• Journal of the Korean Data and Information Science Society
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• v.9 no.1
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• pp.63-70
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• 1998

The singular value decomposition is a tool which is used to find a linear structure of reduced dimension and to give interpretation of the lower dimensional structure about multivariate data. In this paper the singular value decomposition is reviewed from both algebraic and geometric point of view and, is illustrated the way which the tool is used in the multivariate techniques finding a simpler geometric structure for the data.

• International Journal of Air-Conditioning and Refrigeration
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• v.12 no.1
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• pp.37-49
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• 2004

Four different data reduction methods for the heat transfer coefficients from experimental data under dehumidifying conditions are compared. The four methods consist of two heat and mass transfer models and two fin efficiency models. Data are obtained from two heat exchanger samples having plain fins or wave fins. Comparison of the reduced heat transfer coefficients revealed that the single potential heat and mass transfer model yielded the humidity-independent heat transfer coefficients. Two fin efficiency models-enthalpy model and humidity model-yielded approximately the same fin efficiencies, and accordingly approximately the same heat transfer coefficients. The heat transfer coefficients under wet conditions were approximately the same as those of the dry conditions for the plain fin configuration. For the wave fin configuration, however, wet surface heat transfer coefficients were approximately 12% higher. The pressure drops of the wet surface were 10% to 45% larger than those of the dry surface.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.4
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• pp.115-120
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• 2010

The reduction rate of fuel consumption by showing the fuel injection data for driver was measured in this study. The fuel injection data are composed of injection period, real time fuel economy and average fuel economy. The fuel consumption was measured by processing the voltage signal of injector and driven distance by GPS sensor. The fuel consumption was reduced by driving more carefully, i.e driving more steady without sudden acceleration and deceleration watching these fuel injection data. The reduction rate was up to 37% and the rate increased as the driver is customed to this driving pattern.

• Smart Structures and Systems
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• v.5 no.4
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• pp.415-426
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• 2009

This study focuses on the concept of multi-scale wireless sensor networks for damage detection in civil infrastructure systems by first over viewing the general network philosophy and attributes in the areas of data acquisition, data reduction, assessment and decision making. The data acquisition aspect includes a scalable wireless sensor network acquiring acceleration and strain data, triggered using a Restricted Input Network Activation scheme (RINAS) that extends network lifetime and reduces the size of the requisite undamaged reference pool. Major emphasis is given in this study to data reduction and assessment aspects that enable a decentralized approach operating within the hardware and power constraints of wireless sensor networks to avoid issues associated with packet loss, synchronization and latency. After over viewing various models for data reduction, the concept of a data-driven Bivariate Regressive Adaptive INdex (BRAIN) for damage detection is presented. Subsequent examples using experimental and simulated data verify two major hypotheses related to the BRAIN concept: (i) data-driven damage metrics are more robust and reliable than their counterparts and (ii) the use of heterogeneous sensing enhances overall detection capability of such data-driven damage metrics.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.345-346
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• 2023

As the shape and size of detectable objects diversifying recognition and segmentation algorithms have been developed to acquire accurate shape information. Although a high density of data captured by the repetition of scanning improves the accuracy of algorithms the high dense data decreases the efficiency due to its large size. This paper proposes standardization algorithms using the feature of structural members on indoor point cloud data to improve the process. First of all we determine the reduction rate of the density based on the features of the target objects then the data reduction algorithm compresses the data based on the reduction rate. Second the data arrangement algorithm rotates the data until the normal vector of data is aligned along the coordinate axis to allow the following algorithms to operate properly. Final the data arrangement algorithm separates the rotated data into their leaning axis. This allows reverse engineering of indoor point clouds to obtain the efficiency and accuracy of refinement processes.

• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.93-100
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• 2011

In this paper, we propose a FDR(False Data Reduction) strategy for P2P environment that reduces false data. The key idea of our strategy is that we use FDR algorithm to stop transmitting of false data and to delete that. If a user recognizes false data in downloaded-data and the user's peer requests the others to stop the transmission of the false data immediately. Also, the FDR algorithm notifies the other peers to prohibit spreading of the false data in the environment. All this procedure is possible to be executed in each peer without any lookup server. The FDR algorithm needs only a little data exchange among peers. Through simulation, we show that it is more effective to reduce the network traffic than the previous P2P strategy. We also show that the proposed strategy improves the performance of network compared to previous P2P strategy. As a result, The FDR strategy is decreased 9.78 ~ 16.84% of mean true data transmission time.

• Journal of Korea Water Resources Association
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• v.48 no.3
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• pp.221-231
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• 2015

This is a continuous study on the dam effects for the spatial extension of flood data. In this study, flood reduction rates of dams and their influences on downstream using the spatially extended flood data were implemented. Nam-Han River was selected for measuring the impacts of ChoongJu and HoangSung dams. In the evaluations of flood reduction rate at dams, the larger flood events have the lower flood reduction rates for both dams. At the YeoJoo water level station, the analyses of the relations between flood reduction rates and the sizes of watersheds dams located were performed. the sizes of watersheds having a functional dam have highly influenced on the reduction rates of flood. The average of flood reduction rates was smaller than the area rate. For instances, area rates of HoangSung (0.02) and ChoongJu dams (0.6) are larger than the average flood reduction rates for HoangSung (0.01) and ChoongJu dams (0.51), respectively. However, the water level station follows the dam flood reduction characteristics of dams themselves. The spatial effects of dam flood reductions are analyzed based on the three water level stations (GangChun, YeoJoo, YangPyung). The distance of flood reduction rates lower than 0.1 as average flood reduction rate was the area 7 times of watershed having a dam with 0.02 as a minimum reduction rate.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.567-576
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• 2006

In this paper, we applied the multiblock dimension reduction methods to the classification of tumor based on microarray gene expressions data. This procedure involves clustering selected genes, multiblock dimension reduction and classification using linear discrimination analysis and quadratic discrimination analysis.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1272-1274
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• 2003

In this paper, we present a new algorithm for high dimensional data reduction based on wavelet decomposition and Genetic Algorithm (GA). Comparative results show the superiority of our algorithm for dimensionality reduction and accuracy improvement.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.49 no.3
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• pp.30-36
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• 2012

block truncation coding(BTC) image compression is known as a simple and efficient technology for image compression algorithm. In this paper, we propose RMC-BTC algorithm(RMC : reduction method chrominace data) for color image compression. To compress chrominace data, in every BTC block, the RMC-BTC coding employs chrominace data expressed with average of chrominace data and using method of luminance data bit-map to represented chrominance data bit-map. Experimental results shows efficiency of proposed algorithm, as compared with PSNR and compression ratio of the conventional BTC method.