• Journal of Energy Engineering
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• v.15 no.1 s.45
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• pp.74-81
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• 2006

In this study, on the basis of an analytic hierarchy process (AHP) method and through a questionnaire on subjective preference and importance, various power supply systems were comprehensively compared with multiple decision criteria such as environmental, social, healthy, and economic viewpoints and then overall priority was assessed. When a decision-making problem is modelled by a hierarchy structure, the AHP method is regarded as a useful tool for extracting subjective opinions via the aforementioned questionnaire. Here, the overall preferences were obtained by linearly aggregating weighting vector and preference matrix. The energy systems such as nuclear, coal, and LNG power plants were selected because they took share over 90% of domestic electricity supply in Korea. Furthermore, wind power and photovoltaic solar systems were included as representative renewable energy systems in Korea. According to the results of this demonstration study, the following comprehensive comparison indicators were yielded: 1) weighting factors for 4 types of main criteria as well as for 11 types of sub-criteria; 2) preference valuation for 7 types of energy systems under consideration; 3) overall score for each energy systems.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.1
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• pp.64-73
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• 2011

Oil price prediction is an important issue for the regulators of the government and the related industries. When employing the time series techniques for prediction, however, it becomes difficult and challenging since the behavior of the series of oil prices is dominated by quantitatively unexplained irregular external factors, e.g., supply- or demand-side shocks, political conflicts specific to events in the Middle East, and direct or indirect influences from other global economical indices, etc. Identifying and quantifying the relationship between oil price and those external factors may provide more relevant prediction than attempting to unclose the underlying structure of the series itself. Technically, this implies the prediction is to be based on the vectoral data on the degrees of the relationship rather than the series data. This paper proposes a novel method for time series prediction of using Semi-Supervised Learning that was originally designed only for the vector types of data. First, several time series of oil prices and other economical indices are transformed into the multiple dimensional vectors by the various types of technical indicators and the diverse combination of the indicator-specific hyper-parameters. Then, to avoid the curse of dimensionality and redundancy among the dimensions, the wellknown feature extraction techniques, PCA and NLPCA, are employed. With the extracted features, a timepointspecific similarity matrix of oil prices and other economical indices is built and finally, Semi-Supervised Learning generates one-timepoint-ahead prediction. The series of crude oil prices of West Texas Intermediate (WTI) was used to verify the proposed method, and the experiments showed promising results : 0.86 of the average AUC.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.6
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• pp.1355-1362
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• 2011

This paper describes a multi-mode LDPC decoder which supports three block lengths(648, 1296, 1944) and four code rates(1/2, 2/3, 3/4, 5/6) of IEEE 802.11n WLAN standard. Our LDPC decoder adopts a block-serial architecture based on min-sum algorithm and layered decoding scheme. A novel way to store check-node values and parity check matrix reduces the sizes of check-node memory and H-ROM. An efficient scheme for check-node memory addressing is used to achieve stall-free read/write operations. The designed LDPC decoder is verified by FPGA implementation, and synthesized with a $0.18-{\mu}m$ CMOS cell library. It has 219,100 gates and 45,036 bits RAM, and the estimated throughput is about 164~212 Mbps at 50 MHz@2.5v.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2844-2851
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• 2015

How to recover from the multiple 2D images into 3D object has been widely studied in the field of computer vision. In order to improve the accuracy of the recovered 3D shape, it is more important that noise must be minimized and the number of image frames must be guaranteed. However, potential noise is implied when tracking feature points. And the number of image frames which is consisted of an observation matrix usually decrease because of tracking failure, occlusions, or low image resolution, and so on. Therefore, it is obviously essential that the number of image frames must be secured by recovering the missing feature points under noise. Thus, we propose the analytic approach which can control directly the error distance and orientation of missing feature point by the geometrical properties under noise distribution. The superiority of proposed method is demonstrated through experimental results for synthetic and real object.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1246-1257
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• 2004

To find an optimal solution with genetic algorithm, it is desirable to maintain the population sire as large as possible. In some cases, however, the cost to evaluate each individual is relatively high and it is difficult to maintain large population. To solve this problem we propose a novel genetic algorithm based on fuzzy clustering, which considerably reduces evaluation number without any significant loss of its performance by evaluating only one representative for each cluster. The fitness values of other individuals are estimated from the representative fitness values indirectly. We have used fuzzy c-means algorithm and distributed the fitness using membership matrix, since it is hard to distribute precise fitness values by hard clustering method to individuals which belong to multiple groups. Nine benchmark functions have been investigated and the results are compared to six hard clustering algorithms with Euclidean distance and Pearson correlation coefficients as fitness distribution method.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.4
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• pp.622-627
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• 2012

The processes to determine the composition, dynamics, and activity of infection mechanisms by the rhizosphere microflora have attracted the interest of scientists from multiple disciplines although considerable progress of the infection pathways and plant-pathogen interactions by soil borne fungal pathogens have been made. Soilborne pathogens are confined within a three-dimensional matrix of mineral soil particles, pores, organic matter in various stages of decomposition and a biological component. Among the physical and chemical properties of soils soil texture and matric water potential may be the two most important factors that determine spread exudates by soil borne fungal pathogens, based on the size of the soil pores. Pathogenic invasion of plant roots involves complex molecular mechanisms which occur in the diffuse interface between the root and the soil created by root exudates. The initial infection by soilborne pathogens can be caused by enzymes which breakdown cell wall layers to penetrate the plant cell wall for the fungus. However, the fate and mobility of the exudates are less well understood. Therefore, it needs to develop methods to control disease caused by enzymes produced by the soilborne pathogens by verifying many other possible pathways and mechanisms of infection processes occurring in soils.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2599-2613
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• 2015

