Invasive predators are one of the most damaging species groups to biodiversity. In the Nakdong River, the lake skygazer Chanodichthys erythropterus is a dominant species that is fiercely carnivorous and a concern for commercial fish. Although it is important to understand the ecological characteristics related to the feeding habit, studies on the diets of lake skygazer in Nakdong River have been limited to studies of gut contents. In this study, the trophic position (TP) and feeding habits of C. erythropterus were studied by calculating TPs using samples collected from 13 sites throughout the Nakdong River. Compound-specific isotopic analysis of amino acids provided reliable TPs from the muscle of Lake skygazer C. erythropterus without any isotope baseline. The results were approximately 3 to 3.6 and suggesting a carnivorous but size-dependent prey variation. In particular, the TP variability of C. erythropterus observed in the Nakdong River showed that it had a selective feeding habit compared to carnivorous fish species of relatively similar trophic levels.

This study compared research trends in universities general English program before and after the COVID-19 pandemic. After analyzing 248 articles from KCI using frequency analysis, centrality analysis, and topic modeling, this study found consistent keywords indicating a focus on learning objectives, effectiveness analysis, satisfaction surveys, and level-based learning before and after the COVID-19 pandemic. Centrality analysis revealed keywords like “teaching, research, analysis” before COVID-19 and “satisfaction, study, level, activity, effect” after COVID-19, indicating a shift towards learner satisfaction, level-based learning, and effectiveness analysis due to the transition to online learning. Topic modeling revealed shifts in research trends: Pre-COVID-19 focused on effective teaching methods, evaluation techniques, and cultural content, while Post-COVID-19 prioritized online teaching methods, web-based platforms, and selfdirected learning. Future research should address self-directed learning, attitudes and goal setting, closing learning gaps in online/blended learning, and developing effective online assessment tools and evaluation strategies. This study provides valuable insights and directions for further research in general English programs.

Around the 1980s, with government’s promotion and dissemination policies for FRP (Fiberglass Reinforced Plastics) of the government as a main material of fishing boats, approximately 97% of the entire fishing boats in Korea have utilized FRP until now. Nevertheless, diverse social and environmental issues have emerged due to the susceptibility to fire and the generation of substances detrimental to human health during the construction process of FRP fishing vessels. Especially, the high disposal cost and the limitation of recycling technology in the disposal process of FRP fishing boats have elicited attention to circular economy. This research intended to grasp the management status and problems of disposed FRP fishing boats in Korea, and to assess the level of competitive advantage of FRP fishing boats’ recycling technologies of FRP fishing boats based on VRIO (Value, Rarity, Imitability, Organization) analysis through domestic and foreign management policies and related recycling examples. According to the survey of 161 respondents, including the industry, stakeholders and experts related to the collection, treatment and recycling of fisheries wastes, it was revealed that FRP fishing boats’ recycling technologies of FRP fishing boats are at the level of ‘unused competitive advantage’ that satisfied the level of value, rarity and imitability, but not the level of organization.

In this review, we examine the latest technological developments in the utilization of truffles, a gourmet ingredient reputed to be one of the "world's three greatest delicacies," considering changing global consumption trends. Global demand for truffles is expected to increase steadily, with an average annual growth rate of 8.9% from 2023 to 2030. As truffles are expensive, the demand for truffles is expected to be concentrated in developed countries such as the United States, European countries, and Japan. In Korea, truffles are utilized in various industries, including food, functional foods, and cosmetics. Korean consumer demand for truffles has consistently remained high since 2019, and truffle products have been performing well in the market. Consequently, there exists substantial potential demand for newly developed truffle-related products and technologies. This review aims to provide objective research information through the systematic analysis of patent applications in Korea and internationally, focusing on technologies involving truffles, and can aid in setting directions for research and development.

The importance of biocomposites has increased owing to the changes in global consumption trends and rapid climate change. Technologies using mushroom mycelium cultivation, and molding methods for mycelial application have gained attention as potential strategies for producing eco-friendly composites. Currently, mushroom mycelia are used as raw materials for food and cosmetics; however, research on their utilization as biocomposite materials is limited. Therefore, the potential for the development of mushroom mycelium-related products and technologies is high. This review analyzes the domestic and international patent application trends related to the technologies for composite (packaging, insulation, adhesives, and leather) and food (substitute for meat) materials using mushroom mycelium, as an eco-friendly biocomposite material, to provide objective patent information that can further research and development (R&D) in this field.

