• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.109-125
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• 2019

As interest on intelligent search engines increases, various studies have been conducted to extract and utilize the features related to products intelligencely. In particular, when users search for goods in e-commerce search engines, the 'color' of a product is an important feature that describes the product. Therefore, it is necessary to deal with the synonyms of color terms in order to produce accurate results to user's color-related queries. Previous studies have suggested dictionary-based approach to process synonyms for color features. However, the dictionary-based approach has a limitation that it cannot handle unregistered color-related terms in user queries. In order to overcome the limitation of the conventional methods, this research proposes a model which extracts RGB values from an internet search engine in real time, and outputs similar color names based on designated color information. At first, a color term dictionary was constructed which includes color names and R, G, B values of each color from Korean color standard digital palette program and the Wikipedia color list for the basic color search. The dictionary has been made more robust by adding 138 color names converted from English color names to foreign words in Korean, and with corresponding RGB values. Therefore, the fininal color dictionary includes a total of 671 color names and corresponding RGB values. The method proposed in this research starts by searching for a specific color which a user searched for. Then, the presence of the searched color in the built-in color dictionary is checked. If there exists the color in the dictionary, the RGB values of the color in the dictioanry are used as reference values of the retrieved color. If the searched color does not exist in the dictionary, the top-5 Google image search results of the searched color are crawled and average RGB values are extracted in certain middle area of each image. To extract the RGB values in images, a variety of different ways was attempted since there are limits to simply obtain the average of the RGB values of the center area of images. As a result, clustering RGB values in image's certain area and making average value of the cluster with the highest density as the reference values showed the best performance. Based on the reference RGB values of the searched color, the RGB values of all the colors in the color dictionary constructed aforetime are compared. Then a color list is created with colors within the range of ${\pm}50$ for each R value, G value, and B value. Finally, using the Euclidean distance between the above results and the reference RGB values of the searched color, the color with the highest similarity from up to five colors becomes the final outcome. In order to evaluate the usefulness of the proposed method, we performed an experiment. In the experiment, 300 color names and corresponding color RGB values by the questionnaires were obtained. They are used to compare the RGB values obtained from four different methods including the proposed method. The average euclidean distance of CIE-Lab using our method was about 13.85, which showed a relatively low distance compared to 3088 for the case using synonym dictionary only and 30.38 for the case using the dictionary with Korean synonym website WordNet. The case which didn't use clustering method of the proposed method showed 13.88 of average euclidean distance, which implies the DBSCAN clustering of the proposed method can reduce the Euclidean distance. This research suggests a new color synonym processing method based on RGB values that combines the dictionary method with the real time synonym processing method for new color names. This method enables to get rid of the limit of the dictionary-based approach which is a conventional synonym processing method. This research can contribute to improve the intelligence of e-commerce search systems especially on the color searching feature.

• The Journal of Korean Society for Radiation Therapy
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• v.30 no.1_2
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• pp.9-16
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• 2018

