• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11C
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• pp.1025-1028
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• 2009

This paper presents upper bounds of Maximum Likelihood (ML) decoding performance of a few irregular LDPC codes using the simple bound and ML input output weight distributions and it is shown that contrary to general opinion that as block length becomes longer, BP decoding performance becomes simply closer to ML decoding performance, before peak degradation, as block length becomes longer, BP decoding performance falls behind ML decoding performance more and after peak degradation, general opinion holds.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.2
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• pp.76-82
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• 2002

This paper presents the method of sail-resistivity estimation using the backpropagation(BP) neural network. Existing estimation programs are expensive, and their estimation methods need complex techniques and take much time. Also, those programs have not become well spreaded in Korea yet. Soil resistivity estimation method using BP algorithm has studied for the reason mentioned above. This paper suggests the method which differs from expensive program or graphic technology requiring many input stages, complicated calculation and professional knowledge. The equivalent earth resistivity can be presented immediately after inputting apparent resistivity through the personal computer with a simplified Program without many Processing stages. This program has the advantages of reasonable accuracy, rapid processing time and confident of anti users.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.7C
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• pp.594-597
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• 2010

This paper estimates belief-propagation (BP) decoding performance of moderate-length irregular low-density parity-check (LDPC) codes with sphere bounds. We note that for moderate-length($10^3{\leq}N{\leq}4\times10^3$) irregular LDPC codes, BP decoding performance, which is much worse than maximum likelihood (ML) decoding performance, is well matched with one of loose upper bounds, i.e., sphere bounds. We introduce the sphere bounding technique for particular codes, not average bounds. The sphere bounding estimation technique is validated by simulation results. It is also shown that sphere bounds and BP decoding performance of irregular LDPC codes are very close at bit-error-rates (BERs) $P_b$ of practical importance($10^{-5}{\leq}P_b{\leq}10^{-4}$).

• 보존과학연구
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• s.27
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• pp.181-201
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• 2006

In this study, radiocarbon dating by the benzene synthesis method and liquid scintillation counting method were performed for the age determination of coal sample at the Cheongok-dong archaeologicalsite, Ulsan. The results of radiocarbon age(BP year) are section Ga #1 $2920\pm50$ BP year, section Ga #5 $3100\pm55$BP year, section Ga #6 $2820\pm60$ BP year, section Na #5 $2650\pm50$ BP year. These ages were converted to calibrated age(AD/BC year) using high precision curve. The products on the excavation of Cheongok-dong archaeological site, Ulsan, section Ga #1 BC 1260-980 BC, section Ga #5 BC1460-1200 BC, section Ga #6 BC 1110~830 BC, section Na #5 BC 920-760 BC. Comparison of samples with radiocarbon and archaeological ages for revealed the approximation.

• Korean Journal of Veterinary Research
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• v.45 no.1
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• pp.127-130
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• 2005

The objective of present study was to ascertain sex determination for individual identification, parentage control, and sex chromosome anomalies in horse. PCR amplification products of the equine sex determining region of the Y chromosome gene (SRY) and amelogenin gene (AMEL) were detected by using agarose gel electrophoresis. A normal sire and foal II showed 1 SRY band (430 bp) and 3 AMEL (AMELX, AMELY, and AMELX/Y) band, 175 bp, 160 bp, 190 bp, respectively, and a normal dam and foal I showed a single AMELX band (175 bp). These results enables a quick diagnosis for sex determination prior to cytogenetic analysis.

• Journal of Information Technology Applications and Management
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• v.9 no.2
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• pp.1-16
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• 2002

Recently, neural network methods have been studied to find out more valuable information in data bases. But the supervised learning methods of neural networks have an overfitting problem, which leads to errors of target patterns. And the unsupervised learning methods can distort important information in the process of regularizing data. Thus they can't efficiently classify data, To solve the problems, this paper introduces a hybrid neural networks HACAB(Hybrid Algorithm combining a Competition learning model And BP Algorithm) combining a competition learning model and 8P algorithm. HACAB is designed for cases which there is no target patterns. HACAB makes target patterns by adopting a competition learning model and classifies input patterns using the target patterns by BP algorithm. HACAB is evaluated with random input patterns and Iris data In cases of no target patterns, HACAB can classify data more effectively than BP algorithm does.

• KIEE International Transactions on Electrophysics and Applications
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• v.5C no.3
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• pp.138-142
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• 2005

In this paper, we compared recognition rates between NN(neural networks) and clustering method as a scheme of off-line PD(partial discharge) diagnosis which occurs at the stator coil of traction motor. To acquire PD data, three defective models are made. PD data for classification were acquired from PD detector. And then statistical distributions are calculated to classify model discharge sources. These statistical distributions were applied as input data of two classification tools, BP(Back propagation algorithm) and ANFIS(adaptive network based fuzzy inference system) pre-processed FCM(fuzzy c-means) clustering method. So, classification rate of BP were somewhat higher than ANFIS. But other items of ANFIS were better than BP; learning time, parameter number, simplicity of algorithm.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.669-673
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• 2008

In this study, the new methodology which combines Kohonen self-organizing map(KSOM) neural networks model and the conventional neural networks models such as feedforward neural networks model and generalized neural networks model is introduced to forecast flood stage in Nakdong river, Republic of Korea. It is possible to train without output data in KSOM neural networks model. KSOM neural networks model is used to classify the input data before it combines with the conventional neural networks model. Four types of models such as SOM-FFNNM-BP, SOM-GRNNM-GA, FFNNM-BP, and GRNNM-GA are used to train and test performances respectively. From the statistical analysis for training and testing performances, SOM-GRNNM-GA shows the best results compared with the other models such as SOM-FFNNM-BP, FFNNM-BP, and GRNNM-GA and FFNNM-BP shows vice-versa. From this study, we can suggest the new methodology to forecast flood stage and construct flood warning system in river basin.

• Korean Journal of Plant Taxonomy
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• v.51 no.3
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• pp.337-344
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• 2021

The chloroplast genome of Limonium tetragonum (Thunb.) Bullock, a halophytic species, was sequenced to understand genetic differences based on its geographical distribution. The cp genome of L. tetragonum was 154,689 bp long (GC ratio is 37.0%) and has four subregions: 84,572 bp of large single-copy (35.3%) and 12,813 bp of small single-copy (31.5%) regions were separated by 28,562 bp of inverted repeat (40.9%) regions. It contained 128 genes (83 protein-coding genes, eight rRNAs, and 37 tRNAs). Thirty-five single-nucleotide polymorphisms and 33 INDEL regions (88 bp in length) were identified. Maximum-likelihood and Bayesian inference phylogenetic trees showed that L. tetragonum formed a sister group with L. aureum, which is incongruent with certain previous studies, including a phylogenetic analysis.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.106-111
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• 2021

Multiple sales promotions for one product can be simultaneously offered. For instance, marketers can offer a premium along with a price discount. In the present research, we primarily focus on offers containing both a bonus pack and a price discount. In particular, this research examines whether consumer evaluations of offers containing both a bonus pack and a price discount (i.e., BP + PD offers) vary according to consumers' saving orientation. Specifically, we predict that for consumers with strong (vs. weak) saving orientation, offers containing the high PD but low extra amount of BP will be more favorably evaluated than offers with a high extra amount of BP but low PD. A series of two experimental results show that consumers' saving orientation moderates their evaluations of BP and PD offers, which supports the prediction. The findings imply that marketers can evoke more positive consumer responses to BP and PD offers, considering individual differences such as saving orientation.