• Journal of Ginseng Culture
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• v.6
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• pp.51-79
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• 2024

In the 1970s and 1980s, during the nascent phase of ginseng disease research, efforts concentrated on isolating and identifying pathogens. Subsequently, their physiological ecology and pathogenesis characteristics were scrutinized. This led to the establishment of a comprehensive control approach for safeguarding major aerial part diseases like Alternaria blight, anthracnose, and Phytophthora blight, along with underground part diseases such as Rhizoctonia seedling damping-off, Pythium seedling damping-off, and Sclerotinia white rot. In the 1980s, the sunshade was changed from traditional rice straw to polyethylene (PE) net. From 1987 to 1989, focused research aimed at enhancing disease control methods. Notably, the introduction of a four-layer woven P.E. light-shading net minimized rainwater leakage, curbing Alternaria blight occurrence. Since 1990, identification of the bacterial soft stem rot pathogen facilitated the establishment of a flower stem removal method to mitigate outbreaks. Concurrently, efforts were directed towards identifying root rot pathogens causing continuous crop failure, employing soil fumigation and filling methods for sustainable crop land use. In 2000, adapting to rapid climate changes became imperative, prompting modifications and supplements to control methods. New approaches were devised, including a crop protection agent method for Alternaria stem blight triggered by excessive rainfall during sprouting and a control method for gray mold disease. A comprehensive plan to enhance control methods for Rhizoctonia seedling damping-off and Rhizoctonia damping-off was also devised. Over the past 50 years, the initial emphasis was on understanding the causes and control of ginseng diseases, followed by refining established control methods. Drawing on these findings, future ginseng cultivation and disease control methods should be innovatively developed to proactively address evolving factors such as climate fluctuations, diminishing cultivation areas, escalating labor costs, and heightened consumer safety awareness.

• Applied Biological Chemistry
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• v.15 no.3
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• pp.193-198
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• 1972

Twenty seven specimens of deteriolated storage rice were collected all over Korea. Types of deteriolation were classified according to color outlooks, and for 48 grains of each specimen the causative storage microorganisms were isolated and identified. The following results were observed; 1. 27 specimens of deteriolated rice were classified according to color outlooks into 7 types: reddish yellow 1, light reddish yellow 3, light grayish yellow 4, light red 6, dark gray 7, light gray 3, and rice weevil type 3. 2. The most common storage microorganisms group which infected deteriolated Korean rice were Aspergillus glaucus group, especially species of A. amstelodami, A. chevalieri, A. montevidensis, and A. ruber, which were frequently associated with Penicillium, Brevibactereum, and Bacillus. 3. As a specific case sometimes a specimen of deteriolated rice was infected chiefly by one deminant species of microorganism. Five cases were observed: that is, by P. islandicum, P. lanosum, B. lentus, Pseudomonas cohaerans, Brev. lipolyticum. 4. No definite relationship was observed between color outlook types and the deteriolation causing microorganisms. Only the heavily infected rice by Penicillium islandicum expressed discernible reddish yellow color indicative of the infection by this mold. 5. Mycotoxin problem could be noted in one specimen of deteriolated imported rice heavily infected by P. islandicum. Other mycotoxin producing fungi, A. flavus, A. ochraceus, A. fumigatus were also detected, but their growth frequencies were so low that it might not be serious problem.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.562-569
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• 2019

