• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.5
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• pp.9-16
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• 2005

The purpose of this research is to acquire emotional data on luminous source by measuring and evaluating human emotional response to the change of the optical feature of luminous environment Luminous sources used in actual architectural space were selected with the optical feature of luminous soured then to measure and analysis human emotional response on Luminous Source. As a result of that 1) In the result of performance measurement by the item of the clear vision of an optic function the fluorescent lamp of daylight indicated the most excellent Performance. 2) In the item of fatigue and stress, the metal halide lamp and mercury lamp showed the most 3) In $\ulcorner$ suitable in light$\lrcorner$, $\ulcorner$a similar with daylight$\lrcorner$ adjective of the amenity item the fluorescent lamp of daylight which color temperature was high turned up to be high also, in $\ulcorner$brilliant$\lrcorner$, adjective, the metal halide lamp and mercury lamp turned up to be low. 4) In the result of factor analysis, three factors $\ulcorner$activity$\lrcorner$, $\ulcorner$potency$\lrcorner$, $\ulcorner$evaluation$\lrcorner$ were abstracted and $\ulcorner$activity$\lrcorner$ factor has the most influential on evaluating the mood of interior space. 5) For the affection in the mood evaluation by each luminous sources, $\ulcorner$activity$\lrcorner$ factor was the most influential by metal halide lamp and fluorescent lamp of daylight, $\ulcorner$potency$\lrcorner$ factor was most influential by kind of incandescent lamp, $\ulcorner$evaluation$\lrcorner$ factor was most influential by fluorescent lamp of low color temperature.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.665-679
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• 2019

Mesoscale soil moisture measurement from the promising Cosmic-Ray Neutron Probe (CRNP) is expected to bridge the gap between large scale microwave remote sensing and point-based in-situ soil moisture observations. Traditional calibration based on $N_0$ method is used to convert neutron intensity measured at the CRNP to field scale soil moisture. However, the static calibration parameter $N_0$ used in traditional technique is insufficient to quantify long term soil moisture variation and easily influenced by different time-variant factors, contributing to the high uncertainties in CRNP soil moisture product. Consequently, in this study, we proposed a modified traditional calibration method, so-called Dynamic-$N_0$ method, which take into account the temporal variation of $N_0$ to improve the CRNP based soil moisture estimation. In particular, a nonlinear regression method has been developed to directly estimate the time series of $N_0$ data from the corrected neutron intensity. The $N_0$ time series were then reapplied to generate the soil moisture. We evaluated the performance of Dynamic-$N_0$ method for soil moisture estimation compared with the traditional one by using a weighted in-situ soil moisture product. The results indicated that Dynamic-$N_0$ method outperformed the traditional calibration technique, where correlation coefficient increased from 0.70 to 0.72 and RMSE and bias reduced from 0.036 to 0.026 and -0.006 to $-0.001m^3m^{-3}$. Superior performance of the Dynamic-$N_0$ calibration method revealed that the temporal variability of $N_0$ was caused by hydrogen pools surrounding the CRNP. Although several uncertainty sources contributed to the variation of $N_0$ were not fully identified, this proposed calibration method gave a new insight to improve field scale soil moisture estimation from the CRNP.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.3
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• pp.97-110
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• 2019

This study analyzed various factors of influence affecting the will to start a business and established and empirically analyzed a research model to see which factors significantly affect the will to start a business. To this end, we investigated the general characteristics and experiences of individuals, conducted a study on the will to start a business, and analyzed the entrepreneurship passion for startups, the ability to find business opportunities, and the ability to connect with partner companies. The intent to start a business survey was investigated in a recertive style with a 7 point scale, and the reliability and feasibility review were analyzed through the PLS analysis method, which enables the implementation of a measurement model and a structural model. To collect valid data, the survey was conducted using an entrepreneurial curriculum class hours to collect and analyze 421 data. In summary, the results are as follows: First, college students have many opportunities to develop their capabilities through competitions held by universities and support institutions, and by utilizing them, they have no fear of starting a business. Second, the ability of students to discover product clients themselves has been improved by fostering entrepreneurship in the special lectures on startup in universities. Third, it can be seen that it has received various information on startups from support agencies to enhance its commitment to startups. The implications are as follows. First, they should foster entrepreneurship among college students by offering practical oriented courses that can broaden their understanding of startups. Second, it needs to be improved from entrepreneurial enthusiasm to a program that can grow into a company that can collaborate with partner companies and confirm its commitment to corporate establishment and product development and determine market opportunities. Third, it is necessary to establish an ecosystem of start-ups that can carry out systematic planning and performance management as it is weak to carry out projects with will to startups.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.393-404
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• 2022

