• Journal of the Korean Geotechnical Society
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• v.23 no.4
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• pp.33-46
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• 2007

HWAW (Harmonic Wavelet Analysis of Wave) method, which is non-destructive method using body and surface waves, has the advantages of obtaining 2D subsurface imaging because it uses a short receiver spacing to obtain the $V_s$ profile of whole depth. Even though the reliability of HWAW method has already been verified by using the numerical simulation in the various layered models, it is very difficult to evaluate the reliability of HWAW in the field because the exact $V_s$ values of the experimental site are unknown. In this study, a model testing site where the material properties and layer information could be controlled was constructed to verify the reliability of HWAW method. The detailed geometry of the testing site was strictly measured by surveying, and 140 vertical and horizontal geophones were established at the boundary of each layer to evaluate the dynamic material properties. Using the interval travel times between the upper and lower geophones, the body wave velocities of each layer were 2 dimensionally obtained as reference data, and comparative study using HWAW method was performed. By comparing 2D Vs profile obtained by HWAW method to the reference data, the reliability of HWAW method was verified.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.93-101
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• 2009

This paper suggests a new algorithm automatically searching for Region-of-Interest(ROI) with high speed, using the edge information of high frequency subband transformed with wavelet. The proposed method executes a searching algorithm of 4-direction object boundary by the unit of block using the edge information, and detects ROIs. The whole image is splitted by $64{\times}64$ or $32{\times}32$ sized blocks and the blocks can be ROI block or background block according to taking the edges or not. The 4-directions searche the image from the outside to the center and the algorithm uses a feature that the low-DOF image has some edges as one goes to center. After searching all the edges, the method regards the inner blocks of the edges as ROI, and makes the ROI masks and sends them to server. This is one of the dynamic ROI method. The existing methods have had some problems of complicated filtering and region merge, but this method improved considerably the problems. Also, it was possible to apply to an application requiring real-time processing caused by the process of the unit of block.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.576-584
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• 2022

As an important branch of cognitive psychology, "constructivism" is called a "revolution" in contemporary educational psychology, which has a profound influence on the field of pedagogy and psychology. Based on "WOS" database, this study selects "WOS Core database" and "KCI database", uses CiteSpace visualization software as analysis tool, and makes knowledge map analysis on the research literature of "constructivism" in the field of education in recent 35 years. Analysis directions include annual analysis, network connection analysis by country(region) branch, author, institution or University, and keyword analysis. The purpose of the analysis is to grasp the subject areas, research hotspots and future trends of the research on constructivism, and to provide theoretical reference for the research on constructivism. There are three conclusions from the study. 1. Studies on the subject of constructivism have continued from the 1980s to the present. It is now in a period of steady development. 2. Countries concerned with the subject of constructivism mainly include the United States, Canada, Britain, Australia and the Netherlands. The main research institutions and authors are mainly located in these countries. 3. Currently, the keywords constructivism research focus on the clusters of "instructional strategies", and the development of science and technology is affecting individual learning. In the future, instructional strategies will become the focus of structural constructivism research. With the development of instructional technology, it is necessary to conduct research related to the development of new teaching models.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.499-510
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• 2023

Recently, an intelligent and advanced cyber attack attacks a computer network of a public institution using a file containing malicious code or leaks information, and the damage is increasing. Even in public institutions with various information protection systems, known attacks can be detected, but unknown dynamic and encryption attacks can be detected when existing signature-based or static analysis-based malware and ransomware file detection methods are used. vulnerable to The detection method proposed in this study extracts the detection result data of the system that can detect malicious code and ransomware among the information protection systems actually used by public institutions, derives various attributes by combining them, and uses a machine learning classification algorithm. Results are derived through experiments on how the derived properties are classified and which properties have a significant effect on the classification result and accuracy improvement. In the experimental results of this paper, although it is different for each algorithm when a specific attribute is included or not, the learning with a specific attribute shows an increase in accuracy, and later detects malicious code and ransomware files and abnormal behavior in the information protection system. It is expected that it can be used for property selection when creating algorithms.

