Recently, there has been an increasing attempt to replace defect detection inspections in the manufacturing industry using deep learning techniques. However, obtaining substantial high-quality labeled data to enhance the performance of deep learning models entails economic and temporal constraints. As a solution for this problem, semi-supervised learning, using a limited amount of labeled data, has been gaining traction. This study assesses the effectiveness of semi-supervised learning in the defect detection process of manufacturing using the MixMatch algorithm. The MixMatch algorithm incorporates three dominant paradigms in the semi-supervised field: Consistency regularization, Entropy minimization, and Generic regularization. The performance of semi-supervised learning based on the MixMatch algorithm was compared with that of supervised learning using defect image data from the metal casting process. For the experiments, the ratio of labeled data was adjusted to 5%, 10%, 25%, and 50% of the total data. At a labeled data ratio of 5%, semi-supervised learning achieved a classification accuracy of 90.19%, outperforming supervised learning by approximately 22%p. At a 10% ratio, it surpassed supervised learning by around 8%p, achieving a 92.89% accuracy. These results demonstrate that semi-supervised learning can achieve significant outcomes even with a very limited amount of labeled data, suggesting its invaluable application in real-world research and industrial settings where labeled data is limited.

이 연구는 1세대 스마트 온실의 재배환경 데이터와 장미 절 화의 품질 특성 데이터를 수집하고 그 요인들 간의 상관 관계 를 분석하여 절화수명 예측 및 최적 환경 조성의 기초 자료를 얻고자 수행되었다. 이를 위해, 토경재배(SC) 및 암면배지경 양액재배(RWH) 하우스 각 1개소를 선정하여 1년간 기온, 상 대습도(RH) 및 수증기압차(VPD), 일적산광량(DLI), 근권온도 등의 환경 데이터와 매월 말 수확된 장미 ‘Miss Holland’ 절 화의 품질 특성 데이터를 수집하였으며, 이 데이터와 절화수 명과의 상관관계를 분석하였다. 절화수명은 10월과 11월을 제외하고는 SC 하우스에서 RWH 하우스보다 더 길었다. 절 화수명과 환경 및 생육 특성 간의 상관관계 분석에서 SC 하우 스의 상관계수는 RWH 하우스보다 조금 더 높았으며, 절화수 명 예측을 위한 요소들도 두 하우스 간에 차이가 있었다. SC 하우스의 절화수명 Y=0.848X1+0.366X2-0.591X3+2.224X4- 0.171X5+0.47X6+0.321X7+9.836X8-110.219(X1-X8: 최고 RH, RH 일교차, DLI, pH, Hunter’s b value, EC, 절화 장, 잎 두께; R2=0.544)로 예측되었고, RWH 하우스의 절화수명 Y=-1.291X1+52.026X2-0.094X3+0.448X4-3.84X5+0.624X6 - 8.528X7+28.45(X1-X7: 경경, 야간 VPD, 최고 근권온도, 최 저 근권온도, 기온 일교차, RH 일교차, 최고 VPD; R2=0.5243) 로 예측되었다. 이 두 모델식으로부터 SC 하우스에서는 RH, EC 및 pH가, 그리고 RWH 하우스에서는 근권 온도가 절화수명에 더 큰 영향을 미친다는 것을 추론할 수 있다. 따라서 각 재배 방법에 따라 장미의 절화수명에 더 큰 영향을 미치는 환경적 요인을 효율적으로 관리할 필요가 있다.

The purpose of this study is to identify the major peacekeeping activities that the Korean armed forces has performed from the past to the present. To do this, we collected 692 press releases from the National Defense Daily over the past 20 years and performed topic modeling and social network analysis. As a result of topic modeling analysis, 112 major keywords and 8 topics were derived, and as a result of examining the Korean armed forces's peacekeeping activities based on the topics, 6 major activities and 2 related matters were identified. The six major activities were 'Northeast Asian defense cooperation', 'multinational force activities', 'civil operations', 'defense diplomacy', 'ceasefire monitoring group', and 'pro-Korean activities', and 'general troop deployment' related to troop deployment in general. Next, social network analysis was performed to examine the relationship between keywords and major keywords related to topic decision, and the keywords ‘overseas’, ‘dispatch’, and ‘high level’ were derived as key words in the network. This study is meaningful in that it first examined the topic of the Korean armed forces's peacekeeping activities over the past 20 years by applying big data techniques based on the National Defense Daily, an unstructured document. In addition, it is expected that the derived topics can be used as a basis for exploring the direction of development of Korea's peacekeeping activities in the future.

