• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.220-223
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• 2008

A test facility to measure the performance of a KM(Kick Motor) is constructed, and prediction of blast wave propagation over the facility is performed to check if the safety of test personnel in MCC(Main Control Center) can be guaranteed even for the most severe explosion. Assuming that the initial explosion energy is contained in a sphere under the pressure of 500, 1000, 1500 psi, respectively, the radius of the sphere is determined for each pressure to set the mass of contained explosion gas to 35 kg. The material properties of explosion gas are set to be the ones of KM propellant combustion gas under normal condition. To reduce the effort and time required for a complex three-dimensional modeling, the flowfield is approximated to axismmetry. Calculations are performed for all three initial pressure conditions, and the analysis of the result is given for 1500 psi which is expected to be the worst case. The maximum pressure is 3.5 psig while the minimum pressure is -1.2 psig on the outer wall of MCC, and the maximum pressure difference between the inner and outer walls of protection wall amounts to 3.0 psi.

• 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%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.26-32
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• 2018

The protein secondary structures are important information for studying the evolution, structure and function of proteins. Recently, deep learning methods have been actively applied to predict the secondary structure of proteins using only protein sequence information. In these methods, widely used input features are protein profiles transformed from protein sequences. In this paper, to obtain an effective protein profiles, protein profiles were constructed using protein sequence search methods such as PSI-BLAST and HHblits. We adjust the similarity threshold for determining the homologous protein sequence used in constructing the protein profile and the number of iterations of the profile construction using the homologous sequence information. We used the protein profiles as inputs to convolutional neural networks and recurrent neural networks to predict the secondary structures. The protein profile that was created by adding evolutionary information only once was effective.

• Journal of the Korean Society of Safety
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• v.31 no.3
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• pp.8-15
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• 2016

The purpose of this study is to design a model with the structural stability so as not to lose the operational function due to structural plastic or fail of a sliding blast door by blast pressure to this aim, a numerical simulation was performed using full-size experiments and M&S (Modeling & Simulation) of the sliding blast door. The sliding blast door ($W3,000{\times}H2,500mm$) under the blast load is in the form of a sliding type 2-way metal grill, which was applied by a design blast pressure (reflected pressure $P_r$) of 17 bar. According to the experimental results of a real sliding blast door under blast load, the blast pressure reached the sliding blast door approximately 4.3 ms after the explosion and lasted about 4.0 ms thereafter. The maximum blast pressure($P_r$) was 347.7 psi (2,397.3 kPa), it is similar to the UFC 3-340-02 of Parameter(91 %). In addition, operation inspection that was conducted for the sliding blast door after real test showed a problem of losing the door opening function, which was because of the fail of the Reversal Bolt that was installed to prevent the shock due to rebound of the blast door from the blast pressure. According to the reproduction of the experiment through M&S by applying the blast pressure measurement value of the full-size experiments, the sliding blast door showed a similar result to the full-size experiment in that the reversal bolt part failed to lose the function. In addition, as the pressure is concentrated on the failed reversal bolt, the Principal Tensile Failure Stress was exceeded in only 1.25 ms after the explosion, and the reversal bolt completely failed after 5.4 ms. Based on the result of the failed reversal bolt through the full-size experiment and M&S, the shape and size of the bolts were changed to re-design the M&S and re-analyze the sliding blast door. According to the M&S re-analysis result when the reversal bolt was designed in a square of 25 mm ($625mm^2$), the maximum pressure that the reversal bolt receives showed 81% of the principal tensile failure stress of the material, in plastic stage before fail.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04a
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• pp.79-82
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• 2002

단백질 3차 구조 정보는 PDB에서 플랫화일 형태로 제공되고 있으며 이러한 플랫화일 각각의 엔트리들은 단백질 3차 분자 구조를 구성하는 원자들의 공간좌표정보, 서열정보, 실험정보 및 참조정보 등으로 구성된다. 이러한 정보들을 포함하고 있는 플랫파일로부터 필수적인 구조정보 및 서열정보 등의 효율적 검색을 위해서는 플랫파일을 데이터베이스로 구축함과 동시에, 구축된 데이터베이스를 위한 유사성 검색시스템 구축이 요구된다. 따라서, 이 논문에서는 Protein DataBank에서 제공하는 플랫파일을 공간객체 모델링기법에 기반한 관계형 데이터베이스로 구축하고 PSI-BLAST를 적용하여 단백질 서열 유사성 검색 시스템을 구축한다. 이렇게 함으로써 단백질 3자 구조 분자를 구성하는 원자에 대한 검색과 구조에 대한 서열 유사성 검색을 통하여 단백질 3차 구조 분류 및 구조 예측 시스템 구축에 활용할 수 있다.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.419-427
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• 2024

