• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.311-318
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• 2016

IoT(Internet of Things areas) rich information based on the user easy access to service creation must be one of the power system of specificity due following: The IoT spread obstacle to the act be, and 'Smart Grid information of this is not easy under power plants approach the Directive on the protection measures, particularly when stringent security policies IoT technologies applied to Advanced Metering Infrastructure sector has been desired. This is a situation that occurs is limited to the application and use of IoT technologies in the power system. Power Information Network is whilst closed network operating is has a smart grid infrastructure, smart grid in an open two-way communication for review and although information security vulnerabilities increased risk of accidents increases as according to comprehensive security policies and technologies are required and can. In this paper, the IoT platform architecture design of information systems as part of the power of research and development IoT-based energy information platform aims. And to establish a standard framework for a connection to one 'Sensor-Gateway-Network-platform sensors Service' to provide power based on the IoT services and solutions. Framework is divided into "sensor-gateway" platform to link information modeling and gateways that can accommodate the interlocking standards and handling protocols variety of sensors Based on this real-time data collection, analysis and delivery platform that performs the role of the relevant and to secure technology.

• The KIPS Transactions:PartD
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• v.11D no.4
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• pp.983-990
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• 2004

Multiple-count problem is occurred when rectangle objects span across several buckets. The CD histogram is a technique which selves this problem by keeping four sub-histograms corresponding to the four points of rectangle. Although It provides exact results with constant response time, there is still a considerable issue. Since it is based on a query window which aligns with a given grid, a number of errors nay be occurred when it is applied to real applications. In this paper, we propose selectivity estimation techniques using the generalized cumulative density histogram based on two probabilistic models : \circled1 probabilistic model which considers the query window area ratio, \circled2 probabilistic model which considers intersection area between a given grid and objects. Our method has the capability of eliminating an impact of the restriction on query window which the existing cumulative density histogram has. We experimented with real datasets to evaluate the proposed methods. Experimental results show that the proposed technique is superior to the existing selectivity estimation techniques. Furthermore, selectivity estimation technique based on probabilistic model considering the intersection area is very accurate(less than 5% errors) at 20% query window. The proposed techniques can be used to accurately quantify the selectivity of the spatial range query on rectangle objects.

• Journal of IKEEE
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• v.22 no.3
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• pp.829-835
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• 2018

Recently, studies have been actively conducted to reduce spare power that is unnecessarily generated or wasted in existing power systems and to improve energy use efficiency. In this study, smart meter, which is one of the element technologies of smart grid, is implemented to improve the efficiency of energy use by controlling power of electric devices, and predicting trends of energy usage based on deep learning. We propose and develop an algorithm that controls the power of the electric devices by comparing the predicted power consumption with the real-time power consumption. To verify the performance of the proposed smart meter based on the deep running, we constructed the actual power consumption environment and obtained the power usage data in real time, and predicted the power consumption based on the deep learning model. We confirmed that the unnecessary power consumption can be reduced and the energy use efficiency increases through the proposed deep learning-based smart meter.

• Journal of the Korean Geotechnical Society
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• v.29 no.7
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• pp.75-89
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• 2013

This paper describes a GIS-based geohydrologic methodology, called YSGWF (YonSei GroundWater Flow) for predicting the rainfall infiltration-groundwater flow of slopes. This physical-based model was developed by the combination of modified Green-Ampt model that considers the unsaturated soil parameters and GIS-based raster model using Darcy's law that reflects the groundwater flow. In the model, raster data are used to simulate the three dimensional inclination of bedrock surface as actual topographic data, and the groundwater flow is governed by the slope. Also, soil profile is ideally subdivided into three zones, i.e., the wetting band zone, partially saturated zone, and fully saturated zone. In the wetting band and partially saturated zones the vertical infiltration of water (rainfall) from surface into ground is modeled. When the infiltrated water recharges into the fully saturated zone, the horizontal flow of groundwater is introduced. A comparison between the numerical calculation and real landslide data shows a reasonable agreement, which indicate that the model can be used to simulate real rainfall infiltration-groundwater flow.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.824-827
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• 2011

Needs for Smart Grid development are increasing all over the world as a solution to its problem according to depletion of energy resources, climatic and environmental rapidly change and growing demand for electrical power. Especially decentralized power is attracting world's attention. In this mood a new era for a unit scale of decentralized power environment is on its way in building. However there is a problem to have to be solved in the uniformity of power quality because the amount of power generated from renewable energy resources such as wind power and solar light is very sensitive to climate fluctuation. And thus this paper tries to suggest an energy management algorithm on basis of real time monitoring for meteorological data. The proposed EMS model embodies the method for predicting the power generation by monitoring and analyzing the climatic data and controling the efficient power distribution between the renewable energy and the existing power. The ultimate goal of this paper is to provide the technological basis for achieving zero-energy house.

