• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.786-792
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• 2022

In the cloud computing environment, there has been a lot of research into the Fog/Edge Computing (FEC) paradigm for securing user proximity of application services and computation offloading to alleviate service delay difficulties. The method of predicting dynamic location change patterns of edge devices (moving objects) requesting application services is critical in this FEC environment for efficient computing resource distribution and deployment. This paper proposes an optimal moving pattern extraction algorithm in which variable weights (distance, time, congestion) are applied to selected paths in addition to a support factor threshold for frequency patterns (moving objects) of edge devices. The proposed algorithm is compared to the OPE_freq [8] algorithm, which just applies frequency, as well as the A^* and Dijkstra algorithms, and it can be shown that the execution time and number of nodes accessed are reduced, and a more accurate path is extracted through experiments.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.5
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• pp.80-92
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• 2017

The popularity of cloud computing has led to the emergence of various types of cloud services, and the hybrid cloud, a deployment model that integrates public cloud and private cloud and offset their shortcomings, is in the spotlight recently. However, the complexity of different clouds integration and the lack of related integration solutions have delayed the adoption of hybrid cloud and cloud strategy by companies and organizations. Therefore, in this paper, we propose a cloud integration mechanism to solve the integration complexity problem. The cloud integration mechanism proposed in this paper consists of integration script that solves the cloud integration by the script based on the hybrid cloud function, a process of creating and executing it, and a script creation model applying the software design pattern. By integrating the various cloud services, we can quickly generate scripts that meet the user's needs. It is expected that the introduction of hybrid cloud and the acquisition of cloud strategy can be accelerated through this proposed integration mechanism.

• Journal of the Korea Fashion and Costume Design Association
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• v.26 no.1
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• pp.17-31
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• 2024

The purpose of this study is to propose a modern use of traditional culture by developing creative fashion designs that combine modern and traditional styles based on an analysis of traditional costumes of women in the Tang Dynasty of China. The characteristics of the Tang Dynasty women's costume are as follows. The Tang Dynasty women's costume consists of a short coat (衫, Shan), skirt (裙, Qun), half-arm shawl (半臂, Banbi), and short embroidered cape (帔, Pei). The colors are succinct and elegant, commonly red, yellow, green and navy blue in its entirety. It may be classified by pattern that blend plant patterns, animal patterns, geometric patterns, and two or more mixed patterns. On the basis of the characteristics for traditional women's costume during the Tang Dynasty, the CLO 3D program is employed to develop digital fashion design for four pairs of 3D digital clothing and the production of two pairs of work product. The results are as follows. First, the development of fashion design reflecting the design characteristics of traditional women's clothing in the Tang Dynasty of China could be expressed as fashion design reflecting unique values while connecting tradition and modernity. Second, the 3D virtual clothing program displays an extremely important effect in design deployment and pattern arrangement by having efficiency and convenience in clothing production. The CLO 3D program is closely combined with the 2D design and 3D effect and heightened efficiency while being appropriate to realize sustainability while saving processing time and energy for the sample products. Third, the production of an actual product by facilitating the 3D virtual clothing design may lead to time savings and an effective economy and may allow for the comparison of digital fashion design and actual products as well as confirming the effects of digital fashion design.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.6
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• pp.95-100
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• 2009

Nowadays there are many studies and there's huge development about USN/RFID which have great developmental potential to many kinds of applications. More and more real time application apply USN/RFID technology to identify data collect and locate objects. Wide deployment of USN/RFID will generate an unprecedented volume of primitive data in a short time. Duplication and redundancy of primitive data will affect real time performance of application. Thus, security applications must filter primitive data and correlate them for complex pattern detection and transform them to events that provide meaningful, actionable information to end application. In this paper, we design a ECA Rule system for security monitoring of exhibition. This system will process USN/RFID primitive data and event and perform data transformation. It's had applied each now in exhibition hall through this study and efficient data transmission and management forecast that is possible.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.8
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• pp.780-785
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• 2010

The OPRoS (Open Platform for Robotic Services) framework is proposed as an application runtime environment for service robot systems. For the successful deployment of the OPRoS framework, fault tolerance support is crucial on top of its basic functionalities of lifecycle, thread and connection management. In the previous work [1] on OPRoS fault tolerance supports, we presented a framework-based fault tolerance architecture. In this paper, we extend the architecture with component-based fault tolerance techniques, which can provide more simplicity and efficiency than the pure framework-based approach. This argument is especially true for fault detection, since most faults and failure can be defined when the system cannot meet the requirement of the application functions. Specifically, the paper applies two widely-used fault detection techniques to the OPRoS framework: 'bridge component' and 'process model' component techniques for fault detection. The application details and performance of the proposed techniques are demonstrated by the same application scenario in [1]. The combination of component-based techniques with the framework-based architecture would improve the reliability of robot systems using the OPRoS framework.