This study assessed the potential impact of gas leakage resulting from accidental damage to buried urban gas pipelines during perforating operation near subway construction sites. The risk of explosions due to ignition sources such as static electricity, arising from gas infiltrating the subway construction site through storm sewers and sewage pipes, was evaluated using the ALOHA program. The results of the threat zone calculation, which input various parameters of urban gas pipelines such as length, diameter, and pressure, indicated that the flammable area within the vapor cloud extended from 1.2 to 1.4 km (red zone), the blast area ranged from 0.8 to 1.0 km (yellow zone), and the jet fire extended from 45 to 61 m (red zone). This study demonstrates that within the flammable area of the vapor cloud, a specific combination of concentration and conditions can increase flammability. The blast area may experience explosions with a pressure of 1.0 psi, sufficient to break glass windows. In the event of a jet fire, high temperatures and intense radiant heat exposure lead to rapid fire propagation in densely populated areas, posing a high risk of casualties. The findings are presented in terms of the sphere of influence and threat zone ranges.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.173-182
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• 2005

A three dimensional (3D) model for the catalytic region of Type II Pseudomonas sp. USM 4-55 PHA synthase 1 (PhaC1$_{P.sp\;USM\;4-55}$) from residue 267 to residue 484 was developed. Sequence analysis demonstrated that PhaC1$_{P.sp\;USM\;4-55}$ lacked homology with all known structural databases. PSI-BLAST and HMM Superfamily analyses demonstrated that this enzyme belongs to the ${\alpha}/{\beta}$ hydrolase fold family. Threading approach revealed that the most suitable template to use was the Human gastric lipase (1HLG). The superimposition of the predicted PhaC1$_{P.sp\;USM\;4-55}$ model with the 1HLG template structure covering 86.2% of the backbone atoms showed an RMSD of 1.15 ${\AA}$ The catalytic residues comprising of Cys296, Asp451, His452 and His479 were found to be conserved and were located adjacent to each other. We proposed that the catalytic mechanism involved the formation of two tetrahedral intermediates.

• 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.

• International Journal of Industrial Entomology and Biomaterials
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• v.10 no.1
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• pp.25-33
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• 2005

Recently a cDNA clone of apoLp-III from Hyphantria cunea was isolated and subjected to computational analysis to compare with other available sequences. Multiple sequence alignments were carried out using the amino acid sequences of apoLp-III from six insects. It was found that the H. cunea apoLp-III has relatively high sequence identities to Spodoptera litura ($69.5\%$), Manduca sexta ($66.8\%$), Galleria mellonella ($65.1\%$), Bombyx mori N4 ($54.3\%$) but less identity to Locusta migratoria ($18.3\%$). The amino acid composition was compared with other insects using EXPASY tools; it shows that alanine (Ala), glutamine (Gln), leucine (Leu) and lysine (Lys) are the major amino acid components of apoLp-III in H. cunea as well as other lepidopterans. Homology modeling performed using PSI-BLAST (PDB template M. sexta) reveals that the apoLp-III molecules consist of five, long amphipathic alpha helical bundles with short loops connecting the helices and shows homology with other insects. Phylogenetic analysis shows that the orthopteran apoLp-III represented by locust was most distantly related to the lepidopteran insects.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.244-249
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• 2004

There have been many attempts to predict the secondary structure content of a protein from its primary sequence, which serves as the first step in a series of bioinformatics processes to gain knowledge of the structure and function of a protein. Most of them assumed that prediction relying on the information of the amino acid composition of a protein can be successful. Several approaches expanded the amount of information by including the pair amino acid composition of two adjacent residues. Recent methods achieved a remarkable improvement in prediction accuracy by using this expanded composition information. The overall average errors of two successful methods were 6.1% and 3.4%. This work was motivated by the observation that evolutionarily related proteins share the similar structure. After manipulating the values of the frequency matrix obtained by running PSI-BLAST, inputs of an artificial neural network were constructed by taking the ratio of the amino acid composition of the evolutionarily related proteins with a query protein to the background probability. Although we did not utilize the expanded composition information of amino acid pairs, we obtained the comparable accuracy, with the overall average error being 3.6%.