• Ocean and Polar Research
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• v.26 no.3
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• pp.385-400
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• 2004

The Lago Sofia Conglomerate encased in the 2km thick hemipelagic mudstones and thinbedded turbidites of the Cretaceous Cerro Toro Formation, southern Chile, is a deposit of a gigantic submarine channel developed along a foredeep trough. It is hundreds of meters thick kilometers wide, and extends for more than 120km from north to south, representing one of the largest ancient submarine channels in the world. The channel deposits consist of four major facies, including stratified conglomerates (Facies A), massive or graded conglomerates (Facies B), normally graded conglomerates with intraformational megaclasts (Facies C), and thick-bedded massive sandstones (Facies D). Conglomerates of Facies A and B show laterally inclined stratification, foreset stratification, and hollow-fill structures, reminiscent of terrestrial fluvial deposits and are suggestive of highly competent gravelly turbidity currents. Facies C conglomerates are interpreted as deposits of composite or multiphase debris flows associated with preceding hyperconcentrated flows. Facies D sandstones indicate rapidly dissipating, sand-rich turbidity currents. The Lago Sofia Conglomerate occurs as isolated channel-fill bodies in the northern part of the study area, generally less than 100m thick, composed mainly of Facies C conglomerates and intercalated between much thicker fine-grained deposits. Paleocurrent data indicate sediment transport to the east and southeast. They are interpreted to represent tributaries of a larger submarine channel system, which joined to form a trunk channel to the south. The conglomerate in the southern part is more than 300 m thick, composed of subequal proportions of Facies A, B, and C conglomerates, and overlain by hundreds of m-thick turbidite sandstones (Facies D) with scarce intervening fine-grained deposits. It is interpreted as vertically stacked and interconnected channel bodies formed by a trunk channel confined along the axis of the foredeep trough. The channel bodies in the southern part are classified into 5 architectural elements on the basis of large-scale bed geometry and sedimentary facies: (1) stacked sheets, indicative of bedload deposition by turbidity currents and typical of broad gravel bars in terrestrial gravelly braided rivers, (2) laterally-inclined strata, suggestive of lateral accretion with respect to paleocurrent direction and related to spiral flows in curved channel segments around bars, (3) foreset strata, interpreted as the deposits of targe gravel dunes that have migrated downstream under quasi-steady turbidity currents, (4) hollow fills, which are filling thalwegs, minor channels, and local scours, and (5) mass-flow deposits of Facies C. The stacked sheets, laterally inclined strata, and hollow fills are laterally transitional to one another, reflecting juxtaposed geomorphic units of deep-sea channel systems. It is noticeable that the channel bodies in the southern part are of feet stacked toward the east, indicating eastward migration of the channel thalwegs. The laterally inclined strata also dip dominantly to the east. These features suggest that the trunk channel of the Lago Sofia submarine channel system gradually migrated eastward. The eastward channel migration is Interpreted to be due to tectonic forcing imposed by the subduction of an oceanic plate beneath the Andean Cordillera just to the west of the Lago Sofia submarine channel.

• Korean Journal of Culture and Social Issue
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• v.26 no.1
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• pp.25-45
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• 2020

Intentionality judgement in criminal cases is a core area of fact finding that is root of guilty and sentencing judgment on the defendant. However, the third party is not sure the intentionality because it reflects subjective aspect of agent. Thus, mechanism behind intentionality judgment is an important factor to be properly understood by the academia and the criminal justice system. However, previous studies regarding intentionality judgment models have shown inconsistent results. Mental-state models proposed foreseeability(belief) and desire of agent at the time of the offence as key factors in intentionality judgment. These factors consistent with central things on intentionality judgment in criminal law. However, key factors in moral-evaluation models are blameworthiness of agent and badness of outcome reflected on the consequent aspect of act. Recently, deep-self concordance model emerged suggesting important factors on intentionality judgment are not mental states and moral evaluations but individual's deep-self. However, these models are limited in that they do not consider the important features of criminal cases, that the consequence of the case is inevitably negative, and therefore the actor who is a party to legal punishment rarely expresses his or her mental state at the time of the act. Therefore, this study suggests that, based on the existing intentionality judgment studies and the characteristics of the criminal case, the inference about who the agent was originally will play a key role in judging the intentionality in the criminal case. This is the moral-character model. Futhermore, In this regard, this study discussed what the media and criminal justice institutions should keep in mind and the directions for future research.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Journal of the Korean Society of Radiology
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• v.84 no.5
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• pp.1123-1133
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• 2023

