KSCE Journal of Civil and Environmental Engineering Research
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v.30
no.2B
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pp.191-198
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2010
Assessing seasonality of precipitation is necessarily required to establish future plans and policies for water resources management. In this regard, a main objective of the study is to introduce an effective approach for assessing the seasonality of the precipitation and evaluate the seasonality through the proposed one. We have used circular statistics to characterize the seasonality on the precipitation in Korea. The circular statistics allow us to effectively assess changes in timing of the seasonality in detail. It was found that peak time on monthly rainfall occurred between end of June and early July in southern coastal area while the timing was delayed in northern part of Korea because of monsoon moving in from south to north. In case of annual daily peak precipitation, spatio-temporal variation of the peak time was increased. It is mainly because of geophysical effects, frequency and paths of typhoons. Finally, temporal variations on the timing of the peak seasons were evaluated through circular statistics by 30-year moving average data. The peak season in the Northen part of Korea (e.g. Seoul and Gangrung) has been moved back from early July to end of July while the peak season has been moved up from middle of July to early July in the Southern part of Korea (e.g. Busan and Mokpo). It seems that changes in seasonality are mostly modulated by variability in the east-asia monsoon system.
Journal of the Korea Society of Computer and Information
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v.28
no.10
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pp.133-153
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2023
In this paper, we propose a novel approach to investigating brain-signal measurement technology using Electroencephalography (EEG). Traditionally, researchers have combined EEG signals with bio-signals (BSs) to enhance the classification performance of emotional states. Our objective was to explore the synergistic effects of coupling EEG and BSs, and determine whether the combination of EEG+BS improves the classification accuracy of emotional states compared to using EEG alone or combining EEG with pseudo-random signals (PS) generated arbitrarily by random generators. Employing four feature extraction methods, we examined four combinations: EEG alone, EG+BS, EEG+BS+PS, and EEG+PS, utilizing data from two widely-used open datasets. Emotional states (task versus rest states) were classified using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classifiers. Our results revealed that when using the highest accuracy SVM-FFT, the average error rates of EEG+BS were 4.7% and 6.5% higher than those of EEG+PS and EEG alone, respectively. We also conducted a thorough analysis of EEG+BS by combining numerous PSs. The error rate of EEG+BS+PS displayed a V-shaped curve, initially decreasing due to the deep double descent phenomenon, followed by an increase attributed to the curse of dimensionality. Consequently, our findings suggest that the combination of EEG+BS may not always yield promising classification performance.
This study used intersubject representational similarity analysis (IS-RSA) to identify participant-response consistency patterns in previously published data. Additionally, analysis of variance (ANOVA) was utilized to detect any variations in the conditions of each experiment. In each experiment, a combination of ASMR stimulation, visual and auditory stimuli, and time-series emotional video stimulation was employed, and emotional ratings and physiological measurements were collected in accordance with the respective experimental conditions. Every pair of participants' measurements for each stimulus in each experiment was correlated using Pearson correlation coefficient as part of the IS-RSA. The results of study revealed a consistent response pattern among participants exposed to ASMR, visual, and auditory stimuli, in contrast to those exposed to time-series emotional video stimulation. Notably, the ASMR experiment demonstrated a high level of response consistency among participants in positive conditions. Furthermore, both auditory and visual experiments exhibited remarkable consistency in participants' responses, especially when subjected to high arousal levels and visual stimulation. The findings of this study confirm that IS-RSA serves as a valuable tool for summarizing and presenting multidimensional data information. Within the scope of this study, IS-RSA emerged as a reliable method for analyzing multidimensional data, effectively capturing and presenting comprehensive information pertaining to the participants.