Reliable terrestrial rainfall observations from satellites at finer spatial resolution are essential for urban hydrological and microscale agricultural demands. Although various traditional "top-down" approach-based satellite rainfall products were widely used, they are limited in spatial resolution. This study aims to assess the potential of a novel "bottom-up" approach for rainfall estimation, the parameterized SM2RAIN model, applied to the C-band SAR Sentinel-1 satellite data (SM2RAIN-S1), to generate high-spatial-resolution terrestrial rainfall estimates (0.01° grid/6-day) over Central South Korea. Its performance was evaluated for both spatial and temporal variability using the respective rainfall data from a conventional reanalysis product and rain gauge network for a 1-year period over two different sub-regions in Central South Korea-the mixed forest-dominated, middle sub-region and cropland-dominated, west coast sub-region. Evaluation results indicated that the SM2RAIN-S1 product can capture general rainfall patterns in Central South Korea, and hold potential for high-spatial-resolution rainfall measurement over the local scale with different land covers, while less biased rainfall estimates against rain gauge observations were provided. Moreover, the SM2RAIN-S1 rainfall product was better in mixed forests considering the Pearson's correlation coefficient (R = 0.69), implying the suitability of 6-day SM2RAIN-S1 data in capturing the temporal dynamics of soil moisture and rainfall in mixed forests. However, in terms of RMSE and Bias, better performance was obtained with the SM2RAIN-S1 rainfall product over croplands rather than mixed forests, indicating that larger errors induced by high evapotranspiration losses (especially in mixed forests) need to be included in further improvement of the SM2RAIN.

• Korean Journal of Agricultural Science
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• v.2 no.2
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• pp.357-373
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• 1975

This study was conducted to evaluate the winter active polycross progenies of 10 genotypes selected at the hot and dry climate of the Southern Oregon in their performance in the progeny test comparing with a high yielding variety, 'Fawn', and a winter active variety, 'TFM', as the control varieties at Daejon, Korea. Various plant and leaf characteristics, especially which related to photosynthesis, and forage production during the first summer after their establishment, were examined. The important conclusions of this study are summarized as follows: 1. The winter active genotypes and variety had less leaf fresh weight and dry weight per leaf than variety 'Fawn'. Variations among polycross progenies of genotypes for these characteristics were great. 2. The winter active genotypes and variety had less leaf area per leaf than variety 'Fawn'. Leaf area among polycross progenies of genotypes deviated greatly and poly cross progenies of 'genotype-16' had the same average leaf area as 'Fawn'. 3. Differences of specific leaf weight (S. L. W.) in the winter active genotypes and variety were not significant. Probably the genetic diversity for S. L. W were not big and were narrowed down already in this genetic population. It was suggested that the photosynthate production within the population might not be different and there might be differences in the photosynthate production-translocation balance. Further study for the diurnal change in S. L. W. within the population might be useful. 4. The winter active variety and genotypes had less leaf width than 'Fawn' does. Leaf width among polycross progenies of genotypes deviated significantly. 5. Differences among controls and polycross progeny group in the initial plant height were significant and variety 'Fawn' was taller than the winter active genotypes and variety. But the differences were not significant in the regrowth of plant height after the first forage harvest. On the contrary. the differences among polycross progenies of genotypes were not significant in the initial plant but the differences in their polycross progeny performance became obvious and great in the regrowth ability which is an improtent agronomic characteristics for forage crops produced in the pasture and for hay and silage. 6. Plant width of the winter active genotypes and variety was lesser than 'Fawn' variety. 7. Differences of tiller number became evident and variety 'Fawn' had higher tiller number than the winter active genotypes and variety after the first forage cutting. There, deviations among polycross progenies of genotypes were great for this characteristic. It was obvious that the genetic differences became more evident in the second measurement after the first cutting of forage probably because this characteristic were stimulated by defoliation in the cartain genotypes and variety. 8. The winter active genotypes and variety on the initial growth. the regrowth ability andtotal yield had lesser forage yield than variety 'Fawn'. Deviation of forage yield among polycross progenies of genotypes were great and gave basis for selection according to their polycross progeny performance improving the forage yield of these winter active tall fescue population during summer. 9. It was concluded that the winter active variety and genotypes in this study was poorer than variety 'Fawn' for the most of leaf and plant characteristics including forage yield. For these measurements, the variations among polycross progenies of genotypes were great. and plant breeding might able to improve further this winter active tall fescue through the polycross progeny testing method for the higher forage production during summer in Korea. 10. The result of the associations among various characteristics under study were quite agreeable with the results of the analysis of variance and woul be useful in the selection of desirable genotypes for the development of a new variety.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.443-472
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• 2014