다중 에이전트 강화학습의 발전과 함께 게임 분야에서 강화학습을 레벨 디자인에 적용하려는 연구가 계속되 고 있다. 플랫폼의 형태가 레벨 디자인의 중요한 요소임에도 불구하고 지금까지의 연구들은 플레이어의 스킬 수준이나, 스킬 구성 등 플레이어의 매트릭에 초첨을 맞춰 강화학습을 활용하였다. 따라서 본 논문에서는 레 벨 디자인에 플랫폼의 형태가 사용될 수 있도록 시각 센서의 가시성과 구조물의 복잡성을 고려하여 플랫폼 이 플레이 경험에 미치는 영향을 연구한다. 이를 위해Unity ML-Agents Toolkit과MA-POCA 알고리즘, Self-play 방식을 기반으로2vs2 대전 슈팅 게임 환경을 개발하였으며 다양한 플랫폼의 형태를 구성하였다. 분석을 통해 플랫폼의 형태에 따른 가시성과 복잡성의 차이가 승률 밸런스에는 크게 영향을 미치지 않으나 전체 에피소 드 수, 무승부 비율, Elo의 증가폭에 유의미한 영향을 미치는 것을 확인했다.

Maintaining sea superiority through successful mission accomplishments of warships is being proved to be an important factor of winning a war, as in the Ukraine-Russia war. in order to ensure the ability of a warship to perform its duties, the survivability of the warship must be strengthened. In particular, among the survivability factors, vulnerability is closely related to a damage assessment, and these vulnerability data are used as basic data to measure the mission capability. The warship's mission capability is usually measured using a wargame model, but only the operational effects of a macroscopic view are measured with a theater level resolution. In order to analyze the effectiveness and efficiency of a weapon system in the context of advanced weapon systems and equipments, a warship's mission capability must be measured at the engagement level resolution. To this end, not the relationship between the displacement tonnage and the weight of warheads applied in the theater level model, but an engagement level resolution vulnerability assessment method that can specify physical and functional damage at the hit position should be applied. This study proposes a method of measuring a warship’s mission capability by applying the warship vulnerability assessment method to the naval engagement level analysis model. The result can be used as basic data in developing engagement algorithms for effective and efficient operation tactics to be implemented from a single unit weapon system to multiple warships.

Smart factory companies are installing various sensors in production facilities and collecting field data. However, there are relatively few companies that actively utilize collected data, academic research using field data is actively underway. This study seeks to develop a model that detects anomalies in the process by analyzing spindle power data from a company that processes shafts used in automobile throttle valves. Since the data collected during machining processing is time series data, the model was developed through unsupervised learning by applying the Holt Winters technique and various deep learning algorithms such as RNN, LSTM, GRU, BiRNN, BiLSTM, and BiGRU. To evaluate each model, the difference between predicted and actual values was compared using MSE and RMSE. The BiLSTM model showed the optimal results based on RMSE. In order to diagnose abnormalities in the developed model, the critical point was set using statistical techniques in consultation with experts in the field and verified. By collecting and preprocessing real-world data and developing a model, this study serves as a case study of utilizing time-series data in small and medium-sized enterprises.

