토목섬유가 적용된 AER 지주옹벽의 안정성에 관한 연구

최중현 부산대학교 대학원 2017 국내박사

The purpose of this study is to investigate the effect of installing the reinforcement on the newly developed AER retaining wall, which improved the disadvantages of the former retaining wall. Particularly, there is a feature that the length of the bottom plate can be shortened. It consists of a wall, a back support beam and a bottom plate, and the back support beam acts as a constraining pile. The model retaining wall was made 2.1m high. The model retaining wall was equipped with an inclinometer, earth pressure gauges, and strain gauges for ground strain measurement. When a reinforcement was installed on the AER retaining wall, the earth pressure of vertical retaining wall was reduced by about 25% ~ 30%, and the inclined retaining wall was reduced by about 7 ~ 9%. It is shown that the vertical retaining wall has higher reinforcing effect than the inclined retaining wall. The result of the overturn test turned out that the artificial ground failure behavior of the two types of retaining walls were similar trends. In case of vertical retaining walls, the area of failure decreased by 8.3% and the inclined retaining walls decreased by 18.9%. The distance from the retaining wall to the crack was decreased by 0.16m ~ 0.6m, and the artificial failure surface angle increased by about 4°. The analysis of the failure surface indicated that the failure of the ground was deemed not to extend to the bottom of the reinforcement, and the upper ground was considered to have slipped along the reinforcement and fractured. As a result of 3D numerical analysis of the displacement behavior of AER retaining wall under load conditions, it was found effective to reduce the horizontal displacement of the retaining wall and to suppress the displacement of the ground, by installing the reinforcement behind the retaining wall rather than from the back support beams. Moreover, it was analyzed that the installation length of reinforcement was 0.8H~1.0H, and the optimal installation height was 5/10H ~ 6/10H.

Studies on the F1 domain of the aerotaxis transducer aer of Escherichia coli

강민호 건국대학교 2003 국내박사

Escherichia coli utilizes the Aer protein to sense changes in the intemal energy level and guide the bacteria to a niche where the cellular energy level is optimal. Aer contains an F1 domain that connects the N-terminal PAS sensor domain to the transmembrane domain, a HAMP domain and C- terminal signaling domain. The importance of the F1 domain (residues 122- 166) for Aer function was shown in this study. The F1 domain was randomly mutagenized to identify residues in that are essential for function. Single amino acid mutations in the F1 domain abolished FAD binding, likely to the N-terminal PAS sensor domain, and eliminated aerotaxic responses, S28G, A65V, and A99V were identified as second-site suppressors for E122G, L161P, and R166P in the F1 domain. These second-site suppressors complemented the Aer F1 mutants as analyzed by ring formation and swarming ability on succinate swarm plates. S28G, A65V, and A99V restored aerotaxis responses in the Aer F1 mutants as well as Aer HAMP mutants and therefore appear to be non-specific second-site suppressors. It is likely that the S28G, A65V and A99V suppressor mutations increase the affinity of the PAS domain for FAD, thereby counteracting the decreased affinity for FAD in the L161P, R166P and I123N mutants. Secondary structure prediction suggested that the Aer F1 domain is composed of two helical segments that connect to the PAS domain and the TM region by two hinges. The F1 domain appears to be essential for Aer function, as it has been preserved in all Aer-like chemoreceptors. Therefore, it appears that the Fl domain plays the same role in all chemoreceptor proteins, This essential function may be structural or functional in that the F1 domain may be an essential spacer to correctly align the PAS domain with the HAMP domain, or it may play a significant role in signal propagation from the PAS domain to the transmembrane region and down through the HAMF domain.