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.129-141
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• 2023

This study analyzes the dynamic characteristics of daily freight rates of dry bulk and tanker shipping markets and their forecasting accuracy by using the error correction models. In order to calculate the error terms from the co-integrated time series, this study uses the common stochastic trend model (CSTM model) and vector error correction model (VECM model). First, the error correction model using the error term from the CSTM model yields more appropriate results of adjustment speed coefficient than one using the error term from the VECM model. Furthermore, according to the adjusted determination coefficients (adjR²), the error correction model of CSTM-model error term shows more model fitness than that of VECM-model error term. Second, according to the criteria of mean absolute error (MAE) and mean absolute scaled error (MASE) which measure the forecasting accuracy, the results show that the error correction model with CSTM-model error term produces more accurate forecasts than that of VECM-model error term in the 12 cases among the total 15 cases. This study proposes the analysis and forecast tasks 1) using both of the CSTM-model and VECM-model error terms at the same time and 2) incorporating additional data of commodity and energy markets, and 3) differentiating the adjustment speed coefficients based the sign of the error term as the future research topics.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.454-464
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• 2023

This paper proposes a low-power 8-bit asynchronous SAR ADC with a sampling rate of 1 MS/s for sensor node applications. The ADC uses bootstrapped switches to improve linearity and applies a VCM-based CDAC switching technique to reduce the power consumption and area of the DAC. Conventional synchronous SAR ADCs that operate in synchronization with an external clock suffer from high power consumption due to the use of a clock faster than the sampling rate, which can be overcome by using an asynchronous SAR ADC structure that handles internal comparisons in an asynchronous manner. In addition, the SAR logic is designed using dynamic logic circuits to reduce the large digital power consumption that occurs in low resolution ADC designs. The proposed ADC was simulated in a 180-nm CMOS process, and at a 1.8 V supply voltage and a sampling rate of 1 MS/s, it consumed 46.06 𝜇W of power, achieved an SNDR of 49.76 dB and an ENOB of 7.9738 bits, and obtained a FoM of 183.2 fJ/conv-step. The simulated DNL and INL are +0.186/-0.157 LSB and +0.111/-0.169 LSB.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.109-130
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• 2011

One of the major problems in the area of data mining is the size of the data, as most data set has huge volume these days. Streams of data are normally accumulated into data storages or databases. Transactions in internet, mobile devices and ubiquitous environment produce streams of data continuously. Some data set are just buried un-used inside huge data storage due to its huge size. Some data set is quickly lost as soon as it is created as it is not saved due to many reasons. How to use this large size data and to use data on stream efficiently are challenging questions in the study of data mining. Stream data is a data set that is accumulated to the data storage from a data source continuously. The size of this data set, in many cases, becomes increasingly large over time. To mine information from this massive data, it takes too many resources such as storage, money and time. These unique characteristics of the stream data make it difficult and expensive to store all the stream data sets accumulated over time. Otherwise, if one uses only recent or partial of data to mine information or pattern, there can be losses of valuable information, which can be useful. To avoid these problems, this study suggests a method efficiently accumulates information or patterns in the form of rule set over time. A rule set is mined from a data set in stream and this rule set is accumulated into a master rule set storage, which is also a model for real-time decision making. One of the main advantages of this method is that it takes much smaller storage space compared to the traditional method, which saves the whole data set. Another advantage of using this method is that the accumulated rule set is used as a prediction model. Prompt response to the request from users is possible anytime as the rule set is ready anytime to be used to make decisions. This makes real-time decision making possible, which is the greatest advantage of this method. Based on theories of ensemble approaches, combination of many different models can produce better prediction model in performance. The consolidated rule set actually covers all the data set while the traditional sampling approach only covers part of the whole data set. This study uses a stock market data that has a heterogeneous data set as the characteristic of data varies over time. The indexes in stock market data can fluctuate in different situations whenever there is an event influencing the stock market index. Therefore the variance of the values in each variable is large compared to that of the homogeneous data set. Prediction with heterogeneous data set is naturally much more difficult, compared to that of homogeneous data set as it is more difficult to predict in unpredictable situation. This study tests two general mining approaches and compare prediction performances of these two suggested methods with the method we suggest in this study. The first approach is inducing a rule set from the recent data set to predict new data set. The seocnd one is inducing a rule set from all the data which have been accumulated from the beginning every time one has to predict new data set. We found neither of these two is as good as the method of accumulated rule set in its performance. Furthermore, the study shows experiments with different prediction models. The first approach is building a prediction model only with more important rule sets and the second approach is the method using all the rule sets by assigning weights on the rules based on their performance. The second approach shows better performance compared to the first one. The experiments also show that the suggested method in this study can be an efficient approach for mining information and pattern with stream data. This method has a limitation of bounding its application to stock market data. More dynamic real-time steam data set is desirable for the application of this method. There is also another problem in this study. When the number of rules is increasing over time, it has to manage special rules such as redundant rules or conflicting rules efficiently.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.3
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• pp.154-166
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• 2009

