• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.4
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• pp.65-74
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• 2021

In order to efficiently inspect rapidly increasing old tunnels in many well-developed countries, many inspection methodologies have been proposed using imaging equipment and image processing. However, most of the existing methodologies evaluated their performance on a clean concrete surface with a limited area where other objects do not exist. Therefore, this paper proposes a 6-step framework for tunnel crack detection deep learning model development. The proposed method is mainly based on negative sample (non-crack object) training and Cascade Mask R-CNN. The proposed framework consists of six steps: searching for cracks in images captured from real tunnels, labeling cracks in pixel level, training a deep learning model, collecting non-crack objects, retraining the deep learning model with the collected non-crack objects, and constructing final training dataset. To implement the proposed framework, Cascade Mask R-CNN, an instance segmentation model, was trained with 1561 general crack images and 206 non-crack images. In order to examine the applicability of the trained model to the real-world tunnel crack detection, field testing is conducted on tunnel spans with a length of about 200m where electric wires and lights are prevalent. In the experimental result, the trained model showed 99% precision and 92% recall, which shows the excellent field applicability of the proposed framework.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.604-609
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• 2019

In this study, we fabricated a capacitive humidity sensor with interdigitated (IDT) electrodes using a thermosetting polyimide as a humidifying material. First, the number of electrodes, thickness, and spacing of the polyimide film were optimized, and a mask was designed and fabricated. The sensor was fabricated on a silicon substrate using semiconductor processing equipment. The area of the sensor was $1.56{\times}1.66mm^2$, and the width of the electrode and the gap between the electrodes were each $3{\mu}m$. The number of electrodes was 166, and the length of an electrode was 1.294 mm for the sensitivity of the sensor. The sensor was then packaged on a PCB for measurement. The sensor was inserted into a chamber environment with a temperature of $25^{\circ}C$ and connected to an LCR meter to measure the change in capacitance at relative humidity (RH) of 20% to 90%, 1 V, and 20 kHz. The results showed a sensitivity of 26fF/%RH, linearity of < ${\pm}2%RH$, and hysteresis of < ${\pm}2.5%RH$.

• Resources Recycling
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• v.27 no.6
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• pp.23-29
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• 2018

Recently rapid economic growth and technological development have led to an increase in the generation of waste electrical and electronic equipment (WEEE). As the amount of electric and electronic waste generated increases, the importance of processing waste printed circuit boards (PCB) is also increasing. Various studies have been conducted to recycle various valuable metals contained in a waste PCB in an environmentally friendly and economical manner. To get anode slime containing Ag and Au, Anode copper prepared from PCB scraps was used by means of electro-refining. Ag and Au recovery was conducted by leaching, direct reduction, and ion exchange method. In the case of silver, the anode slime was leached at 3 M $HNO_3$, 100 g/L, $70^{\circ}C$, and Ag was recovered by precipitation, alkali dissolution, and reduction method. In the case of gold, the nitrate leaching residues of the anode slime was leached at 25% aqua regia, 200 g/L, $70^{\circ}C$, and Au was recovered by pH adjustment, ion exchange resin adsorption, desorption and reduction method. The purity of the obtained Au and Ag were confirmed to be 99.99%.

• Korean Journal of Heritage: History & Science
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• v.42 no.4
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• pp.4-19
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• 2009

Though admitting that, in light of the recent archaeological trend, the excavation on relics of Sam-gama (a sort of kiln steaming the hemp) is increasingly reported, little efforts by far have been made not only to restore its traditional structure design but also to research hardly the change of hemp-steaming technologies in ages. In this regard, this paper shows the exploration of structural method and design as well as operability with regard to Sam-gut, traditional hemp-processing equipment that was recently reconstructed in Jungsun, Kangwon Province. Samgut, generally positioned at the waterside area, is an traditional device for steaming hemp to get bast fibers from the raw material of hemp, principally consisting of HWA-JIP(fire-place) to obtain steams by feeding fire ad Mong-got(boiling chamber) to make the hemp steamed after stacking. More specifically, thick round-logs were piled at the bottom of Hwajip prior to stacking stones around its circumferential area. When the timber positioned below gets burned with high temperature to heat stones existing in the upper side, waters then poured onto it after laying a bundle of grass and soil up to the boiled stones. If so, there generates hot vapor, which is conveyed to Monggot to steam the hemp. Functionally, it is of outstanding importance that Samgut is capable of producing high-temperature water vapors instantaneously under the intensive manpower, thus being constructed achievable for those purposes. The Samgut made by digging the ground is an instant facility that is closed after use. The remains, which were used to generate higher thermal power for steaming hemp, make it hard to excavate the historic traits because there left little vestiges in the soil, which means keen attention must be paid to find out the trace of Smgama relics. Future research stall be focused on collection of broader data regarding Samgut including technological review in extracting bas fibers from the hemp.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.161-170
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• 2019

Multi-product mass production small and medium enterprise factories have a wide variety of products and a large number of products, wasting manpower and expenses for inventory management. In addition, there is no way to check the status of inventory in real time, and it is suffering economic damage due to excess inventory and shortage of stock. There are many ways to build a real-time data collection environment, but most of them are difficult to afford for small and medium-sized companies. Therefore, smart factories of small and medium enterprises are faced with difficult reality and it is hard to find appropriate countermeasures. In this paper, we implemented the contents of extension of existing inventory management method through character extraction on label with barcode and QR code, which are widely adopted as current product management technology, and evaluated the effect. Technically, through preprocessing using OpenCV for automatic recognition and classification of stock labels and barcodes, which is a method for managing input and output of existing products through computer image processing, and OCR (Optical Character Recognition) function of Google vision API. And it is designed to recognize the barcode through Zbar. We propose a method to manage inventory by real-time image recognition through Raspberry Pi without using expensive equipment.