Purpose : Brain Stereotactic Radiosurgery can treat non-invasive diseases with high rates of complications due to surgical operations. However, brain stereotactic radiosurgery may be accompanied by radiation induced side effects such as fractionation radiation therapy because it uses radiation. The effects of Coplanar Volumetric Modulated Arc Therapy(C-VMAT) and Non-Coplanar Volumetric Modulated Arc Therapy(NC-VMAT) on surrounding normal tissues were analyzed in order to reduce the side effects caused fractionation radiation therapy such as head and neck. But, brain stereotactic radiosurgery these contents were not analyzed. In this study, we evaluated the usefulness of NC-VMAT by comparing and analyzing C-VMAT and NC-VMAT in patients who underwent brain stereotactic radiosurgery. Methods and materials : With C-VMAT and NC-VMAT, 13 treatment plans for brain stereotactic radiosurgery were established. The Planning Target Volume ranged from a minimum of 0.78 cc to a maximum of 12.26 cc, Prescription doses were prescribed between 15 and 24 Gy. Treatment machine was TrueBeam STx (Varian Medical Systems, USA). The energy used in the treatment plan was 6 MV Flattening Filter Free (6FFF) X-ray. The C-VMAT treatment plan used a half 2 arc or full 2 arc treatment plan, and the NC-VMAT treatment plan used 3 to 7 Arc 40 to 190 degrees. The angle of the couch was planned to be 3-7 angles. Results : The mean value of the maximum dose was $105.1{\pm}1.37%$ in C-VMAT and $105.8{\pm}1.71%$ in NC-VMAT. Conformity index of C-VMAT was $1.08{\pm}0.08$ and homogeneity index was $1.03{\pm}0.01$. Conformity index of NC-VMAT was $1.17{\pm}0.1$ and homogeneity index was $1.04{\pm}0.01$. $V_2$, $V_8$, $V_{12}$, $V_{18}$, $V_{24}$ of the brain were $176{\pm}149.36cc$, $31.50{\pm}25.03cc$, $16.53{\pm}12.63cc$, $8.60{\pm}6.87cc$ and $4.03{\pm}3.43cc$ in the C-VMAT and $135.55{\pm}115.93cc$, $24.34{\pm}17.68cc$, $14.74{\pm}10.97cc$, $8.55{\pm}6.79cc$, $4.23{\pm}3.48cc$. Conclusions : The maximum dose, conformity index, and homogeneity index showed no significant difference between C-VMAT and NC-VMAT. $V_2$ to $V_{18}$ of the brain showed a difference of at least 0.5 % to 48 %. $V_{19}$ to $V_{24}$ of the brain showed a difference of at least 0.4 % to 4.8 %. When we compare the mean value of $V_{12}$ that Radione-crosis begins to generate, NC-VMAT has about 12.2 % less amount than C-VMAT. These results suggest that if NC-VMAT is used, the volume of $V_2$ to $V_{18}$ can be reduced, which can reduce Radionecrosis.

• Korean Journal of Heritage: History & Science
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• v.53 no.4
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• pp.150-169
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• 2020

Surveys of Buddhist ruins at Namsan in Gyeongju began in earnest during the Japanese colonial era, undertaken by Japanese scholars. These surveys of Buddhist remains in Namsan made during the colonial period should be seen as previous research which cannot be ignored in any in-depth study of Buddhist ruins in Gyeongju. Full-scale surveys of Buddhist ruins at Namsan began in the 1920s. Previous surveys conducted around the time of the Japanese annexation of Korea in 1910 are generally viewed as only representing preliminary investigations and, thus, have not received much attention. However, these early surveys are significant in that they led to the Buddhist ruins on Namsan becoming widely known in the 1910s and served as the foundations for later studies. The removal of the Seated Medicine Buddha from Samneung Valley in Gyeongju in 1915 and its subsequent exhibition at the Joseon Local Products Expo, which marked the fifth anniversary of the Japanese administration of Korea, was especially important in garnering attention for Namsan's wealth of Buddhist artifacts, as the statue was placed in the main hall of the art museum and attracted a great deal of interest from visitors. It is typically thought that this Seated Medicine Buddha was exhibited in 1915 because it was the most beautiful and well-preserved statue from Namsan. However, the removal of this statue was closely related to the proposed move of the Seokguram statue to Seoul around the time of Korea's annexation. The plan to move Seokguram to Seoul was primarily devised by Terauchi Masatake, and the plan, based on Ilseontongjo-ron ('日鮮同祖論'), a historical theory that prehistoric Korean and Japanese people were of the same blood, and Joseon Jeongcheasoeng-ron ('朝鮮停滯性論'), a historical theory arguing that development had stagnated in Korea, was intended to be a visual demonstration of a new era for Korea. This new era was to proceed under the rule of the Japanese Empire through the dissolution of Gyeongbokgung, the symbol of the Joseon Dynasty, which would be replaced with past glories as symbolized by the statue of Buddha. However, as the plan floundered, the replacement for Seokguram in Seoul ended up being none other than the Seated Medicine Buddha of Samneung Valley. Surveys of the Seated Medicine Buddha began in 1911, administered by Sekino Tadashi, but he likely learned of the statue's location from Moroga Hideo or Kodaira Ryozo, Japanese residents of Gyeongju. It is also probable that these Japanese residents received a request from the Japanese Government General of Korea to find a Buddha statue that was worthy of being displayed at exhibitions. In this way, we can say that the transfer of the Seated Medicine Buddha to Seoul was the result of close cooperation between the Government General, Sekino Tadashi, and Japanese residents of Gyeongju. This also had the effect of removing the magical veil which had shrouded the Buddhist ruins of Namsan. In other words, while the early surveys of Buddhist ruins on Namsan are significant, it is difficult to argue that the surveys were undertaken for purely academic purposes, as they were deeply related to the imperial ambitions of Governor-General Terauchi which encompassed the plans to move Seokguram to Seoul and the successful hosting of the 1915 Expo. It should also be pointed out that the failure of the plan to move Seokguram to Seoul and the preservation of the Seated Stone Buddha of Mireuggok at Namsan was in no small part due to resistance from Korean residents in Gyeongju. Although it is not described in detail in the paper, research is needed which shows that the Korean residents of Gyeongju were not simple bystanders, but agents of history.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Bio-Environment Control
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• v.30 no.1
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• pp.77-84
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• 2021

