• Earthquakes and Structures
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• v.16 no.3
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• pp.321-327
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• 2019

This study investigated the possibility of using the recorded micro tremor data on ground level as ambient vibration input excitation data for investigation and application Operational Modal Analysis (OMA) on the bench-scale earthquake simulator (The Quanser Shake Table) for model chimney. As known OMA methods (such as EFDD, SSI and so on) are supposed to deal with the ambient responses. For this purpose, analytical and experimental modal analysis of a model chimney for dynamic characteristics was performed. 3D Finite element model of the chimney was evaluated based on the design drawing. Ambient excitation was provided by shake table from the recorded micro tremor ambient vibration data on ground level. Enhanced Frequency Domain Decomposition is used for the output only modal identification. From this study, best correlation is found between mode shapes. Natural frequencies and analytical frequencies in average (only) 1.996% are different.

• ETRI Journal
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• v.43 no.1
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• pp.152-162
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• 2021

In the last few years, detection has become a powerful methodology for network protection and security. This paper presents a new detection scheme for data recorded over a computer network. This approach is applicable to the broad scientific field of information security, including intrusion detection and prevention. The proposed method employs bidimensional (time-frequency) data representations of the forms of the short-time Fourier transform, as well as the Wigner distribution. Moreover, the method applies matrix factorization using singular value decomposition and principal component analysis of the two-dimensional data representation matrices to detect intrusions. The current scheme was evaluated using numerous tests on network activities, which were recorded and presented in the KDD-NSL and UNSW-NB15 datasets. The efficiency and robustness of the technique have been experimentally proved.

• Physical Therapy Korea
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• v.13 no.1
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• pp.54-60
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• 2006

This study aimed to compare movement patterns of shoulder joints between the right and left symmetry in stroke patients and control subjects. This study proposes use of the voluntary response index (VRI) calculated from quantitative analysis of surface electromyographic (sEMG) and motion data recorded during voluntary movement as a feeding task. The VRI is comprised of two numeric values, one derived from the total muscle activity recorded for the voluntary motor task (magnitude), and the other from the sEMG distribution across the recorded muscles with the similarity index (SI). Five stroke patients and five age-matched healthy controls were recruited. Feeding motion was performed using the provided spoon five times with rests taken on a chair in between tasks. EMG data were digitized and analyzed on the basis of the root mean square (RMS) envelope of activity. The average amplitude of responses was calculated. Responsiveness and clinically meaningful levels of discrimination between stroke patients and control for EMG magnitude and SI were determined. The similarity index of the results from two successive examinations of both sides apart for stroke patients and control subjects were .86 and .95 in motion analysis and .84 and .99 in electromyographic analysis. The SI of sEMG data and motion data was significantly correlated in stroke patients. The data suggest that SI is a sensitive program for comparing and analyzing the symmetry of muscle activity and motion in both sides. This analysis method has a clinical value in grading muscular activity and movement impairment after brain injury.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.9
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• pp.215-223
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• 2017

Various methods for creating interesting games are used in the development process. Because the empirical part is difficult to measure and analyze, it usually only measures and analyzes the parts where data are easy to quantify. This is a clear limit to the fact that the experience of the game is important.This study proposes a system that recognizes the face of a game user and measures the emotion change from the recognized information in order to easily quantify the experience of the user who is playing the game. The system recognizes emotions and records them in real time from the face of the user who is playing the game. These recorded data include time and figures related to the progress of the game, and numerical values for emotions recognized from the face. Using the recorded data, it is possible to judge what kind of emotion the game induces to the user at a certain point in time. Numerical data on the recorded empirical part using the system of this study is expected to help develop the game according to the developer 's intention.

• Water for future
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• v.18 no.1
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• pp.9-11
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• 1985

• Journal of Korean Academy of Nursing
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• v.36 no.2
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• pp.227-235
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• 2006

Purpose: This study aimed to: 1) determine the core nursing interventions, and 2) compare acute interventions with subacute interventions recorded in the nursing notes of patients with cerebrovascular accidents (CVA). Methods: The nursing records covering the first 10 days of 30 patients with a CVA who were admitted from January to December 2004 at C University Hospital in Korea were examined. Data was collected using the nursing interventions classification (NIC) from January to April 2005. Finally, data analysis was carried out using mean, SD, and paired t-test according to domains, classes, and interventions. Results: The most frequent nursing intervention at both stage was 'Neurologic monitoring'. There were differences in interventions belonging to the 'Physiological: complex,' 'Behavioral,' 'Safety,' and 'Health system' domains between the acute and subacute stages. The frequency of interventions belonging to the 'Immobility management,' 'Neurological management,' 'Tissue perfusion management,' 'Patient education,' 'Risk management,' 'Health system mediation,' and 'Information management' classes at the acute stage was higher compared to the subacute stage. Conclusions: This study found out that nurses relatively recorded more nursing interventions during the acute stage hence the unsuccessful documentation of the subacute stage particularly in describing the specific nursing interventions at this stage.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.10a
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• pp.439-446
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• 1998

