• Journal of Distribution Science
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• v.20 no.2
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• pp.55-64
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• 2022

Purpose: This research is aimed to study the level of the intellectual potential distribution, as well as the correlation between economic growth and key indicators of intellectual potential in each region of Kazakhstan. A review of the conceptual framework shows that there is a large body of research evaluating the level of intellectual potential in different ways based on different factors. Research design, data, and methodology: The research methodology is divided into two groups the integral index method using the normalization of indicators, weighting, and ranking; the method of correlation analysis. By the proposed methodological approaches, were calculated a set of factors affect the distribution of the intellectual potential. Statistics are taken for indicators of development of the intellectual potential for 2011-2020 from the Bureau of National Statistics. Results: Ranking results showed gaps between regions in Kazakhstan by the level of intellectual potential. Correlation analysis results revealed a statistically significant relationship on expenditures on R&D, computer literacy, innovative products, number of PhD students, and cultural and leisure indicators. Conclusions: Based on the obtained results of the intellectual potential level development there were given recommendations for the reproduction and regulation of the intellectual potential in the future.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2854-2863
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• 2023

Objective: To present a hybrid approach that incorporates a constrained beam-hardening estimator (CBHE) and deep learning (DL)-based post-refinement for metal artifact reduction in dental cone-beam computed tomography (CBCT). Methods: Constrained beam-hardening estimator (CBHE) is derived from a polychromatic X-ray attenuation model with respect to X-ray transmission length, which calculates associated parameters numerically. Deep-learning-based post-refinement with an artifact disentanglement network (ADN) is performed to mitigate the remaining dark shading regions around a metal. Artifact disentanglement network (ADN) supports an unsupervised learning approach, in which no paired CBCT images are required. The network consists of an encoder that separates artifacts and content and a decoder for the content. Additionally, ADN with data normalization replaces metal regions with values from bone or soft tissue regions. Finally, the metal regions obtained from the CBHE are blended into reconstructed images. The proposed approach is systematically assessed using a dental phantom with two types of metal objects for qualitative and quantitative comparisons. Results: The proposed hybrid scheme provides improved image quality in areas surrounding the metal while preserving native structures. Conclusion: This study may significantly improve the detection of areas of interest in many dentomaxillofacial applications.

• Geomechanics and Engineering
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• v.29 no.5
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• pp.523-533
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• 2022

Several prediction model of penetration rate (PR) of tunnel boring machines (TBMs) have been focused on applying to design stage. In construction stage, however, the expected PR and its trends are changed during tunneling owing to TBM excavation skills and the gap between the investigated and actual geological conditions. Monitoring the PR during tunneling is crucial to rescheduling the excavation plan in real-time. This study proposes a sequential prediction method applicable in the construction stage. Geological and TBM operating data are collected from Gunpo cable tunnel in Korea, and preprocessed through normalization and augmentation. The results show that the sequential prediction for 1 ring unit prediction distance (UPD) is R²≥0.79; whereas, a one-step prediction is R²≤0.30. In modeling algorithm, a gradient boosted regression tree (GBRT) outperformed a least square-based linear regression in sequential prediction method. For practical use, a simple equation between the R² and UPD is proposed. When UPD increases R² decreases exponentially; In particular, UPD at R²=0.60 is calculated as 28 rings using the equation. Such a time interval will provide enough time for decision-making. Evidently, the UPD can be adjusted depending on other project and the R² value targeted by an operator. Therefore, a calculation process for the equation between the R² and UPD is addressed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.226-228
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• 2021

We propose a fall recognition system using the Pose Detection of Google ML kit using video data. Using the Pose detection algorithm, 33 three-dimensional feature points extracted from the body are used to recognize the fall. The algorithm that recognizes the fall by analyzing the extracted feature points uses k-NN. While passing through the normalization process in order not to be influenced in the size of the human body within the size of image and image, analyzing the relative movement of the feature points and the fall recognizes, thirteen of the thriteen test videos recognized the fall, showing an 100% success rate.

• Journal of Information Science Theory and Practice
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• v.11 no.4
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• pp.40-50
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• 2023

Research in interdisciplinary fields like biomechanics is published in a variety of journals whose visibility depends on bibliometric indexing that is often driven by citation analysis of bibliometric databases. This study documented variation in publication metrics and research subject categories assigned to 14 biomechanics journals. Authors, citation, and citation rate (CR) were collected for the top 15 cited articles in the journals retrieved from the Google Scholar service. Research subject categories were also extracted for journals from three databases (Dimensions, Journal Citation Reports, and Scopus). Despite the focus on biomechanics for the journals studied, these biomechanics journals have widely varying CR and subject categories assigned to them. There were significant (p=0.001) and meaningful (77-108%) differences in median CR between average, low, and high CR groups of these biomechanics journals. Since CR are primary data used to calculate most journal metrics and there is no one biomechanics subject category, field normalization for journal citation metrics in biomechanics is difficult. Care must be taken to accurately interpret most citation metrics of biomechanics journals as biased proxies of general usage of research, given a specific database, time frame, and area of biomechanics research.

