• Explosives and Blasting
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• v.14 no.4
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• pp.13-19
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• 1996

Lately, the improtance of smooth blasting is increasing on every construction fields, suchas underground caves, tunnels, and roadconstruction, etc. The main purpose of smooth blasting is to prevent unnecessary cracks from the base rockwhich preserved permanently and is to gain the smooth fracture plane. So, in smooth blashing, explosives with low detonating velocity are generally used. But it is difficult to discuss general theory on the smooth blashing because the mechanical properties of pertienent rocks are difficult regionally. Accordingly basic reserches on the smooth blasting are demended. In this paper, the mechanisms of the smooth blasting on the rocks containing discontinuities were discussd. Firstly, the writer predicted the formation of fracture plane and unevenness using mathematical methodology, the next the model blast tests were conducted in order to simulate the crack propagation modes from the blast holes. Through the research, the following conclusions were obtained l)The blast test results were in reasonally good agreement with the theoretical prediction. 2)The degree of discontinuity has an influence on the fracture morphology.

• 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 Korea Concrete Institute
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• v.16 no.5 s.83
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• pp.621-626
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• 2004

The use of blast-furnace slag (BFS) in making not only normal concrete but also high-performance concrete has several advantages with respect to workability, long-term strength and durability. However, slag concrete tends to show more shrinkage than normal concrete, especially autogenous shrinkage. High autogenous shrinkage would result in severe cracking if they are not controlled properly. Therefore, in order to minimize the shrinkage stress and to ensure the service life of concrete structures, the autogenous shrinkage behavior of concrete containing BFS should be understood. In this study, small prisms made of concrete with water-binder (cement+BFS) ratio (W/B) ranging from 0.27 to 0.42 and BFS replacement level of $0\%$, $30\%$, and $50\%$, were prepared to measure the autogenous shrinkage. Based on the test results, thereafter, material constants in autogenous shrinkage prediction model were determined. In particular, an effective autogenous shrinkage defined as the shrinkage that contributes to the stress development was introduced. Moreover, an estimation formula of the 28-day effective autogenous shrinkage was proposed by considering various W/B's. Test results showed that autogenous shrinkage increased with replacement level of BFS at the same W/B. Interestingly, the increase of autogenous shrinkage is dependent on the W/B at the same content of BFS; the lower W/B, the smaller increasing rate. In concluding, it is necessary to use the combination of other mineral admixtures such as shrinkage reducing admixture or to perform sufficient moisture curing on the construction site in order to reduce the autogenous shrinkage of BFS concrete.

• Explosives and Blasting
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• v.38 no.4
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• pp.16-25
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• 2020

Multi-setting smart-investigation of the ground and large uncharged hole boring (MSP) method to reduce the blast-induced vibration in a tunnel excavation is carried out over 50m of long-distance boring in a horizontal direction and thus has been accompanied by deviations in boring alignment because of the heavy and one-directional rotation of the rod. Therefore, the deviation has been adjusted through the boring machine's variable setting rely on the previous construction records and expert's experience. However, the geological characteristics, machine conditions, and inexperienced workers have caused significant deviation from the target alignment. The excessive deviation from the boring target may cause a delay in the construction schedule and economic losses. A deep learning-based prediction model has been developed to discover an ideal initial setting of the MSP machine. Dropout, early stopping, pre-training techniques have been employed to prevent overfitting in the training phase and, significantly improved the prediction results. These results showed the high possibility of developing the model to suggest the boring machine's optimum initial setting. We expect that optimized setting guidelines can be further developed through the continuous addition of the data and the additional consideration of the other factors.

• Genomics & Informatics
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• v.10 no.1
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• pp.58-64
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• 2012

Intron prediction is an important problem of the constantly updated genome annotation. Using two model plant (rice and $Arabidopsis$) genomes, we compared two well-known intron prediction tools: the Blast-Like Alignment Tool (BLAT) and Sim4cc. The results showed that each of the tools had its own advantages and disadvantages. BLAT predicted more than 99% introns of whole genomic introns with a small number of false-positive introns. Sim4cc was successful at finding the correct introns with a false-negative rate of 1.02% to 4.85%, and it needed a longer run time than BLAT. Further, we evaluated the intron information of 10 complete plant genomes. As non-coding sequences, intron lengths are not limited by a triplet codon frame; so, intron lengths have three phases: a multiple of three bases (3n), a multiple of three bases plus one (3n + 1), and a multiple of three bases plus two (3n + 2). It was widely accepted that the percentages of the 3n, 3n + 1, and 3n + 2 introns were quite similar in genomes. Our studies showed that 80% (8/10) of species were similar in terms of the number of three phases. The percentages of 3n introns in $Ostreococcus$ $lucimarinus$ was excessive (47.7%), while in $Ostreococcus$ $tauri$, it was deficient (29.1%). This discrepancy could have been the result of errors in intron prediction. It is suggested that a three-phase evaluation is a fast and effective method of detecting intron annotation problems.