본 논문에서는 대규모 실시간 매칭의 생존 게임에서 플레이를 위한 유저들의 소셜 관계에 대해 연구한다. 특 히 “사전 팀 구성”을 통한 자의적인 팀 구성이 어떤 방식으로 유저들을 연결하는 지 연구하고자 한다. 다수 의 사람 간 집단 역학에서 나타나는 특성이나 패턴에 대한 조사를 중심으로 하였으며, 개인의 특성은 보조적 인 수단으로만 사용된다. 이번 연구에서는 게임을 플레이하는 유저들의 익명화 된 대규모 데이터를 활용하며 이에 대한 간소화된 집계 방법을 제안한다. 데이터 세트에는 사전 팀 구성에 관한 11,259만 줄의 속성이 포 함되어 있으며, 데이터에서 우리는 250만개의 노드와 1,182만개의 무방향 에지가 있는 협업 네트워크를 구성 하여 대규모 게임 내 협동 네트워크를 만듭니다. 연결 정도, 경로 길이, 클러스터링 및 소속 하위 컴포넌트의 크기 등 네트워크에 관한 수치를 통해 게임내 소셜 활동에 대한 이해를 높이고자 한다. 본 논문에서는 다음 의 두가지 특성을 중심으로 결론을 제시한다. 첫째, 네트워크 내에는 대규모로 연결된 2개(전체의 44% 및 2%)와 나머지의 파편화된 하위 컴포넌트로 구성 되어있다. 이 대규모 컴포넌트 중 작은 쪽은 한국 유저로만 구성되어 있다. 둘째, 컴포넌트 크기 별 평균 연결 거리와 군집화 계수, k-core를 확인함으로써 기타 다른 네 트워크 대비 이웃 간 연결이 강하면서 전체적으로는 비교적 멀리 떨어져 있음을 확인한다.

Sentiment analysis is a method used to comprehend feelings, opinions, and attitudes in text, and it is essential for evaluating consumer feedback and social media posts. However, creating sentiment dictionaries, which are necessary for this analysis, is complex and time-consuming because people express their emotions differently depending on the context and domain. In this study, we propose a new method for simplifying this procedure. We utilize syntax analysis of the Korean language to identify and extract sentiment words based on the Reason-Sentiment Pattern, which distinguishes between words expressing feelings and words explaining why those feelings are expressed, making it applicable in various contexts and domains. We also define sentiment words as those with clear polarity, even when used independently and exclude words whose polarity varies with context and domain. This approach enables the extraction of explicit sentiment expressions, enhancing the accuracy of sentiment analysis at the attribute level. Our methodology, validated using Korean cosmetics review datasets from Korean online shopping malls, demonstrates how a sentiment dictionary focused solely on clear polarity words can provide valuable insights for product planners. Understanding the polarity and reasons behind specific attributes enables improvement of product weaknesses and emphasis on strengths. This approach not only reduces dependency on extensive sentiment dictionaries but also offers high accuracy and applicability across various domains.

Approximately 40,000 elevators are installed every year in Korea, and they are used as a convenient means of transportation in daily life. However, the continuous increase in elevators has a social problem of increased safety accidents behind the functional aspect of convenience. There is an emerging need to induce preemptive and active elevator safety management by elevator management entities by strengthening the management of poorly managed elevators. Therefore, this study examines domestic research cases related to the evaluation items of the elevator safety quality rating system conducted in previous studies, and develops a statistical model that can examine the effect of elevator maintenance quality as a result of the safety management of the elevator management entity. We review two types: odds ratio analysis and logistic regression analysis models.

In order to support start-ups effectively with limited resources, it is necessary to provide support tailored to start-up companies. Businesses that are operating in local incubation centers may have different demands for the support depending on the region characteristics. This study was worked on companies that have entered into the incubation center 'Youth Cube', which is operated by Ansan City. We investigated the perception of importance in the field of support to identify support policy priorities, and the satisfaction level of each field of support currently supported. Then We conducted an IPA analysis to derive the direction of support. The Ansan area has regional characteristics that it is located in the metropolitan area with well-equipped start-up infrastructure and many innovative institutions, As a result of the importance survey, technology commercialization was recognized as the most important, and education/mentoring was recognized as the least important. Satisfaction with the space and facilities that can be used free of charge was the highest, and the satisfaction was the lowest for entrepreneurship education/mentoring, as was the degree of importance. In addition, according to each quadrant, we derived support that should be maintained, support that should be intensively improved, support that should be given a low policy priority, and support directions that should be kept in mind to avoid excessive support.

