• Spatial Information Research
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• v.16 no.4
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• pp.421-439
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• 2008

The objective of this project is to construct the intelligent underground facility management system based on UFSN (Underground Facility Sensor Network). Total project duration is 6-year. And first two years' project has been finished. First two years' project focused on water supply and sewage facilities among 7 underground facilities and investigated the fundamental construction technology for the underground facilities management system. To contribute the development of systematic and scientific management of underground facilities, KRIHS implemented 3 researches for 'Underground facilities management system for an intelligent city' like followings: (1) to investigate an integration plan for current water supply and sewage management systems, (2) to derivate of water supply and sewage monitoring items for the monitoring technology development, and (3) to implement a basic research for sensor installation plans on different types of current and new underground facility systems. This research paper contains the first two years' outcome of researches from KRIHS (Korea Research Institute for Human Settlements).

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.2
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• pp.87-96
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• 2004

When watershed spatial database is constructed from DEM, hydrological geographic characteristics of watershed can be easily extracted. And the characteristics can be assigned and managed as the attribute of spatial database. In this study the scheme of constructing framework spatial database which is basic information for managing watershed information is examined. We established framework spatial data and defined the relationship of the data. And framework spatial database of test site was constructed. In this study, HyGIS(Hydrological Geographic Information System) which is developed by domestic technology for making hydrological spatial data and developing water resources system is used. Hydrological geographic characteristics and spatial data is extracted by HyGIS. And the data from HyGIS is used for constructing framework spatial database of test site. Finally, this study suggests the strategy of constructing framework spatial database for developing watershed management system based on river network.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.771-775
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• 2008

Concerns related to protecting, identifying, and isolating of subsystems of a water distribution network have led to the realization of the increased importance of valves in the system. The most important purpose of valves in water distribution systems is to isolate subsystems due to breakage, maintenance activities, or contamination. A subsystem called segment is isolated by the closure of adjacent valves and an efficient algorithm should identify the adjacent valves to minimize the pipe failure impact. In this paper, an algorithm to identify adjacent valves to be closed to isolate a subsystem from the remainder of a network in case of a pipe failure is presented. An application to the water distribution system in Ottawa, Canada demonstrates the developed algorithm efficiently locates the adjacent valves for the isolation of a broken pipe.

• Journal of Soil and Groundwater Environment
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• v.27 no.2
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• pp.24-35
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• 2022

This study was conducted to estimate groundwater demand and supply for agricultural activities in a frequent-drought area that requires implementation of optimal distribution plan utilizing the well-network system (WNS). The WNS has been considered as a viable strategic way of supplying groundwater to farmlands by connecting groundwater wells physically or virtually. The study area heavily relied on groundwater resources for irrigation up to 53% due to a lack of surface water resources. Two kinds of methods, HOMWRS software and FAO approach, were used for estimating irrigation water requirements for paddy and upland fields, respectively. During the latest 10 years (2010~2019), the water requirements was estimated to be 1,106 m³/day. The requirements notably increased to 1,121~4,004 m³/day during active farming season (May to September), which exceeded the total yield capacity of pre-existing groundwater wells (2,356 m³/day) in the area. Detailed and definite determination for groundwater demand and supply helped to determine optimal scale parameters of WNS. The WNS has achieved more balanced distribution of groundwater resources for irrigation over the study area.

• Geomechanics and Engineering
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• v.28 no.6
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• pp.599-611
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• 2022

Tendon reinforced cemented soil is applied extensively in foundation stabilisation and improvement, especially in areas with soft clay. To solve the deterioration problem led by steel corrosion, the glass fiber-reinforced polymer (GFRP) tendon is introduced to substitute the traditional steel tendon. The interface bond strength between the cemented soil matrix and GFRP tendon demonstrates the outstanding mechanical property of this composite. However, the lack of research between the influence factors and bond strength hinders the application. To evaluate these factors, back propagation neural network (BPNN) is applied to predict the relationship between them and bond strength. Since adjusting BPNN parameters is time-consuming and laborious, the particle swarm optimisation (PSO) algorithm is proposed. This study evaluated the influence of water content, cement content, curing time, and slip distance on the bond performance of GFRP tendon-reinforced cemented soils (GTRCS). The results showed that the ultimate and residual bond strengths were both in positive proportion to cement content and negative to water content. The sample cured for 28 days with 30% water content and 50% cement content had the largest ultimate strength (3879.40 kPa). The PSO-BPNN model was tuned with 3 neurons in the input layer, 10 in the hidden layer, and 1 in the output layer. It showed outstanding performance on a large database comprising 405 testing results. Its higher correlation coefficient (0.908) and lower root-mean-square error (239.11 kPa) were obtained compared to multiple linear regression (MLR) and logistic regression (LR). In addition, a sensitivity analysis was applied to acquire the ranking of the input variables. The results illustrated that the cement content performed the strongest influence on bond strength, followed by the water content and slip displacement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1540-1560
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• 2022

