• Computational Structural Engineering
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• v.33 no.2
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• pp.28-34
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• 2020

• Interdisciplinary Bio Central
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• v.4 no.4
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• pp.14.1-14.6
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• 2012

Splice site prediction in DNA sequence is a basic search problem for finding exon/intron and intron/exon boundaries. Removing introns and then joining the exons together forms the mRNA sequence. These sequences are the input of the translation process. It is a necessary step in the central dogma of molecular biology. The main task of splice site prediction is to find out the exact GT and AG ended sequences. Then it identifies the true and false GT and AG ended sequences among those candidate sequences. In this paper, we survey research works on splice site prediction based on support vector machine (SVM). The basic difference between these research works is nucleotide encoding technique and SVM kernel selection. Some methods encode the DNA sequence in a sparse way whereas others encode in a probabilistic manner. The encoded sequences serve as input of SVM. The task of SVM is to classify them using its learning model. The accuracy of classification largely depends on the proper kernel selection for sequence data as well as a selection of kernel parameter. We observe each encoding technique and classify them according to their similarity. Then we discuss about kernel and their parameter selection. Our survey paper provides a basic understanding of encoding approaches and proper kernel selection of SVM for splice site prediction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1686-1692
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• 2012

Spreading sequence play an important role in wireless communications, such as in a CDMA(code division multiple access) communication system and multi-carrier spectrum communication system. Spreading sequences with low cross-correlation, in a direct-sequence spread spectrum communication system, help to minimize multiple access interference and to increase security degree of system. Analysis of cross-correlations between the sequences is a necessary process to design sequences. However it require lots of computing time for analysis of cross-correlations between sequences. In this paper we propose a method which is possible to compute effectively cross-correlation using subfield in the process of practical computation of cross-correlation between nonlinear binary sequences.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.2
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• pp.137-145
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• 1989

In this paper we consider the design of bent sequence generators employing a modified bent function. Through the experimental results and simulation, we show that the period of sequences is $2^n$-1, the number of sequences in the set is $2n/2$, the largest magnitude of correlation is $2n/2$+1, and the sequence generators easy to randomly initialize into any assigned code and hence can be rapidly hopped from sequence to sequence. And we construct a code synchronization system with bent sequence generator as the local code generator, a sliding correlator and DLL as the acquisition subsystem and the tracking subsystem, respectively for DS-CODA, and verify the experimenta results for accomplishing code synchronism.

• Macromolecular Research
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• v.17 no.8
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• pp.597-602
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• 2009

Although the existing design principle of full-sequence ionic complement is convenient for the development of peptides, it greatly constrains the exploration of peptides with other possible assembly mechanisms and different yet essential functions. Herein, a novel designed half-sequence ionic complementary peptide (referred to as P9), AC-Pro-Ser-Phe-Asn-Phe-Lys-Phe-Glu-Pro-$NH_2$, is reported. When transferred from pure water to sodium chloride solution, P9 underwent a dramatic morphological transformation from globular aggregations to nanofibers. Moreover, the rheological experiment showed that the P9 could form a hydrogel with a storage modulus of about 30 Pa even at very low peptide concentration (0.5% (wt/vol)). The P9 hydrogel formed in salt solution could recover in a period of about 1,800 sec, which is faster than that in the pure water. The data suggestcd that the half-sequence, ionic complementary peptide might be worthy of further research for its special properties.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.526-538
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• 2021

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.

• Composites Research
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• v.20 no.1
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• pp.1-7
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• 2007

This study presents a methodology for optimization of static characteristics of golf club shafts. Stacking sequence for the optimal composite shaft performance is searched. A new objective function is defined for the simultaneous optimization of flexural and torsional stiffnesses. Classical lamination theory is used for the static analysis. As the optimization tool, genetic algorithm is applied with the stacking sequence as design. variables. With the optimal stacking sequence, dynamic characteristics of the shaft is also studied.

• Proceedings of the Korea Society of Design Studies Conference
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• 2005.05a
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• pp.218-219
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• 2005

• Structural Engineering and Mechanics
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• v.52 no.6
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• pp.1157-1176
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• 2014

The objective of this research is to develop a practical design and assessment approach of steel frames with steel slit walls (SSWs) that focuses on the damage-control behavior to enhance the structural resilience. The yielding sequence of SSWs and frame components is found to be a critical issue for the damage-control behavior and the design of systems. The design concept is validated by the full-scale experiments presented in this paper. Based on a modified energy-balance model, a procedure for designing and assessing the system motivated by the framework regarding the equilibrium of the energy demand and the energy capacity is proposed. The damage-control spectra constructed by strength reduction factors calculated from single-degree-of-freedom systems considering the post stiffness are addressed. A quantitative damage-control index to evaluate the system is also derived. The applicability of the proposed approach is validated by the evaluation of example structures with nonlinear dynamic analyses. The observations regarding the structural response and the prediction during selected ground motions demonstrate that the proposed approach can be applied to damage-control design and assessment of systems with satisfactory accuracy.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2000.10a
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• pp.132-135
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• 2000

A process design expert system for rotationally symmetric deep drawing products has been developed The application of the expert system to non-axisymmetric components, however, has not been reported yet. Thus, in this present study, the expert system for non-axisymmetric deep drawing products with elliptical shape was constructed by using process sequence design. The system developed in this work consists of four modules. The first one is a recognition of shape module to recognize non-axisymmetric products. The second one is three dimensional (3-D) modeling module to calculate the surface area for non-axisymmetric products. The third one is a blank design module to create an oval-shaped blank with the identical surface area. The forth one is a process planning module based on the production rules that play the best important role in an expert system for manufacturing. The production rules are generated and upgraded by interviewing with field engineers.