• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

• Journal of Sensor Science and Technology
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• v.31 no.5
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• pp.318-323
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• 2022

Barium titanate (BaTiO₃) is considered to be a beneficial ceramic material for multilayer ceramic capacitor (MLCC) applications because of its high dielectric constant and low dielectric loss. Numerous attempts have been made to improve the physical properties of BaTiO₃ in response to recent market trends by employing multicomponent alloying strategies. However, owing to its significant number of atomic combinations and unpredictable physical properties, finding a traditional experimental approach to develop multicomponent systems is difficult; the development of such systems is also time-consuming. In this study, 168 new structures were fabricated using special quasi-random structures (SQSs) of Ba_1-xCa_xTi_1-yZryO₃, and 1680 physical properties were extracted from first-principles calculations. In addition, we built an integrated database to manage the computational results, and will provide big data solutions by performing data analysis combined with AI modeling. We believe that our research will enable the global materials market to realize digital transformation through datalization and intelligence of the material development process.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.4B
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• pp.323-338
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• 2003

The main memory database systems (MMDBMS) efficiently supports various database applications that require high performance since it employs main memory rather than disk as a primary storage. Recently, it has been increased needs that have the fast data processing as well as the efficient modeling of application requiring for a complicated structure, and conformity to applications that need the strict dta consistency. In MMDBMS, because all the data is located in the main memory, it can support the usable expression methods of data satisfying their needs without performance overhead. The method has the operation to manipulate the data and the constraint such as referential integrity in more detail. The data model consists of this methods is an essential component to decide the expression power of DBMS. In this paper, we discuss about various requests to provide the communication services and propose the data model that support it. The mainly discussed issues are 1) definition of the relationship between tables using the pointer, 2) navigation of the data using the relationship, 3) support of the referential integrity for pointer, 4) support of the uniform processing time for the join, 5) support of the object-oriented concepts, and 6) sharing of an index on multi-tables. We discuss the pointer-based data model that designed to include these issues to efficiently support complication environments.

• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.131-146
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• 1999

The satellite visible data have been successfully applied to study the ocean color. Another ocean color sensor, the Ocean Scanning Multi-spectral Imager (OSMI) on the Korea Multi-Purpose Satellite (KOMPSAT) will be launched in 1999. In order to understand the characteristics of future OSMI images, we have first discussed the simulation models and procedures in detail, and produced typical patterns of radiances at visible bands by using radiative transfer models. The various simulated images of full satellite passes and Korean local areas for different seasons, water types, and the satellite crossing equator time (CET) are presented to illustrate the distribution of each component of radiance (i.e., aerosol scattering, Rayleigh scattering, sun glitter, water-leaving radiance, and total radiance). A method to evaluate the image quality and availability is then developed by using the characteristics of image defined as the Complex Signal Noise Ratio (CSNR). Meanwhile, a series of CSNR images are generated from the simulated radiance components for different cases, which can be used to evaluate the quality and availability of OSMI images before the KOMPSAT will be placed in orbit. Finally, the quality and availability of OSMI images are quantitatively analyzed by the simulated CSNR image. It is hoped that the results would be useful to all scientists who are in charge of OSMI mission and to those who plan to use the data from OSMI.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1373-1387
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• 2021

Forest fire poses a significant threat to the environment and society, affecting carbon cycle and surface energy balance, and resulting in socioeconomic losses. Widely used multi-spectral satellite image-based approaches for burned area detection have a problem in that they do not work under cloudy conditions. Therefore, in this study, Sentinel-1 Synthetic Aperture Radar (SAR) data from Europe Space Agency, which can be collected in all weather conditions, were used to identify forest fire damaged area based on a series of processes including Principal Component Analysis (PCA) and K-means clustering. Four forest fire cases, which occurred in Gangneung·Donghae and Goseong·Sokcho in Gangwon-do of South Korea and two areas in North Korea on April 4, 2019, were examined. The estimated burned areas were evaluated using fire reference data provided by the National Institute of Forest Science (NIFOS) for two forest fire cases in South Korea, and differenced normalized burn ratio (dNBR) for all four cases. The average accuracy using the NIFOS reference data was 86% for the Gangneung·Donghae and Goseong·Sokcho fires. Evaluation using dNBR showed an average accuracy of 84% for all four forest fire cases. It was also confirmed that the stronger the burned intensity, the higher detection the accuracy, and vice versa. Given the advantage of SAR remote sensing, the proposed statistical processing and K-means clustering-based approach can be used to quickly identify forest fire damaged area across the Korean Peninsula, where a cloud cover rate is high and small-scale forest fires frequently occur.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.141-149
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• 2008

Recently we frequently analyze multivariate data with quite large number of variables. In such data sets, virtually duplicated variables may exist simultaneously even though they are conceptually distinguishable. Duplicate variables may cause problems such as the distortion of principal axes in principal component analysis and factor analysis and the distortion of the distances between observations, i.e. the input for cluster analysis. Also in supervised learning or regression analysis, duplicated explanatory variables often cause the instability of fitted models. Since real data analyses are aimed often at multiple purposes, it is necessary to reduce the number of variables to a parsimonious level. The aim of this paper is to propose a practical algorithm for selection of a subset of variables from a given set of p input variables, by the criterion of minimum trace of partial variances of unselected variables unexplained by selected variables. The usefulness of proposed method is demonstrated in visualizing the relationship between selected and unselected variables, in building a predictive model with very large number of independent variables, and in reducing the number of variables and purging/merging categories in categorical data.

