• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.11
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• pp.1231-1238
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• 2015

In this paper, we proposed an efficient algorithm using Node monitoring (NM) and Piecewise Linear Function Approximation(: NP) for reducing the complexity of LDPC code decoding. Proposed NM algorithm is based on a new node-threshold method together with message passing algorithm. Piecewise linear function approximation is used to reduce the complexity of the algorithm. This new algorithm was simulated in order to verify its efficiency. Complexity of our new NM algorithm is improved to about 20% compared with well-known methods according to simulation results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.4
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• pp.622-628
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• 2012

This paper proposes an environment monitoring algorithm using a behavior-based multiple robot system. This paper handles an escort and a boundary-tracking especially. Unlike previous research works, the proposed environment monitoring system which is based on the behavior-based multiple robot control allows the system to employ the reusable code and general algorithm. Also, the proposed method can be applied to cheaper process with low performances. In the proposed method, escort and boundary-tracking missions are constructed by weighted sum of predefined basic behaviors after redefining the basic behaviors in previous works and introducing the novel basic behavior. Simulation results of the proposed method are included to demonstrate the practical application of the proposed algorithm.

• 한국정보통신설비학회:학술대회논문집
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• 2006.08a
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• pp.232-234
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• 2006

In this paper, we propose a new real-time traffic monitoring scheme for the BcN, the RBC algorithm and investigate its operation. We implemented RBC algorithm and constructed the testbed and we confirmed the performance for this. The simulation result shows that traffic monitoring log size is reduced remarkably by using the RBC. An operation hour is reduced and a feedback support of Real-time is possible also. Consequently, RBC algorithm confirmed we are fit for Real-time TE plan for BcN.

• Journal of Korean Society of Water and Wastewater
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• v.26 no.1
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• pp.1-12
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• 2012

The purpose of water distribution system is supplying water to users by maintaining appropriate pressure and water quality. For efficient monitoring of the water distribution system, determination of optimal locations for pressure monitoring is essential. In this study, entropy theory was applied to determine the optimal locations for pressure monitoring. The entropy which is defined as the amount of information was calculated from the pressure change due to the variation of demand reflected the abnormal conditions at nodes, and the emitter function (fire hydrant) was used to reproduce actual pressure change pattern in EPANET. The optimal combination of monitoring points for pressure detection was determined by selecting the nodes receiving maximum information from other nodes using genetic algorithm. The Ozger's and a real network were evaluated using the proposed model. From the results, it was found that the entropy theory can provide general guideline to select the locations of pressure sensors installation for optimal design and monitoring of the water distribution systems. During decision-making phase, optimal combination of monitoring points can be selected by comparing total amount of information at each point especially when there are some constraints of installation such as limitation of available budget.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.5
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• pp.1119-1128
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• 2024

This study developed an anomaly detection algorithm for underground hydrogen pipelines using Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) technologies. The LSTM-AE-based algorithm was tested in a real-world testbed, showing high performance in detecting third-party construction activities and gas leaks. The model achieved 99.86% accuracy, 100% precision, 99.74% recall, and a 99.87% F1 score for DAS data, and 99.95% accuracy, 100% precision, 95.24% recall, and a 97.44% F1 score for DTS data. These results demonstrate the algorithm's effectiveness in real-time monitoring and its potential to enhance the safety of hydrogen pipeline infrastructure. Future research will focus on optimizing the algorithm for broader applications.

• Journal of Sensor Science and Technology
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• v.16 no.6
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• pp.467-474
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• 2007

The real-time monitoring about the activity of the human provides useful information about the activity quantity and ability. The present study implemented a small-size and low-power acceleration monitoring system for convenient monitoring of activity quantity and recognition of emergent situations such as falling during daily life. For the wireless transmission of acceleration sensor signal, we developed a wireless transmission system based on a wireless sensor network. In addition, we developed a program for storing and monitoring wirelessly transmitted signals on PC in real-time. The performance of the implemented system was evaluated by assessing the output characteristic of the system according to the change of posture, and parameters and acontext recognition algorithm were developed in order to monitor activity volume during daily life and to recognize emergent situations such as falling. In particular, recognition error in the sudden change of acceleration was minimized by the application of a falling correction algorithm

• Journal of the Optical Society of Korea
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• v.14 no.1
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• pp.22-27
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• 2010

Bit error rate (BER) monitoring is the ultimate goal of performance monitoring in all digital transmission systems as well as optical fiber transmission systems. To achieve this goal, optimization of the decision threshold must also be considered because BER is dependent on the level of decision threshold. In this paper, we analyze a pseudo-error counting scheme and propose an algorithm to achieve both BER monitoring and adaptive decision threshold optimization in optical fiber transmission systems. To verify the effectiveness of the proposed algorithm, we conduct computer simulations in both Gaussian and non-Gaussian distribution cases. According to the simulation results, BER and the optimum decision threshold can be estimated with the errors of < 20% and < 10 mV, respectively, within 0.1-s processing time in > 40-Gb/s transmission systems.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.504-514
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• 2009

Recently, fire monitoring application systems have been an active research area due to the safety of industries, historical monuments and so on. The fire monitoring application systems can reduce the damage of properties by providing earlier warning for possible fire situation. However, the existing systems have a drawback that they detect fire with delay due to their uniform epoch in fire detection algorithm. Moreover, they do not provide user-friendly graphical user interfaces in their fire monitoring systems. To resolve the problems, First, we propose a new fire detection algorithm (Early Fire Detection Fire Algorithm) which uses the distribution of sensing data for early fire detection. Our fire detection algorithm is better in terms of fire detection time than the existing work because it can set a start time of fire detection epoch dynamically based on data distribution. Second, we develop a fire monitoring application system which provides users with both a user-friendly graphical user interface and a fire alarm message when fire occurs. Finally, we show from our experiment that our developed system is effectively used for early fire warning in a variety of fire situations.

• Smart Structures and Systems
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• v.21 no.5
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

• Journal of the Korean Society of Safety
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• v.29 no.5
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• pp.47-53
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• 2014

Despite the automatic fire alarm system, according to the national fire data system of national emergency management agency, the fires account for 40,932 incidents, 2,184 injuries and about 430 billion won in property losses in 2013. Since the conventional automatic fire alarm system has several weaknesses related to electrical signal such as noise, surge, lighting, etc. Most fires are mainly caused by electrical faults, mechanical problem, chemical, carelessness and natural. The electrical faults such as line to ground fault, line to line fault, electrical leakage and arc are one of the major problems in fire. This paper describes the development of a novel real-time fire monitoring system algorithm including fault detection function which puts the existing optic smoke and heat detectors for fire detection with current and voltage sensors in order to utility fault monitoring using high accuracy DAQ measurement system with LabVIEW program. The fire detection and electrical fault monitoring with a proposed a new detection algorithm are implemented under several test. The fire detection and monitoring system operates according to the proposed algorithm well.