Purpose Our study aimed to evaluate the association between automated quantified body composition on CT and pulmonary function or quantitative lung features in patients with chronic obstructive pulmonary disease (COPD). Materials and Methods A total of 290 patients with COPD were enrolled in this study. The volume of muscle and subcutaneous fat, area of muscle and subcutaneous fat at T12, and bone attenuation at T12 were obtained from chest CT using a deep learning-based body segmentation algorithm. Parametric response mapping-derived emphysema (PRM^emph), PRM-derived functional small airway disease (PRM^fSAD), and airway wall thickness (AWT)-Pi10 were quantitatively assessed. The association between body composition and outcomes was evaluated using Pearson's correlation analysis. Results The volume and area of muscle and subcutaneous fat were negatively associated with PRM^emph and PRM^fSAD (p < 0.05). Bone density at T12 was negatively associated with PRM^emph (r = -0.1828, p = 0.002). The volume and area of subcutaneous fat and bone density at T12 were positively correlated with AWT-Pi10 (r = 0.1287, p = 0.030; r = 0.1668, p = 0.005; r = 0.1279, p = 0.031). However, muscle volume was negatively correlated with the AWT-Pi10 (r = -0.1966, p = 0.001). Muscle volume was significantly associated with pulmonary function (p < 0.001). Conclusion Body composition, automatically assessed using chest CT, is associated with the phenotype and severity of COPD.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.85-92
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• 2017

As accessibility toward traditional cultural contents drops compared to its increase in production, the need for higher accessibility for continued management and research to exist. For this, this paper introduces an image classifier model for traditional images based on artificial neural networks, which converts the input image's features into a vector space and by utilizing a RNN based model it recognizes and compares the details of the input which enables the classification of traditional images. This enables the classifiers to classify similarly looking traditional images more precisely by focusing on the details. For the training of this model, a wide range of images were arranged and collected based on the format of the Korean information culture field, which contributes to other researches related to the fields of using traditional cultural images. Also, this research contributes to the further activation of demand, supply, and researches related to traditional culture.

• The Journal of Korean Academy of Prosthodontics
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• v.42 no.2
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• pp.192-209
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• 2004

Purpose : The purpose of this study was to evaluate the machining accuracy and consistency of implant/abutment/screw combination or internal connection type. Material and methods: In this study, each two randomly selected internal implant fixtures from ITI, 3i, Avana, Bicon, Friadent, Astra, and Paragon system were used. Each abutment was connected to the implant with 32Ncm torque value using a digital torque controller or tapping. All samples were cross-sectioned with grinder-polisher unit (Omnilap 2000 SBT Inc) after embeded in liquid unsaturated polyester (Epovia, Cray Valley Inc). Then optical microscopic and scanning electron microscopic(SEM) evaluations of the implant-abutment interfaces were conducted to assess quality of fit between the mating components. Results : 1) Generally, the geometry of the internal connection system provided for a precision fit of the implant/abutment into interface. 2) The most precision fit of the implant/abutment interface was provided in the case of Bicon System which has not screw. 3) The fit of the implant/abutment interface was usually good in the case of ITI, 3I and Avana system and the amount of fit of the implant/abutment interface was similar to each other. 4) The fit of the implant/abutment interface was usually good in the case of Friadent, Astra and Paragon system. The case of Astra system with the inclined contacting surface had the most Intimate contact among them. 5) Amount of intimate contact in the abutment screw thread to the mating fixture was larger in assembly with two-piece type which is separated screw from abutment such as Friadent, Astra and Paragon system than in that with one-piece type which is not seperated screw from abutment such as ITI, 3I and Avana system. 6) Amount of contact in the screw and the screw seat of abutment was larger in assembly of Friadent system than in asembly of Astra system of Paragon system. Conclusion: Although a little variation in machining accuracy and consistency was noted in the samples, important features of all internal connection systems were the deep, internal implant-abutment connections which provides intimate contact with the implant walls to resist micro-movement, resulting in a strong stable interface. From the results of this study, further research of the stress distribution according to the design of internal connection system will be required.