To survive in the global competitive environment, enterprise should be able to solve various problems and find the optimal solution effectively. The big-data is being perceived as a tool for solving enterprise problems effectively and improve competitiveness with its' various problem solving and advanced predictive capabilities. Due to its remarkable performance, the implementation of big data systems has been increased through many enterprises around the world. Currently the big-data is called the 'crude oil' of the 21st century and is expected to provide competitive superiority. The reason why the big data is in the limelight is because while the conventional IT technology has been falling behind much in its possibility level, the big data has gone beyond the technological possibility and has the advantage of being utilized to create new values such as business optimization and new business creation through analysis of big data. Since the big data has been introduced too hastily without considering the strategic value deduction and achievement obtained through the big data, however, there are difficulties in the strategic value deduction and data utilization that can be gained through big data. According to the survey result of 1,800 IT professionals from 18 countries world wide, the percentage of the corporation where the big data is being utilized well was only 28%, and many of them responded that they are having difficulties in strategic value deduction and operation through big data. The strategic value should be deducted and environment phases like corporate internal and external related regulations and systems should be considered in order to introduce big data, but these factors were not well being reflected. The cause of the failure turned out to be that the big data was introduced by way of the IT trend and surrounding environment, but it was introduced hastily in the situation where the introduction condition was not well arranged. The strategic value which can be obtained through big data should be clearly comprehended and systematic environment analysis is very important about applicability in order to introduce successful big data, but since the corporations are considering only partial achievements and technological phases that can be obtained through big data, the successful introduction is not being made. Previous study shows that most of big data researches are focused on big data concept, cases, and practical suggestions without empirical study. The purpose of this study is provide the theoretically and practically useful implementation framework and strategies of big data systems with conducting comprehensive literature review, finding influencing factors for successful big data systems implementation, and analysing empirical models. To do this, the elements which can affect the introduction intention of big data were deducted by reviewing the information system's successful factors, strategic value perception factors, considering factors for the information system introduction environment and big data related literature in order to comprehend the effect factors when the corporations introduce big data and structured questionnaire was developed. After that, the questionnaire and the statistical analysis were performed with the people in charge of the big data inside the corporations as objects. According to the statistical analysis, it was shown that the strategic value perception factor and the inside-industry environmental factors affected positively the introduction intention of big data. The theoretical, practical and political implications deducted from the study result is as follows. The frist theoretical implication is that this study has proposed theoretically effect factors which affect the introduction intention of big data by reviewing the strategic value perception and environmental factors and big data related precedent studies and proposed the variables and measurement items which were analyzed empirically and verified. This study has meaning in that it has measured the influence of each variable on the introduction intention by verifying the relationship between the independent variables and the dependent variables through structural equation model. Second, this study has defined the independent variable(strategic value perception, environment), dependent variable(introduction intention) and regulatory variable(type of business and corporate size) about big data introduction intention and has arranged theoretical base in studying big data related field empirically afterwards by developing measurement items which has obtained credibility and validity. Third, by verifying the strategic value perception factors and the significance about environmental factors proposed in the conventional precedent studies, this study will be able to give aid to the afterwards empirical study about effect factors on big data introduction. The operational implications are as follows. First, this study has arranged the empirical study base about big data field by investigating the cause and effect relationship about the influence of the strategic value perception factor and environmental factor on the introduction intention and proposing the measurement items which has obtained the justice, credibility and validity etc. Second, this study has proposed the study result that the strategic value perception factor affects positively the big data introduction intention and it has meaning in that the importance of the strategic value perception has been presented. Third, the study has proposed that the corporation which introduces big data should consider the big data introduction through precise analysis about industry's internal environment. Fourth, this study has proposed the point that the size and type of business of the corresponding corporation should be considered in introducing the big data by presenting the difference of the effect factors of big data introduction depending on the size and type of business of the corporation. The political implications are as follows. First, variety of utilization of big data is needed. The strategic value that big data has can be accessed in various ways in the product, service field, productivity field, decision making field etc and can be utilized in all the business fields based on that, but the parts that main domestic corporations are considering are limited to some parts of the products and service fields. Accordingly, in introducing big data, reviewing the phase about utilization in detail and design the big data system in a form which can maximize the utilization rate will be necessary. Second, the study is proposing the burden of the cost of the system introduction, difficulty in utilization in the system and lack of credibility in the supply corporations etc in the big data introduction phase by corporations. Since the world IT corporations are predominating the big data market, the big data introduction of domestic corporations can not but to be dependent on the foreign corporations. When considering that fact, that our country does not have global IT corporations even though it is world powerful IT country, the big data can be thought to be the chance to rear world level corporations. Accordingly, the government shall need to rear star corporations through active political support. Third, the corporations' internal and external professional manpower for the big data introduction and operation lacks. Big data is a system where how valuable data can be deducted utilizing data is more important than the system construction itself. For this, talent who are equipped with academic knowledge and experience in various fields like IT, statistics, strategy and management etc and manpower training should be implemented through systematic education for these talents. This study has arranged theoretical base for empirical studies about big data related fields by comprehending the main variables which affect the big data introduction intention and verifying them and is expected to be able to propose useful guidelines for the corporations and policy developers who are considering big data implementationby analyzing empirically that theoretical base.

