• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.10
• /
• pp.3989-4006
• /
• 2020

The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.

• Journal of Communications and Networks
• /
• v.14 no.6
• /
• pp.662-671
• /
• 2012

Plug-in hybrid electric vehicles (PHEVs) will be widely used in future transportation systems to reduce oil fuel consumption. Therefore, the electrical energy demand will be increased due to the charging of a large number of vehicles. Without intelligent control strategies, the charging process can easily overload the electricity grid at peak hours. In this paper, we consider a smart charging and discharging process for multiple PHEVs in a building's garage to optimize the energy consumption profile of the building. We formulate a centralized optimization problem in which the building controller or planner aims to minimize the square Euclidean distance between the instantaneous energy demand and the average demand of the building by controlling the charging and discharging schedules of PHEVs (or 'users'). The PHEVs' batteries will be charged during low-demand periods and discharged during high-demand periods in order to reduce the peak load of the building. In a decentralized system, we design an energy cost-sharing model and apply a non-cooperative approach to formulate an energy charging and discharging scheduling game, in which the players are the users, their strategies are the battery charging and discharging schedules, and the utility function of each user is defined as the negative total energy payment to the building. Based on the game theory setup, we also propose a distributed algorithm in which each PHEV independently selects its best strategy to maximize the utility function. The PHEVs update the building planner with their energy charging and discharging schedules. We also show that the PHEV owners will have an incentive to participate in the energy charging and discharging game. Simulation results verify that the proposed distributed algorithm will minimize the peak load and the total energy cost simultaneously.

• Journal of Korea Spatial Information System Society
• /
• v.10 no.4
• /
• pp.37-45
• /
• 2008

Increased demand for the latest spatial information, it is necessary to study the complement current map updating systems for responding the diverse environment of the national territory information. This study suggests the way to update 1/5,000 digital maps with the application of as-built drawings based on current systems. This study compares current as-built drawings with digital maps, and consider intra and international related cases and regulations. This study also selects case areas based on different types of as-built drawings and updates digital maps with the application of as-built drawings. After the consideration of systems and regulations related to as-built drawings, the research results, including the improvement and the application of as-built drawings, are presented. It is expected that the foundation to provide more rapid and accurate geographic information can be achieved from the research result.

• Nuclear Engineering and Technology
• /
• v.54 no.4
• /
• pp.1363-1373
• /
• 2022

The recent seismic events that occurred in South Korea have increased the interest in the re-evaluation of the seismic capacity of nuclear power plant (NPP) equipment, which is often conservatively estimated. To date, various approaches-including the Bayesian method proposed by the United States (US) Electric Power Research Institute -have been developed to quantify the seismic capacity of NPP equipment. Among these, the Bayesian approach has advantages in accounting for both prior knowledge and new information to update the probabilistic distribution of seismic capacity. However, data availability and region-specific issues exist in applying this Bayesian approach to Korean NPP equipment. Therefore, this paper proposes to construct an earthquake experience database by combining available earthquake records at Korean NPP sites and the general location of equipment within NPPs. Also, for the better representation of the seismic demand of Korean earthquake datasets, which have distinct seismic characteristics from those of the US at a high-frequency range, a broadband frequency range optimization is suggested. The proposed data construction and seismic demand optimization method for seismic capacity re-evaluation are demonstrated and tested on a 480 V motor control center of a South Korea NPP.

• Journal of Korea Spatial Information System Society
• /
• v.11 no.1
• /
• pp.71-78
• /
• 2009

A demand on the spatial data management has been rapidly increased with the introduction and diffusion process of ITS, Telematics, and Wireless Sensor Network. And many different users use the digital map that offers various thematic spatial data. Spatial data for digital map can be managed by tile-based and feature-based data. The existing tile-based digital map management systems have difficult problems such as data construction, history management, and update data based on a spatial object. In order to solve these problems, we proposed the data model for feature-based digital map management system for representation of feature-based seamless map, history management, real-time update of spatial data, and analyzed the validity and utility of the proposed model.

• International conference on construction engineering and project management
• /
• 2011.02a
• /
• pp.582-585
• /
• 2011

During disaster response, prioritization of limited resources is one of the most important bust challenging tasks. At the same time, it is imperative to timely provide the rescuers with the adequate equipment to facilitate lifesaving operations. However, supply of high demand equipment was insufficient during the initial phase of disaster response, challenging lifesaving operations in the case of the 9-11 terrorist attacks. In respond to the Haiti Earthquake, spatial information of the geographic area was not sufficient to support the search and rescue operations in the early phase of disaster response. However, with the help of civilians, information such as road names, infrastructure damage, and victim locations were updated into the spatial data repository. At the same time, resource outside of the disaster affected zone converges into the area to assist the response efforts, which is the effect of convergence that often made resource coordination challenging in large scale disasters. To efficiently collect information and utilize the converging resources, this paper proposes a flexible data repository for information update for equipment utilization in large scale disaster response scenarios.

