• Science of Emotion and Sensibility
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• v.21 no.4
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• pp.55-62
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• 2018

The two main requirements of wearable optical fiber fabrics are that they must presuppose a high degree of flexibility and they must maintain the luminance effect in both flat and bent conformations. Therefore, woven optical fiber fabrics that satisfy the above conditions were developed by both weaving and by using computer embroidery. First, we measured the brightness of the wearable optical fiber fabric in the flat state at a total of 10 measurement points at intervals of 1 cm. Second, the wearable optical fiber fabric was placed horizontally on the forearm, where three-dimensional bending occurs, and the luminance values were recorded at the same 10 measurement points. For the woven fabric in the flat state, the maximum, minimum, average, and standard deviation luminance values were $5.23cd/m^2$, $2.74cd/m^2$, $3.56cd/m^2$, and $1.11cd/m^2$, respectively. The corresponding luminance values from the bent forearm were $7.92cd/m^2$ (maximum), $2.37cd/m^2$ (minimum), $4.42cd/m^2$ (average), and $2.16cd/m^2$ (standard deviation). In the case of the computer-embroidered fabric, the maximum, minimum, average, and standard deviation luminance values in the flat state were $7.56cd/m^2$, $3.84cd/m^2$, $5.13cd/m^2$, and $1.04cd/m^2$, respectively, and in the bent forearm state were $9.6cd/m^2$, $3.63cd/m^2$, $6.13cd/m^2$, and $2.26cd/m^2$, respectively. Therefore, the computer-embroidered fabric exhibited a higher luminous effect than the woven fabric because the detailed structure reduced light-loss due to the backside fabric. In both types of wearable optical fiber fabric the luminance at the forearm was 124% and 119%, respectively, and the light emitting effect of the optical fiber fabric was maintained even when bent by the human body. This is consistent with the principle of Huygens, which defines the wave theory of light, and also the Huygens-Fresnel-Kirchhoff principle, which states that the intensity of light increases according to the magnitude of the angle of propagation of the light wavefront (${\theta}$).

• Journal of National Security and Military Science
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• s.15
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• pp.57-86
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• 2018

The ROKAF has been training for a number of exercise for victory in the war, but the lack of aviation ammunition has become a big issue every year. However, due to the limitation of defense resources, there are many difficulties in securing and stockpiling ammunition for the war readiness. Therefore, there is a need to find a way to secure aviation ammunition for war readiness in a more economical way, so In this study, we analyze the precedent research case and the case of the reduction of the purchase cost of weapon system of other countries, and then I have suggested a plan that is appropriate for our situation. As a result of examining previous research cases for this study, there were data that KIDA studied in 2012, Precision-guided weapons acquisition cost reduction measures pursued by US Air Force And the use of procurement agencies that are being implemented by NATO member countries. Based on this study, the following four measures were proposed to reduce the purchase cost of aviation ammunition. First, the mutual aid support agreement was developed to sign the ammunition joint operation agreement. Second, join the NATO Support & Procurement Agency (NSPA) Third, it builds a purchasing community centered on the countries operating the same ammunition Fourth, participating in the US Air Force's new purchase plan for ammunition and purchase it jointly. The main contents of these four measures are as follows. 1. the mutual aid support agreement was developed to sign the ammunition joint operation agreement. Korea has signed agreements on mutual logistics support with 14 countries including the United States, Israel, Indonesia, Singapore, Australia, and Taiwan. The main purpose of these agreements is mutual support of munitions and materials, also supporting the training of the peace time and promoting exchange and cooperation. However, it is expected that there will be many difficulties in requesting or supporting mutual support in actual situation because the target or scope of mutual aid of ammunition is not clearly specified. Thus, a separate agreement on the mutual co-operation of more specific and expanded concepts of aviation ammunition is needed based on the current mutual aid support agreements 2. join the NATO Support & Procurement Agency (NSPA) In the case of NATO, there is a system in which member countries purchase munitions at a low cost using munitions purchase agencies. It is the NATO Purchasing Agency (NSPA) whose mission is to receive the purchasing requirements of the Member Nations and to purchase them quickly and efficiently and effectively to the Member Nations. NSPA's business includes the Ammunition Support Partnership (ASP), which provides ammunition purchase and disarming services. Although Korea is not a member of NATO, NSPA is gradually expanding the scope of joint procurement of munitions, and it is expected that Korea will be able to join as a member. 3. it builds a purchasing community centered on the countries operating the same ammunition By benchmarking the NSPA system, this study suggested ways to build a purchasing community with countries such as Southeast Asia, Australia, and the Middle East. First, it is necessary to review prospectively how to purchase ammunition by constructing ammunition purchasing community centered on countries using same kind of ammunition. 4. participating in the US Air Force's new purchase plan for ammunition When developing or purchasing weapons systems, joint participation by several countries can reduce acquisition costs. Therefore, if the US Air Force is planning to acquire aviation ammunition by applying it to the purchase of aviation ammunition, we will be able to significantly reduce the purchase cost by participating in this plan. Finally, there are some limitations to the method presented in this study, but starting from this study, I hope that the research on these methods will be actively pursued in the future.

