• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.6-6
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• 2018

최근 집중 호우로 인한 내수침수 피해가 도시화와 기후변화로 늘어나고 있다. 내수침수 피해로 인한 복구비용과 시간이 증가하고 있으며 향후에는 이보다 더 크게 늘어날 것으로 예상된다. 이러한 문제를 해결하기 위하여 충분한 선행시간을 가지고 내수 침수 구역을 제시할 수 있어야 한다. 기존의 물리적 모델은 정확하고 정교한 결과를 제공하지만, 시뮬레이션을 준비하고 마치는 데에 시간이 많이 소요된다. 그 이유로서는 강우량, 지형적 특성, 배수관망 시스템, 수문학적 매개변수 등의 다양한 데이터도 필요하기 때문이다. 이는 도시유역에 대한 내수침수의 실시간 예측이 어렵게 되었으며, 충분한 선행시간을 확보하지 못하는 원인이 되었다. 본 연구에서는 이 문제에 대한 해결책으로 결정론적 방법과 확률론적 방법을 자료지향형 모형으로 결합하여 해결책을 제시하고자 하며, 특정 강우 조건하에 도시유역에서의 내수침수에 영향을 미치는 맨홀에 대한 정보를 제공하고자 한다. 위와 같은 과정을 수행하기 위하여 입력자료 조합에 대한 비선형 분석을 실시하였으며, 그 결과로 특정 강우 조건에 대하여 각 맨홀에 대한 누적월류량을 예측할 수 있는 비선형 인공신경망을 구축할 수 있었다. 본 연구에서 제시된 방법론은 국내의 강남 배수분구에 대하여 적용이 되었으며, 내수침수 예측결과와 2차원 해석결과를 비교하고자 하였다. 본 연구에서는 위 과정을 통하여 1차원 도시유출해석을 위한 입력 자료를 준비하는 시간을 절약하고, 다양한 강우 조건과 내수침수지도 사이의 연관성을 학습하는 예측 모형을 이용하여 도시유역의 내수침수에 대한 충분한 선행시간을 확보하고자 한다. 결론적으로, 이 연구의 결과는 도시유역에 대한 비구조적 대책 수립에 도움을 줄 것으로 확인이 되며 도시 유역 내에 맨홀 위치들을 고려한 위험지구를 파악하는 데에 유용할 것으로 판단된다.

• Journal of the Korean Magnetics Society
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• v.21 no.6
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• pp.204-207
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• 2011

We developed a time-resolved magneto-optical Kerr effect microscope system to investigate ultrafast magnetization dynamics. Based on the pump-probe method, 0.1-ps time resolution was achieved by use of a fs Ti:Sapphire laser. The magnetization dynamics was then measured on Pt/Co/Pt thin films with various Co thicknesses. All the samples exhibited ultrafast demagnetization within a few ps by direct heating of pump laser. Some thicker samples showed precessional motion of magnetization, from which the Gilbert damping constant was determined based on the Landau-Lifshitz-Gilbert equation.

• Hwankyungkyoyuk
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• v.24 no.3
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• pp.18-25
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• 2011

This study was conducted to evaluate the effects of volatile organic compounds and formaldehyde of newly-built school classroom indoor air on the neurobehavioral functions of students. The elementary schools that were opened in September 2008(as of September 2008) was selected for newly-built school and the elementary school that were opened in March 2006 was selected for control group schools. The concentration of formaldehyde(HCHO), a hazardous organic compound that exists in the air of classrooms, exceeded the standard value of $108.2{\mu}g/m^3$ in newly-built schools while it was $60.8{\mu}g/m^3$ in control group schools, which is around 60% of the standard concentration. However, the concentration of the total volatile organic compounds(TVOCs) was $788.9{\mu}g/m^3$ and $756.1{\mu}g/m^3$ in newly-built schools and control group schools respectively, which are approximately two times higher than the standard concentration. In newly-built schools, the mean reaction time of additions and symbol digit, respectively 3,020ms and 2,398ms in pre-exposure were increased to 3,167ms and 2,514ms respectively in post-exposure. The difference of mean reaction time between pre and post exposure was 146.8 ms, or 4.6%, and 116.7ms, or 4.8%, respectively, showing statistically-significant increase of reaction time(p<0.05). On the contrary, the difference of reaction time of both tests were not statistically significant in the control group schools. These results showed that the neurobehavioral performance of newly-built schools students were affected by volatile organic compounds and formaldehyde of classroom indoor air.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.65-74
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• 2021

Recently, studies on Korean spelling correction have been actively conducted based on machine translation and automatic noise generation. These methods generate noise and use as train and data set. This has limitation in that it is difficult to accurately measure performance because it is unlikely that noise other than the noise used for learning is included in the test set In addition, there is no practical error type standard, so the type of error used in each study is different, making qualitative analysis difficult. This paper proposes new 'error type classification' for deep learning-based Korean spelling correction research, and error analysis perform on existing commercialized Korean spelling correctors (System A, B, C). As a result of analysis, it was found the three correction systems did not perform well in correcting other error types presented in this paper other than spacing, and hardly recognized errors in word order or tense.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.37-44
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• 2021

Quality estimation (QE) can evaluate the quality of machine translation output even for those who do not know the target language, and its high utilization highlights the need for QE. QE shared task is held every year at Conference on Machine Translation (WMT), and recently, researches applying Pretrained Language Model (PLM) are mainly being conducted. In this paper, we conduct a survey on the QE task and research trends, and we summarize the features of PLM. In addition, we used a multilingual BART model that has not yet been utilized and performed comparative analysis with the existing studies such as XLM, multilingual BERT, and XLM-RoBERTa. As a result of the experiment, we confirmed which PLM was most effective when applied to QE, and saw the possibility of applying the multilingual BART model to the QE task.

