• Wind and Structures
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• v.8 no.6
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• pp.407-426
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• 2005

A hybrid RANS/LES approach, based on the Limited Numerical Scales concept, is applied to the numerical simulation of the flow around a square cylinder. The key feature of this approach is a blending between two eddy-viscosities, one given by the $k-{\varepsilon}$ RANS model and the other by the Smagorinsky LES closure. A mixed finite-element/finite-volume formulation is used for the numerical discretization on unstructured grids. The results obtained with the hybrid approach are compared with those given by RANS and LES simulations for three different grid resolutions; comparisons with experimental data and numerical results in the literature are also provided. It is shown that, if the grid resolution is adequate for LES, the hybrid model recovers the LES accuracy. For coarser grid resolutions, the blending criterion appears to be effective to improve the accuracy of the results with respect to both LES and RANS simulations.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.27 no.11
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• pp.603-608
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• 2015

The hybrid desiccant dehumidifier is an energy-effective system in comparison with the existing desiccant dehumidifier. Its main feature is to use the heat given off by the condenser as the react heat source. Through analysis of the elements for a more efficient design of the hybrid desiccant dehumidifier, it is evident that those energy-saving components do not work individually, but organically influence the efficiency of the equipment. Therefore, the hybrid desiccant dehumidifier may be an important product in the dehumidification industry.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.2
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• pp.230-234
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• 1996

A new trenched Hybrid Schottky INjection Field Effect Transistor (HSINFET) is proposed and verified by 2-D semiconductor device simulation. The feature of the proposed structure is that the hybrid Schottky injector is implemented at the trench sidewall and p-n junction injector at the upper sidewall and bottom of a trench. Two-dimensional simulation has been performed to compare the new HSINFET with the SINFET, conventional HSINFET and lateral insulated gate bipolar transistor(LIGBT). The numerical results shows that the current handling capability of the proposed HSINFET is significantly increased without sacrificing turn-off characteristics. The proposed HSINFET exhibits higher latch-up current density and much faster switching speed than the lateral IGBT. The forward voltage drop of the proposed HSINFET is 0.4 V lower than that of the conventional HSINFET and the turn-off time of the trenched HSINFET is much smaller than that of LIGBT.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.73-82
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• 2013

In pattern classification, feature selection is an important factor in the performance of classifiers. In particular, when classifying a large number of features or variables, the accuracy and computational time of the classifier can be improved by using the relevant feature subset to remove the irrelevant, redundant, or noisy data. The proposed method consists of two parts: a wrapper part with an improved genetic algorithm(GA) using a new reproduction method and a filter part using mutual information. We also considered feature selection methods based on mutual information(MI) to improve computational complexity. Experimental results show that this method can achieve better performance in pattern recognition problems than other conventional solutions.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.794-802
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• 2022

The recovery of reasonable depth information from different scenes is a popular topic in the field of computer vision. For generating depth maps with better details, we present an efficacious monocular depth prediction framework with coordinate attention and feature fusion. Specifically, the proposed framework contains attention, multi-scale and feature fusion modules. The attention module improves features based on coordinate attention to enhance the predicted effect, whereas the multi-scale module integrates useful low- and high-level contextual features with higher resolution. Moreover, we developed a feature fusion module to combine the heterogeneous features to generate high-quality depth outputs. We also designed a hybrid loss function that measures prediction errors from the perspective of depth and scale-invariant gradients, which contribute to preserving rich details. We conducted the experiments on public RGBD datasets, and the evaluation results show that the proposed scheme can considerably enhance the accuracy of depth prediction, achieving 0.051 for log10 and 0.992 for δ<1.25³ on the NYUv2 dataset.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.649-653
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• 2006

In this paper, we propose a hybrid feature extraction method in which Principal Components Analysis is combined with optimized k-Means clustering technique. Our approach hierarchically reduces the redundancy of features with high explanation in principal components analysis for choosing a good subset of features critical to improve the performance of classifiers. Based on this result, we evaluate the performance of intrusion detection by using Support Vector Machine and a nonparametric approach based on k-Nearest Neighbor over data sets with reduced features. The Experiment results with KDD Cup 1999 dataset show several advantages in terms of computational complexity and our method achieves significant detection rate which shows possibility of detecting successfully attacks.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1326-1335
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• 2021

As part of the cell division method, we proposed a method for segmenting images generated by topography microscopes through deep learning-based feature generation and graph segmentation. Hybrid vector shapes preserve the overall shape and boundary information of cells, so most cell shapes can be captured without any post-processing burden. NIH-3T3 and Hela-S3 cells have satisfactory results in cell description preservation. Compared to other deep learning methods, the proposed cell image segmentation method does not require postprocessing. It is also effective in preserving the overall morphology of cells and has shown better results in terms of cell boundary preservation.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.146-158
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• 2022

With the success of the digital economy and the rapid development of its technology, network security has received increasing attention. Intrusion detection technology has always been a focus and hotspot of research. A hybrid model that combines particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is presented in this work. Continuous-valued PSO and binary PSO (BPSO) are adopted together to determine the parameter combination and the feature subset. A fitness function based on the detection rate and the number of selected features is proposed. The results show that the method can simultaneously determine the parameter values and select features. Furthermore, competitive or better accuracy can be obtained using approximately one quarter of the raw input features. Experiments proved that our method is slightly better than the genetic algorithm-based KELM model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3658-3679
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• 2022

Classification of persons wearing and not wearing face masks in images has emerged as a new computer vision problem during the COVID-19 pandemic. In order to address this problem and scale up the research in this domain, in this paper a hybrid technique by employing ResNet-101 and multi-layer perceptron (MLP) classifier has been proposed. The proposed technique is tested and validated on a self-created face masks classification dataset and a standard dataset. On self-created dataset, the proposed technique achieved a classification accuracy of 97.3%. To embrace the proposed technique, six other state-of-the-art CNN feature extractors with six other classical machine learning classifiers have been tested and compared with the proposed technique. The proposed technique achieved better classification accuracy and 1-6% higher precision, recall, and F1 score as compared to other tested deep feature extractors and machine learning classifiers.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.600-602
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• 1997

In this study, a hybrid neural net consisting of an Adaptive LVQ(ALVQ) algorithm and MLP is proposed to perform speaker identification task. ALVQ is a new learning procedure using adaptively feature vector sequence instead of only one feature vector in training codebooks initialized by LBG algorithm and the optimization criterion of this method is consistent with the speaker classification decision rule. ALVQ aims at providing a compressed, geometrically consistent data representation. It is fit to cover irregular data distributions and computes the distance of the input vector sequence from its nodes. On the other hand, MLP aim at a data representation to fit to discriminate patterns belonging to different classes. It has been shown that MLP nets can approximate Bayesian "optimal" classifiers with high precision, and their output values can be related a-posteriori class probabilities. The different characteristics of these neural models make it possible to devise hybrid neural net systems, consisting of classification modules based on these two different philosophies. The proposed method is compared with LBG algorithm, LVQ algorithm and MLP for performance.