• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.1-8
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• 2021

In this paper, we propose reinforced VGG style network structure for low performance embedded system to classify low resolution infrared image. The combination of reinforced VGG style network structure and global average pooling makes lower computational complexity and higher accuracy. The proposed method classify the synthesize image which have 9 class 3,723,328ea images made from OKTAL-SE tool. The reinforced VGG style network structure composed of 4 filters on input and 16 filters on output from max pooling layer shows about 34% lower computational complexity and about 2.4% higher accuracy then the first parameter minimized network structure made for embedded system composed of 8 filters on input and 8 filters on output from max pooling layer. Finally we get 96.1% accuracy model. Additionally we confirmed the about 31% lower inference lead time in ported C code.

• Journal of Aerospace System Engineering
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• v.10 no.3
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• pp.63-69
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• 2016

The properties of composite materials, when compared to those of metallic materials, are highly variable due to many factors including the batch-to-batch variability of raw materials, the prepreg manufacturing process, material handling, part-fabrication techniques, ply-stacking sequences, environmental conditions, and test procedures. It is therefore necessary to apply reliable statistical-analysis techniques to obtain the design allowables of composite materials. A new composite-material qualification process has been developed by the Advanced General Aviation Transport Experiments (AGATE) consortium to yield the lamina-design allowables of composite materials according to standardized coupon-level tests and statistical techniques; moreover, the generated allowables database can be shared among multiple users without a repeating of the full qualification procedure by each user. In 2005, NASA established the National Center for Advanced Materials Performance (NCAMP) with the purpose of refining and enhancing the AGATE process to a self-sustaining level to serve the entire aerospace industry. In this paper, the statistical techniques and procedures for the generation of the allowables of aerospace composite materials will be discussed with a focus on the pooling-across-environments method.

• Journal of IKEEE
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• v.21 no.3
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• pp.244-247
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• 2017

In this paper, we implemented a convolution neural network using GP-GPU. After defining the structure, CNN performed inferencing using the GP-GPU with 256 threads, which was the previous study, using the weight obtained from the training. Training used Intel i7-4470 CPU and Matlab. Dataset used Daimler Pedestrian Dataset. The GP-GPU is controlled by the PC using PCIe and operates as an FPGA. We assigned a thread according to the depth and size of each layer. In the case of the pooling layer, we used over warpping pooling to perform additional operations on the horizontal and vertical regions. One inferencing takes about 12 ms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2603-2609
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• 2010

Currently at port, various trials about the improvement of operating method for raising operation efficiency of transferring equipment are made, but if the delay is occurred to quay crane due to individual team method in YT operation, the problem which even arranged YT should stop the work. Therefore, this study installed wireless location determining device within yard and measured the location of YT with real-time. Also, in order to raise the location determination effect, we converted operating method into pooling operation method and arranged YT. Through this study, we can trace the movement of YT with real-time and the work control method which anlayzes and evaluates congestion of yard objectively is prepared.

• The Korean Journal of Nuclear Medicine
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• v.39 no.3
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• pp.151-162
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• 2005

Hemangioma is the most common benign tumor of the liver, with a prevalence estimated as high as 7%. Tc-99m red blood cell (RBC) hepatic blood pool scan with single photon omission computed tomography (SPECT) imaging is extremely useful for the confirmation or exclusion of hepatic hemangiomas. The classic finding of absent or decreased perfusion and increased blood pooling ("perfusion/blood pool mismatch") is the key diagnostic element in the diagnosis of hemangiomas. The combination of early arterial flow and delayed blood pooling ("perfusion/blood pool match") is shown uncommonly. In giant hemangioma, filling with radioactivity appears first in the periphery, with progressive central fill-in on sequential RBC blood pool scan. However, the reverse filling pattern, which begins first in the center with progressive peripheral filling, is also rarely seen. Studies with false-positive blood pooling have been reported infrequently in nonhemangiomas, including hemangiosarcoma, hepatocellular carcinoma, hepatic adenoma, and metastatic carcinomas (adenocarcinoma of the colon, small cell carcinoma of the lung, neruroendocrine carcinoma). False-negative results have been also reported rarely except for small hemagniomas that are below the limits of spatial resolution of gamma camera.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1778-1797
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• 2021

