• Journal of the Korean Society of Safety
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• v.26 no.4
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• pp.34-40
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• 2011

This paper is purposed to develop an leakage current detecting system(LCDS) which can prevent electrical fires on breaker capacity expansion as well as ruptures of XL(Extra long) pipelines and power failure by operation of ELB(Earth leakage breaker) at auto water machine in winter. In order to develop LCDS, this paper studied field state investigation, field state experiment, development of leakage alarm system and verification experiments. Field states investigation at livestock companies(10 companies) in cheong-won location to deduce the problems of auto water machine is analyzed. The field state experiment is conducted at B livestock company in cheong won location. The field state experiment method is measured with leakage current when ELB tripped by environment factor(fine, cloudy, and rainy day). The LCDS is developed as MCU(Micro Control Unit) part applied leakage current values at B livestock company. Verification experiments for the leakage current detecting system were conducted by two methods of current supply and field test. Results show that LCDS suggested in this paper are valuable and usable in auto water machine based on environment factor, which will prevent severe damage to human beings and properties and reduce the electrical fires in livestock.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.416-421
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• 2000

Today, manufacturing market is highly competitive due to more complex and variable products, shorter delivery lead times, intensified product development rate, customers's increasing concern about quality, durability, maintainability, safety and environmental performance. To survive this environment, development of the automated design system is needed using AutoCAD. This paper represents a computer aided design system for watergate by using AutoCAD R2000 system and its Visual LISP computer language. The developed system ultimately generates the design for water gate through AutoCAD.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.87-92
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• 1999

The biggest challenge facing today manufacturing industry is better quality and high productivity. From an economic point of view, productivity is the most important parameter, as high productivity will reduce the cost. However, the customers of today are not only cost concerned, but also quality conscious. So high accuracy levels should also be achieved in the manufacturing process. This paper reports the development of a automatic design system based on AutoCAD program. This work is composed of three section that are design of top down menu, guide frame and gate lifter for water-gate programed by AutoLISP language and runned Windows system. The developed system ultimately generates the design for a water gate through AutoCAD program. In the design of the water gate, it needs about 23 hours with an expert, but this system can be only 80 seconds without an expert.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.330-335
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• 2001

Characteristics of the present world market include demanding and sophisticated customers, requirement of high quality and innovative products, greater product diversity, increasing labour and products costs, availability of diverse alternatives to the customers and smaller batch sizes to satisfy a variety of customer profiles. To fulfil these characteristics, manufacturing companies need to be flexible adaptable, proactive and able to produce variety of products in short time at low cost. The aim of the study is to develop a computer-aided design system for water-gate on AutoCAD R2000 system. The developed system has been written in AutoCAD and VisualLISP with a personal computer, and is composed four modules which are the gate-lifter input module, guide-frame input module, and upgrade module. Based on knowledge-based rules, the system is designed by considering several factors, such as width and height of a water-gate, material, object of product and maximum depth of water.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.307-307
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• 2022

최근 기후변화와 댐 상류 토지이용 변화 등과 같은 다양한 원인에 의해 댐 유입량의 변동성이 증가하면서 댐 관리 및 운영조작 의사 결정에 어려움이 발생하고 있다. 따라서 이러한 댐 유입량의 변동 특성을 반영하여 댐 유입량을 정확하고 효율적으로 예측할 수 있는 방안이 필요한 실정이다. 머신러닝 기술이 발전하면서 Auto-ML(Automated Machine Learning)이 다양한 분야에서 활용되고 있다. Auto-ML은 데이터 전처리, 최적 알고리즘 선택, 하이퍼파라미터 튜닝, 모델 학습 및 평가 등의 모든 과정을 자동화하는 기술이다. 그러나 아직까지 수문 분야에서 댐 유입량을 예측하기 위한 모델을 개발하는데 있어서 Auto-ML을 활용한 사례는 부족하고, 특히 댐 유입량의 예측 정확성을 확보하기 위해 High-inflow and low-inflow 의 변동 특성을 고려한 하이브리드 결합 방식을 통해 Auto-ML 기반 앙상블 모델을 개발하고 평가한 연구는 없다. 본 연구에서는 Auto-ML의 패키지 중 Auto-sklearn을 통해 홍수기, 비홍수기 유입량 변동 특성을 반영한 하이브리드 앙상블 댐 유입량 예측 모델을 개발하였다. 소양강댐을 대상으로 적용한 결과, 하이브리드 Auto-sklearn 앙상블 모델의 댐 유입량 예측 성능은 R² 0.868, RMSE 66.23 m³/s, MAE 16.45 m³/s로 단일 Auto-sklearn을 통해 구축 된 앙상블 모델보다 전반적으로 우수한 것으로 나타났다. 특히 FDC (Flow Duration Curve)의 저수기, 갈수기 구간에서 두 모델의 유입량 예측 경향은 큰 차이를 보였으며, 하이브리드 Auto-sklearn 모델의 예측 값이 관측 값과 더욱 유사한 것으로 나타났다. 이는 홍수기, 비홍수기 구간에 대한 앙상블 모델이 독립적으로 구축되는 과정에서 각 모델에 대한 하이퍼파라미터가 최적화되었기 때문이라 판단된다. 향후 본 연구의 방법론은 보다 정확한 댐 유입량 예측 자료를 생성하기 위한 방안 수립뿐만 아니라 다양한 분야의 불균형한 데이터셋을 이용한 앙상블 모델을 구축하는데도 유용하게 활용될 수 있을 것으로 사료된다.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.3
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• pp.50-54
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• 2001