• The Korean Journal of Nuclear Medicine Technology
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• v.23 no.1
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• pp.45-49
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• 2019

Purpose Lung Ventilation Scan(LVS) images directly inhaled radiation gas to evaluate lung ventilation ability. Therefore, it is influenced by various factors related to inhalation, including number of breaths, respiratory duration, respiration rate, and breathing method. In actual LVS examinations, it is difficult for objectify the patient's ability to inhale, and there is currently no known index related to inhalation. Therefore, this study confirms the correlation between counts per second(cps) in LVS and the results of pulmonary function test(PFT) and evaluate its usefulness as an objective indicator of inhalation. Materials and Methods From October 2010 to September 2018, 36 Chronic Obstructive Pulmonary Disease(COPD) patients who had both LVS and PFT were classified by severity(Mild, Moderate, Severe). LVS was performed by creating Technegas with Vita Medical's Technegas Generator and inhaling it to the patient. LVS images were acquired with Philips's Forte equipment., and PFT used Carefusion's Vmax Encore 22. The correlation between the cps measured by setting the region of interest(ROI) of both lungs on the LVS and the forced vital capacity(FVC), forced expiratory volume in one second($FEV_1$), $FEV_1/FVC$ of the results of PFT was compared and analyzed. Results We analyzed the correlation between cps of LVS using Technegas and the results of PFT by classifying COPD patients according to severity. Correlation coefficient between $FEV_1/FVC$ and cps was Severe -0.773, Moderate -0.750, and Mild -0.437. The Severe and Modulate result values were statistically significant(P<0.05) and Mild was not significant(P=0.155). On the other hand, the correlation coefficient between FVC and cps was statistically significant only in Mild and it was 0.882(P<0.05). Conclusion According to the study, we were able to analyze correlation between cps of LVS using Technegas and the results of PFT in COPD Patients. Using this result, when performing a LVS, the results of PFT can be used as an index of inhaling capacity. In addition, it is thought that it will be more effective for the operation of the exam rooms.

• Journal of Science and Technology Studies
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• v.19 no.1
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• pp.1-51
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• 2019

