbecco's Modified Eagle Medium (DMEM) with 10% or 20% hFF respectively. Up to five oocytes were inseminated with approximately 200,000 sperm cells/2 ml in each well. Fertilization was examined in the following morning and fertilized oocytes were co-cultured until embryo transfer. Vero cells for co-culture were prepared in Tissue Culture Medium - 199 (TCM-199) with 10% fetal bovine serum. At the two to four cell and blastocyst on day 2 and day 5, embryo and blstocyst grading were evaluated. Pregnancy rate was determined after transfer of human embryos at the two to four cell stage on day 2 (Group I) or subsequent transfer of embryos on day 2 and at the blastocyst stage on day 5 (Group II). For statistical analysis, Student's t-test and Chi-square (${\chi}^2$_test) were used. Results were considered statistically significant when p value was less than 0.05. Results: No differences was found in the fertilization between Group I (81.0%, 98/121) and Group II (81.8%, 180/220). In case of cleavage rate, no difference was found in Group I (95.9%, 94/98) and Group II (97.8%, 174/178). However, the rate of-clinical pregnancy was significantly higher (p=0.014) in Group II (66.7%, 12/18) than in Group I (26.3%, 5/19). Conclusion: The results of this study showed that SET is safe and effective, and significantly increases the pregnancy rate.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.75-84
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• 2019

In this study, the random forest approach was used to predict the national mean rice yield of South Korea by using mean climatic factors at a national scale. A random forest model that used monthly climate variable and year as an important predictor in predicting crop yield. Annual yield change would be affected by technical improvement for crop management as well as climate. Year as prediction factor represent technical improvement. Thus, it is likely that the variables of importance identified for the random forest model could result in a large error in prediction of rice yield in practice. It was also found that elimination of the trend of yield data resulted in reasonable accuracy in prediction of yield using the random forest model. For example, yield prediction using the training set (data obtained from 1991 to 2005) had a relatively high degree of agreement statistics. Although the degree of agreement statistics for yield prediction for the test set (2006-2015) was not as good as those for the training set, the value of relative root mean square error (RRMSE) was less than 5%. In the variable importance plot, significant difference was noted in the importance of climate factors between the training and test sets. This difference could be attributed to the shifting of the transplanting date, which might have affected the growing season. This suggested that acceptable yield prediction could be achieved using random forest, when the data set included consistent planting or transplanting dates in the predicted area.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.10a
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• pp.647-653
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• 2009

Taguchi parameter design is an approach to reducing performance variation of quality characteristic value in products and processes. Taguchi has used SN (Signal to Noise) ratio to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. This paper describes the prediction technique of vehicle performance using parameter analysis to reduce man hour and test development period as well as to achieve stable NVH performance. Design engineer could efficiently decide the design variable using parameter analysis database in early design stage. These improvements can reduce the time needed to develop better vehicles.

• Journal of the East Asian Society of Dietary Life
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• v.21 no.1
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• pp.53-59
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• 2011

The purpose of this study was to investigate the properties of frozen cookies containing buckwheat flour. Buckwheat flour was added to cake flour bases at concentrations of 3%, 6%, and 9%. Falling numbers, RVA (rapid visco analyzer) increased with the addition of buckwheat flour, whereas initial pasting temperature decreased. The set back value, which is a prediction of retrogradation, did not show any significant difference. The alveographic P values of the treatment were lower than that of the Control. In the farinogram test, water absorption increased upon the addition of buckwheat flour, whereas stability and farinogram quality number decreased. In conclusion, the addition of 6% buckwheat flour is recommended to obtain the optimal volume of frozen cookies.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.10 no.4
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• pp.381-388
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• 1998

Hot and humid weather in summer generally brings about discomfort. Experiments on which relative humidity makes effects on the comfort sensation were performed to the young and the aged using sensation vote. From July to October 1996, seven college students and eleven aged people were exposed for 2 hours under six different conditions in the Pukyong National University test chamber so as to determine the effects of relative humidity on thermal and comfort sensations. Subjects were wearing same clothes, and the mean clo value was 0.5. The mean radiant temperature was equal to the air temperature and air velocity in the occupied zone around 0.lm/s. In the experiments, it was found that discomfort could be largely reduced when the humidity is controlled to low values in the settled high temperature.