• Journal of Internet of Things and Convergence
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• v.7 no.1
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• pp.21-25
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• 2021

Augmented reality systems are widely used in the fields of entertainment, shopping, education, and training, and the augmented reality technology is gradually increasing in importance. When augmented reality technology is used for education or training, it must be possible to represent different virtual objects depending on the work stage even for the same marker. Also, since the training content varies depending on the situation, it is necessary to describe it using a training scenario. In order to solve this problem, we propose a scenario markup language for an augmented reality system that can create training content based on a scenario and connect it with an augmented reality system. The scenario markup language for augmented reality provides functions such as a method for connecting a scene, a marker and a virtual object, a method for grasping the state of equipment or sensor value, and a method for moving a scene according to conditions. The augmented reality scenario markup language can flexibly increase the usefulness and expandability of the augmented reality system usage method and content usage.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.577-583
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• 2012

Objective: This paper describes the process and results related to the development of gesture recognition-based natural user interface(NUI) for vehicle maintenance e-Training system. Background: E-Training refers to education training that acquires and improves the necessary capabilities to perform tasks by using information and communication technology(simulation, 3D virtual reality, and augmented reality), device(PC, tablet, smartphone, and HMD), and environment(wired/wireless internet and cloud computing). Method: Palm movement from depth camera is used as a pointing device, where finger movement is extracted by using OpenCV library as a selection protocol. Results: The proposed NUI allows trainees to control objects, such as cars and engines, on a large screen through gesture recognition. In addition, it includes the learning environment to understand the procedure of either assemble or disassemble certain parts. Conclusion: Future works are related to the implementation of gesture recognition technology for a multiple number of trainees. Application: The results of this interface can be applied not only in e-Training system, but also in other systems, such as digital signage, tangible game, controlling 3D contents, etc.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.413-435
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• 2018

Although the accuracy of handwritten character recognition based on deep networks has been shown to be superior to that of the traditional method, the use of an overly deep network significantly increases time consumption during parameter training. For this reason, this paper took the training time and recognition accuracy into consideration and proposed a novel handwritten character recognition algorithm with newly designed network structure, which is based on an extended nonlinear kernel residual network. This network is a non-extremely deep network, and its main design is as follows:(1) Design of an unsupervised apriori algorithm for intra-class clustering, making the subsequent network training more pertinent; (2) presentation of an intermediate convolution model with a pre-processed width level of 2;(3) presentation of a composite residual structure that designs a multi-level quick link; and (4) addition of a Dropout layer after the parameter optimization. The algorithm shows superior results on MNIST and SVHN dataset, which are two character benchmark recognition datasets, and achieves better recognition accuracy and higher recognition efficiency than other deep structures with the same number of layers.

• Journal of Navigation and Port Research
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• v.47 no.4
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• pp.191-201
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• 2023

The provision of onboard Internet services is recognized as one of the measures to enhance the appeal of seafarers and improve seafarer welfare. This study aims to investigate the influence of seafarers' leisure activities using the Internet on shipboard culture. Shipboard culture was examined using Hofstede's cultural dimensions theory. An empirical analysis was conducted on crewmembers regarding their Internet-based leisure activities and the shipboard culture. As a result, it was observed that sociability activities through the Internet while onboard significantly influenced power distance, uncertainty avoidance, and long-term orientation. The investigation of shipboard culture revealed uncertainty avoidance, masculinity, and long-term orientation, along with low power distance and individualism cultures. In addition, an analysis of shipboard culture according to seafarers' characteristics showed significant differences in certain shipboard cultures based on seafarers' attributes.

• Journal of Korea Multimedia Society
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• v.16 no.3
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• pp.375-387
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• 2013

Advanced internet service and smart equipment have caused an environment supporting various online learning anytime and anywhere, which requires learning contents optimized on a new media. Among various on/off line education related to IT, most part if it is office SW. Many oh them cannot make a good education for effective training in practical because many instructors are tend to focus on teaching simple function and use examples of formality repeatedly. In this paper we propose a new office SW education system that make use of LET(Live EduTainer) based on RTE(Real Training Environment) which maximize the effect of learning and it is integrated with GBL(Game Based Learning) which gives rise to interesting in a knowledge as well as simple teaching so that learners are absorbed on it. We'll elaborate a method for teaching and learning required in this system, design and configuration of the system.

