• The Journal of Asian Finance, Economics and Business
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• 제7권2호
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• pp.169-180
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• 2020

Globalization has led to a dramatic increase in intercultural service encounters between services providers and customers from diverse cultural backgrounds. This paper explores the causal relationship between service employees‟ cultural intelligence and adaptive sales behavior in intercultural service encounters, and the mediating effect of cognitive and emotional empathy on this relationship. A quantitative survey methodology was utilized to collect data on 341 salespeople at duty-free shops located on Jeju Island, Korea. Data analysis was conducted using SPSS 18 and Amos 18. The results show that cultural intelligence has a significant impact on cognitive empathy, emotional empathy, and adaptive sales behavior. Cognitive empathy has a positive impact on adaptive sales behavior, whereas the relationship between emotional empathy and adaptive sales behavior is not significant. Additionally, cognitive empathy mediates the relationship of cultural intelligence and adaptive sales behavior. This study has useful managerial implications for employee selection, training, and development in service firms engaged in intercultural service encounters. This study extends prior research on intercultural service encounters by exploring the direct impact of cultural intelligence on intercultural adaptation and the mediating effect of empathy, suggesting the presence of a cognitive mechanism that plays a key role in the impact of cultural intelligence on adaptive sales behavior.

• 한국보육지원학회지
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• 제17권6호
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• pp.15-28
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• 2021

Objective: The purpose of this study was to examine the mediating effect of teacher emotional intelligence in the relationship between job environments and teacher efficacy. Methods: Participants of this study were 205 child care teachers from U-city. Correlation analysis between variables was conducted with the collected data, and regression analysis was conducted to verify the mediating effect of emotional intelligence in the relationship between the job environment of daycare centers and teacher efficacy. Results: First, there was a positive correlation between job environments, teacher efficacy, and teacher emotional intelligence. Second, job environments and teacher emotional intelligence had a significant direct effect on teacher efficacy and teacher emotional intelligence also had a significant indirect effect between job environments and teacher efficacy. Conclusion/Implications: The way to improve the quality of child care is to improve the quality of teachers. Among the teacher's competencies, it is very important to increase teacher efficacy. Teacher efficacy plays a very important role in the quality of childcare and the healthy development of toddlers and children. In order to increase teacher efficacy, policy support for the job environment and training support to increase teacher efficacy are considered necessary.

• 유통과학연구
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• 제20권9호
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• pp.109-126
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• 2022

Purpose: The role of salespersons' emotions in effective selling behavior garners attention among scholars and practitioners. Previous studies have investigated the effects of emotional intelligence and emotional labor on sales success separately. However, to understand the whole process, the relationships among salespersons' cognition, emotions, and behaviors should be considered simultaneously. Accordingly, we uniquely examined how salespersons' emotional intelligence (emotional antecedent) and customer orientation (cognitive antecedent) influence their emotional labor (deep acting vs. surface acting), adaptive selling behavior, and the selling results in the retail environment. Research design, data, and methodology: To improve methodological rigor, we used the dyadic approach. We measured 182 salespersons' emotional intelligence, customer orientation, and emotional labor, and 364 customers assessed the salespersons' adaptive selling behavior and selling results in the insurance and duty-free department retailing sectors. Result: The findings suggest that salespersons' customer orientation and emotional intelligence relate to deep-acting of emotional labor, affecting their adaptive selling behavior and relationship quality with customers. Conclusions: As for managerial implications, sales managers may well consider emotional intelligence levels when selecting salespersons in the retail industry. Additionally, practical training programs are required to cultivate customer orientation, emotional intelligence, and deep acting while performing emotional labor.

• 융합보안논문지
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• 제20권2호
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• pp.123-135
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• 2020

2016년 알파고의 대국 이후, 여러 산업 분야에서 인공지능 적용에 대한 요구가 많아지고 있고 그와 관련된 연구가 활발하게 진행되고 있다. 군사 분야도 마찬가지 인데, 지금까지 인공지능이 적용된 무기체계가 없었기 때문에 그 구현에 대한 노력이 도전으로 작용하고 있다. 한편 알파고를 이긴 알파고 제로는 인공지능의 자기학습에 의한 데이터 기반 접근법이 기존의 사람에 의한 지식 기반 접근법보다 좋은 결과를 도출할 수 있다는 결과를 보여주었다. 본 논문에서는 이러한 점을 착안하여, 알파고 제로의 기반이 되는 강화학습을 함정전투체계 또는 전투관리체계에 적용하는 것을 제안한다. 이는 일정한 승률을 보이는 최적의 전술적 결과물이 사용자 즉, 함장과 작전요원에게 권고할 수 있도록 하는 인공지능 어플리케이션을 함정전투체계에 적용하는 방법이다. 이를 위해 전투성능에 관한 체계의 정의, 함정전투체계 설계 방안과 실 체계와의 Mapping, 훈련체계가 현 작전 수행에 원활히 적용될 수 있는 방안을 더불어 제시한다.

