• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.147-149
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• 2022

This paper proposes a state machine design methodology in which an interactive robot that mimics discrete trial training (DTT protocol) can support social interaction training for children with autism. The robot applied to social interaction training uses the response to the provided training stimulus as a quantitative indicator by processing the data received from the sensors measuring the behavioral response of the child. In this process, the state machine is used as information that classifies the state of the acquired data and provides the subsequent stimulus for DTT protocol. Through the joint attentional training, it can be used as evidence-based treatment information by quantitatively classifying the data on the number of sustainable and DTT protocol and the child's response, as well as the current reaction status of the child to the observer performing remote monitoring. At the same time, it was confirmed that it is possible to properly respond to misrecognition situations.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Journal of the Korean Geotechnical Society
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• v.30 no.7
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• pp.41-53
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• 2014

This study examined the magnitude and distribution of the earth pressure on the support system in a jointed rock mass by considering different joint shear strength, rock type, and joint inclination angle. The study particularly focused on the effect of joint cohesive strength for a certain condition. Based on a physical model test (Son and Park, 2014), extended parametric studies were conducted considering rock-structure interactions based on the discrete element method, which can consider the rock and joint characteristics of rock mass. The results showed the earth pressure was strongly affected by the joint cohesive strength as well as the rock type and joint inclination angle. The study indicated that the effect of joint cohesive strength was particularly significant when a rock mass was under the condition of joint sliding. This paper investigates the magnitude of joint cohesive strength to prevent a joint sliding for each different condition. The test results were also compared with Peck's earth pressure, which has been frequently used for soil ground. The comparison indicated that the earth pressure in a jointed rock mass can be significantly different from that in soil ground. This study is expected to provide a better understanding of the earth pressure on the support system in a jointed rock mass.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.1
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• pp.132-148
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• 2013

Via the Internet, the information infrastructure of modern health care has already established medical information systems to share electronic health records among patients and health care providers. Data hiding plays an important role to protect medical images. Because modern medical devices have improved, high resolutions of medical images are provided to detect early diseases. The high quality medical images are used to recognize complicated anatomical structures such as soft tissues, muscles, and internal organs to support diagnosis of diseases. For instance, 16-bit depth medical images will provide 65,536 discrete levels to show more details of anatomical structures. In general, the feature of low utilization rate of intensity in 16-bit depth will be utilized to handle overflow/underflow problem. Nowadays, most of data hiding algorithms are still experimenting on 8-bit depth medical images. We proposed a novel reversible data hiding scheme testing on 16-bit depth CT and MRI medical image. And the peak point and zero point of a histogram are applied to embed secret message k bits without salt-and-pepper.

• Korean Journal of Remote Sensing
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• v.30 no.3
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• pp.399-415
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• 2014

The intensity of stormwater runoff is particularly acute across cities located in arid climates. During flash floods loose sediment and pollutants are typically transported across sun-hardened surfaces contributing to widespread degradation of water quality. Rapid, dense urbanization exacerbates the problem by creating continuous areas of impervious surfaces, perforated only by a few green patches. Our work demonstrates how the latest techniques in remote sensing can be used to routinely measure urban land cover types, impervious cover, and vegetated areas. In addition, multiple regression models can then infer relationships between urban land use and land cover types with stormwater quality data, initially sampled at discrete monitoring sites, and then extrapolated annually across an arid city; in our case, the city of Phoenix in Arizona, USA. Results reveal that from 30 storm event samples, solids and heavy metal pollutants were found to be highly related with general impervious surfaces; in particular, with industrial and commercial land use types. Repercussions stemming from this work include support for public policies that advocate environmental sustainability and the more recent focus on urban livability. Also, advocacy for new urban construction and re-development that both steer away from vast unbroken impervious surfaces, in place of more fragmented landscapes that harmonize built and green spaces.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.6
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• pp.773-785
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• 2014

For decades, modeling of pilots has been intensively studied due to its advantages in reducing costs for training and enhancing safety of pilots. In particular, research for modeling of pilots' non-kinetic behaviors which refer to the decisions made by pilots is beneficial as the expertise of pilots can be inherent in the models. With the recent growth in the amount of combat logs accumulated, employing statistical learning methods for the modeling becomes possible. However, the combat logs consist of heterogeneous data that are not only continuous or discrete but also sequence independent or dependent, making it difficult to directly applying the learning methods without modifications. Therefore, in this paper, we present a kernel function named hybrid sequence kernel which addresses the problem by using multiple kernel learning methods. Based on the empirical experiments by using combat logs obtained from a simulator, the proposed kernel showed satisfactory results.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2106-2117
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• 2017

An effective statistical feature extraction approach of data sampling of fault in the combined transmission system is presented in this paper. The proposed algorithm leads to high accuracy at minimum cost to predict fault location and fault type classification. This algorithm requires impedance measurement data from one end of the transmission line. Modal decomposition is used to extract positive sequence impedance. Then, the fault signal is decomposed by using discrete wavelet transform. Statistical sampling is used to extract appropriate fault features as benchmark of decomposed signal to train classifier. Support Vector Machine (SVM) is used to illustrate the performance of statistical sampling performance. The overall time of sampling is not exceeding 1 1/4 cycles, taking into account the interval time. The proposed method takes two steps of sampling. The first step takes 3/4 cycle of during-fault and the second step takes 1/4 cycle of post fault impedance. The interval time between the two steps is assumed to be 1/4 cycle. Extensive studies using MATLAB software show accurate fault location estimation and fault type classification of the proposed method. The classifier result is presented and compared with well-established travelling wave methods and the performance of the algorithms are analyzed and discussed.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.2
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• pp.53-59
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• 2009

Simulation Based Acquisition(SBA) which pursues to use M&S in manner of integrated collaboration is being applied in defense acquisition. To accomplish SBA efficiently, reusability, reconfiguration and scalability of M&S components are important factors. To avoid constraints caused by coupling of components, PBA is designed to add and configure components easily by enabling independent interface and interaction among the components and provides common development infrastructure also. And PBA framework is implemented to support the development of a simulator which uses the PBA. It is expected that deployment of PBA framework as common development infrastructure can raise efficiency of M&S works.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.3111-3122
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• 1997

Since the advent of an information era, the traffic of communication has been remarkably increasing and the information service has gradually become speedy and popular. With these trends, it is necessary to extend the utilization of existing twist pair cables for PSTN(Public Switched Telephone Network) to provide high speed data services for telephone subscribers, In this paper, we have studied on a structure of discrete multi-tone ADSL(Asymmetric Digital Subscriber Lines) system which can support high speed multimedia service using a twist pair cable, and analyzed the transmission performance by computer simulations.