• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.370-372
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• 2014

This study questions, and an automatic scoring system written in HTML, and XML-based system that is at issue, the issue questions in a convenient offline automatically how to register, Easy to manage questions of issues, questions and problems of merging the PC and the mobile device in a place that can be obtained without taking the test system study. Server systems, and real-time registration questions merging problem, such as difficulty adjusting to the test required to build the system. Clients communicate with the server using the mobile device and the PC is required to take the exam in the View application, and responses are sent for treatment research.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.428-439
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• 2020

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4664-4681
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• 2020

Modulation recognition (MR) plays a key role in cognitive radar, cognitive radio, and some other civilian and military fields. While existing methods can identify the signal modulation type by extracting the signal characteristics, the quality of feature extraction has a serious impact on the recognition results. In this paper, an end-to-end MR method based on long short-term memory (LSTM) and the gated recurrent unit (GRU) is put forward, which can directly predict the modulation type from a sampled signal. Additionally, the sliding window method is applied to fast-changing mixed-modulation signals for which the signal modulation type changes over time. The recognition accuracy on training datasets in different SNR ranges and the proportion of each modulation method in misclassified samples are analyzed, and it is found to be reasonable to select the evenly-distributed and full range of SNR data as the training data. With the improvement of the SNR, the recognition accuracy increases rapidly. When the length of the training dataset increases, the neural network recognition effect is better. The loss function value of the neural network decreases with the increase of the training dataset length, and then tends to be stable. Moreover, when the fast-changing period is less than 20ms, the error rate is as high as 50%. As the fast-changing period is increased to 30ms, the error rates of the GRU and LSTM neural networks are less than 5%.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.4
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• pp.181-195
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• 2014

The game Industry has been a major leader in business world with its size and volume in terms of profit and culture contents, and ever increasing at the moment. Cloud Game has appeared as a new, combined game format, playable on smart TV and smart phone with its upgraded storage size and fast spreading N-screen. This research studies the present reality of the cloud industry by focusing on three categories which are device type, Platform, and game contents consequently in order to determine the future prospect of cloud games. First, the cloud game business will thrive as devices such as smart TV and smart phone are used widely. Second, the cloud game industry will have a new era when OS systems of Platform are united effectively. Third, the previous platform holders will have to face new challenges brought up by cloud games' service providers. Forth, the gamer, developer, and service provider need each other in order to widen the spectrum of business in cloud game industry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1216-1231
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• 2021

With the increase in the population of our cities and the rapid increase in the number of private cars, urban traffic has become more and more congested. At this stage, urban public transportation has become one of the main ways to improve urban traffic congestion. Aiming at the problem of how to improve the basic capacity of buses in multi-line transfer stations, this paper conducts simulation research based on anylogic software. Through micro-simulation analysis of vehicles entering, stopping, and exiting the station, combined with the delay model theory, the vehicle is given Stop organization optimization and station layout improvement methods, so that vehicles can run in the station more stably, smoothly and safely. Case analysis shows that applying this method to the roadside parking problem, the main and auxiliary bus stations have a significant improvement in operating capacity compared with the conventional tandem double bus stations, and the service level of the main and auxiliary bus stations has been significantly improved.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.158-170
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• 2021

Virtualization technologies are being adopted and broadly utilized in many fields and at different levels. In cloud computing, achieving load balancing across large distributed virtual machines is considered a complex optimization problem with an essential importance in cloud computing systems and data centers as the overloading or underloading of tasks on VMs may cause multiple issues in the cloud system like longer execution time, machine failure, high power consumption, etc. Therefore, load balancing mechanism is an important aspect in cloud computing that assist in overcoming different performance issues. In this research, we propose a new approach that combines the advantages of different task allocation algorithms like Round robin algorithm, and Random allocation with different threshold techniques like the VM utilization and the number of allocation counts using least connection mechanism. We performed extensive simulations and experiments that augment different scheduling policies to overcome the resource utilization problem without compromising other performance measures like makespan and execution time of the tasks. The proposed system provided better results compared to the original round robin as it takes into consideration the dynamic state of the system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1798-1813
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• 2021

