• Journal of the Korea Society of Computer and Information
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• v.17 no.10
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• pp.185-192
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• 2012

Call Center requires an ability of agents a lot more than face-to-face contact due to being achieved communication by non face-to face channel for contact with customers. In order to improve the ability of agents, Call Center carries out various educational training according to their work experience and function and with the accomplishment of educational training, Call Center is going to fulfill to develop its quality of counseling and productivity. On the other hand, due to investment of a lot of time and budget to educational training, it is needed to grasp and manage about its effectiveness that how helpful the training is for performance of work-site operations through evaluation of educational training. Having Seen researches about evaluation of educational training until these days, most researches have mainstream to measure satisfaction and a level of learning or degree that how the learning transfers to actions. It is found that a research about an entire evaluation model should be required. This study aims to investigate effectiveness of Call Center educational training from the level of recognition by reflecting Kirkpatrick's the four levels of learning evaluation. By the four levels, reaction, learning, behavior and results, the study found out a connection with standards of evaluation about each levels. In addition, by using structural equation modeling, it was examined goodness of fit about the entire model. Furthermore, by an alternative model, considering a direct relation between a factor of reaction and behavior, it was compared and examined goodness of fit of overall model of the study model and the alternative one.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1679-1692
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• 2023

South Korea is a country that emits a large amount of pollutants as a result of population growth and industrial development and is also severely affected by transboundary air pollution due to its geographical location. As pollutants from both domestic and foreign sources contribute to air pollution in Korea, the location of air pollutant emission sources is crucial for understanding the movement and distribution of pollutants in the atmosphere and establishing national-level air pollution management and response strategies. Based on this background, this study aims to effectively acquire spatial information on domestic and international air pollutant emission sources, which is essential for analyzing air pollution status, by utilizing high-resolution optical satellite images and deep learning-based image segmentation models. In particular, industrial parks and quarries, which have been evaluated as contributing significantly to transboundary air pollution, were selected as the main research subjects, and images of these areas from multi-purpose satellites 3 and 3A were collected, preprocessed, and converted into input and label data for model training. As a result of training the U-Net model using this data, the overall accuracy of 0.8484 and mean Intersection over Union (mIoU) of 0.6490 were achieved, and the predicted maps showed significant results in extracting object boundaries more accurately than the label data created by course annotations.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.281-288
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• 2022

AGVs are often used in industrial applications to transport heavy materials around a large industrial building, such as factories or warehouses. In particular, in fulfillment centers their usefulness is maximized for automation. To increase productivity in warehouses such as fulfillment centers, sophisticated path planning of AGVs is required. We propose a scheme that can be applied to QMIX, a popular cooperative MARL algorithm. The performance was measured with three metrics in several fulfillment center layouts, and the results are presented through comparison with the performance of the existing QMIX. Additionally, we visualize the transport paths of trained AGVs for a visible analysis of the behavior patterns of the AGVs as heat maps.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.905-910
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• 2022

Drowsy driving requires a lot of social attention because it increases the incidence of traffic accidents and leads to fatal accidents. The number of accidents caused by drowsy driving is increasing every year. Therefore, in order to solve this problem all over the world, research for measuring various biosignals is being conducted. Among them, this paper focuses on non-contact biosignal analysis. Various noises such as engine, tire, and body vibrations are generated in a running vehicle. To measure the driver's heart rate and respiration rate in a driving vehicle with a piezoelectric sensor, a sensor plate that can cushion vehicle vibrations was designed and noise generated from the vehicle was reduced. In addition, we developed a system for classifying whether the driver is sleeping or not by extracting the model using the CNN-LSTM ensemble learning technique based on the signal of the piezoelectric sensor. In order to learn the sleep state, the subject's biosignals were acquired every 30 seconds, and 797 pieces of data were comparatively analyzed.

• Proceedings of the Korea Contents Association Conference
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• 2014.11a
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• pp.79-80
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• 2014

본 연구는 오늘날 대학 교육과정의 일부로 중요시되고 있는 봉사학습에 대한 것으로써 봉사학습 교육과정의 하나인 해외봉사활동 프로그램을 어떻게 개발하고 체계적으로 운영하여 교육적 효과를 높일 것인지를 삼육대학교의 사례를 중심으로 제안한다. 대학은 봉사학습의 일환으로 해외봉사프로그램을 운영함에 있어 지도교수, 학생, 학교 봉사관련부서(사회봉사단 혹은 봉사센터)가 역할의 분담과 상호 협력을 통해 일관성 있으면서도 조화롭게 운영되도록 한다. 프로그램 성공의 핵심은 학교의 봉사교육에 대한 분명한 철학과 비전 제시, 지도교수들의 봉사정신과 세밀한 준비, 봉사활동 참여자들에 대한 철저한 사전교육, 봉사자들이나 팀들에 대한 봉사단의 체계적 지원활동 등이다.