• Economic and Environmental Geology
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• v.45 no.4
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• pp.377-384
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• 2012

The main field component of the Earth's magnetic field was modeled from the tri-axial magnetometer onboard KOrean MultiPurpose SATellite-II (KOMPSAT-II) for the purpose of satellite attitude control. The model computed by the KOMPSAT-II magnetometer measurement data is compared with the International Geomagnetic Reference Field (IGRF) model of a degree of up to 13 in spherical harmonic coefficients. The previous study with KOMPSAT-I (Kim et al. 2004) indicated a good correlation of power spectrum of spherical harmonic coefficients with respect to the degree up to 5. This study, however, showed an agreement of the degree up to 8-9 of the coefficient power spectrum and a discrepancy between degrees 10 and 13. We have concluded that relevant data selection process, removal of the external field from the data in the high latitude region, an accuracy of the magnetometer all play an important role in finding a coherence with the IGRF model. This study will be extended to the secular variation model of geomagnetism if longer-period data become available.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.587-596
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• 2022

Sea levels are rising as a result of climate change, and low-lying areas along the coast are at risk of flooding. Therefore, we tried to investigate the relationship between sea level change and climate indices using satellite altimeter data (Topex/Poseidon, Jason-1/2/3) and southern oscillation index (SOI) and the Pacific decadal oscillation (PDO) data. If time domain data were converted to frequency domain, the original data can be analyzed in terms of the periodic components. Fourier transform and Wavelet transform are representative periodic analysis methods. Fourier transform can provide only the periodic signals, whereas wavelet transform can obtain both the periodic signals and their corresponding time location. The cross-wavelet transformation and the wavelet coherence are ideal for analyzing the common periods, correlation and phase difference for two time domain datasets. Our cross-wavelet transform analysis shows that two climate indices (SOI, PDO) and sea level height was a significant in 1-year period. PDO and sea level height were anti-phase. Also, our wavelet coherence analysis reveals when sea level height and climate indices were correlated in short (less than one year) and long periods, which did not appear in the cross wavelet transform. The two wavelet analyses provide the frequency domains of two different time domain datasets but also characterize the periodic components and relative phase difference. Therefore, our research results demonstrates that the wavelet analyses are useful to analyze the periodic component of climatic data and monitor the various oceanic phenomena that are difficult to find in time series analysis.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.1
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• pp.101-113
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• 2013

This paper intends to develop mechanical analysis models that are able to predict complete nonlinear behavior in the bolted connector subjected to cyclic loads. In addition, experimental data which were obtained from loading tests performed on the T-stub connections are utilized to validate the accuracy of analytical prediction and the adequacy of numerical modeling. The behavior of connection components including tension bolt uplift, bending of the T-stub flange, stem elongation, relative slip deformation, and bolt bearing are simulated by the multi-linear stiffness models obtained from the observation of their individual force-deformation mechanisms in the connection. The component springs, which involve the stiffness properties, are implemented into the simplified joint element in order to numerically generate the behavior of full-scale connections with considerable accuracy. The analytical model predictions are evaluated against the experimental tests in terms of stiffness, strength, and deformation. Finally, it can be concluded that the mechanical models proposed in this study have the satisfactory potential to estimate stiffness response and strength capacity at failure.

• Journal of Korean Academy of Nursing
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• v.36 no.7
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• pp.1183-1192
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• 2006

Purpose: The purpose of this survey was to investigate clinical nurses' understanding of delirium and their educational need of delirious patient care. Method: A survey questionnaire regarding nurses' general perception and understanding of delirium, experience with delirious patients and educational need was developed and conducted with 179 clinical nurses in a university hospital in Seoul. Data was analyzed using descriptive statistics. Results: Nurses thought that delirium was one of the most important nursing problems and they considered it to be more treatable than to be preventable. However, the majority of nurses were ilot confident in caring for delirious patients. Nurses reported that delirium happened most often after surgery, and that possible contributing factors could be changes in physical environment and anxiety/stress, as well as medication and long-term isolation. Thirteen nursing interventions were identified but half of the nurses utilized only one or two of the thirteen. The most frequently used intervention was reorienting the patient followed by medication and emotional support, presenting family, and close observation. 99.5% of nurses addressed the importance of professional education on delirium care, especially in the area of intervention and management. Conclusion: The results support the strong need for development of a multi-component educational program on delirium care.