• Proceedings of the Materials Research Society of Korea Conference
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• 2003.03a
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• pp.109-109
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• 2003

III- V ferromagnetic semiconductor has attracted great attention as a potential application for spintronics due to a successful demonstration of spin injection from ferromagnetic GaNnAs into semiconductor. GaMnN may be one of the possible candidates for room temperature operation. Samples were grown on sapphire (0001) substrate at $650^{\circ}C$ via molecular beam epitaxy with a single Precursor of (Et$_2$Ga(N$_3$)NH$_2$$CH_3$) and solid source of Mn at different Mn source temperature. The background pressure is low 10$^{-10}$ Torr and the samples growth pressure was 1.4 $\times$ 10$^{-6}$ Torr.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.965-974
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• 2007

In this paper, we proposed the feature selection algorithm for multi-class genre classification. In our proposed algorithm, we developed GMM separation score based on Gaussian mixture model for measuring separability between two genres. Additionally, we improved feature subset selection algorithm based on sequential forward selection for multi-class genre classification. Instead of setting criterion as entire genre separability measures, we set criterion as worst genre separability measure for each sequential selection step. In order to assess the performance proposed algorithm, we extracted various features which represent characteristics such as timbre, rhythm, pitch and so on. Then, we investigate classification performance by GMM classifier and k-NN classifier for selected features using conventional algorithm and proposed algorithm. Proposed algorithm showed improved performance in classification accuracy up to 10 percent for classification experiments of low dimension feature vector especially.

• Korean Journal of Construction Engineering and Management
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• v.10 no.3
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• pp.139-147
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• 2009

Accurate construction cost estimation in the initial stage of building project plays a key role for project success and for mitigation of disputes. Total construction cost(TCC) estimation of apartment projects in Vietnam has become more important because those projects increasingly rise in quantity with the urbanization and population growth. This paper presents the application of artificial neural networks(ANNs) in estimating TCC of apartment projects. Ninety-one questionnaires were collected to identify input variables. Fourteen data sets of completed apartment projects were obtained and processed for training and generalizing the neural network(NN). MATLAB software was used to train the NN. A program was constructed using Visual C++ in order to apply the neural network to realistic projects. The results suggest that this model is reasonable in predicting TCCs for apartment projects and reinforce the reliability of using neural networks to cost models. Although the proposed model is not validated in a rigorous way, the ANN-based model may be useful for both practitioners and researchers. It facilitates systematic predictions in early phases of construction projects. Practitioners are more proactive in estimating construction costs and making consistent decisions in initial phases of apartment projects. Researchers should benefit from exploring insights into its implementation in the real world. The findings are useful not only to researchers and practitioners in the Vietnam Construction Industry(VCI) but also to participants in other developing countries in South East Asia. Since Korea has emerged as the first largest foreign investor in Vietnam, the results of this study may be also useful to participants in Korea.

• Journal of agriculture & life science
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• v.45 no.1
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• pp.33-40
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• 2011

An efficient method for the rapid micropropagation of Camptotheca acuminata from axillary buds was established by application of various plant growth regulators. Among various cytokinins, $0.5mg\;L^{-1}$ BA showed the best performance on shoot multiplication, number average multiple shoots up to 10.8. The propagated shoot cuttings in vitro were elongated on NN basal medium without plant growth regulators. The secondary multiple shoots were induced at the site of initially induced buds. Rooting was induced directly near the base of the shoot on half-strength NN medium containing $0.5mg\;L^{-1}$ of IBA, whereas high concentration of $1.0mgL^{-1}$ IBA could induce callus at the base of the shoot. The camptothecin content, anticancer compound of the micropropagated plants was contained in various tissues. Camptothecin contents were 1.8 and $2.5mg\;g^{-1}$ dry weight in stems from propagated in vitro and mother plant, respectively. This result may be used to develop strategies for large-scale propagation of elite C. acuminata trees.

