• Journal of the Korean Society of Clothing and Textiles
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• v.37 no.8
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• pp.1060-1074
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• 2013

This study measured and analyzed pressure at each measurement part of imported compression stockings sold in Korea to provide basic information to establish a pressure standard and grade ranking. This study used 40 medical compression stockings imported from 6 countries. Pressure measurements were taken at 11 points: front side and back side of ankle, end-point of the gastrocnemius muscle, front, inner side, back, and outer side of calf, back side of below knew girth, inner side, and outer side of mid-thigh girth, and inner side of thigh girth. AMI 3037-10 and AMI 3037-2 were used for measurements taken inside an environmental chamber at a temperature of $21^{\circ}C$ and a relative humidity (RH) of 65%. For the measurements, 11 air pack sensors were attached to a wooden model leg (Hohenstein) and three measurements were taken at each measurement point in three minutes. The average of these measurements was used for analysis. The findings of this study were as follows. As for the front side of the ankle, of the 40 products, 14 products (6 USA, 2 Swiss, 3 Italian, and 2 Taiwanese) were within the pressure range indicated on the product label; however, no German products fell within the pressure range. A total of 8 products (5 USA, 1 Swiss, 1 Italian, and 1 German) were gradient compression type; however, no Japanese or Taiwanese product were of this type. The majority of products had the highest pressure at the end-point of the gastrocnemius muscle. Only 3 products, 1 USA (Jobst Opaque 30-40mmHg), 1 Swiss (Sigvaris Cotton 34-46mmHg) and 1 Italian (Jobstocking 25-32mmHg), had measurements that met the indicated standard pressure, were a gradient compression type, and met the overall standard for compression stockings.

• The Korean Journal of Physiology and Pharmacology
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• v.4 no.1
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• pp.63-72
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• 2000

Chronic exposure to cadmium (Cd) results in an inhibition of protein endocytosis in the renal proximal tubule, leading to proteinuria. In order to gain insight into the mechanism by which Cd impairs the protein endocytosis, we investigated the effect of Cd on the acidification of renal cortical endocytotic vesicles (endosomes). The endosomal acidification was assessed by measuring the pH gradient-dependent fluorescence change, using acridine orange or FITC-dextran as a probe. In renal endosomes isolated from Cd-intoxicated rats, the $V_{max}$ of ATP-driven fluorescence quenching ($H^+-ATPase$ dependent intravesicular acidification) was significantly attenuated with no substantial changes in the apparent $K_m,$ indicating that the capacity of acidification was reduced. When endosomes from normal animals were directly exposed to free Cd in vitro, the $V_{max}$ was slightly reduced, whereas the $K_m$ was markedly increased, implying that the biochemical property of the $H^+-ATPase$ was altered by Cd. In endosomes exposed to free Cd in vitro, the rate of dissipation of the transmembrane pH gradient after $H^+-ATPase$ inhibition appeared to be significantly faster compared to that in normal endosomes, indicating that the $H^+-conductance$ of the membrane was increased by Cd. These results suggest that in long-term Cd-exposed animals, free Cd ions liberated in the proximal tubular cytoplasm by lysosomal degradation of cadmium-metallothionein complex (CdMT) may impair endosomal acidification 1) by reducing the $H^+-ATPase$ density in the endosomal membrane, 2) by suppressing the intrinsic $H^+-ATPase$ activity, and 3) possibly by increasing the membrane conductance to $H^+$ ion. Such effects of Cd could be responsible for the alterations of proximal tubular endocytotic activities, protein reabsorption and various transporter distributions observed in Cd-exposed cells and animals.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.12
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• pp.1513-1519
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• 2013

A genetic algorithm (GA) is an optimization technique based on natural evolution theory to find the global optimal solution. Unlike the gradient-based method, it can design nanoscale structures in the electric field because it does not require sensitivity calculation. This research intends to design a nanoaperture with an unprecedented shape by the topology optimization scheme based on the GA and ON/OFF method in the visible frequency range. This research mainly aims to maximize the transmission rate at a measuring area located 10nm under the exit plane and to minimize the electric distribution at other locations. The finite element analysis (FEA) and optimization process are performed by using the commercial package COMSOL combined with the Matlab programming. The final results of the optimized model are analyzed by a comparison of the electric field intensity and the spot size of near field with those of the initial model.

