• The Korean Journal of Archival Studies
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• no.79
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• pp.5-32
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• 2024

The Korean government ranked 3rd among 193 UN member countries in the UN's 2022 e-Government Development Index. Korea, which has consistently been evaluated as a top country, can clearly be said to be a leading country in the world of e-government. The lubricant of e-government is data. Data itself is neither information nor a record, but it is a source of information and records and a resource of knowledge. Since administrative actions through electronic systems have become widespread, the production and technology of data-based records have naturally expanded and evolved. Technology may seem value-neutral, but in fact, technology itself reflects a specific worldview. The digital order of new technologies, armed with hyper-connectivity and super-intelligence, not only has a profound influence on traditional power structures, but also has an a similar influence on existing information and knowledge transmission media. Moreover, new technologies and media, including data-based generative artificial intelligence, are by far the hot topic. It can be seen that the all-round growth and spread of digital technology has led to the augmentation of human capabilities and the outsourcing of thinking. This also involves a variety of problems, ranging from deep fakes and other fake images, auto profiling, AI lies hallucination that creates them as if they were real, and copyright infringement of machine learning data. Moreover, radical connectivity capabilities enable the instantaneous sharing of vast amounts of data and rely on the technological unconscious to generate actions without awareness. Another irony of the digital world and online network, which is based on immaterial distribution and logical existence, is that access and contact can only be made through physical tools. Digital information is a logical object, but digital resources cannot be read or utilized without some type of device to relay it. In that respect, machines in today's technological society have gone beyond the level of simple assistance, and there are points at which it is difficult to say that the entry of machines into human society is a natural change pattern due to advanced technological development. This is because perspectives on machines will change over time. Important is the social and cultural implications of changes in the way records are produced as a result of communication and actions through machines. Even in the archive field, what problems will a data-based archive society face due to technological changes toward a hyper-intelligence and hyper-connected society, and who will prove the continuous activity of records and data and what will be the main drivers of media change? It is time to research whether this will happen. This study began with the need to recognize that archives are not only records that are the result of actions, but also data as strategic assets. Through this, author considered how to expand traditional boundaries and achieves reterritorialization in a data-driven society.

• Korean Journal of Heritage: History & Science
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• v.57 no.3
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• pp.24-35
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• 2024

Recently, research using deep learning technologies such as artificial intelligence, convolutional neural networks, etc. has been actively conducted in various fields including healthcare, manufacturing, autonomous driving, and security, and is having a significant influence on society. In line with this trend, the present study attempted to apply deep learning to the classification of archaeological artifacts, specifically ancient Korean roof-end tiles. Using 100 images of roof-end tiles from each of the Goguryeo, Baekje, and Silla dynasties, for a total of 300 base images, a dataset was formed and expanded to 1,200 images using data augmentation techniques. After building a model using transfer learning from the pre-trained EfficientNetB0 model and conducting five-fold cross-validation, an average training accuracy of 98.06% and validation accuracy of 97.08% were achieved. Furthermore, when model performance was evaluated with a test dataset of 240 images, it could classify the roof-end tile images from the three dynasties with a minimum accuracy of 91%. In particular, with a learning rate of 0.0001, the model exhibited the highest performance, with accuracy of 92.92%, precision of 92.96%, recall of 92.92%, and F1 score of 92.93%. This optimal result was obtained by preventing overfitting and underfitting issues using various learning rate settings and finding the optimal hyperparameters. The study's findings confirm the potential for applying deep learning technologies to the classification of Korean archaeological materials, which is significant. Additionally, it was confirmed that the existing ImageNet dataset and parameters could be positively applied to the analysis of archaeological data. This approach could lead to the creation of various models for future archaeological database accumulation, the use of artifacts in museums, and classification and organization of artifacts.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.65-72
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• 2024

