본 연구는 빈곤지역 아동청소년의 건강한 성장은 지역사회의 구성원들이 주체가 되어 빈곤 아동청소년의 문제를 지역 과제로 인식하고 함께 해결해갈 때 가능하다고 보았다. 따라서 우리 의 과제를 우리 스스로 고민하고 궁리하여 실천 활동에 적용하고자 􍾧참여실천연구􍾨를 진행하였 다. 실천 내용으로는 􍾧월산동마을교육활동가협의회􍾨라는 주체들 간 협의􍾶소통 구조를 조직하였 고, 워크숍, 세미나, 컨설팅 등을 통해 지역 교육 문제를 논의하고 학습하였다. 이를 기반으로 학교 교사와 마을활동가의 협동을 통해 마을교육과정을 개발􌝆운영하였고, 마을 공유공간이자 청소년 거점 공간을 마련하였다. 실천 과정에서의 특징은 첫째, 주체나 공간, 활동을 구상하는 데 있어서 지역(마을)에 대한 지향이 강하게 드러났다. 둘째, 활동의 시작이 주체들이 함께 논 의하고 공유, 합의하는 과정에서부터 비롯하였다. 셋째, 중간지원자인 화월주의 독특한 역량과 역할로 초반에는 주도적으로 기반을 조성하는 역할을 하다가 당사자들을 활동의 주체로 세운 다음 권한과 역할을 넘겼다. 넷째, 실천 과정에서 자기 또는 상호 간 반성적 실천 모습이 확인 되었다. 본 연구의 의의는 당사자 참여실천연구 과정을 통해 지역 과제를 해결해나가고 마을 주민의 주체적 성장과 마을교육공동체 형성의 실체를 확인하였다는 데에 있다.

The buffer is a critical barrier component in an engineered barrier system, and its purpose is to prevent potential radionuclides from leaking out from a damaged canister by filling the void in the repository. No experimental parameters exist that can describe the buffer expansion phenomenon when Kyeongju bentonite, which is a buffer candidate material available in Korea, is exposed to groundwater. As conventional experiments to determine these parameters are time consuming and complicated, simple swelling pressure tests, numerical modeling, and machine learning are used in this study to obtain the parameters required to establish a numerical model that can simulate swelling. Swelling tests conducted using Kyeongju bentonite are emulated using the COMSOL Multiphysics numerical analysis tool. Relationships between the swelling phenomenon and mechanical parameters are determined via an artificial neural network. Subsequently, by inputting the swelling tests results into the network, the values for the mechanical parameters of Kyeongju bentonite are obtained. Sensitivity analysis is performed to identify the influential parameters. Results of the numerical analysis based on the identified mechanical parameters are consistent with the experimental values.

The successful implementation of green supply chain management(GSCM) practices requires a level of cooperation that can be difficult to conduct. Despite this challenge, limited scholarly attention has been paid to exploring how the implementation of GSCM practices can be effectively facilitated and enhanced through accumulated social capital with suppliers. Based on social capital theory, this study postulates that supplier network characteristics derived from social capital with key suppliers can be critical antecedents of GSCM, which in turn enhances the firm’s environmental performance. To test hypotheses, data were collected from 330 firms in 15 countries, and structural equation modeling was employed. Results show that GSCM improves environmental performance, and structural and cognitive social capitals of the supplier network act as antecedents and lead to GSCM implementation.