This paper proposes a positive experience design approach for Internet of Things (IoT) intelligent products to improve users' subjective well-being in the fields of artificial intelligence and big data. First, the authors selected six target users and taking the Xiaomi IoT intelligent products for the research objects and conducted a thorough observation on how the target users used IoT intelligent products in their own homes over two weeks via a home-visiting interview, group diary, and focus group. Second, they constructed an individual activities table for the participants' IoT intelligent product experience using a hierarchical task analysis (HTA). Third, two researchers sorted out the sub-tasks of happiness in the HTA table. Finally, the authors found the positive experience design approach of IoT intelligent products. The positive experience design approach of IoT intelligent products is proposed from focusing on the personal pleasure experience to individual life meaningful design and group social relationship design, including individual pleasure experience, personal goal realization, group needs satisfaction and the harmony of group relations. The paper uses the two design examples of an interactive kettle and a harmonious chair to further discuss the feasibility of the design approach. In the era of big data, it is helpful for designers to use this design approach to improve the users' sense of sustainable pleasure, achievement perception of their future goal realization, and the well-being of the group's social relationships.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.3
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• pp.91-98
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• 2022

With the recent increase in interest in metaverse in which virtual and physical spaces are digitally fused, many activities in physical spaces are expected to take place in web-based virtual spaces. Therefore, there is a need for research on a self-sovereign identity system that can secure privacy and mutual trust in a DID(decentralized identifier)-based virtual space environment. We, in this paper, developed and validated a reliable communication method consisting of DIDComm messages, a procedure for generating distributed identifiers, asymmetric keys, and DID documents based on Hyperledger Indy and DIDComm open sources. The developed communication method can be applied to verify each other by exchanging additional information and verifiable credentials for trust among communication participants.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.2
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• pp.77-83
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• 2022

In this paper, the two-stage object detection approach is proposed to implement a deep learning-based label inspection system on edge computing environments. Since the label printed on the products during the production process contains important information related to the product, it is significantly to check the label information is correct. The proposed system uses the lightweight deep learning model that able to employ in the low-performance edge computing devices, and the two-stage object detection approach is applied to compensate for the low accuracy relatively. The proposed Two-Stage object detection approach consists of two object detection networks, Label Area Detection Network and Character Detection Network. Label Area Detection Network finds the label area in the product image, and Character Detection Network detects the words in the label area. Using this approach, we can detect characters precise even with a lightweight deep learning models. The SF-YOLO model applied in the proposed system is the YOLO-based lightweight object detection network designed for edge computing devices. This model showed up to 2 times faster processing time and a considerable improvement in accuracy, compared to other YOLO-based lightweight models such as YOLOv3-tiny and YOLOv4-tiny. Also since the amount of computation is low, it can be easily applied in edge computing environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2305-2327
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• 2022

Recent improvements in technology have increased the consumption of virtual reality (VR) contents on immersive displays. The VR experience depends on the type of displays as well as the quality of VR contents. However, research on the impacts of VR content quality on VR experience and comparisons among different types of immersive display devices are lacking. In this study, VR contents created with our VR framework, are provided to participants on conventional two-dimensional (2D) immersive displays and VR headset. The geometric alignment of VR contents is improved with the addition of two calibration modes (i.e. preprocessing and straightening). The subjective feelings of presence and cybersickness experienced by participants while consuming VR contents created by our framework and commercial solutions are recorded in the form of questionnaires. The results of this study indicate that the improvements in VR quality lead to a better presence and less cybersickness in both conventional 2D displays and VR headset. Furthermore, the level of presence and cybersickness increases in VR headsets as compared to conventional 2D displays. Finally, the VR content quality improvements lead to a better VR experience for our VR framework as compared to commercial solutions.