• KEPCO Journal on Electric Power and Energy
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• 제8권2호
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• pp.137-141
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• 2022

To inspect the power facility faults using artificial intelligence, it need that improve the accuracy of the diagnostic model are required. Data augmentation skill using generative adversarial network (GAN) is one of the best ways to improve deep learning performance. GAN model can create realistic-looking fake images using two competitive learning networks such as discriminator and generator. In this study, we intend to verify the effectiveness of virtual data generation technology by including the fake image of power facility generated through GAN in the deep learning training set. The GAN-based fake image was created for damage of LP insulator, and ResNet based normal and defect classification model was developed to verify the effect. Through this, we analyzed the model accuracy according to the ratio of normal and defective training data.

• ETRI Journal
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• 제45권5호
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• pp.862-873
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• 2023

Determining whether an autonomous self-driving agent is in the middle of an intersection can be extremely difficult when relying on visual input taken from a single camera. In such a problem setting, a wider range of views is essential, which drives us to use three cameras positioned in the front, left, and right of an agent for better intersection recognition. However, collecting adequate training data with three cameras poses several practical difficulties; hence, we propose using data collected from one camera to train a three-camera model, which would enable us to more easily compile a variety of training data to endow our model with improved generalizability. In this work, we provide three separate fusion methods (feature, early, and late) of combining the information from three cameras. Extensive pedestrian-view intersection classification experiments show that our feature fusion model provides an area under the curve and F1-score of 82.00 and 46.48, respectively, which considerably outperforms contemporary three- and one-camera models.

• 대한산업공학회지
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• 제41권4호
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• pp.396-407
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• 2015

The literature about intelligence assets allocation focused on mainly single or partial assets such as TOD and GSR. Thus, it is limited in application to the actual environment of operating various assets. In addition, field units have generally vulnerabilities because of depending on qualitative analysis. Therefore, we need a methodology to ensure the validity and reliability of intelligence asset allocation. In this study, detection probability was generated using digital geospatial data in MGIS (Military Geographic Information System) and simulation logic of BCTP (Battle Commander Training Programs) in the R.O.K army. Then, the optimal allocation mathematical model applied concept of simultaneous integrated management, which was developed based on the partial set covering model. Also, the proposed GA (Genetic Algorithm) provided superior results compared to the mathematical model. Consequently, this study will support effectively decision making by the commander by offering the best alternatives for optimal allocation within a reasonable time.

• 한국군사과학기술학회지
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• 제23권6호
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• pp.650-658
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• 2020

The research of the AI-based command decision support system was insufficient both quantitatively and qualitatively. In particular, in Korea, there was no research on concrete concept design at the current concept research level. This paper proposed the conceptual design of a tactical echelon command decision support system based on artificial intelligence(AI) according to the current army's doctrine of the operation process. The suggested conceptual design clarified the problem and proposed an appropriate process for design, and applied the function analysis method among rational techniques that enable conceptual design systematically.

• 한국BIM학회 논문집
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• 제12권1호
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• pp.1-9
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• 2022

Concrete formwork is a crucial component for any construction project. Artificial intelligence offers great potential to automate formwork design by offering various design options and under different criteria depending on the requirements. This study applied image segmentation in 2D formwork drawings to extract sheathing, strut and pipe support formwork elements. The proposed artificial intelligence model can recognize, classify, and extract formwork elements from 2D CAD drawing image and training and test results confirmed the model performed very well at formwork element recognition with average precision and recall better than 80%. Recognition systems for each formwork element can be implemented later to generate 3D BIM models.

• Structural Engineering and Mechanics
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• 제85권1호
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• pp.15-27
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• 2023

Using artificial intelligence and internet of things methods in engineering and industrial problems has become a widespread method in recent years. The low computational costs and high accuracy without the need to engage human resources in comparison to engineering demands are the main advantages of artificial intelligence. In the present paper, a deep neural network (DNN) with a specific method of optimization is utilize to predict fundamental natural frequency of a cylindrical structure. To provide data for training the DNN, a detailed numerical analysis is presented with the aid of functionally modified couple stress theory (FMCS) and first-order shear deformation theory (FSDT). The governing equations obtained using Hamilton's principle, are further solved engaging generalized differential quadrature method. The results of the numerical solution are utilized to train and test the DNN model. The results are validated at the first step and a comprehensive parametric results are presented thereafter. The results show the high accuracy of the DNN results and effects of different geometrical, modeling and material parameters in the natural frequencies of the structure.