• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.1
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• pp.47-53
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• 2012

Sensor networks and car networks that have different structure from that of conventional TCP/IP network require extreme network environment due to frequent change of connectivity. Because such extreme network environment has characteristics like unreliable link connectivity, long delay time, asymmetrical data transfer rate, and high error rate, etc., it is difficult to perform normally with the conventional TCP/P-based routing. DTNs (delay and disruption tolerant network) was designed to support data transfer in extreme network environment with long delay time and no guarantee for continuous connectivity between terminals. This study suggests an algorithm that limits the maximum number of copying transferred message to L by improving the spray and wait routing protocol, which is one of the conventional DTNs routing protocols, and using the azimuth and density data of the mobile nods. The suggested algorithm was examined by using ONE, a DTNs simulator. As a result, it could reduce the delay time and overhead of unnecessary packets compared to the conventional spray and wait routing protocol.

• Proceedings of the Korea Society for Simulation Conference
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• 2005.05a
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• pp.139-143
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• 2005

The P2P is a network environment supporting data exchange which is directly connected peers without limit of existing server-client and intervening central server for resource or offered service with each peer. JXTA is a representative of P2P system. JXTA is a typical distributed computing model that proposed by Sun Microsystems. JXTA that doesn't require centralized services or resources is adaptable in extreme changes of network organization. MANET(Mobile Ad Hoc Network) is a representative wireless network that is composed of mobile nodes without infrastructure. So MANET establishes the path for the communication of each peers and maintains the newest routing information by exchanging routing information. In this paper, we propose a technique of JXTAMAUET which implements JXTA which is the P2P network system from the wireless network which is becoming the foundation of ubiquitous computing. For the performance evaluation of the JXTAMANET, we use simulation.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.469-472
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• 2008

In general, structures in service gradually lose original performance according to time due to initial defects in design and construction, or exposure to unfavorable external conditions such as repeated loading or deteriorating environment, and in extreme cases, may collapse in large disaster. Therefore, in order to maintain the serviceability of structures at optimal level, advanced structure measuring system which can inform optimal time point and method of maintenance is required in addition to accurate prediction of residual life the structure by periodic inspection. To guarantee the safety level of bridge structure and to prevent from disaster, the integration of safety network for bridge structures are needed. Therefore in this study, to enhance the effectiveness of safety network for bridge, the connection methodologies between safety network and pre-installed bridge monitoring system are investigated.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1548-1555
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• 2017

This paper attempts to employ and investigate neural based approaches as interpolation tools for modeling of Switched Reluctance Motor (SRM) drive. Precise modeling of SRM is essential to analyse the performance of control strategies for variable speed drive application. In this work the suitability of Generalized Regression Neural Network (GRNN) and Extreme Learning Machine (ELM) in addition to conventional neural network are explored for improving the modeling accuracy of SRM. The neural structures are trained with the data obtained by modeling of SRM using Finite Element Analysis (FEA) and the trained neural network is incorporated in the model of SRM drive. The results signify the modeling accuracy with GRNN model. The closed loop drive simulation is performed in MATLAB/Simulink environment and the closeness of the results in comparison with the experimental prototype validates the modeling approach.

• Journal of Navigation and Port Research
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• v.40 no.5
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• pp.265-270
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• 2016

Extreme tropospheric anomalies such as typhoons or regional torrential rain can degrade positioning accuracy of the GPS signal. It becomes one of the main error terms affecting high-precision positioning solutions in network RTK. This paper proposed a detection algorithm to be used during atmospheric anomalies in order to detect the tropospheric irregularities that can degrade the quality of correction data due to network errors caused by inhomogeneous atmospheric conditions between multi-reference stations. It uses an atmospheric grid that consists of four meteorological stations and estimates the troposphere zenith total delay difference at a low performance point in an atmospheric grid. AWS (automatic weather station) meteorological data can be applied to the proposed tropospheric anomaly detection algorithm when there are different atmospheric conditions between the stations. The concept of probability density distribution of the delta troposphere slant delay was proposed for the threshold determination.

• Smart Structures and Systems
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• v.17 no.5
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• pp.743-751
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• 2016

An ongoing research project is devoted to the design and implementation of a satellite based asset tracking for supporting emergency management in crisis operations. Due to the emergency environment, one has to rely on a low power consumption wireless communication. Therefore, the communication hardware and software must be designed to match requirements, which can only be foreseen at the level of more or less likely scenarios. The latter aspect suggests a deep use of a simulator (instead of a real network of sensors) to cover extreme situations. The former power consumption remark suggests the use of a minimal computer (Raspberry Pi) as data collector.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2000.11a
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• pp.69-73
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• 2000

We systematically analyze the influence of fracture junction, solute transfer characteristics on transport patterns in discrete, two-dimensional fracture network models. Regular lattices and random fracture networks with power-law length distributions are considered in conjunction with particle tracking methods. Solute transfer probabilities at fracture junctions are determined from analytical considerations and from simple complete mixing and streamline routing models. For regular fracture networks, mixing conditions at fracture junctions are always dominated by either complete mixing or streamline routing end member cases. Moreover bulk transport properties such as the spreading and the dilution of solute are highly sensitive to the mixing rule. However in power-law length networks there is no significant difference in bulk transport properties, as calculated by assuming either of the two extreme mixing rules. This apparent discrepancy between the effects of mixing properties at fracture junctions in regular and random fracture networks is explained by the statistics of the coordination number and of the flow conditions at fracture intersections. We suggest that the influence of mixing rules on bulk solute transport could be important in systematic orthogonal fracture networks but insignificant in random networks.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• Journal of Environmental Science International
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• v.26 no.9
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• pp.1023-1029
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• 2017

We studied the distribution of air temperature using the high density urban climate observation network data of Daegu. The observation system was established in February 2013. We used a total of 38 air temperature observation points (23 thermometers and 18 AWSs). From the distribution of monthly averaged air temperatures, air temperatures at the center of Daegu were higher than in the suburbs. The daily minimum air temperature was more than or equal to $25^{\circ}C$ and the daily maximum air temperature was more than or equal to $35^{\circ}C$ at the elementary school near the center of Daegu. Also, we compared the time elements, which are characterized by the diurnal variation of surface air temperature. The warming and cooling rates in rural areas were faster than in urban areas. This is mainly due to the difference in surface heat capacity. These results indicate the influence of urbanization on the formation of the daily minimum temperature in Daegu.

• Current Optics and Photonics
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• v.8 no.2
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• pp.127-137
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• 2024

The distribution of tags is an important factor that affects the performance of radio-frequency identification (RFID). To study RFID performance, it is necessary to obtain RFID tags' coordinates. However, the positioning method of RFID technology has large errors, and is easily affected by the environment. Therefore, a new method using optical measurement is proposed to achieve RFID performance analysis. First, due to the possibility of blurring during image acquisition, the paper derives a new image prior to removing blurring. A nonlocal means-based method for image deconvolution is proposed. Experimental results show that the PSNR and SSIM indicators of our algorithm are better than those of a learning deep convolutional neural network and fast total variation. Second, an RFID dynamic testing system based on photoelectric sensing technology is designed. The reading distance of RFID and the three-dimensional coordinates of the tags are obtained. Finally, deep learning is used to model the RFID reading distance and tag distribution. The error is 3.02%, which is better than other algorithms such as a particle-swarm optimization back-propagation neural network, an extreme learning machine, and a deep neural network. The paper proposes the use of optical methods to measure and collect RFID data, and to analyze and predict RFID performance. This provides a new method for testing RFID performance.