Journal of Korea Society of Industrial Information Systems
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v.27
no.4
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pp.19-27
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2022
In general, the performance of ML(Machine Learning) application is determined by various factors such as the type of ML model, the size of model (number of parameters), hyperparameters setting during the training, and training data. In particular, the recognition accuracy of ML may be deteriorated or experienced overfitting problem if the amount of dada used for training is insufficient. Existing studies focusing on image recognition have widely used open datasets for training and evaluating the proposed ML models. However, for specific applications where the sensor used, the target of recognition, and the recognition situation are different, it is necessary to build the dataset manually. In this case, the performance of ML largely depends on the quantity and quality of the data. In this paper, training data used for motion recognition application is augmented using the kernel density estimation algorithm which is a type of non-parametric estimation method. We then compare and analyze the recognition accuracy of a ML application by varying the number of original data, kernel types and augmentation rate used for data augmentation. Finally experimental results show that the recognition accuracy is improved by up to 14.31% when using the narrow bandwidth Tophat kernel.
Shin, Jaewook;Lee, Joonsung;Kim, Min-Oh;Choi, Narae;Seo, Jin Keun;Kim, Dong-Hyun
Investigative Magnetic Resonance Imaging
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v.18
no.4
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pp.303-313
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2014
Purpose : In-vivo conductivity reconstruction using transmit field ($B_1{^+}$) information of MRI was proposed. We assessed the accuracy of conductivity reconstruction in the presence of statistical noise in complex $B_1{^+}$ map and provided a parametric model of the conductivity-to-noise ratio value. Materials and Methods: The $B_1{^+}$ distribution was simulated for a cylindrical phantom model. By adding complex Gaussian noise to the simulated $B_1{^+}$ map, quantitative conductivity estimation error was evaluated. The quantitative evaluation process was repeated over several different parameters such as Larmor frequency, object radius and SNR of $B_1{^+}$ map. A parametric model for the conductivity-to-noise ratio was developed according to these various parameters. Results: According to the simulation results, conductivity estimation is more sensitive to statistical noise in $B_1{^+}$ phase than to noise in $B_1{^+}$ magnitude. The conductivity estimate of the object of interest does not depend on the external object surrounding it. The conductivity-to-noise ratio is proportional to the signal-to-noise ratio of the $B_1{^+}$ map, Larmor frequency, the conductivity value itself and the number of averaged pixels. To estimate accurate conductivity value of the targeted tissue, SNR of $B_1{^+}$ map and adequate filtering size have to be taken into account for conductivity reconstruction process. In addition, the simulation result was verified at 3T conventional MRI scanner. Conclusion: Through all these relationships, quantitative conductivity estimation error due to statistical noise in $B_1{^+}$ map is modeled. By using this model, further issues regarding filtering and reconstruction algorithms can be investigated for MREPT.
Environmental DNA (eDNA) metabarcoding is effective method with high detection sensitivity for evaluating fish biodiversity and detecting endangered fish from natural water samples. We compared the richness of operational taxonomic units(OTUs) and composition of freshwater fishes according to filters(cellulose nitrate filter vs. glass fiber filter), extraction kits(DNeasy2® Blood & Tissue Kit vs. DNeasy2® PowerWater Kit), primer sets (12S rDNA vs. 16S rDNA), and PCR methods (conventional PCR vs. touchdown PCR) to determine the optimal conditions for metabarcoding analysis of Korean freshwater fish. The glass fiber filter and DNeasy2® Blood & Tissue Kit combination showed the highest number of freshwater fish OTUs in both 12S and 16S rDNA. Among the four types, the primer sets only showed statistically significant difference in the average number of OTUs in class Actinopterygii (non-parametric Wilcoxon signed ranks test, p=0.005). However, there was no difference in the average number of OTUs in freshwater fish. The species composition also showed significant difference according to primer sets (PERMANOVA, Pseudo-F=6.9489, p=0.006), but no differences were observed in the other three types. The non-metric multidimensional scaling (NMDS) results revealed that species composition clustered together according to primer sets based on similarity of 65%; 16S rDNA primer set was mainly attributed to endangered species such as Microphysogobio koreensis and Pseudogobio brevicorpus. In contrast, the 12S rDNA primer set was mainly attributed to common species such as Zacco platypus and Coreoperca herzi. This study provides essential information on species diversity analysis using metabarcoding for environmental water samples obtained from rivers in Korea.
