• Magazine of the Korean Society of Agricultural Engineers
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• v.34 no.E
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• pp.45-59
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• 1992

At downstream part of the hydraulic structures such as spiliway or drainage gate, jet flow can occur by gate opening. If stream bed is not hard or bed protection is not sufficient, scour hole will be formed due to high shear stress of the jet flow. We call this primary scour. Once the scour hole is formed, a vortex occurs in it and this vortex causes additional scour. We call this secondary scour. The primary scour proceeds to downstream together with flow direction but the secondary one proceeds to upstream direction opposite to it. If the secondary one continues and reaches to the hydraulic structure, it can undermine the bottom of hydraulic structure and this will lead to failure of structure itself. Thus, it is necessary to know the physical features of the vortex structure in a scour hole, which is the main mechanism of the secondary scour. This study deals with the characteristics of the vortex structure and its shear stress which causes the secondary scour.

• BMB Reports
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• v.44 no.2
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• pp.118-122
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• 2011

Most widely used secondary structure assignment methods such as DSSP identify structural elements based on N-H and C=O hydrogen bonding patterns from X-ray or NMR-determined coordinates. Secondary structure assignment algorithms using limited $C{\alpha}$ information have been under development as well, but their accuracy is only ~80% compared to DSSP. We have hereby developed SABA (Secondary Structure Assignment Program Based on only Alpha Carbons) with ~90% accuracy. SABA defines a novel geometrical parameter, termed a pseudo center, which is the midpoint of two continuous $C{\alpha}s$. SABA is capable of identifying $\alpha$-helices, $3_{10}$-helices, and $\beta$-strands with high accuracy by using cut-off criteria on distances and dihedral angles between two or more pseudo centers. In addition to assigning secondary structures to $C{\alpha}$-only structures, algorithms using limited $C{\alpha}$ information with high accuracy have the potential to enhance the speed of calculations for high capacity structure comparison.

• Journal of Microbiology and Biotechnology
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• v.12 no.1
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• pp.130-136
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• 2002

The internal transcribed spacer (ITS) region in the genus Tricholoma was analyzed, including for its primary nucleotide sequence and secondary structural characterization. The secondary structures of the ITS2 region in the genus Tricholoma were identified for use in bioinformatic processes to study molecular evolution and compare secondary structures. Ten newly sequenced ITS regions were added to the analysis and submitted to the GenBank database. The resulting structure from a minimum energy algorithm indicated the four-domain model, as previously suggested by others. The conserved secondary structure of the ITS2 sequences of the genus Tricholoma exhibited certain unique features, including pyrimidine tracts in the loops of domain A and a complete structure containing four domains, with motifs identified in other ITS2 secondary structures. A phylogenetic tree was derived from sequence alignment based on the secondary structures. From the resulting maximum parsimonious tree, it was found that the species in the genus Tricholoma had evolved monophyletically and were composed of four groups, as supported by the bootstrapping values and pileus color.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.280-282
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• 2005

A ribonucleic acid (RNA) is one of the two types of nucleic acids found in living organisms. An RNA molecule represents a long chain of monomers called nucleotides. The sequence of nucleotides of an RNA molecule constitutes its primary structure, and the pattern of pairing between nucleotides determines the secondary structure of an RNA. Non-coding RNA genes produce transcripts that exert their function without ever producing proteins. Predicting the secondary structure of non-coding RNAs is very important for understanding their functions. We focus on Nussinov's algorithm as useful techniques for predicting RNA secondary structures. We introduce a new traceback matrix and scoring table to improve above algorithm. And the improved algorithm provides better levels of performance than the originals.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.4
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• pp.314-318
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• 2010

Predicting Alpha-helicies, Beta-sheets and Turns of a proteins secondary structure is a complex non-linear task that has been approached by several techniques such as Neural Networks, Genetic Algorithms, Decision Trees and other statistical or heuristic methods. This project introduces a new machine learning method by combining Bayesian Inference with offline trained Multilayered Perceptron (MLP) models as the likelihood for secondary structure prediction of proteins. With varying window sizes of neighboring amino acid information, the information is extracted and passed back and forth between the Neural Net and the Bayesian Inference process until the posterior probability of the secondary structure converges.