The understanding of law and science as fundamentally different two systems, in which fact stands against justice, rapid progress against prudent process, is far too simple to be valid. Nonetheless, such account is commonly employed to explain the tension between law and science or justice and truth. Previous STS research raises fundamental doubts upon the off-the-shelf concept of "scientific truth" that can be introduced to the court for legal judgment. Delimiting the qualification of the expert, the value of the expert knowledge, or the criteria of the scientific expertise have always included social negotiation. What are the values that are affecting the boundary-making of the thing called "modern science" that is supposedly useful in solving legal conflicts? How do the value of law and the meaning of justice change as the boundaries of modern science take shapes? What is the significance of "science" when it is emphasized, particularly in relation to the legal provisions of paternity, and how does this perception of science affect unfoldings of legal disputes? In order to explore the answers to the above questions, we follow a process in which a type of "knowledge-deficient model" of a court-that is, law lags behind science and thus, under-employs its useful functions-can be closely examined. We attend to a series of discussions and subsequent changes that occurred in the US courts between 1930s and 1970s, when blood type tests began to be used to determine parental relations. In conclusion, we argue that it was neither nature nor truth in itself that was excavated by forensic scientists and legal practitioners, who regarded blood type tests as a truth machine. Rather, it was their careful practices and crafty narratives that made the roadmaps of modern science, technology, and society on which complex tensions between modern states, families, and courts were seen to be "resolved".

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.221-238
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• 2019

Recently, companies and public institutions have been actively introducing chatbot services in the field of customer counseling and response. The introduction of the chatbot service not only brings labor cost savings to companies and organizations, but also enables rapid communication with customers. Advances in data analytics and artificial intelligence are driving the growth of these chatbot services. The current chatbot can understand users' questions and offer the most appropriate answers to questions through machine learning and deep learning. The advancement of chatbot core technologies such as NLP, NLU, and NLG has made it possible to understand words, understand paragraphs, understand meanings, and understand emotions. For this reason, the value of chatbots continues to rise. However, technology-oriented chatbots can be inconsistent with what users want inherently, so chatbots need to be addressed in the area of the user experience, not just in the area of technology. The Fourth Industrial Revolution represents the importance of the User Experience as well as the advancement of artificial intelligence, big data, cloud, and IoT technologies. The development of IT technology and the importance of user experience have provided people with a variety of environments and changed lifestyles. This means that experiences in interactions with people, services(products) and the environment become very important. Therefore, it is time to develop a user needs-based services(products) that can provide new experiences and values to people. This study proposes a chatbot development process based on user needs by applying the design thinking approach, a representative methodology in the field of user experience, to chatbot development. The process proposed in this study consists of four steps. The first step is 'setting up knowledge domain' to set up the chatbot's expertise. Accumulating the information corresponding to the configured domain and deriving the insight is the second step, 'Knowledge accumulation and Insight identification'. The third step is 'Opportunity Development and Prototyping'. It is going to start full-scale development at this stage. Finally, the 'User Feedback' step is to receive feedback from users on the developed prototype. This creates a "user needs-based service (product)" that meets the process's objectives. Beginning with the fact gathering through user observation, Perform the process of abstraction to derive insights and explore opportunities. Next, it is expected to develop a chatbot that meets the user's needs through the process of materializing to structure the desired information and providing the function that fits the user's mental model. In this study, we present the actual construction examples for the domestic cosmetics market to confirm the effectiveness of the proposed process. The reason why it chose the domestic cosmetics market as its case is because it shows strong characteristics of users' experiences, so it can quickly understand responses from users. This study has a theoretical implication in that it proposed a new chatbot development process by incorporating the design thinking methodology into the chatbot development process. This research is different from the existing chatbot development research in that it focuses on user experience, not technology. It also has practical implications in that companies or institutions propose realistic methods that can be applied immediately. In particular, the process proposed in this study can be accessed and utilized by anyone, since 'user needs-based chatbots' can be developed even if they are not experts. This study suggests that further studies are needed because only one field of study was conducted. In addition to the cosmetics market, additional research should be conducted in various fields in which the user experience appears, such as the smart phone and the automotive market. Through this, it will be able to be reborn as a general process necessary for 'development of chatbots centered on user experience, not technology centered'.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.63-88
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• 2019

