• Korean Journal of Ecology and Environment
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• v.55 no.1
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• pp.10-18
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• 2022

This study analyzed the relationship between the community structure of benthic macroinvertebrates and habitat changes in open estuaries among the sites included in the national estuary monitoring program. The estuary survey was conducted under the "Guidelines for Investigation and Evaluation of Biometric Networks" and classified by sea area, 80 places in the East Sea, 102 places in the South Sea, and 19 places in the West Sea were investigated. In a total of 201 open estuaries, benthic macroinvertebrates were identified with 4 phyla, 9 classes, 41 orders, 139 families, 269 species and 196 species in the East Sea, 182 species in the South Sea, and 90 species in the West Sea. The highest population densities were Insecta in the East Sea, the Malacostraca in the South Sea, and the Annelida in the West Sea. Through SIMPER analysis, species contributing to the similarity of benthic macroinvertebrates communities in each sea area were identified. Some species greatly influenced the similarity of clusters. The benthic community in the East Sea was affected by the salinity, so the contribution rate of freshwater species was high. On the other hand, the benthic communities of the South and West Seas showed species compositions are influenced by the substrate composition. As results, the benthic macroinvertebrate community in Korean estuaries was impacted by salinity and substrate simultaneously, and the close relationship with geographical distance was not observed. The result of this study is expected to be used to respond to environmental changes by identifying and predicting changes in the diversity and distribution of benthic macroinvertebrates in Korea estuaries.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.131-154
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• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.

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

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

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.287-310
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• 2022

Companies' interest in developing AI-based intelligent new products is increasing. Recently, the main concern of companies is to innovate customer experience and create new values by developing new products through the effective use of Artificial intelligence technology. However, due to the nature of products based on radical technologies such as artificial intelligence, intelligent products differ from existing products and development methods, so it is clear that there is a limitation to applying the existing development methodology as it is. This study proposes a new research method based on KANO-TOPSIS for the successful development of AI-based intelligent new products by using car voice assistants as an example. Using the KANO model, select and evaluate functions that customers think are necessary for new products, and use the TOPSIS method to derives priorities by finding the importance of functions that customers need. For the analysis, major categories such as vehicle condition check and function control elements, driving-related elements, characteristics of voice assistant itself, infotainment elements, and daily life support elements were selected and customer demand attributes were subdivided. As a result of the analysis, high recognition accuracy should be considered as a top priority in the development of car voice assistants. Infotainment elements that provide customized content based on driver's biometric information and usage habits showed lower priorities than expected, while functions related to driver safety such as vehicle condition notification, driving assistance, and security, also showed as the functions that should be developed preferentially. This study is meaningful in that it presented a new product development methodology suitable for the characteristics of AI-based intelligent new products with innovative characteristics through an excellent model combining KANO and TOPSIS.

• Korean Journal of Plant Taxonomy
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• v.52 no.1
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• pp.54-63
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• 2022

Prunus subg. Cerasus is the most planted street and landscaping tree in South Korea, but it is difficult to identify species according to their macro-morphologies, leading to problems when attempting properly to manage species quantities. The purpose of this study is to understand the current status of plant types and species compositions in Bundang Central Park in Bundang-gu of Seongnam City and to discuss the necessity of the management of landscaping tree planting. In April of 2021, during the cherry blossom season, a total of 5,866 planted cherry trees were investigated within an area of 6 km² of Bundang-gu in Seongnam City. As a result, 5,744 trees were sorted into eleven taxa, but the remaining 122 trees were not determined due to their complex morphologies. Prunus ×yedoensis Matsum. accounted for the highest proportion (52.1%), followed by P. serrulata Lindl. var. pubescens Nakai and P. jamasakura Siebold ex Koidz. P. ×nudiflora (Koehne) Koidz., a plant native to Jejudo Island, was not found in this survey. In order to help identify cherry trees based on micro-morphologies, an identification key was presented for the eleven taxa planted as major landscaping trees. It is known that cherry trees frequently form interspecific hybrids in nature. In order to prevent a loss of the genetic originality of native species due to hybridization and gene introgression from foreign cherry trees, it is necessary to manage planting species near the habitats of native taxa and track their origins.

