• Coupled systems mechanics
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• v.12 no.6
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• pp.503-517
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• 2023

Standard Digital Volume Correlation (DVC) approaches are based on pattern matching between two reconstructed volumes acquired at different stages. Such frameworks are limited by the number of scans (due to acquisition duration), and time-dependent phenomena can generally not be captured. Projection-based Digital Volume Correlation (P-DVC) measures displacement fields from series of 2D radiographs acquired at different angles and loadings, thus resulting in richer temporal sampling (compared to standard DVC). The sought displacement field is decomposed over a basis of separated variables, namely, temporal and spatial modes. This study utilizes an alternative route in which spatial modes are con-structed via scan-wise DVC, and thus only the temporal amplitudes are sought via P-DVC. This meth-od is applied to a glass fiber mat reinforced polymer specimen containing a machined notch, subjected to in-situ cyclic tension, and imaged via X-Ray Computed Tomography. Different temporal interpolations are exploited. It is shown that utilizing only one DVC displacement field (as spatial mode) was sufficient to properly capture the complex kinematics up to specimen failure.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.8
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• pp.782-791
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• 1991

In the fields of pattern recognition such as speech recognition, several new techniques using Artifical Neural network Models have been proposed and implemented. In particular, the Multilayer Perception Model has been shown to be effective in static speech pattern recognition. But speech has dynamic or temporal characteristics and the most important point in implementing speech recognition systems using Artificial Neural Network Models for continuous speech is the learning of dynamic characteristics and the distributed cues and contextual effects that result from temporal characteristics. But Recurrent Multilayer Perceptron Model is known to be able to learn sequence of pattern. In this paper, the results of applying the Recurrent Model which has possibilities of learning tedmporal characteristics of speech to phoneme recognition is presented. The test data consist of 144 Vowel+ Consonant + Vowel speech chains made up of 4 Korean monothongs and 9 Korean plosive consonants. The input parameters of Artificial Neural Network model used are the FFT coefficients, residual error and zero crossing rates. The Baseline model showed a recognition rate of 91% for volwels and 71% for plosive consonants of one male speaker. We obtained better recognition rates from various other experiments compared to the existing multilayer perceptron model, thus showed the recurrent model to be better suited to speech recognition. And the possibility of using Recurrent Models for speech recognition was experimented by changing the configuration of this baseline model.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.6
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• pp.650-662
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• 2011

The statistical tools such as empirical orthogonal function (EOF), and singular value decomposition (SVD) have been applied to analyze the characteristic of air pollutant over southeast Asia as well as to evaluate Zimeke's tropospheric column ozone (ZTO) determined by tropospheric residual method. In this study, we found that the EOF and SVD analyses are useful methods to extract the most significant temporal and spatial pattern from enormous amounts of satellite data. The EOF analyses with OMI $NO_2$ and OMI HCHO over southeast Asia revealed that the spatial pattern showed high correlation with fire count (r=0.8) and the EOF analysis of CO (r=0.7). This suggests that biomass burning influences a major seasonal variability on $NO_2$ and HCHO over this region. The EOF analysis of ZTO has indicated that the location of maximum ZTO was considerably shifted westward from the location of maximum of fire count and maximum month of ZTO occurred a month later than maximum month (March) of $NO_2$, HCHO and CO. For further analyses, we have performed the SVD analyses between ZTO and ozone precursor to examine their correlation and to check temporal and spatial consistency between two variables. The spatial pattern of ZTO showed latitudinal gradient that could result from latitudinal gradient of stratospheric ozone and temporal maximum of ZTO in March appears to be associated with stratospheric ozone variability that shows maximum in March. These results suggest that there are some sources of error in the tropospheric residual method associated with cloud height error, low efficiency of tropospheric ozone, and low accuracy in lower stratospheric ozone.

• Journal of Korea Water Resources Association
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• v.44 no.12
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• pp.941-954
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• 2011

In this study, past method and recent method for flood discharge with domestic multi-purpose dams in Korea were compared and analyzed with respect to the scale of watershed. Rainfall depth, temporal pattern, rainfall excess, rainfall-runoff model, parameter estimation and base flow were selected as the principal factors affecting flood discharge and effects on flood discharge were analyzed quantitatively by using sensitivity analysis. The results showed that the flood discharges calculated by past and recent method increased and decreased with a wide range of discharge with respect to the scale of watershed. The reason for decrease of flood discharge is the exchange of temporal pattern of rainfall and the principal reasons for increase of flood discharge are the increase of rainfall depth by unusual weather phenomena and the difference of estimation method for parameters of unit hydrograph.