• Tuberculosis and Respiratory Diseases
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• v.47 no.4
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• pp.442-450
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• 1999

Background : Isoniazid(INH) and rifampicin(RFP) are the most effective anti-tuberculosis drugs which make the short-course chemotherapy possible. Although prescribed dosages of INH and RFP in Korea are different from those recommended by American Thoracic Society, there has been few study about pharmacokinetic profiles of INH and RFP in Korean patients who receive INH, RFP, ethambutol(EMB) and pyrazinamide(PZA) simultaneously. Methods : Among the patients with active tuberculosis from Dec. 1997 to July 1998, we selected 17 patients. After an overnight fast, patients were given INH 300mg, RFP 450mg, EMB 800mg and PZA 1500mg daily. Blood samples for the measurement of plasma INH(n=15) and RFP(n=17) level were drawn each at 0, 0.5, 1, 1.5, 2, 4, 6, 8 and 12hrs, and urine was also collected. INH and RFP level in the plasma and the urine were measured by high-performance liquid chromatography(HPLC). Pharmacokinetic parameters such as peak serum concentration(Cmax), time to reach to peak serum concentration(Tmax), half-life, elimination rate constant(Ke), total body clearance(CLtot), nonrenal clearance(CLnr), and renal clearance(CLr) were calculated. Results : 1) Pharmacokinetic parameters of INH were as follows: Cmax; $7.63{\pm}3.20{\mu}g/ml$, Tmax; $0.73{\pm}0.22hr$, half-life; $2.12{\pm}0.84hrs$, Ke; $0.83{\pm}0.15hrs^{-1}$, CLtot; $17.54{\pm}8.89L/hr$, CLnr; $14.74{\pm}8.35L/hr$, CLr; $2.79{\pm}1.31L/hr$. 2) Pharmacokinetic parameters of RFP were as follows: Cmax; $8.93{\pm}3.98{\mu}g/ml$, Tmax; $1.76{\pm}1.13hrs$, half-life; $2.27{\pm}0.54hrs$, Ke; $0.32{\pm}0.08hrs^{-1}$, CLtot; $14.63{\pm}6.60L/hr$, CLr; $1.04{\pm}0.55L/hr$, CLnr; $13.59{\pm}6.21L/hr$. 3) While the correlation between body weight and Cmax of INH was not statistically significant (r=-0.514, p value>0.05), Cmax of RFP was significantly affected by body weight of the patients(r=-0.662, p value<0.01). Conclusion : In Korean patients with tuberculosis, 300mg of INH will be sufficient to reach the ideal peak blood level even in the patients over 50kg of body weight However, 450mg of RFP will not be the adequate dose in the patients who weigh over 50~60kg.