Database query and reporting tools, OLAP tools and data mining tools are typical front-end tools in Business Intelligence environment which is able to support gathering, consolidating and analyzing data produced from business operation activities and provide access to the result to enterprise's users. Traditional reporting tools have an advantage of creating sophisticated dynamic reports including SQL query result sets, which look like documents produced by word processors, and publishing the reports to the Web environment, but data source for the tools is limited to RDBMS. On the other hand, OLAP tools and data mining tools have an advantage of providing powerful information analysis functions on each own way, but built-in visualization components for analysis results are limited to tables or some charts. Thus, this paper presents a system that integrates three typical front-end tools to complement one another for BI environment. Traditional reporting tools only have a query editor for generating SQL statements to bring data from RDBMS. However, the reporting tool presented by this paper can extract data also from OLAP and data mining servers, because editors for OLAP and data mining query requests are added into this tool. Traditional systems produce all documents in the server side. This structure enables reporting tools to avoid repetitive process to generate documents, when many clients intend to access the same dynamic document. But, because this system targets that a few users generate documents for data analysis, this tool generates documents at the client side. Therefore, the tool has a processing mechanism to deal with a number of data despite the limited memory capacity of the report viewer in the client side. Also, this reporting tool has data structure for integrating data from three kinds of data sources into one document. Finally, most of traditional front-end tools for BI are dependent on data source architecture from specific vendor. To overcome the problem, this system uses XMLA that is a protocol based on web service to access to data sources for OLAP and data mining services from various vendors.

• Journal of Biomedical Engineering Research
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• v.25 no.5
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• pp.391-401
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• 2004

3D imaging systems using 2D phased arrays have a large number of active channels, compelling to use a very expensive and bulky beamforming hardware, and suffer from low volume rate because, in principle, at least one ultrasound transmit-receive event is necessary to construct each scanline. A high speed 3D imaging method using a cross array proposed previously to solve the above limitations can implement fast scanning and dynamic focusing in the lateral direction but suffer from low resolution except at the fixed transmit focusing along the elevational direction. To overcome these limitations, we propose a new real-time volumetric imaging method using a cross array based on the synthetic aperture technique. In the proposed method, ultrasound wave is transmitted successively using each elements of an 1D transmit array transducer, one at a time, which is placed along the elevational direction and for each firing, the returning pulse echoes are received using all elements of an 1D receive array transducer placed along the lateral direction. On receive, by employing the conventional dynamic focusing and synthetic aperture method along lateral and elevational directions, respectively, ultrasound waves can be focused effectively at all imaging points. In addition, in the proposed method, a volume of interest consisting of any required number of slice images, can be constructed with the same number of transmit-receive steps as the total number of transmit array elements. Computer simulation results show that the proposed method can provide the same and greatly improved resolutions in the lateral and elevational directions, respectively, compared with the 3D imaging method using a cross array based on the conventional fixed focusing. In the accompanying paper, we will also propose a new real-time 3D imaging method using a cross array for improving transmit power and elevational spatial resolution, which uses linear wave fronts on transmit.

• Journal of Biomedical Engineering Research
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• v.25 no.5
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• pp.403-414
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• 2004

In the accompanying paper, we proposed a real. time volumetric imaging method using a cross array based on receive dynamic focusing and synthetic aperture focusing along lateral and elevational directions, respetively. But synthetic aperture methods using spherical waves are subject to beam spreading with increasing depth due to the wave diffraction phenomenon. Moreover, since the proposed method uses only one element for each transmission, it has a limited transmit power. To overcome these limitations, we propose a new real. time volumetric imaging method using cross arrays based on synthetic aperture technique with linear wave fronts. In the proposed method, linear wave fronts having different angles on the horizontal plane is transmitted successively from all transmit array elements. On receive, by employing the conventional dynamic focusing and synthetic aperture methods along lateral and elevational directions, respectively, ultrasound waves can be focused effectively at all imaging points. Mathematical analysis and computer simulation results show that the proposed method can provide uniform elevational resolution over a large depth of field. Especially, since the new method can construct a volume image with a limited number of transmit receive events using a full transmit aperture, it is suitable for real-time 3D imaging with high transmit power and volume rate.