Due to cognitive differences, traditional perceptual engineering (KE) frequently relies too heavily on designers' experience in analyzing customers' emotional demands, which can result in product designs that deviate from users' expectations. This work suggests a thorough evaluation approach that combines the particle swarm optimization-support vector regression (PSO-SVR) model and perceptual engineering to increase the scientificity and precision of design choices. The approach first determines the subjective weights of users' emotional needs using spherical fuzzy hierarchical analysis (SFAHP). Next, it uses the entropy weighting method to determine the objective weights. Finally, it combines the subjective and objective data using game theory to produce a more rational evaluation system. Finally, the emotional prediction model based on PSO-SVR is constructed to realize the accurate mapping between emotional needs and design features. The empirical study shows that“speed”, “dynamic”and“luxury” are the core emotional demands of users, and the algorithm's prediction results are highly consistent with users' actual evaluations, which strongly verifies the accuracy of the model. Compared with the traditional KE method, the model better integrates subjective experience and objective data and provides more practical support for the design of flybridge yachts.

Recently, steel dampers are widely used as seismic reinforcement devices. Steel dampers have the advantage of being easy to manufacture and being able to absorb a lot of energy through stable hysteresis behavior. However, there is a possibility that the steel damper may be damaged due to fatigue caused by repeated seismic loads. In this study, the seismic performance of steel slit dampers and engineering plastic slit dampers with different physical characteristics were compared and analyzed. It can be seen that the yield load of the steel slit damper, which has relatively high rigidity, is much larger than that of the engineering plastic slit damper. Therefore, the engineering plastic damper is more effective when applied to a structure that experiences a large displacement rather than a structure that experiences a large load.

본 연구는 우즈베키스탄 자동차 산업 발전에 있어 리버스 엔지니어링 의 역할을 탐구하며, 특히 노나카와 타케우치의 지식 창출 모델(SECI Model)과의 통합에 중점을 두고 있다. 우즈베키스탄이 경쟁력 있는 국내 산업을 구축하고자 노력함에 따라, 리버스 엔지니어링은 해외 기술의 습 득, 적용, 그리고 국산화를 가능하게 했다. 본 연구는 정성적 사례 연구 접근법을 활용하여 기업 보고서, 정책 문서, 학술 문헌을 바탕으로 1996 년부터 2024년까지의 발전 상황을 분석한다. 연구 결과에 따르면 리버스 엔지니어링은 지식 이전과 혁신을 지원해 왔지만, 제한된 R&D 역량, 수 입 부품 의존도, 그리고 취약한 지식재산권 보호 등의 과제가 여전히 남 아 있다. 이러한 장벽을 극복하기 위해 본 연구는 전략적 정책, 국내 혁 신에 대한 투자 확대, 그리고 AI 기반 설계 프로세스 도입을 권고한다.

Being in a stable continental region (SCR) with a limited history of instrumentation, South Korea has not collected sufficient instrumental data for data-driven ground motion models. To address this limitation, we investigated the suitability of the hybrid ground motion simulation method that Graves and Pitarka (2010, 2015) proposed for simulating earthquake ground motions in South Korea. The hybrid ground motion simulation method used in this study relies on region-specific parameters to accurately model phenomena associated with the seismic source and the wave propagation. We initially employed relevant models and parameters available in the literature as a practical approach. We incorporated a three-dimensional velocity model developed by Kim et al. (2017) and a one-dimensional velocity model presented by Kim et al. (2011) to account for the crustal velocity structure of the Korean peninsula. To represent the earthquake source, we utilized Graves and Pitarka’s rupture generator algorithm along with a magnitude-area scaling relationship developed for SCR by Leonard (2014). Additionally, we assumed the stress and attenuation parameters based on studies of regional seismicity. Using the implemented platform, we simulated the 2016 Mw5.57 Gyeongju earthquake and the 2017 Mw5.4 Pohang earthquake. Subsequently, we compared results with recorded accelerations and an empirical ground motion prediction equation at strong motion stations. Our simulations had an overall satisfactory agreement with the recorded ground motions and demonstrated the potential of broadband hybrid ground motion simulation for engineering applications in South Korea. However, limitations remain, such as the underestimation of long-period ground motions during the 2017 Pohang earthquake and the lack of a model to predict the ground motion amplification associated with the near-surface site response accurately. These limitations underscore the importance of careful validation and refinement of region-specific models and parameters for practically implementing the simulation method.