To faithfully represent the intention of the work in the exhibition space, lighting that provides high color reproduction like natural light is required. Thus, many lighting technologies have been introduced to improve CRI, but most of them only evaluated the general color rendering index (CRI Ra), which considers eight pastel colors. Natural light provides excellent color rendering performance for all colors, including red and blue, expressed by color rendering index of R9 and R12, but most artificial lighting has the problem that color rendering performance such as R9 and R12 is significantly lower than that of natural light. Recently, lighting technology that provides CRI at the level of natural light is required to realistically express the colors of works including primary colors but related research is very insufficient. Therefore this paper proposes exhibition hall lighting that fulfills CRI with a focus on CRI Ra, R9, and R12 based on the characteristics of natural light. First reinforcement wavelength bands for improving R9 and R12 are selected through analysis of the actual measurement SPD of natural and artificial lighting. Afterward virtual SPDs with a peak wavelength within the reinforcement wavelength band are created and then SPD combination conditions that satisfy CRI Ra≥95, R9, and R12≥90 are derived through combination simulation with a commercial LED light source. Through this, after specifying two types of light sources with 405,630nm peak wavelength that had the greatest impact on the improvement of R9 and R12, the exhibition hall lighting applied with two W/C White LEDs is designed and a control Index DB of the lighting is constructed. Afterward experiments with the proposed method showed that it was possible to achieve high CRI at the level of natural light with average CRI Ra 96.5, R9 96.2, and R12 94.0 under the conditions of illuminance (300-1,000 Lux) and color temperature (3,000-5,000K).

• Journal of Korean Home Economics Education Association
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• v.35 no.3
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• pp.47-66
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• 2023

The purpose of this study was to develop an educational smart app for classes by reconstructing some of the teaching-learning contents of the clothing preparation planning within the 'clothing preparation planning and selection' curriculum unit. To this end, a teaching-learning process plan was planned for the classes, a smart app was developed for classes, and feedback from home economics teachers and app development experts was received for the developed app. The main composition of the developed app consists of five steps. The first step is to set up a profile using a real photo, ZEPETO or Galaxy emoji, or iPhone Memoji. In the second step, students make a list of clothes by figuring out the types, quantities and conditions of their exisitng wardrobe items. Each piece of clothing is assigned an individual registration number, and stduents can take pictures of the front and back, along with describing key attributes such as type, color, season-appropriateness, purchase date, and current status. Step three guides students in deciding which garments to retain and which to discard. Building on the clothing inventory from the previous step, students classify items to keep and items to dispose of. In Step 4, Deciding How to Arrange Clothing, students decide how to arrange clothing by filling out an alternative scorecard. Through this process, students can learn in advance the subsection of resource management and self-reliance, laying the foundationa for future learning in 'Practice of Rational Consumption Life'. Lastly, in the fifth stage of determining the disposal method, this stage is to develop practical problem-oriented classes on how to dispose of the clothes to be discarded in the thirrd stage by exploring various disposal methods, engaging in group discussions, and sharing opinions. This study is meaningful as a case study as an attempt to develop a smart app for education by an instructor to align teaching plans and educational content with achievement standards for the class. In the future, upgrades will have to be made through user application.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.434-441
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• 2023

Determining the size or area of a plant's leaves is an important factor in predicting plant growth and improving the productivity of indoor farms. In this study, we developed a convolutional neural network (CNN)-based model to accurately predict the length and width of lettuce leaves using photographs of the leaves. A callback function was applied to overcome data limitations and overfitting problems, and K-fold cross-validation was used to improve the generalization ability of the model. In addition, ImageDataGenerator function was used to increase the diversity of training data through data augmentation. To compare model performance, we evaluated pre-trained models such as VGG16, Resnet152, and NASNetMobile. As a result, NASNetMobile showed the highest performance, especially in width prediction, with an R_squared value of 0.9436, and RMSE of 0.5659. In length prediction, the R_squared value was 0.9537, and RMSE of 0.8713. The optimized model adopted the NASNetMobile architecture, the RMSprop optimization tool, the MSE loss functions, and the ELU activation functions. The training time of the model averaged 73 minutes per Epoch, and it took the model an average of 0.29 seconds to process a single lettuce leaf photo. In this study, we developed a CNN-based model to predict the leaf length and leaf width of plants in indoor farms, which is expected to enable rapid and accurate assessment of plant growth status by simply taking images. It is also expected to contribute to increasing the productivity and resource efficiency of farms by taking appropriate agricultural measures such as adjusting nutrient solution in real time.

