• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.33.3-33.3
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• 2019

Automatic, yet reliable methods to find and classify barred galaxies are going to be more important in the era of large galaxy surveys. Here, we introduce a new approach to classify barred galaxies by analyzing the butterfly pattern that Buta & Block (2001) reported as a bar signature on the potential map. We make it easy to find the pattern by moving the ratio map from a Cartesian coordinate to a polar coordinate. Our volume-limited sample consists of 1698 spiral galaxies brighter than Mr = -15.2 with z < 0.01 from the Sloan Digital Sky Survey/DR7 visually classified by Ann et al. (2015). We compared the results of the classification obtained by four different methods: visual inspection, ellipse fitting, Fourier analysis, and our new method. We obtain, for the same sample, different bar fractions of 63%, 48%, 36%, and 56% by visual inspection, ellipse fitting, Fourier analysis, and our new approach, respectively. Although automatic classifications detect visually determined, strongly barred galaxies with the concordance of 74% to 86%, automatically selected barred galaxies contain different amount of weak bars. We find a different dependence of bar fraction on the Hubble type for strong and weak bars: SBs are preponderant in early-type spirals, whereas SABs are in late-type spirals. Moreover, the ellipse fitting method often misses strongly barred galaxies in the bulge-dominated galaxies. These explain why previous works showed the contradictory dependence of the bar fraction on the host galaxy properties. Our new method has the highest agreement with visual inspection in terms of the individual classification and the overall bar fraction. In addition, we find another signature on the ratio map to classify barred galaxies into new two classes that are probably related to the age of the bar.

• Journal of IKEEE
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• v.24 no.4
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• pp.1141-1147
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• 2020

The hornet species are so similar in shape that they are difficult for non-experts to classify, and because the size of the objects is small and move fast, it is more difficult to detect and classify the species in real time. In this paper, we developed a system that classifies hornets species in real time based on a deep learning algorithm using a boundary box. In order to minimize the background area included in the bounding box when labeling the training image, we propose a method of selecting only the head and body of the hornet. It also experimentally compares existing boundary box-based object recognition algorithms to find the best algorithms that can detect wasps in real time and classify their species. As a result of the experiment, when the mish function was applied as the activation function of the convolution layer and the hornet images were tested using the YOLOv4 model with the Spatial Attention Module (SAM) applied before the object detection block, the average precision was 97.89% and the average recall was 98.69%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.576-583
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• 2020

This paper proposes a technique to determine the spam comments on YouTube, which have recently seen tremendous growth. On YouTube, the spammers appeared to promote their channels or videos in popular videos or leave comments unrelated to the video, as it is possible to monetize through advertising. YouTube is running and operating its own spam blocking system, but still has failed to block them properly and efficiently. Therefore, we examined related studies on YouTube spam comment screening and conducted classification experiments with six different machine learning techniques (Decision tree, Logistic regression, Bernoulli Naive Bayes, Random Forest, Support vector machine with linear kernel, Support vector machine with Gaussian kernel) and ensemble model combining these techniques in the comment data from popular music videos - Psy, Katy Perry, LMFAO, Eminem and Shakira.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.1-15
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• 2022

The MobileNetV3 is specially designed for mobile devices with limited memory and computing power. To reduce the network parameters and improve the network inference speed, a new lightweight network is proposed based on MobileNetV3. Firstly, to reduce the computation of residual blocks, a partial residual structure is designed by dividing the input feature maps into two parts. The designed partial residual structure is used to replace the residual block in MobileNetV3. Secondly, a dual-path feature extraction structure is designed to further reduce the computation of MobileNetV3. Different convolution kernel sizes are used in the two paths to extract feature maps with different sizes. Besides, a transition layer is also designed for fusing features to reduce the influence of the new structure on accuracy. The CIFAR-100 dataset and Image Net dataset are used to test the performance of the proposed partial residual structure. The ResNet based on the proposed partial residual structure has smaller parameters and FLOPs than the original ResNet. The performance of improved MobileNetV3 is tested on CIFAR-10, CIFAR-100 and ImageNet image classification task dataset. Comparing MobileNetV3, GhostNet and MobileNetV2, the improved MobileNetV3 has smaller parameters and FLOPs. Besides, the improved MobileNetV3 is also tested on CPU and Raspberry Pi. It is faster than other networks

• Journal of Sensor Science and Technology
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• v.12 no.3
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• pp.128-138
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• 2003

In this paper, we proposed the algorithm for locating destination address block (DAB) from automatic system to classify packages. For locating DAB, because the size of obtained images is are very large, we select the region of interesting (ROI) to reduce time carrying into algorithm. After selecting the ROI, proposed algorithm is carried out within the ROI. We extract the outline of the handwriting part of the DAB and the rest components within the obtained ROI using thresholding. We carry out labeling to extract each connected component for extracted outline and the rest components. We extract the outline of the handwriting part of the DAB using the geometrical characteristic of the outline of the handwriting part of the DAB among many connected components. The last, we extract the locating DAB using the outline of the handwriting part of the DAB.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.11
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• pp.2264-2275
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• 1994

