• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.23-33
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• 2016

The current smart lighting is shaped to offer the lighting environment suitable for current context, after identifying user's action and location through a sensor. The sensor-based context awareness technology just considers a single user, and the studies to interpret many users' various context occurrences and conflicts lack. In existing studies, a fuzzy theory and algorithm including ReBa have been used as the methodology to solve context conflict. The fuzzy theory and algorithm including ReBa just avoid an opportunity of context conflict that may occur by providing services by each area, after the spaces where users are located are classified into many areas. Therefore, they actually cannot be regarded as customized service type that can offer personal preference-based context conflict. This paper proposes a priority-based LED lighting control system interpreting multiple context conflicts, which decides services, based on the granted priority according to context type, when service conflict is faced with, due to simultaneous occurrence of various contexts to many users. This study classifies the residential environment into such five areas as living room, 'bed room, study room, kitchen and bath room, and the contexts that may occur within each area are defined as 20 contexts such as exercising, doing makeup, reading, dining and entering, targeting several users. The proposed system defines various contexts of users using an ontology-based model and gives service of user oriented lighting environment through rule based on standard and context reasoning engine. To solve the issue of various context conflicts among users in the same space and at the same time point, the context in which user concentration is required is set in the highest priority. Also, visual comfort is offered as the best alternative priority in the case of the same priority. In this manner, they are utilized as the criteria for service selection upon conflict occurrence.

• Journal of Animal Science and Technology
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• v.55 no.5
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• pp.399-403
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• 2013

This study was conducted to investigate the basic concept of multiple trait effective daughter contribution (MTEDC) for dairy cattle sires and calculate effective daughter contribution (EDC) by applying a five lactation multiple trait model using milk yield test records of daughters for the Multiple-trait Across Country Evaluation (MACE). Milk yield data and pedigree information of 301,551 cows that were the progeny of 2,046 Korean and imported dairy bulls were collected from the National Agricultural Cooperative Federation and used in this study. For MTEDC approximation, the reliability of the breeding value was separated based on parents average, own yield deviation and mate adjusted progeny contribution. EDC was then calculated by lactation using these reliabilities. The average number of recorded daughters per sire by lactations were 140.57, 94.24, 55.14, 29.20 and 14.06 from the first to fifth lactation, respectively. However, the average EDC per sire by lactation using the five lactation multiple trait model was 113.49, 89.28, 73.56, 54.02 and 35.08 from the first to fifth lactation, respectively, while the decrease of EDC in late lactations was comparably lower than the average number of recorded daughters per sire. These findings indicate that the availability of daughters without late lactation records is increased by genetic correlation using the multiple trait model. Owing to the relatedness between the EDC and reliability of the estimated breeding value for sire, understanding the MTEDC algorithm and continuous monitoring of EDC is required for correct MACE application of the five lactation multiple trait model.

• The Journal of Korean Society for Radiation Therapy
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• v.28 no.2
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• pp.139-148
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• 2016

Purpose : Range Compensator used for proton therapy compensates the proton beam dose which delivers to the normal tissues according to the Target's Distal Margin dose. We are going to check the improvement of dose on the target part by comparing the dose of PTV and OAR according to applying in different method of Smooth Thickness of Range Compensator which is used in brain tumor therapy. Materials and Methods : For 10 brain tumor patients taking proton therapy in National Cancer Center, Apply Smooth Thickness applied in Range Compensator in order from one to five by using Compensator Editor of Eclipse Proton Planning System(Version 10.0, Varian, USA). The therapy plan algorithm used Proton Convolution Superposition(version 8.1.20 or 10.0.28), and we compared Dmax, Dmin, Homogeneity Index, Conformity Index and OAR dose around tumor by applying Smooth Thickness in phase. Results : When Smooth Thickness was applied from one to five, the Dmax of PTV was decreased max 4.3%, minimum at 0.8 and average of 1.81%. Dmin increased max 1.8%, min 1.8% and average. Difference between max dose and minimum dose decreased at max 5.9% min 1.4% and average 2.6%. Homogeneity Index decreased average of 0.018 and Conformity Index didn't had a meaningful change. OAR dose decreased in Brain Stem at max 1.6%, min 0.1% and average 0.6% and in Optic Chiasm max 1.3%, min 0.3%, and average 0.5%. However, patient C and patient E had an increase each 0.3% and 0.6%. Additionally, in Rt. Optic Nerve, there was a decrease at max 1.5%, min 0.3%, and average 0.8%, however, patient B had 0.1% increase. In Lt. Optic Nerve, there was a decrease at max 1.8%, min 0.3%, and average 0.7%, however, patient H had 0.4 increase. Conclusion : As Smooth Thickness of Range Compensator which is used as the proton treatment for brain tumor patients is applied in stages, the resolution of Compensator increased and as a result the most optimized amount of proton beam dose can be delivered. This is considered to be able to irradiate the equal amount at PTV and reduce the unnecessary dose applied at OAR to reduce the side effects.

