• Journal of Korea Multimedia Society
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• v.5 no.4
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• pp.458-467
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• 2002

There has been proposed many simplification algorithms for effectively decreasing large-volumed polygonal surface data. These algorithms apply their own cost function for collapse to one of fundamental simplification unit, such as vertex, edge and triangle, and minimize the simplification error occurred in each simplification steps. Most of cost functions adopted in existing works use the error estimation method based on distance optimization. Unfortunately, it is hard to define the local characteristics of surface data using distance factor alone, which is basically scalar component. Therefore, the algorithms cannot preserve the characteristic features in surface areas with high curvature and, consequently, loss the detailed shape of original mesh in high simplification ratio. In this paper, we consider the vector component, such as surface orientation, as one of factors for cost function. The surface orientation is independent upon scalar component, distance value. This means that we can reconsider whether or not to preserve them as the amount of vector component, although they are elements with low scalar values. In addition, we develop a simplification algorithm based on half-edge collapse manner, which use the proposed cost function as the criterion for removing elements. In half-edge collapse, using one of endpoints in the edge represents a new vertex after collapse operation. The approach is memory efficient and effectively applicable to the rendering system requiring real-time transmission of large-volumed surface data.

• Korean Journal of Clinical Laboratory Science
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• v.51 no.1
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• pp.64-70
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• 2019

Dirofilaria immitis (D. immitis) is a filarial nematode that causes cardiopulmonary dirofilariasis in dogs. In the late stages of infection, infected dogs show one or more symptoms and advanced heart disorder with perivascular inflammation. To detect D. immitis specifically and efficiently in the early stages of infection, a duplex TaqMan qPCR assay was developed based on previous studies using primers and probes specialized to detect D. immitis cytochrome c oxidase subunit I (COI) and dog glyceraldehyde-3-phosphate dehydrogenase (GAPDH). As positive controls, plasmid DNAs were constructed from D. immitis COI or dog GAPDH and a TA-cloning vector. Simplex and duplex TaqMan qPCR assays were performed using the specific primers, probes, and genomic or plasmid DNA. The duplex reaction developed could detect D. immitis COI and dog GAPDH in the same sample simultaneously after optimization of the primer concentrations. The limit of detection was 25 copies for the simplex and duplex assays, and both showed good linearity, high sensitivity, and excellent PCR efficiency. The duplex assays for pathogen detection reduce the costs, labor, and time compared to simplex reactions. Therefore, the duplex TaqMan qPCR assay developed herein will allow efficient D. immitis detection and quantification from a large number of samples simultaneously.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.64-74
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• 2019

Although a non-rigid registration has high demands in clinical practice, it has a high computational complexity and it is very difficult for ensuring the accuracy and robustness of registration. This study proposes a method of applying a non-rigid registration to 3D magnetic resonance images of brain in an unsupervised learning environment by using a deep-learning network. A feature vector between two images is produced through the network by receiving both images from two different patients as inputs and it transforms the target image to match the source image by creating a displacement vector field. The network is designed based on a U-Net shape so that feature vectors that consider all global and local differences between two images can be constructed when performing the registration. As a regularization term is added to a loss function, a transformation result similar to that of a real brain movement can be obtained after the application of trilinear interpolation. This method enables a non-rigid registration with a single-pass deformation by only receiving two arbitrary images as inputs through an unsupervised learning. Therefore, it can perform faster than other non-learning-based registration methods that require iterative optimization processes. Our experiment was performed with 3D magnetic resonance images of 50 human brains, and the measurement result of the dice similarity coefficient confirmed an approximately 16% similarity improvement by using our method after the registration. It also showed a similar performance compared with the non-learning-based method, with about 10,000 times speed increase. The proposed method can be used for non-rigid registration of various kinds of medical image data.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• Information Systems Review
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• v.24 no.4
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• pp.23-40
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• 2022

Although Support Vector Machine(SVM) has been used in various fields such as bankruptcy prediction model, the hyperplane learned by SVM in class imbalance problem can be severely skewed toward minority class and has a negative impact on performance because the area of majority class is expanded while the area of minority class is invaded. This study proposed optimized uneven margin SVM(OPT-UMSVM) combining threshold moving or post scaling method with UMSVM to cope with the limitation of the traditional even margin SVM(EMSVM) in class imbalance problem. OPT-UMSVM readjusted the skewed hyperplane to the majority class and had better generation ability than EMSVM improving the sensitivity of minority class and calculating the optimized performance. To validate OPT-UMSVM, 10-fold cross validations were performed on five sub-datasets with different imbalance ratio values. Empirical results showed two main findings. First, UMSVM had a weak effect on improving the performance of EMSVM in balanced datasets, but it greatly outperformed EMSVM in severely imbalanced datasets. Second, compared to EMSVM and conventional UMSVM, OPT-UMSVM had better performance in both balanced and imbalanced datasets and showed a significant difference performance especially in severely imbalanced datasets.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.581-591
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• 2005