The goal of this paper is to investigate changes in North Korea's domestic and foreign policies through automated text analysis over North Korea represented in South Korean mass media. Based on that data, we then analyze the status of text mining research, using a text mining technique to find the topics, methods, and trends of text mining research. We also investigate the characteristics and method of analysis of the text mining techniques, confirmed by analysis of the data. In this study, R program was used to apply the text mining technique. R program is free software for statistical computing and graphics. Also, Text mining methods allow to highlight the most frequently used keywords in a paragraph of texts. One can create a word cloud, also referred as text cloud or tag cloud. This study proposes a procedure to find meaningful tendencies based on a combination of word cloud, and co-occurrence networks. This study aims to more objectively explore the images of North Korea represented in South Korean newspapers by quantitatively reviewing the patterns of language use related to North Korea from 2016. 11. 1 to 2019. 5. 23 newspaper big data. In this study, we divided into three periods considering recent inter - Korean relations. Before January 1, 2018, it was set as a Before Phase of Peace Building. From January 1, 2018 to February 24, 2019, we have set up a Peace Building Phase. The New Year's message of Kim Jong-un and the Olympics of Pyeong Chang formed an atmosphere of peace on the Korean peninsula. After the Hanoi Pease summit, the third period was the silence of the relationship between North Korea and the United States. Therefore, it was called Depression Phase of Peace Building. This study analyzes news articles related to North Korea of the Korea Press Foundation database(www.bigkinds.or.kr) through text mining, to investigate characteristics of the Kim Jong-un regime's South Korea policy and unification discourse. The main results of this study show that trends in the North Korean national policy agenda can be discovered based on clustering and visualization algorithms. In particular, it examines the changes in the international circumstances, domestic conflicts, the living conditions of North Korea, the South's Aid project for the North, the conflicts of the two Koreas, North Korean nuclear issue, and the North Korean refugee problem through the co-occurrence word analysis. It also offers an analysis of South Korean mentality toward North Korea in terms of the semantic prosody. In the Before Phase of Peace Building, the results of the analysis showed the order of 'Missiles', 'North Korea Nuclear', 'Diplomacy', 'Unification', and ' South-North Korean'. The results of Peace Building Phase are extracted the order of 'Panmunjom', 'Unification', 'North Korea Nuclear', 'Diplomacy', and 'Military'. The results of Depression Phase of Peace Building derived the order of 'North Korea Nuclear', 'North and South Korea', 'Missile', 'State Department', and 'International'. There are 16 words adopted in all three periods. The order is as follows: 'missile', 'North Korea Nuclear', 'Diplomacy', 'Unification', 'North and South Korea', 'Military', 'Kaesong Industrial Complex', 'Defense', 'Sanctions', 'Denuclearization', 'Peace', 'Exchange and Cooperation', and 'South Korea'. We expect that the results of this study will contribute to analyze the trends of news content of North Korea associated with North Korea's provocations. And future research on North Korean trends will be conducted based on the results of this study. We will continue to study the model development for North Korea risk measurement that can anticipate and respond to North Korea's behavior in advance. We expect that the text mining analysis method and the scientific data analysis technique will be applied to North Korea and unification research field. Through these academic studies, I hope to see a lot of studies that make important contributions to the nation.

• Korean Journal of Heritage: History & Science
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• v.52 no.3
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• pp.112-129
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• 2019

