• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.598-603
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• 2019

Three-dimensional shape measurement technology is used in various industries. Among them, optical three-dimensional shape measurement techniques based on the optical trigonometry are mainly used in the field of semiconductor product inspection, where large quantities of three-dimensional shape measurements are made daily in factories and fine measurements are also required. The light source and the drive circuit, which are components of three-dimensional measurement equipment based on this optical trigonometry, produce heat generated by prolonged operation, and may be exposed to conditions where the ambient temperature is not constant, resulting in temperature-induced measurement errors. In this study, the compensation method of the Thermal Errors for Phase Measuring Profilometry is proposed. Three-Dimensional Shape Measurement Equipment based on Phase Measuring Profilometry is implemented to measure the height of an object and ambient temperature for 10 Hours, and a regression line was obtained line by making simple linear regression using measured temperature and height values. This regression line was used to correct the error of the height measurement according to the temperature, and thermal error was from 139.88 um(Micrometer) to 13.12 um.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.327-336
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• 2019

Soybean is one of the most important crops of which the grains contain high protein content and has been consumed in various forms of food. Soybean plants are generally cultivated on the field and their yield and quality are strongly affected by climate change. Recently, the abnormal climate conditions, including heat wave and heavy rainfall, frequently occurs which would increase the risk of the farm management. The real-time assessment techniques for quality and growth of soybean would reduce the losses of the crop in terms of quantity and quality. The objective of this work was to develop a simple model to estimate the growth of soybean plant using a multispectral sensor mounted on a rotor-wing unmanned aerial vehicle(UAV). The soybean growth model was developed by using simple linear regression analysis with three phenotypic data (fresh weight, dry weight, leaf area index) and two types of vegetation indices (VIs). It was found that the accuracy and precision of LAI model using GNDVI (R²= 0.789, RMSE=0.73 ㎡/㎡, RE=34.91%) was greater than those of the model using NDVI (R²= 0.587, RMSE=1.01 ㎡/㎡, RE=48.98%). The accuracy and precision based on the simple ratio indices were better than those based on the normalized vegetation indices, such as RRVI (R²= 0.760, RMSE=0.78 ㎡/㎡, RE=37.26%) and GRVI (R²= 0.828, RMSE=0.66 ㎡/㎡, RE=31.59%). The outcome of this study could aid the production of soybeans with high and uniform quality when a variable rate fertilization system is introduced to cope with the adverse climate conditions.

• Plant Resources
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• v.5 no.3
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• pp.196-201
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• 2002

This study was conducted to investigate the relationships between yearly variations of climatic elements and yearly variations of productivity in sesame. In addition, correlation coefficients among yield and yield components were estimated. The data of yield and yield components were investigated for 10 years from 1992 to 2001. The meteorological data gathered at the Yeosu Weather Station for the same period were used to find out the relationships between climatic elements and productivity. Yearly variation of the amount of precipitation in July and September were large with coefficients of variation(c.v.) of 64.59, 92.47%, respectively, but the variation of the average temperature in June and August were relative small. Yield and plant height greatly with c. v. of 26.24, 23.41 %, respectively, 1, 000 grain weights show more or less c.v. of 3.83% and length capsule setting show still less variation. Correlation coefficients between maximun temperature in period of cultivation(from June to September) and yield are positively significant at the level of 5.1 %, respectively. Correlation coefficients amount the plant height, length capsule setting, number of capsules per plant, weight of 1, 000 grains and seed yield were positively significant at the level of 1 %, respectively. Simple linear regression equations by the least square method are estimated for number of capsules per plant(Y$_1$) and the maximun temperature in August(X) as $Y_1$=10.1255+0.1725X, and for yield(Y$_2$) and the maximun temperature in August(X) as $Y_2$=21.6151 + 1.3724X.

• Smart Structures and Systems
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• v.11 no.4
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• pp.411-433
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• 2013

Due to its easy operation and wide applicability, the ambient vibration method is commonly adopted to determine the cable force by first identifying the cable frequencies from the vibration signals. With given vibration length and flexural rigidity, an analytical or empirical formula is then used with these cable frequencies to calculate the cable force. It is, however, usually difficult to decide the two required parameters, especially the vibration length due to uncertain boundary constraints. To tackle this problem, a new concept of combining the modal frequencies and mode shape ratios is fully explored in this study for developing an accurate method merely based on ambient vibration measurements. A simply supported beam model with an axial tension is adopted and the effective vibration length of cable is then independently determined based on the mode shape ratios identified from the synchronized measurements. With the effective vibration length obtained and the identified modal frequencies, the cable force and flexural rigidity can then be solved using simple linear regression techniques. The feasibility and accuracy of the proposed method is extensively verified with demonstrative numerical examples and actual applications to different cable-stayed bridges. Furthermore, several important issues in engineering practice such as the number of sensors and selection of modes are also thoroughly investigated.

