• The Korean Journal of Mycology
• /
• v.38 no.2
• /
• pp.105-111
• /
• 2010

Lentinus lepideus, known as train wrecker fungus, has been used for nutritional and medicinal purposes. Recently, commercial cultivation technique and a new cultivar of the mushroom were developed. To investigate the genetic diversity and phylogenetic relationship for identifying the mushroom strains and cultivar, one commercial and 13 strains of Lentinus lepideus from different geographical regions of Korea were analyzed by ITS regions of rDNA and RAPD of genomic DNA. Three strains of Lentinus edodes were also used for the analysis. The size of the ITS1 and ITS2 regions of rDNA from the different strains varied from 173 to 179 bp and 203 to 205 bp, respectively. The sequence of ITS1 was more variable than that of ITS2, while the 5.8S sequences were identical with 156 base pairs. A phylogenetic tree based on the ITS region sequences indicated that selected strains could be classified into four clusters, while 3 strains of L. edodes was divided into a new cluster. Ten primers out of 20 arbitrary primers used in the RAPD-PCR efficiently amplified the genomic DNA. The numbers of amplified DNA bands varied with the primers and strains, with polymorphic DNA fragments in the range from 0.2 to 2.6 kb. The results showed that phylogenetic relationship among Korean strains of Lentnus lepideus is high, but genetic diversity is low.

• Cross-Cultural Studies
• /
• v.43
• /
• pp.429-457
• /
• 2016

This article is an analysis of the status of the modern Korean novels translated into Chinese over the past five years and how they are perceived by readers. Translation of modern Korean novels over the past five years has a few important characteristics as the following. The first characteristic is diversity. Books written by the most representative modern Korean writers, like Lee Gwang-soo, Kim Yu-jung, Kim Dong-ri, and books of the authors with very unique ideas, such as Park Kyung-ri, Lee Mun-yeol, Shin Kyung-suk, Gong Ji-young, Kim Young-ha, Park Min-kyu, Cheon Myung-gwan, and Kim Ae-ran have been translated and introduced to the Chinese population. Secondly, there are active translation of the books written by female writers. Lastly, without the support of the Literature Translation Institute of Korea or the Daesan Foundation, the number of works translated and published is slowly increasing. As a result of the increasing number of translations, the quality of translation is improving. However, interest on the part of Chinese readers in the modern Korean novel is not very high. But, the works of authors like Kim Young-ha, Cheon Myung-gwan, Kim Ae-ran, and Park Min-kyu, who began their literary careers after the mid-90s, are drawing relatively more attention. The common features of such works are the novelty of the narrative methods, attachment to reality, and readability. The interest shown by Chinese readers is significant in explaining the two following factors. First, it is true that many modern Korean novels are available in China, but only those that have been read will continue to be read. Second, the indifference of Chinese readers to modern Korean novels is because they are not yet aware of the existence of such works. It is important to train professional translators who can properly translate literature and also to focus on introducing the differences in modern Korean novels through canonical translation. To achieve this aim, not only supportive policies, but also cooperation between researchers in the field of modern Korean literature, translators, and publishers is essential.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.43-61
• /
• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Phonetics and Speech Sciences
• /
• v.11 no.1
• /
• pp.51-61
• /
• 2019

The purpose of this study was to develop a smartphone based voice therapy program for patients with voice disorders. Contents of voice therapy were collected through analysis of mobile contents related to voice therapy in Korea, experts and users' demand survey, and the program was developed using Android Studio. Content needed for voice therapy was collected through analysis of mobile contents related to voice therapy. The user satisfaction evaluation for application was conducted for five patient with functional voice disorders. The results showed that the mobile contents related to voice therapy in Korea were mostly related to breathing, followed by voice and singing, but only 13 applications were practically practiced for voice therapy. Expert and user demand surveys showed that the patients and therapists both had a high need for content that could provide voice training in places other than the treatment room. Based on this analysis, 'Home Voice Trainer', an smartphone based voice therapy program, was developed. Home Voice Trainer is an application for voice therapy and management based on Android smartphones. It is designed to train voice therapy activities at home that have been trained offline. In addition, the records of voice training of patients were managed online so that patients can maintain voice improvement through continuous voice consulting even after the end of voice therapy. User evaluations show that patients are satisfied with the difficulty and content of voice therapy programs provided by home voice trainers, but lack of a portion of user interface, such as the portion of home button and interface between screens. Further study suggests the clinical application of home voice trainer to the patients with voice disorders. It is expected that the development study and the clinical application of smart contents related to voice therapy will be actively conducted.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.21 no.1
• /
• pp.29-41
• /
• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Journal of Intelligence and Information Systems
• /
• v.28 no.1
• /
• pp.69-88
• /
• 2022