• Journal of Convergence for Information Technology
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• v.9 no.5
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• pp.158-164
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• 2019

Recently many studies to reduce the noise of dental hand piece which generate inevitably mechanical sound to offend to the ear of a patient have been spotlighted. Generally, methods of adding a sound absorbing material inside the exhaust valve, air pump of machine or automobile are widely reported as optimal way to reduce the mechanical noise. In this paper we studied a new UV laser aided manufacturing of micro-porous structure of EPP substrate and applied dental hand piece to improve the efficiency of sound absorption. A lot of micro-sized pores were fabricated with UV laser processing on the surface of sliced EPP substrate. From fundamental experiments, more high-performance of micro-porous EPP substrate has finally demonstrated for sound-absorbing structure of the micro muffler inside dental hand piece, which actually has the excellent potential to apply a lot of potable machine.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.7-14
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• 2020

This study analyzed the effect of the number of deaths of circulatory system diseases according to 12-day short-term exposure of carbon monoxide from January 2010 to December 2018, and predicted the future treatment cost of circulatory system diseases according to increased carbon monoxide concentration. Data were extracted from Air Korea of Korea Environment Corporation and Korea Statistical Office, and analyzed using Poisson regression analysis and ARIMA intervention model. For statistical processing, SPSS Ver. 21.0 program was used. The results of the study are as follows. First, as a result of analyzing the relationship between the impact of short-term carbon monoxide exposure on death of circulatory system diseases from the day to the previous 11 days, it was found that the previous 11 days had the highest impact. Second, with the increase in carbon monoxide concentration, the future circulatory system disease treatment cost was estimated at 10,123 billion won in 2019, higher than the observed value of 9,443 billion won at the end of December 2018. In addition, when summarized by month, it can be seen that the cost of treatment for circulatory diseases increases from January to December, reflecting seasonal fluctuations. Through such research, the future for a healthy life for all citizens can be realized by distributing various devices and equipment utilizing IoT to preemptively respond to the increase in air pollutants such as carbon monoxide.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.77-83
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• 2020

The heat treatment facility is in a situation where the scope of application of the remote IOT system is expanding due to the harsh environment caused by high heat and long working hours among the root industries. In this heat treatment process environment, the IOT middleware is required to play a pivotal role in interpreting, managing and controlling data information of IoT devices (sensors, etc.). Until now, the system controlled by the heat treatment remotely was operated with the command of the operator's batch system without overall monitoring of the site situation. However, for the safety and precise control of the heat treatment facility, it is necessary to control various sensors and recognize the surrounding work environment. As a solution to this, the heat treatment safety support system presented in this paper proposes a support system that can detect the access of the work manpower to the heat treatment furnace through thermal image detection and operate safely when ordering work from a remote location. In addition, an OPEN CV-based deterioration analysis system using DNN deep learning network was constructed for faster and more accurate recognition than general fixed hot spot monitoring-based thermal image analysis. Through this, we would like to propose a system that can be used universally in the heat treatment environment and support the safety management specialized in the heat treatment industry.

• Journal of Platform Technology
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• v.8 no.3
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• pp.10-21
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• 2020

Partial discharge occurs a lot in high-voltage power equipment such as switchgear, transformers, and switch gears. Partial discharge shortens the life of the insulator and causes insulation breakdown, resulting in large-scale damage such as a power outage. There are several types of partial discharge that occur inside the product and the surface. In this paper, we design a predictive model that can predict the pattern and probability of occurrence of partial discharge. In order to analyze the designed model, learning data for each type of partial discharge was collected through the UHF sensor by using a simulator that generates partial discharge. The predictive model designed in this paper was designed based on CNN during deep learning, and the model was verified through learning. To learn about the designed model, 5000 training data were created, and the form of training data was used as input data for the model by pre-processing the 3D raw data input from the UHF sensor as 2D data. As a result of the experiment, it was found that the accuracy of the model designed through learning has an accuracy of 0.9972. It was found that the accuracy of the proposed model was higher in the case of learning by making the data into a two-dimensional image and learning it in the form of a grayscale image.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.555-564
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• 2021

Thalamus is known to play an important role in the regulation of nerve function. Thalamus, located in the center of the brain, is involved in sleep, arousal, and emotional regulation, and has been reported to be associated with multiple sclerosis, essential tremors, and neurodegenerative diseases such as Parkinson's disease. In addition, it has been reported that iron deposits in the thalamus can cause depressive symptoms with age. Although there are discrepancies between studies, it can be deduced that the thalamus region has a clear effect on neurological disorders due to a strong relationship between the thalamus and neurological functions such as emotional control and processing. Through tractography analysis, the connectivity between the detailed areas of each subcortical region was investigated in the form of a matrix, showing strong connectivity and weak interhemispheric connectivity. In the 59> group, the WM connectivity of thalamus was found to be weaker than those of the two groups. Comparisons between the two groups showed that the young groups (10-39 and 40-59) had higher connection intensity than the 59> group and that statistically significant differences in 3 connection pathways were found in each hemisphere. A decrease in thalamus-related connection strength in aging has shown that it can affect emotional and neurological disorders such as anxiety and depression, and network measurements can help assess cognitive impairment across clinical conditions.