• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.951-960
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• 2016

Tower insulators in electric power transmission network play a crucial role in preserving the reliability of the system. Electrical utilities frequently face the problem of flashover of insulators due to pollution deposition on their surface. Several research works based on leakage current (LC) measurement has been already carried out in developing diagnostic techniques for these insulators. Since the LC signal is highly intermittent in nature, estimation of pollution severity based on LC signal measurement over a short period of time will not produce accurate results. Reports on the measurement and analysis of LC signals over a long period of time is scanty. This paper attempts to use Random Forest (RF) classifier, which produces accurate results on large data bases, to analyze the pollution severity of high voltage tower insulators. Leakage current characteristics over a long period of time were measured in the laboratory on porcelain insulator. Pollution experiments were conducted at 11 kV AC voltage. Time domain analysis and wavelet transform technique were used to extract both basic features and histogram features of the LC signal. RF model was trained and tested with a variety of LC signals measured over a lengthy period of time and it is noticed that the proposed RF model based pollution severity classifier is efficient and will be helpful to electrical utilities for real time implementation.

• The Journal of the Korea Contents Association
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• v.11 no.7
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• pp.70-79
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• 2011

The multi-path routing schemes that assigns labels to sensor nodes for the reliability of data transmission and the accuracy of an aggregation query over the sensor networks where data transfer is prone to defect have been proposed. However, the existing schemes have high costs for reassigning labels to nodes when the network topology is changed. In this paper, we propose a novel routing method that avoids duplicated data and reduces the update cost of a sensor node. In order to show the superiority of the proposed scheme, we compare it with the existing scheme through the various experiments. Our experimental results show that our proposed method reduces about 95% the amount of the transmitted data for restoration to node failure and about 220% the amount of the transmitted data for query processing over the existing method on average.

• Journal of KIISE:Information Networking
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• v.37 no.2
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• pp.109-121
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• 2010

Recently, the military of Korea has been establishing the next generation wireless tactical communication system so called Tactical Information Communication Network (TICN). A routing protocol for TICN transport networks has to be able to select a path with high survivality, reliability, and stability while accommodating as many flows as possible with minimum QoS guarantees. The OSPF(Open Short Path First) used widely is determined to be the routing protocol for TICN. With the typical deployment practices OSPF, however, it cannot satisfy the requirements of TICN. In this paper, we propose a cost function for OSPF and a way to tune the OSPF protocol parameters for the TICN transport networks. Through simulations, it is shown that the OSPF with the proposed cost function provides better performance than the OSPF in terms of both the services provided to the applications and the network resources utilization.

• Journal of Advanced Navigation Technology
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• v.12 no.2
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• pp.154-163
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• 2008

In a mobile broadband wireless access (MBWA) network, many users access a base station (BS), which relays data transferred from high-speed wired network to low-speed wireless network. For this difference of their data rate, a BS suffers from the lack of its buffer space when many users run multiple applications at the same time, and thus packet losses occur. TCP, which guarantees end-to-end reliability, is used as transport protocol also in wireless networks. But TCP lowers their transmission rate incorrectly and frequently whenever packet losses occur. And they increase their transmission rate differently with each other; finally TCP throughput of each TCP flow varies largely, and then TCP fairness goes worse. In this paper, a scheme that controls packet transmission rate adaptively according to TCP flows' transmission rate, that prevents buffer overflows at BS, and that guarantees TCP fairness at a certain degree is proposed. As it is analyzed by simulations, the proposed scheme enhances TCP fairness by maintaining TCP throughput of each TCP sender similarly with each other.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2007.11a
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• pp.252-255
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• 2007

Recently military industrial company, utilizing company funded R&D and goverment and industry contracts, has developed ACTS/DACS technology. This technology can be utilized to rapidly steer "smart" bullets, "smart" rounds, tactical missile, cruise missile and kill vehicles for both endo- and exoatmospheric applications. The ACTS/DACS typically consists of a Smart Bus Controller(SCB), a proprietary network firing bus, Smart Pyrotechnic Devices(SPD), rocket motors, and a structure. The SCB communicates with the SPDs over the propretary network firing bus. Each rocket motor contains an SPD which provides rocket motor ignition. Firing energy is stored locally in the SPD so surge currents do not occur in the system as rocket motors are fired. This approach allows multiple, truly simultaneous firings without the need for large, dedicated batteries. Each SPD also functions as a network tranceiver and high reliability fir set all in the space of a single-sided 10 millimeter diameter circuit. The present work develops a new means for igniting explosive materials. The volume of semiconductor bridge (SCB) is over 30 times smaller than a conventional hot wire. We believe that the present work has a potential for development of a new igniter such as smart pyrotechnic device.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.75-81
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• 2008