• Journal of the Korean Magnetic Resonance Society
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• v.18 no.1
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• pp.36-40
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• 2014

Predicting secondary structure of protein through assigned backbone chemical shifts has been used widely because of its convenience and flexibility. In spite of its usefulness, chemical shift based analysis has some defects including isotopic shifts and solvent interaction. Here, it is shown that corrected chemical shift analysis for secondary structure of protein. It is included chemical shift correction through consideration of deuterium isotopic effect and calculate chemical shift index using probability-based methods. Enhanced method was applied successfully to one of the proteins from Mycobacterium tuberculosis. It is suggested that correction of chemical shift analysis could increase accuracy of secondary structure prediction of protein and small molecule in solution.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1816-1821
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• 2016

Protein secondary structure is important for the study of protein evolution, structure and function of proteins which play crucial roles in most of biological processes. This paper try to effectively extract protein secondary structure information from the large protein structure database in order to predict the protein secondary structure of a query protein sequence. To find more remote homologous sequences of a query sequence in the protein database, we used PSI-BLAST which can perform gapped iterative searches and use profiles consisting of homologous protein sequences of a query protein. The secondary structures of the homologous sequences are weighed combined to the secondary structure prediction according to their relative degree of similarity to the query sequence. When homologous sequences with a neural network predictor were used, the accuracies were higher than those of current state-of-art techniques, achieving a Q3 accuracy of 92.28% and a Q8 accuracy of 88.79%.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.133-138
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• 2003

The prediction of protein secondary structure has been an important bioinformatics tool that is an essential component of the template-based protein tertiary structure prediction process. It has been known that the predicted secondary structure information improves both the fold recognition performance and the alignment accuracy. In this paper, we describe several novel ideas that may improve the prediction accuracy. The main idea is motivated by an observation that the protein's structural information, especially when it is combined with the evolutionary information, significantly improves the accuracy of the predicted tertiary structure. From the non-redundant set of protein structures, we derive the 'potential' parameters for the protein secondary structure prediction that contains the structural information of proteins, by following the procedure similar to the way to derive the directional information table of GOR method. Those potential parameters are combined with the frequency matrices obtained by running PSI-BLAST to construct the feature vectors that are used to train the support vector machines (SVM) to build the secondary structure classifiers. Moreover, the problem of huge model file size, which is one of the known shortcomings of SVM, is partially overcome by reducing the size of training data by filtering out the redundancy not only at the protein level but also at the feature vector level. A preliminary result measured by the average three-state prediction accuracy is encouraging.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.4
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• pp.307-313
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• 2021

Modernized GNSS signal structures tend to use tiered codes, and all GNSSs use binary codes as secondary codes. However, recently, signals using polyphase codes such as Zadoff-Chu sequence have been proposed, and are expected to be utilized in GNSS. For example, there is Tiered Differential Polyphase Code (TDPC) using polyphase code as secondary code. In TDPC, the phase of secondary code changes every one period of the primary code and a time-variant error is added to the carrier tracking error, so carrier tracking ambiguity exists until the secondary code phase is found. Since the carrier tracking ambiguity cannot be solved using the general GNSS receiver architecture, a new receiver architecture is required. Therefore, in this paper, we describe the carrier tracking ambiguity and its cause in signal tracking, and propose a receiver structure that can solve it. In order to prove the proposed receiver structure, we provide three signal tracking results. The first is the differential decoding result (secondary code sync) using the general GNSS receiver structure and the proposed receiver structure. The second is the IQ diagram before and after multiplying the secondary code demodulation when carrier tracking ambiguity is solved using the proposed receiver structure. The third is the carrier tracking result of the legacy GPS (L1 C/A) signal and the signal using TDPC.

• Language and Information
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• v.2 no.2
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• pp.155-183
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• 1998

The purpose of this paper is to explore the syntactic criteria for determining a secondary predicates as a predicate modifier or a conjunction, and to formalize the semantic aspects of the [-ke] structure as a predicate in Korean. Syntactically, the [-ke] structure is considered to be a secondary predicate when the shared arguments appear in both the [-ke] structure and the main verb structure. On the other hand, if they do not appear in both structures, the [-ke] structure is considered to be a connective element. Semantically the [-ke] structure has numerous aspects such as depictives, resultatives, objectivity, and emphasis. The depictives of the secondary predicate can be formalize as $p{\wedge}q$ where p represents a propositional expression of the secondary predicate and q is a propositional expression of the main verb. Resultatives have the logical form $q{\rightarrow}{\Box}p$, because the consequence has to always be true. However, objectivity has the logical form $q{\rightarrow}{\diamondsuit}p$, because the consequence can be either true or false. Emphasis is represented as $q{\rightarrow}p{\uparrow}$ because the secondary predicate represents the polarity of the event.