• Journal of the Korea Organic Resources Recycling Association
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• v.32 no.3
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• pp.5-18
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• 2024

Chemical oxygen demand (COD), an organic material measurement index, has a limit to the management of the total amount of all organic materials including non-degradable organic materials due to low oxidation rate. So total organic carbon (TOC) that can measure organic materials more accurately is introduced and used as a measurement index. Several environmental companies including company A in Gyeonggi-do dilute raw wastewater first and then treats it with chemicals. And an activated carbon is used at the rear stage to treat total organic carbon even though various treatment processes can be applied to reduce TOC in wastewater. There are some problems such as use of a lot of diluting water and generation of an excessive amount of sludge, so it is urgent to come up with an alternative plan. Therefore, in this study, an application experiment was conducted on two different methods for improving the TOC reduction efficiency of waste water from Company A. The first method is the evaluation of the substitution potential of powered activated carbon(PAC), an adsorbent currently used, by manufacturing cellulose-based graphene like carbon (CGLC). This first study showed that CGLC had about 10% higher TOC adsorption efficiency than commercial PAC, showing the possibility of being applied as an alternative adsorbent for PAC in water treatment sites. The second method relates to the removal of TOC by sulfate radials produced by persulfate (PS) activation. Two activation methods were applied: using CGLC and PAC as carbon-based catalyst and using the high temperature of wastewater for PS activation. As a result of using PAC and CGLC as PS activation materials, the TOC removal rate was lower than the adsorption amount of TOC by CGLC and PAC due to excessive chlorine ions present in the real wastewater. However, as a result of using the high water temperature (55~60℃) of the field wastewater for PS activation, it showed a much greater TOC removal efficiency than PAC alone, CGLC alone, and using a carbon-based catalyst for PS activation. When PS was injected more than 0.5%, it showed a TOC removal efficiency of 95% or more within 24 hr. In addition, when PS was injected more than 0.3%, the TOC concentration could be lowered to less than 75 ppm, which is the wastewater discharge standard applied to company A. When these results were summarized, raw wastewater of high temperature can be treated with a simple process of only adding of PS and discharged by treating TOC below the wastewater discharge standard applied to company A.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.

• Tuberculosis and Respiratory Diseases
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• v.42 no.6
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• pp.900-912
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• 1995

Background: Although graded exercise stress tests are widely used for the evaluation of cardiorespiratory performance, normal standards on respiratory gas exchange and ventilatory functions at maximal exercise in Koreans have not been well established. The purpose of this study is to provide reference values on these by sex and age, along with derivation of some of their prediction equations. Method: Symptom-limited maximal exercise test was carried out by Bruce protocol in 1,000 healthy adults consisting of 603 males and 397 females, aged 20~66 years. Among them VC, $FEV_1$ and MVV were also determined in 885 cases. All the subjects were members of a health center, excluding athletes. During the exercise, subjects were allowed to hold on to front hand rail of the treadmill for safety purpose. Results: The $VO_2\;max/m^2$, $VCO_2\;max/m^2$ and $V_E\;max/m^2$ were greater in males than in females and decreased with age. The RR max in men and women was similar but decreased slightly with age. The $V_T$ max was markedly greater in men but showed no significant changes with age in either gender. The mean of $V_T$ max/VC, $V_E$ max/MVV and BR revealed that there were considerable ventilatory reserves at maximal exercise even in older females. The regression equations of the cardinal parameters obtained using exercise time(ET, min), age(A, yr), height(Ht, cm), weight(W, kg), sex(S, 0=male; 1=female), VC(L), $FEV_1$(L) and $V_E$ max(L) as variables are as follows: $VO_2\;max/m^2$(L/min)=1.449+0.073 ET-0.007A+0.010W-0.006Ht-0.209S, $VCO_2\;max/m^2$(L/min)=1.672+0.063ET-0.008A+0.010W-0.005Ht-0.319S, VE max/$m^2$(L/min)=58.161+1.503ET-0.315A-9.871S or VE max/$m^2$(L/min)=47.873+6.548 $FEV_1$-5.715 S, and VT max(L)=1.497+0.223VC-0.493S. Conclusion: Respiratory gas exchange and ventilatory variables at maximal exercise were studied in 1,000 non-athletes by Bruce protocol. During exercise, the subjects were allowed to hold on to hand rail of the treadmill for safety purpose. We feel that our results would provide ideal target values for patients and healthy individuals to be achieved, since our study subjects were members of a health center whose physical fitness levels were presumably higher than ordinary population.

