• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Korean Journal of Environment and Ecology
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• v.35 no.1
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• pp.24-36
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• 2021

Countries around the world, including the Republic of Korea, are participating in efforts to preserve biodiversity. Concerning species, in particular, studies that aim to find potential habitats and establish conservation plans by conducting habitat suitability analysis for specific species are actively ongoing. However, few studies on mid- to long-term changes in suitable habitat areas are based on accumulated information. Therefore, this study aimed to analyze the time-series changes in the habitat suitable area and examine the otters' changing pattern (Lutra lutra) designated as Level 1 endangered wildlife in Gangwon-do. The time-series change analysis used the data on otter species' presence points from the 2nd, 3rd, and 4th national natural environment surveys conducted for about 20 years. Moreover, it utilized the land cover map consistent with the survey period to create environmental variables to reflect each survey period's habitat environment. The suitable habitat area analysis used the MaxEnt model that can run based only on the species presence information, and it has been proven to be reliable by previous studies. The study derived the habitat suitability map for otters in each survey period, and it showed a tendency that habitats were distributed around rivers. Comparing the response curves of the environmental variables derived from the modeling identified the characteristics of the habitat favored by otters. The examination of habitats' change by survey period showed that the habitats based on the 2nd National Natural Environment Survey had the widest distribution. The habitats of the 3rd and 4th surveys showed a tendency of decrease in area. Moreover, the study aggregated the analysis results of the three survey periods and analyzed and categorized the habitat's changing pattern. The type of change proposed different conservation plans, such as field surveys, monitoring, protected area establishment, and restoration plan. This study is significant because it produced a comprehensive analysis map that showed the time-series changes of the location and area of the otter habitat and proposed a conservation plan that is necessary according to the type of habitat change by region. We believe that the method proposed in this study and its results can be used as reference data for establishing a habitat conservation and management plan in the future.

• Korean Journal of Environment and Ecology
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• v.35 no.1
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• pp.81-91
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• 2021

The Democratic People's Republic of Korea did not have direct legislation on natural environmental conservation until the early 1970s when the regime was still in the early stage. The Law on Land was enacted in 1977 to provide the legal basis for protecting the natural environment, including land protection, protection zones, and forest formation and protection. The enactment of the Law on Environmental Protection in 1986 made progress on environmental conservation in the DPRK. The constitutional amendment in 1992 stipulated "the preservation and creation of the natural environment as the responsibility of the state." Based on the Framework Law on Environmental Protection, subordinate statutes in various fields were enacted after the1990s. While the committee designated and managed the protected zones in the early days, the Framework Law on Environmental Protection established the ground for the designation of legally protected areas, and the Law on Protection of Scenic Spots and Natural Monuments enacted in 1995, and the Law on Environmental Protection enacted in 2009 provided the details. Furthermore, the types of nature reserves include biosphere reserves, primeval forest reserves, animal reserves, plant reserves, and scenic reserves. The 2nd National Biodiversity Strategy and Action Plan established in 2007 based on the Convention on Biological Diversity(CBD) stated 326 protected zones in the DPRK. However, the 2018 United Nations list of Protected Areas shows only 31 registered zones, indicating the need to establish basic information on protected areas in DPRK. This study can provide basic information for a better understanding of the nature conservation system in the DPRK. Considering that environmental protection activities such as protection of endangered species and recovery of environmental pollution are subject to exceptions under the current sanctions against North Korea (UN Security Council, the United States), it will be possible to contribute to identifying possible inter-Korean cooperation projects in the field of the natural environment.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.182-193
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• 2022

Purpose: By measuring and evaluating the risk of biodiesel through non-volatile residue (NVR) and flash point and explosion limit measurement at a specific temperature according to ASTM test standards, the risk of chemical fire causative substances is identified and a universal evaluation method By derivation and securing the risk-related data of the material, it can be used for the identification and analysis of the cause of the fire, and it can be applied to the risk assessment of other chemical substances Method: In order to measure the risk of biodiesel, it was measured using the non-volatile residue(NVR) measurement method, which measures how much flammable liquid is generated at a specific temperature. Heating was tested by applying KS M 5000: 2009 Test Method 4111. In addition, the flash point was measured using the method specified in ASTM E659-782005, and the energy supply method was measured using the constant temperature method. In addition, the explosion limit measurement was conducted in accordance with ASTM E 681-04 「Standard test method for concentration limits of flammability of chemicals(Vapors and gases)」 test standard. Result: As a result of checking the amount of combustible liquid by the non-volatile residue (NVR)measurement method, the non-volatile residue(NVR) of general diesel when left at 105±2℃ for 3 hours was about 30% (70% of volatile matter) and about 4% of biodiesel. In addition, similar results were obtained for the non-volatile residue(NVR)heating temperature of 150±2℃, 3 hours and 200±2℃ for 1 hour, and white smoke was generated at 200℃ or higher. In addition, similar values were obtained as a result of experimentally checking the explosion (combustion) limits of general diesel, general diesel containing 20% biodiesel, and 100% biodiesel. Therefore, it was confirmed that the flammability risk did not significantly affect the explosion risk. Conclusion: The results of this study suggested the risk judgment criteria for mixtures through experimental research on flammable mixtures for the purpose of securing the effectiveness, reliability, and reproducibility of the details of the criteria for determining dangerous substances in the existing Dangerous Materials Safety Management Act. It will be possible to provide reference data for the judgment criteria for flammable liquids that are regulated in the field. In addition, if the know-how for each test method is accumulated through this study, it is expected that it will be used as basic data in the research on risk assessment of dangerous substances and as a basis for research on the determination of dangerous substances.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.127-146
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• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.251-273
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• 2022

