• Journal of Environmental Science International
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• v.24 no.4
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• pp.481-494
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• 2015

Groundwater level hydrographs from observation wells in Jeju island clearly illustrate distinctive features of recharge showing the time-delaying and dispersive process, mainly affected by the thickness and hydrogeologic properties of the unsaturated zone. Most groundwater flow models have limitations on delineating temporal variation of recharge, although it is a major component of the groundwater flow system. Recently, a convolution model was suggested as a mathematical technique to generate time series of recharge that incorporated the time-delaying and dispersive process. A groundwater flow model was developed to simulate transient groundwater level fluctuations in Pyoseon area of Jeju island. The model used the convolution technique to simulate temporal variations of groundwater levels. By making a series of trial-and-error adjustments, transient model calibration was conducted for various input parameters of both the groundwater flow model and the convolution model. The calibrated model could simulate water level fluctuations closely coinciding with measurements from 8 observation wells in the model area. Consequently, it is expected that, in transient groundwater flow models, the convolution technique can be effectively used to generate a time series of recharge.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.63-68
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• 2022

In this study, we produced defect detection models using parallel convolution neural networks. If convolution neural networks are constructed parallel type, the model's detection accuracy will increase and detection time will decrease. We produced parallel-type defect detection models using 4 types of convolutional algorithms. The performance of models was evaluated using evaluation indicators. The model's performance is detection accuracy and detection time. We compared the performance of each parallel model. The detection accuracy of the model using AlexNet is 97 % and the detection time is 0.3 seconds. We confirmed that when AlexNet algorithm is constructed parallel type, the model has the highest performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3598-3614
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• 2020

With the increase of motor vehicles and tourism demand, some traffic problems gradually appear, such as traffic congestion, safety accidents and insufficient allocation of traffic resources. Facing these challenges, a model of Spatio-Temporal Dilated Convolutional Network (STDGCN) is proposed for assistance of extracting highly nonlinear and complex characteristics to accurately predict the future traffic flow. In particular, we model the traffic as undirected graphs, on which graph convolutions are built to extract spatial feature informations. Furthermore, a dilated convolution is deployed into graph convolution for capturing multi-scale contextual messages. The proposed STDGCN integrates the dilated convolution into the graph convolution, which realizes the extraction of the spatial and temporal characteristics of traffic flow data, as well as features of road occupancy. To observe the performance of the proposed model, we compare with it with four rivals. We also employ four indicators for evaluation. The experimental results show STDGCN's effectiveness. The prediction accuracy is improved by 17% in comparison with the traditional prediction methods on various real-world traffic datasets.

• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.547-557
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• 2019

To extract the depth map from a single image, a number of CNN-based deep learning methods have been performed in recent research. In this study, the GAN structure of Pix2Pix is maintained. this model allows to converge well, because it has the structure of the generator and the discriminator. But the convolution in this model takes a long time to compute. So we change the convolution form in the generator to a depthwise convolution to improve the speed while preserving the result. Thus, the seven down-sizing convolutional hidden layers in the generator U-Net are changed to depthwise convolution. This type of convolution decreases the number of parameters, and also speeds up computation time. The proposed model shows similar depth map prediction results as in the case of the existing structure, and the computation time in case of a inference is decreased by 64%.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.455-457
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• 2019

In this paper we study the effect of decomposed spatiotemporal convolutions for action recognition in videos. Our motivation emerges from the empirical observation that spatial convolution applied on solo frames of the video provide good performance in action recognition. In this research we empirically show the accuracy of factorized convolution on individual frames of video for action classification. We take 3D ResNet-18 as base line model for our experiment, factorize its 3D convolution to 2D (Spatial) and 1D (Temporal) convolution. We train the model from scratch using Kinetics video dataset. We then fine-tune the model on UCF-101 dataset and evaluate the performance. Our results show good accuracy similar to that of the state of the art algorithms on Kinetics and UCF-101 datasets.