Manufacturing is characterized by increasing product variety and technical complexity, decreasing levels of demand, expanding global competition and declining profitability of organizations. To survive in such a complex environment, development of the automatic design system into design and manufacturing can be introduced to increase the flexibility and adaptability to markets. This paper presents the development of an automatic water-gate design system that composed of a main program and three modules, and was programmed by AutoLISP language under AutoCAD system. The devel-oped system is capable of generating water-gate design automatically according to input data as customer requirement.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.03a
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• pp.40-52
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• 1998

Among the non-traditional machining methods, Abrasive waterjet machining process shows big promise in drilling difficult-to-machine materials due to its numerous advantages such as absence of heat affect zone and thermal distortion. Acoustic emission signal technique is used to understand about material removal mechanisms during abrasive waterjet drilling process. More information about the drilling process is derived through frequency decomposition of auto regressive moving average modeling representing acoustic emission signals.

• Journal of Technologic Dentistry
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• v.31 no.4
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• pp.37-43
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• 2009

This study evaluated the effect of concentration of glass fiber reinforcement on the flexural properties of auto and heat polymerized denture base resin. The test specimens($64{\times}10{\times}3.3mm$) were made of auto and heat polymerized resin(Vertex, Dentimax, Netherlands). Glass fiber(ER 270FW, Hankuk Fiber Glass, Korea) were used to reinforce the denture base resin. The 2.6%, 5.3% and 7.9% volume pre-impregnated fiber were located at the bottom of specimen. The test specimens(n=7) of each group were stored in distilled water at $37^{\circ}C$ for 50 hours before test. The flexural strength and modulus were measured by an universal testing machine(Z020, Zwick, Germany) at a crosshead speed of 5 mm/min in a three-point bending mode. The data was analyzed by one-way ANOVA and the Duncan's multiple range test(${\alpha}$=0.05). The difference of auto polymerized resin groups and heat polymerized resin groups were statistically analyzed by t-test(${\alpha}$=0.05). Glass fiber showed significant reinforcing effects on auto and heat polymerized resin. For flexural strength and modulus, auto polymerized resin was the highest in 7.9% volume, while heat polymerized resin was the highest in 5.3% volume. In this study, glass fiber at 7.9% volume ratio showed most effective reinforcing effect on auto polymerized resin and glass fiber at 5.3% volume ratio showed most effective reinforcing effect on heat polymerized resin in terms of flexural strength and flexural modulus.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.307-325
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• 2023

Due to the prolonged COVID-19 pandemic, the frequency of people who are tired of living indoors visiting nearby mountains and national parks to relieve depression and lethargy has exploded. There is a place where thousands of people who came out of nature stop walking and breathe and rest, that is the mineral spring. Even in mountains or national parks, there are about 600 mineral springs that can be found occasionally in neighboring parks or trails in the metropolitan area. However, due to irregular and manual water quality tests, people drink mineral water without knowing the test results in real time. Therefore, in this study, we intend to develop a model that can predict the quality of the spring water in real time by exploring the factors affecting the quality of the spring water and collecting data scattered in various places. After limiting the regions to Seoul and Gyeonggi-do due to the limitations of data collection, we obtained data on water quality tests from 2015 to 2020 for about 300 mineral springs in 18 cities where data management is well performed. A total of 10 factors were finally selected after two rounds of review among various factors that are considered to affect the suitability of the mineral spring water quality. Using AutoML, an automated machine learning technology that has recently been attracting attention, we derived the top 5 models based on prediction performance among about 20 machine learning methods. Among them, the catboost model has the highest performance with a prediction classification accuracy of 75.26%. In addition, as a result of examining the absolute influence of the variables used in the analysis through the SHAP method on the prediction, the most important factor was whether or not a water quality test was judged nonconforming in the previous water quality test. It was confirmed that the temperature on the day of the inspection and the altitude of the mineral spring had an influence on whether the water quality was unsuitable.