목적 : 본 연구의 목적은 저상버스 이용현황을 파악하고, PEO 모델기반 해석 과정을 통해 개인적, 작업적, 그리고 환경적인 부분의 잠재적 문제 요인을 파악해보려고 한다. 연구방법 : 본 연구는 지체장애 및 뇌병변 장애를 경험하고 있는 48명의 대상자를 중심으로 설문조사를 실시하였 고, 장애인이 저상버스를 이용하는데 어려운 요인들을 PEO모델로 분류하여 해석하였다. 결과 : 본 연구결과, 작업과 환경요소에서는 운전자의 지원부족 및 승, 하차 과정의 작업수행문제가, 사람과 환경 요소에서는 이용자의 사회적 통합시도의 부족, 낮은 보조기기 사용률과 사회적 인식수준의 문제가, 사람과 작업 요소에서는 이용자의 건강상태 및 장애에 대한 부정적 인식과 성공적인 작업수행의 경험 부족이 잠재적인 문제 요소로 확인되었다. 결론 : 이동권 보장을 위한 서비스 개선을 위한 노력은 매우 중요할 것이며, 향후, 다각적인 분석을 통한 저상버 스 이용자의 서비스 개선을 위한 노력이 필요할 것이다.

Elevators are the main means of transport in buildings. A malfunction of an elevator in operation may cause in convenience to users. Furthermore, fatal accidents, such as injuries and death, may occur to the passengers also. Therefore, it is important to prevent failure before accidents happen. In related studies, preventive measures are proposed through analyzing failures, and the lifespan of elevator components. However, these methods are limited to existing an elevator model and its surroundings, including operating conditions and installed environments. Vibration occurs when the elevator is operated. Experts have classified types of faults, which are symptoms for malfunctions (failures), via analyzing vibration. This study proposes an artificial intelligent model for classifying faults automatically with deep learning algorithms through elevator vibration data, hereby preventing failures before they occur. In this study, the vibration data of six elevators are collected. The proposed methodology in this paper removes "the measurement error data" with incorrect measurements and extracts operating sections from the input datasets for proceeding deep learning models. As a result of comparing the performance of training five deep learning models, the maximum performance indicates Accuracy 97% and F1 Score 97%, respectively. This paper presents an artificial intelligent model for detecting elevator fault automatically. The users’ safety and convenience may increase by detecting fault prior to the fatal malfunctions. In addition, it is possible to reduce manpower and time by assisting experts who have previously classified faults.

본 연구는 중국 C2C 중고거래 플랫폼의 사용자 연구대상으 로 정보기술 수용과 사용 태도에 대해 설명력이 높다고 인정받 고 있는 기술수용모델(TAM)을 토대로 핵심 변수 외 다양화된 정보시스템 환경을 반영하는 변수로 주목받고 있는 지각된 유 희성을 추가 변수로 지정하였다. 본 연구는 가설을 검증하기 위해 중국 C2C 중고거래 플랫폼에 대한 경험 있는 사용자들을 대상으로 조사를 실시하였다. 총 400부의 설문지를 배포하였으 며 회수된 설문지를 선별하여 총 362부 유효 설문지가 선출되 었다. 또한, C2C 중고거래 플랫폼 특성 변인인 정보 대칭성을 규명하고, 이를 바탕으로 정보의 이용 가치를 높이고 활성하기 위한 전략 방향에 시사점을 제시하는 데 그 목적을 둔다. 그리 고 마케팅 연구의 학문적 발전에 기여하고 기업에는 중고 플랫 폼을 활용한 마케팅 전략 수립 시 실무적인 도움을 제공할 수 있을 것으로 기대한다.

과학과 기술의 발달로 복합재료, 합금, 고강도 탄소섬유, 고분자 재료 등 지능형 소재가 개발되고 있다. 다양한 엔지 니어링 분야에서 이러한 첨단 재료의 응용을 연구하기 위해 전 세계적으로 광범위한 연구가 진행되고 있다. 초탄성 형상기억합 금(SSMA)은 깃발 모양의 히스테리시스 거동을 가지며 추가적인 열처리 없이 응력 완화로 인한 잔류 변형이 거의 없는 신뢰성 이 높은 내진 재료이다. 그러나 공학 문제에서 SSMA 효율성을 연구하기 위한 수치 모델의 개발은 여전히 어려운 작업이다. 본 연구에서는 SSMA 인장시험의 실험결과를 통해 유한요소해석 프로그램인 Abaqus와 수치해석 프로그램인 OpenSEES를 이용하여 재료 모델을 구현한 후 해석결과의 거동 특성 및 에너지 소산을 분석하였다.

This paper introduces a container loading problem and proposes a theoretical approach that efficiently solves it. The problem is to determine a proper weight of products loaded on a container that is delivered by third party logistics (3PL) providers. When the company pre-loads products into a container, typically one or two days in advance of its delivery date, various truck weights of 3PL providers and unpredictability of the randomness make it difficult for the company to meet the total weight regulation. Such a randomness is mainly due to physical difference of trucks, fuel level, and personalized equipment/belongings, etc. This paper provides a theoretical methodology that uses historical shipping data to deal with the randomness. The problem is formulated as a stochastic optimization where the truck randomness is reflected by a theoretical distribution. The data analytics solution of the problem is derived, which can be easily applied in practice. Experiments using practical data reveal that the suggested approach results in a significant cost reduction, compared to a simple average heuristic method. This study provides new aspects of the container loading problem and the efficient solving approach, which can be widely applied in diverse industries using 3PL providers.

