• Journal of Korea Multimedia Society
• /
• v.23 no.8
• /
• pp.1075-1087
• /
• 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.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.11
• /
• pp.4984-4993
• /
• 2012

This study was initiated with the need of studies to promote learning and use of collaboration skills that learners should have in collaborative learning in CSCL. The researcher carried out research on 96 students taking the course of 'educational methods and educational technology' in K collage to take a look at the impact of prior training on collaboration in CSCL and interaction of types of collaborative scripts. To answer the first research question, the scores of each group's chatting in collaborative learning process and messages represented in the process of task performance based on collaborative skills were measured and analyzed. In addition, to answer the second research question, the scores of each group's shared mental model formulation based on relevant evaluation standards were analyzed. This study results, First, there was no significant difference in the acquisition of collaboration skills caused by interaction of prior training on collaboration and collaboration skills and collaborative scripts. However, it turned out that types of collaborative scripts give significant impacts on acquisition of collaboration skills. Second, there was also no significant difference between prior training on collaboration and the formulation of shared mental model by the interaction of collaborative scripts. However, it is showed that types of collaborative scripts have significant impacts on the formation of shared mental model.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.7
• /
• pp.3172-3193
• /
• 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)
• /
• v.17 no.3
• /
• pp.958-979
• /
• 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.

• Korean Journal of Computational Design and Engineering
• /
• v.20 no.4
• /
• pp.420-430
• /
• 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.

• Journal of The Korean Association of Information Education
• /
• v.19 no.3
• /
• pp.261-270
• /
• 2015

In this paper, a strategy of cooperation activities was conducted to analyze on the impact of what effect appears in teacher training. We classified with satisfaction, effectiveness and academic achievement as effects of teacher training. We were divided into three groups that are cooperative-reflection activity group using the SNS, self-reflection activity group and general training group. Depending on the type of reflection activity, we have one-way ANOVA analysis for the effectiveness of teacher training. By the results of the analysis, we found to have a positive impact that cooperative reflection activity group were more an academic achievement, satisfaction and effectiveness of training. Accordingly, we have found the SNS-based collaborative reflection activity is very effective in teacher training.

• Journal of Navigation and Port Research
• /
• v.40 no.4
• /
• pp.189-196
• /
• 2016

We propose a virtual sailor training platform which supports emergency drills for ship's fire in virtual environment. Proposed platform not only enhances training efficiency by providing immersiveness, but also enables a consolidated virtual training due to the network-based multiplayer capabilities. Based on the offline fire simulation results using FDS and CFAST the platform visualizes a realistic fire spread in real-time. The training platform on the basis of the fire training material of the maritime safety education institute induces equipment proficiency and environment adaptation throughout immersive virtual environment in addition to procedure proficiency as well. In the implementation we showed that the equipment and environment controls and telepresence improve the training proficiency and enable collaborative virtual training that participates multiple trainees and induces cooperation for a common goal. Implementation of the platform demonstrated the skill mastery capability of the drill such as efficient fire apparatus controls and passenger controls.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.7
• /
• pp.3480-3500
• /
• 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.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2020.05a
• /
• pp.228-230
• /
• 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.

• Journal of Society of Preventive Korean Medicine
• /
• v.15 no.3
• /
• pp.83-99
• /
• 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.