• Journal of Applied Biological Chemistry
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• v.62 no.3
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• pp.305-313
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• 2019

Men's climactic syndrome, andropause, or testosterone deficit syndrome, is one of the new problems with the health of older men in the age of aging. This phenomenon is a natural phenomenon occurring in men as they age, clinically characterized by a decrease in blood testosterone levels and a marked decrease in physical and mental activity. The purpose of this study was to investigate the effect of hydrothermal extract of Taraxacum coreanum by comparing the levels of nitric oxide (NO) in the cavernosum and the levels of male hormone in the blood. Taraxacum coreanum extract increased NO production in vitro and in vivo in a dose-dependent manner. Levels of dihydrotestosterone and 17-hydroxyysteroid dehydrogenases, as well as levels of neurogenic nitric oxide synthase and cGMP, increased significantly in elderly rats (22 weeks) after 4 weeks of daily intake of Taraxacum coreanum extract. However, prostaglandin $E_2$, testosterone, and sexually-hormone-binding globulin levels were not different among all groups. Furthermore, total sperm and motile sperm counts were also no significant difference. Overall, these results suggest the possibility of Taraxacum coreanum extract as a safe and effective natural substance for enhancing NO, cGMP and free testosterone.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.2
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• pp.65-72
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• 2021

Recently, attacks on the network environment have been rapidly escalating and intelligent. Thus, the signature-based network intrusion detection system is becoming clear about its limitations. To solve these problems, research on machine learning-based intrusion detection systems is being conducted in many ways, but two problems are encountered to use machine learning for intrusion detection. The first is to find important features associated with learning for real-time detection, and the second is the imbalance of data used in learning. This problem is fatal because the performance of machine learning algorithms is data-dependent. In this paper, we propose the HSF-DNN, a network intrusion detection model based on a deep neural network to solve the problems presented above. The proposed HFS-DNN was learned through the NSL-KDD data set and performs performance comparisons with existing classification models. Experiments have confirmed that the proposed Hybrid Feature Selection algorithm does not degrade performance, and in an experiment between learning models that solved the imbalance problem, the model proposed in this paper showed the best performance.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.37-44
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• 2021

Automatic classification of brain MRI images play an important role in early diagnosis of brain tumors. In this work, we present a deep learning-based brain tumor classification model in MRI images using ensemble of deep features. In our proposed framework, three different deep features from brain MR image are extracted using three different pre-trained models. After that, the extracted deep features are fed to the classification module. In the classification module, the three different deep features are first fed into the fully-connected layers individually to reduce the dimension of the features. After that, the output features from the fully-connected layers are concatenated and fed into the fully-connected layer to predict the final output. To evaluate our proposed model, we use openly accessible brain MRI dataset from web. Experimental results show that our proposed model outperforms other machine learning-based models.

• The Journal of Korean Institute of Information Technology
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• v.17 no.9
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• pp.99-112
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• 2019

Cracks on pavement surfaces are critical signs and symptoms of the degradation of pavement structures. Image-based pavement crack detection is a challenging problem due to the intensity inhomogeneity, topology complexity, low contrast, and noisy texture background. In this paper, we address the problem of pavement crack detection and segmentation at pixel-level based on a Deep Neural Network (DNN) using gray-scale images. We propose a novel DNN architecture which contains a modified U-net network and a high-level features network. An important contribution of this work is the combination of these networks afforded through the fusion layer. To the best of our knowledge, this is the first paper introducing this combination for pavement crack segmentation and detection problem. The system performance of crack detection and segmentation is enhanced dramatically by using our novel architecture. We thoroughly implement and evaluate our proposed system on two open data sets: the Crack Forest Dataset (CFD) and the AigleRN dataset. Experimental results demonstrate that our system outperforms eight state-of-the-art methods on the same data sets.

• Clinical Pain
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• v.20 no.2
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• pp.105-121
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• 2021

Objective: Physiologically, the vastus medialis muscle is the first muscle to undergo muscle atrophy, and it was thought that pain in patients with knee osteoarthritis could be reduced if this muscle could be strengthened and stabilized. The purpose of this study was to prove the effectiveness in knee osteoarthritis using polydioxanone sutures that have been tried in other musculoskeletal areas. Method: Forty knee osteoarthritis patients voluntarily participated in the study, and divided into 30 polydioxanone suture needle (MEST-B2375 produced by Ovmedi Co.) and 10 sham needle (without suture). And the needles were inserted into the vastus medialis muscle. In all patients, safety evaluation including blood tests and ultrasonography as well as efficacy evaluation including isometric maximal contractile strength of quadriceps muscle, weight bearing pain, impression of change, quadriceps angle, rescue drug intake were evaluated up to 30 weeks after the procedure. Results: Isometric maximal contractile strength showed a significant improvement at 4 weeks after the procedure in the polydioxanone suture group, and the weight-bearing pain showed a significant improvement at every visit in the polydioxanone suture group compared with baseline values. Patient global impression of change score showed significant improvement at 20 and 30 weeks, and clinical score showed improvement at every visit. Conclusion: Insertion of polydioxanone sutures showed improvement in muscle strength and knee pain by supporting and fixation of the vastus medialis muscle in patients with degenerative knee osteoarthritis. Insertion of polydioxanone sutures is considered to have a therapeutic effect in knee osteoarthritis patients.