Recently, many studies have been conducted to improve quality by applying machine learning models to semiconductor manufacturing process data. However, in the semiconductor manufacturing process, the ratio of good products is much higher than that of defective products, so the problem of data imbalance is serious in terms of machine learning. In addition, since the number of features of data used in machine learning is very large, it is very important to perform machine learning by extracting only important features from among them to increase accuracy and utilization. This study proposes an anomaly detection methodology that can learn excellently despite data imbalance and high-dimensional characteristics of semiconductor process data. The anomaly detection methodology applies the LIME algorithm after applying the SMOTE method and the RFECV method. The proposed methodology analyzes the classification result of the anomaly classification model, detects the cause of the anomaly, and derives a semiconductor process requiring action. The proposed methodology confirmed applicability and feasibility through application of cases.

As the environmental impacts of fossil fuel energy sources increase, the South Korean government has tried to change non-environmental- friendly enery sources to environmental-friendly energy sources in order to mitigate environmental effects, which lead to global warming and air pollution. With both a limited budget and limited time, it is essential to accurately evaluate the economic and environmental effects of renewable energy projects for the efficient and effective operation of renewable energy plants. Although the traditional economic evaluation methods are not ideal for evaluating the economic impacts of renewable energy projects, they can still be used for this purpose. Renewable energy projects involve many risks due to various uncertainties. For this reason, this study utilizes a real option method, the Geske compound model, to evaluate the renewable energy projects on Jeju Island in terms of economic and environmental values. This study has developed an economic evaluation model based on the Geske compound model to investigate the influences of flexibility and uncertainty factors on the evaluation process. This study further conducts a sensitivity analysis to examine how two uncertainty factors (namely, investment cost and wind energy production) influence the economic and environmental value of renewable energy projects.

To mitigate the environmental impacts of the energy sector, the government of South Korea has made a continuous effort to facilitate the development and commercialization of renewable energy. As a result, the efficiency of renewable energy plants is not a consideration in the potential site selection process. To contribute to the overall sustainability of this increasingly important sector, this study utilizes the Black-Scholes model to evaluate the economic value of potential sites for off-site wind farms, while analyzing the environmental mitigation of these potential sites in terms of carbon emission reduction. In order to incorporate the importance of flexibility and uncertainty factors in the evaluation process, this study has developed a site evaluation model focused on system dynamics and real option approaches that compares the expected revenue and expected cost during the life cycle of off-site wind farm sites. Using sensitivity analysis, this study further investigates two uncertainty factors (namely, investment cost and wind energy production) on the economic value and carbon emission reduction of potential wind farm locations.

2007년 메타버스 로드맵에서 메타버스 정의가 공식 발표된 바 있으며, 당시 국내외 연구자들에 의해 많은 논문이 발표되면서 학문적 이론적으로 정립하려는 노력을 해왔다. 이와는 달리, 산업계에서는 ‘메타버스’키 워드가 비대면 장기화와 맞물려 과열 양상을 보이고 있다. 정작 언급되는 메타버스 성공사례는 '게임'이거나 '게이미피케이션' 또는 ‘게임융합’ 사례가 다수다. 따라서, '메타버스'영역에도 게이미피케이션 이론과 원리의 적용은 바람직하다. 이에, 필자가 발표했던 DMGL 모델을 확장하여, 메타버스 내 습관형성 위한 다이내믹 모델을 제시하였다. 저자는 플레이어 경험을 향상시키기 위해 메타월드 습관 형성을 위한 4가자 양상의 모 델을 제안했다. P1) 플레이어는 세계관에 따라 전개되는 배경스토리를 따르며, 유발된 호기심으로 탐험할 동 기가 생성되고 게임의 조작법을 익히고 게임의 목표를 향하는 여정이다. P2) 플레이어는 메타월드의 조작법 익히기 단계로 온보딩 한다. P3) 플레이어는 아바타 꾸미기,아이템 획득/거래,커뮤니티 활동 등을 수행하면 서, 점차 목적(표)를 향한다. P4) 플레이어는 목적기능을 바로 수행할 수 있는 방식으로 온보딩한다. 이 모델 은 게임의 원리와 요소의 융합(게이미피케이션)으로, 후속연구에서 보다 심도 있는 연구 모델을 제시할 예 정이다.

The recent earthquake in Korea caused a lot of damage to reinforced concrete (RC) columns with non-seismic details. The nonlinear analysis enables predicting the hysteresis behavior of RC columns under earthquakes, but the analytical model used for the columns must be accurate and practical. This paper studied the nonlinear analysis models built into a commercial structural analysis program for the existing RC columns. The load-displacement relationships, maximum strength, initial stiffness, and energy dissipation predicted by the three analysis models were compared and analyzed. The results were similar to those tested in the order of the fiber, Pivot, and Takeda models, whereas the fiber model took the most time to build. For columns subjected to axial load, the Pivot model could predict the behavior at a similar level to that of the fiber model. Based on the above, it is expected that the Pivot model can be applied most practically for existing RC columns.

We explored the frequency response of krill target strength (TS) to understand the Antarctic krill (Euphausia superba) and ice krill (Euphausia crystallorophias) using the stochastic distorted-wave Born approximation (SDWBA) model. The results showed that the distribution of orientation and the fatness factor could significantly impact on the frequency response of TS. Krill TS is clearly depended on acoustic properties, which could affect to estimate the biomass of two krill species. The results provide insight into the importance of understanding TS variation to estimate the Antarctic krill and ice krill biomass, and their ecology related to the environmental features in the Southern Ocean.