• Journal of Environmental Science International
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• v.23 no.4
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• pp.625-635
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• 2014

Temporal variation of groundwater levels in Jeju Island reveals time-delaying and dispersive process of recharge, mainly caused by the hydrogeological feature that thickness of the unsaturated zone is highly variable. Most groundwater flow models have limitations on delineating temporal variation of recharge, although it is a major component of the groundwater flow system. A new mathematical model was developed to generate time series of recharge from precipitation data. The model uses a convolution technique to simulate the time-delaying and dispersive process of recharge. The vertical velocity and the dispersivity are two parameters determining the time series of recharge for a given thickness of the unsaturated zone. The model determines two parameters by correlating the generated recharge time series with measured groundwater levels. The model was applied to observation wells of Jeju Island, and revealed distinctive variations of recharge depending on location of wells. The suggested model demonstrated capability of the convolution method in dealing with recharge undergoing the time-delaying and dispersive process. Therefore, it can be used in many groundwater flow models for generating a time series of recharge.

• International Journal of Advanced Culture Technology
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• v.4 no.4
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• pp.23-29
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• 2016

In this paper, the learning technique for CNN processor on vehicle is proposed. In the case of conventional CNN processors, weighted values learned through training are stored for use, but when there is distortion in the image due to the weather conditions, the accuracy is decreased. Therefore, the method of enhancing the input image for classification is general, but it has the weakness of increasing the processor size. To solve this problem, the CNN performance was improved in this paper through the learning method of the distorted image. As a result, the proposed method showed improvement of approximately 38% better accuracy than the conventional method.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.2
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• pp.69-78
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• 1995

Nonlinear analysis methods are developed which will enable the reliable prediction of the dynamic behavior of the space shuttle main engine(SSME) turbopumps in the presence of bearing clearances and other local nonlinearities. A computationally efficient convolution method, based on discretized Duhamel and transition matrix integral formulations, is developed for the transient analysis. In the formulation, the coupling forces due to the onlinearities are treated as external forces acting on the coupled subsystems. Iteration is utilized to determine their magnitudes at each time increament. The method is applied to a nonlinear generic model of the high pressure oxygen turthods, the convolution approach proved to be more accurate and highly more efficient. For determining the nonlinear, steady-state periodic responses, an incremental harmonic balance(IHB) method was also developed. The method was successfully used to determine dominantly harmonic and subharmonic(subsynchronous) responses of the HPOTP generic model with bearing clearances. A reduction method similar to the impedance formulation utilized with linear systems is used to reduce the housing-totor models to their coordinates at the bearing clearances.

• ETRI Journal
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• v.40 no.4
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• pp.537-545
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• 2018

In this paper, we present a comprehensive analytical study of the symbol error rate (SER) of single-carrier frequency-division multiple access (SC-FDMA) with zero-forcing frequency domain equalization (ZF-FDE) over a Rayleigh fading channel. SC-FDMA is considered as a potential waveform candidate for fifth-generation (5G) radio access networks (RANs). First, the $N_C$ fold convolution of the noise distribution of an orthogonal frequency-division multiplexing (OFDM) system is computed for each value of the signal-to-noise ratio (SNR) in order to determine the noise distribution of the SC-FDMA system. $N_C$ is the number of subcarriers assigned to a user or the size of the discrete Fourier transform (DFT) precoding. Here, we present a simple alternative method of calculating the SER by simplifying the $N_C$ fold convolution using time and amplitude scaling properties. The effects of the $N_C$ fold convolution and SNR over the computation of the SER of the SC-FDMA system has been separated out. As a result, the proposed approach only requires the computation of the $N_C$ fold convolution once, and it is used for different values of SNR to calculate the SER of SC-FDMA systems.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1129-1138
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• 2014

CAPTCHA is a technique which distinguishes human and machine using what human can judge easily but machine can't. Though Text-based-CAPTCHA has been widely used and can be implemented easily, it is less security than other CAPTCHAs such as image-based, or audio-based CAPTCHAs. To enhance the security of text-based CAPTCHA, many techniques have been developed. One of them is making CAPTCHA recognized hard using complex background or noise. In this paper, we introduce how to apply convolution filtering effectively to attack CAPTCHA and actually analyze Naver's CAPTCHA which has been used for joining a cafe with this method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4246-4267
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• 2020

As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.1
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• pp.11-20
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• 2014

Many classification studies for the community of densely-connected nodes are limited to the comprehensive analysis for detecting the communities in probabilistic networks with nodes and edge of the probabilistic distribution because of the difficulties of the probabilistic operation. This study aims to use convolution method for operating nodes and edge of probabilistic distribution. For the probabilistic hierarchy network with nodes and edges of the probabilistic distribution, the model of this study detects the communities of nodes to make the new probabilistic distribution with two distribution. The results of our model was verified through comparing with Monte-carlo Simulation and other community-detecting methods.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.216-224
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• 2017

Abnormal situation caused by aggressive behavior of pigs adversely affects the growth of pigs, and comes with an economic loss in intensive pigsties. Therefore, IT-based video surveillance system is needed to monitor the abnormal situations in pigsty continuously in order to minimize the economic demage. Recently, some advances have been made in pig monitoring; however, detecting each pig is still challenging problem. In this paper, we propose a new color image-based monitoring system for the detection of the individual pig using a fast region-based convolution neural network with consideration of detecting touching pigs in a crowed pigsty. The experimental results with the color images obtained from a pig farm located in Sejong city illustrate the efficiency of the proposed method.

• Kyungpook Mathematical Journal
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• v.59 no.3
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• pp.377-389
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• 2019

We evaluate the convolution sum $W_{a,b}(n):=\sum_{al+bm=n}{\sigma}(l){\sigma}(m)$ for (a, b) = (1, 28),(4, 7),(2, 7) for all positive integers n. We use a modular form approach. We also re-evaluate the known sums $W_{1,14}(n)$ and $W_{1,7}(n)$ with our method. We then use these evaluations to determine the number of representations of n by the octonary quadratic form $x^2_1+x^2_2+x^2_3+x^2_4+7(x^2_5+x^2_6+x^2_7+x^2_8)$. Finally we express the modular forms ${\Delta}_{4,7}(z)$, ${\Delta}_{4,14,1}(z)$ and ${\Delta}_{4,14,2}(z)$ (given in [10, 14]) as linear combinations of eta quotients.

• 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.