This study uses FMECA (Failure Modes, Effects, and Criticality Analysis) and HAZOP (Hazard and Operability), which are widely applied in industrial areas, among risk assessment methods, and applies them to the same system. While FMECA evaluates system failure conditions and analyzes risks, HAZOP evaluates the system comprehensively by evaluating operational risks that may occur based on system parameters. According to data released by the Ministry of Land, Infrastructure and Transport, as of December 2021, the length of roads in Korea is 113,405 km, and the repair of guardrails that have expired must be fixed urgently in terms of traffic safety. Replacing all of these guardrails with new ones requires a very large cost, but if the guardrails are repaired with a vehicle equipped with the G-Save method, carbon emissions are reduced, the repair period is shortened, and great economic benefits can be obtained. However, risk assessment for guardrail coating vehicles has not been done so far. Focusing on this point, this study aims to evaluate the risk of these coating vehicles and describe the results. Finally, we found that the Risk Priority Numbers(RPN) in the FMECA risk assessment were greatly reduced, and 6 risk factors from HAZOP risk assessment and actions were taken.

This study conducted a big data analysis targeting the concept of "Ruralism," which is important in rural development, and the keywords "Rurality" and "Rural-amenity," which are similarly used. Keyword frequency analysis, key keyword analysis, network analysis, and emotional analysis were conducted by applying text mining techniques, which are methods of analyzing unstructured data online, to present basic data on the establishment of the concept of Ruralism for rural space maintenance. As a result of the study, conservation for Ruralism was the most important in the case of keyword frequency analysis, and differences between the Rurality and Rural-amenity keywords, which were used similarly to Ruralism, were identified through major keywords, indicating that Ruralism is a concept that has a more comprehensive range of rural areas than other concepts. In the case of the network, the network shown in Ruralism was prominent in improving the quality of life of invisible residents, and the last emotional analysis showed that it was necessary to prepare standards to overcome the possibility of preserving Ruralism and difficult policy directions. This study is meaningful in that it identifies online perceptions and looks at them from a macro perspective so as not to be confused by looking at keywords used similarly to Ruralism at a time when setting mid- to long-term policy directions is important.

아날로그TV 방송 시대에는 Mono/Stereo 오디오 방송과 음 성다중방송이 있었고, 디지털HDTV 방송 시대에는 Dolby AC-3 기술에 의한 5.1채널 입체오디오 방송이 추가 서비스로 이루어졌다. 지금의 4K-UHDTV 방송 시대에는 채널기반 오디 오(Channel-Based Audio) 7.1.4채널 입체오디오 방송을 비롯하 여 객체기반 오디오(Object-Based Audio) 기본방송 및 편파방 송과 해설방송 등 유저의 선택과 맞춤형 방송이 가능하고, 장 면기반 오디오(Scene-Based Audio) 몰입형 공간수음효과 3D 입체음향 방송 등이 MPEG-H 기술에 의해 활용될 수 있다. 본 논문은 국내 UHDTV 방송 전송방식(ATSC 3.0)과 함께 Audio 규격으로 규정된 MPEG-H 기술 응용에 대한 실험을 통 해 오디오 서비스 개발의 가능성을 연구하는데 그 목적을 두고 있다. 이러한 연구를 위해 ‘2022 카타르월드컵’ 기간 중 카타르와 SBS 간 전송링크 구간의 국제신호(IS) 현장음 및 SBS 아나운 서와 해설자 음성 전송용 음향채널 오디오를 소스로 사용하였 으며, 객체기반의 기본, 해설, 현장, 집중 4가지 오디오 모드를 구성하여 SBSi 채널을 통해 모바일 스트리밍 전송을 하였다. 그리고 수신은 SBS 모바일 앱에 Decoder를 설치하여 유저의 음향모드 선택과 활용 및 향후 MPEG-H 기술에 의한 다양한 형태의 오디오 서비스 개발의 가능성을 실험하였다.

This study aims to investigate the effects of using AI chatbots in Korean English education from a macro perspective. For this purpose, 19 experimental studies are selected to conduct a meta-analysis, synthesizing the results of 51 individual study cases. The results of this study are as follows: First, it is found that the overall effect size of using chatbots is more than medium size meaning that a chatbot is an effective tool to learn English. Second, in the aspects of linguistic competence and affective categories, each shows over medium sizes like the overall effect size. In details of the dependent variables, vocabulary and speaking in linguistic competence and motivation in affective categories, large effect sizes are shown. Third, the effect sizes are getting larger, as the younger the students are, the longer the experiment period lasts, and the more purpose-built the chatbot is. But the differences in the effect sizes in terms of these moderators (e.g., school level, experiment period, and chatbot type) are not significant. Lastly, it is suggested that follow-up studies are needed to collect a sufficient number of experimental study cases and subdivide the variables for performing a more detailed meta-anlysis.