가상의 공간에서 함께 즐기는 콘텐츠에 대해 관심이 늘어나고 있다. 더불어 가상공간에서 나만의 아바타도 꾸미고 게임 콘텐츠 역시 즐기고 있다. 이러한 가상의 공간을 메타버스라고 하며 대표적인 플랫폼으로는 제 페토와 로블록스가 있다. 메타버스 플랫폼은 플랫폼의 취지에 맞게 유저가 직접 제작하기 편리한 컨텐츠 제 작 도구를 지원한다. 그러나 일반 유저가 제작한 콘텐츠는 메타버스에서 활동하고 있는 다양한 유저의 관심 과 흥미를 지속적으로 유발하기에는 부족한 부분이 있어 꾸준한 흥행이 어렵다. 본 논문은 기존 게임 엔진을 활용해서 콘텐츠를 제작하는 콘텐츠 개발자로서 메타버스 플랫폼이 제공하는 제작 도구의 효용성을 검증하 고자 게임 엔진의 선택 기준을 참고하여 인터페이스, 개발 언어, 개발 공정의 기준으로 대표하는 플랫폼의 제작 도구를 비교하였다.

In this study, project information of government-funded research institute in the food field was collected and analyzed to systematically identify the factors affecting the process of transferring technological achievements of public research institute to the private sector. This study hypothesized that human resources, financial resources, and technological characteristics as input factors of R&D projects affect output factors, such as research papers or patents produced by R&D projects. Moreover, these outputs would serve as drivers of the technology transfer as one of the R&D outcomes. Linear Regression Analysis and Poisson Regression Analysis were conducted to empirically and sequentially investigate the relationship between input factors and output and outcome of R&D projects and the results are as follows: First, the principle investigator's career and participating researcher's size as human resource factors have an influence on both the number of SCI (science citation index) papers and patent registration. Second, the research duration and research expenses for the current year have an influence on the number of SCI papers and patent registrations, which are the main outputs of R&D projects. Third, the technology life cycle affects the number of SCI papers and patent registrations. Lastly, the higher the number of SCI papers and patent registrations, the more it affected the number of technology transfers and the amount of technology transfer contract.

This study investigates the influence of particulate matter concentrations on the incidence of asthma, focusing on the delayed onset of symptoms and subsequent medical consultations. Analysis incorporates a four-day lag from the initiation of fine dust exposure and compares asthma patterns before and after the World Health Organization's (WHO) classification of fine dust as a Group 1 carcinogen in November 2013. Utilizing daily PM10 data and asthma-related medical visit counts in Seoul from 2008 to 2016, the study additionally incorporates Google search frequencies and newspaper article counts on fine dust to assess public awareness. Results reveal a surge in search frequencies and article publications after WHO announcement, indicating heightened public interest. To standardize the long-term asthma occurrence trend, the daily asthma patient numbers are ratio-adjusted based on annual averages. The analysis uncovers an increase in asthma medical visits 2 to 3 days after fine dust events. Additionally, greater public awareness of fine dust hazards correlates with a significant reduction in asthma occurrence after such events, even within 'normal' fine dust concentrations. Notably, behavioral changes, like limiting outdoor activities, contribute to this decrease. This study highlights the importance of analyzing accumulated medical data over an extended period to identify general public behavioral patterns, deviating from conventional survey methods in social sciences. Future research aims to extend data collection beyond 2016, exploring recent trends and considering the potential impact of decreased fine dust awareness amid the COVID-19 pandemic.

In this study, we propose a novel approach to analyze big data related to patents in the field of smart factories, utilizing the Latent Dirichlet Allocation (LDA) topic modeling method and the generative artificial intelligence technology, ChatGPT. Our method includes extracting valuable insights from a large data-set of associated patents using LDA to identify latent topics and their corresponding patent documents. Additionally, we validate the suitability of the topics generated using generative AI technology and review the results with domain experts. We also employ the powerful big data analysis tool, KNIME, to preprocess and visualize the patent data, facilitating a better understanding of the global patent landscape and enabling a comparative analysis with the domestic patent environment. In order to explore quantitative and qualitative comparative advantages at this juncture, we have selected six indicators for conducting a quantitative analysis. Consequently, our approach allows us to explore the distinctive characteristics and investment directions of individual countries in the context of research and development and commercialization, based on a global-scale patent analysis in the field of smart factories. We anticipate that our findings, based on the analysis of global patent data in the field of smart factories, will serve as vital guidance for determining individual countries' directions in research and development investment. Furthermore, we propose a novel utilization of GhatGPT as a tool for validating the suitability of selected topics for policy makers who must choose topics across various scientific and technological domains.