Multi-index hashing (MIH) is the state-of-the-art method for indexing binary codes, as it di-vides long codes into substrings and builds multiple hash tables. However, MIH is based on the dataset codes uniform distribution assumption, and will lose efficiency in dealing with non-uniformly distributed codes. Besides, there are lots of results sharing the same Hamming distance to a query, which makes the distance measure ambiguous. In this paper, we propose a data-oriented multi-index hashing method (DOMIH). We first compute the covariance ma-trix of bits and learn adaptive projection vector for each binary substring. Instead of using substrings as direct indices into hash tables, we project them with corresponding projection vectors to generate new indices. With adaptive projection, the indices in each hash table are near uniformly distributed. Then with covariance matrix, we propose a ranking method for the binary codes. By assigning different bit-level weights to different bits, the returned bina-ry codes are ranked at a finer-grained binary code level. Experiments conducted on reference large scale datasets show that compared to MIH the time performance of DOMIH can be improved by 36.9%-87.4%, and the search accuracy can be improved by 22.2%. To pinpoint the potential of DOMIH, we further use near-duplicate image retrieval as examples to show the applications and the good performance of our method.

• Journal of Microbiology and Biotechnology
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• v.14 no.3
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• pp.620-627
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• 2004

On the foundation of a database of genome sequences and protein analyses, the ability to clone a gene based on a peptide analysis is becoming more feasible and effective for identifying a specific gene and its protein product of interest. As such, the current study conducted a protein analysis using 2-D PAGE followed by MALDI- TOF and ESI-MS to identify a highly expressed gene product of C. parasitica. A distinctive and highly expressed protein spot with a molecular size of 47.2 kDa was randomly selected and MALDI-TOF MS analysis was conducted. A homology search indicated that the protein appeared to be a fungal enolase (enol). Meanwhile, multiple alignments of fungal enolases revealed a conserved amino acid sequence, from which degenerated primers were designed. A screening of the genomic $\lambda$ library of C. parasitica, using the PCR amplicon as a probe, was conducted to obtain the full-length gene, while RT-PCR was performed for the cDNA. The E. coli-expressed eno 1 exhibited enolase enzymatic activity, indicating that the cloned gene encoded the C. parasitica enolase. Moreover, ESI-MS of two of the separated peptides resolved from the protein spot on 2-D PAGE revealed sequences identical to the deduced sequences, suggesting that the cloned gene indeed encoded the resolved protein spot. Northern blot analysis indicated a consistent accumulation of an eno1 transcript during the cultivation.

• Journal of Korean Neurosurgical Society
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• v.49 no.1
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• pp.8-12
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• 2011

Objective: Ossification of the posterior longitudinal ligament (OPLL) has a strong genetic component. Specific gene polymorphisms may be associated with OPLL in several genes which regulate calcification in chondrocytes, change of extracellular collagen matrix and secretions of many growth factors and cytokines controlling bone morphogenesis. Toll-like receptor 5 (TLR5) may playa role in the pathogenesis of OPLL by intermediate nuclear factor-kappa B (NF-${\kappa}B$). The current study focused on coding single nucleotide polymorphisms (SNPs) of TLR5 for a case-control study investigating the relationship between TLR5 and OPLL in a Korean population. Methods: A total of 166 patients with OPLL and 231 controls were recruited for a case-control association study investigating the relationship between SNPs of TLR5 gene and OPLL. Four SNPs were genotyped by direct sequencing (rs5744168, rs5744169, rs2072493, and rs5744174). SNP data were analyzed using the SNPStats, SNPAnalyzer, Haploview, and Helixtree programs. Multiple logistic regression analysis with adjustment for age and gender was performed to calculate an odds ratio (OR). Results: None of SNPs were associated with OPLL in three alternative models (codominant, dominant, and recessive models; p> 0.05). A strong linkage disequilibrium block, including all 4 SNPs, was constructed using the Gabriel method. No haplotype was significantly associated with OPLL in three alternative models. Conclusion: These results suggest that Toll-like receptor 5 gene may not be associated with ossification of the posterior longitudinal ligament risk in Korean population.

• Structural Engineering and Mechanics
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• v.53 no.3
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• pp.481-495
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• 2015

Many hydropower stations in southwest China are located in regions of brittle rock mass with high geo-stresses. Under these conditions deep fractured zones often occur in the sidewalls of the underground caverns of a power station. The theory and methods of fracture and damage mechanics are therefore adopted to study the phenomena. First a flexibility matrix is developed to describe initial geometric imperfections of a jointed rock mass. This model takes into account the area and orientation of the fractured surfaces of multiple joint sets, as well as spacing and density of joints. Using the assumption of the equivalent strain principle, a damage constitutive model is established based on the brittle fracture criterion. In addition the theory of fracture mechanics is applied to analyze the occurrence of secondary cracks during a cavern excavation. The failure criterion, for rock bridge coalescence and the damage evolution equation, has been derived and a new sub-program integrated into the FLAC-3D software. The model has then been applied to the stability analysis of an underground cavern group of a hydropower station in Sichuan province, China. The results of this method are compared with those obtained by using a conventional elasto-plastic model and splitting depth calculated by the splitting failure criterion proposed in a previous study. The results are also compared with the depth of the relaxation and fracture zone in the surrounding rock measured by field monitoring. The distribution of the splitting zone obtained both by the proposed model and by the field monitoring measurements are consistent to the validity of the theory developed herein.