This study attempted to find a way to maintain the quality of mixing baby wild leaf vegetables with existing baby leaf vegetables in various ratios. The crops for mixing baby leaf vegetables were Peucedanum japoincum Thunberg and Ligularia stenocephala, as wild vegetables, and red romaine, which is widely used in young leafy vegetables. The mixing ratio of red romaine and wild vegetables was red romaine 0: mantilla oil 5: L. stenocephala ratio 5 (R0: P5: L5), red romaine 3.3: P. japoincum 3.3: L. stenocephala ratio 3.3 (R3.3: P3.3: L3.3), red romaine 5: P. japoincum 2.5: L. stenocephala 2.5 (R5: P2.5: L2.5), red romaine 8: P. japoincum 1: L. stenocephala 1 (R8: P1: L1), red romaine 10: P. japoincum 0: L. stenocephala 0 (R10: P0: L0). All treatments were packaged in OTR (oxygen transmittance) 10,000 cc m-2·day-1·atm-1 film and stored for 27 days at 2℃/85% RH conditions. Fresh weight, carbon dioxide, oxygen, and ethylene concentrations of the baby leaf packages were examined approximately every 3 days, and visual quality, chlorophyll content, and chromaticity were examined on the 27th day of storage. The oxygen and carbon dioxide concentration in the packages were affected by the respiration rate of the crop. As the mixing ratio of lettuce, which had a low respiration rate, increased, the oxygen concentration in the packages was higher and the carbon dioxide concentration was lower. Oxygen concentration decreased significantly after 15 days, but was remained above 16%, and on the contrary, carbon dioxide concentration was kept at 1-4% until the 15th, and then gradually increased to 2-5% on the 27th day. The concentration of ethylene was maintained at 3-6 µL·L-1 until the end of storage (27th day). Visual quality score measured at the end of storage was slightly less than 3.0, which is the limit of marketability of all treatments. Although there was no significant difference, the chlorophyll content (SPAD) of red romaine and P. japoincum were most similar with an initial value in R8:P1:1 treatment, and L. stenocephala was higher value in R8:P1:L1 and R5:P2.5:L2.5 treatments at the end of storage. The leaf color (L∗, a∗, b∗, chroma) of the three crops at end of storage compared with the heat map showed the least change in the R5:P2.5:L2.5 and R8:P1:L1 treatments at the end of storage. Among them, R8:P1:L1 treatment maintained the highest chlorophyll content, the second lowest ethylene concentration, and adequate carbon dioxide concentration of 2-3%. Therefore, it is judged that the mixed ratio of red romaine 8: P. japoincum 1: L. stenocephala 1 (R8: P1: L1) is most suitable for the mixed package of baby leaf vegetables of these three crops.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.191-207
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• 2021