오늘날 정보통신기술의 발달로 정보매체가 다양해지고 정보 가 유통되는 속도 또한 빨라지고 있지만, 노인 등의 정보취약 계층에게는 정보를 습득하는 것이 더욱 어려워지고 있다. 이러 한 노년층의 정보격차는 또 다른 형태의 삶의 격차를 야기하고 있다. 이에 본 연구는 노인의 정보화기기 활용 수준을 파악하고 초 급, 중급, 고급 등 노인의 정보화기기 활용 수준이 삶의 만족도 에 미치는 영향을 분석하였다. 구체적인 연구의 방향과 목적은 다음과 같다. 첫째, 노인의 정보화기기 활용수준별(초급, 중급, 고급) 삶의 만족도 간의 관계 및 자아통합감의 매개효과 등 정보화기기 활 용수준이 노인의 삶의 만족도에 미치는 영향을 알아보고자 하 였다. 둘째, 도시와 농촌 등 거주지역별 노인의 정보화기기 활용 수준에 따른 자아통합감, 노인의 삶의 만족도의 차이를 알아보 고자 한다. 이러한 목적을 달성하기 위해 서울 및 도시지역 그리고 경기 도 포천의 읍면지역에 거주하는 60세 이상의 노인 320명을 대 상으로 설문조사를 실시하였고 성실한 설문 응답지 250부를 선 택하여 정보화기기 활용 수준별 자아통합감과 삶의 만족도에 미치는 영향을 분석한 결과, 다음과 같은 결과를 확인하였다. 첫째, 정보화기기 활용수준이 자아통합감에 미치는 영향을 알아보기 위해 구조모형의 평가를 실시한 바, 정보화기기 활용 수준 초급, 중급, 고급 모두 자아통합감을 향상시키는 것으로 나타났다. 둘째, 정보화기기 활용수준 초급에서는 노인의 삶의 만족도 에 의미있는 영향이 없었지만 중급, 상급에서는 노인의 삶의 만족도를 더욱 향상시키는 것으로 나타났다. 셋째, 자아통합감이 높으면 노인의 삶의 만족도 역시 높아지 는 것으로 나타났다. 넷째, 정보화기기 활용수준이 초급수준일수록 자아통합감이 높아지는 정도는 농촌지역에 거주하는 노인들이 도시지역에 거 주하는 노인보다 상대적으로 더 높게 나타났다. 다섯째, 정보화기기 활용수준이 고급 수준일수록 자아통합감 이 높아지는 정도는 도시지역에 거주하는 노인들이 농촌지역에 거주하는 노인보다 상대적으로 더 높게 나타났다. 이러한 연구결과를 토대로 첫째, 지속가능한 노인의 정보화 교육을 위해서 노인이 쉽게 이해하고 다가갈 수 있는 정보제공 및 정보화기기 활용교육 활 성화 방안이 필요함을 알게 되었다. 둘째, 노인 집단의 다양성을 반영한 다각적인 측면에서의 자 아통합감, 삶의 만족도를 증대시킬 수 있도록 정보화기기 활용 수준에 따른 맞춤형 교육이 필요함을 알게 되었다. 셋째 노인 세대의 정보 활용에 대한 적극적인 관심이 필요하 고 그동안 노인층들이 정보격차가 큰 것은 최근에 나타난 정보 화기기를 노인들이 배우거나 활용할 기회가 없었던 것이지 노 인들은 원래 정보화기기를 사용할 수 없는 사람들이 아니었음 을 인식해야 함을 알게 되었다. 마지막으로 노년층 정보화 교육은 농촌, 도시의 환경과 특성 을 고려한 다양한 지역자원과의 협력과 다양한 인력 활용 운영 방안이 필요함을 알게 되었다.

At a time when the demand for drones is increasing, a plan to utilize drone images was sought for efficient promotion of cadastral resurvey. To achieve the purpose of this study, the technical and legal status of drone images was reviewed, and through this, the possibility of using it for cadastral resurvey was primarily reviewed. subsequently, an experiment was conducted targeting the project district to examine whether drone images were applied to boundary extraction, which is the primary process of cadastral resurvey. As a result of the experiment, it was found that boundary extraction from images is possible. However, in some cases, it is impossible due to field conditions or image quality. Therefore, it is necessary first to apply cases where boundary extraction is possible to cadastral resurvey and seek solutions for some impossible cases. In particular, the image quality problem may have problems with the current technology, but it will also have problems with the existing drone equipment. So, standard for drone calibration should also be established. Finally, the cadastral resurvey surveying procedure using drones was also presented.