Journal of the Institute of Electronics Engineers of Korea SP
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v.48
no.6
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pp.48-60
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2011
Nowadays, the 3D community is actively researching on 3D imaging and free-viewpoint video (FVV). The free-viewpoint rendering in multi-view video, virtually move through the scenes in order to create different viewpoints, has become a popular topic in 3D research that can lead to various applications. However, there are restrictions of cost-effectiveness and occupying large bandwidth in video transmission. An alternative to solve this problem is to generate virtual views using a single texture image and a corresponding depth image. A critical issue on generating virtual views is that the regions occluded by the foreground (FG) objects in the original views may become visible in the synthesized views. Filling this disocclusions (holes) in a visually plausible manner determines the quality of synthesis results. In this paper, a new approach for handling disocclusions using depth based inpainting algorithm in synthesized views is presented. Patch based non-parametric texture synthesis which shows excellent performance has two critical elements: determining where to fill first and determining what patch to be copied. In this work, a noise-robust filling priority using the structure tensor of Hessian matrix is proposed. Moreover, a patch matching algorithm excluding foreground region using depth map and considering epipolar line is proposed. Superiority of the proposed method over the existing methods is proved by comparing the experimental results.
KSCE Journal of Civil and Environmental Engineering Research
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v.38
no.4
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pp.579-586
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2018
Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.
Statement of problem. There have been many studies about marginal discrepancy of single restorations made by various systems and materials. But most of statistical inferences are not definite because of sample size, measurement number, measuring , instruments, etc. And there have been few studies about the marginal fit of Computer-aided Cercon crowns. Purpose. The purpose of this study was to compare the marginal fit of the anterior single restorations made using computer-aided milled Cercon crowns with metal-ceramic restorations and to obtain more accurate information by using a large enough sample size and by making sufficient measurements per specimen. Material and methods. The in vitro marginal discrepancies of computer-aided milled Cercon crowns and control groups (metal ceramic crowns) were evaluated and compared. The crowns were made from one extracted maxillary central incisor prepared by milling machine. 30 crowns per each system were fabricated. Measurements of a crown were recorded at 50 points that were randomly selected for marginal gap evaluation. Parametric statistical analysis was performed for the results. Results. The means and standard deviations of the marginal fit were 85$\pm$22$\mu$m for the control group and 91$\pm$15$\mu$m for the Cercon crowns. The t-test of the marginal discrepancies between Cercon crowns and metal-ceramic crowns were performed. Significant differences were not found between groups (P=0.230>.05). Based on the criterion of 120$\mu$m as the limit of clinical acceptability, the mean marginal fits of Cercon crowns and metal-ceramic crowns were acceptable. Conclusion. Within the limitations of this in vitro study, the following conclusions were drawn: 1. Mean gap dimensions and standard deviations at the marginal opening for maxillary incisal crowns were 85$\pm$22$\mu$m for the control (metal-ceramic crowns), 91$\pm$15$\mu$m for Cercon crowns. 2. The Cercon crowns showed slightly larger marginal gap discrepancy than the control but marginal gap between Computer-aided milled Cercon crowns and metal ceramic crowns did not showed significant difference (P>.05). 3. The Cercon crowns and metal ceramic crowns showed clinically acceptable marginal discrepancy.
Synthetic Aperture Radar is able to provide images of wide coverage in day, night, and all-weather conditions. Recently, as the SAR image resolution improves up to the sub-meter level, their applications are rapidly expanding accordingly. Especially there is a growing interest in the use of geographic information of high resolution SAR images and the change detection will be one of the most important technique for their applications. In this paper, an automatic threshold tracking and change detection algorithm is proposed applicable to high-resolution SAR images. To detect changes within SAR image, a reference image is generated using log-ratio operator and its amplitude distribution is estimated through K-S test. Assuming SAR image has a non-gaussian amplitude distribution, a generalized thresholding technique is applied using Kittler and Illingworth minimum-error estimation. Also, MoLC parametric estimation method is adopted to improve the algorithm performance on rough ground target. The implemented algorithm is tested and verified on the simulated SAR raw data. Then, it is applied to the spaceborne high-resolution SAR images taken by Cosmo-Skymed and KOMPSAT-5 and the performances are analyzed and compared.