• Nuclear Engineering and Technology
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• v.47 no.2
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• pp.176-186
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• 2015

Pattern classifications have become important tools for fault diagnosis in nuclear power plants (NPP). However, it is often difficult to obtain training data under fault conditions to train a supervised classification model. By contrast, normal plant operating data can be easily made available through increased deployment of supervisory, control, and data acquisition systems. Such data can also be used to train classification models to improve the performance of fault diagnosis scheme. In this paper, a fault diagnosis scheme based on semisupervised classification (SSC) scheme is developed. In this scheme, new measurements collected from the plant are integrated with data observed under fault conditions to train the SSC models. The trained models are subsequently applied to new measurements for fault diagnosis. In comparison with supervised classifiers, the proposed scheme requires significantly fewer data collected under fault conditions to train the classifier. The developed scheme has been validated using different fault scenarios on a desktop NPP simulator as well as on a physical NPP simulator using a graph-based SSC algorithm. All the considered faults have been successfully diagnosed. The results have demonstrated that SSC is a promising tool for fault diagnosis in NPPs.

• Proceedings of the KSR Conference
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• 2005.11a
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• pp.246-251
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• 2005

Accident scenarios analysis is a course to understand, analyze, and describe a process of an accident and behavior pattern of the parties to an accident. The method of accident scenarios is that we described patterns represented between accidents and hazardous conditions, and then provide data to prevent the accident. We have carried out scenarios analysis in various fields so far, but it was not taking account of system. In this research, we made a study on technology of accident scenarios analysis using QFD (Quality Function Deployment) to analyze systematically and evaluate quantitatively types of hazards and scenarios of railway accident. And we analyses accident scenarios of a subject of work-related fatality accident to railway employee and conducted risk assessment for different scenarios. Also we defined relation between unsafe events and hazardous conditions caused to work-related fatality accident, and attempted to quantitatively assess work-related fatality accident and the parties to accidents. The results of this research will be used in analyzing for important causes and contributing factors of work-related fatality accidents at the step of risk assessment of railway system, and quantitatively assessing frequency of work-related accidents and risk.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.1-10
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• 2018

Wide-area monitoring of tree-related high impedance fault (THIF) efficiently contributes to increase reliability of large-scaled network, since the failure to early location of them may results in critical lines tripping and consequently large blackouts. In the first place, this wide-area monitoring of THIF requires managing the placement of sensors across large power grid network according to THIF detection objective. For this purpose, current paper presents a framework in which sensors are distributed according to a predetermined risk map. The proposed risk map determines the possibility of THIF occurrence on every branch in a power network, based on electrical conductivity of trees and their positions to power lines which extracted from spectral data. The obtained possibility value can be considered as a weight coefficient assigned to each branch in sensor placement problem. The next step after sensors deployment is to on-line monitor based on moving data window. In this on-line process, the received data window is evaluated for obtaining a correlation between low frequency and high frequency components of signal. If obtained correlation follows a specified pattern, received signal is considered as a THIF. Thereafter, if several faulted section candidates are found by deployed sensors, the most likely location is chosen from the list of candidates based on predetermined THIF risk map.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.2982-3003
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• 2014

With the explosive deployment of the wireless communications technology, the increased QoS requirement has sparked keen interest in network planning and optimization. As the major players in wireless network optimization, the BS's resource utilization and mobile user's QoS can be improved a lot by the load-balancing technology. In this paper, we propose a load-balancing strategy that uses Coordinated Multiple Points (CoMP) technology among the Base Stations (BS) to effectively extend network coverage and increase edge users signal quality. To use universally, different patterns of load-balancing based on CoMP are modeled and discussed. We define two QoS metrics to be guaranteed during CoMP load balancing: call blocking rate and efficient throughput. The closed-form expressions for these two QoS metrics are derived. The load-balancing capacity and QoS performances with different CoMP patterns are evaluated and analyzed in low-dense and high-dense traffic system. The numerical results present the reasonable CoMP load balancing pattern choice with guaranteed QoS in each system.

• Smart Structures and Systems
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• v.29 no.1
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.