The purpose of this study is to examine the relationships between citations and the research trends of The Research Journal of the Costume Culture (RJCC) using bibliometric and network analyses. The results are as follows. First, the RJCC has been cited by a greater number of journals and high-reputation journals today. The RJCC has been mentioned in global academic journals in various fields, and it has been noted the most in environmental science. Second, because of examining the articles published in the RJCC over the past three years (2019–2021), it was found that the number of topics was evenly distributed in various subfields of the clothing and textiles sector. The RJCC principally deals with traditional clothing, ethics and sustainability, and technology, which means that the RJCC reflects the past, present, and future. As a result of conducting a cluster analysis using the Wakita-Tsurumi algorithm, the subjects of ethical fashion and sustainability were derived from the subdivisions of the RJCC. This suggests that the RJCC is a journal specialized in ethical fashion and sustainability sectors such as environmental, animal, and labor ethics. This study outlined the current status and future direction of academic journals in the field of clothing through an analysis of the RJCC’s influence change and the relationship between citations. In addition, it is academically significant because it identifies research trends and knowledge-structure changes in the apparel science field by identifying changes in research keywords and significant research topics by sector.

This study attempted to obtain a comprehensive insight into future education by systematically analyzing the contents of the research report from national research institutes on future education. To achieve this, 11 research reports on future education were analyzed using the semantic network analysis method. As a result, first, the main keywords of future education are 'future-oriented', 'artificial intelligence', 'learning topic', 'diversity', 'convergence', 'future core competency', 'democratic citizenship education', 'thinking ability', 'education policy', and 'literacy'. Second, among these keywords, 'education reform' is strongly linked with 'future-oriented', 'future core competency' and 'education policy'; 'future core competency' is strongly linked with 'future science' and 'thinking ability'; 'future science' was found to be strongly connected with 'learning subject' and 'convergence'. Also, 'education content' was found to be strongly connected with 'diversity', 'literacy' and 'artificial intelligence'. Third, as a result of the centrality index, 'future-oriented' is located at the center of the network, followed by 'learning topics', 'diversity', 'literacy', and 'artificial intelligence' are located within a short distance. In addition, 'learning topics', 'artificial intelligence', and 'future orientation' were found to play an important role in the overall semantic structure. The main points of discussion are discussed, based on these results, with previous research results.

Future autonomous vehicles need to recognize the ego lanes required for lane change and the side left and right lanes differently. Therefore, multi-lane recognition is needed. In this study, using the YOLO network, mainly used for object recognition, the proposed method recognizes the ego, left and right side lanes as different objects and identifies the correct lanes. As a result of the performance evaluation on the TuSimple test data, the proposed method recognized the ego lanes and the left and right side lanes differently. It showed very stable lane recognition results. And by detecting lanes that do not exist in the ground truth of TuSimple data, the proposed method is very robust in lanes detection. Nevertheless, studies related to learning data reinforcement in which lanes are located in the center or at the left and right edges of the image and accurate network learning for lanes are needed.

With the rapid change of social outlook and the emergence of new things, neologisms and neologisms have also become an eye-catching and vigorous field of vocabulary research. Since the reform and opening up, the social outlook has greatly changed , and new things have emerged one after another. New words and new expressions have also become an eye-catching, changing and vigorous field of vocabulary research. According to the definition of neologisms and neologisms, the author sorted out 100 neologisms and neologisms released in the past ten years (2011-2020) of "Chinese inventory of the top ten neologisms and neologisms of the year", supplemented some examples with reference to the report on the living conditions of Chinese language (2005-2020), and selected 200 high-frequency neologisms and neologisms in the past 15 years based on the standard of modern Chinese neologisms and neologisms (including more than 10000 entries from 1990 to 2016). From definition to Motivation and semantic classification, phonetic　and grammatical word-building features, word-making methods and teaching strategies ,all of the above are thoroughly analyzed from theory to practice.