AER 지주옹벽의 거동에 관한 연구

김홍선 부산대학교 대학원 2017 국내박사

The purpose of this study was to investigate the installation interval space and its effect of the back supporting beams in Assembled Earth Retaining Wall(=AER-wall). A comprehensive experimental and analytical study has been conducted. In this test, a prototype model of AER-wall was made and tested. Major variables include the installation interval space of the back supporting beams and the slope of the retaining wall. From this test, it is found that the structural stability is improved compared to the other retaining wall structures, by adding the back supporting beams. The resisting ability of this wall on the soil pressure is improved. The observed soil pressure tested in this retaining wall reduced about 17 percent compared to the calculated soil pressure. It is found that the optimal area ratio of the back supporting beams is 0.2, considering the rotation of retaining wall and soil pressure according to the area ratio of back supporting beam. A three dimensional numerical analysis is conducted in this study. From this analysis, it is found that the installation of the back supporting beams is very efficient, especially in the case of the difficulty in sufficient base length of retaining wall structure.

조립식 지주옹벽(AER)의 배면보강재 효과에 관한 실험적 연구

황은아 부산대학교 대학원 2014 국내석사

A retaining wall is a structure designed and constructed to resist the lateral pressure of soil when there is a desired change in ground elevation that exceeds the angle of repose of the soil. Many types of retaining walls that have various advantages are used, but retaining walls should be developed for stability and economy. The paper proposes the efficient installation method of reinforcement reinforced retaining wall. Optimal height and optimal length of reinforcement is determined by laboratory model test. Optimal height of reinforcement is point of application of total force on the retaining wall. Optimal length of reinforcement is 500/700(reinforcement length / height of retaining wall). Effect of fixing reinforcement to the retaining wall is increased by increasing lateral displacements.

전기동력원을 포함하는 자동차의 주행모드조건 변화에 따른 연비 및 배출가스 특성에 관한 연구

강은정 한국교통대학교 일반대학원 2016 국내석사

Greenhouse gas abatement policies are one of the most important social issues with strengthening policies of emission regulation. With such a background, Green car market such as EV(Electric Vehicle), HEV(Hybrid Electric Vehicle), PHEV(Plug-in Hybrid Vehicle) and the like (‘xEV’) is rapidly growing. Conventional I.C. engine automotive is driven only by the engine. but energy consumption characteristics of xEV using electric energy source(such as motor) as the main or additional power source are shown different result due to the characteristics of the electric motor according to the driving pattern and driving conditions. The purpose of this study is the analysis of fuel efficiency and emission characteristics of xEV according to driving cycle conditions. The analysis was shown by dividing into three parts. First, It was analysed fuel efficiency and emission effect of HEV and PHEV according to change of driving cycle conditions. Second, It was analysed the all electric range of the electric vehicles and charge depleting mode of plug-in hybrid vehicle according to the change of driving mode conditions. Finally, It was analysed SOC change of HEV during mode test of vehicle in order to confirm the influence according to initial SOC change of HEV. 기존의 내연기관 자동차는 엔진으로만 차량을 구동하는 반면에 전기에너지를 주동력원 또는 추가적인 동력원으로 포함하는 전기자동차(EV), 하이브리드 전기자동차(HEV) 및 플러그인 하이브리드자동차(PHEV)의 경우 전기모터가 갖는 특성으로 인하여 운전패턴이나 주행조건에 따라 에너지 소비특성 및 에너지 소비효율, 온실가스 배출량 특성 등이 다소 상이하게 나타난다. 따라서 본 연구에서는 전기동력원을 포함하는 자동차(이하 ‘xEV’라고 칭한다.)의 주행모드 조건에 따른 연비 특성을 크게 세 가지로 구분하여 비교분석하였다. 첫 번째로 하이브리드 전기자동차, 플러그인 하이브리드자동차와 같은 xEV의 차종별 특성에 따라 주행모드 조건 변화에 따른 연비 및 유해 배출가스 영향도를 분석하였다. 두 번째로 전기자동차 및 플러그인 하이브리드자동차의 전기구간에서 주행모드조건 변화에 따른 차량의 총 주행거리 및 에너지소비효율에 미치는 특성을 비교함으로써 향후 xEV의 상용화를 위한 검토를 수행하였다. 마지막으로 타입별(mild, power type) 하이브리드 전기자동차의 초기 SOC 변화가 연비 및 배출가스에 미치는 영향을 알아보기 위하여 모드를 주행하는 동안 SOC의 변화를 비교분석하였다.