Objectives : The aim of this study was to identify the trend of school health research by analyzing articles related to school health for the last 10 years. this information can be used to guide research direction for the future. Methods : This study is a descriptive study that analyzed annual data. using an objective frame of evaluation about the methodology and research domain in each paper, all the papers included in the journals concerning school health from January 1993 to December 2000 were analyzed. The data was processed statistically by frequency and percentage. Results : 455 papers in 9 journals related to school health were published. The Journal of the Korean Society of School Health had 204 articles, the highest number of any journal. most of the articles were descriptive, but the number of experimental studies increased over time. the most common research subjects were students were the greatest ones, but the trend to study both parents and teachers increased near the end of the sampling period. the most common selection of subjects appeared to be based on convenience, but probability sampling gradually increasing annually. the most common research instrument was the questionnaire and the reliability and the validity of instruments were described in approximately half of the studies. The survey was the most commonly used method of data collection. The papers that met ethical issue in data collection were less than those that did not. In addition, the papers that provided the rationale for the calculation of sample size were less than those that did not. parametric statistics were the main methods of data analysis, but some advanced statistics were used more often than simple descriptive statistics in the latter part of the sampling period. In general, limit of the studies were not frequently mentioned but more recommendations were made. regarding the characteristics of the research area, the assesment domain was remarkable. The rate of school health problem assesment was the highest among research subjects. Sex- related subjects were the highest in detail research subjects. Conclusions : The research of school health has increased quantitatively, but it is difficult to ascertain its qualitative development. Therefore, on the basis of the research completed up until now, more school-based intervention studies and longitudinal studies need to be another target for the evaluation of the effects of the school health service. as well, policy suggestion through international and cross-sectional comparison studies are needed to assist in the establishment of the long term direction of school health.
This paper proposes a stable and robust method to distinct the color similarity for clothes using the LBG algorithm under various light sources, Since the conventional methods, such as the histogram intersection and the accumulated histogram, are profoundly sensitive to the changing of light environments, the distinction of color similarity for the same cloth can be different due to the complicated light sources. To reduce the effects of the light sources, the properties of hue and saturation which consistently sustain the characteristic of the color under the various changes of light sources are analyzed to define the characteristic of the color distribution. In a two-dimensional space determined by the properties of hue and saturation, the LBG algorithm, a non-parametric clustering approach, is applied to examine the color distribution of images for each clothes. The color similarity of images is defined by the average of Euclidean distance between the mapping clusters which are calculated from the result of clustering of both images. To prove the stability of the proposed method, the results of the color similarity between our method and the traditional histogram analysis based methods are compared using a dozen of cloth examples that obtained under different light environments. Our method successively provides the classification between the same cloth image pair and the different cloth image pair and this classification of color similarity for clothe images obtains the 91.6% of success rate.
Journal of the Korea Academia-Industrial cooperation Society
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v.21
no.11
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pp.510-518
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2020
With the promotion of digitalization in the construction industry, BIM has become an indispensable technology. On the other hand, it has not been actively utilized in practice because of the difficulty of BIM modeling. The reason is that 3D modeling is less productive not only because of the difficulty of learning BIM software but also the modeling work is done manually. Therefore, this study proposes a method and system that can improve the productivity of BIM-based modeling. For this reason, in the study, a slit caisson, which is a typical structure of a port, was selected as a development target, and various parameters were derived through interviews with experts so that it could be used in practice. This study presents a UI construction plan that considers user convenience for efficient management and operation of diverse and complex parameters. Based on this, this study used visual programming and Excel VBA to develop a BIM-based design automation system for slit caissons. The developed system can use many parameters to quickly develop slit caisson models suitable for various design conditions that can contribute to BIM-based modeling and productivity improvement.
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