• The Journal of Engineering Geology
• /
• v.22 no.4
• /
• pp.459-466
• /
• 2012

A difficulty encountered in engineering seismic mapping is that reflection events from shallow discontinuities are commonly overlapped with coherent noise such as air wave, direct waves, head waves, and high-amplitude surface waves. Here, the radial trace transform, a simple geometric re-mapping of a trace gather (x-t domain) to another trace gather (v-t domain), is applied to investigate the rejection effect of coherent linear noises. Two different types of data sets were selected as a representative database: good-quality data for intermediate sounding (hundreds of meters) in a sedimentary basin and very noisy data for shallow (${\leq}50m$) mapping of the weathered zone and bedrock surface. Results obtained with cascaded application of the radial transform and low-cut filtering proved to be as good as, or better than, those produced using f-k filtering, and were especially effective for air wave and direct wave. This simple transform enables better understanding of the characteristics of various types of noise in the RT domain, and can be generally applied to overcoming diffractions and back-scatterings caused by joints, fractures, and faults commonly that are encountered in geotechnical problems.

• Proceedings of the Korea Association of Information Systems Conference
• /
• 2001.12a
• /
• pp.64-82
• /
• 2001

HTML revolutionized the way we specify the appearance of data on the Internet. Today, XML (the eXtensible Markup Language) is changing the way we specify the meaning of data. XML, lets document authors define their own markup tags and attribute names to assign meaning to the data elements in the document. Further, XML elements can be nested and include references to indicate data relationships, as Listing One. Unlike HTML, XML markup tags do not describe how to render the data. Rather, they provide descriptions of data, allowing software to understand the meaning of the data automatically For publishing, instead, XSL, the eXtensible Stylesheet Language as a separate language , is in charge of specifying the presentation of XML documents. The purpose of this study is to discover how to transform your organizations relational data into potential e-commerce, business-to-business, and web application with XML and XSL documents. For this purpose, the literature survey, first of all, was undertaken to understand the basic structures of XML documents. Second, one case implementation was performed to understand how to transform Access 2002 XML Files into HTML with XSLTand VB script. The results come out to be successful, more or less. But the limitations of it still exist. One immediate limitation is that XML documents are essentially tree structure, as dictated by the nesting of elements. However, relational database tables are two dimensional matrix structure. In addition, real-world data often is graph structured-a single data element may be referenced in multiple ways. However, this study is useful for understanding how to convert relational database into XML documents and to publish them using XSL or VB script.

• International conference on construction engineering and project management
• /
• 2015.10a
• /
• pp.559-563
• /
• 2015

Identification of energy use patterns in buildings has a great opportunity for energy saving. To find what energy use patterns exist, clustering analysis has been commonly used such as K-means and hierarchical clustering method. In case of high dimensional data such as energy use time-series, data reduction should be considered to avoid the curse of dimensionality. Principle Component Analysis, Autocorrelation Function, Discrete Fourier Transform and Discrete Wavelet Transform have been widely used to map the original data into the lower dimensional spaces. However, there still remains an ongoing issue since the performance of clustering analysis is dependent on data type, purpose and application. Therefore, we need to understand which data reduction techniques are suitable for energy use management. This research aims find the best clustering method using energy use data obtained from Seoul National University campus. The results of this research show that most experiments with data reduction techniques have a better performance. Also, the results obtained helps facility managers optimally control energy systems such as HVAC to reduce energy use in buildings.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.5
• /
• pp.1-7
• /
• 2009

In recent years, compressed sensing (CS) algorithm has been studied in various research areas including medical imaging. To use the CS algorithm, the signal that is to be reconstructed needs to have the property of sparsity But, most medical images generally don't have this property. One method to overcome this problem is by using sparsifying transform. However, MR imaging, compared to other medical imaging modality, has the unique property that by using appropriate image acquisition pulse sequences, the image contrast can be modified. In this paper, we propose the possibility of applying the CS algorithm with non-sparsifying transform to the pulse sequence modified MR images and improve the reconstruction performance of the CS algorithm by using an appropriate sparsifying transform. We verified the proposed contents by computer simulation using Shepp-Logan phantom and in vivo data.

• KIISE Transactions on Computing Practices
• /
• v.22 no.5
• /
• pp.201-208
• /
• 2016

Collection and storing of multiple time series data in real time requires large memory space. To solve this problem, the usage of varying sample size is proposed in the compression scheme using discrete cosine transform technique. Time series data set has characteristics such that a higher compression ratio can be achieved with smaller amount of value changes and lower frequency of the value changes. The coefficient of variation and the variability of the differences between adjacent data elements (VDAD) are presumed to be very good measures to represent the characteristics of the time series data and used as key parameters to determine the varying sample size. Test results showed that both VDAD-based and the coefficient of variation-based scheme generate excellent compression ratios. However, the former scheme uses much simpler sample size decision mechanism and results in better compression performance than the latter scheme.