• Journal of Korea Water Resources Association
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• v.43 no.12
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• pp.1083-1091
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• 2010

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. However, widely used estimation schemes fail to describe the realistic variability of daily precipitation field. We compare and contrast the performance of statistical methods for the spatial estimation of precipitation in two hydrologically different basins, and propose a two-step process for effective daily precipitation estimation. The methods assessed are: (1) Inverse Distance Weighted Average (IDW); (2) Multiple Linear Regression (MLR); (3) Climatological MLR; and (4) Locally Weighted Polynomial Regression (LWP). In the suggested simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before applying IDW scheme (one of the local scheme) to estimate the amount of precipitation separately on wet days. As the results, the suggested method shows the better performance of daily rainfall interpolation which has spatial differences compared with conventional methods. And this technique can be used for streamflow forecasting and downscaling of atmospheric circulation model effectively.

• Therapeutic Science for Rehabilitation
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• v.7 no.1
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• pp.79-88
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• 2018

Objective : Conventional therapy approaches for stroke survivors have required considerable demands on therapist's effort and patient's expense. Thus, new robotics rehabilitation therapy technologies have been proposed but they have suffered from less than optimal control algorithms. This article presents a novel technical healthcare solution for the real-time, simultaneous and propositional myoelectric control for stroke survivors' upper limb robotic rehabilitation therapy. Methods : To implement an appropriate computational algorithm for controlling a portable rehabilitative robot, a linear regression model was employed, and a simple game experiment was conducted to identify its potential of clinical utilization. Results : The results suggest that the proposed device and computational algorithm can be used for stroke robot rehabilitation. Conclusion : Moreover, we believe that these techniques will be used as a prominent tool in making a device or finding new therapy approaches in robot-assisted rehabilitation for stroke survivors.

• Spatial Information Research
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• v.22 no.1
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• pp.55-63
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• 2014

Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.4
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• pp.1751-1758
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• 2012

Capillary blood sampling on the forearm reduces pain caused by skin puncture. The present study compared the blood glucose test results performed at different sampling sites of the forearm, finger, and vein to evaluate clinical validity of this alternative site blood sampling technique. Subjects numbered 555 including 61 diabetic patients participated to measure the glucose concentration on the finger ($G_F$) and the forearm ($G_A$) with a portable glucometer under overnight fasting state. Then, the venous glucose concentration ($G_V$) was measured in 514 subjects in less than 1 hour. The test results were analyzed by simple linear regression, intraclass correlation, and Passing-Bablok regression techniques. $G_A$ was highly correlated with $G_F$ or $G_V$ showing the correlation coefficients (r) of approximately 0.97 (P<0.0001) in the normal group. The patient group also resulted similarly high correlation with only slightly lower r value. The mean differences in glucose concentration were less than ${\pm}10mg/dL$ regardless of the sampling sites. Intraclass correlation coefficients were slightly smaller than r but very much similar in value in both groups. The 95% confidence intervals of the slope as well as the intercept in the Passing-Bablok regression analysis were < ${\pm}20%$ and < ${\pm}20mg/dL$, respectively, which were within the clinically acceptable ranges. These three statistical techniques introduced in the present study well demonstrated the consistency of $G_A$ with $G_F$ and $G_V$. Therefore, the forearm blood glucose test could be considered as clinically valid under fasting condition.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1091-1104
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• 2022

Climate change is predicted to increase the frequency and intensity of rainfall worldwide, and the pattern is changing due to inundation damage in urban areas due to rapid urbanization and industrialization. Accordingly, the impact assessment of climate change is mentioned as a very important factor in urban planning, and the World Meteorological Organization (WMO) is emphasizing the need for an impact forecast that considers the social and economic impacts that may arise from meteorological phenomena. In particular, in terms of traffic, the degradation of transport systems due to urban flooding is the most detrimental factor to society and is estimated to be around £100k per hour per major road affected. However, in the case of Korea, even if accurate forecasts and special warnings on the occurrence of meteorological disasters are currently provided, the effects are not properly conveyed. Therefore, in this study, high-resolution analysis and hydrological factors of each area are reflected in order to suggest the depth of flooding of urban floods and to cope with the damage that may affect vehicles, and the degree of flooding caused by rainfall and its effect on vehicle operation are investigated. decided it was necessary. Therefore, the calculation formula of rainfall-immersion depth-vehicle speed is presented using various machine learning techniques rather than simple linear regression. In addition, by applying the climate change scenario to the rainfall-inundation depth-vehicle speed calculation formula, it predicts the flooding of urban rivers during heavy rain, and evaluates possible traffic network disturbances due to road inundation considering the impact of future climate change. We want to develop technology for use in traffic flow planning.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.