Up to this day, mobile communications have evolved rapidly over the decades, mainly focusing on speed-up to meet the growing data demands of 2G to 5G. And with the start of the 5G era, efforts are being made to provide such various services to customers, as IoT, V2X, robots, artificial intelligence, augmented virtual reality, and smart cities, which are expected to change the environment of our lives and industries as a whole. In a bid to provide those services, on top of high speed data, reduced latency and reliability are critical for real-time services. Thus, 5G has paved the way for service delivery through maximum speed of 20Gbps, a delay of 1ms, and a connecting device of 106/㎢ In particular, in intelligent traffic control systems and services using various vehicle-based Vehicle to X (V2X), such as traffic control, in addition to high-speed data speed, reduction of delay and reliability for real-time services are very important. 5G communication uses high frequencies of 3.5Ghz and 28Ghz. These high-frequency waves can go with high-speed thanks to their straightness while their short wavelength and small diffraction angle limit their reach to distance and prevent them from penetrating walls, causing restrictions on their use indoors. Therefore, under existing networks it's difficult to overcome these constraints. The underlying centralized SDN also has a limited capability in offering delay-sensitive services because communication with many nodes creates overload in its processing. Basically, SDN, which means a structure that separates signals from the control plane from packets in the data plane, requires control of the delay-related tree structure available in the event of an emergency during autonomous driving. In these scenarios, the network architecture that handles in-vehicle information is a major variable of delay. Since SDNs in general centralized structures are difficult to meet the desired delay level, studies on the optimal size of SDNs for information processing should be conducted. Thus, SDNs need to be separated on a certain scale and construct a new type of network, which can efficiently respond to dynamically changing traffic and provide high-quality, flexible services. Moreover, the structure of these networks is closely related to ultra-low latency, high confidence, and hyper-connectivity and should be based on a new form of split SDN rather than an existing centralized SDN structure, even in the case of the worst condition. And in these SDN structural networks, where automobiles pass through small 5G cells very quickly, the information change cycle, round trip delay (RTD), and the data processing time of SDN are highly correlated with the delay. Of these, RDT is not a significant factor because it has sufficient speed and less than 1 ms of delay, but the information change cycle and data processing time of SDN are factors that greatly affect the delay. Especially, in an emergency of self-driving environment linked to an ITS(Intelligent Traffic System) that requires low latency and high reliability, information should be transmitted and processed very quickly. That is a case in point where delay plays a very sensitive role. In this paper, we study the SDN architecture in emergencies during autonomous driving and conduct analysis through simulation of the correlation with the cell layer in which the vehicle should request relevant information according to the information flow. For simulation: As the Data Rate of 5G is high enough, we can assume the information for neighbor vehicle support to the car without errors. Furthermore, we assumed 5G small cells within 50 ~ 250 m in cell radius, and the maximum speed of the vehicle was considered as a 30km ~ 200 km/hour in order to examine the network architecture to minimize the delay.

• The Korean Journal of Nuclear Medicine Technology
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• v.25 no.2
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• pp.35-40
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• 2021

Purpose Glomerular filtration rate(GFR) is an important indicator for the diagnosis, treatment, and follow-up of kidney disease and is also used by healthy individuals for drug use and evaluating kidney function in donors. The gold standard method of the GFR test is to measure by continuously injecting the inulin which is extrinsic marker, but it takes a long time and the test method is complicated. so, the method of measuring the serum concentration of creatinine is used. Estimated glomerular filtration rate (eGFR) is used instead. However, creatinine is known to be affected by age, gender, muscle mass, etc. eGFR formulas that are currently used include the Cockroft-Gault formula, the modification of diet in renal disease (MDRD) formula, and the chronic kidney disease epidemilogy collaboration (CKD-EPI) formula for adults. For children, the Schwartz formula is used. Measurement of GFR using ⁵¹Cr-EDTA (diethylenetriamine tetraacetic acid), ^99mTc-DTPA (diethylenetriamine pentaacetic acid) can replace inulin and is currently in use. Therefore, We compared the GFR measured using ^99mTc-DTPA with the eGFR using CKD-EPI formula. Materials and Methods For 200 kidney transplant donors who visited Asan medical center.(96 males, 104 females, 47.3 years ± 12.7 years old) GFR was measured using plasma(Two-plasma-sample-method, TPSM) obtained by intravenous administration of ^99mTc-DTPA(0.5mCi, 18.5 MBq). eGFR was derived using CKD-EPI formula based on serum creatinine concentration. Results GFR average measured using ^99mTc-DTPA for 200 kidney transplant donors is 97.27±19.46(ml/min/1.73m²), and the eGFR average value using the CKD-EPI formula is 96.84±17.74(ml/min/1.73m²), The concentration of serum creatinine is 0.84±0.39(mg/dL). Regression formula of ^99mTc-DTPA GFR for serum creatinine-based eGFR was Y = 0.5073X + 48.186, and the correlation coefficient was 0.698 (P<0.01). Difference (%) was 1.52±18.28. Conclusion The correlation coefficient between the ^99mTc-DTPA and the eGFR derived on serum creatinine concentration was confirmed to be moderate. This is estimated that eGFR is affected by external factors such as age, gender, and muscle mass and use of formulas made for kidney disease patients. By using ^99mTc-DTPA, we can provide reliable GFR results, which is used for diagnosis, treatment and observation of kidney disease, and kidney evaluation of kidney transplant patients.

