• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5412-5419
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• 2011

The need is growing for high-speed spindle because various equipment are becoming more precise, miniaturization and high speed with the development of industries. Air-lubricated dynamic bearings are widely used in the optical lithographic manufacturing of wafers to realize nearly zero friction for the motion of the stage. Air-lubricated dynamic bearing can be used in high-speed, high-precision spindle system and hard disk drive(HDD) because of its advantages such as low frictional loss, low heat generation, averaging effect leading better running accuracy. In the paper, numerical analysis is undertaken to calculate the performance of air-lubricated dynamic bearing with herringbone groove. The static performances of herringbone groove bearings which can be used to support the thrust load are calculated. Electrochemical micro machining($EC{\mu}M$) which is non-contact ultra precision machining method has been developed to fabricate the air-lubricated dynamic bearing and optimum parameters which are inter electrode gap size, concentration of electrolyte, machining time are simulated using numerical analysis program.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1835-1845
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• 2021

To accurately identify black ice and warn the drivers of information in advance so they can control speed and take preventive measures. In this paper, we propose a lightweight black ice detection network based on infrared road images. A black ice recognition network model based on CNN transfer learning has been developed. Additionally, to further improve the accuracy of black ice recognition, an enhanced lightweight network based on MobileNetV2 has been developed. To reduce the amount of calculation, linear bottlenecks and inverse residuals was used, and four bottleneck groups were used. At the same time, to improve the recognition rate of the model, each bottleneck group was connected to a 3×3 convolutional layer to enhance regional feature extraction and increase the number of feature maps. Finally, a black ice recognition experiment was performed on the constructed infrared road black ice dataset. The network model proposed in this paper had an accurate recognition rate of 99.07% for black ice.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.528-535
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• 2022

Although the domestic aviation industry has made rapid progress with the development of aircraft manufacturing and transportation technologies, aviation safety accidents continue to occur. The supervisory agency classifies hazards and risks based on risk-based aviation safety data, identifies safety trends for each air transportation operator, and conducts pre-inspections to prevent event and accidents. However, the human classification of data described in natural language format results in different results depending on knowledge, experience, and propensity, and it takes a considerable amount of time to understand and classify the meaning of the content. Therefore, in this journal, the fine-tuned KoBERT model was machine-learned over 5,000 data to predict the classification value of new data, showing 79.2% accuracy. In addition, some of the same result prediction and failed data for similar events were errors caused by human.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.3 s.303
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• pp.17-24
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• 2005

Various defects are found in PDP manufacturing process. Detecting these defects early and reprocessing them is an important factor that reduces the cost of production. In this paper, the image processing system for the PDP pattern inspection is designed and implemented using the high performance and accuracy CCD line scan camera. For the preprocessing of the high speed line image data, the Image Processing Part (IPP) is designed and implemented using high performance DSP, FIFO and FPGA. Also, the Data Management and System Control Part (DMSCP) are implemented using ARM processor to control many IPP and cameras and to provide remote users with processed data. For evaluating implemented system, experiment environment which has an area camera for reviewing and moving shelf is made. Experimental results showed that proposed system was quite successful.

• Elastomers and Composites
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• v.43 no.3
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• pp.173-182
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• 2008

Rubber-pad forming process for materials such as metal in which portions of the die which act upon the material is composed of a natural or synthetic rubber or elastomer material. This makes the rubber pad forming process relatively cheap and flexible, high accuracy for small product series in particular. In this study, we carried out the physical test and finite element analysis of elastomer such as natural rubber and urethane for steel rack rube forming. The non-linear property of elastomer which are described as strain energy function are important parameter to design and evaluate of elastomer component. These are determined by material tests which are uni-axial tension and bi-axial tension. This study is concerned with simulation and investigation of the significant parameters associated with this process.