• Journal of Korean Neurosurgical Society
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• v.30 no.6
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• pp.699-704
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• 2001

Objectives : The rupture of middle cerebral artery(MCA) aneurysm usually cause or is associated with higher incidence of intracerebral hemorrhages(ICH) than any other aneurysmal ruptures. Also, the outcome of patients who had ICH is known to be worse than patients who had subarachnoid hemorrhage(SAH) only. The authors report the bleeding pattern and outcome of ruptured MCA aneurysm patients. Patients and Methods : A total 106 ruptured MCA aneurysm patients who were surgically treated were included and they were divided into 2 groups by the initial brain CT findings according to the presence or absence of ICH over 10cc in amount. The clinical data were analysed retrospectively. Results : The overall mortality was 18.9%. Among 81 patients(76.4%) who had subarachnoid hemorrhage(SAH) only, 68 patients(84%) showed favorable outcome. Twenty five patients(23.6%) had ICH over 10cc in amount with or without SAH, and among them, 11 patients(44%) showed favorable outcome. The ICH was located in temporal lobe(15 patients, 60%), frontal lobe(3, 12%), sylvian fissure(6, 24%) and frontal-temporal lobe(1, 4%). Among 15 patients who had ICH in temporal lobe, only 4 patients(26.6%) showed favorable outcome and all 3 patients who had ICH in frontal lobe showed favorable outcome. Conclusion : ICH was presented in 23.6% of ruptured MCA aneurysm patients and the prognosis of patients with ICH was worse than patients with SAH only. The ICH was located mainly in the temporal lobe and sylvian fissure.

• Journal of the Korean Geographical Society
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• v.42 no.4
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• pp.632-647
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• 2007

Lots of geographic phenomena have dynamic spatial patterns with time changes, and there have been lots of researches on exploring these dynamic spatial patterns. However, most of these researches focused on the static pattern analysis in a given period, rather than dealing with dynamic changes in the spatial pattern over time with the continual or cumulative perspective. For this reason, investigation of the inertia of spatial process in terms of temporal changes is needed. From this background, the purpose of this paper is to propose the methodology to explore the changes in spatial pattern cumulatively by considering the inertia of the spatial statistics over time, and to apply it to the case study That is, we introduce the new spatial statistic, and produce the z-values of the statistic using Monte Carlo Simulation, and then to explore the changes in spatial patterns over time cumulatively. To do this, the method to combine the J statistic with CUSUM statistic for exploring spatial patterns, and to apply it to the changes in the commercial landuse in Erie County, New York State. Through the proposed method for spatio-temporal Patterns, we could explore continual changes effectively in the spatial patterns reflecting the statistics by temporal spot cumulatively.

• Journal of Korea Spatial Information System Society
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• v.10 no.2
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• pp.63-79
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• 2008

With the recent development of advanced GIS and complex spatial analysis technologies, the more sophisticated technologies are being required to support the advanced knowledge for solving geographical or spatial problems in various decision support systems. In addition, necessity for research on scientific crime investigation and forensic science is increasing particularly at law enforcement agencies and investigation institutions for efficient investigation and the prevention of crimes. There are active researches on geographic profiling to predict the base location such as criminals' residence by analyzing the spatial patterns of serial crimes. However, as previous researches on geographic profiling use simply statistical methods for spatial pattern analysis and do not apply a variety of spatial and temporal analysis technologies on serial crimes, they have the low prediction accuracy. Therefore, this paper identifies the typology the spatio-temporal patterns of serial crimes according to spatial distribution of crime sites and temporal distribution on occurrence of crimes and proposes STA-BLP(Spatio-Temporal Analysis based Base Location Prediction) algorithm which predicts the base location of serial crimes more accurately based on the patterns. STA-BLP improves the prediction accuracy by considering of the anisotropic pattern of serial crimes committed by criminals who prefer specific directions on a crime trip and the learning effect of criminals through repeated movement along the same route. In addition, it can predict base location more accurately in the serial crimes from multiple bases with the local prediction for some crime sites included in a cluster and the global prediction for all crime sites. Through a variety of experiments, we proved the superiority of the STA-BLP by comparing it with previous algorithms in terms of prediction accuracy.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.429-432
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• 1999

Vegetation cover classification is examined based on a time series NOAA/AVHRR data. Time series data analysis methods including Fourier transform, Auto-Regressive (AR) model and temporal signature similarity matching are developed to extract phenological features of vegetation from a time series NDVI data from NOAA/AVHRR and to classify vegetation types. In the Fourier transform method, typical three spectral components expressing the phenological features of vegetation are selected for classification, and also in the AR model method AR coefficients are selected. In the temporal signature similarity matching method a new index evaluating the similarity of temporal pattern of the NDVI is introduced for classification.

• Speech Sciences
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• v.13 no.1
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• pp.43-54
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• 2006

The purpose of this study is to examine temporal structures of English words beginning with an /s/ plus stop sequence through production experiments with native speakers of Korean learning English and native speakers of English. According to the results of our production experiment, both a beginner and an advanced group of Korean English learners produced /s/ shorter than a following stop, while the opposite pattern was observed in English native speakers' production. An advanced group of Korean English learners were good at producing a stop after /s/ as unaspirated, but their production of a stop following /s/ was different from English native speakers' production in that the closure duration of the stop was much longer.

• The Korean Journal of Financial Studies
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• v.2 no.1
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• pp.145-158
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• 1995

According to recent empirical studies, there is a systematic pattern in temporal behaviors of asset returns, and that systematic pattern is related to the business cycle. I propose a model which captures this evidence. This is done by considering a state dependent preference structure where state dependency is related to the business cycle. In this setting, the three main puzzles(i.e., the volatility puzzle, the equity premium puzzle, mean reversion) are understood as interrelated behaviors.