• Korean Journal of Heritage: History & Science
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• v.55 no.2
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• pp.200-211
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• 2022

The celadon stools with an openwork ring design which consist of four items as one collection were excavated from Gaeseong, Gyeonggi-do Province. The celadon stools were designated and managed as treasures due to their high arthistorical value in the form of demonstrating the excellence of celadon manufacturing techniques and the fanciful lifestyles during the Goryeo Dynasty. However, one of the items, which appeared to have been repaired and restored in the past, suffered a decline in aesthetic value due to the aging of the treatment materials and the lack of skill on the part of the conservator, raising the need for re-treatment as a result of structural instability. An examination of the conservation condition prior to conservation treatment found structural vulnerabilities because physical damage had been artificially inflicted throughout the area that was rendered defective at the time of manufacturing. The bonded surfaces for the cracked areas and detached fragments did not fit, and these areas and fragments had deteriorated because the adhesive trickled down onto the celadon surface or secondary contaminants, such as dust, were on the adhesive surface. The study identified the position, scope, and conditions of the bonded areas at the cracks UV rays and microscopy in order to investigate the condition of repair and restoration. By conducting Fourier-transform infrared spectroscopy(FT-IR) and portable x-ray fluorescence spectroscopy on the materials used for the former conservation treatment, the study confirmed the use of cellulose resins and epoxy resins as adhesives. Furthermore, the analysis revealed the addition of gypsum(CaSO₄·2H₂O) and bone meal(Ca₁₀ (PO₄)₆(OH)₂) to the adhesive to increase the bonding strength of some of the bonded areas that sustained force. Based on the results of the investigation, the conservation treatment for the artifact would focus on completely dismantling the existing bonded areas and then consolidating vulnerable areas through bonding and restoration. After removing and dismantling the prior adhesive used, the celadon stool was separated into 6 large fragments including the top and bottom, the curved legs, and some of the ring design. After dismantling, the remaining adhesive and contaminants were chemically and physically removed, and a steam cleaner was used to clean the fractured surfaces to increase the bonding efficacy of the re-bonding. The bonding of the artifact involved applying the adhesive differently depending on the bonding area and size. The cyanoacrylate resin Loctite 401 was used on the bonding area that held the positions of the fragments, while the acrylic resin Paraloid B-72 20%(in xylene) was treated on cross sections for reversibility in the areas that provided structural stability before bonding the fragments using the epoxy resin Epo-tek 301-2. For areas that would sustain force, as in the top and bottom, kaolin was added to Epo-tek 301-2 in order to reinforce the bonding strength. For the missing parts of the ring design where a continuous pattern could be assumed, a frame was made using SN-sheets, and the ring design was then modeled and restored by connecting the damaged cross section with Wood epos. Other restoration areas that occurred during bonding were treated by being filled with Wood epos for aesthetic and structural stabilization. Restored and filled areas were color-matched to avoid the feeling of disharmony from differences of texture in case of exhibitions in the future. The investigation and treatment process involving a variety of scientific technology was systematically documented so as to be utilized as basic data for the conservation and maintenance.

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.10
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• pp.1664-1672
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• 2013