The advent of big data has brought about the need for analytics. Natural language processing (NLP), a field of big data, has received a lot of attention. Topic modeling among NLP is widely applied to identify key topics in various academic journals. The Korean Society of Industrial and Systems Engineering (KSIE) has published academic journals since 1978. To enhance its status, it is imperative to recognize the diversity of research domains. We have already discovered eight major research topics for papers published by KSIE from 1978 to 1999. As a follow-up study, we aim to identify major topics of research papers published in KSIE from 2000 to 2022. We performed topic modeling on 1,742 research papers during this period by using LDA and BERTopic which has recently attracted attention. BERTopic outperformed LDA by providing a set of coherent topic keywords that can effectively distinguish 36 topics found out this study. In terms of visualization techniques, pyLDAvis presented better two-dimensional scatter plots for the intertopic distance map than BERTopic. However, BERTopic provided much more diverse visualization methods to explore the relevance of 36 topics. BERTopic was also able to classify hot and cold topics by presenting ‘topic over time’ graphs that can identify topic trends over time.

The air transport industry is experiencing unprecedented fluctuations in aviation demand through the Covid-19 pandemic, and is more focused than ever on maintaining and generating business profitability. During the pandemic, demand for air cargo has soared, and the conversion business from passenger aircrafts to freighters(P2F) is drawing attention as a new business in the aviation maintenance industry. This study derives important factors to be considered in order to successfully carry out the P2F project through a wide range of cases and related literature, and analyzes the relative importance of each factor using the analytic hierarchy process. Through a survey of 33 aviation maintenance experts with more than 20 years of field experience, the importance of main factors and their sub factors was determined and implications were drawn. As a primary result, in order to succeed in the P2F project, the main factors were identified in the order of skill, finance, and location. The most important sub factors for each main factors were identified in order of securing airframe modification skill, securing infrastructure construction cost, and creating P2F business complex and district. The quantified success factors suggested the critical direction for the successful development of Korea's P2F business, and presented viable and specific business strategies and implementation plans for each factors.

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

The increasing number of technology transfers from public research institutes in Korea has led to a growing demand for patent recommendation platforms for SMEs. This is because selecting the right technology for commercialization is a critical factor in business success. This study developed a patent recommendation system that uses technology transfer data from the past 10 years to recommend patents that are suitable for SMEs. The system was developed in three stages. First, an item-based collaborative filtering system was developed to recommend patents based on the similarities between the patents that SMEs have previously transferred. Next, a content-based recommendation system based on TF-IDF was developed to analyze patent names and recommend patents with high similarity. Finally, a hybrid system was developed that combines the strengths of both recommendation systems. The experimental results showed that the hybrid system was able to recommend patents that were both similar and relevant to the SMEs' interests. This suggests that the system can be a valuable tool for SMEs that are looking to acquire new technologies.

Recently, the importance of impact-based forecasting has increased along with the socio-economic impact of severe weather have emerged. As news articles contain unconstructed information closely related to the people’s life, this study developed and evaluated a binary classification algorithm about snowfall damage information by using media articles text mining. We collected news articles during 2009 to 2021 which containing ‘heavy snow’ in its body context and labelled whether each article correspond to specific damage fields such as car accident. To develop a classifier, we proposed a probability-based classifier based on the ratio of the two conditional probabilities, which is defined as I/O Ratio in this study. During the construction process, we also adopted the n-gram approach to consider contextual meaning of each keyword. The accuracy of the classifier was 75%, supporting the possibility of application of news big data to the impact-based forecasting. We expect the performance of the classifier will be improve in the further research as the various training data is accumulated. The result of this study can be readily expanded by applying the same methodology to other disasters in the future. Furthermore, the result of this study can reduce social and economic damage of high impact weather by supporting the establishment of an integrated meteorological decision support system.

