• Journal of Applied Biological Chemistry
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• v.56 no.2
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• pp.119-129
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• 2013

Thirteen different Streptomyces isolates were evaluated for their ability to produce keratinase using chicken feather as a sole carbon and nitrogen sources under solid state fermentation (SSF). Streptomyces sp. NRC 13S produced the highest keratinase activity [1,792 U/g fermented substrate (fs)]. The phenotypic characterization and analysis of 16S rDNA sequencing of the isolate were studied. Optimization of SSF medium for keratinase production by the local isolate, Streptomyces sp. NRC13S, was carried out using the one-variable-at-a-time and the statistical approaches. In the first optimization step, the effect of incubation period, initial moisture content, initial pH value of the fermentation medium, and supplementation of some agro-industrial by-products on keratinase production were evaluated. The strain produced about 2,310 U/gfs when it grew on chicken feather with moisture content of 75% (w/w), feather: fodder yeast ratio of 70:30 (w/w), and initial pH 7 using phosphate buffer after 8 days. Based on these results, the Box-Behnken design and response surface methodology were applied to find out the optimal conditions for the enzyme production. The corresponding maximal production of keratinase was about 2,569.38 U/gfs.

• Korean Journal of Computational Design and Engineering
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• v.16 no.3
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• pp.178-186
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• 2011

Static CAD models are the CAD models that do not have feature information and modeling history. These static models are generated by translating CAD models in a specific CAD system into neutral formats such as STEP and IGES. When a CAD model is translated into a neutral format, its precious feature information such as feature parameters and modeling history is lost. Once the feature information is lost, the advantage of feature based modeling is not valid any longer, and modification for the model is purely dependent on geometric and topological manipulations. However, the capabilities of the existing methods to modify static CAD models are limited, Direct modification methods such as tweaking can only handle the modifications that do not involve topological changes. There was also an approach to modify static CAD model by using volume decomposition. However, this approach was also limited to modifications of protrusion features. To address this problem, we extend the volume decomposition approach to handle not only protrusion features but also depression features in a static CAD model. This method first generates the model that contains the volume of depression feature using the bounding box of a static CAD model. The difference between the model and the bounding box is selectively decomposed into so called the feature volume and the base volume. A modification of depression feature is achieved by manipulating the feature volume of the static CAD model.

• Korean Chemical Engineering Research
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• v.61 no.2
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• pp.328-339
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• 2023

Methanol steam reforming (MSR) is a promising method for hydrogen supplying as a critical step in hydrogen fuel cell commercialization in mobile applications. Modelling and understanding of the reactor behavior is an attractive research field to develop an efficient reformer. Three-layer feed-forward artificial neural network (ANN) and Box-Behnken design (BBD) were used to modelling of MSR process using the Cu-SiO₂ aerogel catalyst. Furthermore, impacts of the basic operational variables and their mutual interactions were studied. The results showed that the most affecting parameters were the reaction temperature (56%) and its quadratic term (20.5%). In addition, it was also found that the interaction between temperature and Steam/Methanol ratio is important on the MSR performance. These models precisely predict MSR performance and have great agreement with experimental results. However, on the basis of statistical criteria the ANN technique showed the greater modelling ability as compared with statistical BBD approach.

• Computers and Concrete
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• v.16 no.1
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• pp.49-65
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• 2015

It is hard to guarantee the strict synchronization of all the jacking-up points in the integral jacking of a large-span continuous box girder bridge. This paper took the Hengliaojing Bridge as background, which need jacking up as an object with 295m length and more than 10,000tons weight, adopted 3D software to calculate the unsynchronized jacking-up working conditions, and studied the relationships between the unsynchronized vertical difference and the girder's deformation behaviour. The aim is to verify the maximum value of the unsynchronized vertical difference, and guide the construction and ensure safety. The monitoring system with its contents is introduced corresponding to the analysis. The results of the deck relative elevations prove that it is difficult to avoid the deck torsional deformation for jacking different; especially the side span shows more deformations for its smaller stiffness. The maximum difference is smaller than the limited value with acceptable stresses in the sections. The jacking heights of the pier in each construction step are controlled regularly according to the design. The shifting of the whole bridge in longitudinal direction is smaller than in transverse direction. The several beginning steps are the key to adjust their support reactions. This study is one parts of the fundamental research for the code "Technical specification for bridge jacking-up and reposition of China". The whole synchronous jacking project of the main bridge set a world record by the World Record Association for the whole bridge jacking project with the longest span of the world.

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.375-380
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• 2018

This paper proposes a classification method using the Convolutional Neural Network(CNN) which can obtain the type of trucks from the input image without the feature extraction step. To automatically classify vehicle images according to the type of truck cargo box, the top view images of the vehicle are used as input image and we design the structure of the CNN suitable for the input images. Learning images and correct output results is generated and the weights of neural network are obtained through the learning process. The actual image is input to the CNN and the output of the CNN is calculated. The classification performance is evaluated through comparison CNN output with actual vehicle types. Experimental results show that vehicle images could be classified with more than 90 percent accuracy according to the type of cargo box and this method can be used for pre-classification for inspecting loading defect.