• Journal of Animal Reproduction and Biotechnology
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• v.36 no.2
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• pp.91-98
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• 2021

The objective of this study was to establish a selection process for high quality sperm in bovine semen using sperm separation by magnetic activation (MACS). For this, semen from 21 Nellore bulls was collected using an artificial vagina. To guarantee the presence of pathologies in the ejaculate, animals previously declassified in four consecutive spermiogram were used. Semen was analyzed in five statuses: (1) fresh semen (fresh); (2) density gradient centrifugation (DGC), percoll column; (3) non-apoptotic fraction after separation by MACS (MAC); (4) apoptotic fraction from the separation (MACPOOR); and (5) MAC followed by DGC (MACDGC). Using a computerized analysis system (CASA), motility was measured. The sperm morphology was evaluated by phase contrast, and the supravital test was completed with eosin/nigrosin staining. For DGC, 20 × 10⁶ cells were used in a gradient of 90% and 45% percoll. MACS used 10 × 10⁶ cells with 20 μL of nanoparticles attached to annexin V, and filtered through the MiniMACS magnetic separation column. Membrane integrity was assessed with SYBR-14/IP and mitochondrial potential with JC-1 by flow cytometry. Processing sperm by MACDGC, was more effective in obtaining samples with high quality sperm, verified by the total of abnormalities in the samples: 35.04 ± 2.29%, 21.50 ± 1.47%, 17.30 ± 1.10%, 30.68 ± 1.94% and 10.50 ± 1.46%, respectively for fresh, DGC, MAC, MACPOOR, and MACDGC. The subpopulation of non-apoptotic sperm had a high number of live cells (82.65%), membrane integrity (56.60%) and mitochondrial potential (83.98%) (p < 0.05). These findings suggest that this nanotechnological method, that uses nanoparticles, is efficient in the production of high-quality semen samples for assisted reproduction procedures in cattle.

• Industry Promotion Research
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• v.7 no.1
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• pp.1-8
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• 2022

We investigated various types of machine learning methods that can be applied to censored data. Exploratory data analysis reveals the distribution of each feature, relationships among features. Next, classification problem has been set up where the dependent variable is death_event while the rest of the features are independent variables. After applying various machine learning methods to the data, it has been found that just like many other reports from the artificial intelligence arena random forest performs better than logistic regression. But recently well performed artificial neural network and gradient boost do not perform as expected due to the lack of data. Finally Kaplan-Meier and Cox proportional hazard model have been employed to explore the relationship of the dependent variable (t_i, δ_i) with the independent variables. Also random forest which is used in machine learning has been applied to the survival analysis with censored data.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.143-143
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• 2023

최근 대청댐('17), 평림댐('19) 등 광역 취수원에서 망간의 먹는 물 수질기준(0.05mg/L 이하) 초과 사례가 발생되어, 다수의 민원이 제기되는 등 취수원의 망간 관리 중요성이 부각되고 있다. 특히, 동절기 전도(Turn-over)시기에 고농도 망간이 발생되는 경우가 많은데, 현재 정수장에서는 망간을 처리하기 위해 유입구간에 필터를 설치하고 주기적으로 교체하는 방식으로 처리하고 있다. 그러나 단기간에 고농도 망간 다량 유입 시 처리용량의 한계 등 정수장에서의 공정관리가 어려워지므로 사전 예측에 의한 대응 체계 고도화가 필요한 실정이다. 본 연구는 광역취수원인 주암댐을 대상으로 망간 예측의 정확도 향상 및 예측기간 확대를 위해 다양한 머신러닝 기법들을 적용하여 비교 분석하였으며, 독립변수 및 초매개변수 최적화를 진행하여 모형의 정확도를 개선하였다. 머신러닝 모형은 수심별 탁도, 저수위, pH, 수온, 전기전도도, DO, 클로로필-a, 기상, 수문 자료 등의 독립변수와 화순정수장에 유입된 망간 농도를 종속변수로 각 변수에 해당하는 실측치를 학습데이터로 사용하였다. 그리고 데이터기반 모형의 정확도를 개선하기 위해서 성층의 수준을 판별하는 지표로서 PEA(Potential Energy Anomaly)를 도입하여 데이터 분석에 활용하고자 하였다. 분석 결과, 망간 유입률은 계절 주기에 따라 농도가 달라지는 것을 확인하였고 동절기 전도시점과 하절기 장마기간 난류생성 시기에 저층의 고농도 망간이 유입이 되는 것을 분석하였다. 또한, 두 시기의 망간 농도의 변화 패턴이 상이하므로 예측 모델은 각 계절별로 구축해 학습을 진행함으로써 예측의 정확도를 향상할 수 있었다. 다양한 머신러닝 모델을 구축하여 성능 비교를 진행한 결과, 동절기에는 Gradient Boosting Machine, 하절기에는 eXtreme Gradient Boosting의 기법이 우수하여 추론 모델로 활용하고자 하였다. 선정 모델을 통한 단기 수질예측 결과, 전도현상 발생 시기에 대한 추종 및 예측력이 기존의 데이터 모형만 적용했을 경우대비 약 15% 이상 예측 효율이 향상된 것으로 나타났다. 본 연구는 머신러닝 모델을 활용한 망간 농도 예측으로 정수장의 신속한 대응 체계 마련을 지원하고, 수처리 공정의 효율성을 높이는 데 기여할 것으로 기대되며, 후속 연구로 과거 시계열 자료 활용 및 물리모형과의 연결 등을 통해 모델의 신뢰성을 제고 할 계획이다.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.35 no.6
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• pp.164-172
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• 2023

