• Journal of Animal Science and Technology
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• v.50 no.6
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• pp.793-798
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• 2008

The semen collection process in the porcine is far from being a sterile procedure. Consequently, porcine ejaculates commonly contain bacterial contaminants. The aim of this study is to identify the bacteria in porcine semen and to find the antibiotics resistance of bacteria. Twelve porcine originating from four AI center were used to collect semen. Bacteria were identified by automated instrument for rapid organism identification system and bacterial sensitivities of 8 antibiotics were tested. The Bacterial contaminants of Staphylococcus genus(37.8%), Proteus genus(7.0%), Bacillus genus (6.1%), Pasteulla genus(5.7%), Acinetobacte genus(5.2%), Serratia genus(4.3%) and others(33.9%) were frequently isolated. However, amikacin showed higher antibiotic sensitivity than other antibiotics. General sanitation protocols can contribute partly to inhibit the bacterial contamination, with monitoring boar housing, semen collection areas and the extended semen. But, proper selection of preservative antibiotics by microbial sensitivities can minimize the influence of bacteria.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.2
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• pp.173-180
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• 2013

In this paper, we studied on measurement of soil deformation around the tip of model pile by close-range photogrammetry. The rigorous bundle adjustment method was utilized to monitor the soil deformation in the laboratory model pile-load test as function of incremental penetration of the pile. Control points were installed on the frame of the laboratory model box case and more than 150 target points were inserted inside the soil around the model pile and on the surface. Four overlapping images including three horizontal and one vertical image were acquired by a non-metric camera for each penetration step. The images were processed to automatically locate the control and target points in the images for the self-calibration and the bundle adjustment. During the bundle adjustment, the refraction index of the acrylic case of the laboratory model was accounted for accurate measurement. The experiment showed the proposed approach enabled the automated photogrammetric monitoring of soil deformation around the tip of model pile.

• Journal of Plant Biotechnology
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• v.31 no.4
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• pp.307-312
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• 2004

The bioreactor system for the large-scale plant tissue culture was developed to control the pH concentration and DO (dissolved oxygen), and air flowrate. The system controlling the proper air flow rate for each bulblet growth stage and monitoring the contamination of bioreactor using the pH change was controled by computer program. For the uniform bulblet distribution in bioreactor, the proper air flow rate was 300 cc/min at the beginning of bulblet culture, 400 cc/min after 20 days, 500 cc/min after 40 days, 600 cc/min after 60days, and 700 cc/min after 80 days. It was possible to maintain the pH concentration within 5.5$\pm$0.5 during the culture by control system of bioreactor.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1258-1258
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• 2001

Estimation of sugar and reducing sugar content in molasses is very important task in sugar refineries. Conventional methods of determination of sugar content in molasses samples are highly time consuming and employ hazardous chemicals. Due to the physical properties of molasses, probability of error in conventional analytical techniques is high. These methods have proven to be inefficient for a process control in any sugar industry. Hence development of a rapid, inexpensive, physical and also accurate method for sugar determination in molasses will be highly useful. Near Infrared spectroscopy is being widely used worldwide as an analytical technique in food industry. The technique offers the advantage of being non-destructive and rapid. The present paper highlights the potential of near infrared reflectance spectroscopy as a rapid and automated analytical technique for determination of sugar and reducing sugar content in molasses. A number of molasses samples were collected during and after the sugar season from Havana Sugar Industry, Havana. The samples were chosen so as to obtain a wide range of concentration of sugar and reducing sugars. This was done in order to achieve a good calibration curve with widely spread data points. These samples were scanned in the region of 1100 - 2500 nm in diffuse reflectance mode. An indigenous ELICO NIR spectrophotometer, modified according to the requirements of sugar industry was used for this purpose. Each sample was also analyzed simultaneously by standard chemical methods. Chemical values were taken as reference for near infrared analysis. In order to obtain the most accurate calibration for the set of samples, various mathematical treatments were employed. Partial Least Square method was found to be most suitable for the analysis. A comparison is made between the actual values (chemical values) and the predicted values (NIR values). The actual values agree very well with the predicted values showing the accuracy of the technique. The validity of the technique is checked by predicting the concentration of sugar in unknown molasses samples using the calibration curve. The present investigation assesses the feasibility of the technique for on-line monitoring of sugars present in molasses in sugar industries.

• Atmosphere
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• v.17 no.4
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• pp.339-348
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• 2007

Global Environment Laboratory at Yonsei University in Seoul ($37.57^{\circ}N$, $126.95^{\circ}E$) has carried out the ozone layer monitoring program in the framework of the Global Ozone Observing System of the World Meteorlogical Organization (WMO/GAW/GO3OS Station No. 252) since May of 1984. The daily measurements of total ozone and the vertical distribution of ozone amount have been made with the Dobson Spectrophotometer (No.124) on the roof of the Science Building on Yonsei campus. From 2004 through 2006, major parts of the manual operations are automated in measuring total ozone amount and vertical ozone profile through Umkehr method, and calibrating instrument by standard lamp tests with new hardware and software including step motor, rotary encoder, controller, and visual display. This system takes full advantage of Windows interface and information technology to realize adaptability to the latest Windows PC and flexible data processing system. This automatic system also utilizes card slot of desktop personal computer to control various types of boards in the driving unit for operating Dobson spectrophotometer and testing devices. Thus, by automating most of the manual work both in instrument operation and in data processing, subjective human errors and individual differences are eliminated. It is therefore found that the ozone data quality has been distinctly upgraded after automation of the Dobson instrument.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.237-244
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• 2018