Recently, research on applying text analysis to deep learning has steadily continued. In particular, researches have been actively conducted to understand the meaning of words and perform tasks such as summarization and sentiment classification through a pre-trained language model that learns large datasets. However, existing pre-trained language models show limitations in that they do not understand specific domains well. Therefore, in recent years, the flow of research has shifted toward creating a language model specialized for a particular domain. Domain-specific pre-trained language models allow the model to understand the knowledge of a particular domain better and reveal performance improvements on various tasks in the field. However, domain-specific further pre-training is expensive to acquire corpus data of the target domain. Furthermore, many cases have reported that performance improvement after further pre-training is insignificant in some domains. As such, it is difficult to decide to develop a domain-specific pre-trained language model, while it is not clear whether the performance will be improved dramatically. In this paper, we present a way to proactively check the expected performance improvement by further pre-training in a domain before actually performing further pre-training. Specifically, after selecting three domains, we measured the increase in classification accuracy through further pre-training in each domain. We also developed and presented new indicators to estimate the specificity of the domain based on the normalized frequency of the keywords used in each domain. Finally, we conducted classification using a pre-trained language model and a domain-specific pre-trained language model of three domains. As a result, we confirmed that the higher the domain specificity index, the higher the performance improvement through further pre-training.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.185-209
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• 2023

With the advent of the 4th industrial revolution, various technologies have received much attention. Technologies related to the 4th industry include the Internet of Things (IoT), big data, artificial intelligence, virtual reality (VR), 3D printers, and robotics, and these technologies are often converged. In particular, the robotics field is combined with technologies such as big data, artificial intelligence, VR, and digital twins. Accordingly, much research using robotics is being conducted, which is applied to distribution, airports, hotels, restaurants, and transportation fields. In the given situation, research on human-robot interaction is attracting attention, but it has not yet reached the level of user satisfaction. However, research on robots capable of perfect communication is steadily being conducted, and it is expected that it will be able to replace human emotional labor. Therefore, it is necessary to discuss whether the current human-robot interaction technology can be applied to business. To this end, this study first examines the trend of human-robot interaction technology. Second, we compare LDA (Latent Dirichlet Allocation) topic modeling and BERTopic topic modeling methods. As a result, we found that the concept of human-robot interaction and basic interaction was discussed in the studies from 1992 to 2002. From 2003 to 2012, many studies on social expression were conducted, and studies related to judgment such as face detection and recognition were conducted. In the studies from 2013 to 2022, service topics such as elderly nursing, education, and autism treatment appeared, and research on social expression continued. However, it seems that it has not yet reached the level that can be applied to business. As a result of comparing LDA (Latent Dirichlet Allocation) topic modeling and the BERTopic topic modeling method, it was confirmed that BERTopic is a superior method to LDA.

• Journal of the Society of Disaster Information
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• v.19 no.2
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• pp.395-410
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• 2023