• Journal of Korean Society of Archives and Records Management
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• v.5 no.2
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• pp.27-48
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• 2005

This study investigates the current preservation and management of four records and documentary heritage in Korea that is in the UNESCO's Memory of the World Register. The study analyzes their problems and corresponding solutions in digitizing those world records heritages. This study also reviews additional four documentary books in Korea that are in the wish list to add to UNESCO's Memory of the World Register. This study is organized as the following: Chapter 2 examines the value and meanings of world records and documentary heritage in Korea. The registry requirements and procedures of UNESCO's Memory of the World Register are examined. The currently registered records of Korea include Hunmin-Chongum, the Annals of the Choson Dynasty, the Diaries of the Royal Secretariat (Seungjeongwon Ilgi), and Buljo- Jikji-Simche-Yojeol (vol. II). These records heritage's worth and significance are carefully analyzed. For example, Hunmin-Chongum("訓民正音") is consisted of unique and systematic letters. Letters were delicately explained with examples in its original manual at the time of letter's creation, which is an unparalleled case in the world documentary history. The Annals of the Choson Dynasty("朝鮮王朝實錄") are the most comprehensive historic documents that contain the longest period of time in history. Their truthfulness and reliability in describing history give credits to the annals. The Royal Secretariat Diary (called Seungjeongwon-Ilgi("承政院日記")) is the most voluminous primary resources in history, superior to the Annals of Choson Dynasty and Twenty Five Histories in China. Jikji("直指") is the oldest existing book published by movable metal print sets in the world. It evidences the beginning of metal printing in the world printing history and is worthy of being as world heritage. The review of the four registered records confirms that they are valuable world documentary heritage that transfers culture of mankind to next generations and should be preserved carefully and safely without deterioration or loss. Chapter 3 investigates the current status of preservation and management of three repositories that store the four registered records in Korea. The repositories include Kyujanggak Archives in Seoul National University, Pusan Records and Information Center of National Records and Archives Service, and Gansong Art Museum. The quality of their preservation and management are excellent in all of three institutions by the following aspects: 1) detailed security measures are close to perfection 2) archiving practices are very careful by using a special stack room in steady temperature and humidity and depositing it in stack or archival box made of paulownia tree and 3) fire prevention, lighting, and fumigation are thoroughly prepared. Chapter 4 summarizes the status quo of digitization projects of records heritage in Korea. The most important issue related to digitization and database construction on Korean records heritage is likely to set up the standardization of digitization processes and facilities. It is urgently necessary to develop comprehensive standard systems for digitization. Two institutions are closely interested in these tasks: 1) the National Records and Archives Service experienced in developing government records management systems; and 2) the Cultural Heritage Administration interested in digitization of Korean old documents. In collaboration of these two institutions, a new standard system will be designed for digitizing records heritage on Korean Studies. Chapter 5 deals with additional Korean records heritage in the wish list for UNESCO's Memory of the World Register, including: 1) Wooden Printing Blocks(經板) of Koryo-Taejangkyong(高麗大藏經) in Haein Temple(海印寺); 2) Dongui-Bogam("東醫寶鑑") 3) Samguk-Yusa("三國遺事") and 4) Mugujeonggwangdaedaranigyeong. Their world value and importance are examined as followings. Wooden Printing Blocks of Koryo-Taejangkyong in Haein Temple is the worldly oldest wooden printing block of cannon of Buddhism that still exist and was created over 750 years ago. It needs a special conservation treatment to disinfect germs residing in surface and inside of wooden plates. Otherwise, it may be damaged seriously. For its effective conservation and preservation, we hope that UNESCO and Government will schedule special care and budget and join the list of Memory of the Word Register. Dongui-Bogam is the most comprehensive and well-written medical book in the Korean history, summarizing all medical books in Korea and China from the Ancient Times through the early 17th century and concentrating on Korean herb medicine and prescriptions. It is proved as the best clinical guidebook in the 17th century for doctors and practitioners to easily use. The book was also published in China and Japan in the 18th century and greatly influenced the development of practical clinic and medical research in Asia at that time. This is why Dongui Bogam is in the wish list to register to the Memory of the World. Samguk-Yusa is evaluated as one of the most comprehensive history books and treasure sources in Korea, which illustrates foundations of Korean people and covers histories and cultures of ancient Korean peninsula and nearby countries. The book contains the oldest fixed form verse, called Hyang-Ka(鄕歌), and became the origin of Korean literature. In particular, the section of Gi-ee(紀異篇) describes the historical processes of dynasty transition from the first dynasty Gochosun(古朝鮮) to Goguryeo(高句麗) and illustrates the identity of Korean people from its historical origin. This book is worthy of adding to the Memory of the World Register. Mugujeonggwangdaedaranigyeong is the oldest book printed by wooden type plates, and it is estimated to print in between 706 and 751. It contains several reasons and evidence to be worthy of adding to the list of the Memory of the World. It is the greatest documentary heritage that represents the first wooden printing book that still exists in the world as well as illustrates the history of wooden printing in Korea.