• Reproductive and Developmental Biology
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• v.30 no.1
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• pp.59-64
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• 2006

This study was conducted to establish a convenient freezing method of boar semen. Boar semen was cooled until $5^{\circ}C$ for 3 hrs using cell freezer and loaded into straws. Semen straws were frozen in different steps in strofoam box filled with $LN_2$. Highest sperm viability (54.0%) was obtained by 1-step freezing(holding at 10 cm height from the surface of $LN_2$ for 10 min). Sperm viability increased by holding at $-102^{\circ}C$ for 10min (74.0%, P<0.05). In thawing regime, sperm viability was significantly higher in $37^{\circ}C$ group than in $52^{\circ}C$ group. The sperm characteristics did not differ between 1-step and 3-step. After IVF using frozen-thawed boar semen, developmental rate of embryos to the morula+blastocyst stage was in 1-step freezing group than that of 3-step freezing group (27.5 vs 14.7%, P<0.05). The result shows that the 1-step freezing with holding at $-102^{\circ}C$ for 10min before plunging into $LN_2$ is a convenient and easy freezing method for boar semen.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.81-88
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• 2020

This purpose of this study is to develop content that enables repetitive carving practice of the maxillary right central incisor (MRCI) based on augmented reality (AR). For a step-by-step practice of achieving the tooth shape, after creation of the storyboard from the square box shape in step 1 to the completed MRCI block in step 16, three-dimensional (3D) modeling data reflecting the characteristics of the mesial, distal, lingual, and labial surface of the MRCI were generated. An application was built in which 3D modeling data were output on the screen of the learner's mobile device, and image markers suitable for 3D modeling in steps 1 to 16 of the MRCI model were respectively generated. Using this information, the learner could carve a high-quality MRCI by repeatedly performing the tooth shape carving exercises. With AR, we intend to contribute to improved tooth morphology carving skills by linking the theory and practical techniques for a beginners in dentistry.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.2
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• pp.104-115
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• 2002

Core is the main component of inductor. This core should be classified into around 10 classes according to the value of inductance and Q. The coil should be winded with the outer-boundary of this core by different number of turns. Theses kind of precise inductors would be required in the future environment which PCs and communication devices demand more high speed and lower voltage level. It would be quite unefficient that only one core is classified once a time. There, it will be developed so that 10 cores are classified simultaneously. For the operation of classifying 10 cores once in a time, suppose 10 test instruments could be used. In this case, it would take much cost since a test instrument Is expensive. So, by using only one test instrument, it is really more desirable that this system is developed. Each core classified by 10 different classes is to be stored into the corresponding box through the corresponding rubber hose. 10 cores are passed on a serial line and are placed on each testing slot. Here, each core located at each slot is tested, and then the bowl located on the top of a step motor is moved into the corresponding spot by rotating step motor with some angles. Each bowl connected with the corresponding box through rubber hose. Actually 100 hoses are connected, 10 step motors are rotated at 10 different angles, so the size is really so big, the shape of connecting 100 hoses is so complicated. Therefore it is anticipated that the system would be going to be easily out of ordered. In this paper the main purpose is to make several suggestions to be able to work well in these kinds of being affected by the abnormal operation of motors and the flow of cores.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.45-55
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• 2021

This paper has proposed an algorithm that detecting for dense small vehicle in large image efficiently. It is consisted of two Ensemble Deep-Learning Network algorithms based on Coarse to Fine method. The system can detect vehicle exactly on selected sub image. In the Coarse step, it can make Voting Space using the result of various Deep-Learning Network individually. To select sub-region, it makes Voting Map by to combine each Voting Space. In the Fine step, the sub-region selected in the Coarse step is transferred to final Deep-Learning Network. The sub-region can be defined by using dynamic windows. In this paper, pre-defined mapping table has used to define dynamic windows for perspective road image. Identity judgment of vehicle moving on each sub-region is determined by closest center point of bottom of the detected vehicle's box information. And it is tracked by vehicle's box information on the continuous images. The proposed algorithm has evaluated for performance of detection and cost in real time using day and night images captured by CCTV on the road.

• Journal of the Korean Society of Safety
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• v.31 no.2
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• pp.10-17
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• 2016

The purpose of this paper is to deduct components that are in the group of highest risk(top 10%). the group is conducted for classification into groups by values according to risk priority through risk priority number(RPN) of FMEA(Failure modes and effects analysis) sheet. Top 10% of failure mode among total potential failure modes(72 failure modes) of ESS included 5 BMS(battery included) failure modes, 1 invert failure mode, and 1 cable connectors failure mode in which BMS was highest. This is because ESS is connected to module, try, and lack in the battery part as an assembly of electronic information communication and is managed. BMS is mainly composed of the battery module and communication module. There is a junction box and numerous connectors that connect these two in which failure occurs most in the connector part and module itself. Finally, this paper proposes RPN by each step from the starting step of ESS design to installation and operation. Blackouts and electrical disasters can be prevented beforehand by managing and removing the deducted risk factors in prior.