• Proceedings of the SAREK Conference
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• 2008.11a
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• pp.487-492
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• 2008

The cooling of data centers has emerged as a significant challenge as the density of IT server increases. Server installations, along with the shrinking physical size of servers and storage systems, has resulted in high power density and high heat density. The introduction of high density enclosures into a data center creates the potential for "hot spots" within the room that the cooling system may not be able to address, since traditional designs assume relatively uniform cooling patterns within a data center. The cooling system for data center consists of a CRAC or CRAH unit and the associated air distribution system. It is the configuration of the distribution system that primarily distinguishes the different types of data center cooling systems, this is the main subject of this paper.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.57-65
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• 2021

We propose a novel method to detect abnormal data of specific symptoms using deep learning in air pollution measurement system. Existing methods generally detect abnomal data by classifying data showing unusual patterns different from the existing time series data. However, these approaches have limitations in detecting specific symptoms. In this paper, we use DeepLab V3+ model mainly used for foreground segmentation of images, whose structure has been changed to handle one-dimensional data. Instead of images, the model receives time-series data from multiple sensors and can detect data showing specific symptoms. In addition, we improve model's performance by reducing the complexity of noisy form time series data by using 'piecewise aggregation approximation'. Through the experimental results, it can be confirmed that anomaly data detection can be performed successfully.

• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.139-145
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• 2018

In this paper, we propose the methodology and system to show weather information to spatial system. When using the spatial information system, it is easy and convenient to show information such as target location, mission contents, enemy threats and so on. However, drawing 1-dimensional weather information on 3-dimensional space in spatial information system is hard task. To fuse data, we need to add a spatial layer including weather information to spatial layers and perform space modeling for showing weather information as spatial data in a virtual space. The virtual space is shown by receiving meteorological data and then changing in real time through weather database linkage.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.363-370
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• 2017

Data assimilation is an initializing method for air quality forecasting such as PM10. It is very important to enhance the forecasting accuracy. Optimal interpolation is one of the data assimilation techniques. It is very effective and widely used in air quality forecasting fields. The technique, however, requires too much memory space and long execution time. It makes the PM10 air quality forecasting difficult in real time. We propose a fast optimal interpolation data assimilation method for PM10 air quality forecasting using a new kernel tridiagonal sparse matrix and CUDA massively parallel processing architecture. Experimental results show the proposed method is 5~56 times faster than conventional ones.

• Proceedings of the SAREK Conference
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• 2006.06a
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• pp.938-943
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• 2006

The primary purpose of a computer room of data center and associated infrastructure is to support the operation of critical IT equipment. Traditionally, most owners of large critical data centers have been more than willing to accept a reasonable amount of computer room worker discomfort if necessary to support critical IT systems. All electrical equipment produces heat, which must be removed to prevent the equipment temperature from rising to an unacceptable level. Most information technology equipment and other equipment found in a data center or network room is air-cooled. Sizing a cooling system requires an understanding of the amount of heat produced by the equipment contained in the enclosed space, along with the heat produced by the other heat sources typically encountered.

• Journal of Environmental Impact Assessment
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• v.9 no.4
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• pp.323-338
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• 2000

A Korean air dispersion modeling software, AirMaster, was developed on a basis of dispersion theories adopted in U.S. EPA's ISC3 (Industrial Source Complex - version 3) model to assess the air quality impact from the stacks. Key characteristics of AirMaster are as follows: 1) The building downwash effect can be easily simulated; 2) The screen, long term, and short term models can be run independently; 3) The input data to run the model such as meteorological and terrain data are supplied automatically from the databases in AirMaster; and 4) The modeling procedure is easy and simple under the GUI window environment. In order to validate AirMaster, comparisons with ISC3 model and Indianapolis tracer experiment were carried out. It was shown that AirMaster was identical to ISCST3 and ISCLT3 models in predicting the 1 hr to annual concentrations from the stack under various stack emission and meteorological conditions. The 1 hr concentrations predicted by AirMaster also showed a good agreement with the Indianapolis tracer measurements.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.260-269
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• 2022