Raman spectroscopy has been receiving increased attention as a standoff explosive detection technique. In addition, there is a growing need for a fast search method that can identify raman spectrum for measured chemical substances compared to known raman spectra in large database. By far the most simple and widely used method is to calculate and compare the Euclidean distance between the given spectrum and the spectra in a database. But it is non-trivial problem because of the inherent high dimensionality of the data. One of the most serious problems is the high computational complexity of searching for the closet spectra. To overcome this problem, we presented the MPS Sort with Sorted Variance+PDS method for the fast algorithm to search for the closet spectra in the last paper. the proposed algorithm uses two significant features of a vector, mean values and variance, to reject many unlikely spectra and save a great deal of computation time. In this paper, we present two new methods for the fast algorithm to search for the closet spectra. the PCA+PDS algorithm reduces the amount of computation by reducing the dimension of the data through PCA transformation with the same result as the distance calculation using the whole data. the Hierarchical Cluster Tree algorithm makes a binary hierarchical tree using PCA transformed spectra data. then it start searching from the clusters closest to the input spectrum and do not calculate many spectra that can not be candidates, which save a great deal of computation time. As the Experiment results, PCA+PDS shows about 60.06% performance improvement for the MPS Sort with Sorted Variance+PDS. also, Hierarchical Tree shows about 17.74% performance improvement for the PCA+PDS. The results obtained confirm the effectiveness of the proposed algorithm.

• Journal of Nutrition and Health
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• v.54 no.3
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• pp.277-291
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• 2021

Purpose: This study was undertaken to identify the alcohol drinking status of industrial workers, their health care practice, and dietary behavior, as well as their nutrient intake. Methods: In July 2019, 220 male subjects working in the Chungnam area were enrolled in the study. Their alcohol drinking status was evaluated by applying the Alcohol Use Disorder Identification-K (AUDIT-K) system. Demographic characteristics, status of health care practice, and dietary behaviors were assessed using a self-administered questionnaire; nutrient intakes were analyzed using 24-hour recalls. Data were analyzed by applying χ²-test, ANOVA, Duncan test, and Pearson's correlation analysis with SPSS v. 25.0. Results: Workers were classified by their alcohol drinking status as 'normal' (84, 38.2%), 'problem drinker' (45, 20.5%), 'alcohol dependence I' (60, 27.3%), and 'alcohol dependence II' (31, 16.0%). The alcohol drinking status showed significant differences with age (p < 0.05), monthly income (p < 0.05), smoking status (p < 0.05), and need for weight control (p < 0.05). Moreover, increased alcohol intake resulted in significantly decreased levels of health care practice and dietary behaviors (p < 0.05, p < 0.01, respectively). The energy intake was highest in the 'alcohol dependence I' group, followed by 'alcohol dependence II', 'problem drinker', and 'normal drinker' (p < 0.05). Intakes of vitamin E, vitamin C, and niacin in the 'alcohol dependence I' group were found to be higher than the other groups (p < 0.05). A negative correlation was obtained between alcohol drinking status, health care practice, and dietary behaviors, whereas a positive correlation was determined between alcohol drinking status, energy and water intakes. Conclusion: Considering these results, we conclude the necessity to consider nutritional and alcoholic education programs for improving the quality of work life of industrial workers, based on their alcohol drinking status.

• (The) Research of the performance art and culture
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• no.22
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• pp.93-121
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• 2011

This article is concerned with the withdrawal of the compositional principle of Jinogigut which has been performed in Seoul and the identification of its meaning based on the withdrawal. Jinogigut is a world where a god is connected to humans in complicated manners, this world and the world of the dead coexist, and it is a process of demonstrating that the dead, who have stayed in the world of humans, enter the world of a god. Jinogigut shows the process of leading the dead to the world of the dead one after another. First, the god-centered street is continued, and the gut displays through which process a god will guide the dead to the world of the dead. Next, is a human-centered street, which exhibits the appearance of the dead heading to the world of the dead following the death angel, more in detail. Finally, a human-centered structure shows how humans enter the world of the dead. Through this repetition, it reveals that the dead take a seat in the world of the dead, at last. The organization of the later part of the world of the dead-oriented gut in Jinogigut, which is god-centered, continues to a human-centered gut through the meeting between a god and humans. and , which are continued, followed by , are ceremonial rituals that confirm the dead entering the world of the dead without any problem. Begareugi shows that the entering of the dead into the world of the dead was completed with perfection by cutting hemp cloth, and informs the living that the dead expressed gratitude for holding the ritual for him/her by appearing at the venue of the gut once again and that the dead settled into the world of death. , which finally holds ancestral rites to the god of ancestors who is seated in the world of the dead, reveals that the dead, who had been a human, has been transformed into the god of ancestors through Jinogigut. Jinogigut also performs the function of comforting a client (who is the family of the dead) of the gut, who has faced a sudden death in his/her family. What is the most important for consoling the client is to display that the dead has entered the world of the dead without any problem. Jinogigut shows this process through a three-layered structure. It exhibits how the dead would be moved to the world of gods, as well as the safe entering of the dead who followed Jeoseung-saja(envoy from the world of the dead) and who had appeared to this world from the world of the dead. Then, it demonstrates again the appearance of the dead entering the world of the dead following Barigongu; thus, it placates the heart of the client's family.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• The Korean Journal of Air & Space Law and Policy
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• v.30 no.1
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• pp.207-244
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• 2015