사람들의 인터넷 활동으로 많은 인터넷 용어를 탄생시켰다. 이 중 일부 용어는 널리 사용되어 유행하게 되었으며, 이는 그 당시의 사회 발전 상 태, 사람들의 이데올로기를 대표하는 용어로 선정되기도 하였다. 본 연구 는 중국 국가언어자원감시연구센터가 2021년 12월 6일 발표한 ‘2021년도 10대 인터넷 용어’를 바탕으로 생태언어학 관련 이론을 적용해 분석하고 연구한다. 그리고 이것을 통해 유익한 담화의 사용을 장려함으로써 파괴 적 담화의 발생을 줄이고, 사용을 자제하여 인류사회의 생태가 지속적으 로 건전하고 잘 발전하도록 유도한다. 생태언어학자 스티베는 ‘할라테이 모델’을 기반으로 비판적 생태 언어 학을 발전시켰다. 그는 전 세계인의 관점에서 7개 단어로 구성된 생태관 을 정리하고 그에 맞는 대화 분석 모델을 제시하였다. 본고에서는 이 생 태관과 대화 분석 모델을 바탕으로 조국사랑, 단결추구, 용감한 활동, 근 검절약 등과 같은 가치와 결합하여 생태관을 생성하고 10개의 인터넷 용 어에 대한 분석을 시행하였다. 10개의 인터넷 용어 중 먼저, ‘躺平’은 퇴폐적이고 부정적인 정서 때문 에 사람들이 쉽게 포기하고, 생태관이 맞지 않아 파괴적 담화로 분류된다. 다음으로, ‘YYDS’, ‘破防’, ‘元宇宙’, ‘绝绝子’, ‘伤害性不高, 侮辱性极强’, ‘我 看不懂, 但我大受震撼’ 6개의 단어는 생태환경에 도움이 되는 측면과 생 태환경을 해치는 측면이 있어 양면적 담화로 분류된다. 끝으로, ‘觉醒年 代’, ‘双减’, ‘强国有我’는 삶을 중시하고 현재와 미래를 주시하며, 자원의 공평한 분배에 유리하며 유익한 담화로 분류된다. 이런 분석을 통해 유익한 담화를 많이 쓰도록 권장하고 긍정적인 영향 을 미쳐, 파괴적인 담화의 사용을 자제하고 부정적인 영향을 줄임으로써 사회 생태계가 장기적으로 더욱 지속적이고 건강하게 발전할 수 있다.

PURPOSES : In this study, model-agnostic methods are applied for interpreting machine learning models, such as the feature global effect, the importance of a feature, the joint effects of features, and explaining individual predictions. METHODS : Model-agnostic global interpretation techniques, such as partial dependence plot (PDP), accumulated local effect (ALE), feature interaction (H-statistics), and permutation feature importance, were applied to describe the average behavior of a machine learning model. Moreover, local model-agnostic interpretation methods, individual conditional expectation curves (ICE), local surrogate models (LIME), and Shapley values were used to explain individual predictions. RESULTS : As global interpretations, PDP and ALE-Plot demonstrated the relationship between a feature and the prediction of a machine learning model, where the feature interaction estimated whether one feature depended on the other feature, and the permutation feature importance measured the importance of a feature. For local interpretations, ICE exhibited how changing a feature changes the interested instance’s prediction, LIME explained the relationship between a feature and the instance’s prediction by replacing the machine model with a locally interpretable model, and Shapley values presented how to fairly contribute to the instance’s prediction among the features. CONCLUSIONS : Model-agnostic methods contribute to understanding the general relationship between features and a prediction or debut a model from the global and/or local perspective, securing the reliability of the learning model.

Visual inspection methods have limitations, such as reflecting the subjective opinions of workers. Moreover, additional equipment is required when inspecting the high-rise buildings because the height is limited during the inspection. Various methods have been studied to detect concrete cracks due to the disadvantage of existing visual inspection. In this study, a crack detection technology was proposed, and the technology was objectively and accurately through AI. In this study, an efficient method was proposed that automatically detects concrete cracks by using a Convolutional Neural Network(CNN) with the Orthomosaic image, modeled with the help of UAV. The concrete cracks were predicted by three different CNN models: AlexNet, ResNet50, and ResNeXt. The models were verified by accuracy, recall, and F1 Score. The ResNeXt model had the high performance among the three models. Also, this study confirmed the reliability of the model designed by applying it to the experiment.