• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.413-418
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• 2011

The thermodynamic properties of steam table are obtained by measurement or approximate calculation under appropriate assumptions. Therefore they are supposed to have basic measurement errors. And thermodynamic properties should be modeled through function approximation for using in numerical analysis. In order to make noised thermodynamic properties corresponding to measurement errors, random numbers are generated, adjusted to appropriate magnitudes and added to original thermodynamic properties. Both neural networks and quadratic spline interpolation method are introduced for function approximation of these modified thermodynamic properties in the saturated water based on pressure and temperature. In analysis spline interpolation method gives much less relative errors than neural networks at both ends of data. Excluding the both ends of data, the relative errors of neural networks is generally within ${\pm}0.2%$ and those of spline interpolation method within ${\pm}0.5$~1.5%. This means that the neural networks give smaller relative errors compared with quadratic spline interpolation method within range of use. From this fact it was confirmed that the neural networks trace the original values better than the quadratic interpolation method and neural networks are more appropriate method in modelling the saturated steam table.

• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.663-672
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• 2002

In this paper, Using an automatic tracking system, behavior of an aquatic insect, Chironomus sp. (Chironomidae), was observed in semi-natural conditions in response to sub-lethal treament of a carbamate insecticide, carbofuran. The fourth instar larvae were placed in an observation cage $(6cm\times{7cm}\times{2.5cm)}$ at temperature of $18^\circ{C}$ and the light condition of 10 time (light) : 14 time (dark). The tracking system was devised to detect the instant, partial movement of the insect body. Individual movement was traced after the treatment of carbofuran (0.1ppm) for four days 2days : before treatment, 2 days : after treatment). Along with the other irregular behaviors, "ventilation activity", appearing as a shape of "compressed zig-zag", was more frequently observed after the treatment of the insecticide. The activity of the test individuals was also generally depressed after the chemical treatment. In order to detect behavioral changes of the treated specimens, wavelet analysis was implemented to characterize different movement patterns. The extracted parameters based on Discrete Wavelet Transforms (DWT) were subsequently provided to artificial neural networks to be trained to represent different patterns of the movement tracks before and after treatments of the insecticide. This combined model of wavelets and artificial neural networks was able to point out the occurrence of characteristic movement patterns, and could be an alternative tool for automatically detecting presences of toxic chemicals for water quality monitoring. quality monitoring.

• Applied Chemistry for Engineering
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• v.16 no.6
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• pp.809-814
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• 2005

In this work, the thermal properties of epoxy coating system on the liner plate in the containment structure of nuclear power plants had been examined by irradiation and design basis accident (DBA) conditions. The effect of immersion in hot water on adhesion strength of the coating system had been also studied. The glass transition temperature ($T_g$) and thermal stability of ET-5290/carbon steel A 32 epoxy coating systems were measured by DSC and TGA analyses, respectively. Contact angle measurements were used to determine the effect of immersion on the surface energetics of epoxy coating system, with a viewpoint of surface free energy. Adhesion tests were also executed to evaluate the adhesion strength at interfaces between carbon steel plate and epoxy resins. As a result, it was found that the irradiation led to an improvement of internal crosslinked structure in cured epoxy systems, resulting in significantly increasing the thermal stability, as well as the $T_g$. Also, the immersion in hot water made a role in the post-curing of epoxy resins and increased the mechanical interlocking of the network system, resulting in increasing the adhesion strength of the epoxy coating system.

• Resources Recycling
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• v.22 no.4
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• pp.38-45
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• 2013

The porous ceramic beads using mine tailing were prepared and applied to catalytic converter for NOx/SOx removal. Catalytic support was used synthesized mesoporous silica (SBA-15) which coated on surface. Internal structure for porous ceramic beads was composed of three-dimensional network structure and porosity was about 80%. In addition, the specific surface area for mesoporous silica(SBA-15) coated on converter was significantly increased 55 $m^2/g$ compared with 0.8 $m^2/g$ before coating. NOx/SOx removal experiment was performed using $V_2O_5$ and $V_2O_5$/CuO converter. NOx conversion ratio for $V_2O_5$/CuO converter was approximately increased 10% compared to $V_2O_5$ converter. In addition, catalytic converter of $V_2O_5$/CuO was shown to remove 95% of NOx and 90% of SOx at reaction temperature of $350^{\circ}C$, space velocity of 10000 $h^{-1}$ and $O_2$ concentrations of 5%, respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.581-584
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• 2009

Recently, the cameras which used for observation are installed in children protection area and local crime prevention area in order to protect life and property and by its work being recognized and are installed more. Normal cameras have cost problem to observe multiple area and detail, because they can observe only one place. PTZ camera can observe multiple area by moving focus by schedule or remote control, but it can't automatically move the focus of it to the place where event occurred, because it can't recognize the place. In this study, we can monitor multiple area effectively, by installing a wireless sensor node equipped with temperature, lighting, gas and human detection sensor to each area, to monitor many place low-price and actively and to move the focus of PTZ camera to preset position, and send recorded video to the user, when the various sensor data received from wireless sensors in observation area are to be determined abnormal by analyzing. In addition, at night we can record a scene using infrared, but to reduce power consumption of lighting system which are installed to improve resolution, it supplies power to the lighting system when event occurred. So we were able to implement low power green monitoring system.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.77-88
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• 2021