• Journal of the Korean Society of Food Science and Nutrition
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• v.38 no.6
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• pp.694-708
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• 2009

The principal objective of this study was to determine the effects of smoking & drinking on the diet, nutrient intake, and overall health. A sample of 262 youths, aged 16 to 18 year-old, was randomly selected from Seoul and its vicinity. The subjects participated by answering survey questions including general questions, questions regarding health, smoking & drinking habits, dietary habits, nutrient intake, physical characteristics, and smoking cessation plans. The average height, weight, and BMI of the subjects were $173.5{\pm}6.8\;cm$, $64.8{\pm}11.8\;kg$, and $21.4{\pm}3.7\;kg/m^2$, respectively. Among the subjects, 88% appeared to be interested in health and 43.5% of youth asserted that the best way to keep healthy was to engage in regular exercise. Among 63 smokers, 52 students (82.5%) used alcoholic beverages while 11 students (17.5%) did not use alcoholic beverages, meaning that smoking was a causative factor in drinking. 55.6% of youth reported beginning to smoke in middle school, and 38.1% of them asserted that curiosity was the motive for smoking. The youth reported that the craving for smoking was highest when hungry, and the best place to smoke was the restroom. 20 students (69.0%) answered that the only way to quit smoking was just to stop. 12 students (44.4%) reported that the main reason for failures in smoking cessation attempts was a lack of intention or willpower. 87.1% of all subjects answered that they were inclined to quit smoking, and 56.7% of them would be interested in attending a smoking cessation program if they had the opportunity. Among the smoking and drinking group, 50% of drinkers began to drink in high school, and the reason for drinking given was peer pressure-40% of drinkers answered that they wished to quit drinking. 34.4% of students appeared to have breakfast everyday, but 16.4% of students answered that they had quit eating breakfast. 52.5% of all students reported that the principal reason for overeating was the presence of one's favorite food, and the smoking and drinking group reported overeating more frequently than other groups (p<0.05). 72.6% of all subjects reported eating interim meals $1{\sim}2$ times daily, 36.4% of smokers ate carbonated beverages, 38.5% ate ice cream as a interim meal, and 38.5% of the drinking and smoking groups ate fruits, 26.9% of them ate fried foods, and some of them ate fast foods as a interim meal. Among smokers, the ratio of eating fat-rich foods, and meats such as kalbi and samgyupsal more than two times per week was higher, and 54.3% of smokers ate ice cream, cookies, and carbonated beverages more than two times per week (p<0.05). The total nutrient intake of the $15{\sim}19$-year youth group was much higher than the standard value. The energy intake of the smoking group and the drinking and smoking group was significantly higher than that of the normal group (p<0.05). Intakes of phosphorus (p<0.05), cholesterol (p<0.05), and sodium (p<0.05) were the highest among all groups. Accordingly, it is recommended that practical education programs be implemented to teach young students to resist peer pressures to smoke and drink. Additionally, education that acknowledges the importance of nutrition is necessary to avoid preferential eating and overeating due to smoking and drinking.such education can also teach students to eat a balanced diet and improve their physical development.