• Journal of Service Research and Studies
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• v.14 no.2
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• pp.37-47
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• 2024

Currently, social interest in post-cremation funerals is growing due to the establishment of cremation culture. In addition, as awareness of nature-friendly funeral methods spreads in modern times, the demand for tree burial grounds and tree groves, which are representative natural burial methods, is increasing. However, if the current method of relying on trees is used, the forest burial may damage the forest and turn it into another cemetery. The tree decoration is a funeral method that contains the temporal meaning of humans returning to the space of nature that we have, and the philosophical meaning that humans return in compliance with nature. Like this, there are quite a few concerns. Even though tree burials are not the traditional burial facilities we are familiar with, many of the facilities and operating systems adopt the standards of park cemeteries and have stricter standards and restrictions than natural burials under the law. This rigidity is intended to preserve the forest, but the reality is that it limits the expansion and operation of tree plantations. To this end, this study seeks to find specific improvement measures for sustainable tree plantation operation. To this end, we look at the types of natural fields in foreign countries and find directions for tree planting that can be effectively applied and established in accordance with the sentiments of the people. Specific improvement measures include an enshrinement method that does not rely on memorial trees, the operation of anonymous or anonymous tree planting, a change in the method of visiting and commemorating, and various mountaineering methods, thereby suggesting alternatives to sustainable tree planting in Korea. The place where tree planting is implemented is the forest, that is, the forest itself. I should be a place where the spirit of natural return, which is the essence of the deceased, can be celebrated through the forest, not a funeral facility. By doing so, it will be possible to provide the public value of the forest, that is, the social function of the forest, in the name of an eco-friendly funeral service.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.293-300
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• 2024

On October 31, 2023, the revision of the Medical Technologist Act made it mandatory to complete field training courses in order to obtain a license as a radiologic technologist. Therefore, we would like to survey the actual situation of field training in medical institutions to inform the revised Medical Technologist Act and propose improvement measures to increase the effectiveness of field training. A survey was conducted from March to April, 2023, among radiologic technologists working in medical institutions. The questionnaire was sent through a form on a domestic portal site, Company N, and 120 respondents completed it. Eighty-two respondents, or 68.3 percent, had experience in educating on-the-job training students. 58% of the respondents were aware of the fact that the amendment to the Act on Medical Technologist etc. made field training mandatory to obtain a radiologic technologist license. In accordance with Article 9 of the Medical Technologist Act, which prohibits unlicensed persons from practicing, 50% of the respondents were aware that those who are in training to complete an education course equivalent to the license they are seeking to obtain at a university or other institution are allowed to practice as medical Technologists. When asked what is currently taught during fieldwork, 6% of respondents said that they are required to perform radiation-generating activities in addition to observing, guiding patients, and positioning and moving patients. When asked about the future direction of education as fieldwork becomes mandatory for licensure, 77% of respondents said that they will teach more than they currently do. When asked about the appropriate total length of fieldwork, 35% said 12 weeks and 480 hours, 33% said 8 weeks and 320 hours, and 27% said 16 weeks and 640 hours. It can be seen that the current on-the-job training is inadequate according to various regulations, and students' satisfaction is low. However, with the revision of the Act on Medical Technologists, field training has become mandatory to obtain a license as a radiologist, and it is necessary to improve the educational conditions of field training. Therefore, it is necessary to comply with the Nuclear Safety Act and the Rules on the Safety Management of Diagnostic Radiation Generating Devices, introduce standardized training objectives and evaluation systems, designate training hospitals and radiologists in charge of training, and introduce extended training periods and simulation exercises to internalize field training.

• 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.

• Journal of Venture Innovation
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• v.7 no.1
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• pp.183-200
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• 2024

Recently, with the MZ generation's entry into society and the social participation of the female population, conflicts are occurring between workplace groups that value WLB and existing groups that emphasize collaboration due to differences in work orientation. Public institutions and companies that utilize work-life balance support systems show differences in job Commitment depending on the nature of the work and the activation of the support system. Accordingly, it is necessary to verify the effectiveness of the WLB support system actually operated by the company and present universally valid standards. The purpose of this study is, first, to verify the effectiveness of the support system for work-life balance and to find practical consensus amid changes in policies and perceptions of the working environment. Second, the influence of work-life balance level and job immersion according to work characteristics was analyzed to verify the mutual influence in order to establish standards for WLB operation that reflects work characteristics. For the study, a 2X2 matrix model was used to analyze the impact of work-life balance and work characteristics on job commitment, and four hypotheses were established. First, analysis of the job involvement level of conflict-type group members, second, analysis of the job involvement level of leading group members, third, analysis of the job involvement level of agreeable group members, and fourth, analysis of the job involvement level of cooperative group members. To conduct this study, an online survey was conducted targeting employees working in public institutions and large corporations. The survey was conducted for a total of 9 days from October 23 to 31, 2023, and 163 people responded, and the analysis was based on a valid sample of 152 people, excluding 11 copies that were insincere responses or gave up midway. As a result of the study's hypothesis testing, first, the conflict type group was found to have the lowest level of job engagement at 1.43. Second, the proactive group showed the highest level of job engagement at 4.54. Third, the conformity group showed a slightly lower level of job involvement at 2.58. Fourth, the cooperative group showed a slightly higher level of job involvement at 3.80. The academic implications of the study are that it subdivides employees' personalities into factors based on the level of work-life balance and nature of work. The practical implications of the study are that it analyzes the effectiveness of WLB support systems operated by public institutions and large corporations by grouping them.

