• 전력전자학회논문지
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• 제26권3호
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• pp.183-191
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• 2021

A battery management system (BMS) provides some functions for ensuring safety and reliability that includes algorithms estimating battery states. Given the changes caused by various operating conditions, the state-of-health (SOH), which represents a figure of merit of the battery's ability to store and deliver energy, becomes challenging to estimate. Machine learning methods can be applied to perform accurate SOH estimation. In this study, we propose a Long-Term Recurrent Convolutional Network (LRCN) that combines the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) to extract aging characteristics and learn temporal mechanisms. The dataset collected by the battery aging experiments of NASA PCoE is used to train models. The input dataset used part of the charging profile. The accuracy of the proposed model is compared with the CNN and LSTM models using the k-fold cross-validation technique. The proposed model achieves a low RMSE of 2.21%, which shows higher accuracy than others in SOH estimation.

• Asian Journal of Innovation and Policy
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• 제10권1호
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• pp.68-89
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• 2021

The expansion of irrigated agricultural production can be appropriate for the southeast region in the U.S. as a climate change adaptation strategy. This study investigated the effect of supplemental development of irrigated agriculture on the regional economy by applying the supply side Georgia multiregional input-output (MRIO) model. For the analysis, 100% conversion of non-irrigated cultivable acreage into irrigated acreage for cotton, peanuts, corn, and soybeans in 42 counties of southwest Georgia is assumed. With this assumption, the difference in total net returns of production between the non-irrigation and irrigation method is calculated as input data of the Georgia MRIO model. Based on the information of a 95% confidence interval for each crop's average price, the lower and upper bounds of estimated results are also presented. The total impact of cotton production was $60 million with the range of $35 million to $85 million: The total impact of peanuts, soybeans, corn was $10.2 million (the range of $3.28 million to $23.7 million), $6.6 million (the range of $3.1 million to $10.2 million), $1.2 million (the range of -$6 million to $8.5 million), respectively.

• Journal of Medicine and Life Science
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• 제17권3호
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• pp.86-93
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• 2020

The study aimed to understand medical students' experiences of emotional changes, including their method of adapting to experiences, and the effect of the experiences in shaping their identities. We interviewed 12 medical students who were finishing their 1-year clinical internship in 2016. Data on their opinions and reasons for emotional changes during their school life were obtained. The descriptive phenomenological approach was applied to analyze the interviews. Their stress came from disappointment in themselves, competitive environment, observing a change in their personalities, meeting their parents' expectations, and interpersonal relations. The interviewees adjusted to the medical study by exercising self-control in their studies and daily lives, by practicing self-acceptance and observing their state of mind, and by breaking free from the competition-driven environment and obsession with grades. In addition, they cultivated endurance and found external support. Finally, they achieved self-efficacy and were comfortable in their identity as medical students. They still had to address the stress from working relationships and the difficulty in balancing studies and life. The medical students' self-evaluation and compulsive tendencies increased during the medical course due to the burden of studies. They evolved by learning self-control and introspection and seeking ways to adapt. Understanding this growth process of medical students will improve student support in medical schools.

• 한국정보시스템학회지:정보시스템연구
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• 제31권1호
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• pp.271-287
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• 2022

Purpose This research aims to study the changes in recruitment needed for the growth and survival of companies in the rapidly changing industry. In particular, we built a real company's worklist accounting for the rapidly advancing data-driven digital transformation, and presented the capabilities and conditions required for work. Design/methodology/approach we selected 37 jobs based on NCS to develop the employment search requirements by analyzing the business characteristics and work capabilities of the industry and company. The business specification indicators were converted into a matrix through the TF-IDF process, and the NMF algorithm is used to extract the features of each document. Also, the cosine distance measurement method is utilized to determine the similarity of the job specification conditions. Findings Companies tended to prefer "IT competency," which is a specification related to computer use and certification, and "experience competency," which is a specification for experience and internship. In addition, 'foreign language competency' was additionally preferred depending on the job. This analysis and development of job requirements would not only help companies to find the talents but also be useful for the jobseekers to easily decide the priority of their specification activities.

• 로봇학회논문지
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• 제17권1호
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• pp.76-85
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• 2022

One of the most fundamental challenges when designing controllers for dynamic systems is the adjustment of controller parameters. Usually the system model is used to get the initial controller, but eventually the controller parameters must be manually adjusted in the real system to achieve the best performance. To avoid this manual tuning step, data-driven methods such as machine learning were used. Recently, reinforcement learning became one alternative of this problem to be considered as an agent learns policies in large state space with trial-and-error Markov Decision Process (MDP) which is widely used in the field of robotics. However, on initial training step, as an agent tries to explore to the new state space with random action and acts directly on the controller parameters in real systems, MDP can lead the system safety-critical system failures. Therefore, the issue of 'safe exploration' became important. In this paper we meet 'safe exploration' condition with Control Barrier Function (CBF) which converts direct constraints on the state space to the implicit constraint of the control inputs. Given an initial low-performance controller, it automatically optimizes the parameters of the control law while ensuring safety by the CBF so that the agent can learn how to predict and control unknown and often stochastic environments. Simulation results on a quadrotor UAV indicate that the proposed method can safely optimize controller parameters quickly and automatically.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.937-944
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• 2022