• Korean Journal of Geomatics
• /
• v.3 no.2
• /
• pp.99-106
• /
• 2004

In order to meet the users' demand, who needs faster and more accurate data in geographic information, it is necessary to obtain and process the data more effectively. Now more effective data obtainments about geographic information is possible through the development of integration technology, which is applied to the field of geographic information, as well as through the development of hardware and software engineering. With the fast and precise correction and update, the development of integrate technology can bring the reduction of the time and money. To obtain fast and precise geographic information using Aerial Photogrammetry method, it is necessary to develop Airborne GPS/INS integration system, which makes GCP to the minimum. For this reason, this study has tried to develop a system which could unite and process both GPS and INS data. For this matter, code-processing module for DGPS and OTF initializaion module, which can decide integer ambiguity even in motion, have been developed. And also, continuous kinematic carrier-processing module has been developed to calculate the location at the moment of filming. In addition, this study suggests a possibility of using a module, which can unite GPS and INS, using Kalman filtering, and also shows the INS navigation theory.

• Journal of Construction Engineering and Project Management
• /
• v.1 no.3
• /
• pp.45-51
• /
• 2011

During disaster response, prioritization of limited resources is one of the most important but challenging tasks. At the same time, it is imperative to timely provide to the rescuers with the adequate heavy equipment to facilitate lifesaving operations. However, supply of high demand equipment is usually insufficient during the initial phase of disaster response, challenging lifesaving operations. At the same time, resources outside of the disaster affected zone converge into the area to assist the response efforts, which is the effect of convergence that often made resource coordination challenging in large scale disasters. Meanwhile, the initial condition of the disaster is usually best known by civilians already at the area before and during impact of the disaster. The knowledge of the civilians is not always received and considered by the responding organizations. With the help of these civilians, critical information such as victim location, infrastructure damage, and risk condition could be better know before any response actions are taken. To efficiently collect information and utilize the converging resources, this paper proposes a geospatial information repository for initial condition reporting and update to guide search and rescue operations and deployment of equipment with safety considerations for the rescuers in large scale disaster response scenarios.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.2
• /
• pp.542-558
• /
• 2016

Vehicular Ad Hoc Networks (VANETs) are self-configuring networks where the nodes are vehicles equipped with wireless communication technologies. In such networks, limitation of signal coverage and fast topology changes impose difficulties to the proper functioning of the routing protocols. Traditional Mobile Ad Hoc Networks (MANET) routing protocols lose their performance, when communicating between vehicles, compromising information exchange. Obviously, most applications critically rely on routing protocols. Thus, in this work, we propose a methodology for investigating the performance of well-established protocols for MANETs in the VANET arena and, at the same time, we introduce a routing protocol, called Genetic Network Protocol (G-NET). It is based in part on Dynamic Source Routing Protocol (DSR) and on the use of Genetic Algorithms (GAs) for maintenance and route optimization. As G-NET update routes periodically, this work investigates its performance compared to DSR and Ad Hoc on demand Distance Vector (AODV). For more realistic simulation of vehicle movement in urban environments, an analysis was performed by using the VanetMobiSim mobility generator and the Network Simulator (NS-3). Experiments were conducted with different number of vehicles and the results show that, despite the increased routing overhead with respect to DSR, G-NET is better than AODV and provides comparable data delivery rate to the other protocols in the analyzed scenarios.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.43 no.1
• /
• pp.123-131
• /
• 2020

There have been a lot of studies in the past for the method of predicting the failure of a machine, and recently, a lot of researches and applications have been generated to diagnose the physical condition of the machine and the parts and to calculate the remaining life through various methods. Survival models are also used to predict plant failures based on past anomaly cycles. In particular, special machine that reflect the fluid flow and process characteristics of chemical plants are connected to hundreds or thousands of sensors, so there are not many factors that need to be considered, such as process and material data as well as application of derivative variables. In this paper, the data were preprocessed through time series anomaly detection based on unsupervised learning to predict the abnormalities of these special machine. Next, clustering results reflecting clustering-based data characteristics were applied to produce additional variables, and a learning data set was created based on the history of past facility abnormalities. Finally, the prediction methodology based on the supervised learning algorithm was applied, and the model update was confirmed to improve the accuracy of the prediction of facility failure. Through this, it is expected to improve the efficiency of facility operation by flexibly replacing the maintenance time and parts supply and demand by predicting abnormalities of machine and extracting key factors.