• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.65-70
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• 2022

In manufacturing and semiconductor industries, transfer robots increase productivity through accurate and continuous work. Due to the nature of the semiconductor process, there are environments where humans cannot intervene to maintain internal temperature and humidity in a clean room. So, transport robots take responsibility over humans. In such an environment where the manpower of the process is cutting down, the lack of maintenance and management technology of the machine may adversely affect the production, and that's why it is necessary to develop a technology for the machine failure diagnosis system. Therefore, this paper tries to identify various causes of failure of transport robots that are widely used in semiconductor automation, and the Prognostics and Health Management (PHM) method is considered for determining and predicting the process of failures. The robot mainly fails in the driving unit due to long-term repetitive motion, and the core components of the driving unit are motors and gear reducer. A simulation drive unit was manufactured and tested around this component and then applied to 6-axis vertical multi-joint robots used in actual industrial sites. Vibration data was collected for each cause of failure of the robot, and then the collected data was processed through signal processing and frequency analysis. The processed data can determine the fault of the robot by utilizing machine learning algorithms such as SVM (Support Vector Machine) and KNN (K-Nearest Neighbor). As a result, the PHM environment was built based on machine learning algorithms using SVM and KNN, confirming that failure prediction was partially possible.

• International Journal of Industrial Entomology and Biomaterials
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• v.29 no.1
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• pp.135-144
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• 2014

Rearing of tropical tasar silkworm, Antheraea mylitta Drury is mainly conducted in outdoor on Terminalia tomentosa W. & A. a nature grown primary host plant available in forest and also on raised primary host plant Terminalia arjuna Bedd. Temperature, relative humidity and rainfall are the main environmental factors for occurrence of pests (parasites and predators) of tasar silkworm during I, II and III crop rearing in the tropical tasar producing zones. The present study was aimed to study the influence of abiotic factors on prevalence of tasar silkworm pests. The study was conducted at different agro-climatic regions viz., Central Tasar Research &Training Institute, Ranchi, Jharkhand, Regional Extension Centre, Katghora, Chattisgarh and Regional Extension Centre, Hatgamaria during 2010-13 covering 3 seed crop and 6 commercial crops. Data on incidence of tropical tasar silkworm endo-parasitoids like Uzi Fly, Blepharipa zebina Walker and Ichneumon fly (Yellow Fly), Xathopimpla pedator, Fabricius and Predators such as Stink bug (Eocanthecona furcellata Wolf), Reduviid bug (Sycanus collaris Fabricius) and Wasp (Vespa orientalis Linnaeus) was recorded Weekly. The meteorological data was collected daily. Data was collected from 4 different agro-climatic zones of tasar growing areas. Analysis of the data revealed a significant negative correlation between abiotic factors and incidence of ichneumon fly and uzi fly. Based on the 3 years data on prevalence of pests region-wise pest calendars and prediction models were developed.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.6
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• pp.505-513
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• 2005

We measured the time-dependent concentration of VOCs emitted from Ondol floor, furniture, and the wall made of various building materials. After obtaining results from the previous measurement, we developed the estimation equations of the concentration decay, and obtained the estimated graphs for the concentration decay under different air change rates and loading factor conditions by using the estimated equations. We conducted our tests by applying our measurements to real residences for 110 days in the case of furniture and for 40 days in the case of the floor. We also conducted experiments in the cases of various wall materials for 7 days which totaled 10 times. We used the GC/FID for experiments for real residences accord-ing to the specified procedures of the NIOSH 1501, and carried out experiments for wall materials according to the specified procedures of the ASTM 5116-97. When conducting experiments for wall materials, we set the temperature and relative humidity at $23^{\circ}C$ and $50\%$, respectively. We also set the air change rate and loading factor at 0.7/h and $1.617 m^2/m^3$, respectively. Our results showed that it is possible to predict proplrly the time-dependent concentration decay of VOCs by using logarithmic functions in both cases of experiments for real residences and for wall materials. Furthermore, we found that the concentration decay rate of VOCs increased rapidly as the air exchange rate increased while the concentration decay rate decreased as the loading factor increased.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.4
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• pp.3-12
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• 2007

