• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.33-39
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• 2019

In this paper, we propose deep convolutional neural network(CNN) with bottleneck structure which improves the performance of earthquake classification. In order to address all possible forms of earthquakes including micro-earthquakes and artificial-earthquakes as well as large earthquakes, we need a representation and classifier that can effectively discriminate seismic waveforms in adverse conditions. In particular, to robustly classify seismic waveforms even in low snr, a deep CNN with 1x1 convolution bottleneck structure is proposed in raw seismic waveforms. The representative experimental results show that the proposed method is effective for noisy seismic waveforms and outperforms the previous state-of-the art methods on domestic earthquake database.

• Journal of Korea Multimedia Society
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• v.23 no.7
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• pp.803-811
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• 2020

This paper proposes an image classification algorithm that transforms the number of convolution layers in the residual block of ResNet, CNN's representative method. The proposed method modified the structure of 34/50 layer of ResNet structure. First, we analyzed the performance of small and many convolution layers for the structure consisting of only shortcut and 3 × 3 convolution layers for 34 and 50 layers. And then the performance was analyzed in the case of small and many cases of convolutional layers for the bottleneck structure of 50 layers. By applying the results, the best classification method in the residual block was applied to construct a 34-layer simple structure and a 50-layer bottleneck image classification model. To evaluate the performance of the proposed image classification model, the results were analyzed by applying to the cifar10 dataset. The proposed 34-layer simple structure and 50-layer bottleneck showed improved performance over the ResNet-110 and Densnet-40 models.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.7
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• pp.915-921
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• 2005

Genetic variation at 25 microsatellite loci, population structure, and genetic bottleneck hypothesis were examined for Ankleshwar poultry population found in Gujrat, India. The estimates of genetic variability such as effective number of alleles and gene diversities revealed substantial genetic variation frequently displayed by microsatellite markers. The average polymorphism across the studied loci and the expected gene diversity in the population were 6.44 and 0.670${\pm}$0.144, respectively. The population was observed to be significantly differentiated into different groups, and showed fairly high level of inbreeding (f = 0.240${\pm}$0.052) and global heterozygote deficit. The bottleneck analysis indicated the absence of genetic bottleneck in the past. The study revealed that the Ankleshwar poultry breed needs appropriate genetic management for its conservation and improvement. The information generated in this study may further be utilized for studying differentiation and relationships among different Indian poultry breeds.

• Journal of the Korean Mathematical Society
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• v.35 no.3
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• pp.727-740
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• 1998

Controlling traffic lights at a bottleneck, in [5] a time of open passage is called optimal, if it minimizes the first moment of the asymptotic distribution of the queue length. The discussion of the first moment as function of the time of open passage is based on an analysis of the behavior of a fixed point when varying control parameters and delivers theoretical and computational aspects of the traffic problem.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.66-69
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• 2002

This paper proposes a new dynamic storage structure and methods fur geometric data with detail levels. Using geometric data with detail levels, we can search geometric data quickly. However, the previous structures for detail levels form the bottleneck in the design of database and do not support all types of geometric data with detail levels. Our structure supports all types of geometric data with detail levels. Moreover, our structure does not form bottleneck in the design of database. This paper presents the structure and algorithms for searching and updating of geometric data with detail levels. Experiments are then performed.

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.892-904
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• 2021

An image semantic segmentation model is proposed based on improved ENet network in order to achieve the low accuracy of image semantic segmentation in complex environment. Firstly, this paper performs pruning and convolution optimization operations on the ENet network. That is, the network structure is reasonably adjusted for better results in image segmentation by reducing the convolution operation in the decoder and proposing the bottleneck convolution structure. Squeeze-and-excitation (SE) module is then integrated into the optimized ENet network. Small-scale targets see improvement in segmentation accuracy via automatic learning of the importance of each feature channel. Finally, the experiment was verified on the public dataset. This method outperforms the existing comparison methods in mean pixel accuracy (MPA) and mean intersection over union (MIOU) values. And in a short running time, the accuracy of the segmentation and the efficiency of the operation are guaranteed.

• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• Journal of KIISE:Information Networking
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• v.29 no.1
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• pp.40-47
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• 2002

This paper analyzes the performance of an optical line termination system in ATM based Passive Optical Network(PON) using the operational analysis. We analyze it using system parameters such as utilization, throughput and routing frequency based on the buffer structure in each block of ATM-PON. Furthermore, we derive the mean response time and the visit ratio of each block, and then search the bottleneck block that hinders the system performance. We found that the 622Mbps 16x16 switching block is the bottleneck block for ATM-PON. In this bottleneck block, the loss probability increases rapidly when the cell arrival rate increases.

• Journal of Digital Convergence
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• v.6 no.1
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• pp.43-52
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• 2008

Managing of local government public service business is innovation strategy of local government public service business using choice and competition principle. Managing strategy of local government public service business is new paradigm for innovating public service business and role structure of government. It is important to understand properly substance and characteristics of local government public service business. Accordingly, the purpose of this study find bottleneck of local government public service business and take out improvement plan and suggest policy plan of Seo Gu District Office in Gwang Ju City in the future.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.11
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• pp.102-113
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• 1997

In this paepr, a new image segmentation method reducing efficiently contour information causing bottleneck problem at segmentatio-based very low bit rate codingis proposed, while preserving objective and subjective quality. It consists of 4-level hierarchical image segmentation based on mathematical morphology and 1-leve region merging structure using contast of two adjacent regions. For two adjacent region pairs at the fourth level included in each region of the thid level, contrast is calculated. Among the pairs of two adjacent regions with less value than threshold, two adjacent regions having the minimum contrast are merged first. After region merging, texture of the merged region is updated. The procedure is performed recursively for all the adjacent region pairs at the fourth level included in each region of the third level. Compared with the previous method, the objective and subjective image qualities are similar. But it reduces 46.65% texture information on the average by decreasing total region number to be tansmitted. Specially, it shows reduction of the 23.95% contour information of the average. Thus, it can improve efficiently the bottleneck problem at segementation-based very low bit rate coding.