Document image binarization is an important pre-processing step in document analysis and archiving. The state-of-the-art models for document image binarization are variants of encoder-decoder architectures, such as FCN (fully convolutional network) and U-Net. Despite their success, they still suffer from three limitations: (1) reduced feature map resolution due to consecutive strided pooling or convolutions, (2) multiple scales of target objects, and (3) reduced localization accuracy due to the built-in invariance of deep convolutional neural networks (DCNNs). To overcome these three challenges, we propose an improved semantic segmentation model, referred to as DP-LinkNet, which adopts the D-LinkNet architecture as its backbone, with the proposed hybrid dilated convolution (HDC) and spatial pyramid pooling (SPP) modules between the encoder and the decoder. Extensive experiments are conducted on recent document image binarization competition (DIBCO) and handwritten document image binarization competition (H-DIBCO) benchmark datasets. Results show that our proposed DP-LinkNet outperforms other state-of-the-art techniques by a large margin. Our implementation and the pre-trained models are available at https://github.com/beargolden/DP-LinkNet.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.547-559
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• 2022

For a chi-squared test, which is a statistical method used to test the independence of a contingency table of two factors, the expected frequency of each cell must be greater than 5. The percentage of cells with an expected frequency below 5 must be less than 20% of all cells. However, there are many cases in which the regional expected frequency is below 5 in general small area studies. Even in large-scale surveys, it is difficult to forecast the expected frequency to be greater than 5 when there is small area estimation with subgroup analysis. Another statistical method to test independence is to use the Bayes factor, but since there is a high ratio of data dependency due to the nature of the Bayesian approach, the low expected frequency tends to decrease the precision of the test results. To overcome these limitations, we will borrow information from areas with similar characteristics and pool the data statistically to propose a pooled Bayes test of independence in target areas. Jo et al. (2021) suggested hierarchical Bayesian pooling models for small area estimation of categorical data, and we will introduce the pooled Bayes factors calculated by expanding their restricted pooling model. We applied the pooled Bayes factors using bone mineral density and body mass index data from the Third National Health and Nutrition Examination Survey conducted in the United States and compared them with chi-squared tests often used in tests of independence.

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.3
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• pp.316-327
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• 2007

In this study, a Korean native cattle strain (Hanwoo) evidencing high performance in terms of both meat quality and quantity was employed in the generation of 150,000 BAC clones with an average insert size of 140 kb, and corresponding to about a 6X coverage of bovine chromosomal DNA. The BAC clones were pooled in a mini-scale via three rounds of a pooling protocol, and the efficiency of this pooling protocol was evaluated by testing the accuracy of accessibility to the positive clones, via a PCR-based screening method. Two sets of primers designed from each of two known genes were tested, and each yielded 2 or 3 positive clones for each gene, thereby indicating that the BAC library pooling system was appropriate with regard to the accession of the target BAC clones. Analyses of $3.3{\times}10^6$ base pairs obtained from the 7,090 BAC end sequence (BES) showed that 34.88% of the DNA sequence harbored the repetition sequence. Analysis of the 7,090 BES to the $1^{st}$ and $2^{nd}$ generation radiation hybrid map of the cattle genome, using the COMPASS program designed for the construction of a cattle-human comparative mapping, resulted in the localization of a total of 1,374 clones proximal to 339 $1^{st}$ generation markers, and 1,721 clones proximal to 664 $2^{nd}$ generation markers. Collectively, the BAC library and pooling system of the BAC clones from the Korean cattle, coupled with the chromosome-localized BAC clones, will provide us with novel tools for the excavation of desired clones for genome mapping and sequencing, and will also furnish us with additional information regarding breed differences in cattle.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.4-4
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• 2005

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.763-774
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• 2015

Hand posture recognition has been a wide region of applications in Human Computer Interaction and Computer Vision for many years. The problem arises mainly due to the high dexterity of hand and self-occlusions created in the limited view of the camera or illumination variations. To remedy these problems, a hand posture recognition method using 3-D point cloud is proposed to explicitly utilize 3-D information from depth maps in this paper. Firstly, hand region is segmented by a set of depth threshold. Next, hand image normalization will be performed to ensure that the extracted feature descriptors are scale and rotation invariant. By robustly coding and pooling 3-D facets, the proposed descriptor can effectively represent the various hand postures. After that, SVM with Gaussian kernel function is used to address the issue of posture recognition. Experimental results based on posture dataset captured by Kinect sensor (from 1 to 10) demonstrate the effectiveness of the proposed approach and the average recognition rate of our method is over 96%.