The purpose of this paper is to analyze the relationship between meteorological factors and agricultural reservoir storage, and predict the reservoir storage by multiple regression equation selected by high correlated meteorological factors. Two agricultural reservoirs (Geumgwang and Gosam) located in the upsteam of Gongdo water level gauging station of Anseong-cheon watershed were selected. Monthly reservoir storage data and meteorological data in Suwon weather station of 21 years (1985-2005) were collected. Three cases of correlation (case 1: yearly mean, case 2: seasonal mean dividing a year into 3 periods, and case 3: lagging the reservoir storage from 1 month to 3 months under the condition of case 2) were examined using 8 meteorological factors (precipitation, mean/maximum/minimum temperature, relative humidity, sunshine hour, wind velocity and evaporation). From the correlation analysis, 4 high correlated meteorological factors were selected, and multiple regression was executed for each case. The determination coefficient ($R^{2}$) of predicted reservoir storage for case 1 showed 0.45 and 0.49 for Geumgwang and Gosam reservoir respectively. The predicted reservoir storage for case 2 showed the highest $R^{2}$ of 0.46 and 0.56 respectively in the period of April to June. The predicted reservoir storage for 1 month lag of case 3 showed the $R^{2}$ of 0.68 and 0.85 respectively for the period of April to June. The results showed that the status of agricultural reservoir storage could be expressed with couple of meteorological factors. The prediction enhanced when the storage data are divided into periods rather than yearly mean and especially from the beginning time of paddy irrigation (April) to high decrease of reservoir storage (June) before Jangma.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.3
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• pp.95-100
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• 2009

Fine fuel moisture code (FFMC), a main component of forest fire weather index(FWI) in the Canadian forest fire danger rating system(CFFDRS), indicated a probability of ignition through expecting a dryness of fine fuels. According to this code, a rising of temperature and wind velocity, a decreasing of precipitation and decline of humidity in a weather condition showed a rising of a danger rate for the forest fire. In this study, we analyzed a weather condition during 5 years in Kangwon province, calculated a FFMC and examined an application of FFMC. Very low humidity and little precipitation was a characteristic during spring and fall fire season in Kangwon province. 75% of forest fires during 5 years occurred in this season and especially 90% of forest fire during fire season occurred in spring. For developing of the prediction model for a forest fire occurrence probability, we used a logistic regression function with forest fire occurrence data and classified mean FFMC during 10 days. Accuracy of a developed model was 63.6%. To improve this model, we need to deal with more meteorological data during overall seasons and to associate a meteorological condition with a forest fire occurrence with more research results.

• Korean Journal of Food Science and Technology
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• v.19 no.6
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• pp.463-470
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• 1987

In order to develop a rapid predicting method of the shelf-life of moisture sensitive foods and establish their proper packaging methods, the qualify changes and shelf-life of dired laver as a model food were studied by the computer simulation. A mathematical model of the relationship between the rate constants of chlorophyll a and water activity was established at $10^{\circ}C,\;25^{\circ}C$ and $40^{\circ}C$. Computer simulation to predict water activity and chlorophyll a was developed by considering the simultaneous influence of storage conditions such as water content of products, storage temperature and relative humidity, packaging materials. Simulated quality changes of dried laver was in good agreement with the experiment data. Chlorophyll a and sensory score decreased as the water activity increased. And correlation coefficient between the sensory scores and the contents of chlorophyll a was very high as 0.991. The critical water activity by sensory evaluation was around 0.55. The shelf-life of dried laver packaged with plastic films could be predicted from the above results in various storage conditions.

• Annual Conference of KIPS
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• 2019.10a
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• pp.581-584
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• 2019

Food consumption is growing worldwide every year owing to a growing population. Hence, the increasing population needs the production of sufficient and good quality food products. Strawberry is one of the world's most famous fruit. To obtain the highest strawberry output, we worked with three strawberry varieties supplied with three kinds of nutrient water in a greenhouse and with the outcome of the strawberry production, the highest yielding strawberry variety is detected. This Study uses the nutrient water consumed every day by the highest yielding strawberry variety. The atmospheric temperature, humidity and CO2 levels within the greenhouse are identified and used for the prediction, since the water consumption by any plant depends primarily on weather conditions. Machine learning techniques show successful outcomes in a multitude of issues including time series and regression issues. In this study, daily nutrient water consumption of strawberry plants is predicted using machine learning algorithms is proposed. Four Machine learning algorithms are used such as Linear Regression (LR), K nearest neighbour (KNN), Support Vector Machine with Radial Kernel (SVM) and Gradient Boosting Machine (GBM). Gradient Boosting System produces the best results.