Recently, steel dampers are widely used as seismic reinforcement devices. Steel dampers have the advantage of being easy to manufacture and being able to absorb a lot of energy through stable hysteresis behavior. However, there is a possibility that the steel damper may be damaged due to fatigue caused by repeated seismic loads. In this study, the seismic performance of steel dampers and engineering plastic dampers with different physical characteristics were compared and analyzed. In addition, numerical analysis was performed on a hybrid damper that combines a steel damper and an engineering plastic damper. It is more effective to apply engineering plastic dampers to structures that experience significant displacement due to seismic loads. The behavior of hybrid dampers combining steel dampers and engineering plastic dampers is dominated by steel dampers. A hybrid damper in which an engineering plastic damper yields after a steel damper yields can effectively respond to various seismic loads and secure high ductility and excellent seismic performance.

최근 인공지능 분야에서 가장 활발히 연구되고 있는 거대 언어 모델은 교육에 대한 응용 가능성을 보 이며, 교육학의 거의 모든 분야에서 그 활용 방안이 연구되고 있다. 이러한 연구는 공학 교육에서도 주목 받고 있다. 그러나 구체적인 활용 분야와 방법에 대해서는 아직 많은 연구가 필요한 상황이다. 특히, 거대 언어 모델을 이용한 교육과정 설계와 개선에 대한 연구는 인공지능 공학과 교육학 두 분야에서 모두 중요한 연구 과제로 부각되고 있다. 이러한 응용 필요성에 대한 예시이자 전략으로써, 본 연구는 OpenAI에서 발표한 최신 거대 언어 모델인 ChatGPT-4o를 이용하여 한국과학기술원(KAIST) 공과대학 학부 전공 과 목과 S전자 DS부문(반도체사업부) 직무 사이의 연관성을 분석하고, 그 결과를 기반으로 대학과 기업체 양측에 반도체 산업 인력 양성과 채용에 대한 실질적인 응용 전략을 제안한다. 이를 위해 KAIST 공과대 학 학부과정에 개설된 모든 전공 과목과 S전자 DS부문(반도체사업부)의 직무기술서를 ChatGPT-4o에 학습시켜 각 과목이 특정 제품군, 직무와 가지는 연관성을 특정 범위와 기준에 의거하여 정량화된 점수로 평가했다. 또한, 각각의 직무, 전공, 과목별로 확보한 데이터를 기초적인 통계 분석을 통해 평가했으며, 구직자와 구인자의 활용 가능성에 초점을 두고 특정 전공의 각 직무별 연관성과 특정 직무의 각 전공별 연관성, 그리고 특정 직무 및 전공의 반도체 제품군별 연관성 등 다양한 조건에서 분석을 진행하였다. 또 한 본 전략에 대한 반도체 산업 실무자 견해를 수집하여 실제 전략으로의 활용 가능성을 검증하였다. 분 석 결과, 간단한 질문과 분석만으로도 전공, 교과목별로 유의미한 직무 연관성의 차이를 확인했다. 이러한 결과를 바탕으로 본 연구는 대학 교육과정의 개선과 기업 채용 및 양성 과정에서의 응용 전략을 제시한 다. 이 연구는 대학과 산업 간의 협력을 통해 인적자원 개발과 채용 효율성 증대에 기여할 것으로 기대한 다. 또한, 후속 연구로 구직자와 구인자, 교수자 등 본 연구의 효과를 확인할 수 있는 집단을 대상으로 한 대규모 설문조사 및 전문가그룹 대상 질적연구 등을 제안하여 실제 활용도와의 비교 분석 연구를 제안 한다. 결론적으로, 본 연구는 거대 언어 모델을 활용하여 필요한 인재를 양성하기 위한 교육 과정 설계의 구체적인 응용 가능성을 제시함으로써, 인공지능을 이용한 교육 분야에 대한 기여 방안을 모색한다.