Collaborative filtering systems have problems involving sparsity and the provision of recommendations by making correlations between only two users' preferences. These systems recommend items based only on the preferences without taking in to account the contents of the items. As a result, the accuracy of recommendations depends on the data from user-rated items. When users rate items, it can be expected that not all users ran do so earnestly. This brings down the accuracy of recommendations. This paper proposes a collaborative recommendation method for extracting typical group preferences using user-item matrix optimization and user profiles in collaborative tittering systems. The method excludes unproven users by using entropy based on data from user-rated items and groups users into clusters after generating user profiles, and then extracts typical group preferences. The proposed method generates collaborative user profiles by using association word mining to reflect contents as well as preferences of items and groups users into clusters based on the profiles by using the vector space model and the K-means algorithm. To compensate for the shortcoming of providing recommendations using correlations between only two user preferences, the proposed method extracts typical preferences of groups using the entropy theory The typical preferences are extracted by combining user entropies with item preferences. The recommender system using typical group preferences solves the problem caused by recommendations based on preferences rated incorrectly by users and reduces time for retrieving the most similar users in groups.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.55-62
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• 2022

The perovskite solar cell is an active part of research in renewable energy fields such as solar energy, wind, hydroelectric power, marine energy, bioenergy, and hydrogen energy to replace fossil fuels such as oil, coal, and natural gas, which will gradually disappear as power demand increases due to the increase in use of the Internet of Things and Virtual environments due to the 4th industrial revolution. The perovskite solar cell is a solar cell device using an organic-inorganic hybrid material having a perovskite structure, and has advantages of replacing existing silicon solar cells with high efficiency, low cost solutions, and low temperature processes. In order to optimize the light absorption layer thin film predicted by the existing empirical method, reliability must be verified through device characteristics evaluation. However, since it costs a lot to evaluate the characteristics of the light-absorbing layer thin film device, the number of tests is limited. In order to solve this problem, the development and applicability of a clear and valid model using machine learning or artificial intelligence model as an auxiliary means for optimizing the light absorption layer thin film are considered infinite. In this study, to estimate the light absorption layer thin-film optimization of perovskite solar cells, the regression models of the support vector machine's linear kernel, R.B.F kernel, polynomial kernel, and sigmoid kernel were compared to verify the accuracy difference for each kernel function.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.4B
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• pp.771-782
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• 2000

In this paper, an H.263 video codec is implemented by adopting the concept of hardware and software co-design. Each module of the codec is investigated to find which approach between hardware and software is better to achieve real-time processing speed as well as flexibility. The hardware portion includes motion-related engines, such as motion estimation and compensation, and a memory control part. The remaining portion of theH.263 video codec is implemented in software using a RISC processor. This paper also introduces efficient design methods for hardware and software modules. In hardware, an area-efficient architecture for the motion estimator of a multi-resolution block matching algorithm using multiple candidates and spatial correlation in motion vector fields (MRMCS), is suggested to reduce the chip size. Software optimization techniques are also explored by using the statistics of transformed coefficients and the minimum sum of absolute difference (SAD)obtained from the motion estimator.

• Development and Reproduction
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• v.17 no.4
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• pp.379-387
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• 2013

Embryonic stem (ES) cells can self-renew and differentiate to various cells depending on the culture condition. Although ES cells are a good model for cell type specification and can be useful for application in clinics in the future, studies on ES cells have many experimental restraints including low transfection efficiency and transgene expression. Here, we observed that transgene expression after transfection was enhanced by treatment with histone deacetylse (HDAC) inhibitors such as trichostatin A, sodium butyrate, and valproic acid. Transfection was performed using conventional transfection reagents with a retroviral vector encoding GFP under the control of CMV promoter as a reporter. Treatment of ES cells with HDAC inhibitors after transfection increased population of GFP positive cells up to 180% compared with untreated control. ES cells showed normal expression of stem cell markers after treatment with HDAC inhibitors. Transgene expression was further enhanced by modifying transfection procedure. GFP positive cells selected after transfection were proved to have the stem cell properties. Our improved protocol for enhanced gene delivery and expression in mouse ES cells without hampering ES cell properties will be useful for study and application of ES cells.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.11
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• pp.1529-1540
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• 2011

Silencing of a specific gene using RNAi (RNA interference) is a valuable tool for functional analysis of a target gene. However, information on RNAi for analysis of gene function in farm animals is relatively nil. In the present study, we have validated the interfering effects of siRNA (small interfering RNA) using both quantitative and qualitative gene silencing in buffalo granulosa cells. Qualitative gene knockdown was validated using a fluorescent vector, enhanced green fluorescence protein (EGFP) and fluorescently labeled siRNA (Cy3) duplex. While quantitatively, siRNA targeted against the luciferase and CYP19 mRNA was used to validate the technique. CYP19 gene, a candidate fertility gene, was selected as a model to demonstrate the technique optimization. However, to sustain the expression of CYP19 gene in culture conditions using serum is difficult because granulosa cells have the tendency to luteinize in presence of serum. Therefore, serum free culture conditions were optimized for transfection and were found to be more suitable for the maintenance of CYP19 gene transcripts in comparison to culture conditions with serum. Decline in fluorescence intensity of green fluorescent protein (EGFP) was observed following co-transfection with plasmid generating siRNA targeted against EGFP gene. Quantitative decrease in luminescence was seen when co-transfected with siRNA against the luciferase gene. A significant suppressive effect on the mRNA levels of CYP19 gene at 100 nM siRNA concentration was observed. Also, measurement of estradiol levels using ELISA (enzyme-linked immunosorbent assay) showed a significant decline in comparison to control. In conclusion, the present study validated gene silencing using RNAi in cultured buffalo granulosa cells which can be used as an effective tool for functional analysis of target genes.