One of the most important roles in the nonlinear dynamic structural analysis is to select a proper ground excitation, which dominates the response of a structure. Because of the lack of recorded accelerograms in Korea, a stochastic model of ground excitation with various dynamic properties rather than recorded accelerograms is necessarily required. If all information is not available at site, the information from other sites with similar features can be used by the procedure of seismic hazard analysis. Eliopoulos and Wen identified the parameters of the ground motion model by the empirical relations or expressions developed by Trifunac and Lee. Because the relations used in the parameter identification are largely empirical, it is required to apply the artificial neural networks instead of the empirical model. Additionally, neural networks have the advantage of the empirical model that it can continuously re-train the new recorded data, so that it can adapt to the change of the enormous data. Based on the redefined traditional processes, three neural-networks-based models (FAS_NN, PSD_NN and INT_NN) are proposed to individually substitute the Fourier amplitude spectrum, the parameter identification of power spectral density function and intensity function. The paper describes the first half of the research for the development of Neural-Networks-based model for the generation of an Artificial earthquake and a Response Spectrum(NNARS).

• Journal of Digital Convergence
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• v.13 no.1
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• pp.289-295
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• 2015

Laboratory Research Notebook is the recorded data of the process and the result of research and also the most basic data. To record and manage R&D information systematically is a very basic method to secure R&D quality and becomes a foundation of performing a project successfully. Especially, as the laboratory environment is getting digitalized due to development of IT technology, most R&D information is recorded as electronic documents. Therefore, Laboratory Research Notebook is used to effectively manage R&D information recorded as electronic documents. This study would like to look at Laboratory Research Notebook which is used to manage R&D information systematically and then present a model which suits research environment.

• Magazine of the Korean Society of Agricultural Engineers
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• v.36 no.1
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• pp.63-72
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• 1994

The objective of the study is to develop weather generators for daily rainfall and small pan evaporation and to test the applicability with recorded data. Daily rainfall forecasting model(DRFM) was developed that uses a first order Markov chain to describe rainfall seque- nces and applies an incomplete Gamma function to predict the amount of precipitation. Daily evaporation forecasting model(DEFM) that adopts a normal distribution function to generate the evaporation for dry and wet days was also formulated. DRFM and DEFM were tested with twenty year weather data from eleven stations using Chi-square and Kolmogorov and Smirnov goodness of fit tests. The test results showed that the generated sequences of rainfall occurrence, amount of rainfall, and pan evaporation were statistically fit to recorded data from eleven, seven, and seven stations at the 5% level of significance. Generated rainfall data from DRFM were very close in frequency distri- bution patterns to records for stations all over the country. Pan evaporation for rainy days generated were less accurate than that for dry days. And the proposed models may be used as tools to provide many mathematical models with long-term daily rainfall and small pan evaporation data. An example is an irrigation scheduling model, which will be further detailed in the paper.

• Smart Structures and Systems
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• v.31 no.2
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• pp.183-197
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• 2023

For long-span bridges with a structural health monitoring (SHM) system, environmental temperature-driven responses are proved to be a main component in measurements. However, anomalous structural behavior may be hidden incomplicated recorded data. In order to receive reliable assessment of structural performance, it is important to study therelationship between temperature and monitoring data. This paper presents an application of the cointegration based methodology to detect anomalies that may be masked by temperature effects and then forecast the temperature-induced deflection (TID) of long-span suspension bridges. Firstly, temperature effects on girder deflection are analyzed with fieldmeasured data of a suspension bridge. Subsequently, the cointegration testing procedure is conducted. A threshold-based anomaly detection framework that eliminates the influence of environmental temperature is also proposed. The cointegrated residual series is extracted as the index to monitor anomaly events in bridges. Then, wavelet separation method is used to obtain TIDs from recorded data. Combining cointegration theory with autoregressive moving average (ARMA) model, TIDs for longspan bridges are modeled and forecasted. Finally, in-situ measurements of Xihoumen Bridge are adopted as an example to demonstrate the effectiveness of the cointegration based approach. In conclusion, the proposed method is practical for actual structures which ensures the efficient management and maintenance based on monitoring data.