In this paper, We propose an anomaly detection model using deep neural network to automate the identification of outliers of the national air pollution measurement network data that is previously performed by experts. We generated training data by analyzing missing values and outliers of weather data provided by the Institute of Environmental Research and based on the BeatGAN model of the unsupervised learning method, we propose a new model by changing the kernel structure, adding the convolutional filter layer and the transposed convolutional filter layer to improve anomaly detection performance. In addition, by utilizing the generative features of the proposed model to implement and apply a retraining algorithm that generates new data and uses it for training, it was confirmed that the proposed model had the highest performance compared to the original BeatGAN models and other unsupervised learning model like Iforest and One Class SVM. Through this study, it was possible to suggest a method to improve the anomaly detection performance of proposed model while avoiding overfitting without additional cost in situations where training data are insufficient due to various factors such as sensor abnormalities and inspections in actual industrial sites.

• Journal of ILASS-Korea
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• v.19 no.2
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• pp.64-68
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• 2014

A driving vehicle performance which is driven by FTP-75 mode was simulated by computer. Throttle valve position, engine speed, air mass flow rate, fuel consumption et al. were computer simulated. A set of engine part load performance data, automatic transmission shift map and vehicle specifications were used for the computer simulation. Throttle valve position, engine speed, air mass flow rate et al. measured for evaluating the computer simulation results by driving the vehicle with FTP-75 mode on a chassis dynamometer. GT-Power$^{(R)}$ software was used for the computer simulation of the driving vehicle performance. Experimental fuel consumption rate was measured by using an ECU HILS fuel injection system. The experimental data and simulation results were compared. The computer simulation of the driving vehicle performance predicts the measured data well comparatively.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.30 no.2
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• pp.83-91
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• 2018

A datacenter is a high energy consuming facility whose cooling energy consumption rate is 10~20 times larger than general office buildings. The higher the temperature of supply air from a CRAC (computer room air-conditioner) is supplied, the more energy efficient cooling is possible because of improving the COP of a chiller and advanced range of outdoor air temperature available for the economizer cycles. However, because the temperature of cold air flowing into server computers varies depending on air mixing configurations in a computer room, the proper supply air temperature must be considered based on the investigation of air mixing and heat dissipation. By these, this study aims to understand the effects of variation of the supply air temperature on the air flow distributions, temperature distributions and rack cooling efficiencies. Computational fluid dynamics (CFD) aided in conducting the investigation. As a result, the variation of the supply air temperature does not affect the air flow distributions. However, it mainly affects the temperature distribution. From the results of CFD simulations, Rack cooling indices (RCIHI and RCILO) were evaluated and showed the ideal state set at $19^{\circ}C$ of the supply air temperature.

• International Journal of Air-Conditioning and Refrigeration
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• v.10 no.4
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• pp.220-228
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• 2002

The cool-down performance after soaking is very important in an automotive air-conditioning system and is considered as a key design variable. Therefore, transient characteristics of each system component are essential to the preliminary design as well as steady-state performance. The objective of this study is to develop a computer simulation model and ostinato theoretically the transient performance of an automotive air-conditioning system. To do that, the mathematical modelling of each component, such as compressor, condenser, receiver/drier, expansion valve, and evaporator, is presented first of all. The basic balance equations about mass and energy are used in modelling. For detailed calculation, condenser and evaporator are divided into many sub-sections. Each sub-section is an elemental volume for modelling. In models of expansion valve and compressor, dynamic behaviors are not considered in this analysis, but the quasisteady state ones are just considered, such as the relation between mass flow rate and pressure drop in expansion device, polytropic process in compressor, etc. Also it is assumed that there are no heat loss and no pressure drop in discharge, liquid, and suction lines. The developed simulation model is validated by comparing with the laboratory test data of an automotive air-conditioning system. The overall time-tracing properties of each component agreed well with those of test data in this case.