• Journal of Animal Science and Technology
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• v.54 no.2
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• pp.71-75
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• 2012

The present study data were obtained from 36,894 cows in Korea Animal Improvement Association from 2001 to 2009 which was subjected for ultrasound measurements (eye muscle area, back-fat thickness, marbling score) and descent. Repeated record models were carried out using 7,913 of 36,894 of total animal traits. The ultrasound measured traits and performance test data were used to study the chest girth, body condition score, eye muscle area, back-fat thickness and marbling score with genetic correlation and parameters for the ultrasound measured traits using REMLF90 program. Genetic correlation of eye muscle area with back-fat thickness, marbling score and back-fat thickness with marbling score were noticed in repeated records animal model as 0.69, 0.54, and 0.59, whereas in multiple trait animal model method were 0.07, 0.66, and 0.39, respectively. Repeated records of animal models were used as positive correlation of traits. Multiple trait animal models were used as negative correlation of eye muscle area with marbling score. The analysis on repeat records of animal models using ultrasound measurements about Korean cattle showed positive effects for each traits. In comparison differences between the repeat records of animal models and multiple trait animal models was found with higher traits of her, the heritability and repeatability was found higher in repeat records animal models. In light of these assessments, carcass traits by ultrasound measurements are expected to help and improve an accurate analysis of each trait and if the research analysis using repeat records of animal models continue when we estimate genetic ability of these traits.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.17-23
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• 2020

Biometric information indicating measurement items related to human characteristics has attracted great attention as security technology with high reliability since there is no fear of theft or loss. Among these biometric information, fingerprints are mainly used in fields such as identity verification and identification. If there is a problem such as a wound, wrinkle, or moisture that is difficult to authenticate to the fingerprint image when identifying the identity, the fingerprint expert can identify the problem with the fingerprint directly through the preprocessing step, and apply the image processing algorithm appropriate to the problem. Solve the problem. In this case, by implementing artificial intelligence software that distinguishes fingerprint images with cuts and wrinkles on the fingerprint, it is easy to check whether there are cuts or wrinkles, and by selecting an appropriate algorithm, the fingerprint image can be easily improved. In this study, we developed a total of 17,080 fingerprint databases by acquiring all finger prints of 1,010 students from the Royal University of Cambodia, 600 Sokoto open data sets, and 98 Korean students. In order to determine if there are any injuries or wrinkles in the built database, criteria were established, and the data were validated by experts. The training and test datasets consisted of Cambodian data and Sokoto data, and the ratio was set to 8: 2. The data of 98 Korean students were set up as a validation data set. Using the constructed data set, five CNN-based architectures such as Classic CNN, AlexNet, VGG-16, Resnet50, and Yolo v3 were implemented. A study was conducted to find the model that performed best on the readings. Among the five architectures, ResNet50 showed the best performance with 81.51%.