• Explosives and Blasting
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• v.40 no.3
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• pp.44-53
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• 2022

This study evaluates the applicability of the TNT Equivalency Method, Multi-Energy Method, and Baker-Strehlow-Tang (BST) Method, which are blast prediction models used to determine the overpressure of blast wave generated from vapor cloud explosion. It is assumed that the propane leaked from a propane storage container with a capacity of 2000 kg installed in an area where studio houses and shopping centers are concentrated causes a vapor cloud explosion. The equivalent mass of TNT calculated by applying the TNT Equivalency Method is found to be 4061 kg. Change of overpressure with the distance obtained by the TNT Equivalency Method, Multi-Energy Method, and BST Method is rapid and the magnitude of overpressure obtained by the TNT Equivalency Method and BST method is generally similar within 100 m from explosion center. As a result of comparing the overpressure observed in the actual vapor cloud explosion case with the overpressure obtained by applying the TNT Equivalent Method, Multi-Energy Method, and BST Method, the BST Method is found to be the best fit. As a result of comparing the overpressure with the distance obtained by each explosion prediction model with the damage criteria for structure, it is estimated that the structure located within 90 m from explosion center would suffer a damage more than partial destruction, and glass panes of the structure separated by 600 m would be fractured.

• Explosives and Blasting
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• v.41 no.3
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• pp.26-37
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• 2023

In order to determine the amount of explosives that can minimize the vibration generated during tunnel construction using the blasting method, it is necessary to derive the blasting vibration coefficients, K and n, by analyzing the vibration records of trial blasting in the field or under similar conditions. In this study, we aimed to develop a technique that can derive reasonable K and n when trial blasting cannot be performed. To this end, we collected full-scale trial blast data and studied how to predict the blast vibration coefficient (K, n) according to the type of explosive, center cut blasting method, rock origin and type, and rock grade using deep learning (DL). In addition, the correction value between full-scale and borehole trial blasting results was calculated to compensate for the limitations of the borehole trial blasting results and to carry out a design that aligns more closely with reality. In this study, when comparing the available explosive amount according to the borehole trial blasting result equation, the predictions from deep learning (DL) exceed 50%, and the result with the correction value is similar to other blast vibration estimation equations or about 20% more, enabling more economical design.

• Genomics & Informatics
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• v.18 no.1
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• pp.5.1-5.5
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• 2020

Highly pathogenic avian influenza (HPAI) viruses have caused severe respiratory disease and death in poultry and human beings. Although most of the avian influenza viruses (AIVs) are of low pathogenicity and cause mild infections in birds, some subtypes including hemagglutinin H5 and H7 subtype cause HPAI. Therefore, sensitive and accurate subtyping of AIV is important to prepare and prevent for the spread of HPAI. Next-generation sequencing (NGS) can analyze the full-length sequence information of entire AIV genome at once, so this technology is becoming a more common in detecting AIVs and predicting subtypes. However, an analysis pipeline of NGS-based AIV sequencing data, including AIV subtyping, has not yet been established. Here, in order to support the pre-processing of NGS data and its interpretation, we developed a user-friendly tool, named prediction of avian influenza virus subtype (PAIVS). PAIVS has multiple functions that support the pre-processing of NGS data, reference-guided AIV subtyping, de novo assembly, variant calling and identifying the closest full-length sequences by BLAST, and provide the graphical summary to the end users.

• Explosives and Blasting
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• v.28 no.1
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• pp.11-18
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• 2010

This study improved construction and cost efficiency that are disadvantages of existing low vibration crackers(low vibration cracker, plasma, etc) and introduced cases of development and practical applications of Low vibration explosives(NewFINECKER) that minimizes blast vibration. The low vibration explosives(NewFINECKER) is suitable to Type-1 among standard blasting patterns of Ministry of Land, Transport and Maritime Affairs(MLTM) and delay blasting is possible. Moreover, the low vibration explosives improve construction and work efficiency while the level of vibration is shown to be about 60~70% of normal emulsion explosives. Additionally, this study suggested standard blasting patterns, the prediction equation of blasting vibration, and construction methods.

• Computers and Concrete
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• v.10 no.6
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• pp.557-573
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• 2012

This paper discusses the combined application of two different techniques, Neural Networks (NN) and Principal Component Analysis (PCA), for improved prediction of concrete properties. The combination of these approaches allowed the development of six neural networks models for predicting slump and compressive strength of concrete with mineral additives such as blast furnace slag, fly ash and silica fume. The Back-Propagation Multi-Layer Perceptron (BPMLP) with Bayesian regularization was used in all these models. They are produced to implement the complex nonlinear relationship between the inputs and the output of the network. They are also established through the incorporation of a huge experimental database on concrete organized in the form Mix-Property. Thus, the data comprising the concrete mixtures are much correlated to each others. The PCA is proposed for the compression and the elimination of the correlation between these data. After applying the PCA, the uncorrelated data were used to train the six models. The predictive results of these models were compared with the actual experimental trials. The results showed that the elimination of the correlation between the input parameters using PCA improved the predictive generalisation performance models with smaller architectures and dimensionality reduction. This study showed also that using the developed models for numerical investigations on the parameters affecting the properties of concrete is promising.