• Journal of agricultural medicine and community health
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• v.22 no.1
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• pp.1-18
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• 1997

This study was carried out to reveal the status of health examination among farmers and to attract more attention to the health care system for farmers. Ten pre-trained medical students interviewed the rural residents 18 years of age and older in eight villages which were randomly selected from a county near Taegu city in Korea, in August 1996. Finally 751 persons were interviewed of whom the percentages of male and female were 41.8%, 58.2% respectively. Among the subjects, 361(48.3%) were fully engaged in farming, 184(24.4%) were partly engaged, and the remaining 206(27.3%) were not engaged in farming at all. The overall prevalence of farmer's disease was 23.0% and there was no significant difference between the group of fully engaged in farming(23.3%) and the group of not-fully engaged(22.9%). But the prevalence of farmer's disease in female subjects(27.8%) was significantly higher than that in male(16.2%)(p<0.01). Among the 288 farmer engaged in spraying pesticide, 113(39.2%) had experienced one or more pesticide related symptoms during last one year, but only 18(15.9%) of them had visited medical facilities due to their symptoms. The experience of receiving education about pesticide was significantly correlated with the degree of wearing protectors during pesticide spraying(p<0.001). Among the 736 persons excluding non-respondents, 281(38.2%) received health examination during last one year ; 176(62.6%) of them received free health examination, and 105(37.4%) received charged one. Among the 533 persons 40 years age and older, only 124(23.3%) had received the 'health examination for the elderly' during last one year, which is provided for the 40 years age and older by Korea medical insurance corporation and medical insurance societies. Most of all beneficiaries of self-employed medical insurance thought the imposed contributions as very expensive(77.4%) or moderately expensive(13.2%). The great majority of farmers are exposed to various health risk factors including pesticide, high temperature, overwork etc. comparable to industrial workers. But farmers are excluded from the regular yearly worker's health examination because of not belonging to a company despite they pay relatively more medical insurance contributions compared with the industrial workers and the urban self-employed medical insureds. It is necessary to develop special health management program for farmers such as the special health examination for the industrial workers exposed harmful agents.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• The korean journal of orthodontics
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• v.36 no.5
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• pp.339-348
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• 2006

Objective: With development of the skeletal anchorage system, orthodontic mini-implant (OMI) assisted on masse sliding retraction has become part of general orthodontic treatment. But compared to the emphasis on successful anchorage preparation, the control of anterior teeth axis has not been emphasized enough. Methods: A 3-D finite element Base model of maxillary dental arch and a Lingual tipping model with lingually inclined anterior teeth were constructed. To evaluate factors influencing the axis of anterior teeth when OMI was used as anchorage, models were simulated with 2 mm or 5 mm retraction hooks and/or by the addition of 4 mm of compensating curve (CC) on the main archwire. The stress distribution on the roots and a 25000 times enlarged axis graph were evaluated. Results: Intrusive component of retraction force directed postero-superiorly from the 2 mm height hook did not reduce the lingual tipping of anterior teeth. When hook height was increased to 5 mm, lateral incisor showed crown-labial and root-lingual torque and uncontrolled tipping of the canine was increased.4 mm of CC added to the main archwire also induced crown-labial and root-lingual torque of the lateral incisor but uncontrolled tipping of the canine was decreased. Lingual tipping model showed very similar results compared with the Base model. Conclusion: The results of this study showed that height of the hook and compensating curve on the main archwire can influence the axis of anterior teeth. These data can be used as guidelines for clinical application.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of the Korean Society of Radiology
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• v.7 no.2
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• pp.113-120
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• 2013

Nowadays, the medical system towards patients changes into the medical services. As the human rights are improved and the capitalism is enlarged, the rights and needs of patients are gradually increasing. Also, based on this change, several systems in hospitals are revised according to the convenience and needs of patients. Thus, the cases of mobile portable among examinations are getting augmented. Because the number of mobile portable examinations in patient's room, intensive care unit, operating room and recovery room increases, neighboring patients are unnecessarily exposed to radiation so that the examination is legally regulated. Hospitals have to specify that "In case that the examination is taken out of the operating room, emergency room or intensive care units, the portable medical X-ray protective blocks should be set" in accordance with the standards of radiation protective facility in diagnostic radiological system. Some keep this regulation well, but mostly they do not keep. In this study, we shielded around the Collimator where the radiation is detected and then checked the change of dose regarding that of angles in portable tube and collimator before and after shielding. Moreover, we tried to figure out the effects of shielding on dose according to the distance change between patients' beds. As a result, the neighboring areas around the collimator are affected by the shielding. After shielding, the radiation is blocked 20% more than doing nothing. When doing the portable examination, the exposure doses are increased $0^{\circ}C$, $90^{\circ}C$ and $45^{\circ}C$ in order. At the time when the angle is set, the change of doses around the collimator decline after shielding. In addition, the exposure doses related to the distance of beds are less at 1m than 0.5m. In consideration of the shielding effects, putting the beds as far as possible is the best way to block the radiation, which is close to 100%. Next thing is shielding the collimator and its effect is about 20%, and it is more or less 10% by controlling the angles. When taking the portable examination, it is better to keep the patients and guardians far enough away to reduce the exposure doses. However, in case that the bed is fixed and the patient cannot move, it is suggested to shield around the collimator. Furthermore, $90^{\circ}C$ of collimator and tube is recommended. If it is not possible, the examination should be taken at $0^{\circ}C$ and $45^{\circ}C$ is better to be disallowed. The radiation-related workers should be aware of above results, and apply them to themselves in practice. Also, it is recommended to carry out researches and try hard to figure out the ways of reducing the exposure doses and shielding the radiation effectively.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.