• The Journal of Korean Medical History
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• v.22 no.2
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• pp.7-13
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• 2009

Background : To understand Korean medicine it is crucial to first understand medical aspect of 'DongUiBoGam'. It is also meaningful that it became one of the influential book even to posterity. Also based on the apprehension, we can take a view of development of Korean medicine led by 'DongUiBoGam'. Objectives : This study aims to review the status, study field, specialist of DongUiBoGam. In the process, this review will grasp trends in this field of studies and will direct further researches into the right direction. Method : The computerized Korean databases were searched from their respective inceptions up to December 2008. The search terms used were 'DongUiBoGam' and random or Korean language terms related to DongUiBoGam. Several specialized journals were also manually searched for relevant articles. Result : Since the 2000s, DongUiBoGam papers in the Korean Literature is increased. Published 58 papers on The Korean Journal of Oriental Medical Prescription were more than the other journals. 58 papers published in The Korean Journal of Oriental Medical Prescription are the best in many areas related to Korean medicine. Most people have submitted papers related to DongUiBoGam was Woo-yeal Jeong. Conclusions : 'DongUiBoGam' research is conducted and current tendency and outlook for 'DongUiBoGam' is carried out focusing on several associations.

• Perspectives in Nursing Science
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• v.10 no.1
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• pp.65-76
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• 2013

Purpose: To identify an associations between health care workers' uniforms and health care-associated infection. Methods: Electronic databases, including Ovid-Medline, the Cochrane Library, CINAHL, EMBASE, KMbase, and KoreaMed, were searched. The search terms included doctor, nurse, health care worker/staff/assistant, clothing, (white) gown, uniform, (neck)tie, and attire. Only papers published in English and Korean were included. Results: 16 studies were selected from 1,900 references screened. All of the studies were non-comparative studies except for one. Four were conducted with doctors, six with nurses, one with health care workers including physiotherapists and one for medical staff plus visitors in a neonatal intensive care unit. Doctors more frequently changed their uniforms than neckties; therefore, the degree of contamination was more serious in neckties. The cuff zone was more likely to be heavily contaminated than other areas of long-sleeve gowns. Coats become contaminated quickly once worn, and colony counts reached a similar level within the first few hours after wearing them. Wearing a plastic apron or protective clothing did not prevent the bacterial contamination of nurses' uniforms, and the best way to decrease the contamination was changing to newly laundered uniforms before starting every duty. Conclusion: Healthcare workers' uniforms are a potential source of health care-associated infection although there was no robust evidence. The government must establish standards for laundering of uniforms or a requirement for institutions to provide a laundering service for healthcare workers' uniforms.

• Journal of the Korean Society of Safety
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• v.35 no.5
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• pp.15-21
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• 2020

Quantitative risk assessment (QRA) is used as a legal or voluntary safety management tool for the hazardous material industry and the utilization of the method is gradually increasing. Therefore, a leak frequency analysis based on reliable generic data is a critical element in the evolution of QRA and safety technologies. The aim of this paper is to derive the leak frequency function that can be applied more flexibly in QRA based on OGP report with high reliability and global utilization. For the purpose, we first reviewed the data on the 16 equipments included in the OGP report and selected the predictors. And then we found good equations to fit the OGP data using non-linear regression analysis. The various expectation functions were applied to search for suitable parameter to serve as a meaningful reference in the future. The results of this analysis show that the best fitting parameter is found in the form of DNV function and connection function in natural logarithm. In conclusion, the average percentage error between the fitted and the original value is very small as 3 %, so the derived prediction function can be applicable in the quantitative frequency analysis. This study is to contribute to expand the applicability of QRA and advance safety engineering as providing the generic equations for practical leak frequency analysis.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.98-105
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• 2021

The sensory stimulation of a cosmetic product has been deemed to be an ancillary aspect until a decade ago. That point of view has drastically changed on different levels in just a decade. Nowadays cosmetic formulators should unavoidably meet the needs of consumers who want sensory satisfaction, although they do not have much time for new product development. The selection of new products from candidate products largely depend on the panel of human sensory experts. As new product development cycle time decreases, the formulators wanted to find systematic tools that are required to filter candidate products into a short list. Traditional statistical analysis on most physical property tests for the products including tribology tests and rheology tests, do not give any sound foundation for filtering candidate products. In this paper, we suggest a deep learning-based analysis method to identify hand cream products by raw electric signals from tribological sliding test. We compare the result of the deep learning-based method using raw data as input with the results of several machine learning-based analysis methods using manually extracted features as input. Among them, ResNet that is a deep learning model proved to be the best method to identify hand cream used in the test. According to our search in the scientific reported papers, this is the first attempt for predicting test cosmetic product with only raw time-series friction data without any manual feature extraction. Automatic product identification capability without manually extracted features can be used to narrow down the list of the newly developed candidate products.

• Natural Product Sciences
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• v.24 no.4
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• pp.235-240
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• 2018

Betula platyphylla var. japonica (Betulaceae), also known as Asian white birch, is an endemic medicinal tree, the bark of which has been used in Chinese traditional medicine for the treatment of various inflammatory diseases. In our continuing search for bioactive compounds from Korean natural resources, a phytochemical investigation of the bark of B. platyphylla var. japonica led to the isolation of 7-oxo-${\beta}$-sitosterol (1) and soyacerebroside I (2) from its ethanol extract as main components by liquid chromatography (LC)/mass spectrometry (MS)-based analysis. The structures of isolates were identified by comparison of $^1H$ and $^{13}C$ nuclear magnetic resonance spectroscopic data and physical data with the previously reported values and LC/MS analyses. To the best of our knowledge, this is the first study to demonstrate that the isolated compounds, 7-oxo-${\beta}$-sitosterol and soyacerebroside I, were isolated in B. platyphylla var. japonica. We examined the effects of the isolates on the regulation of adipocytes and osteoblast differentiation. These isolates (1 and 2) produced fewer lipid droplets compared to the untreated negative control in Oil Red O staining of the mouse mesenchymal stem cell line without altering the amount of alkaline phosphatase staining. The results demonstrated that both compounds showed marginal inhibitory effects on adipocyte differentiation but did not affect osteoblast differentiation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.2074-2093
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• 2022