• Journal of the Daesoon Academy of Sciences
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• v.36
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• pp.153-199
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• 2020

The teachings of Sangje teachings have been spread to humanity and were provided as basis for building the earthly paradise due to His having performed the Reordering Works of the Universe (Cheonjigongsa) for nine years. The work that remains will be completed year by year following the cosmic program that Sangje set for the universe. The chapters titled 'Gyoun (Progress of the Order)' in Jeon-gyeong (The Canonical Scripture) can be summarized into three parts: Viewing Gyoun, Spreading Gyoun, and Establishing the firm ground of Gyoun. Viewing Gyoun is seeing how the teachings would be transmitted from the beginning to end. The work of Gyoun was established by Sangje and promoted as the teachings of Sangje which will ultimately unfold into the realization of an earthly paradise. Spreading Gyoun is performed by disciples who received the teachings from Sangje and then the successor to whom Sangje transmitted the religious authority. Since chapter two of Gyoun is about the hagiography of Doju Jo Jeongsan, it is shown that Doju unfolded and developed Sangje's teachings. Establishing the firm ground of Gyoun is carried out to enable practitioners to understand that Dotong-gunja ('Dao-Empowered Sages,' Earthly Immortals) will be produced as a result of Sangje's Reordering Works of Heaven and Earth and that humans can perfect themselves through cultivating the Dao. In conclusion, Gyoun can be summarized as a process that started during Doju Jo Jeongsan's 50 years (1909~1958) of holy works and spreading of the teachings. Next, it was continued through the time of Dojeon who was bestowed with religious authority through Doju's last words. Dojeon, like Doju before him, spread the teachings. In later times, there will be Dotong-gunjas who transmit Sangje's teachings to the whole world. Although the above characterizations are accurate, I compared some verses from Chapter 1 of Progress of the Order (Gyoun) in The Canonical Scripture (Jeon-gyeong) of Daesoon Jinrihoe to the 6 th edition (1965) of Daesoon Jeongyeong, a key scripture from the earliest strata of Jeungsanist scriptures, and found that there were a few earlier variations of the same content. The use of words and sentences were different though in several of these verses. Also, some of the verses indicated alternative historical dates (years), and some of the verses from Chapter 1 of Progress of the Order from The Canonical Scripture do not appear anywhere in the 6^th edition of Daesoon Jeong-gyeong.

• Journal of Food Hygiene and Safety
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• v.37 no.2
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• pp.39-45
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• 2022

This study aimed to investigate the validation and modify the analytical method to determine quercetin-3-𝑜-gentiobioside and isoquercitrin in Abelmoschus esculentus L. Moench for the standardization of ingredients in development of functional health products. The analytical method was validated based on the ICH (International Conference for Harmonization) guidelines to verify the reliability and validity there of on the specificity, linearity, accuracy, precision, detection limit and quantification limit. For the HPLC analysis method, the peak retention time of the index component of the standard solution and the peak retention time of the index component of A. esculentus L. Moench powder sample were consistent with the spectra thereof, confirming the specificity. The calibration curves of quercetin-3-𝑜-gentiobioside and isoquercitrin showed a linearity with a near-one correlation coefficient (0.9999 and 0.9999), indicating the high suitability thereof for the analysis. A. esculentus L. Moench powder sample of a known concentration were prepared with low, medium, and high concentrations of standard substances and were calculated for the precision and accuracy. The precision of quercetin-3-𝑜-gentiobioside and isoquercitrin was confirmed for intra-day and daily. As a result, the intra-day precision was found to be 0.50-1.48% and 0.77-2.87%, and the daily precision to be 0.07-3.37% and 0.58-1.37%, implying an excellent precision at level below 5%. As a result of accuracy measurement, the intra-day accuracy of quercetin-3-𝑜-gentiobioside and isoquercitrin was found to be 104.87-109.64% and the daily accuracy thereof was found to be 106.85-109.06%, reflecting high level of accuracy. The detection limits of quercetin-3-𝑜-gentiobioside and isoquercitrin were 0.24 ㎍/mL and 0.16 ㎍/mL, respectively, whereas the quantitation limits were 0.71 ㎍/mL and 0.49 ㎍/mL, confirming that detection was valid at the low concentrations as well. From the analysis, the established analytical method was proven to be excellent with high level of results from the verification on the specificity, linearity, precision, accuracy, detection limit and quantitation limit thereof. In addition, as a result of analyzing the content of A. esculentus L. Moench powder samples using a validated analytical method, quercetin-3-𝑜-gentiobioside was analyzed to contain 1.49±0.01 mg/dry weight g, while isoquercitrin contained 1.39±0.01 mg/dry weight g. The study was conducted to verify that the simultaneous analysis on quercetin-3-𝑜-gentiobioside and isoquercitrin, the indicators of A. esculentus L. Moench, is a scientifically reliable and suitable analytical method.