• The Journal of Korea Robotics Society
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• v.1 no.1
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• pp.102-106
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• 2006

지능형 서비스 로봇은 인공지능 및 센서인터페이스 등의 다양한 기능과 IT기술의 접목으로 우리의 생활을 윤택하게 해줄 수 있는 인간 지향적인 성격으로 발전하고 있다. 또한 많은 분야에서 새로운 서비스 개발을 통해서 좀더 인간의 생활에 가까이 접근되어 지고 있다. 한편 국내 초등학교에서 초등교사는 학생 수의 과다로 능동적이기 보다는 수동적인 수업을 하고 있어서 학습 효율성이나 능률성이 떨어지고 있다. 이에 본 연구에서는 발전하고 있는 서비스 로봇 기술로 능동적인 수업형태와 자율적이고 창의적인 학습 및 교육서비스를 제공할 수 있는 교사도우미 로봇을 개발하였다. 본 연구에서 개발된 로봇은 기존의 지능형 서비스 로봇과 ICT(Information & Communication Technology) 교육을 접목할 수 있는 로봇의 제반 환경의 구성과 더불어 교육 현장에 알맞도록 HRI 및 시나리오를 기반으로 개발되었고, 로봇의 주요기능은 교사지원, 학습보조, 수업지원 등으로 사전 설문조사를 통하여 필요성이 높은 기능을 중심으로 설정되었다. 개발된 초등학교 지원 로봇은 2006년 10월부터 대전지능로봇산업화센터에서 초등학교 학생을 대상으로 시범수업을 준비하고 있고, 2007년도 중반에는 초등학교에서 시범사업을 추진할 예정이다.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.4
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• pp.361-370
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• 2023

Swarm has recently become a critical component of offensive and defensive systems. Multi-agent reinforcement learning(MARL) empowers swarm systems to handle a wide range of scenarios. However, the main challenge lies in MARL's scalability issue - as the number of agents increases, the performance of the learning decreases. In this study, transfer learning is applied to advanced MARL algorithm to resolve the scalability issue. Validation results show that the training efficiency has significantly improved, reducing computational time by 31 %.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.437-442
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• 2023

The purpose of this study is to verify the effect of gamification-based classes on the learning motivation and learning immersion of junior college students and to explore the meaning. This study was conducted on 80 students from two departments as part of the teaching and learning community activities supported by the D University Teaching and Learning Development Center in Busan. The research problem of this study is, first, does gamification-based classes affect the strengthening of learning motivation of junior college students? Second, does gamification-based classes affect the learning immersion of junior college students?. As a result of conducting a survey before and after the application of gamification-based classes and examining the effectiveness, gamification-based classes showed statistically significant changes in all categories of learners' learning motivation, learning immersion. Through this, it can be seen that gamification-based classes are valuable as teaching and learning methods suitable for improving the learning motivation and learning immersion of junior college students.

• Industry Promotion Research
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• v.8 no.4
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• pp.163-176
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• 2023

Through a literature review, this study examined the concept of slow learners and the lifelong learning characteristics of slow learners, and sought ways to achieve successful lifelong learning by utilizing grit and growth mindset among non-cognitive characteristics. Slow learners were experiencing difficulties in cognitive, academic, linguistic, social and emotional, and behavioral characteristics. For successful lifelong learning of slow learners, it was necessary to set long-term goals rather than short-term goals and to maintain effort and consistency of interest to achieve the goals. In addition, it was confirmed that in order to achieve long-term goals, it is necessary to believe that change can be achieved through effort and learning. In other words, the need for learning using grit and growth mindset was confirmed. Based on these previous research results, it was presented as a lifelong learning strategy for slow learners that applied grit and growth mindset, which are non-cognitive characteristics, rather than cognitive characteristics such as intelligence.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.462-464
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• 2022

의료영상기반의 인공지능 연구는 질환의 조기진단 및 예측 분야에 눈부신 기술발전이 되어왔다. 장기 섬유증은 만성 염증성 질환의 질병 진행을 특징짓고 전 세계적으로 모든 원인으로 인한 사망률의 45%에 기여하며, 그중 간 섬유증은 주로 삶의 질과 예후를 결정한다. 해당 질환은 임상 현장에서 혈액데이터 분석 그리고 간생검을 통해 진단을 하고 있으나 최근 의료영상 분석을 통해 진단에 활용하고 있는 추세이다. 본 논문에서는 인공지능을 기반으로 하여, 간 섬유화를 진단하기 위해 MRI영상을 학습하여 질환에 대한 중증도 진단을 돕는 인공지능 모델을 제시하고자 한다. 이를 위해 인공지능 모델을 개발하는 과정과 그 결과를 보인다. 본 논문에서 제시한 모델을 통해 간 섬유화를 빠르게 진단할 수 있을 것으로 기대한다.