Duck industry had a rapid growth in recent years. Nevertheless, researches to improve duck house environment are still not sufficient enough. Moisture generation of duck house litter is an important factor because it may cause severe illness and low productivity. However, the measuring process is difficult because it could be disturbed with animal excrements and other factors. Therefore, it has to be calculated according to the environmental data around the duck house litter. To cut through all these procedures, we built several machine learning regression model forecasting moisture generation of litter by measured environment data (air temperature, relative humidity, wind velocity and water contents). 5 models (Multi Linear Regression, k-Nearest Neighbors, Support Vector Regression, Random Forest and Deep Neural Network). have been selected for regression. By using R-Square, RMSE and MAE as evaluation metrics, the best accurate model was estimated according to the variables for each machine learning model. In addition, to address the small amount of data acquired through lab experiments, bootstrapping method, a technique utilized in statistics, was used. As a result, the most accurate model selected was Random Forest, with parameters of n-estimator 200 by bootstrapping the original data nine times.

• The Journal of Society for e-Business Studies
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• v.24 no.3
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• pp.1-18
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• 2019

Due to the great advances in e-Marketplace and changes in type of items purchased from the online market, it has been dramatically increased the demand of the storage and transportation under the special conditions such as restricted temperature. Especially, the cold chain needs the way to transparently measure and monitor the entire network in realtime because it has a very complicated structure and requires totally different criteria at the every different steps and items. In this research, it has been presented the performance evaluation metrics to make contract using service level agreement (SLA), the way to apply the smart contract based on blockchain, the structure of blocks, service platform and application in order to build cold chain which can prevent the risk factors by measuring and sharing information in realtime using block chain technology. In addition, we have proposed the way to store the measured performance and reputation of each player in the block using smart contract based on SLA. With the presented framework, all players including service providers as well as users can secure the information for making the rational decisions. When the service platform is actually built and operated, it seems possible to secure the information in transparently and realtime. Also, it is possible to prevent the risk factors or prepare the preemptive plans to react on them.

• The Journal of Bigdata
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• v.3 no.2
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• pp.59-70
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• 2018

In order to provide stable district heat supplying service to the certain limited residential area, it is the most important to forecast the short-term future demand more accurately and produce and supply heat in efficient way. However, it is very difficult to develop a universal heat demand forecasting model that can be applied to general situations because the factors affecting the heat consumption are very diverse and the consumption patterns are changed according to individual consumers and regional characteristics. In particular, considering all of the various variables that can affect heat demand does not help improve performance in terms of accuracy and versatility. Therefore, this study aims to develop a demand forecasting model using deep learning based on only limited information that can be acquired in real time. A demand forecasting model was developed by learning the artificial neural network of the Tensorflow using past data consisting only of the outdoor temperature of the area and date as input variables. The performance of the proposed model was evaluated by comparing the accuracy of demand predicted with the previous regression model. The proposed heat demand forecasting model in this research showed that it is possible to enhance the accuracy using only limited variables which can be secured in real time. For the demand forecasting in a certain region, the proposed model can be customized by adding some features which can reflect the regional characteristics.

• The Journal of Bigdata
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• v.5 no.2
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• pp.17-27
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• 2020

District heating system supplies heat from low-cost high-efficiency heat production facilities to heat demand areas through a heat pipe network. For efficient heat supply system operation, it is important to accurately predict the heat demand within the region and optimize the heat production plan accordingly. In this study, a heat demand forecasting model is proposed considering real-time calorimeter information from local heat demands. Previous models considered ambient temperature and heat demand history data to predict future heat demands. To improve forecast accuracy, the proposed heat demand forecast model added big data from real-time calorimeters installed in the heat demands within the target region. By employing calorimeter information directly in the model, it is expected that the proposed forecast model is to reflect heat use pattern of each demand. Computational experiemtns based on the actual heat demand data shows that the forecast accuracy of the proposed model improved when the calorimeter big data is reflected.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.95-104
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• 2020

In this paper, we propose the real time indoor air quality monitoring and controlling platform on cloud using IoT sensor data such as PM10, PM2.5, CO2, VOCs, temperature, and humidity which has direct or indirect impact to indoor air quality. The system is connected to air ventilator to manage and optimize the indoor air quality. The proposed system has three main parts; First, IoT data collection service to measure, and collect indoor air quality in real time from IoT sensor network, Second, Big data processing pipeline to process and store the collected data on cloud platform and Finally, Big data analysis and visualization service to give real time insight of indoor air quality on mobile and web application. For the implication of the proposed system, IoT sensor kits are installed on three different public day care center where the indoor pollution can cause serious impact to the health and education of growing kids. Analyzed results are visualized on mobile and web application. The impact of ventilation system to indoor air quality is tested statistically and the result shows the proper optimization of indoor air quality.