• Atmosphere
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• v.24 no.2
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• pp.151-158
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• 2014

This study investigated a method for the discrimination of precipitation type using thickness of geopotential height at 1000~850 hPa and improved Matsuo's scheme over South Korea using 7 upper-level observations data during winter time from 2003 to 2008. With this research, it was suggested that thickness between snow and rain should range from 1281 to 1297 gpm at 1000~850 hPa. This threshold was suitable for determining precipitation type such as snow, sleet and rain and it was verified by investigation at 7 upper-level observation and 10 surface observation data for 3 years (2009~2011). In addition, precipitation types were separated properly by Matsuo's scheme and its improved one, which is a fuction of surface air temperature and relative humidity, when they lie in mixed sectors. Precipitation types in the mixed sector were subdivided into 5 sectors (rain, rain and snow, snow and rain, snow, and snow cover). We also present the decision table for monitoring and predicting precipitation types using model output of Korea Local Analysis and Prediction System (KLAPS) and observation data.

• Journal of Environmental Science International
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• v.11 no.7
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• pp.687-696
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• 2002

Air quality monitoring data and meteorology data which had collected from 1995. 1. to 1999. 2. in six areas of Daegu, Manchondong, Bokhyundong, Deamyungdong, Samdukdong, Leehyundong and Nowondong, were investigated to determine the distribution and characteristic of ozone. A equation of multiple regression was suggested after time series analysis of contribution factor and meteorology factor were investigated during the day which had high concentration of ozone. The results show the following; First, 63.6% of high ozone concentration days, more than 60 ppb of ozone concentration, were in May, June and September. The percentage of each area showed that; Manchondong 14.4%, Bokhyundong 15.4%, Deamyungdong 15.6%, Samdukdong 15.6%, Leehyundong 17.3% and Nowondong 21.6%. Second, correlation coefficients of ozone, $SO_2$, TSP, $NO_2$ and CO showed negative relationship; the results were respectively -0.229, -0.074, -0.387, -0.190(p<0.01), and humidity were -0.677. but temperature, amount of radiation and wind speed had positive relationship; the results were respectively 0.515, 0.509, 0.400(p<0.01). Third, $R^2$ of equation of multiple regression at each area showed that; Nowondong 45.4%, Lee hyundong 77.9%, Samdukdong 69.9%, Daemyungdong 78.8%, Manchondong 88.6%, Bokhyundong 77.6%. Including 1 hour prior ozone concentration, $R^2$ of each area was significantly increased; Nowondong 75.2%, Leehyundong 89.3%, Samdukdong 86.4%, Daemyungdong 88.6%, Manchondong 88.6%, Bokhyundong 88.0%. Using equation of multiple regression, There were some different $R^2$ between predicted value and observed value; Nowondong 48%, Leehyundong 77.5%, Samdukdong 58%, Daemyungdong 73.4%, Manchondong 77.7%, Bokhyundong 75.1%. $R^2$ of model including 1 hour prior ozone concentration was higher than equation of current day; Nowondong 82.5%, Leehyundong 88.3%, Samdukdong 80.7%, Daemyungdong 82.4%, Manchondong 87.6%, Bokhyundong 88.5%.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.1
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• pp.135-140
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• 2019

Upon chemical agent release, it is of importance to study the characteristic persistence and evaporation of chemical agents from surfaces for the prediction of dispersion hazard. We have recently developed a fast and near real-time wind tunnel system proving the controlled environment(air flow, temperature, and humidity), continuously collects agent vapor and analyzes it. A thermal sorber/desorber is unnecessary to collect the vapor in the system we have developed. Instead, a tandem thermal sorber collects the vapor, which is then directly transferred to a fast gas chromatography(GC) for analysis. As a proof of concept, the evaporation of sulfur mustard agent(HD) was studied from glass, sand and concrete. The results were in an excellent agreement with those obtained from the conventional wind tunnel system.