• Journal of the Earthquake Engineering Society of Korea
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• v.27 no.6
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• pp.311-320
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• 2023

When an earthquake occurs, the severity of damage is determined by natural factors such as the magnitude of the earthquake, the epicenter distance, soil properties, and type of the structures in the affected area, as well as the socio-economic factors such as the population, disaster prevention measures, and economic power of the community. This study evaluated the direct economic loss due to building damage and the community's recovery ability. Building damage was estimated using fragility functions due to the design earthquake by the seismic design code. The usage of the building was determined from the information in the building registrar. Direct economic loss was evaluated using the standard unit price and estimated building damage. The standard unit price was obtained from the Korean Real Estate Board. The community's recovery capacity was calculated using nine indicators selected from regional statistical data. After appropriate normalization and factor analysis, the recovery ability score was calculated through relative evaluation with neighboring cities.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of the Korean earth science society
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• v.28 no.1
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• pp.87-104
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• 2007

As part of the SPVS (Short-Period Variability Survey) which is a wide-field $(90'{\times}60')$ photometric monitering program at Bohyunsan Optical Astronomy (BOAO), we performed V band time-series CCD photometric observations ofthe young open cluster NGC 129 for 11 nights between October 12, 2004 and November 3, 2005 using the 155mm refractor equipped with $3K{\times}2K$ CCD camera. From the observation we obtained 2400 V band CCD frames and color-magnitude diagram of the cluster. To transform instrumental magnitude to standard magnitude, we applied ensemble normalization technique to all observed time-series data. After the photometric reduction process, we examined variations of 9537 stars. As a result, sixty six of the new variable stars were discovered. To determine the periods of the sevariables, we used DFT(Discrete Fourier Transform) and phase-matching technique. According to light curve shape, period, amplitude and the position on a C-M diagram, we classified these variables as 9 SPB type, 9 ${\delta}$ Scuti type, 29 eclipsing, 17 long term variables. However, two of them were not classified. From this study, we learned that small telescopes could be a very useful tool to observe variable stars in the open cluster in survey program.

• Investigative Magnetic Resonance Imaging
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• v.13 no.1
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• pp.63-73
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• 2009

Purpose : To investigate the effects of various intracranial volume (ICV) measurement methods on the sensitivity of hippocampal volumetry and modulated voxel-based morphometry (mVBM) in female patients with major depressive disorder (MDD). Materials and Methods : T1 magnetic resonance imaging (MRI) data for 41 female subjects (21 MDD patients, 20 normal subjects) were analyzed. Hippocampal volumes were measured manually, and ICV was measured manually and automatically using the FreeSurfer package. Gray and white matter volumes were measured separately. Results : Manual ICV normalization provided the greatest sensitivity in hippocampal volumetry and mVBM, followed by FreeSurfer ICV, GWMV, and GMV. Manual and FreeSurfer ICVs were similar in normal subjects (p = 0.696), but distinct in MDD patients (p = 0.000002). Manual ICV-corrected total gray matter volume (p = 0.0015) and Manual ICV-corrected bilateral hippocampal volumes (right, p = 0.014; left, p = 0.004) were decreased significantly in MDD patients, but the differences of hippocampal volumes corrected by FreeSurfer ICV, GWMV, or GMV were not significant between two groups (p > 0.05). Only manual ICV-corrected mVBM analysis was significant after correction for multiple comparisons. Conclusion : The method of ICV measurement greatly affects the sensitivity of hippocampal volumetry and mVBM. Manual ICV normalization showed the ability to detect differences between women with and without MDD for both methods.

• Analytical Science and Technology
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• v.25 no.2
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• pp.133-145
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• 2012

The quantitative composition of total suspended particulates (TSP) in the atmosphere is identified to consist mainly of ions, organic carbon (OC), element carbon (EC), and metals. In terms of environmental analysis, the rest of the TSP composition may be defined as unknown fraction (${\Sigma}X$) which is yet difficult to analyze both quantitatively and qualitatively. In this study, the major components of TSP were measured at an urban residential area (Gang Seo) in Seoul, Korea from February to December 2009. These TSP data were analyzed in various respects to explain the relationship between known and unknown constituents. During this study period, TSP was comprised mainly of unknown compounds (48.6%) followed by ions, OC, EC, and metals. The results of this study indicate that the distribution of ${\Sigma}X$ exhibits a strong similarity with ${\Sigma}Anions$, as they both increase with increasing TSP levels. However, if the concentrations of ${\Sigma}X$ and ${\Sigma}Anion$ are normalized against TSP, they exhibit a strong inverse correlation with each other due probably to larges differences in solubility. To establish a better strategy for air quality control in urban atmosphere, more efforts are needed to characterize unidentified proportion of particulate matters.