Aviation safety is the stage in which the risk of harm to persons or of property damage is reduced to, and maintained at or below, an acceptable level through a continuing process of hazard identification and risk management. Many accidents and incidents have been taking place since 2014, while there had been relatively safer skies before 2014. International civil aviation community has been exerting great efforts to deal with these emerging issues, thus enhancing and ensuring safety throughout the world over the years. The Preamble of the Chicago Convention emphasizes safety and order of international air transport, and so many Articles in the Convention are related to the safety. Furthermore, most of the Annexes to the Convention are International Standards and Recommended Practices pertaining to the safety. In particular, Annex 19, which was promulgated in Nov. 2013, dealing with safety management system. ICAO, as law-making body, has Air Navigation Commission, Council, Assembly to deliberate and make decisions regarding safety issues. It is also implementing USOAP and USAP to supervise safety functions of member States. After MH 370 disappeared in 2014, ICAO is developing Global Tracking System whereby there should be no loophole in tracking the location of aircraft anywhere in world with the information provided by many stakeholders concerned. MH 17 accident drove ICAO to install web-based repository where information relating to the operation in conflict zones is provided and shared. In addition, ICAO has been initiating various solutions to emerging issues such as ebola outbreak and operation under extreme meteorological conditions. Considering the necessity of protection and sharing of safety data and information to enhance safety level, ICAO is now suggesting enhanced provisions to do so, and getting feedback from member States. It has been observed that ICAO has been approaching issues towards problem-solving from four different dimensions. First regarding time, it analyses past experiences and best practices, and make solutions in short, mid and long terms. Second, from space perspective, ICAO covers States, region and the world as a whole. Third, regarding stakeholders it consults with and hear from as many entities as it could, including airlines, airports, community, consumers, manufacturers, air traffic control centers, air navigation service providers, industry and insurers. Last not but least, in terms of regulatory changes, it identifies best practices, guidance materials and provisions which could become standards and recommended practices.

• Analytical Science and Technology
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• v.31 no.4
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• pp.161-170
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• 2018

The epidemic of disorders associated with synthetic stimulants, such as methamphetamine (MA) and amphetamine (AP), is a health, social, legal, and financial problem. Owing to the high potential of their abuse and addiction, reliable analytical methods are required to detect and identify MA, AP, and their metabolites in biological samples. Thus, a dilute-and-shoot liquid chromatography-tandem mass spectrophotometry (LC-MS/MS) was developed for simultaneous determination of MA, 4-hydroxymethamphetamine (4HMA), AP, and 4-hydroxyamphetamine (4HA) in urine. Urine sample ($100{\mu}L$) was mixed with $50{\mu}L$ of mobile phase consisting of 0.4 % formic acid and methanol and $50{\mu}L$ of working internal-standard solution. Aliquots of $8{\mu}L$ diluted urine was injected into the LC-MS/MS system. For all analytes, chromatographic separation was performed using a C18 reversed-phase column with gradient elution and a total run time of 5 min. The identification and quantification were performed by multiple reaction monitoring (MRM). Linear least-squares regression was conducted to generate a calibration curve, with $1/x^2$ as the weighting factor. The linear ranges were 2.0-200, 1.0-800, and 10-2500 ng/mL for 4HA and 4HMA, AP, and MA, respectively. The inter- and intraday precisions were within 6.6 %, whereas the inter- and intraday accuracies ranged from -14.9 to 11.3 %. The low limits of quantification were 2.0 ng/mL (4HA and 4HMA), 1.0 ng/mL (AP), and 10 ng/mL (MA). The proposed method exhibited satisfactory selectivity, dilution integrity, matrix effect, and stability, which are required for validation. Moreover, the purification efficiency of high-speed centrifugation was clearly higher than 6-15 % for QC samples (n=5), which was higher than that of the membrane-filtration method. The applicability of the proposed method was tested by forensic analysis of urine samples from drug abusers.