Electric vehicles (EVs) have been growing to reduce energy consumption and greenhouse gas (GHG) emissions in the transportation sector. The increasing number of EVs requires adequate recharging infrastructure, and at the same time, adopts low- or zero-emission electricity production because the GHG emissions are highly dependent on primary sources of electricity production. Although previous research has studied solar photovoltaic (PV) -integrated EV charging stations, it is challenging to optimize spatial areas between where the charging stations are required and where the renewable energy sources (i.e., solar photovoltaic (PV)) are accessible. Therefore, the primary objective of this research is to support decisions of siting EV charging stations using a spatial data clustering method integrated with Geographic Information System (GIS). This research explores spatial relationships of PV power outputs (i.e., supply) and traffic flow (i.e., demand) and tests a community in the state of Indiana, USA for optimal sitting of EV charging stations. Under the assumption that EV charging stations should be placed where the potential electricity production and traffic flow are high to match supply and demand, this research identified three areas for installing EV charging stations powered by rooftop PV in the study area. The proposed strategies will drive the transition of existing energy infrastructure into decentralized power systems. This research will ultimately contribute to enhancing economic efficiency and environmental sustainability by enabling significant reductions in electricity distribution loss and GHG emissions driven by transportation energy.

• 한국의류학회지
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• 제47권6호
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• pp.1137-1156
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• 2023

With the advent of artificial intelligence (AI) during the Fourth Industrial Revolution, the fashion industry has simplified the production process and overcome the technical difficulties of design. This study anticipates likely changes in the digital age and develops a model that will allow consumers to design textile patterns using AI technology. Previous studies and industrial examples of AI technology's use in the textile design industry were investigated, and a textile pattern was developed using an AI algorithm. A new textile design model was then proposed based on its application to both virtual and physical clothing. Inspired by traditional Korean masks and props, AI technology was used to input color data from open application programming interface images. By inserting these into various repeating structures, a textile design was developed and simulated as garments for both virtual and real garments. We expect that this study will establish a new textile design development method for Generation Z, who favor customized designs. This study can inform the use of personalization in generative textile design as well as the systemization of technology-driven methods for customized and participatory textile design.

• 한국의류학회지
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• 제48권1호
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• pp.108-119
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• 2024

In this study, we aimed to propose enhancements to the dimensions and design of walking aids tailored for elderly women. Specifically, we focused on wheeled walking assistance devices and aligned each structural component with the appropriate human body dimensions to suggest appropriate product dimensions organized by size clusters, aiming to maximize the practicality of the results. We extracted essential factors required for product design, including human body size elements. The dimension extraction method was clustered to establish connections between key human body parameters-such as height, weight, and age groups-and product dimensions. We conducted a comparative analysis of walking aid product dimensions according to the design elements and sizes of models currently available in the market. The outcomes of this study offer objective, data-driven insights into areas where existing models on the market could benefit from improvement and we anticipate that the findings of this study will provide a solid, quantitative foundation for individuals when selecting the most suitable model for their needs.

• 풍력에너지저널
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• 제14권3호
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• pp.34-42
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• 2023

The reduction of operation and maintenance (O&M) costs is a critical factor in determining the competitiveness of wind energy. Predictive maintenance based on the estimation of remaining useful life (RUL) is a key technology to reduce logistic costs and increase the availability of wind turbines. Although a mechanical component usually has sudden changes during operation, most RUL estimation methods use the trend of a state index over the whole operation period. Therefore, overestimation of RUL causes confusion in O&M plans and reduces the effect of predictive maintenance. In this paper, two RUL estimation methods (load based and data driven) are proposed for the bearings of a wind turbine with the results of trend classification, which differentiates constant and increasing states of the state index. The proposed estimation method is applied to a bearing degradation test, which shows a conservative estimation of RUL.

• 한국재난정보학회 논문집
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• 제15권4호
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• pp.548-559
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• 2019

연구목적: 인력에 의한 조사를 대처할 수 있는 V2I 데이터 기반의 고속도로 휴게소 이용행태 분석. 연구방법: 휴게소 구간 개별차량의 통행속도 자료 분포 특성을 활용하여 통행상태 그룹 분할 및 휴게소 이용경계(Boundary) 설정. 연구결과: 검증 결과 점심시간 휴게소 이용률이 급증, 통행속도 자료 분포 상태간 경계가 명확하거나 불명확한 모든 경우 휴게소 이용경계를 정교하게 산정. 결론: 인력에 의한 휴게소 이용실태 조사를 대처할 수 있어 비용절감의 효과가 크며 조사의 시공간적 범위에 제한이 없음. V2I 시스템이 구축된 휴게소/졸음쉼터/간이 휴게소 등 각 시간대별 동적 이용률 산정이 가능. 단시간/중시간/장시간 이용의 구분이 가능. 차종별 이용실태 도출 가능. 도로구간 불법 주정차 여부 파악 가능. 도로구간 내 돌발상황 검지 가능. 실시간 휴게소 이용률 및 혼잡도 정보 제공등 다양한 분석과 운영전략 수립 가능.