The grain size distribution of sand provides crucial information for understanding coastal erosion and sediment deposition. The commonly used sieve analysis for grain size distribution analysis has limitations such as time-consuming processes and the inability to obtain information about individual particle shapes and colors. In this study, we propose a grain size distribution analysis method using smartphone digital images, which is simpler and more efficient than the sieve analysis method. During the image analysis process, we effectively detect particles from relatively low-resolution smartphone digital images by extracting particle boundaries through image gradient calculation. Using samples collected from four beaches in Gyeongsangbuk-do, we compare and validate the proposed boundary extraction image analysis method with the analysis method that does not extract boundaries, against sieve analysis results. The proposed method shows an average error rate of 8.21% at D₅₀, exhibiting a 65% lower error compared to the method without boundary extraction. Therefore, grain size distribution analysis using smartphone digital images is convenient, efficient, and demonstrated accuracy comparable to sieve analysis.

• Journal of Environmental Impact Assessment
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• v.33 no.2
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• pp.74-83
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• 2024

This study aims to estimate roadkill occurrences and investigate influential factors in Chungcheongnam-do, contributing to the establishment of roadkill prevention measures. By comprehensively considering weather, road, and environmental information, machine learning was utilized to estimate roadkill incidents and analyze the importance of each variable, deriving primary influencing factors. The Gradient Boosting Machine (GBM) exhibited the best performance, achieving an accuracy of 92.0%, a recall of 84.6%, an F1-score of 89.2%, and an AUC of 0.907. The key factors affecting roadkill included average local atmospheric pressure (hPa), average ground temperature (℃), month, average dew point temperature (℃), presence of median barriers, and average wind speed (m/s). These findings are anticipated to contribute to roadkill prevention strategies and enhance traffic safety, playing a crucial role in maintaining a balance between ecosystems and road development.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.2
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• pp.121-135
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• 2014

Object-based classification methods provide a valid alternative to traditional pixel-based methods. This study reports the results of an object-based classification to examine land cover in the demilitarized zones(DMZs) of Inje-gun. We used land cover classes(7 classes for main category and 13 classes for sub-category) selected from the criteria by Korea Ministry of Environment. The average and standard deviation of the spectrum values, and homogeneity of GLCM were chosen to map land cover types in an hierarchical approach using the nearest neighborhood method. We then identified the distributional characteristics of land cover by considering 3 topographic characteristics (altitude, slope gradient, distance from the Southern Limited Line(SLL)) within the DMZs. The results showed that scale 72, shape 0.2, color 0.8, compactness 0.5 and smoothness 0.5 were the optimum weight values while scale, shape and color were most influenced parameters in image segmentation. The forests (92%) were main land cover type in the DMZs; the grassland(5%), the urban area (2%) and the forests (broadleaf forest: 44%, mixed forest: 42%, coniferous forest: 6%) also occupied mostly in land cover classes for sub-category. The results also showed that facilities and roads had higher density within 2 km from the SLL, while paddy, field and bare land were distributed largely outside 6 km from the SLL. In addition, there was apparent distinction in land cover by topographic characteristics. The forest had higher density at above altitude 600m and above slope gradient $30^{\circ}$ while agriculture, bare land and grass land were distributed mainly at below altitude 600m and below slope gradient $30^{\circ}$.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.10
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• pp.387-396
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• 2018

Stock price prediction has been a difficult problem to solve. There have been many studies to predict stock price scientifically, but it is still impossible to predict the exact price. Recently, a variety of types of cryptocurrency has been developed, beginning with Bitcoin, which is technically implemented as the concept of distributed ledger. Various approaches have been attempted to predict the price of cryptocurrency. Especially, it is various from attempts to stock prediction techniques in traditional stock market, to attempts to apply deep learning and reinforcement learning. Since the market for cryptocurrency has many new features that are not present in the existing traditional stock market, there is a growing demand for new analytical techniques suitable for the cryptocurrency market. In this study, we first collect and process seven cryptocurrency price data through Bithumb's API. Then, we use the gradient boosting model, which is a data-driven learning based machine learning model, and let the model learn the price data change of cryptocurrency. We also find the most optimal model parameters in the verification step, and finally evaluate the prediction performance of the cryptocurrency price trends.