One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.

• Journal of Intelligence and Information Systems
• /
• v.28 no.1
• /
• pp.329-352
• /
• 2022

It is reported that particulate matter(PM) penetrates the lungs and blood vessels and causes various heart diseases and respiratory diseases such as lung cancer. The subway is a means of transportation used by an average of 10 million people a day, and although it is important to create a clean and comfortable environment, the level of particulate matter pollution is shown to be high. It is because the subways run through an underground tunnel and the particulate matter trapped in the tunnel moves to the underground station due to the train wind. The Ministry of Environment and the Seoul Metropolitan Government are making various efforts to reduce PM concentration by establishing measures to improve air quality at underground stations. The smart air quality management system is a system that manages air quality in advance by collecting air quality data, analyzing and predicting the PM concentration. The prediction model of the PM concentration is an important component of this system. Various studies on time series data prediction are being conducted, but in relation to the PM prediction in subway stations, it is limited to statistical or recurrent neural network-based deep learning model researches. Therefore, in this study, we propose four transformer-based models including spatiotemporal transformers. As a result of performing PM concentration prediction experiments in the waiting rooms of subway stations in Seoul, it was confirmed that the performance of the transformer-based models was superior to that of the existing ARIMA, LSTM, and Seq2Seq models. Among the transformer-based models, the performance of the spatiotemporal transformers was the best. The smart air quality management system operated through data-based prediction becomes more effective and energy efficient as the accuracy of PM prediction improves. The results of this study are expected to contribute to the efficient operation of the smart air quality management system.

• Journal of Intelligence and Information Systems
• /
• v.29 no.2
• /
• pp.101-114
• /
• 2023

In the wartime, aircraft carrying out a mission to strike the enemy deep in the depth are exposed to the risk of being shoot down. As a key combat force in mordern warfare, it takes a lot of time, effot and national budget to train military flight personnel who operate high-tech weapon systems. Therefore, this study studied the path problem of predicting the route of emergency escape from enemy territory to the target point to avoid obstacles, and through this, the possibility of safe recovery of emergency escape military flight personnel was increased. based problem, transforming the problem into a TSP, VRP, and Dijkstra algorithm, and approaching it with an optimization technique. However, if this problem is approached in a network problem, it is difficult to reflect the dynamic factors and uncertainties of the battlefield environment that military flight personnel in distress will face. So, MDP suitable for modeling dynamic environments was applied and studied. In addition, GIS was used to obtain topographic information data, and in the process of designing the reward structure of MDP, topographic information was reflected in more detail so that the model could be more realistic than previous studies. In this study, value iteration algorithms and deterministic methods were used to derive a path that allows the military flight personnel in distress to move to the shortest distance while making the most of the topographical advantages. In addition, it was intended to add the reality of the model by adding actual topographic information and obstacles that the military flight personnel in distress can meet in the process of escape and escape. Through this, it was possible to predict through which route the military flight personnel would escape and escape in the actual situation. The model presented in this study can be applied to various operational situations through redesign of the reward structure. In actual situations, decision support based on scientific techniques that reflect various factors in predicting the escape route of the military flight personnel in distress and conducting combat search and rescue operations will be possible.

• Journal of Internet Computing and Services
• /
• v.25 no.1
• /
• pp.99-107
• /
• 2024

Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.