• Journal of Internet Computing and Services
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• v.11 no.1
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• pp.117-128
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• 2010

There are many researches related to a simulated shooting training system for replacing the real military and police shooting training. In this paper, we propose the point of impact from a simulated shooting target based on image processing instead of using a sensor based approach. The point of impact is extracted by analyzing the image extracted from the camera on the muzzle of a gun. The final shooting result is calculated by mapping the target and the coordinates of the point of impact. The recognition system is divided into recognizing the projection zone, extracting the point of impact on the projection zone, and calculating the shooting result from the point of impact. We find the vertices of the projection zone after converting the captured image to the binary image and extract the point of impact in it. We present the extracting process step by step and provide experiments to validate the results. The experiments show that exact vertices of the projection area and the point of impact are found and a conversion result for the final result is shown on the interface.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3589-3605
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• 2018

In this paper, we investigate an individual channel estimation problem for multiple-input multiple-output (MIMO) two-way amplify-and-forward (AF) relay networks. To avoid self-interference during the estimation of the individual MIMO channels, a novel blind interference cancellation (BIC) approach is proposed based on an orthogonal preceding framework, where a pair of orthogonal precoding matrices is utilized at the source nodes. By designing an optimal decoding scheme, we propose to decompose the bidirectional transmission into a pair of unidirectional transmissions. Unlike most existing approaches, we make the practical assumption that the nonreciprocal MIMO channel and the mutual interference of multiple antennas are both taken into consideration. Under the precoding framework, we employ an orthogonal superimposed training strategy to obtain the individual MIMO channels. However, the AF strategy causes the noise at the terminal to be the sum of the local noise and the relay-propagated noise. To remove the relay-propagated noise during the estimation of the second-hop channel, a partial noise-nulling method is designed. We also derive a closed-form expression for the total mean square error (MSE) of the MIMO channel from which we compute the optimal power allocation. The simulation results demonstrate that the analytical and simulated curves match fully.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2993-3010
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• 2021

The job shop scheduling problem (JSSP) plays a critical role in smart manufacturing, an effective JSSP scheduler could save time cost and increase productivity. Conventional methods are very time-consumption and cannot deal with complicated JSSP instances as it uses one optimal algorithm to solve JSSP. This paper proposes an effective scheduler based on deep learning technology named self-supervised long-short term memory (SS-LSTM) to handle complex JSSP accurately. First, using the optimal method to generate sufficient training samples in small-scale JSSP. SS-LSTM is then applied to extract rich feature representations from generated training samples and decide the next action. In the proposed SS-LSTM, two channels are employed to reflect the full production statues. Specifically, the detailed-level channel records 18 detailed product information while the system-level channel reflects the type of whole system states identified by the k-means algorithm. Moreover, adopting a self-supervised mechanism with LSTM autoencoder to keep high feature extraction capacity simultaneously ensuring the reliable feature representative ability. The authors implemented, trained, and compared the proposed method with the other leading learning-based methods on some complicated JSSP instances. The experimental results have confirmed the effectiveness and priority of the proposed method for solving complex JSSP instances in terms of make-span.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3714-3732
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• 2019

With the rapid development of network, Intrusion Detection System(IDS) plays a more and more important role in network applications. Many data mining algorithms are used to build IDS. However, due to the advent of big data era, massive data are generated. When dealing with large-scale data sets, most data mining algorithms suffer from a high computational burden which makes IDS much less efficient. To build an efficient IDS over big data, we propose a classification algorithm based on data clustering and data reduction. In the training stage, the training data are divided into clusters with similar size by Mini Batch K-Means algorithm, meanwhile, the center of each cluster is used as its index. Then, we select representative instances for each cluster to perform the task of data reduction and use the clusters that consist of representative instances to build a K-Nearest Neighbor(KNN) detection model. In the detection stage, we sort clusters according to the distances between the test sample and cluster indexes, and obtain k nearest clusters where we find k nearest neighbors. Experimental results show that searching neighbors by cluster indexes reduces the computational complexity significantly, and classification with reduced data of representative instances not only improves the efficiency, but also maintains high accuracy.

• Journal of Internet of Things and Convergence
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• v.5 no.1
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• pp.1-6
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• 2019

This study was intended to verify the impact on the quality of service for employees of youth training facilities. The purpose of this study is to provide basic data for improving service quality by analyzing the factors influencing service quality of youth training facility workers. Data were collected from 110 youth training facilities in Busan. The results showed that social support, emotional labor and self-efficacy had a static effect on the quality of service. Based on the results of this study, the following suggestions were made. First, in order to enhance social support, it is necessary to strengthen regular networking with other agency workers, interview with middle managers, and counseling. Second, to raise emotional labor, it is necessary to imprint a sense of mission as a youth leader. Psychological and emotional programs should also be developed and implemented. Third, in order to increase the self-efficacy, it is necessary to strengthen the administrative super vision and strengthen related education such as image making.