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항공사 객실 승무원의 자아상태가 직무만족과 고객지향성에 미치는 영향 - 교류분석을 중심으로 -
문지원 ( Jiwon Moon ),연지영 ( Jiyoung Yeon ),최정일 ( Jeongil Choi ) 한국품질경영학회 2018 품질경영학회지 Vol.46 No.1
Purpose: This study attempted to analyze how the ego state of flight attendants affects their job satisfaction and customer orientation using Berne’s (1966) transactional analysis and further compare the difference between job satisfaction and customer orientation depending on demographic characteristics, position, and ego state. Methods: The data was collected by using the structured questionnaires to flight attendant of major airline companies. The proposed research model is tested using 164 valid questionnaires using SPSS 23 and Smart PLS 2. Results: This research indicated the only free child ego sate among ego state factors of flight attendant was found to have a positive impact on job satisfaction. In the relationship between ego states and customer orientation, all ego state factors were found to have a significant influence on customer orientation. Conclusions: The study offered a theoretical and empirical foundation for future research by empirically identifying the relationship between ego state factors and customer orientation in the in-flight service and suggested the strategic implications to increase job satisfaction and customer orientation based on the psychology and ego state of flight attendant.
MADFlow : Normalizing Flow를 활용한 다변량 시계열 이상 탐지
문지원(Jiwon Moon),송승환(Seunghwan Song),백준걸(Jun-Geol Baek) 대한산업공학회 2023 대한산업공학회지 Vol.49 No.3
With the recent advancement of smart factories in manufacturing processes, high-dimensional data is being collected in real-time from multiple sensors in production facilities. However, it is very difficult to detect anomalies that reflect both correlations and temporal dependency between high-dimensional variables. In this study, we propose Multivariate Time Series Anomaly Detection via Normalizing Flow (MADFlow), which can reflect both correlation between variables and temporal dependency. MADFlow consists of a temporal encoder to reflect temporal dependency and a flow module to learn the distribution of high-dimensional data and is trained in an end-to-end manner. Experimental results on multivariate time series data with similar characteristics to data generated in manufacturing processes show that MADFlow has significantly better anomaly detection performance than existing models. Therefore, we expect MADFlow to be able to efficiently detect anomalies in real-world manufacturing processes.
다중 마커와 코너점 추적을 활용한 마커 기반 증강현실 시스템
박동우(Dongwoo Park),문지원(Jiwon Moon),정현석(Hyunsuk Jung),김영헌(Younghun Kim),황성수(Sung Soo Hwang) 대한전자공학회 2018 대한전자공학회 학술대회 Vol.2018 No.6
In this paper, we present a marker-based augmented system which augments a virtual object even when a camera image is rapidly changed or a part of a marker is hidden. For this purpose, we utilize multiple markers and perform tracking and detection simultaneously. We selectively use the result of detection and tracking such that stable pose estimation can be performed. Simulation results show that the proposed system drastically reduces the jittering of pose estimation compared to the previous work.