This study investigates the influence and implications of the supervised process of production of flower vessels (花器) in 1411. The type, the production method, and the purpose of flower vessels (花器) were determined based on the workshops appearing in King Sejong-Sillok, Chiriji ("世宗實錄" "地理志") and Gyeongsang-do Chiriji ("慶尙道地理志"), considering articles excavated from Sangju kiln sites. In addition, the implications and the starting point of production of flower vessels (花器) in the Joseon Dynasty were identified. During the Joseon Dynasty, an effort was made to reorganize the government offices, to align ritual systems in the early 15th century. Preparation for rituals, preparation of supplemental utensils used in ancestral rites (祭器), the construction of architecture related to the Royal Family, and the production of weaponry (武器) were supervised. In 1411, flower vessels (花器) had a preferred supervised process of production as well, which means being recognized as a subject of maintenance for the Joseon Dynasty's aims. Flower vessels (花器) had been produced using grayish-blue powdered celadon (粉靑沙器) as flower pots (花盆), and as celadon flower pot-support (花臺), at Sangju kiln sites in particular, since 1411. Interestingly, products had been manufactured in royal kilns as well as in a few other kilns similar to the supervised process of production of flower vessels (花器) in the middle of the 15th century. It means that this effected the Gyeon-yang (見樣) supervised process of flower vessel (花器) production in 1411. At that time, the Joseon Dynasty used Gyeon-yang (見樣) for imperial gifts for the Ming Dynasty and on separate manufactured articles to ensure the standards of production. Gyeon-yang (見樣) affected the production of ceramic utensils used in ancestral rites (祭器), and government officials in Saongwon (司饔院) supervised the production of ceramics for the Royal Family year after year. In sum, it was flower vessels (花器) using Gyeon-yang (見樣) that provided precise production rules to supervise the process of production in 1411.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.179-200
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• 2019

Word-of-mouth (WoM) is defined by consumer activities that share information concerning consumption. WoM activities have long been recognized as important in corporate marketing processes and have received much attention, especially in the marketing field. Recently, according to the development of the Internet, the way in which people exchange information in online news and online communities has been expanded, and WoM is diversified in terms of word of mouth, score, rating, and liking. Social media makes online users easy access to information and online WoM is considered a key source of information. Although various studies on WoM have been preceded by this phenomenon, there is no meta-analysis study that comprehensively analyzes them. This study proposed a method to extract major researches by applying text mining techniques and to grasp the main issues of researches in order to find the trend of WoM research using scholarly big data. To this end, a total of 4389 documents were collected by the keyword 'Word-of-mouth' from 1941 to 2018 in Scopus (www.scopus.com), a citation database, and the data were refined through preprocessing such as English morphological analysis, stopwords removal, and noun extraction. To carry out this study, we adopted main path analysis (MPA) and word co-occurrence network analysis. MPA detects key researches and is used to track the development trajectory of academic field, and presents the research trend from a macro perspective. For this, we constructed a citation network based on the collected data. The node means a document and the link means a citation relation in citation network. We then detected the key-route main path by applying SPC (Search Path Count) weights. As a result, the main path composed of 30 documents extracted from a citation network. The main path was able to confirm the change of the academic area which was developing along with the change of the times reflecting the industrial change such as various industrial groups. The results of MPA revealed that WoM research was distinguished by five periods: (1) establishment of aspects and critical elements of WoM, (2) relationship analysis between WoM variables, (3) beginning of researches of online WoM, (4) relationship analysis between WoM and purchase, and (5) broadening of topics. It was found that changes within the industry was reflected in the results such as online development and social media. Very recent studies showed that the topics and approaches related WoM were being diversified to circumstantial changes. However, the results showed that even though WoM was used in diverse fields, the main stream of the researches of WoM from the start to the end, was related to marketing and figuring out the influential factors that proliferate WoM. By applying word co-occurrence network analysis, the research trend is presented from a microscopic point of view. Word co-occurrence network was constructed to analyze the relationship between keywords and social network analysis (SNA) was utilized. We divided the data into three periods to investigate the periodic changes and trends in discussion of WoM. SNA showed that Period 1 (1941~2008) consisted of clusters regarding relationship, source, and consumers. Period 2 (2009~2013) contained clusters of satisfaction, community, social networks, review, and internet. Clusters of period 3 (2014~2018) involved satisfaction, medium, review, and interview. The periodic changes of clusters showed transition from offline to online WoM. Media of WoM have become an important factor in spreading the words. This study conducted a quantitative meta-analysis based on scholarly big data regarding WoM. The main contribution of this study is that it provides a micro perspective on the research trend of WoM as well as the macro perspective. The limitation of this study is that the citation network constructed in this study is a network based on the direct citation relation of the collected documents for MPA.