• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.9B
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• pp.778-790
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• 2004

In this paper, we propose the functional models of optical burst switching (OBS) routers to apply GMPLS (Generalized Multiprotocol Label Switching) to optical networks based on OBS. In addition, we introduce a connection admission control (CAC) algorithms which are operated in this models and can accommodate the required QoS. Firstly, the characteristics of current GMPLS and OBS for the optical Internet are basically considered. With this consideration, the models are proposed to accept OBS features which include the recognition of data bounda η with control information and the statistical multiplexing in terms of bursts. Secondly, we use an offset time decision (OTD) algorithm on behalf of controlling the connection admission with taking QoS parameters such as burst loss rate(BLR) and service-differentiation ratio(SDR) into consideration. The proposed CAC algorithms use the offered load of LSP (Label Switched Path), wavelength information, and QoS parame ‘ ers as inputs of OTD algorithm. A call setup request will be accepted when the offset time decided by OTD algorithm is reasonable for guaranteeing its requested QoS. Simulation is used for performance evaluation Results show the proposed schemes can guarantee the required QoS and those are better than the previous one in terms of channel utilization.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.55-63
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• 2017

Due to spatial problems and system size,conventional measurement methods used to acquire the information needed for existing electrical energy and management have been limited to new buildings or areas where replacement is possible. This electric load management method is problematic when applying it to energy and safety management of vulnerable areas or existing homes or offices. The problem with installing a measurement module in every branch is that the system is too large. Even if the measurement module is installed, the type of load is not recognized, and efficient management is not performed. In particular, it is very difficult to apply it to traditional markets and backward facilities in Korea. In this paper, we apply NIALM technology and arc detection technology to solve these problems and verify the feasibility of NIALM for normal arc generation. Also, based on the verification results, we propose a new smart home safety management system that can effectively manage electrical safety and that can be applied to conventional market and existing home safety management systems. The proposed system conducts a comparative performance test with an existing safety management system. In addition, it achieves 95% or more load recognition for four loads, which is impossible in 40% of the existing systems, and the arc detection function was confirmed.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.615-622
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• 2018

Recent advancements in camera communication (CamCom) technology using visible light exploited to use display as an luminance source to modulate the data for visible light data communication. The existing display-CamCom techniques uses the selected region of interest based camera capturing approach to detect and decode the 2D color coded data on display screen. This is not effective way to do communicate when the user on mobility. This paper propose the automatic display detection using Lambertian color segmentation combined with canny edge detection algorithms for CamCom in order to avoid manual region of interest selection to establish communication link between display and camera. The automatic display detection methods fails using conventional edge detection algorithms when content changes dynamically in displays. In order to solve this problem lambertian color segmentation combined with canny edge detection algorithms are proposed to detect display automatically. This research analysed different algorithms on display edge recognition and measured the performance on rendering dynamically changing content with color code on display. The display detection rate is achieved around 96% using this proposed solutions.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.6
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• pp.503-513
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• 2005

With the recognition of the importance of computational approach for protein-protein interaction prediction, many techniques have been developed to computationally predict protein-protein interactions. However, few techniques are actually implemented and announced in service form for general users to readily access and use the techniques. In this paper, we design and implement a protein interaction prediction service system based on the domain combination based protein-protein interaction prediction technique, which is known to show superior accuracy to other conventional computational protein-protein interaction prediction methods. In the prediction accuracy test of the method, high sensitivity($77\%$) and specificity($95\%$) are achieved for test protein pairs containing common domains with teaming sets of proteins in a Yeast. The stability of the method is also manifested through the testing over DIP CORE, HMS-PCI, and TAP data. Performance, openness and flexibility are the major design goals and they are achieved by adopting parallel execution techniques, web Services standards, and layered architecture respectively. In this paper, several representative user interfaces of the system are also introduced with comprehensive usage guides.

• Journal of the Korean Institute of Gas
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• v.24 no.4
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• pp.84-92
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• 2020

Most facilities in chemical plants operate in environments that are outside the range of temperature and pressure that can be encountered on a daily basis, and are vulnerable to aging due to these stresses and environmental conditions. The facilities exposed to these conditions are not only likely to fail due to cumulative damage, but also lead to accidents if maintenance and replacement are not performed.Recommendation guidelines called risk-based inspection are widely used around the world-wide. However, limits exist for facilities that have already elapsed for a certain. As a result of the survey on the aging of Ulsan industrial complex in Korea, which carries out proper inspection, many of the facilities have been used for 20 years. Also, most of the facilities where the accident occurred have been in operation for more than 20 years. Therefore, this study suggested criteria for classifying devices that have exceeded a certain period of use as obsolete facilities. In addition, quantitative risk assessment was conducted. The safety investment method using the cost-benefit analysis method was proposed in order to calculate the loss cost and reduce the risk by expressing the risks of the corresponding aged facility as an Economic index. By utilizing the method of cost-benefit analysis of old facilities using the quantitative risk assessment presented in this study, it can be expected to improve the performance and life of old facilities, improve production efficiency and reliability of the system of facilities, change the recognition of safety management costs, increase employee stability, and reduce loss costs.

