• Korean Journal of Materials Research
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• v.23 no.10
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• pp.550-554
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• 2013

As continued scaling becomes increasingly difficult, 3D integration has emerged as a viable solution to achieve higher bandwidths and good power efficiency. 3D integration can be defined as a technology involving the stacking of multiple processed wafers containing integrated circuits on top of each other with vertical interconnects between the wafers. This type of 3D structure can improve performance levels, enable the integration of devices with incompatible process flows, and reduce form factors. Through silicon vias (TSVs), which directly connect stacked structures die-to-die, are an enabling technology for future 3D integrated systems. TSVs filled with copper using an electro-plating method are investigated in this study. DC and pulses are used as a current source for the electro-plating process as a means of via filling. A TiN barrier and Ru seed layers are deposited by plasma-enhanced atomic layer deposition (PEALD) with thicknesses of 10 and 30 nm, respectively. All samples electroplated by the DC current showed defects, even with additives. However, the samples electroplated by the pulse current showed defect-free super-filled via structures. The optimized condition for defect-free bottom-up super-filling was established by adjusting the additive concentrations in the basic plating solution of copper sulfate. The optimized concentrations of JGB and SPS were found to be 10 and 20 ppm, respectively.

• Journal of the East Asian Society of Dietary Life
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• v.27 no.4
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• pp.459-472
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• 2017

This study provides preliminary data to help organize improvements in analyzing the importance and performance of sanitation management items and the management of foodservice facilities in Community Child Centers in Daegu and Gyeongbuk Area. Questionnaires were distributed to 173 participants in sanitation and safety education at the center from April~June 2013 and 121 questionnaires were used as analysis data to investigate the management of foodservice facility at Community Children Centers in Daegu Gyeongbuk area. Most of the Community Child Centers are privately owned, and 62.0% had 20 to 29 children. Only 6.6% and 50.4% of the centers had nutritionists or cooks, respectively, due to budget deficits, and the foodservices were run by employees holding other positions. An investigation of sanitation management found that 84.3% of employees had a regular health inspection with significant differences between Daegu and Gyeongbuk (p<0.05). Most of the sanitation education was necessary, and the contents of sanitation education were applied to the fields in 66.1% of facilities. The reasons why the contents of them were not used in the fields included, the shortage of facilities and devices at 20.7%, which was the most common explanation. The separation separated of contaminated and non-contaminated areas were observed in 45.5% of facilities (p<0.01), separated sinks for pre-processing and cooking were found in 50.4%, and a show significant higher rate was noted in Daegu than in Gyeongbuk (p<0.05). An interior wall and, floor tile installation were observed 43.8% of facilities and a significantly higher rate was noted in Daegu than in Gyeongbuk (p<0.05). 30.9% of centers in Daegu and 11.3% of centers in Gyeongbuk area were equipped with a hot holding table(p<0.05). Overall, there is a need for education of foodservice to managers because most facilities do not have dietitians. In addition, facilities and equipment should be supplied continuously to foodservice facilities in community child centers.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.487-499
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• 2021

Purpose:The purpose of the paper is to review the problems of performance enhancement of safety certification standards and to suggest directions for improvement in order to rationalize safety certification standards for future industrial development and environmental changes. Method: The problems and limitations of the safety certification system are summarized through literature review and interview with manager, and the status of safety certification standards is classified into design standards, performance standards, and detailed standards, and the status analysis is performed. In addition, by synthesizing the results of the investigation and analysis, improvements are suggested to improve the performance of the safety certification standards. Result: Through the survey, the problems and limitations of safety certification could be grouped into six categories: government-led certification system operation, standardized certification standards, long time required to improve certification, poor certification standards preparation system, and lack of reflection of industry opinions. And, as a result of analyzing the certification standards by dividing them into performance and design standards, in the case of machinery, equipment, and protection devices, the design standards were high at 69.7% and 64.9%, whereas in the case of protective equipment, the performance standards were high at 61.1%. In order to improve the performance of safety certification standards centered on design standards, it is necessary to determine the possibility of performance enhancement of the certification standards and determine the feasibility of the inspection test method. In order to improve performance, it was reviewed that it was necessary to establish a systemic foundation and infrastructure, such as strengthening the Product Liability Act, systematizing market monitoring, etc., distributing certification test tasks, and participating in the preparation of certification standards by the private sector. Conclusion: Through this study, the problems and limitations of Korea's safety certification system were summarized and the necessity for performance improvement was reviewed. Performance improvement of safety certification standards is a matter that requires preparatory work, such as legislative revision and infrastructure construction, and requires mid-to-long-term promotion. In addition, rather than improving the overall safety certification standards, the performance requirements for each item subject to certification should be reviewed and promoted, and details should be specified through additional research.

• Korean Journal of Cognitive Science
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• v.21 no.2
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• pp.309-338
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• 2010

In this paper, we studied the brain-computer interface (BCI). BCIs help severely disabled people to control external devices by analyzing their brain signals evoked from motor imageries. The findings in the field of neurophysiology revealed that the power of $\beta$(14-26 Hz) and $\mu$(8-12 Hz) rhythms decreases or increases in synchrony of the underlying neuronal populations in the sensorymotor cortex when people imagine the movement of their body parts. These are called Event-Related Desynchronization / Synchronization (ERD/ERS), respectively. We implemented a BCI-based mouse interface system which enabled subjects to control a computer mouse cursor into four different directions (e.g., up, down, left, and right) by analyzing brain signal patterns online. Tongue, foot, left-hand, and right-hand motor imageries were utilized to stimulate a human brain. We used a non-invasive EEG which records brain's spontaneous electrical activity over a short period of time by placing electrodes on the scalp. Because of the nature of the EEG signals, i.e., low amplitude and vulnerability to artifacts and noise, it is hard to analyze and classify brain signals measured by EEG directly. In order to overcome these obstacles, we applied statistical machine-learning techniques. We could achieve high performance in the classification of four motor imageries by employing Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) which transformed input EEG signals into a new coordinate system making the variances among different motor imagery signals maximized for easy classification. From the inspection of the topographies of the results, we could also confirm ERD/ERS appeared at different brain areas for different motor imageries showing the correspondence with the anatomical and neurophysiological knowledge.