Purpose: The purpose of this study was to analyze the current state of drone terrorism response at such critical national facilities and derive improvements, especially to identify problems in laws and systems to effectively utilize the anti-drone system and present directions for improvement. Method: A qualitative research method was used for this study by analyzing a variety of issues not discussed in existing research papers and policy documents through in-depth interviews with subject matter experts. In-depth interviews were conducted based on 12 semi-structured interviews by selecting 16 experts in the field of anti-drone and terrorism in Korea. The interview contents were recorded with the prior consent of the study participants, transcribed back to the Korean file, and problems and improvement measures were derived through coding. For this, the threats and types were analyzed based on the cases of drone terrorism occurring abroad and measures to establish anti-drone system were researched from the perspective of laws and systems by evaluating the possibility of drone terrorism in the Republic of Korea. Result: As a result of the study, improvements to some of the problems that need to be preceded in order to effectively respond to drone terrorism at critical national facilities in the Republic of Korea, have been identified. First, terminologies related to critical national facilities and drone terrorism should be clearly defined and reflected in the Integrated Defense Act and the Terrorism Prevention Act. Second, the current concept of protection of critical national facilities should evolve from the current ground-oriented protection to a three-dimensional protection concept that considers air threats and the Integrated Defense Act should reflect a plan to effectively install the anti-drone system that can materialize the concept. Third, a special law against flying over critical national facilities should be enacted. To this end, legislation should be enacted to expand designated facilities subject to flight restrictions while minimizing the range of no fly zone, but the law should be revised so that the two wings of "drone industry development" and "protection of critical national facilities" can develop in a balanced manner. Fourth, illegal flight response system and related systems should be improved and reestablished. For example, it is necessary to prepare a unified manual for general matters, but thorough preparation should be made by customizing it according to the characteristics of each facility, expanding professional manpower, and enhancing response training. Conclusion: The focus of this study is to present directions for policy and technology development to establish an anti-drone system that can effectively respond to drone terrorism and illegal drones at critical national facilities going forward.

• Journal of agriculture & life science
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• v.50 no.3
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• pp.1-11
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• 2016

With increasing interest, there have been studies on LiDAR(Light Detection And Ranging)-based DEM(Digital Elevation Model) to acquire three dimensional topographic information. For producing LiDAR DEM with better accuracy, Filtering process is crucial, where only surface reflected LiDAR points are left to construct DEM while non-surface reflected LiDAR points need to be removed from the raw LiDAR data. In particular, the changes of input values for filtering algorithm-constructing parameters are supposed to produce different products. Therefore, this study is aimed to contribute to better understanding the effects of the changes of the levels of GroundFilter Algrothm's Mean parameter(GFmn) embedded in FUSION software on the accuracy of the LiDAR DEM products, using LiDAR data collected for Hwacheon, Yangju, Gyeongsan and Jangheung watershed experimental area. The effect of GFmn level changes on the products' accuracy is estimated by measuring and comparing the residuals between the elevations at the same locations of a field and different GFmn level-produced LiDAR DEM sample points. In order to test whether there are any differences among the five GFmn levels; 1, 3, 5, 7 and 9, One-way ANOVA is conducted. In result of One-way ANOVA test, it is found that the change in GFmn level significantly affects the accuracy (F-value: 4.915, p<0.01). After finding significance of the GFmn level effect, Tukey HSD test is also conducted as a Post hoc test for grouping levels by the significant differences. In result, GFmn levels are divided into two subsets ('7, 5, 9, 3' vs. '1'). From the observation of the residuals of each individual level, it is possible to say that LiDAR DEM is generated most accurately when GFmn is given as 7. Through this study, the most desirable parameter value can be suggested to produce filtered LiDAR DEM data which can provide the most accurate elevation information.

• The Journal of Bigdata
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• v.8 no.1
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• pp.111-129
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• 2023

With the diffusion of digital innovation, the adoption of innovative medical technologies based on artificial intelligence is increasing in the medical field. This is driving the launch and adoption of AI-based SaMD(Software as a Medical Device), but there is a lack of research on the factors that influence the adoption of SaMD by medical institutions. The purpose of this study is to identify key factors that influence medical institutions' decisions to adopt AI-based SaMDs, and to analyze the weights and priorities of these factors. For this purpose, we conducted Delphi surveys based on the results of literature studies on technology acceptance models in healthcare industry, medical AI and SaMD, and developed a research model by combining HOTE(Human, Organization, Technology and Environment) framework and HABIO(Holistic Approach {Business, Information, Organizational}) framework. Based on the research model with 5 main criteria and 22 sub-criteria, we conducted an AHP(Analytical Hierarchy Process) analysis among the experts from domestic medical institutions and SaMD providers to empirically analyze SaMD adoption factors. The results of this study showed that the priority of the main criteria for determining the adoption of AI-based SaMD was in the order of technical factors, economic factors, human factors, organizational factors, and environmental factors. The priority of sub-criteria was in the order of reliability, cost reduction, medical staff's acceptance, safety, top management's support, security, and licensing & regulatory levels. Specifically, technical factors such as reliability, safety, and security were found to be the most important factors for SaMD adoption. In addition, the comparisons and analyses of the weights and priorities of each group showed that the weights and priorities of SaMD adoption factors varied by type of institution, type of medical institution, and type of job in the medical institution.