• Journal of radiological science and technology
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• v.40 no.4
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• pp.533-542
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• 2017

In diagnostic radiology, the imaging system has been changed from film/screen to digital system. However, the method for removing scatter radiation such as anti-scatter grid has not kept pace with this change. Therefore, authors have devised the indirect flat panel detector (FPD) system with net-like lead in substrate layer which can remove the scattered radiation. In clinical context, there are many radiographic examinations with angulated incident X-ray. However, our proposed FPD has net-like lead foil so the vertical lead foil to the angulate incident X-ray would have bad effect on its performance. In this study, we identified the effect of vertical/horizontal lead foil component on the novel system's performance and improved the structure of novel system for clinical usage with angulated incident X-ray. Grid exposure factor and image contrast were calculated to investigate various structure of novel system using Monte Carlo simulation software when the incident X-ray was tilted ($0^{\circ}$, $15^{\circ}$, and $30^{\circ}$ from the detector plane). More photons were needed to obtain same image quality in the novel system with vertical lead foil only then the system with horizontal lead foil only. An optimal structure of novel system having different heights of its vertical and horizontal lead foil component showed improved performance compared with the novel system in a previous study. Therefore, the novel system will be useful in a clinical context with the angulated incident X-ray if the height and direction of lead foil in the substrate layer are optimized as the condition of conventional radiography.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.1
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• pp.74-82
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• 2019

The revolution of industry 4.0 is enabling us to build an intelligent connection society called smart cities. The use of renewable energy in particular solar energy is extremely important for modern society due to the growing power demand in smart cities, but its difficult to monitor and manage in each buildings since need to be deploy low energy sensors and information need to be transfer via wireless sensor network (WSN). The Internet of Things (IoT) / low-power wide-area (LPWA) is an emerging WSN technology, to collect and monitor data about environmental and physical electrical / electronics devices conditions in real time. However, providing power to IoT sensor end devices and other public electrical loads such as street lights, etc is an important challenging role because the sensor are usually battery powered and have a limited life time. In this paper, we proposes an efficient solar energy-based power management scheme for smart city based on IoT technology using LoRa wide-area network (LoRaWAN). This approach facilitates to maintain and prevent errors of solar panel based energy systems. The proposed solution maximizing output the power generated from solar panels system to distribute the power to the load and the grid. In this paper, we proved the efficiency of the proposed system with Simulink based system modeling and real-time emulation.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.397-418
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• 2019

It is very important to understand and analyze the interactive behaviour between ground and adjacent structures due to tunneling. With many technological advancement in modern society, numerous methods for analyzing the interactive behaviour are used in a wide range of civil engineering fields. Close range photogrammetry is mainly being used in the field of geotechnical engineering and research on measuring methods associated with GeoPIV has been currently increased. Originally, the close range photogrammetry using target points and aluminum rods for VMS (Vision Measurement System) program has been used. However, applying this has a problem that external errors can be occurred because the target points are artificially installed by hand, and if the grid between points is being wider or narrower, deficient data can be obtained. Therefore, in this study, MATLAB-based No-target program that can analyze displacement without using target was developed. Additionally, this study focused on comparison and verification with existing program through numerical analysis and laboratory model test. Three cases of Greenfield condition, Strip foundation, and Pile foundation were analyzed. From results of VMS program and No-target program, the error rate and reliability of the total displacement and the vertical displacement were analyzed. It was also compared and verified through the finite element numerical program, PLAXIS.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.525-531
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• 2020

In today's IT environment where various pieces of information are distributed in large volumes, recommendation systems are in the spotlight capable of figuring out users' needs fast and helping them with their decisions. The current recommendation systems, however, have a couple of problems including that user preference may not be reflected on the systems right away according to their changing tastes or interests and that items with no relations to users' preference may be recommended, being induced by advertising. In an effort to solve these problems, this study set out to propose a Fuzzy-AHP-based movie recommendation system by applying the BRNN(Bidirectional Recurrent Neural Network) language model. Applied to this system was Fuzzy-AHP to reflect users' tastes or interests in clear and objective ways. In addition, the BRNN language model was adopted to analyze movie-related data collected in real time and predict movies preferred by users. The system was assessed for its performance with grid searches to examine the fitness of the learning model for the entire size of word sets. The results show that the learning model of the system recorded a mean cross-validation index of 97.9% according to the entire size of word sets, thus proving its fitness. The model recorded a RMSE of 0.66 and 0.805 against the movie ratings on Naver and LSTM model language model, respectively, demonstrating the system's superior performance in predicting movie ratings.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.339-346
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• 2022

Recently, the resource depletion and climate change problem caused by the massive usage of fossil fuels for electric power generation has become a critical issue worldwide. According to this issue, interest in renewable energy resources that can replace fossil fuels is increasing. Especially, photovoltaic power has gaining much attention because there is no risk of resource exhaustion compared to other energy resources and there are low restrictions on installation of photovoltaic system. In order to use the power generated by the photovoltaic system efficiently, a more accurate photovoltaic power forecasting model is required. So far, even though many machine learning and deep learning-based photovoltaic power forecasting models have been proposed, they showed limited success in terms of interpretability. Deep learning-based forecasting models have the disadvantage of being difficult to explain how the forecasting results are derived. To solve this problem, many studies are being conducted on explainable artificial intelligence technique. The reliability of the model can be secured if it is possible to interpret how the model derives the results. Also, the model can be improved to increase the forecasting accuracy based on the analysis results. Therefore, in this paper, we propose an explainable photovoltaic power forecasting scheme based on BiLSTM (Bidirectional Long Short-Term Memory) and SHAP (SHapley Additive exPlanations).