• Smart Structures and Systems
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• v.29 no.5
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• pp.677-691
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• 2022

Water cycle algorithm (WCA) has been a very effective optimization technique for complex engineering problems. This study employs the WCA for simultaneous prediction of heating load (L_H) and cooling load (L_C) in residential buildings. This algorithm is responsible for optimally tuning a neural network (NN). Utilizing 614 records, the behavior of the L_H and L_C is explored and the captured knowledge is then used to predict for 154 unanalyzed building conditions. Since the WCA is a population-based algorithm, different numbers of the searching agents were tested to find the most optimum configuration. It was observed that the best solution is discovered by 500 agents. A comparison with five newly-developed benchmark optimizers, namely equilibrium optimizer (EO), multi-tracker optimization algorithm (MTOA), slime mould algorithm (SMA), multi-verse optimizer (MVO), and electromagnetic field optimization (EFO) revealed that the WCANN predicts the desired parameters with considerably larger accuracy. Obtained root mean square errors (1.4866, 2.1296, 2.8279, 2.5727, 2.5337, and 2.3029 for the L_H and 2.1767, 2.6459, 3.1821, 2.9732, 2.9616, and 2.6890 for the L_C) indicated that the most reliable prediction was presented by the proposed model. The EFONN, however, provided a more time-effective solution. Lastly, an explicit predictive formula was elicited from the WCANN.

• Journal of the Korean Ceramic Society
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• v.22 no.4
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• pp.61-65
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• 1985

$BaTiO_3$ powders doped with $BaTiO_3$ and $Nb_2O_5$ at 9$0^{\circ}C$ for 1hr. were synthesized by Direct Wet Process. These powders were very homogeneous and fine particle size. To obtain the highe PTCR effect AST($1/3Al_2O_3$.$3/4SiO_2$.$1/4TiO_2$) and $MnO_2$ were added in the semiconduc-ting $BaTiO_3$. In this case $Bi_2O_3$ and $MnO_2$ were used in the form of $Bi(NO)_3$ and $MnCl_2$.$4H_2O$ solution for Direct Wet Process. $BaTiO_3$ doped Nb2O5 and $MnO_2$ demostrated greater PTCR effect than $BaTiO_3$ doped $Nn_2O_5$ only.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.691-692
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• 2008

유해 문서를 분류하기 위한 고정된 등급에 의한 분류가 아닌 사용자의 필요에 의해 다양한 등급으로 분류할 수 있는 분류기를 구현하였다. 자질 생성을 위해 ${\chi}^2$, IG, DF, ICF를 이용하였으며, 분류를 위해 나이브 베이지언, C4.5, kNN, SVM을 이용하였다.

• 한국HCI학회:학술대회논문집
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• 2007.02c
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• pp.322-325
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• 2007

Graphics processing unit (GPU) is used for a faster artificial neural network. It is used to implement the matrix multiplication of a neural network to enhance the time performance of a text detection system. Preliminary results produced a 20-fold performance enhancement using an ATI RADEON 9700 PRO board. The parallelism of a GPU is fully utilized by accumulating a lot of input feature vectors and weight vectors, then converting the many inner-product operations into one matrix operation. Further research areas include benchmarking the performance with various hardware and GPU-aware learning algorithms. (c) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.123-124
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• 2019

This paper proposes an improved deep learning method based on small data sets for animal image classification. Firstly, we use a CNN to build a training model for small data sets, and use data augmentation to expand the data samples of the training set. Secondly, using the pre-trained network on large-scale datasets, such as VGG16, the bottleneck features in the small dataset are extracted and to be stored in two NumPy files as new training datasets and test datasets. Finally, training a fully connected network with the new datasets. In this paper, we use Kaggle famous Dogs vs Cats dataset as the experimental dataset, which is a two-category classification dataset.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.899-902
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• 2021

본 논문은 이미지 저작권 유무 판별을 분류 문제로 정의하고 기계학습과 합성곱 신경망 모델을 적용하여 해결한다. 학습을 위해 입력 데이터를 고정된 크기로 변환하고 정규화 과정을 수행하여 학습 데이터셋을 준비한다. 저작권 유무 판별 실험에서 SVM, k-NN, 랜덤포레스트, VGG-Net 모델의 분류 성능을 비교 분석한다. VGG-Net C 모델의 결과가 다른 알고리즘과 비교 시 10.65% 높은 성능을 나타냈으며 배치 정규화 층을 이용하여 과적합 현상을 개선했다.