• The Korean Nurse
• /
• v.29 no.2
• /
• pp.48-65
• /
• 1990

The government has adopted a policy to introduce Home Health Care Program, and has established a three stage plan to implement it. The three stage plan is : First, to amend Article 54 (Nurses for Different Types of Services) of the Regulations for Implementing the Law of Medical Services; Second, to tryout the new system through pilot projects established in public hospitals and clinics; and third, to implement at all hospitals and equivalent medical institutions. In accordance with the plan, the Regulation has been amend and it was promulgated on January 9,1990, thus establishing a legal ground for implementing the policy. Subsequently, however, the Medical Association raised its objection to the policy, causing a delay in moving into the second stage of the plan. Under these circumstances, a study was conducted by collecting and evaluating the opinions of physicians, nurses, non-medical personnel and patients on the need and expected result from the home health care for the purpose of help facilitating the implementation of the new system. As a result of this study, it was revealed that: 1. Except the physicians, absolute majority of all other three groups - nurses, non-medical personnel and patients -gave positive answers to all 11 items related to the need for establishing a program for Home Health Care. Among the physicians, the opinions on the need for the new services were different depending on their field of specialty, and those who have been treating long term patients were more positive in supporting the new system. 2. The respondents in all four groups held very positive view for the effectiveness and the expected result of the program. The composite total of scores for all of 17 items, however, re-veals that the physicians were least positive for the- effectiveness of the new system. The people in all four groups held high expectation on the system on the ground that: it will help continued medical care after the discharge from hospitals; that it will alleviate physical and economic burden of patient's family; that it will offer nursing services at home for the patients who are suffering from chronic disease, for those early discharge from hospital, or those who are without family members to look after the patients at home. 3. Opinions were different between patients( who will receive services) and nurses (who will provide services) on the types of services home visiting nurses should offer. The patients wanted "education on how to take care patients at home", "making arrangement to be admitted into hospital when need arises", "IV injection", "checking blood pressure", and "administering medications." On the other hand, nurses believed that they can offer all 16 types of services except "Controlling pain of patients", 4. For the question of "what types of patients are suitable for Home Health Care Program; " the physicians, the nurses and non-medical personnel all gave high score on the cases of "patients of chronic disease", "patients of old age", "terminal cases", and the "patients who require long-term stay in hospital". 5. On the question of who should control Home Health Care Program, only physicians proposed that it should be done through hospitals, while remaining three groups recommended that it should be done through public institutions such as public health center. 6. On the question of home health care fee, the respondents in all four groups believed that the most desireable way is to charge a fixed amount of visiting fee plus treatment service fee and cost of material. 7. In the case when the Home Health Care Program is to be operated through hospitals, it is recommended that a new section be created in the out-patient department for an exclusive handling of the services, instead of assigning it to an existing section. 8. For the qualification of the nurses for-home visiting, the majority of respondents recommended that they should be "registered nurses who have had clinical experiences and who have attended training courses for home health care". 9. On the question of if the program should be implemented; 74.0% of physicians, 87.5% of non-medical personnel, and 93.0% of nurses surveyed expressed positive support. 10. Among the respondents, 74.5% of -physicians, 81.3% of non-medical personnel and 90.9% of nurses said that they would refer patients' to home health care. 11. To the question addressed to patients if they would take advantage of home health care; 82.7% said they would if the fee is applicable to the Health Insurance, and 86.9% said they would follow advises of physicians in case they were decided for early discharge from hospitals. 12. While 93.5% of nurses surveyed had heard about the Home Health Care Program, only 38.6% of physicians surveyed, 50.9% of non-medical personnel, and 35.7% of patients surveyed had heard about the program. In view of above findings, the following measures are deemed prerequisite for an effective implementation of Home Health Care Program. 1. The fee for home health care to be included in the public health insurance. 2. Clearly define the types and scope of services to be offered in the Home Health Care Program. 3. Develop special programs for training nurses who will be assigned to the Home Health Care Program. 4. Train those nurses by consigning them at hospitals and educational institutions. 5. Government conducts publicity campaign toward the public and the hospitals so that the hospitals support the program and patients take advantage of them. 6. Systematic and effective publicity and educational programs for home heath care must be developed and exercises for the people of medical professions in hospitals as well as patients and their families. 7. Establish and operate pilot projects for home health care, to evaluate and refine their programs.