This study evaluates the balance between cellular removal and extracellular matrix (ECM) preservation in cardiac tissue engineering by comparing chemical and physical decellularization methods. Cardiac tissues were treated with chemical agents (sodium dodecyl sulfate and Triton X-100) and physical methods (freeze-thawing and ultrasound). These methods were assessed based on residual cellular content, DNA quantification, ECM structural integrity, and preservation of key ECM components like collagen and glycosaminoglycan (GAG). The results revealed that while chemical methods, particularly SDS, achieved more complete cell removal, they significantly compromised ECM integrity. In contrast, physical methods, such as freeze-thawing, preserved ECM structure more effectively, despite moderate cellular removal. The findings underscore the importance of tailoring decellularization techniques to specific cardiac tissue engineering needs, with chemical methods excelling in cell removal and physical methods offering superior ECM preservation. Future research should aim to optimize these methods to achieve a better balance between decellularization efficiency and ECM integrity.

One of the key challenges for the commercialization of carbon nanotube fibers (CNTFs) is their large-scale economic production. Among CNTF spinning methods, surfactant-based wet spinning is one of the promising techniques for mass producing CNTFs. Here, we investigated how the coagulation bath composition affects the spinnability and the properties of CNTFs in surfactant-based wet spinning. We used acetone, DMAc, ethanol, and IPA as coagulants and analyzed the relationship between coagulation bath composition and the properties of CNTFs in terms of kinetic and thermodynamic coagulation parameters. From a kinetic perspective, we found that a low mass transfer rate difference (MTRD) is favorable for wet spinning. Based on this finding, we mixed the coagulant bath with solvent in a proper ratio to reduce the MTRD, which generally improved the wet spinning. We also showed that the coagulation strength, a thermodynamic parameter, should be considered. We believe that our research can contribute to establishment of surfactant-based wet spinning of CNTFs.

This paper presents a path planning optimization model for the engineering units to install obstacles in the shortest time during wartime. In a rapidly changing battlefield environment, engineering units operate various engineering obstacles to fix, bypass, and delay enemy maneuvers, and the success of the operation lies in efficiently planning the obstacle installation path in the shortest time. Existing studies have not reflected the existence of obstacle material storage that should be visited precedence before installing obstacles, and there is a problem that does not fit the reality of the operation in which the installation is continuously carried out on a regional basis. By presenting a Mixed Integrer Programming optimization model reflecting various constraints suitable for the battlefield environment, this study attempted to promote the efficient mission performance of the engineering unit during wartime.

CO2 photocatalytic reduction is a carbon–neutral renewable energy technology. However, this technology is restricted by the low utilization of photocatalytic electrons. Therefore, to improve the separation efficiency of photogenerated carriers and enhance the performance of CO2 photocatalytic reduction. In this paper, g-C3N4/Pd composite with Schottky junction was synthesized by using g-C3N4, a two-dimensional material with unique interfacial effect, as the substrate material in combination with the co-catalyst Pd. The composite of Pd and g-C3N4 was tested to have a strong localized surface plasmon resonance effect (LSPR), which decreased the reaction barriers and improved the electron utilization. The combination of reduced graphene oxide (rGO) created a π–π conjugation effect at the g-C3N4 interface, which shortened the electron migration path and further improved the thermal electron transfer and utilization efficiency. The results show that the g-C3N4/ rGO/Pd (CRP) exhibits the best performance for photocatalytic reduction of CO2, with the yields of 13.57 μmol g− 1 and 2.73 μmol g− 1 for CO and CH4, respectively. Using the in situ infrared test to elucidate the intermediates and the mechanism of g-C3N4/rGO/Pd (CRP) photocatalytic CO2 reduction. This paper provides a new insight into the interface design of photocatalytic materials and the application of co-catalysts.