• Analytical Science and Technology
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• v.27 no.6
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• pp.333-338
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• 2014

A simple and sensitive analytical method for fluoride in urine by ion selective electrode (ISE) method was presented. Traditional buffer for fluoride determination using ISE is acetate-based one. Researchers have pointed out some drawbacks of the buffer for fluoride ISE analysis, and some other buffers including citrate-ammonium buffer and MES buffer have been studied for accurate determination of fluoride in urine here. These buffers provided promising results in environmental field, and this author focused on overcoming the interference of co-existing aluminium. The results show that MES-CyDTA buffer gave the best recovery with accuracy of 95-97.5% and precision of 1.9-7.9% for reference sample of 1.8-7.8 mg/L fluoride in urine, with smaller amount of samples and shorter analysis time compared with the traditional method which used acetate buffer. The method was applied to field samples, and which showed urinary of $0.98{\pm}0.38mg/g$ creatinine for workers in electric cable manufacturing factory (n=15) and $0.59{\pm}0.30mg/g$ creatinine for non-exposed workers (n=12).

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.215-222
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• 2001

Neural network based models of semiconductor manufacturing processes have been shown to offer advantages in both accuracy and generalization over traditional methods. However, model development is often complicated by the fact that back-propagation neural networks contain several adjustable parameters whose optimal values unknown during training. These include learning rate, momentum, training tolerance, and the number of hidden layer neurOnS. This paper presents an investigation of the use of genetic algorithms (GAs) to determine the optimal neural network parameters for the modeling of plasma-enhanced chemical vapor deposition (PECVD) of silicon dioxide films. To find an optimal parameter set for the neural network PECVD models, a performance index was defined and used in the GA objective function. This index was designed to account for network prediction error as well as training error, with a higher emphasis on reducing prediction error. The results of the genetic search were compared with the results of a similar search using the simplex algorithm.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.5
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• pp.381-387
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• 2017

Fiber-reinforced composites not only have a direction of thermal expansion coefficient, but also inevitably suffer thermal stress effects due to the difference between the manufacturing process temperature and the actual use temperature. The damage caused by thermal stress is more prominent in the case of thick composite laminates, which are increasingly applied in the aerospace industry, and have a great influence on the mechanical function and fracture strength of the laminates. In this study, the dimensional reduction and thermal stress recovery theory of composite beam structure having high slenderness ratio is introduced and show the efficiency and accuracy of the thermal stress comparison results between the 3-D finite element model and the dimension reduction beam model. Efficient recovery analysis study will be introduced by reconstructing the thermal stress of the composite beam section applied to the thermal environment by constructing the dimensional reduction modeling and recovery relations.

• Transactions of Materials Processing
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• v.27 no.6
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• pp.370-378
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• 2018

Cold forging, carried out at room temperature, leads to high dimensional accuracy and excellent surface integrity as compared to other forging methods such as warm and hot forgings. In the cold forging process, WC-Co (Tungsten Carbide-Cobalt) alloy is the mainly used material as a core dies because of its superior hardness and strength as compared to other structural materials. For cold forging, die life is the most significant factor because it is directly related to the manufacturing cost due to periodic die replacement in mass production. To investigate die life of WC-Co alloy for cold forging, mechanical properties such as strength and fatigue are essentially necessary. Generally, uniaxial tensile test and fatigue test are the most efficient and simplest testing method. However, uniaxial tension is not efficiently application to WC-Co alloy because of its sensitivity to alignment of the specimen due to its brittleness and difficulty in thread machining. In this study, shape of specimen, tools, and testing methods, which are appropriate for uniaxial tensile test for WC-Co alloy, are proposed. The test results such as Young's modulus, tensile strength and stress-strain curves are compared to those in previous literature to validate the proposed testing methods. Based on the validation of test results it was concluded that the newly developed testing method is applicable to other cemented carbides like Titanium carbides with high strength and brittleness, and also can be utilized to carry out fatigue tests for further investigation on die life of cold forging.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.129-138
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• 2021

Many organizations are applying robotic process automation (RPA) to automate repetitive and rule based tasks to enhance the accuracy and efficiency of works. Some members are willing to join the projects hoping to eliminate annoying and meaningless tasks, but others are resisting this innovation fearing that they may lose their jobs. In this study, both positive and negative antecedents are posited to influence the performance in adopting RPA. The effects of relative advantage, compatibility, change management effect, innovation resistance and satisfaction, conclusively to performance improvement were examined via a survey of 109 employees involved in the 11 RPA projects in a manufacturing company, and the structural equation model analysis. The research considering the consumer characteristics of the innovation resistance model can be followed for the development of individualized change management strategy.