• Investigative Magnetic Resonance Imaging
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• v.9 no.1
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• pp.43-49
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• 2005

A few studies have shown that the function of fusiform face area is selectively involved in the perception of faces including a race difference. We investigated the neural substrates of the face-selective region called fusiform face area in the ventral occipital-temporal cortex and same-race memory superiority in the fusiform face area by the event-related fMRI. In our fMRI study, subjects (Oriental-Korean) performed the implicit distinction of the race while they consciously made familiar-judgments, regardless of whether they considered a face as Oriental-Korean or European-American. For race distinction as an implicit task, the fusiform face areas (FFA) and the right parahippocampal gyrus had a greater response to the presentation of Oriental-Korean faces than for the European-American faces, but in the conscious race distinction between Oriental-Korean and European-American faces, there was no significant difference observed in the FFA. These results suggest that different activation in the fusiform regions and right parahippocampal gyrus resulting from superiority of same-race memory could have implicitly taken place by the physiological processes of face recognition.

• Korean Journal of Remote Sensing
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• v.34 no.2_2
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• pp.377-391
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• 2018

In order to efficiently monitor red tide over a wide range, the need for red tide detection using remote sensing is increasing. However, the previous studies focus on the development of red tide detection algorithm for ocean colour sensor. In this study, we propose the use of multi-sensor to improve the inaccuracy for red tide detection and remote sensing data in coastal areas with high turbidity, which are pointed out as limitations of satellite-based red tide monitoring. The study area were selected based on the red tide information provided by National Institute of Fisheries Science, and spatial fusion and spectral-based fusion were attempted using GOCI image as ocean colour sensor and Landsat OLI image as terrestrial sensor. Through spatial fusion of the two images, both the red tide of the coastal area and the outer sea areas, where the quality of Landsat OLI image was low, which were impossible to observe in GOCI images, showed improved detection results. As a result of spectral-based fusion performed by feature-level and rawdata-level, there was no significant difference in red tide distribution patterns derived from the two methods. However, in the feature-level method, the red tide area tends to overestimated as spatial resolution of the image low. As a result of pixel segmentation by linear spectral unmixing method, the difference in the red tide area was found to increase as the number of pixels with low red tide ratio increased. For rawdata-level, Gram-Schmidt sharpening method estimated a somewhat larger area than PC spectral sharpening method, but no significant difference was observed. In this study, it is shown that coastal red tide with high turbidity as well as outer sea areas can be detected through spatial fusion of ocean colour and terrestrial sensor. Also, by presenting various spectral-based fusion methods, more accurate red tide area estimation method is suggested. It is expected that this result will provide more precise detection of red tide around the Korean peninsula and accurate red tide area information needed to determine countermeasure to effectively control red tide.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.807-819
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• 2013

This study proposes a new methodology for future water balance projection considering climate change by assigning a weight to each scenario instead of inputting future streamflows based on GCMs into a water balance model directly. K-nearest neighbor algorithm was employed to assign weights and streamflows in non-flood period (October to the following June) was selected as the criterion for assigning weights. GCM-driven precipitation was input to TANK model to simulate future streamflow scenarios and Quantile Mapping was applied to correct bias between GCM hindcast and historical data. Based on these bias-corrected streamflows, different weights were assigned to each streamflow scenarios to calculate water shortage for the projection periods; 2020s (2010~2039), 2050s (2040~2069), and 2080s (2070~2099). As a result by applying the proposed methodology to project water shortage over the Korean Peninsula, average water shortage for 2020s is projected to increase to 10~32% comparing to the basis (1967~2003). In addition, according to getting decreased in streamflows in non-flood period gradually by 2080s, average water shortage for 2080s is projected to increase up to 97% (516.5 million $m^3/yr$) as maximum comparing to the basis. While the existing research on climate change gives radical increase in future water shortage, the results projected by the weighting method shows conservative change. This study has significance in the applicability of water balance projection regarding climate change, keeping the existing framework of national water resources planning and this lessens the confusion for decision-makers in water sectors.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.1-12
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• 2005