• Journal of Korean Academy of Nursing
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• v.25 no.1
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• pp.61-79
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• 1995

When children are admitted to hospital, they have to adapt to new and unfamiliar stimuli. Children may respond with fear to stimuli such as pain or unfamiliar experiences. One goal of nursing is to help hospitalized children to adapt effectively to their hospital experience. Accordingly, nurses need to assess childrens' fears of their hospital experience to contribute to the planning of care to alleviate these fears. The problem addressed by this study was to identify and measure hospital-related fears(hereafter called HRF) in hospitalized school-aged children. The study was conceptualized with Roy's model. A descriptive qualitative approach was used first, followed by a quantitative approach. This study was conducted from November 30, 1989 to January 12, 1991. The sample consisted of 395 hospitalized school-aged children selected through an allocated sampling technique in nine general hospitals. The HRF questionnaire (three point likert scale ) was developed by a delphi technique. The data were analyzed by an SAS program. Factor analysis was used for the examination of component factors. Differences in the HRF related to demographic variables were examined by t-test, analysis of variance and the Scheffe test. The crude scores of the HRF scale were transformed into T- scores to calculate the standard scores. The results included the following : 1. Forty-four items were derived from 188 statements identifying the childrens' hospital-re-lated fears. These items clustered into 14 factors, fear of injections, operations, bodily harm others' pain, medical rounds, physical examinations, medical staff, disease process, blood and X-rays, drugs and cockroaches, tests, harsh discipline from parents or staff, being absent from school, and separation from family. The 14 factors was classified into four categories,'pain','the unfamiliar','the un-known' and 'separation'. 2. The reliability of the HRF instruments was .92(Cronbach's alpha). In the factor analysis, Cronbach's alpha coefficients for the 14 factors ranged from .84 to .86 and Cronbach's alpha coefficients for the four categories ranged from .70 to .84. Pearson correlation coefficient scores for relationships among the 14 factors ranged from ,11 to .50, and among the four categories, from ,44 to ,63, indicating their relative independence. 3. The total group HRF score ranged from 45 to 130 in a possible range of H to 132, with a mean of 74.51. The fears identified by the children were, in order, injections, harsh discipline by parents or staff, bodily harm, operations, medical staff, disease process, and medical rounds ; the least feared was others' pain. The fear item with the highest mean score was surgery and the lowest was examination by a doctor. HRF scores were higher for girls than for boys, and for grade 1 students than for grade 6 students. HRF scores were lower for children whose fathers were over 40 than for those whose fathers were in the 30 to 39 age group, and whose mothers were over 35 than for those whose mothers were in the 20 to 34 age group. HRF scores were lower when the mother rather than any other person stayed with the child. The expressed fear of pain, the unfamiliar, the un-known and of separation directs nurses' concern to the threat felt by hospitalized children to their concept of self. This study contributes to the assessment of fears of hospitalized children and of stimuli impinging on those fears. Accordingly, nursing practice will be directed to the alleviation of pain, pre-admission orientation to the hospital setting and routines, initiation of information about procedures and experiences and arrangments for mothers to stay with their children. Recommendations were made for further research in different settings and for development and testing of the instrument.