• Journal of the Korean Orthopaedic Association
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• v.54 no.2
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• pp.157-163
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• 2019

Purpose: To investigate the incidence and associated factors of delirium after orthopedic surgery. Materials and Methods: A total of 2,122 cases, who were older than 20 years and underwent orthopedic surgery at a single medical center during a one year period were included. Among them, 132 patients who were diagnosed with delirium after surgery under the Diagnostic and Statistical Manual of Mental Disorders-V criteria and medicated under the consultation of a psychiatrist were included in the study The differences in the incidence of delirium and several affecting factors were analyzed. Results: The overall incidence of delirium after surgery was 6.2% (132 in 2,122 cases). The mean age of the delirium group was 77.4 years (range, 54-92 years), which was higher than that of the non-delirium group (58.1 years). The percentage of women in the delirium group was 63.6% (84 in 132 cases), which was higher than that of the women in the non-delirium group (49.0%). The incidence of delirium after surgery was 9.3% (85 in 916 cases) due to trauma and 3.9% (47 in 1206 cases) due to disease. The incidence of postoperative delirium according to the surgical region was 29.2% (7 in 24 cases) in two or more regions, 13.7% (72 in 526 cases) in the hip, and 9.6% (14 in 146 cases) in the spine, 3.5% (20 in 577 cases) in the knee-lower leg, 2.5% (5 in 199 cases) in the foot-ankle, 2.4% (11 in 457 cases) in the shoulder-elbow, and 1.6% (3 in 189 cases) in the forearm-wrist-hand. Delirium occurred more rapidly in women and surgery due to disease, and the duration of delirium was longer in patients with dementia and major depressive disorders. Conclusion: The incidence of postoperative delirium was high in cases of surgery due to trauma and in cases of surgery in two or more sites. The incidence of postoperative delirium according to a single surgical region was higher in the order of the hip, spine, and knee. Active intervention is needed regarding the correctable risk factor.

• Journal of the Korean Orthopaedic Association
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• v.54 no.1
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• pp.37-44
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• 2019

Purpose: Tumor infiltration around the knee joint or skip metastasis, repeated infection sequelae after tumor prosthesis implantation, regional recurrence, and mechanical failure of the megaprosthesis might require combined distal femur and proximal tibia replacement (CFTR). Among the aforementioned situations, there are few reports on the indication, complications, and implant survival of CFTR in temporarily arthrodesed patients who had a massive bony defect on either side of the knee joint to control infection. Materials and Methods: Thirty-four CFTR patients were reviewed retrospectively and 13 temporary arthrodesed cases switched to CFTR were extracted. All 13 cases had undergone a massive bony resection on either side of the knee joint and temporary arthrodesis state to control the repeated infection. This paper describes the diagnosis, tumor location, number of operations until CFTR, duration from the index operation to CFTR, survival of CFTR, complications, and Musculoskeletal Tumor Society (MSTS) score. Results: According to Kaplan-Meier plot, the 5- and 10-year survival of CFTR was 69.0%±12.8%, 46.0%±20.7%, respectively. Six (46.2%) of the 13 cases had major complications. Three cases underwent removal of the prosthesis and were converted to arthrodesis due to infection. Two cases underwent partial change of the implant due to loosening and periprosthetic fracture. The remaining case with a deep infection was resolved after extensive debridement. At the final follow-up, the average MSTS score of 10 cases with CFTR was 24.6 (21-27). In contrast, the MSTS score of 3 arthrodesis cases with failed CFTR was 12.3 (12-13). The average range of motion of the 10 CFTR cases was 67° (0°-100°). The mean extension lag of 10 cases was 48° (20°-80°). Conclusion: Although the complication rates is substantial, conversion of an arthrodesed knee to a mobile joint using CFTR in a patient who had a massive bony defect on either side of the knee joint to control infection should be considered. The patient's functional outcome was different from the arthrodesed one. For successful conversion to a mobile joint, thorough the eradication of scar tissue and creating sufficient space for the tumor prosthesis to flex the knee joint up to 60° to 70° without soft tissue tension.