중국 대학생의 AI리터러시가 혁신행동에 미치는 영향 : 자기조절학습과 비판적 사고의 매개효과오 수 경(?守景)

오수경 중부대학교 일반대학원 박사과정 2026 국내박사

본 연구의 목적은 인공지능 기술이 급속도로 발전하는 환경에서 대학생의 AI리 터러시가 어떠한 경로와 메커니즘을 통해 혁신행동에 영향을 미치는지를 규명하고, 더 나아가 대학생이 주관적으로 지각하는 적절한 평가와 보상이 이러한 관계에서 상황적 조절역할을 수행하는지를 분석하는 데 있다. 이를 통해 대학 인재 양성과 학습지원 체계 구축의 관점에서 대학생 혁신행동과 AI교육의 심층적 융합을 촉진 하기 위한 실증적 근거와 교육적 시사점을 도출하고자 하였다. 이를 위해 다음 네 가지 연구문제를 설정하여 검증하였다. 첫째, 대학생의 AI리터 러시와 혁신행동 간에는 어떠한 상관관계가 있는가? 둘째, AI리터러시와 혁신행동 간 관계에서 자기조절학습은 어떠한 영향 역할을 수행하는가? 셋째, AI리터러시와 혁신행동 간 관계에서 비판적 사고는 어떠한 영향 역할을 수행하는가? 넷째, 적절 한 평가와 보상에 대한 지각 수준이 높은 집단과 낮은 집단 간에는 AI리터러시, 자 기조절학습, 비판적 사고 및 혁신행동 간 작동 메커니즘에 차이가 존재하는가? 연구대상은 중국 동부·중부·서부 지역 10개 대학에 재학 중인 학부생으로, 최종적 으로 565부의 유효 표본을 확보하였다. 측정 변인의 설정에 있어 AI리터러시는 독 립변인으로, 자기조절학습과 비판적 사고는 매개변인으로, 혁신행동은 종속변인으 로, 적절한 평가와 보상(평가 적합성, 피드백 품질, 보상 적절성 등 하위요인을 포 함)은 조절변인으로 설정하였다. 측정도구의 경우, AI리터러시 척도는 Ma와 Chen(2024)이 중국 대학생을 대상으로 개발·타당화한 AILS-CCS를 사용하였고, 혁 신행동 척도는 중국 대학생 집단의 학습 및 생활 특성을 반영하여 李宪印 외(2019) 가 개발한 혁신행동 척도를 활용하였다. 자기조절학습 척도는 이은희(2025)가 사용· 검증한 척도를, 비판적 사고 척도는 侯玉波 외(2022)가 개발한 척도를 사용하였다. ‘적절한 평가와 보상’ 척도는 평가·피드백·보상 관련 선행연구에서 사용된 기존 척 도를 참고하고, 대학 맥락에 부합하도록 수정·보완하여 구성하였다. 자료 분석은 SPSS와 AMOS 통계 프로그램을 활용하여 기술통계, 신뢰도 및 타당도 검증, 확인 적 요인분석, 구조방정식모형 분석을 순차적으로 실시하였으며, 평가 및 보상 수준 의 상·하 집단을 구분한 다중집단분석을 통해 연구모형과 가설을 검증하였다. 연구결과는 다음과 같이 요약할 수 있다. 첫째, 대학생의 AI리터러시, 자기조절학 습, 비판적 사고는 모두 혁신행동에 유의한 정(+)의 영향을 미치는 것으로 나타났 다. 이는 AI가 광범위하게 개입된 학습환경에서 기술 관련 리터러시와 학습 관련 심리 특성이 혁신행동을 촉진하는 중요한 기반을 이룬다는 점을 시사한다. 둘째, 자 기조절학습과 비판적 사고는 AI리터러시와 혁신행동 간 관계에서 매개역할을 수행 하는 것으로 나타났다. 즉, AI리터러시는 한편으로는 혁신행동에 직접적인 정적 영 향을 미치고, 다른 한편으로는 자기조절학습 능력과 비판적 사고 수준을 향상함으 로써 혁신행동을 간접적으로 촉진하는 이중 경로를 형성하고 있으며, 일부 매개 경 로는 상대적으로 더 높은 설명력을 지니는 것으로 분석되었다. 셋째, 적절한 평가와 보상 수준에 따른 다중집단분석결과, 적절한 평가와 보상은 상기 작동 메커니즘에 유의한 조절효과를 갖는 것으로 확인되었다. 구체적으로, 평가 기준이 명확하고 공 정하며, 피드백이 시의적절하고 구체적이며, 보상 방식이 학생들에 의해 ‘적절하다’ 고 지각되는 고수준 맥락에서는 AI리터러시가 자기조절학습과 비판적 사고를 거쳐 혁신행동에 이르는 경로계수가 유의미하게 강화되었으나, 평가 및 보상 수준이 상 대적으로 낮은 경우는 동일한 경로의 영향력이 뚜렷이 약화하는 것으로 나타났다. 이러한 결과를 토대로 본 연구는 대학 수업 운영과 학습지원체계 구축의 관점에 서 다음과 같은 시사점을 제시한다. 첫째, 대학은 생성형AI를 수업과 학습과정에 도 입·확산하는 동시에, AI리터러시를 대학생의 기초적 핵심역량으로 간주하고, 교육과 정과 비교과과정을 통해 AI 관련 지식·기술 및 윤리의식을 함양해야 한다. 둘째, AI 지원 학습환경을 설계할 때는 자기조절학습 훈련과 비판적 사고 함양 요소를 의도적으로 내재화할 필요가 있다. 예를 들어, 학습목표 관리, 학습과정 모니터링 지도, 실제 문제 기반 탐구활동 등을 통해 학생의 주도적 계획, 성찰 및 비판적 질 문 능력을 강화해야 한다. 셋째, 대학이 대학생의 혁신행동 향상 방안을 설계·구축 함에 있어 평가 및 보상 메커니즘의 최적화를 중시할 필요가 있다. 구체적으로, 평 가 기준의 투명성과 적절성을 제고하고, 고품질의 실행 가능한 피드백을 제공하며, 혁신에 대한 투입과 과정의 질에 상응하는 보상 체계를 설계함으로써 혁신행동이 지속적으로 발현될 수 있는 수업 및 캠퍼스 환경을 조성해야 한다. 향후 후속 연구를 위해 다음과 같은 제언을 제시한다. 첫째, 표본 측면에서 연구 대상의 표집 지역을 확대하고 고등교육기관의 특성에 따른 확장을 통해 결론의 외 적 타당성을 제고할 필요가 있다. 둘째, 연구설계 측면에서 종단추적, 실험연구 또 는 준실험연구를 수행함으로써 AI리터러시와 혁신행동 간의 동태적 작용 과정을 더 정교하게 규명할 필요가 있다. 셋째, 변인 구성 측면에서 개인차 특성 및 상황 요인을 추가 반영하여 보다 종합적인 이론 모형을 구축할 필요가 있다. 