• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.1075-1087
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• 2020

Aviation ICT technology is a convergence technology between aviation and electronics, and has a wide variety of applications, including navigation and education. Among them, in the field of aerial pilot training, there are many problems such as the possibility of accidents during training and the lack of coping skills for various situations. This raises the need for a simulated pilot training system similar to actual training. In this paper, pilot training data were collected in pilot training system using VR/AR to increase immersion in flight training, and Customized Pilot Training Platform with Collaborative Deep Learning in VR/AR Environment that can recommend effective training courses to pilots is proposed. To verify the accuracy of the recommendation, the performance of the proposed collaborative deep learning algorithm with the existing recommendation algorithm was evaluated, and the flight test score was measured based on the pilot's training data base, and the deviations of each result were compared. The proposed service platform can expect more reliable recommendation results than previous studies, and the user survey for verification showed high satisfaction.

• 한국산학기술학회논문지
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• 제13권11호
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• pp.4984-4993
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• 2012

본 연구는 협력능력에 대한 사전훈련과 협력 스크립트의 상호작용이 협력능력과 공유정신모형 형성에 미치는 영향을 알아보았다. K대 '교육방법 및 교육공학'을 수강하는 96명에게 사전훈련과 서로 다른 협력스크립트를 제공하고, 각 집단 별로 협력학습과정에서 이루어지는 메시지와 협력학습 후 개념도를 분석하였다. 연구 결과, 첫째 사전훈련과 협력 스크립트의 상호작용에 따른 협력 능력에는 유의미한 차이가 없었으나, 협력 스크립트 유형은 협력능력에 유의미한 영향을 미치는 것으로 나타났다. 둘째, 협력에 대한 사전 훈련과 협력 스크립트의 상호작용에 따른 공유정신모형 형성에는 유의미한 차이가 없었으나, 협력 스크립트 유형은 공유정신모형에 유의미한 영향을 미치는 것으로 나타났다. 이 연구는 CSCL 환경에서 효과적인 협력학습활동을 위한 전략 제시라는 점에서 의의가 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3172-3193
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• 2018

Recently, several studies have shown that linear representation based approaches are very effective and efficient for image classification. One of these linear-representation-based approaches is the Collaborative representation (CR) method. The existing algorithms based on CR have two major problems that degrade their classification performance. First problem arises due to the limited number of available training samples. The large variations, caused by illumintion and expression changes, among query and training samples leads to poor classification performance. Second problem occurs when an image is partially noised (contiguous occlusion), as some part of the given image become corrupt the classification performance also degrades. We aim to extend the collaborative representation framework under limited training samples face recognition problem. Our proposed solution will generate virtual samples and intra-class variations from training data to model the variations effectively between query and training samples. For robust classification, the image patches have been utilized to compute representation to address partial occlusion as it leads to more accurate classification results. The proposed method computes representation based on local regions in the images as opposed to CR, which computes representation based on global solution involving entire images. Furthermore, the proposed solution also integrates the locality structure into CR, using Euclidian distance between the query and training samples. Intuitively, if the query sample can be represented by selecting its nearest neighbours, lie on a same linear subspace then the resulting representation will be more discriminate and accurately classify the query sample. Hence our proposed framework model the limited sample face recognition problem into sufficient training samples problem using virtual samples and intra-class variations, generated from training samples that will result in improved classification accuracy as evident from experimental results. Moreover, it compute representation based on local image patches for robust classification and is expected to greatly increase the classification performance for face recognition task.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.958-979
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• 2023

Due to laws, regulations, privacy, etc., between 70-90 percent of providers do not share medical data, forming a "data island". It is essential to collaborate across multiple institutions without sharing patient data. Most existing methods adopt distributed learning and centralized federal architecture to solve this problem, but there are problems of resource heterogeneity and data heterogeneity in the practical application process. This paper proposes a collaborative deep learning modelling method based on the blockchain network. The training process uses encryption parameters to replace the original remote source data transmission to protect privacy. Hyperledger Fabric blockchain is adopted to realize that the parties are not restricted by the third-party authoritative verification end. To a certain extent, the distrust and single point of failure caused by the centralized system are avoided. The aggregation algorithm uses the FedProx algorithm to solve the problem of device heterogeneity and data heterogeneity. The experiments show that the maximum improvement of segmentation accuracy in the collaborative training mode proposed in this paper is 11.179% compared to local training. In the sequential training mode, the average accuracy improvement is greater than 7%. In the parallel training mode, the average accuracy improvement is greater than 8%. The experimental results show that the model proposed in this paper can solve the current problem of centralized modelling of multicenter data. In particular, it provides ideas to solve privacy protection and break "data silos", and protects all data.

• 한국CDE학회논문집
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• 제20권4호
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• pp.420-430
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• 2015

Operator Training Simulators (OTSs) provide macroscopic training environment for plant operation. They are equipped with simulation systems for the emulation of remote monitoring and controlling operations. OTSs typically provide 2D block diagram-based graphic user interface (GUI) and connect to process simulation tools. However, process modeling for OTSs is a difficult task. Furthermore, conventional OTSs do not provide real plant field information since they are based on 2D human machine interface (HMI). In order to overcome the limitation of OTSs, we propose a new type of plant training system. This system has the capability required for collaborative training between operators and field workers. In addition, the system provides 3D virtual training environment such that field workers feel like they are in real plant site. For this, we designed system architecture and developed essential functions for the system. For the verification of the proposed system design, we implemented a prototype training system and performed experiments of collaborative training between one operator and two field workers with the prototype system.

