• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2005.04a
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• pp.348-351
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• 2005

In this study, solute breakthrough curves through the unsaturated zone were predicted using artificial neural network (ANN) by numerical tests and laboratory experiments. In the numerical tests, applicability of ANN model to prediction of breakthrough curves was evaluated using synthetic data generated by HYDRUS-2D. An appropriate strategy of ANN application and input data form were recommended. The ANN model was validated by laboratory experiments comparing with HYDRUS-2D simulations. The results show that the ANN model can be an effective method for forecasting solute breakthrough curves through the unsaturated zone when hydraulic data are available.

• Journal of Korean Society for Atmospheric Environment
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• v.11 no.4
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• pp.373-375
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• 1995

In order to turn the Bag Filter to the Purse Filter, the past system that the adverse reason is removed, the input of synthetic materiality of dust and air is placed down, and the output is placed up has been improved so that any adverse effect shall not be raised. The form of filteration mechanism is changed from a cylinder from, and the dust and air is mixed to make the entry velocity and one person can velocity of the mixed materiality become lower by the use of infinity, and the reception of filteration mechanism has been made reasonably. The dust separated from the filteration mechanism is desceded down, snd the mixde materiality is ascended up. So, a cross point is formed, and a collision and a friction is occurred from its process, and at its result, dust is scattered to stick to a filter cloth, therefore, the loss of pressure shall ont be appeared by cause of the increase of dust-loading.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.4
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• pp.44-55
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• 1993

In this paper, we proposed a new optical method for the efficient recognition of Korean characters. There are six filters in the proposed method which employed the concepts of amplitude-modulated phase-only filter(AMPOF) and spatial frequency modulation(SFM). Here, amplitude modulation is used to achieve improved correlation discrimination and SFM is to reduce the number of filters. We also used a simplified synthetic discriminant function(SDF) for distortion invariance of input image. In order to recognize the partial rotation invariant Korean characters, the proposed distortion invariant minimum average correlation energy (MACE) filter is synthesized SFM, partial rotation invariant filter (PRIF), AMPOF and MACE for partial rotation invariance in the frequency domain. The advantage of the proposed filters is to supress the sidelobes of cross correlation peak away from the autocorrelation peak and to produce sharp correlation peaks. We performed simulation and optical experiment for some of Korea characters using the proposed method. The results show that the proposed method has more improved discriminant ability and reduced processing time than the conventional methods.

• Journal of Power Electronics
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• v.16 no.3
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• pp.960-969
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• 2016

High-power three-level voltage-source converters are widely utilized in high-performance AC drive systems. In several ultra-power instances, the harmonics on the grid side should be reduced through multiple rectifications. A combined harmonic elimination method that includes a dual primary-side series-connected winding transformer and selective harmonic elimination pulse-width modulation is proposed to eliminate low-order current harmonics on the primary and secondary sides of transformers. Through an analysis of the harmonic influence caused by dead time and DC magnetic bias, a synthetic compensation control strategy is presented to minimize the grid-side harmonics in the dual primary side series-connected winding transformer application. Both simulation and experimental results demonstrate that the proposed control strategy can significantly reduce the converter input current harmonics and eliminates the DC magnetic bias in the transformer.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.27-31
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• 2005

Vehicle detector system based on image processing technology is a significant domain of ITS (Intelligent Transportation System) applications due to its advantages such as low installation cost and it does not obstruct traffic during the installation of vehicle detection systems on the road[1]. In this paper, we propose architecture for vehicle detection by using image processing. The architecture consists of two main parts such as an image processing part, using high speed FPGA, decision and calculation part using CPU. The CPU part takes care of total system control and synthetic decision of vehicle detection. The FPGA part assumes charge of input and output image using video encoder and decoder, image classification and image memory control.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 1999.05a
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• pp.89-93
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• 1999

Experimental studies are conducted to investigate the flame stability and the thermal/fuel NOx formation characteristics of the low calorific value(LCV) gas fuel. Synthetic LCV fuel gas is produced through mixing carbon monoxide, hydrogen, nitrogen and ammonia on the basis that the thermal input of the syngas fuel into a burner is identical to that of natural gas, and then the syngas mixture is fed to and burnt with air on flat flame burner. Flame behaviors are observed to identify flame instability due to blow-off or flash-back when burning the LCV fuel gas at various equivalence ratio conditions. Measurements of NOx in combustion gas are made for comparing thermal and fuel NOx emissions from the LCV syngas combustion with those of the natural gas one, and for analyzing ammonia to NOx conversion mechanism. In addition, the nitrogen dilution of the LCV syngas is preliminarily attempted as a NOx reduction technique.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.525-528
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• 2019

Atmospheric particle removal is a challenging task and attacks wide interests in computer vision filed. In this paper, we proposed a single image snow removal framework based on deep residual networks. According to the fact that there are various snow sizes in a snow image, the inception module which consists of different filter kernels was adopted to extract multiple resolution features of the input snow image. Except the traditional mean square error loss, the perceptual loss and total variation loss were employed to generate more clean images. Experimental results on synthetic and realistic snow images indicated that the proposed method achieves superior performance in respect of visual perception and objective evaluation.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.111-120
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• 2021

As recent virtual reality technologies provide a more natural three-dimensional interactive environment, users naturally learn to explore space and interact with synthetic objects. The virtual reality researcher develops a technique that realizes realistic sensory feedback to get appropriate feedback to sense input behavior. Although much recent virtual reality research works extensively consider the human factor, it is not easy to adapt to all new virtual environment contents. Among many human factors, distance perception has been treated as very important in virtual environment interaction accuracy. We study the experiential virtual environment with the feature of the virtual object connected with the real object. We divide the three-dimensional interaction, in which distance perception and behavior have a significant influence, into two types (whole-body movement and direct manipulation) and analyze the real and virtual visual distance perception heterogeneity phenomenon. Also, we propose a statistical correction method that can reduce a near-body movement and manipulation error when changing the interaction location and report the experiment results proving its effectiveness.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.3071-3079
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• 2023

Helium production in the nuclear fuel matrix during irradiation plays a critical role in the design and performance of Gen-IV reactor fuel, as it represents a life-limiting factor for the operation of fuel pins. In this work, a surrogate model for the helium production rate in fast reactor MOX fuels is developed, targeting its inclusion in engineering tools such as fuel performance codes. This surrogate model is based on synthetic datasets obtained via the SCIANTIX burnup module. Such datasets are generated using Latin hypercube sampling to cover the range of input parameters (e.g., fuel initial composition, fission rate density, and irradiation time) and exploiting the low computation requirement of the burnup module itself. The surrogate model is verified against the SCIANTIX burnup module results for helium production with satisfactory performance.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.29 no.3
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• pp.163-174
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• 2023

Though box compression strength (BCS) is commonly used as a performance criterion for shipping containers, estimating BCS remains a challenge. In this study, artificial neural networks (ANN) are implemented as a new tool, with a focus on building up ANN architectures for BCS estimation. An Artificial Neural Network (ANN) model can be constructed by adjusting four modeling factors: hidden neuron numbers, epochs, number of modeling cycles, and number of data points. The four factors interact with each other to influence model accuracy and can be optimized by minimizing model's Mean Squared Error (MSE). Using both data from the literature and "synthetic" data based on the McKee equation, we find that model estimation accuracy remains limited due to the uncertainty in both the input parameters and the ANN process itself. The population size to build an ANN model has been identified based on different data sets. This study provides a methodology guide for future research exploring the applicability of ANN to address problems and answer questions in the corrugated industry.