넷째, 대학 의 교수·학습 혁신 실천과 연계하여 AI리터러시 교육과정, 프로젝트 기반 학습, 평 가 및 보상 제도 방안을 구체적으로 설계하고 그 효과를 평가함으로써, AI시대 대 학생의 혁신역량 함양을 위한 실효성 있는 실증 근거를 제공할 필요가 있다. The purpose of this study is to clarify the mechanisms through which AI literacy influences university students’ innovative behavior in the context of rapid advances in artificial intelligence technologies, and to further examine the contextual moderating role of students’ subjective perceptions of the appropriateness of evaluation and reward. Through this process, the study seeks to provide empirical evidence and educational implications for promoting the deep integration of students’ innovative behavior and AI education from the perspective of talent cultivation and the construction of learning support systems in higher education. To this end, the following four research questions were proposed and tested: (1) What is the relationship between university students’ AI literacy and their innovative behavior? (2) In the relationship between AI literacy and innovative behavior, how does self-regulated learning exert its influence? (3) In the relationship between AI literacy and innovative behavior, how does critical thinking exert its influence? (4) Are there differences in the mechanisms linking AI literacy, self-regulated learning, critical thinking, and innovative behavior between student groups with higher versus lower perceived appropriateness of evaluation and reward? The participants were undergraduate students enrolled at 10 universities located in eastern, central, and western China, yielding 565 valid responses. With respect to measurement, AI literacy was specified as the independent variable; self-regulated learning and critical thinking were specified as mediating variables; innovative behavior was specified as the dependent variable; and perceived appropriateness of evaluation and reward (including dimensions such as evaluation alignment, feedback quality, and reward appropriateness) was specified as the moderating variable. Regarding the instruments, AI literacy was measured using the AILS-CCS scale developed and validated by Ma and Chen (2024) for Chinese university students. Innovative behavior was assessed with the scale developed by Li Xianyin et al. (2019), which was constructed based on the learning and daily life characteristics of Chinese undergraduates. Self-regulated learning was measured with the scale employed and validated by Lee Eun-hee (2025). Critical thinking was measured with the scale developed by Hou Yubo et al. (2022). The appropriate evaluation and reward scale was adapted fromexisting instruments used in studies on evaluation, feedback, and reward, and revised to fit the university context. Data analysis was conducted using SPSS and AMOS, including descriptive statistical analysis, reliability and validity testing, confirmatory factor analysis, structural equation modeling, and multi-group analysis based on high- and low-evaluation-and-reward groups, in order to test the research model and its hypotheses. The main findings are as follows. First, AI literacy, self-regulated learning, and critical thinking each exerted a significant positive effect on innovative behavior, indicating that in learning environments where AI is widely embedded, technology-related literacy and learning-related psychological characteristics jointly constitute an