• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.71-76
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• 2013

This paper considers an approach of secondary user selection method in cooperative spectrum sensing, which two users with the best SNR in sensing channel and in reporting channel, respectively, are selected to cooperate with each other in the spectrum sensing. The sensing results reported by two users are then combined to detect PU signal operation. A comparison between this proposed method with conventional selection technique in which only the user having the best sensing channel SNR is selected shows that the proposed method outperforms. We make an assumption that sensing channels experience identical, independent distributed (i.i.d) Rayleigh fading and the reporting channels are invariant and non-identical. Simulation results are derived for demonstration.

• Journal of the Korean Institute of Gas
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• v.8 no.4 s.25
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• pp.70-76
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• 2004

This research was for the analysis of gas industries and 12 Hazard evaluation techniques for the industries, and present the selection guideline of the techniques using 6 factors affecting them. 4 indexes & consequences of incidents into 8 characteristics. Also, combining the indexes with the selection procedure in flowchart format could reduce improper techniques and present alternatives. Also, it is used as guidelines to get safety improvement plan to gas companies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.771-789
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• 2019

Text detection has been a popular research topic in the field of computer vision. It is difficult for prevalent text detection algorithms to avoid the dependence on datasets. To overcome this problem, we proposed a novel unsupervised text detection algorithm inspired by bootstrap learning. Firstly, the text candidate in a novel form of superpixel is proposed to improve the text recall rate by image segmentation. Secondly, we propose a unique text sample selection model (TSSM) to extract text samples from the current image and eliminate database dependency. Specifically, to improve the precision of samples, we combine maximally stable extremal regions (MSERs) and the saliency map to generate sample reference maps with a double threshold scheme. Finally, a multiple kernel boosting method is developed to generate a strong text classifier by combining multiple single kernel SVMs based on the samples selected from TSSM. Experimental results on standard datasets demonstrate that our text detection method is robust to complex backgrounds and multilingual text and shows stable performance on different standard datasets.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.1-6
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• 2021

The purpose of this study is to improve the performance of the artificial neural network system for facial image analysis through the image landmark selection technique. For landmark selection, a CNN-based multi-layer ResNet model for classification of facial image age is required. From the configured ResNet model, a heat map that detects the change of the output node according to the change of the input node is extracted. By combining a plurality of extracted heat maps, facial landmarks related to age classification prediction are created. The importance of each pixel location can be analyzed through facial landmarks. In addition, by removing the pixels with low weights, a significant amount of input data can be reduced.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.45 no.1
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• pp.26-31
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• 2000

This study was carried out to obtain the basic information for breeding cold-tolerant rice varieties with high yield-productivity through wide crosses between indica and japonica rice. Genetic analysis was conducted using 55 F$_1$s obtained from half-diallel crosses among eleven cultivars of various origin including indica and japonica rice. Screening for cold tolerance was done with cold-water irrigation after transplanting until ripening stage. Both general combining ability (GCA) and specific combining ability (SCA) effects were highly significant in all characters associated with dry matter accumulation at 30 and 50days after cold-water irrigation (DAC). The variance of GCA was much larger than that of SCA in plant height, shoot dry weight per plant (DWP), crop growth rate (CGR) and cold-water response index (CRI) of these characters except CRI of shoot dry weight per plant. The DWP, CGR and CRI of these characters of Gaochan 102, Tong88-7 and TR22183 were markedly higher than those of the others. GCA effects of these varieties on DWP, CGR and their CRI were also higher than those of the others, indicating that they are useful as promising parents for breeding cold-tolerant varieties. Analysis of genetic parameters for 11$\times$11 half-diallel F$_1$s revealed that inter-locus gene interaction were concerned in the expression of plant height at 50 DAC, CRI of DWP at 50 DAC, and CRI of CGR, and that intra-locus gene interaction for plant height and the other characters were partial dominance and over-dominance, respectively. Narrow-sense heritability (h$^2$$_{N}$) was the highest in plant height as 0.729, and the lowest in CRI of DWP at 30 DAC as 0.048, suggesting that selection for cold tolerance will be quite effective in case that the selection criterion is the performance itself.f.

• Journal of Internet Computing and Services
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• v.18 no.5
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• pp.47-53
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• 2017

In past few years, high-throughput sequencing, big-data generation, cloud computing, and computational biology are revolutionary. RNA sequencing is emerging as an attractive alternative to DNA microarrays. And the methods for constructing Gene Regulatory Network (GRN) from RNA-Seq are extremely lacking and urgently required. Because GRN has obtained substantial observation from genomics and bioinformatics, an elementary requirement of the GRN has been to maximize distinguishable genes. Despite of RNA sequencing techniques to generate a big amount of data, there are few computational methods to exploit the huge amount of the big data. Therefore, we have suggested a novel gene selection algorithm combining Support Vector Machines and Intensity-dependent normalization, which uses log differential expression ratio in RNAseq. It is an extended variation of support vector machine recursive feature elimination (SVM-RFE) algorithm. This algorithm accomplishes minimum relevancy with subsets of Big-Data, such as NCBI-GEO. The proposed algorithm was compared to the existing one which uses gene expression profiling DNA microarrays. It finds that the proposed algorithm have provided as convenient and quick method than previous because it uses all functions in R package and have more improvement with regard to the classification accuracy based on gene ontology and time consuming in terms of Big-Data. The comparison was performed based on the number of genes selected in RNAseq Big-Data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6A
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• pp.519-536
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• 2007