Latest technological innovation in the development of compact lower power radios has led to the explosion of Internet of Things. With Wi-Fi, Zigbee and other physical layer protocols offering short coverage area there was a need for a RF protocol that had a larger coverage area with low power consumption. LoRa offers Long Range with lower power consumption. LoRa offers point to point and point to multipoint connections. with Single hop communication in place the need for routing protocols are eliminated. LoRa Wide Area Network stack can accommodate thousands of nodes under a single LoRa gateway with a single hop communication between the end nodes and LoRaWAN gateway. This paper takes an experimental approach to analyze the basic physical layer parameters of LoRa and the practical coverage offered by a LoRaWAN under highly dense urban conditions with variable topography. The insights gained from the practical deployment of the LoRaWAN network, and the subsequent performance analysis is used to design a novel public utility monitoring platform. The second half of the papers is designing a robust platform to integrate both existing wired sensor water meters, current and future generation wireless water meters. The Common Automated Metering Platform is designed to integrate both wired sensors and wireless (LoRaWAN and Wi-Fi) supported water meters. This integrated platform reduces the number of nodes under each LoRaWAN gateway and thus improves the scalability of the network. This architecture is currently designed to accommodate one utility application but can be modified to integrate multi-utility applications.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.122-129
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• 2010

High-performance concrete (HPC) is a new terminology used in concrete construction industry. Several studies have shown that concrete strength development is determined not only by the water-to-cement ratio but also influenced by the content of other concrete ingredients. HPC is a highly complex material, which makes modeling its behavior a very difficult task. This paper aimed at demonstrating the possibilities of adapting artificial neural network (ANN) to predict the comprresive strength of HPC. Mahalanobis Distance (MD) outlier detection method used for the purpose increase prediction ability of ANN. The detailed procedure of calculating Mahalanobis Distance (MD) is described. The effects of outlier compared with before and after artificial neural network training. MD outlier detection method successfully removed existence of outlier and improved the neural network training and prediction performance.

• 한국정보통신설비학회:학술대회논문집
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• 2006.08a
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• pp.91-94
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• 2006

In this paper, we deployed the tunnel inspection and monitoring system by wireless sensor network. It is shown that the wireless sensor network which is composed of sensor, wireless communication module, and gateway system can be applied to tunnel monitoring system. Sensors included herein are acceleration transducers, fire-alarm sensors, water-level sensors, and magnetic contact sensors. It is also found that the wireless sensor network can deliver sensing data reliably by ad-hoc networking technology. The gateway system that can send the sensing data to server by CDMA (code division multiple access) is developed. Finally, monitoring system is constructed by web service technology, and it is observed that this system can monitor the present state of tunnel without difficulties. Furthermore, the above system provides an alternative to inspect and monitor the tunnel efficiently where the conventional wired system cannot be applied.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1174-1179
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• 2003

Cast austenitic stainless steel is used for several components, such as primary coolant piping, elbow, pump casing and valve bodies in light water reactors. These components are subject to thermal aging at the reactor operating temperature. Thermal aging results in spinodal decomposition of the delta-ferrite leading to increased strength and decreased toughness. This study shows that ferrite content can be predicted by use of the artificial neural network. The neural network has trained learning data of chemical components and ferrite contents using backpropagation learning process. The predicted results of the ferrite content using trained neural network are in good agreement with experimental ones.

• Computers and Concrete
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• v.10 no.4
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• pp.379-390
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• 2012

In this paper, the mechanical strength of different lightweight mortars made with 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 and 100 percentage of scoria instead of sand and 0.55 water-cement ratio and 350 $kg/m^3$ cement content is investigated. The experimental result showed 7.9%, 16.7% and 49% decrease in compressive strength, tensile strength and mortar density, respectively, by using 100% scoria instead of sand in the mortar. The normalized compressive and tensile strength data are applied for artificial neural network (ANN) generation using generalized regression neural network (GRNN). Totally, 90 experimental data were selected randomly and applied to find the best network with minimum mean square error (MSE) and maximum correlation of determination. The created GRNN with 2 input layers, 2 output layers and a network spread of 0.1 had minimum MSE close to 0 and maximum correlation of determination close to 1.