The Smart Factory level of manufacturing factories of SMEs now lacks a system for grasping the accurate inventory amount associated with inventory movements in managing warehouses at the basic level. Also, it is difficult to manage accurate materials for loss of data due to worker manual work and production method due to experience. In order to solve this problem, in this paper, automatic acquisition of inventory to minimize manual work to grasp workers' Inventory and improve automation is done. In the smart warehouse management system using the IoT-based autonomous mobile module, the autonomous mobile module acquires the data of the inventory storage while moving through the line. In order to grasp the material of the Inventory storage, The Camera module recognizes the name of the inventory storage. And Then, If output matches, the data measured by the sensor is transferred to the server. This data can be processed, saved in a database, and real-time inventory quantity and location can be grasped in a web-based monitoring environment for administrators. The Real-time Automatic Inventory (RAIC) systems is reduce manual tasks and expect the effects of automated inventory management systems.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.42.1-42.1
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• 2018

Deep Ecliptic Patrol of the Southern Sky (DEEP-South) observation is being made during the off-season for exoplanet survey, using Korea Microlensing Telescope Network (KMTNet). An optimal combination of its prime focus optics and the 0.3 billion pixel CCD provides a four square degrees field of view with 0.4 arcsec/pixel plate scale which is also best suited for small body studies. Normal operation of KMTNet started in October 2015, and a significant portion of the allocated telescope time for DEEP-South is dedicated to targeted observation, Opposition Census (OC), of near-Earth asteroids for physical and taxonomic characterization. This is effectively achieved through multiband, time series photometry using Johnson-Cousins BVRI filters. Uninterrupted monitoring of the southern sky with KMTNet is optimized for spin characterization of a broad spectrum of asteroids ranging from the near-Earth space to the main-belt, including binaries, asteroids with satellites, slow/fast- and non-principal axis-rotators, and thus is expected to facilitate the debiasing of previously reported lightcurve observations. Our software subsystem consists of an automated observation scheduler, a pipelined data processing system for differential photometry, and an easy-to-use lightcurve analysis toolkit. Lightcurves, spin periods and provisional determination of class of asteroids to which the lightcurve belongs will be presented, using the dataset from first year operation of KMTNet. Our new taxonomic classification scheme for asteroids will also be summarized.

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.89-101
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• 2008

The most used tool for quality control is control chart in manufacturing industry. But it has limitations at current situation where most of manufacturing facilities are automated and several manufacturing processes have interdependent relationship such as CCM assembly line. To Solve problems, we propose quality management system based on data mining that are consisted of monitoring system where it monitors flows of processes at single window and feature extraction system where it predicts the yield of final product and identifies which processes have impact on the quality of final product. The quality management system uses decision tree, neural network, self-organizing map for data mining. We hope that the proposed system can help manufacturing process to produce stable quality of products and provides engineers useful information such as the predicted yield for current status, identification of causal processes for lots of abnormality.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.7
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• pp.7-15
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• 2020

Recently, the fourth industrial revolution has been presented as a new paradigm and additive manufacturing (AM) has become one of the most important topics. For this reason, process monitoring for each cross-sectional layer of additive metal manufacturing is important. Particularly, deep learning can train a machine to analyze, optimize, and repair defects. In this paper, image classification is proposed by learning images of defects in the metal cross sections using the convolution neural network (CNN) image labeling algorithm. Defects were classified into three categories: crack, porosity, and hole. To overcome a lack-of-data problem, the amount of learning data was augmented using a data augmentation algorithm. This augmentation algorithm can transform an image to 180 images, increasing the learning accuracy. The number of training and validation images was 25,920 (80 %) and 6,480 (20 %), respectively. An optimized case with a combination of fully connected layers, an optimizer, and a loss function, showed that the model accuracy was 99.7 % and had a success rate of 97.8 % for 180 test images. In conclusion, image labeling was successfully performed and it is expected to be applied to automated AM process inspection and repair systems in the future.

• Journal of Korean Society for Atmospheric Environment
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• v.23 no.E1
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• pp.16-28
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• 2007

An improved method was developed to determine gas-phase hydrogen peroxide($H_2O_2$) and organic hydro-peroxides (ROOH) in real-time, The analytical system for $H_2O_2$ is based on formation of hydroxybenzoic acid (OHBA), a strong fluorescent compound. OHBA is formed by a sequence of reactions, photoreduction of Fe(III)-EDTA to Fe(II)-EDTA, the Fenton reaction of Fe(II)-EDTA with $H_2O_2$, and hydroxylation of benzoic acid. By use of this analytical method rather than a previous similar method, Fenton reaction time was reduced from 2 min. to 30s. Air samples were collected by a surfaceless inlet to prevent inlet line losses. With a special arrangement of the sampling apparatus, sample delivery time was drastically reduced from ${\sim}5\;min\;to\;{\sim}20\;s$. The automated system was found to be sensitive, capable of continuous monitoring, and affordable to operate. A comparison of this method with a well-established one showed an excellent linear correlation, validating applicability of this technique to $H_2O_2$ determination. The system was applied to field measurements conducted during summertime of 2004 in Gwangju, South Korea. $H_2O_2$ was found to be a predominant species of peroxides. The diurnal variation of $H_2O_2$ displayed the maximum in early afternoon and the broad minimum throughout night. $H_2O_2$ was correlated positively with ozone, photochemical age, and temperature, however, negatively with $NO_x$ and relative humidity.