important foundation for fostering innovative behavior. Second, self-regulated learning and critical thinking played mediating roles in the relationship between AI literacy and innovative behavior. AI literacy not only directly enhanced students’ innovative behavior but also indirectly facilitated the emergence of innovative behavior by strengthening their capacity for learning regulation and their level of critical thinking, with certain mediating paths exhibiting relatively stronger explanatory power. Third, the multi-group analysis revealed a significant moderating effect of appropriate evaluation and reward on the above mechanisms. Based on these findings, the study offers the following implications from the perspective of curriculum instruction and the construction of learning support systems in higher education. First, as universities advance the integration of generative AI into classroominstruction and learning processes, they should position AI literacy as a foundational core competence for undergraduates and cultivate students’ AI-related knowledge, skills, and ethical awareness through both the formal curriculum and co-curricular programs. Second, in designing AI-supported learning environments, institutions should deliberately embed training in self-regulated learning and the development of critical thinking—for example, by incorporating learning-goal management, process monitoring and scaffolding, and inquiry-based activities grounded in authentic problems—so as to strengthen students’ capacity for proactive planning, reflection, and questioning. Third, when developing interventions to enhance undergraduates’ innovative behavior, universities should prioritize the optimization of evaluation and reward mechanisms by improving the transparency and appropriateness of assessment criteria, providing high-quality and actionable feedback, and designing reward structures aligned with innovative effort and process, thereby fostering classroomand campus environments conducive to the sustained emergence of innovative behavior. For future follow-up research, the following recommendations are proposed. First, at the sampling level, the study population should be extended to universities across a wider range of regions and institutional tiers to improve the external validity of the findings. Second, in terms of research design, longitudinal tracking, experimental, or quasi-experimental studies should be conducted to more precisely elucidate the dynamic processes linking AI literacy and innovative behavior. Third, at the construct level, additional individual and contextual factors should be incorporated to develop a more comprehensive theoretical model. Fourth, in conjunction with ongoing reforms in higher education teaching and learning, researchers should design and evaluate concrete AI literacy curricula, project-based learning approaches, and assessment-and-reward schemes, thereby providing more operationalizable empirical evidence for cultivating undergraduates’ innovative capacity in the AI era.