• Trans-
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• v.12
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• pp.51-79
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• 2022

This study investigates the methods for deriving colors which can serve as a reference to users such as designers and or contents creators who search for online images from the web portal sites using specific words for color planning and more. Two experiments were conducted in order to accomplish this. Digital scenery photos within the geographic scope of Korea were downloaded from web portal sites, and those photos were studied to find out what colors were used to describe daytime and nighttime. Machine learning was used as the study methodology to classify colors in daytime and nighttime, and KSCA was used to derive the color frequency of daytime and nighttime photos and to compare and analyze the two results. The results of classifying the colors of daytime and nighttime photos using machine learning show that, when classifying the colors by 51~100%, the area of daytime colors was approximately 2.45 times greater than that of nighttime colors. The colors of the daytime class were distributed by brightness with white as its center, while that of the nighttime class was distributed with black as its center. Colors that accounted for over 70% of the daytime class were 647, those over 70% of the nighttime class were 252, and the rest (31-69%) were 101. The number of colors in the middle area was low, while other colors were classified relatively clearly into day and night. The resulting color distributions in the daytime and nighttime classes were able to provide the borderline color values of the two classes that are classified by brightness. As a result of analyzing the frequency of digital photos using KSCA, colors around yellow were expressed in generally bright daytime photos, while colors around blue value were expressed in dark night photos. For frequency of daytime photos, colors on the upper 40% had low chroma, almost being achromatic. Also, colors that are close to white and black showed the highest frequency, indicating a large difference in brightness. Meanwhile, for colors with frequency from top 5 to 10, yellow green was expressed darkly, and navy blue was expressed brightly, partially composing a complex harmony. When examining the color band, various colors, brightness, and chroma including light blue, achromatic colors, and warm colors were shown, failing to compose a generally harmonious arrangement of colors. For the frequency of nighttime photos, colors in approximately the upper 50% are dark colors with a brightness value of 2 (Munsell signal). In comparison, the brightness of middle frequency (50-80%) is relatively higher (brightness values of 3-4), and the brightness difference of various colors was large in the lower 20%. Colors that are not cool colors could be found intermittently in the lower 8% of frequency. When examining the color band, there was a general harmonious arrangement of colors centered on navy blue. As the results of conducting the experiment using two methods in this study, machine learning could classify colors into two or more classes, and could evaluate how close an image was with certain colors to a certain class. This method cannot be used if an image cannot be classified into a certain class. The result of such color distribution would serve as a reference when determining how close a certain color is to one of the two classes when the color is used as a dominant color in the base or background color of a certain design. Also, when dividing the analyzed images into several classes, even colors that have not been used in the analyzed image can be determined to find out how close they are to a certain class according to the color distribution properties of each class. Nevertheless, the results cannot be used to find out whether a specific color was used in the class and by how much it was used. To investigate such an issue, frequency analysis was conducted using KSCA. The color frequency could be measured within the range of images used in the experiment. The resulting values of color distribution and frequency from this study would serve as references for color planning of digital design regarding natural scenery in the geographic scope of Korea. Also, the two experiments are meaningful attempts for searching the methods for deriving colors that can be a useful reference among numerous images for content creator users of the relevant field.