• Journal of IKEEE
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• v.20 no.3
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• pp.303-306
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• 2016

In this paper, we propose a method to accelerate convolutional neural network by utilizing a GPGPU. Convolutional neural network is a sort of the neural network learning features of images. Convolutional neural network is suitable for the image processing required to learn a lot of data such as images. The convolutional layer of the conventional CNN required a large number of multiplications and it is difficult to operate in the real-time on the embedded environment. In this paper, we reduce the number of multiplications through Winograd convolution operation and perform parallel processing of the convolution by utilizing SIMT-based GPGPU. The experiment was conducted using ModelSim and TestDrive, and the experimental results showed that the processing time was improved by about 17%, compared to the conventional convolution.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.2
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• pp.91-99
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• 1998

This paper presents a methodology for identifying the cutter runout geometry in end milling process. Cutter runout is common but undesirable phenomenon in multi-tooth machining because it introduces variable chip loading to insert which results in a accelerated tool wear. amplification of force variation and hence enlargement vibration amplitude From understanding of chip load change kinematics, the analytical cutting force convolution model was formulated as the angular domain convolution model was formulated as the angular domain convolution of three dynamic cutting force component functions. By virtue of the convolution integration property, the frequency domain expression of the local cutting forces and the chip width density of the cutter. Experimental study is presented to validate the analytical model. This study provides the in-process monitoring and compensation of dynamic cutter runout to improve machining tolerance and surface quality for industrial application.

• The Journal of the Acoustical Society of Korea
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• v.39 no.6
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• pp.615-621
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• 2020

In this paper, we propose a multi-site based earthquake event classification method using graph convolution networks. In the traditional earthquake event classification methods using deep learning, they used single-site observation to estimate seismic event class. However, to achieve robust and accurate earthquake event classification on the seismic observation network, the method using the information from the multi-site observations is needed, instead of using only single-site data. Firstly, our proposed model employs convolution neural networks to extract informative embedding features from the single-site observation. Secondly, graph convolution networks are used to integrate the features from several stations. To evaluate our model, we explore the model structure and the number of stations for ablation study. Finally, our multi-site based model outperforms up to 10 % accuracy and event recall rate compared to single-site based model.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.5
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• pp.112-119
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• 2001

A variety of input shaper has been proposed to reduce the residual vibration of flexible structures. Multi-hump input shaper is known to be robust for parameter variations. However, existing approach should solve the more complicated nonlinear simultaneous equations to improve the robustness of the input shaper with the additional constraints. In this paper, by proposing a graphical approach which uses convolution of shaper, the multi-hump convolution input shaper could be designed even if the constraints are added for further robustness. With a mass-damper-spring model, the better performance is obtained using the proposed new multi-hump convolution input shaper.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.7 no.3
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• pp.265-276
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• 1995

In this Paper, the Performance of the convolution method has been investigated as an effort to develop a simple system of predicting wind-driven surface current on a real time basis. In this approach wind stress is assumed to be spatially uniform and the effect of atmospheric pressure is neglected. The discrete convolution weights are determined in advance at each point using a linear three-dimensional Galerkin model with linear shape functions(Galerkin-FEM model). Four directions of wind stress(e.g. NE, SW, NW, SE) with unit magnitude are imposed in the model calculation for the construction of data base for convolution weights. Given the time history of wind stress, it is then possible to predict with-driven currents promptly using the convolution product of finite length. An unsteady wind stress of arbitrary form can be approximated by a series of wind pulses with magnitude of 6 hour averaged value. A total of 12 pulses are involved in the convolution product To examine the accuracy of the convolution method a series of numerical experiments has been carried out in the idealized basin representing the scale of the Yellow Sea and the East China Sea. The wind stress imposed varies sinusoidally in time. It was found that the predicted surface currents and elevation fields were in good agreement with the results computed by the direct integration of the Galerkin model. A model with grid 1/8$^{\circ}$ in latitude, l/6$^{\circ}$ in longitude was established which covers the entire region of the Yellow Sea and the East China Sea. The numerical prediction in terms of the convolution product has been carried out with particular attention on the formation of upwind flow in the middle of the Yellow Sea by northerly wind.