In this paper, image compression based on neural networks is presented with block classification and coding. Multilayer neural networks with error back-propagation learning algorithm are used to transform the normalized image date into the compressed hidden values by reducing spatial redundancies. Image compression can basically be achieved with smaller number of hidden neurons than the numbers of input and output neurons. Additionally, the image blocks can be grouped for adaptive compression rates depending on the characteristics of the complexity of the blocks in accordance with the sensitivity of the human visual system(HVS). The quantized output of the hidden neuron can also be entropy coded for an efficient transmission. In computer simulation, this approach lie in the good performances even with images outside the training set and about 25:1 compression rate was achieved using the entropy coding without much degradation of the reconstructed images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.7
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• pp.1735-1755
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• 2012

Wireless sensor networks (WSNs) are becoming increasingly attractive for a variety of applications and have become a hot research area. Routing is a key technology in WSNs and can be coarsely divided into two categories: flat routing and hierarchical routing. In a flat topology, all nodes perform the same task and have the same functionality in the network. In contrast, nodes in a hierarchical topology perform different tasks in WSNs and are typically organized into lots of clusters according to specific requirements or metrics. Owing to a variety of advantages, clustering routing protocols are becoming an active branch of routing technology in WSNs. In this paper, we present an overview on clustering routing algorithms for WSNs with focus on differentiating them according to diverse cluster shapes. We outline the main advantages of clustering and discuss the classification of clustering routing protocols in WSNs. In particular, we systematically analyze the typical clustering routing protocols in WSNs and compare the different approaches based on various metrics. Finally, we conclude the paper with some open questions.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.12
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• pp.815-821
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• 2015

Maritime safety has been more critical recently due to the occurrence of shipboard accidents involving prime movers. As such, the propulsion shafting design and construction plays a vital role in the safe operation of the vessel other than focusing on being cost-efficient. Smaller vessels propulsion shafting system normally install high speed four-stroke diesel engine with reduction gear for propulsion efficiency. Due to higher cylinder combustion pressures, flexible couplings are employed to reduce the increased vibratory torque. In this paper, an actual vibration measurement and theoretical analysis was carried out on a propulsion shafting with V18.3L engine installed on small car-ferry and revealed higher torsional vibration. Hence, a rubber-block type flexible coupling was installed to attenuate the transmitted vibratory torque. Considering the flexible coupling application factor, reduction gear stability due to torque variation was analyzed in accordance with IACS(International Association of Classification Societies) M56 and the results are presented herein.

• Journal of Korea Game Society
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• v.10 no.5
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• pp.39-49
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• 2010

The fundamental characteristic that digital games have is interactivity. Digital games need to be systematically categorized so that similarities and differences can be identified and analyzed. Research in the past, however, has not established common criteria for categorizing digital games. This paper resolves that gap by identifying the fundamental characteristic of games, interactivity, and develops a conceptual framework consisting of primary and corresponding participants, and controlling characters. Through an empirical analysis on some digital games, this study shows that the framework could be comprehensive covering all of interactivity during the game. Future research topics are presented based on this framework.

• Journal of Chest Surgery
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• v.18 no.2
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• pp.151-156
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• 1985

A clinical analysis was done on 50 cases of ventricular septal defect, operated from April 1981 to March 1984 at the department of Thoracic and Cardiovascular Surgery, School of Medicine, Keimyung University. Among 50 cases, 34 cases were males and 16 cases were females. Their age ranged from 1 to 26 years and the mean age was 9.7 years. The main symptoms at admission were frequent upper respiratory infection [50%], exertional dyspnea [42%] and palpitation [34%]. In anatomical classification by Kirklin, type I constituted 20%, type II 76%, type IV 4%. Associated congenital cardiac lesions were pulmonic stenosis [6 cases], patent foramen ovale [5 cases], aortic insufficiency [3 cases] and persistent left superior vena cava [1 case]. When a normal electrocardiogram pattern was present, Qp/Qs, Rp/Rs and pulmonary artery systolic pressure and Pp/Ps were relatively low. Among cases of above 1 cm2/M2 BSA in size of defect, Pp/Ps and pulmonary artery systolic pressure were increased than the cases of below 1 cm2/M2 BSA [P=0.01]. The postoperative right bundle branch block was occurred in 21 cases [75%] among 28 cases of right ventriculotomy approach. The operative mortality was 2% [1 case] among 50 cases and complication rate was 14% [7 cases].