This study proposes a methodology for assessing seismic liquefaction hazard by implementing high-resolution three-dimensional (3D) ground models with high-density/high-precision site investigation data acquired in an area of interest, which would be linked to geotechnical numerical analysis tools. It is possible to estimate the vulnerability of earthquake-induced geotechnical phenomena (ground motion amplification, liquefaction, landslide, etc.) and their triggering complex disasters across an area for urban development with several stages of high-density datasets. In this study, the spatial-ground models for city development were built with a 3D high-precision grid of 5 m x 5 m x 1 m by applying geostatistic methods. Finally, after comparing each prediction error, the geotechnical model from the Gaussian sequential simulation is selected to assess earthquake-induced geotechnical hazards. In particular, with seven independent input earthquake motions, liquefaction analysis with finite element analyses and hazard mappings with LPI and LSN are performed reliably based on the spatial geotechnical models in the study area. Furthermore, various phenomena and parameters, including settlement in the city planning area, are assessed in terms of geotechnical vulnerability also based on the high-resolution spatial-ground modeling. This case study on the high-precision 3D ground model-based zonations in the area of interest verifies the usefulness in assessing spatially earthquake-induced hazards and geotechnical vulnerability and their decision-making support.

Seismic fragility curves play a crucial role in assessing potential seismic losses and predicting structural damage caused by earthquakes. This study compares non-sampling-based methods of seismic fragility curve derivation, particularly the probabilistic seismic demand model (PSDM) and finite element reliability analysis (FERA), both of which require employing sophisticated finite element analysis to evaluate and predict structural damage caused by earthquakes. In this study, a three-dimensional finite element model of API 5L X65, a buried gas pipeline widely used in Korea, is constructed to derive seismic fragility curves. Its seismic vulnerability is assessed using nonlinear time-history analysis. PSDM and a FERA are employed to derive seismic fragility curves for comparison purposes, and the results are verified through a comparison with those from the Monte Carlo Simulation (MCS). It is observed that the fragility curves obtained from PSDM are relatively conservative, which is attributed to the assumption introduced to consider the uncertainty factors. In addition, this study provides a comprehensive comparison of seismic fragility curve derivation methods based on sophisticated finite element analysis, which may contribute to developing more accurate and efficient seismic fragility analysis.

본 연구는 발달장애아동을 대상으로 케어로봇을 치료에 활용하기 위해 발달장애 아동의 부모를 대상으로 소셜로봇이 갖추어야 할 기능과 요구 등을 분석하고자 시행한 질적연구이다. 연구대상자는 자녀 가 발달장애 진단을 받았거나 1년 이상 특수교육을 시행하고 있는 만 18세 미만 자녀를 둔 주양육자 11명 을 대상으로 진행하였다. 면담질문지는 반구조화된 질문지로 구성하여 소셜로봇에 대한 인식, 필요성, 기 대, 염려 및 기타 의견 등으로 구성하였다. 면담은 COVID 19로 인해 비대면 온라인 화상 면담으로 이루어 졌다. 연구결과, 4개의 주제가 도출되었다. 4개의 주제는 낯설지만 곧 마주할 현실, 능동적인 기능이 탑재 된 소셜로봇, 소셜로봇의 외형과 재질에 대한 바람 및 소셜로봇에 대한 염려로 도출되었다. 본 연구를 통하 여 양적연구에서는 파악할 수 없었던 주양육자의 소셜로봇에 대한 기대, 제안 등을 파악할 수 있었고 이를 향후 개발할 소셜로봇에 반영할 수 있다면 보다 수용성이 높은 긍정적인 결과를 도출 할 수 있을 것으로 기대한다.

본 연구에서는 코로나 이후 색조화장품 시장의 소비자들의 온라인 관심 정보에 대한 자료 수집 을 통하여 색조화장품 정보 검색의 특성과 텍스트 마이닝 분석 결과에 나타난 코로나 이후 색조화장품 시 장의 주요 관심정보들을 분석하고자 하였다. 실증분석에서는 “색조화장품” 이라는 단어를 포함하는 뉴스, 블로그, 카페, 웹페이지 등의 모든 문서들을 분석 대상으로 텍스트 마이닝을 수행하였다. 분석 결과 코로나 이후 색조화장품에 대한 온라인 정보 검색은 주로 구매 정보와 피부와 마스크 관련 화장법 등에 관한 정보 와 관심 브랜드와 행사 정보 등의 주요 토픽이 주를 이루고 있었다. 결과적으로 코로나 이후 색조화장품 구매자들은 적극적인 온라인 정보 검색을 통하여 제품 가치와 안전성, 가격 혜택, 매장 정보 등의 구매 정 보에 더욱 민감하게 될 것이므로 이에 대한 대응전략이 요구된다.