• Journal of Popular Narrative
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• v.25 no.4
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• pp.191-221
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• 2019

This study aims to analyze the post-humanistic imagination that has emerged as a major academic thesis in Korean literature, especially novels. In particular, this paper focuses on Cho Ha-hyung's two novels Chimera's Morning(2004) and A Prefabricated Bodhi Tree(2008), published in the early 2000s, for intensive analysis. Post-humanism can be seen as an extension of post-modernism that tried to overcome the limitations of modernity and seek to establish a new world view. In particular, this thought pays attention to the comprehensive understanding of how the rapid development of science and technology, which has developed since the 20th century, has changed the view of humanity and human-centered civilization itself. At the concrete level, it is developing in the direction of constructing a new subject idea by reflecting and dismantling Western-, reason-, and male-centered power mechanisms that are the core of modern civilization. Cho attempts to discover and re-illuminate the surrounding figures, non-humans, and objects that were not noticed in the classic works written in the past. This ideological flow reflects the fact that the concept of human beings, which had been dominated by the humanities in recent years, has been completely changed, and the natural science and technology perspective is applied to the discourse field in various ways. From the point of view of post-humanism, objects that have not been classified as humans and objects that were considered inferior to humans should be included in human or comparable levels. These questions generate interdisciplinary research tasks by involving the large categories of philosophy, such as ontology, epistemology and empirical fields, as well as calling for the participation of the entire literature, science and social sciences. Against the backdrop of a disaster-hit world, Chimera's Morning and A Prefabricated Bodhi Tree depict human beings as variants transformed by bio-technology, and creatures made out of the artificial intelligence built by computer simulations. Post-humanistic ideas in Cho's novels provide a reflective opportunity to comprehensively reconsider the world's shape and human identity reproduced in the text, and to re-explore boundary lines and hierarchy order that distinguish between human and non-human.

• Journal of agriculture & life science
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• v.50 no.5
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• pp.139-152
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• 2016

The comparative in vitro and in situ analysis were conducted to evaluate the rumen degradability and physical structure of domestic roughage as rice straw, timothy, alfalfa, wormwood and green tea. The feedstuffs incubated with rumen fluid and was used to determine gas production, microbial growth rate and pH changes in an in vitro experiment. The gas production was increased during incubation times and was significantly(p<0.05) lower in green tea than other feedstuffs. The microbial growth rate in the feedstuffs was increased during incubation times. However, microbial growth rate was significantly(p<0.05) lower in wormwood and green tea than other feedstuffs. Ruminal pH was decreased during incubation times, and timothy was the lowest, and rice straw was the highest among feedstuffs. The disappearance rate of dry matter(DM) and crude protein(CP) in all feedstuffs were increased during incubation times and green tea was the highest(p<0.05) compared with other feedstuffs. In effective degradability, when rumen out-flow rate was assigned to 4%, wormwood showed the highest in DM, and alfalfa was the highest in CP. Whereas, green tea was the highest in both in situ DM and CP degradability. Many cilia on the surface and stoma of wormwood and stoma in green tea were observed by scanning electron microscopy. Microbes breaked down the cilia at the beginning and then degraded the surface in wormwood. In case of green tea, microbes attached to stoma. Therefore, wormwood and green tea have a potential value as ruminal feed stuffs.

• Korea Science and Art Forum
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• v.23
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• pp.111-122
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• 2016

The purpose of this multi-disciplinary convergent study is to establish Image Copyright Archive Model for Museums to protect image copyright and vitalize the use of images out of necessity of research and development on copyright services over the life cycle of art contents created by the museums and out of the necessity to vitalize distribution market of image copyright contents in creative industry and to formulate management system of copyright services. This study made various suggestions for enhancement of transparency and efficiency of art contents ecosystem through vitalization of use and recycling of image copyright materials by proposing standard system for calculation, distribution, settlement and monitoring of copyright royalty of 1,000 domestic museums, galleries and exhibit halls. First, this study proposed contents and structure design of image copyright archive model and, by proposing art contents distribution service platform for prototype simulation, execution simulation and model operation simulation, established art contents copyright royalty process model. As billing system and technological development for image contents are still in incipient stage, this study used the existing contents billing framework as basic model for the development of billing technology for distribution of museum collections and artworks and automatic division and calculation engine for copyright royalty. Ultimately, study suggested image copyright archive model which can be used by artists, curators and distributors. In business strategy, study suggested niche market penetration of museum image copyright archive model. In sales expansion strategy, study established a business model in which effective process of image transaction can be conducted in the form of B2B, B2G, B2C and C2B through flexible connection of museum archive system and controllable management of image copyright materials can be possible. This study is expected to minimize disputes between copyright holder of artwork images and their owners and enhance manageability of copyrighted artworks through prevention of such disputes and provision of information on distribution and utilization of art contents (of collections and new creations) owned by the museums. In addition, by providing a guideline for archives of collections of museums and new creations, this study is expected to increase registration of image copyright and to make various convergent businesses possible such as billing, division and settlement of copyright royalty for image copyright distribution service.