In this paper, we combine the Hybrid-Automatic Repeat reQuest (HARQ) algorithm with joint Tx and Rx antenna selection based on the reliability of the individual antennas links. The cyclic redundancy check (CRC) is applied on the data before being encoded using the Turbo encoder. In the receiver the CRC is used to detect errors of each antenna stream and to decide whether a retransmission is required or not. The receiver feeds back the transmitter with the Tx antennas ordering and the acknowledgement of each antenna (ACK or NACK). If the number of ACK antennas is higher than the NACK antennas, then the retransmission takes place from the ACK antennas using the Chase Combining (CC). If the number of the NACK antennas is higher than the ACK antennas then the ACK antennas are used to retransmit the data streams using the CC algorithm and additional NACK antennas are used to retransmit the remaining streams using Incremental Redundancy (IR, i.e. the encoder rate is reduced). Furthermore, the HARQ is used with the I-BLAST (Iterative-BLAST) which grantees a high transmission rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.7
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• pp.943-948
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• 2018

In this paper, we propose a hybrid approach of combining $A^*$ and Genetic algorithm in the path search problem. In $A^*$, the cost from a start node to the intermediate node is optimized in principle but the path from that intermediate node to the goal node is generated and tested based on the cumulated cost and the next node in a priority queue is chosen to be tested. In that process, we adopt the genetic algorithm principle in that the group of nodes to generate the next node from an intermediate node is tested by its fitness function. Top two nodes are selected to use crossover or mutation operation to generate the next generation. If generated nodes are qualified, those nodes are inserted to the priority queue. The proposed method is compared with the original sequential selection and the random selection of the next searching path in $A^*$ algorithm and the result verifies the superiority of the proposed method.

• Asian-Australasian Journal of Animal Sciences
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• v.11 no.2
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• pp.117-120
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• 1998

Relationships of breeding values of sires for first lactation milk yield with pedigree information or indices were examined to identify the optimal criteria of selecting young dairy bulls for future use in artificial insemination (AI). Records of performance data on 1087 crossbred daughters (Holstein - Friesian, Jersey and Brown Swiss with Hariana) of 147 sires, generated at Livestock Production Research (Cattle and Buffaloes) Farm, IVRI, Izatnagar, U.P., during 1972 - 1995 were used to obtain the estimates of sire's breeding values (EBV) using the Best Linear Unbiased Prediction Procedures. The correlations between young bull's EBV and the dam's first lactation milk yield was non-significantly different from zero. However, the young bull's EBV was negatively and significantly related (r = - 0.275 ; P < 0.05) to the dam's best lactation milk yield, suggesting that the selection of young dairy bulls from high yielding elite dams is not a suitable criteria for genetic improvement. The correlations of sire's and paternal grandsire's EBV's with young bull's EBV were high and positive (0.532, 0.844; P < 0.01). The maternal grandsire's EBV was positively but non-significantly related to grandson's EBV. The pedigree index incorporating dam's milk records and sire's EBV's showed a negative and non-significant correlation with young bull's EBV. However, the correlation of a pedigree index $(I_3)$ combining information on sire's and paternal grand-sire's EBV's with young bull's EBV's was considerably high and positive (0.797; P < 0.01). The regression coefficients of young bull's EBV on pedigree index $I_3$, was higher than those on other pedigree information. These results revealed that there was no advantage in basing selection on dam's performance or maternal grand-sire's EBV and that sire's and paternal grandsire's EBV's were reliable pedigree information for selection of young dairy bulls for future use in AI.

• Journal of KIISE:Software and Applications
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• v.34 no.9
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• pp.857-868
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• 2007

The good classifier ensemble should have a high complementarity among classifiers in order to produce a high recognition rate and its size is small in order to be efficient. This paper proposes a classifier ensemble selection algorithm with coarse-to-fine stages. for the algorithm to be successful, the original classifier pool should be sufficiently diverse. This paper produces a large classifier pool by combining several different classification algorithms and lots of feature subsets. The aim of the coarse selection is to reduce the size of classifier pool with little sacrifice of recognition performance. The fine selection finds near-optimal ensemble using genetic algorithms. A hybrid genetic algorithm with improved searching capability is also proposed. The experimentation uses the worldwide handwritten numeral databases. The results showed that the proposed algorithm is superior to the conventional ones.