본 연구는 한중일 문화와 무슬림 관광수요와의 인과관계를 실증적으로 규 명하고 무슬림 관광객 유치 경쟁력 개선을 위한 한국의 전략적 시사점을 제 공하는 것을 목적으로 하고 있다. 연구대상 무슬림 국가는 한국과 중국, 일본 을 방문하는 비중이 높은 동남아시아 지역의 말레이시아와 인도네시아로 선 정하였으며 연구 조사 기간은 2006년서 2018년까지로 한정하였다. 본 연구의 국제관광수요 모형에서는 정량적 요인으로 소득, 가격을 선정하 였고 정성적 요인으로는 문화 요인인 문화적 거리를 사용하였다. 문화적 거리 는 홉스테드 문화차원 점수를 활용하여 KSI(Kogut & Singh Index)방식으로 계 측하였다. 연구모형은 고정효과 최소제곱법(OLSFE)으로 추정했는데 이는 패널 고정효 과 모형의 특수 형태이다. 분석 결과 문화 요인인 문화적 거리가 소득과 가격의 경제적 요인보다 무슬림 관광수요에 더 큰 영향을 미치는 것으로 추정되 었다. 이와 같이 본 연구에서 국제관광수요에 영향을 미치는 요인으로 각국의 문화차이를 나타내는 문화적 거리의 중요성을 파악하였다. 이는 관광지 경쟁 력에 국가문화가 왜 필수인지에 대한 이해를 제공한 것으로 실무적인 측면이 나 학술적인 측면에서 활용할 수 있을 것이다. 주제어: 한중일 문화, 무슬림 관광수요, 문화적 거리, 홉스테드 문화차원, KSI(Kogut & Singh Index).

The purpose of this study is to analyze 118 studies in the field of Korean presentation education conducted between 2005 and January 2023 and to provide insight into the future direction of research in this area. The study utilized Topic modeling and direct researcher analysis methods to examine Korean presentation education research materials. The analysis revealed a steady increase in research on Korean presentation education, with high frequency keywords such as ‘academic’, ‘purpose’, ‘discourse’, and ‘class’ indicating that research has been conducted on these topics. Furthermore, the Topic modeling analysis revealed that many studies have focused on ‘study on the analysis of aspects and the use of strategies in presentation discourse’ and ‘study on the use of presentation classes by learners’, particularly for Chinese native learners. In terms of research methods, the analysis conducted through direct researcher analysis showed that the most common methods used were ‘dialogue analysis’ and ‘survey research’, and single research methods were utilized more frequently than mixed methods. Based on these findings, this study suggests that future research should focus on Korean presentation education for language learners with various purposes and employ a variety of research methods.

Recently, the occurrence frequency of earthquake has increased in Korea, and the interests for seismic reinforcement of existing school buildings have been raised. To this end, the seismic performance evaluations for school buildings that did not accomplish the seismic design are required. In particular, this study checks the eigenvalue analysis, pushover curves, maximum base shears, performance points and story drift ratios, and then analyzes the seismic performance characteristics according to bracing configuration of steel frame system reinforcement. Also, this study presents the practical field application methods through the comparison of analysis results for the seismic performance characteristics.

In this study, the seismic response characteristics of the three analysis model with or without TMD were investigated to find out the effective dome shape. The three analysis models are rib type, lattice type and geodesic type dome structure composed of space frame. The maximum vertical and horizontal displacements were evaluated at 1/4 point of the span by applying the resonance harmonic load and historical earthquake loads (El Centro, Kobe, Northridge earthquakes). The study of the effective TMD installation position for the dome structure shows that seismic response control was effective when eight TMDs were installed in all types of analysis model. The investigation of the efficiency of TMD according to dome shape presents that lattice dome and geodesic dome show excellent control performance, while rib dome shows different control performance depending on the historical seismic loads. Therefore, lattice and geodesic types are desirable for seismic response reduction using TMD compared to rib type.