• Applied Microscopy
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• v.39 no.2
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• pp.141-147
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• 2009

The antennae of millipedes have a prominent function in detecting various types of environmental stimuli, and structural modification of the antennae is closely associated with the degree of sense recognition. Although the biological significance of the antennal sensillae to millipedes are widely understood, the structure and function of the antennal sensillae are still not clear and more precise analysis is required. We have analysed the ultrastructural characteristics of the antennal sensillae in a millipede Anaulaciulus koreanus koreanus using field emission scanning electron microscopy (FESEM). According to their morphological and substructural features, we could identify three different types of antennal sensillae as follows: trichoid sensilla (TS), chaetiform sensilla (CS) and basiconic sensilla (BS). The TS on the articles are long, blunt-tipped, almost straight hairs with deep longitudinal grooves in their lower parts whereas, the CS are long, sickleshaped bristles with longitudinal grooves acuminating toward the tip. The BS can be subdivided further into three subtypes which are the large-sized basiconic sensilla ($BS_1$), the small-sized basiconic sensillae ($BS_2$) and the spiniform basiconic sensillae ($BS_3$). The BS between the terminal segment and distal margins of the other segments are clearly discriminated in this species.

• Geophysics and Geophysical Exploration
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• v.13 no.3
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• pp.249-255
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• 2010

Since the 1990's, a number of ULF geomagnetic disturbance associated with earthquake occurrences have actively been reported, and polarization analysis of geomagnetic fields becomes one of potential candidates to be capable of predicting short-term earthquake. This study develops the modified polarization analysis method based on the previous studies, and analyzes three-component geomagnetic fields obtained at Cheongyang geomagnetic observatory using the developed method. A daily polarization value (the ratio of spectral power of horizontal and vertical geomagnetic field) is calculated with a focus on the 0.01 Hz band, which is known to be the most sensitive to seismogenic ULF radiation. We analyze a total of 10 months of geomagnetic data obtained at Cheongyang observatory, and compare the polarization values with the Kp index and the earthquake occurred in the analysis period. The results show that there is little correlation between the temporal variations of polarization values and Kp index, but remarkable increases in polarization values are identified which are associated with two earthquakes. Comparison the polarization values obtained at Cheongyang and Kanoya observatory indicates that the increases of polarization values at Cheongyang might be due to not global geomagnetic induction but the locally occurred earthquakes. Furthermore, these features are clearly shown in normalized polarization values, which take account in the statistical characteristics of each geomagnetic field. On the basis of these results, polarization analysis can be used as promising tool for monitoring the earthquake-precursory phenomenon.

• Geophysics and Geophysical Exploration
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• v.14 no.1
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• pp.69-79
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• 2011

In this study, empirical site amplifications and S-wave velocity profiles for shallow and deep soils are estimated using earthquake ground motion records in metropolitan Manila, the Philippines. We first apply a spectral inversion technique to the earthquake records to estimate effects of source, path, and local site amplification. The earthquake data used were obtained during 36 moderate earthquakes at 10 strong-motion stations of an earthquake observation network in Manila. The estimated Q value of the propagation path is modelled as $54.6f^{1.1}$. Most of the source spectra can be approximated with the omega-square model. The site amplifications show characteristic features according to surface geological conditions. The amplifications at the sites in the coastal lowland and Marikina Valley shows predominant peaks at frequencies from 1 to 5 Hz, while those in the central plateau are characterised by no dominant peaks. These site amplifications are inverted to subsurface S-wave velocity. We, next, discuss the relationship between the amplifications and average S-wave velocity in the top 30m of the S-wave velocity profiles. The amplifications at low frequencies are well correlated with the averaged S-wave velocity. However, high-frequency amplifications cannot be sufficiently explained by the averaged S-wave velocity in the top 30 m. They are correlated more with the average of S-wave velocity over depths less than 30 m.