• Journal of Korea Multimedia Society
• /
• v.19 no.5
• /
• pp.939-947
• /
• 2016

As the number of legacy database systems and the size of data to manipulate have been vastly increased, it has become more difficult and complex to analyze characteristics of data. To improve the efficiency of data analysis and help administrators to make decisions in business life, BI(Business Intelligence) system is used. To construct data warehouse and cube from legacy database systems makes it easy and fast to transform raw data into integrated and categorized meaningful information. In this paper, we built a BI system for an University administration. Several source system databases were integrated to data warehouse to build data cubes. The implemented BI system shows much faster data analysis and reporting ability than the manipulation in legacy systems. It is especially efficient in multi dimensional data analysis, nonetheless in single dimensional analysis.

• The Journal of Korea Robotics Society
• /
• v.15 no.2
• /
• pp.184-189
• /
• 2020

This paper is a study on data augmentation for small dataset by using deep learning. In case of training a deep learning model for recognition and classification of non-mainstream objects, there is a limit to obtaining a large amount of training data. Therefore, this paper proposes a data augmentation method using perspective transform and image synthesis. In addition, it is necessary to save the object area for all training data to detect the object area. Thus, we devised a way to augment the data and save object regions at the same time. To verify the performance of the augmented data using the proposed method, an experiment was conducted to compare classification accuracy with the augmented data by the traditional method, and transfer learning was used in model learning. As experimental results, the model trained using the proposed method showed higher accuracy than the model trained using the traditional method.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.10
• /
• pp.35-42
• /
• 2020

This study aims to investigate the relationship between the variables of Current Ratio (CR), Return-on-Equity (ROE), Return-on-Assets (ROA), Debt-to-Equity Ratio (DER), and Firm Size (FS) on Dividend Policy (DP) in real estate and property companies listed on the Indonesia Stock Exchange in the period 2016-2019, looking at nine real estate companies in Indonesia. The research methodology uses an explanatory analysis approach and linear regression. Based on the eligibility and homogeneity of the data, the number of sample companies selected was nine companies. The company's financial statement data derived from primary data obtained on the Indonesia Stock Exchange, such as current ratio (CR), return-on-equity (ROE), return-on-assets (ROA), debt-to-equity ratio (DER) and firm size and dividend policy variables. The data analysis procedure is first to transform financial data from the original ratio data into interval data and, then, transform it to ordinal data. Furthermore, the validity and reliability process are ignored because the data is primary. Finally, regression testing is part of the hypothesis testing stage. The results of this study showed that the CR, ROE, and firm size had no positive and significant effect on dividend policy. In contrast, DER and ROA have a positive and significant impact on dividend policy.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.4
• /
• pp.1342-1347
• /
• 2010

Various researches have been studied on WSN(wireless sensor network) for barbed wire entanglements surveillance applications such as industry facilities, security area, prison, military area, airport, etc. Currently, barbed wire entanglements surveillance is formed wire sensor network environment. Traditional wire sensor network guarantee high data transmission rate. Therefore, wire sensor network use fast fourier transform of data of high transmission rate for extraction of feature parameter. However, wireless sensor network in comparison with wire sensor network has very low data transmission rate. Therefore, wireless sensor network doesn't use fast fourier transform of wire sensor network for extraction of feature parameter. In this paper, proposed method use 1 level approximation coefficient of DTW(dynamic time-warped) algorithms based on DWT(discrete wavelet transform) for extraction of detection feature parameter and classification feature parameter for barbed wire entanglements surveillance. l level approximation coefficient have time information and frequency information of signal. Therefore, Dynamic time-warped algorithms based on discrete wavelet transform improve detection and classification of target rather than using energy of signal.

• Proceedings of the IEEK Conference
• /
• 1998.10a
• /
• pp.1101-1104
• /
• 1998

The modern multimedia applications which are video processor, video conference or video phone and so forth require real time processing. Because of a large amount of image data, those require high compression performance. In this paper, the prosposed image processing encoder was designed by using wavelet transform encoding. The proposed filter block can process imae data on the high speed because of composing individual function blocks by parallel and compute both highpass and lowpass coefficient in the same clock cycle. When image data is decomposed into multiresolution, the proposed scheme needs external memory and controller to save intermediate results and it can operate within 33MHz.