The induction motor is given a great deal of weight on the industry generally. Therefore, the fault of the induction motor may cause the fault to effect another parts or another faults in the whole system as well as in itself. These are accompany with a lose of the reliability in the industrial system. Accordingly to prevent these situation, the scholars have studies the fault diagnosis of the induction motor. In this paper, we proposed the diagnosis system of the induction motor. The method of diagnosis in proposed system is extracted the feature of the current signal by the wavelet transform. These extracted feature is used the automatic discrimination system by the neural network. We experiment the automatic discrimination system using the three faults imitation that often generated in the induction motor. The proposed system have achieved high reliable result with a simple devices about the three faults.

• Korean Journal of Construction Engineering and Management
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• v.19 no.4
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• pp.82-92
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• 2018

Considering that earlier stage of construction project has a great influence on the possibility of lowering of environmental load, it is important to build and utilize system that can support effective decision making at the initial stage of the project. In this study, we constructed an environmental load estimation model that can be used at the early stage of the project using basic design factors. The model was constructed by using the artificial neural network to estimate environmental load by applying to planning stage (ANN-1), basic design stage (ANN-2). The result of test, shows that average of absolute measuring efficiency and standard deviation of ANN-1 and ANN-2 were 11.19% / 5.30% and 9.59% / 3.09% each. This result indicates that the model using the input variables extended with the project progress has high reliability and it is considered to be effective in decision support at the initial design stage of the project.

• Korean Journal of Construction Engineering and Management
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• v.19 no.4
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• pp.43-51
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• 2018

As the inventory of aged apartments is expected to increase explosively, the importance of maintenance to improve the durability of concrete facilities is increasing. Concrete compressive strength is a representative index of durability of concrete facilities, and is an important item in the precision safety diagnosis for facility maintenance. However, existing methods for measuring the concrete compressive strength and determining the maintenance of concrete facilities have limitations such as facility safety problem, high cost problem, and low reliability problem. In this study, we proposed a model that can predict the concrete compressive strength through images by using deep convolution neural network technique. Learning, validation and testing were conducted by applying the concrete compressive strength dataset constructed through the concrete specimen which is produced in the laboratory environment. As a result, it was found that the concrete compressive strength could be learned by using the images, and the validity of the proposed model was confirmed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.5
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• pp.1057-1068
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• 2012

According to advance on vehicle technology, many kinds of ECU(Electronic Control Unit) are equipped inside the vehicle. In-vehicle communication among ECUs is performed through CAN(Controller Area Networks). CAN have high reliability. However, it has many vulnerabilities because there is not any security mechanism for CAN. Recently, many papers proposed attacks of in-vehicle communication by using these vulnerabilities. In this paper, we propose an wireless attack model using a mobile radio communication network. We propose a secure authentication mechanism for in-vehicle network communication that assure confidentiality and integrity of data packets and also protect in-vehicle communication from the replay attack.

• Journal of Technologic Dentistry
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• v.46 no.2
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• pp.36-41
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• 2024

Purpose: This study aimed to generate virtual mandibular left first molar teeth using deep convolutional generative adversarial networks (DCGANs) and analyze their matching accuracy with actual tooth morphology to propose a new paradigm for using medical data. Methods: Occlusal surface images of the mandibular left first molar scanned using a dental model scanner were analyzed using DCGANs. Overall, 100 training sets comprising 50 original and 50 background-removed images were created, thus generating 1,000 virtual teeth. These virtual teeth were classified based on the number of cusps and occlusal surface ratio, and subsequently, were analyzed for consistency by expert dental technicians over three rounds of examination. Statistical analysis was conducted using IBM SPSS Statistics ver. 23.0 (IBM), including intraclass correlation coefficient for intrarater reliability, one-way ANOVA, and Tukey's post-hoc analysis. Results: Virtual mandibular left first molars exhibited high consistency in the occlusal surface ratio but varied in other criteria. Moreover, consistency was the highest in the occlusal buccal lingual criteria at 91.9%, whereas discrepancies were observed most in the occusal buccal cusp criteria at 85.5%. Significant differences were observed among all groups (p<0.05). Conclusion: Based on the classification of the virtually generated left mandibular first molar according to several criteria, DCGANs can generate virtual data highly similar to real data. Thus, subsequent research in the dental field, including the development of improved neural network structures, is necessary.