Most brachytherapy treatment planning systems employ a dosimetry formalism based on the AAPM TG-43 report which does not appropriately consider tissue heterogeneity. In this study we aimed to set up a simple Monte Carlo-based intracavitary high-dose-rate brachytherapy (IC-HDRB) plan verification platform, focusing particularly on the robustness of the direct Monte Carlo dose calculation using material and density information derived from CT images. CT images of slab phantoms and a uterine cervical cancer patient were used for brachytherapy plans based on the Plato (Nucletron, Netherlands) brachytherapy planning system. Monte Carlo simulations were implemented using the parameters from the Plato system and compared with the EBT film dosimetry and conventional dose computations. EGSnrc based DOSXYZnrc code was used for Monte Carlo simulations. Each $^{192}Ir$ source of the afterloader was approximately modeled as a parallel-piped shape inside the converted CT data set whose voxel size was $2{\times}2{\times}2\;mm^3$. Bracytherapy dose calculations based on the TG-43 showed good agreement with the Monte Carlo results in a homogeneous media whose density was close to water, but there were significant errors in high-density materials. For a patient case, A and B point dose differences were less than 3%, while the mean dose discrepancy was as much as 5%. Conventional dose computation methods might underdose the targets by not accounting for the effects of high-density materials. The proposed platform was shown to be feasible and to have good dose calculation accuracy. One should be careful when confirming the plan using a conventional brachytherapy dose computation method, and moreover, an independent dose verification system as developed in this study might be helpful.
The results from the experiment of natural collection and growth of arkshell(Anadara broughtoni, SCHRENCK) spat, which was collected at Seok-gok Bay located at Chang-weon-gun, Korea, and grown in the 1ittoral zone of Sa-gok-ri, Geo-je-gun, Korea, beginning from August 1,1973 to April 8,1974 are as follows; The larvae of arkshell came out from the very first day of the study August 1, which continued till the end of September, whose amount was not so much varied, but relatively continuous. The drifting larvae were also rather small amount, from which some 200 to 400 larvae were attached through the respective collecting equipment made from straw with the dimension of length 1.5m and breadth 12cm. It is regarded relatively large amount collected and recognised worth while enough to use as the industrial collecting method. The average shell length of spat attached was 0.54mm on 17th September, 4.11mm on 21st October, 10.47mm on 25th November in 1973 and 11.08mm on 8th April in 1974 whose progress was recognized relatively faster, comparing with the other up-to date report, The relationship between the shell length(L) and the longest radial rib(R) was as follows: Below 2.5mm of the shell length: R=0.7393 L -0.0080 Above 2.5mm of the shell length: R=0.8253 L-0.2595 And the relationship between the shell length (L) and the shell breadth (B) was as follows: Below 2.5mm of the shell length: B=0.3505 L +0.0527 Above 2.5mm of the shell length: B=0.4631 L -0.3602 The exponential curve equation between shell length (L) and the total weight (W)was as follows : Below 16mm of the shell length: W=0.19957 $L^{2.5726}$ Above 16mm of the shell length: W=0.20602 $L^{2.8400}$ In view of the above the relative growth among the shell length, the longest radial rib and the shell breadth was changed in the vicinity of 2.5mm of the shell length. And relationship between the shell length and the total weight was changed in the vicinity of 16mm of the shell length.
Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.
Monte Carlo method has been known as the most accurate method for calculating absorbed dose in the human body, and an anthropomorphic phantom has been mainly used as a method of simulating internal organs for using such a calculation method. However, various efforts are made to extract data on several internal organs in the human body directly from CT DICOM files in recent Monte Carlo calculation using Geant4 code and to use by converting them into the geometry necessary for simulation. Such a function makes it possible to calculate the internal absorbed dose accurately while duplicating the actual human anatomical structure. Thus, this study calculated the absorbed dose in the human body by using Geant4 associating with DICOM files, and aimed to confirm the usefulness by compare the result with the measured dose using a Gafchromic EBT2 film. This study compared the dose calculated using simulation and the measured dose in beam central axis using the EBT2 film. The results showed that the range of difference was an average of 3.75% except for a build-up region, in which the dose rapidly changed from skin surface to the depth of maximum dose. In addition, this study made it easy to confirm the target absorbed dose by internal organ and organ through the output of the calculated value of dose by CT slice and the dose value of each voxel in each slice. Thus, the method that outputs dose value by slice and voxel through the use of CT DICOM, which is actual image data of human body, instead of the anthropomorphic phantom enables accurate dose calculations of various regions. Therefore, it is considered that it will be useful for dose calculation of radiotherapy planning system in the future. Moreover, it is applicable for currently-used several energy ranges in current use, so it is considered that it will be effectively used in order to check the radiation absorbed dose in the human body.