RSA is known as one of the best techniques for securing secret information across an unsecured network. The private key which is one of private parameters is the aim for attackers. However, it is exceedingly impossible to derive this value without disclosing all unknown parameters. In fact, many methods to recover the private key were proposed, the performance of each algorithm is acceptable for the different cases. For example, Wiener's attack is extremely efficient when the private key is very small. On the other hand, Fermat's factoring can quickly break RSA when the difference between two large prime factors of the modulus is relatively small. In general, if all private parameters are not disclosed, attackers will be able to confirm that the private key is unquestionably inside the scope [3, n - 2], where n is the modulus. However, this scope has already been reduced by increasing the greatest lower bound to [d_il, n - 2], where d_il ≥ 3. The aim of this paper is to decrease the least upper bound to narrow the scope that the private key will remain within this boundary. After finishing the proposed method, the new scope of the private key can be allocated as [d_il, d_ir], where d_ir ≤ n - 2. In fact, if the private key is extremely close to the new greatest lower bound, it can be retrieved quickly by performing a brute force attack, in which d_ir is decreased until it is equal to the private key. The experimental results indicate that the proposed method is extremely effective when the difference between prime factors is close to each other and one of two following requirement holds: the first condition is that the multiplier of Euler totient function is very close to the public key's small value whereas the second condition is that the public key should be large whenever the multiplier is far enough.

• Steel and Composite Structures
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• v.46 no.3
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• pp.319-334
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• 2023

Localizing damages is an essential task to monitor the health of the structures since they may not be able to operate anymore. Among the damage detection techniques, non-destructive methods are considerably more preferred than destructive methods since damage can be located without affecting the structural integrity. However, these methods have several drawbacks in terms of detecting abilities, time consumption, cost, and hardware or software requirements. Employing artificial intelligence techniques could overcome such issues and could provide a powerful damage detection model if the technique is utilized correctly. In this study, the crack localization in flat and folded plate structures has been conducted by employing a Backpropagated Artificial Neural Network (BPANN). For this purpose, cracks with 18 different dimensions in thin, flat, and folded structures having 15⁰, 30⁰, 45⁰, and 60⁰ folding angle have been modeled and subjected to free vibration analysis by employing the Classical Plate Theory with Finite Element Method. A Four-nodded quadrilateral element having six degrees of freedom has been considered to represent those structures mathematically. The first ten natural frequencies have been obtained regarding healthy and cracked structures. To localize the crack, the ratios of the frequencies of the cracked flat and folded structures to those of healthy ones have been taken into account. Those ratios have been given to BPANN as the input variables, while the crack locations have been considered as the output variables. A total of 500 crack locations have been regarded within the dataset obtained from the results of the free vibration analysis. To build the best intelligent model, a feature search has been conducted for BAPNN regarding activation function, the number of hidden layers, and the number of hidden neurons. Regarding the analysis results, it is concluded that the BPANN is able to localize the cracks with an average accuracy of 95.12%.

• 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.

• Journal of KIISE
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• v.44 no.10
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• pp.1112-1123
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• 2017

As content providers further offload content-centric services to the cloud, data retrieval over the cloud typically results in many redundant items because there is a prevalent near-duplication of content on the Internet. Simply fetching all data from the cloud severely degrades efficiency in terms of resource utilization and bandwidth, and data can be encrypted by multiple content providers under different keys to preserve privacy. Thus, locating near-duplicate data in a privacy-preserving way is highly dependent on the ability to deduplicate redundant search results and returns best matches without decrypting data. To this end, we propose an efficient near-duplicate detection scheme for encrypted data in the cloud. Our scheme has the following benefits. First, a single query is enough to locate near-duplicate data even if they are encrypted under different keys of multiple content providers. Second, storage, computation and communication costs are alleviated compared to existing schemes, while achieving the same level of search accuracy. Third, scalability is significantly improved as a result of a novel and efficient two-round detection to locate near-duplicate candidates over large quantities of data in the cloud. An experimental analysis with real-world data demonstrates the applicability of the proposed scheme to a practical cloud system. Last, the proposed scheme is an average of 70.6% faster than an existing scheme.

• Journal of The Korean Association For Science Education
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• v.22 no.4
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• pp.768-778
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• 2002

Science teachers often suppose that students are able to know the symbolical meaning of graphs when they see the graphs. But such a assumption is not based on the firm theories but a mere image. And we need to search them for holding the abilities to construct and to interpret. In addition, unfortunately, many researchers show that they scarcely have the graphing skills. And then, The Test of Graphing in Science(TOGS) was administered to 535 7th to 12th graders, for we search them for holding the graphing abilities to some degree. Though the higher grade, the better score, they lack the first three among 9 objectives of TOGS which are scaling axes, assigning variables to the axes, using a best fit line, plotting points, translating a graph that displays the data, selecting the corresponding value for y(or x), interrelating/extrapolating graphs, describing the relationship between variables, interrelating the results of the two graphs. It was concluded from this that subjects' graph construction is lower than their graph interpretation in graph skills. It suggests that school science have a bias toward graph interpretation. This tendency represents more strikingly in the case of upper students in TOGS than the others'.