Solar salt is manufactured naturally, and therefore, it contains insoluble substances such as sandy compounds. This study is performed in order to effectively produce clean sea salt by removing the impurities in sea salt through filtration and evaporation in a vacuum condition. Brine was concentrated and crystallized at $90^{\circ}C$ by a rotary vacuum evaporator, which was then recovered as salt crystals by filtration, and then the salt was dehydrated. Manufacturing yields were determined by the amount of water evaporation. Brine was concentrated to 40%, 50% and 60% of the initial volume of brine and manufactured salt were designated as 40S, 50S and 60S, respectively. The salt produced by this process is called ESBS (evaporated salt with brine from salt-farm). The yield of 40S, 50S and 60S were 7.22%, 10.79% and 15.06%, respectively. The NaCl concentration of 40S and 50S were 90.38% and 91.16%, respectively. From a sensory evaluation analysis, the most tasty salt was 40S and the bitter salt was 60S. The average contents of sand compound and insoluble substances in ESBS were 0.001~0.012% and 0.067~0.12%, respectively. The mineral compositions, such as Na, Mg, K, and Ca of 40S and 50S were similar with those of the natural solar salt. In solubility tests, the solubility (g of salt/100 mL $H_2O$/sec) of 40S, 50S, and 60S was 0.69, 0.70, and 0.69, respectively. On the other hand, the solubility of natural solar salt was 0.47. By comparing the water reabsorption rate analysis results, water reabsorption rate of 40S and 50S was about 3 to 5 times lower than that of the solar salt. In the aspects of physical and chemical properties, such as minerals, impurities, solubility and moisture re-absorption rate, salts developed in this study are judged to be better than that of the general solar salt.

• Korean Journal of Food Science and Technology
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• v.21 no.2
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• pp.242-251
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• 1989

The salted-dried mullet(Mugil japonicus) roe is a kind of traditional food particulary in the area of Young-am gun, Chunnam province. This study was conducted to conform the scientific processing conditions and to evaluate the nutritional quality and changes of major components during storage times. The manufacturing method was that the fresh roe was salted for about 20 hours for the preparation of salted-dried roe, washed by clean waters, drained, shaped a flat piece with 1.2cm thickness by pressing, and spreaded sesame oils on the surface of the salted roe periodically during wind drying for 20 days. The dried roe was blanched in heated water$(80^{\circ}C/3min)$ and packaged the dried product for storages. The fractional compositions of free lipid of wind dried roe were 40% of neutral lipids, 12% of glycolipids and 9% of phospholipids and those of bound lipids were 13% of neutral lipids. 10% of glycolipids and 13% of phospholipids respectively. The major fatty acids of the roe were $C_{16:0}$, $C_{18:0}$, $C_{18:1}$, $C_{18:2}$ and $C_{20:0}$ which was consisted of free and bound lipids in wind drying method during processing and storages. Total amino acids were 99.87g/100g and major amino acids were Glu, Pro, Leu, Lys and CySH and the protein score was average 155% and the chemical score was average 109%. Free amino acids was 1,376mg% that had 50.61% of Pro and the major kinds of those were Tyr and CySH.

• Journal of the Korean Applied Science and Technology
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• v.34 no.4
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• pp.874-882
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• 2017

As the interest on the air-pollution is gradually rising up at home and abroad, automotiv e and fuel researchers have been working on the exhaust emission reduction from vehicles through a lot of approaches, which consist of new engine design, innovative after-treatment systems, using clean (eco-friendly alternative) fuels and fuel quality improvement. This research has brought forward three main issues : evaporative, performance, air pollution. In addition, researcher studied the environment problems of the bio-ethanol, bio-butanol, bio-ETBE (Ethyl Tertiary Butyl Ether), MTBE (Methyl Tert iary Butyl Ether) fuel contained in the fuel as octane number improver. The researchers have many dat a about the health effects of ingestion of octane number improver. However, the data support the con clusion that octane number improver is a potential human carcinogen at high doses. Based on the bio-fuel and octane number improver types (bio-ethanol, bio-butanol, bio-ETBE, MTBE), this paper dis cussed the influence of gasoline fuel properties on the evaporative emission characteristics. Also, this p aper assessed the acceleration and power performance of gasoline vehicle for the bio-fuel property. As a result of the experiment, it was found that all the test fuels meet the domestic exhaust gas standards, and as a result of measurement of the vapor pressure of the test fuels, the bio - ethanol : 15 kPa and the biobutanol : 1.6 kPa. thus when manufacturing E3 fuel, Increasing the biobutanol content reduces evaporation gas and vapor pressure. In addition, Similar accelerating and powering performance was shown for the type of biofuel and when bio-butanol and bio-ethanol were compared accelerated perf ormance was improved by about 3.9% and vehicle power by 0.8%.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.