Using airborne magnetic data, magnetic characteristics were studied at the Samryangjin caldera area developed in the volcanics of the Yuchon sub-basin, the south eastern part of the Gyeongsang basin. Residual magnetics, reduction to the pole, horizontal derivative, and vertical derivative maps are prepared. Using these maps, the magnetic lithofaces are zoned and the geological structures such as caldera and faults were qualitatively interpreted. In addition, the two quantitative interpretations were performed. Firstly, the forward modelling were done to the 14.5 line km crossing the caldera area to the northeast-southwest direction. Applying the 3-D Euler deconvolution method to the whole study area, the depth extent and the characteristics of the magnetic anomalous bodies were studied. According to the results, the magnetic lithofaces of the area are zoned by 4 units. In general, these are well matched with the geological distributions. But the biotite granites intruded in the northern boundary of the Samryangjin caldera show the high magnetic intensity, while the biotite granites of the other areas show the low magnetic intensity and the different magnetic lithofaces. Thus, we interpreted that the biotite granites are closely related with the volcanic activity of the Samryngjin caldera, and are intruded in the fracture zones developed along the caldera rim. The Samryangjin caldera and fault structures of the area can be easily recognized by the distinct magnetic structures from the various magnetic anomaly maps. Also the topographic characteristics well reflect these structures. The results of the forward modelling show that the magnetic basement depth of the Gyeongsang sedimentary basin is on the average about 6 km and in maximum 10 km. And the depth becomes shallower toward the caldera boundary due to the shallow intrusion of the volcanics. The results of the 3-D Euler method also show the caldera and fault structures. And the relatively shallow magnetic anomalous bodies which are related with the volcanics are generally developed to the east-west and northeast directions, while the deep magnetic anomalous bodies to the northwest direction.
In this research, a methodology was developed for constructing an appropriate rainfall image database for estimating rainfall intensity based on CCTV video. The database was constructed in the Large-Scale Climate Environment Chamber of the Korea Conformity Laboratories, which can control variables with high irregularity and variability in real environments. 1,728 scenarios were designed under five different experimental conditions. 36 scenarios and a total of 97,200 frames were selected. Rain streaks were extracted using the k-nearest neighbor algorithm by calculating the difference between each image and the background. To prevent overfitting, data with pixel values greater than set threshold, compared to the average pixel value for each image, were selected. The area with maximum pixel variability was determined by shifting with every 10 pixels and set as a representative area (180×180) for the original image. After re-transforming to 120×120 size as an input data for convolutional neural networks model, image augmentation was progressed under unified shooting conditions. 92% of the data showed within the 10% absolute range of PBIAS. It is clear that the final results in this study have the potential to enhance the accuracy and efficacy of existing real-world CCTV systems with transfer learning.
Journal of the Institute of Electronics Engineers of Korea SP
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v.43
no.6
s.312
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pp.28-35
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2006
Statistical models of shape variability based on active shape models (ASMs) have been successfully utilized to perform segmentation and recognition tasks in two-dimensional (2D) images. Three-dimensional (3D) model-based approaches are more promising than 2D approaches since they can bring in more realistic shape constraints for recognizing and delineating the object boundary. For 3D model-based approaches, however, building the 3D shape model from a training set of segmented instances of an object is a major challenge and currently it remains an open problem in building the 3D shape model, one essential step is to generate a point distribution model (PDM). Corresponding landmarks must be selected in all1 training shapes for generating PDM, and manual determination of landmark correspondences is very time-consuming, tedious, and error-prone. In this paper, we propose a novel automatic method for generating 3D statistical shape models. Given a set of training 3D shapes, we generate a 3D model by 1) building the mean shape fro]n the distance transform of the training shapes, 2) utilizing a tetrahedron method for automatically selecting landmarks on the mean shape, and 3) subsequently propagating these landmarks to each training shape via a distance labeling method. In this paper, we investigate the accuracy and compactness of the 3D model for the human liver built from 50 segmented individual CT data sets. The proposed method is very general without such assumptions and can be applied to other data sets.
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