• 정보교육학회논문지
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• 제19권3호
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• pp.261-270
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• 2015

본 연구는 협력 활동의 전략이 교사연수에서 어떤 효과가 나타나는지에 대한 분석을 하였다. 교사 연수의 효과성은 연수 만족도, 효과성, 학업성취도로 설정하였다. 연수에 참가한 초중등 교사들을 대상으로 SNS를 활용한 집단 상호평가 활동군과 개별 자기평가 활동군, 일반 연수군으로 구분하여 연구를 적용하였다. 완전무선화요인설계의 방법으로 교사 연수의 효과성에 대해 변량 분석한 결과, 집단협력 활동이 학습만족도 및 효과성, 학업성취도에 긍정적인 영향을 주는 것으로 나타났다. 이에 따라 SNS기반 협력적 평가활동이 교사연수에 매우 효과적임을 밝혔다.

• 한국항해항만학회지
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• 제40권4호
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• pp.189-196
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• 2016

본 논문은 선박 화재 상황에 대한 비상 대처 훈련을 가상 환경에서 수행하는 가상 선원 훈련 플랫폼을 제안한다. 제안하는 플랫폼은 HMD 기반의 몰입형 가상 환경을 제공하여 훈련 효과를 높일 뿐 아니라 네트워크를 통하여 다중 피훈련자가 함께 훈련에 참여하기 때문에 가상 환경에서의 협동 훈련을 수행할 수 있다. 본 플랫폼은 FDS 및 CFAST 기반의 오프라인 화재 시뮬레이션 결과를 기반으로 사실적인 화재 확산 및 시각화를 제공한다. 선박 화재 상황에 대한 해양 안전 교육 기관의 교재 시나리오를 기반으로 구현한 본 훈련 플랫폼은 기존의 절차 숙달 훈련에 더불어 몰입형 가상 기술을 이용한 장비 숙달 및 환경 숙달 훈련을 제공한다. 구현된 가상 선원 훈련 플랫폼은 장비 작동, 환경 통제, 원격 현장감을 기반하여 선원의 훈련도를 향상시키고 다수의 피훈련자가 실시간으로 임무를 공동 수행하는 가상 협력 훈련이 가능함을 보였다. 또한 구현된 플랫폼은 화재 진압 요령, 승객 유도 방법 등 다양하고 세부적인 기능 숙달 훈련이 가능함을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권7호
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• pp.3480-3500
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• 2017

Accurate locating for the mobile target remains a challenge in various applications of wireless sensor networks (WSNs). Unfortunately, most of the typical localization algorithms perform well only in the WSN with densely distributed sensor nodes. The non-localizable problem is prone to happening when a target moves into the WSN with sparsely distributed sensor nodes. To solve this problem, we propose a collaborative and predictive localization algorithm (CPLA). The Gaussian mixture model (GMM) is introduced to predict the posterior trajectory for a mobile target by training its prior trajectory. In addition, the collaborative and predictive schemes are designed to solve the non-localizable problems in the two-anchor nodes locating, one-anchor node locating and non-anchor node locating situations. Simulation results prove that the CPLA exhibits higher localization accuracy than other tested predictive localization algorithms either in the WSN with sparsely distributed sensor nodes or in the WSN with densely distributed sensor nodes.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2020년도 춘계학술발표대회
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• pp.228-230
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• 2020

The emergence of collaborative learning to the public is to tackle the user's privacy issue in centralized learning by bringing the AI models to the data source or client device for training Collaborative learning employs computing and storage resources on the client's device. Thus, it is privacy preserved by design. In harmony, blockchain is also prominent since it does not require an intermediary to process a transaction. However, these approaches are not yet fully ripe to be implemented in the real world, especially for the complex system (several challenges need to be addressed). In this work, we present the performance of collaborative learning and potential use case of blockchain. Further, we discuss privacy issues in the system.

• 대한예방한의학회지
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• 제15권3호
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• pp.83-99
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• 2011

Objective : To explore and describe coordinators' experiences in collaborative practices between the traditional Korean medicine doctors and the western medicine doctors. Methods : Five coordinators who agreed and completed the informed consent to take part in this qualitative study were interviewed thoroughly and tape-recorded. Transcribed data were analysed thematically with ground theory. Results : Most participants started their coordinating work without sufficient knowledge and systemic support. They, however, could find their identity as coordinators for collaborative practices through preparing manuals and protocols, providing comprehensive patients care, and experiencing the partnership with doctors. To coordinate Korean medicine and western medicine practices efficiently, participants have tried to enhance their professional knowledge and skills, and establish favorable networks. On the other hand, they were in dilemmas of being a multi-player and imbalance of responsibilities and powers in their jobs. Conclusions : It is recommended to clarify job description of coordinator for collaborative practices, develop training programme, and provide the institutional support for wider recognition of coordinator. Findings from this study should be considered in both Korean medicine-western medicine collaborative research and practice.