• Korean Security Journal
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• no.16
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• pp.161-187
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• 2008

The purpose of this study is to suggest a development method of current Korean security system by analyzing the problems shown in the performance of security work in relation to the terrorism, which is enlarging in the word, from various aspects. In order to perform the study, the researcher considered the basic theory concerned to current Korean law concerned to security, principle and methodology of security, terror and new terrorism. The researcher performed the study by selecting qualitative case study focused on Park Geun-Hye case. Through the study, the methods to develop Korean security system are as follows. First, from the legal aspect, it is necessary to establish the law concerned to terrorism prevention and important person security. Moreover, it is necessary to search for the development of private security by revising Security Industry Act, which is a legal ground of private security. Second, it is necessary to improve and reinforce education & training program, which is not still divided in detail from the aspect of private security cultivation. Moreover, it is necessary to activate personal protection work and enlarge market through Security Industry Act and make an effort to change social recognition over security, which is devaluated in the society. From the viewpoint, national license about private security shall be adopted. The department of president security, which is a representative of official security, shall transfer the advanced technology to private security organization. Third, from the aspect of operation, the operation of security based on SCE principle, human shield principle, the nearest person's protection principle, body extension principle, linear protection principle and evacuation priority principle is required. Therefore, the priority shall be given to preventive security and thorough security plan shall be made for the operation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.34-38
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• 2016

This study aims to develop qualification criteria for training and establishing an expert pool of 3D printing operation experts and 3D printing development specialists in the emerging 3D printing industry. We investigate the domestic and international 3D printing industry, as well as 3D printing- related training institutions and quantity of test examinees. Utilizing competency analysis, this study examines the competencies and educational content of both 3D printing operation expert as well as 3D printing development specialists. In addition, a survey was conducted on the issue of institutionalization of qualification criteria for 3D printing operation expert and 3D printer development specialists. This study further proposes evaluation methods for qualification criteria for both 3D printing operation expert and 3D printer development specialists based on simulations of both written and performance tests. Based on the findings, this study proposes instructional program outlines as well as comprehensive guidelines for training and qualification processes for 3D printing operation expert and 3D printer development specialists.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.713-722
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• 2013

This paper proposes a method of using depth information to detect the hand region in real-time based on the cascade method. In order to ensure stable and speedy detection of the hand region even under conditions of lighting changes in the test environment, this study uses only features based on depth information, and proposes a method of detecting the hand region by means of a classifier that uses boosting and cascading methods. First, in order to extract features using only depth information, we calculate the difference between the depth value at the center of the input image and the average of depth value within the segmented block, and to ensure that hand regions of all sizes will be detected, we use the central depth value and the second order linear model to predict the size of the hand region. The cascade method is applied to implement training and recognition by extracting features from the hand region. The classifier proposed in this paper maintains accuracy and enhances speed by composing each stage into a single weak classifier and obtaining the threshold value that satisfies the detection rate while exhibiting the lowest error rate to perform over-fitting training. The trained classifier is used to classify the hand region, and detects the final hand region in the final merger stage. Lastly, to verify performance, we perform quantitative and qualitative comparative analyses with various conventional AdaBoost algorithms to confirm the efficiency of the hand region detection algorithm proposed in this paper.

• Journal of the Korea Society of Computer and Information
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• v.18 no.8
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• pp.65-75
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• 2013

This paper proposes a method of tracking the hand region and detecting the fingertip using only depth images. In order to eliminate the influence of lighting conditions and obtain information quickly and stably, this paper proposes a tracking method that relies only on depth information, as well as a method of using region growing to identify errors that can occur during the tracking process and a method of detecting the fingertip that can be applied for the recognition of various gestures. First, the closest point of approach is identified through the process of transferring the center point in order to locate the tracking point, and the region is grown from that point to detect the hand region and boundary line. Next, the ratio of the invalid boundary, obtained by means of region growing, is used to calculate the validity of the tracking region and thereby judge whether the tracking is normal. If tracking is normal, the contour line is extracted from the detected hand region and the curvature and RANSAC and Convex-Hull are used to detect the fingertip. Lastly, quantitative and qualitative analyses are performed to verify the performance in various situations and prove the efficiency of the proposed algorithm for tracking and detecting the fingertip.

• Convergence Security Journal
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• v.21 no.1
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• pp.191-200
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• 2021

Artificial intelligence technology is developing in an environment where a lot of data is produced due to the development of computing technology, a cloud environment that can store data, and the spread of personal mobile phones. Among these artificial intelligence technologies, the deep neural network provides excellent performance in image recognition and image classification. There have been many studies on image detection for forest fires and fire prevention using such a deep neural network, but studies on detection of cigarette smoking were insufficient. Meanwhile, military units are establishing surveillance systems for various facilities through CCTV, and it is necessary to detect smoking near ammunition stores or non-smoking areas to prevent fires and explosions. In this paper, by reflecting experimentally optimized numerical values such as activation function and learning rate, we did the detection of smoking pictures and non-smoking pictures in two cases. As experimental data, data was constructed by crawling using pictures of smoking and non-smoking published on the Internet, and a machine learning library was used. As a result of the experiment, when the learning rate is 0.004 and the optimization algorithm Adam is used, it can be seen that the accuracy of 93% and F1-score of 94% are obtained.