Image registration is a process to establish the spatial correspondence between images of the same scene, which are acquired at different view points, at different times, or by different sensors. This paper presents a new algorithm for robust registration of the images acquired by multiple sensors having different modalities; the EO (electro-optic) and IR(infrared) ones in the paper. The two feature-based and intensity-based approaches are usually possible for image registration. In the former selection of accurate common features is crucial for high performance, but features in the EO image are often not the same as those in the R image. Hence, this approach is inadequate to register the E0/IR images. In the latter normalized mutual Information (nHr) has been widely used as a similarity measure due to its high accuracy and robustness, and NMI-based image registration methods assume that statistical correlation between two images should be global. Unfortunately, since we find out that EO and IR images don't often satisfy this assumption, registration accuracy is not high enough to apply to some applications. In this paper, we propose a two-stage NMI-based registration method based on the analysis of statistical correlation between E0/1R images. In the first stage, for robust registration, we propose two preprocessing schemes: extraction of statistically correlated regions (ESCR) and enhancement of statistical correlation by filtering (ESCF). For each image, ESCR automatically extracts the regions that are highly correlated to the corresponding regions in the other image. And ESCF adaptively filters out each image to enhance statistical correlation between them. In the second stage, two output images are registered by using NMI-based algorithm. The proposed method provides prospective results for various E0/1R sensor image pairs in terms of accuracy, robustness, and speed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.1
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• pp.25-39
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• 2018

NIST(National Institute of Standards and Technology) has recently published SP 800-90B second draft which is the document for evaluating security of entropy source, a key element of a cryptographic random number generator(RNG), and provided a tool implemented on Python code. In SP 800-90B, the security evaluation of the entropy sources is a process of estimating min-entropy by several estimators. The process of estimating min-entropy is divided into IID track and non-IID track. In IID track, the entropy sources are estimated only from MCV estimator. In non-IID Track, the entropy sources are estimated from 10 estimators including MCV estimator. The running time of the NIST's tool in non-IID track is approximately 20 minutes and the memory usage is over 5.5 GB. For evaluation agencies that have to perform repeatedly evaluations on various samples, and developers or researchers who have to perform experiments in various environments, it may be inconvenient to estimate entropy using the tool and depending on the environment, it may be impossible to execute. In this paper, we propose high-speed implementations and an efficient memory usage technique for min-entropy estimation algorithm of SP 800-90B. Our major achievements are the three improved speed and efficient memory usage reduction methods which are the method applying advantages of C++ code for improving speed of MultiMCW estimator, the method effectively reducing the memory and improving speed of MultiMMC by rebuilding the data storage structure, and the method improving the speed of LZ78Y by rebuilding the data structure. The tool applied our proposed methods is 14 times faster and saves 13 times more memory usage than NIST's tool.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1739-1756
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• 2021

Atmospheric nitrogen dioxide (NO₂) is mainly caused by anthropogenic emissions. It contributes to the formation of secondary pollutants and ozone through chemical reactions, and adversely affects human health. Although ground stations to monitor NO₂ concentrations in real time are operated in Korea, they have a limitation that it is difficult to analyze the spatial distribution of NO₂ concentrations, especially over the areas with no stations. Therefore, this study conducted a comparative experiment of spatial interpolation of NO₂ concentrations based on two linear-regression methods(i.e., multi linear regression (MLR), and regression kriging (RK)), and two machine learning approaches (i.e., random forest (RF), and support vector regression (SVR)) for the year of 2020. Four approaches were compared using leave-one-out-cross validation (LOOCV). The daily LOOCV results showed that MLR, RK, and SVR produced the average daily index of agreement (IOA) of 0.57, which was higher than that of RF (0.50). The average daily normalized root mean square error of RK was 0.9483%, which was slightly lower than those of the other models. MLR, RK and SVR showed similar seasonal distribution patterns, and the dynamic range of the resultant NO₂ concentrations from these three models was similar while that from RF was relatively small. The multivariate linear regression approaches are expected to be a promising method for spatial interpolation of ground-level NO₂ concentrations and other parameters in urban areas.

• Korean Journal of Construction Engineering and Management
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• v.20 no.4
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• pp.22-33
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• 2019

The government has steadily revised and developed laws and systems for activating remodeling of apartments in response to the problems of aged apartments. However, despite such efforts, remodeling has yet to be activated. For many reasons, this study noted that there were no tools for reasonable profitability judgements and decision making in the preliminary stages of the remodeling project. Thus, the feasibility evaluation model was developed. Generally, the profitability judgements are made after the conceptual design. However, decisions to drive remodeling projects are made at the preliminary stage. So a feasibility evaluation model is required at the preliminary stage. Accordingly, In this study, a feasibility evaluation model was developed for determining preliminary stage profitability. Construction costs, business expenses, financial expenses, and generally sales revenue were calculated using the initial available information and remodeling variables derived through the existing cases. Through this process, we developed an algorithm that can give an overview of the return on investment. In addition, the preliminary stage feasibility evaluation model developed was applied to three cases to verify the applicability of the model. Although applied in three cases, the difference between the model's forecast and actual case values is less than 5%, which is considered highly applicable. If cases are expanded in the future, it will be a useful tool that can be used in actual work. The feasibility evaluation model developed in this study will support decision making by union members, and if the model is applied in different regions, it will be expected to help local governments to understand the size of possible remodeling projects.