• Korean Journal of Food Science and Technology
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• v.25 no.2
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• pp.165-170
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• 1993

The average concentration of Pb in 53 samples of canned orange juice currently sold in Korean market was 0.225 mg/kg, and that of Sn, 40.7 mg/kg. There was no appreciable difference in Pb concentration according to elapsed time after manufacturing, whereas Sn concentration increased 0.66 mg/kg per month. During the storage at room temperature or in refrigerator after opening, the Pb concentration increased slowly, reaching 1.7 to 1.8 times of original concentration, whereas Sn concentration increased by 20% per day, resulting in 3 times of original concentration after 7 days. There were no serious changes in Pb and Sn concentration in storage at room temperature or refrigerator for 3 days, when juice samples were opened and transferred to glass container. It is needed that detailed inspection by undertaken to monitor the contents of heavy metals in canned orange juice, since 18% of samples within recommended distribution period exceeded the legal standard for Pb, and recommended that more attention be paid in handling canned orange juice after opening, in order to avoid the hazard from heavy metals.

• The Journal of Bigdata
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• v.5 no.2
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• pp.53-67
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• 2020

This study employees a supervised learning prediction model to detect nonconformity in advance of processed food manufacturing and processing businesses. The study was conducted according to the standard procedure of machine learning, such as definition of objective function, data preprocessing and feature engineering and model selection and evaluation. The dependent variable was set as the number of supervised inspection detections over the past five years from 2014 to 2018, and the objective function was to maximize the probability of detecting the nonconforming companies. The data was preprocessed by reflecting not only basic attributes such as revenues, operating duration, number of employees, but also the inspections track records and extraneous climate data. After applying the feature variable extraction method, the machine learning algorithm was applied to the data by deriving the company's risk, item risk, environmental risk, and past violation history as feature variables that affect the determination of nonconformity. The f1-score of the decision tree, one of ensemble models, was much higher than those of other models. Based on the results of this study, it is expected that the official food control for food safety management will be enhanced and geared into the data-evidence based management as well as scientific administrative system.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.4
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• pp.410-417
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• 2012

Currently domestic thermal and nuclear power plants are comprised of many type's condenser and steam generator tubes to produce the electricity of good quality. There are some methods to inspect these tubes in the event that several defects were discovered in these facilities. Among many non-destructive methods, we used guided wave to inspect the soundness of tubes, because this method is very fast to detect the defect and very simple to install the equipment and also, can inspect up to the long range at a fixed point. Also, this method has a drawback that does not detect a very small size defect. So, we made an effort to overcome this drawback through the experimentation and signal analysis according to the size and shape of the defect through the manufacture of various artificial cracks capable to generate within the small size tube in the study and we anticipate that these detect limits can be overcome along with the development of the signal processing and manufacturing technology of the sensor for the inspection.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.4
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• pp.100-105
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• 2016

The use of 3D scanners has increased gradually according to increasing 3D printer applications. The precision inspection of car parts or electronic components is an important issue not only in the field of mass production, but also in small-scale production. Recently, 3D scanner equipment efficiency and recognition technology has been improved continuously. On the other hand, the spraying time to prepare 3D scanning is time-consuming and has environmental problems. Therefore, an automatic spray system has been in demand by the manufacturing industry. Automatic spray equipment was newly developed for the preparation of a 3D scanner. In this research, the automatic spray system guarantees uniform spray operation. To determine the optimal spray parameters, various spraying methods, solutions and conditions were tested and compared with the experiments. The preparation time for 3D scanning was reduced to 1/10 compared to the manual spraying time, and indicates the optimal spraying conditions through a comparison of various spray coating conditions.

• Design Convergence Study
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• v.16 no.4
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• pp.1-16
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• 2017

As the local manufacturing industry has entered a phase of stagnation, service and product design based on user experience has been highlighted as an alternative for the innovation. However, SMEs(Small and Medium-sized Enterprises) are still struggling to overcome the current crisis. One of the reasons is that SMEs do not have enough contact points with the validated UX firms and experts. Thus, SMEs has a high barrier to invest in new opportunity area, user experience. In this study, we aim to figure out UX experts' competence to perform the UX method to solve the UX problems based on the KSA framework(Knowledge, Skill, Attitude). Based on the literature review and expert workshop, we grouped the UX method according to the similarity of the competence required to conduct the method. With cluster analysis, 5 different groups of UX method were defined based on the competence, Panoramic Analysis, Meticulous Observation and Analysis, Intuitive Interpretation, Agile Visualization, and Logical Inspection. The results would be applied to compose a portfolio of UX experts and to implement a mechanism that could recommend the professional experts to the company.

• Korean Chemical Engineering Research
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• v.57 no.2
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• pp.274-282
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• 2019

In recent years, big data analysis has been expanded to include automatic control through reinforcement learning as well as prediction through modeling. Research on the utilization of image data is actively carried out in various industrial fields such as chemical, manufacturing, agriculture, and bio-industry. In this paper, we applied NASNet, which is an AutoML reinforced learning algorithm, to DeepU-Net neural network that modified U-Net to improve image semantic segmentation performance. We used BRATS2015 MRI data for performance verification. Simulation results show that DeepU-Net has more performance than the U-Net neural network. In order to improve the image segmentation performance, remove dropouts that are typically applied to neural networks, when the number of kernels and filters obtained through reinforcement learning in DeepU-Net was selected as a hyperparameter of neural network. The results show that the training accuracy is 0.5% and the verification accuracy is 0.3% better than DeepU-Net. The results of this study can be applied to various fields such as MRI brain imaging diagnosis, thermal imaging camera abnormality diagnosis, Nondestructive inspection diagnosis, chemical leakage monitoring, and monitoring forest fire through CCTV.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2000.06a
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• pp.16-23
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• 2000

The increasing functional needs of top-quality printing papers and packaging paperboards, and especially the rapid developments in electronic printing processes and various computer printers during past few years, set new targets and requirements for modern paper quality. Most of these paper grades of today have relatively high filler content, are moderately or heavily calendered , and have many coating layers for the best appearance and performance. In practice, this means that many of the traditional quality assurance methods, mostly designed to measure papers made of pure. native pulp only, can not reliably (or at all) be used to analyze or rank the quality of modern papers. Hence, introduction of new measurement techniques is necessary to assure and further develop the paper quality today and in the future. Paper formation , i.e. small scale (millimeter scale) variation of basis weight, is the most important quality parameter of paper-making due to its influence on practically all the other quality properties of paper. The ideal paper would be completely uniform so that the basis weight of each small point (area) measured would be the same. In practice, of course, this is not possible because there always exists relatively large local variations in paper. However, these small scale basis weight variations are the major reason for many other quality problems, including calender blacking uneven coating result, uneven printing result, etc. The traditionally used visual inspection or optical measurement of the paper does not give us a reliable understanding of the material variations in the paper because in modern paper making process the optical behavior of paper is strongly affected by using e.g. fillers, dye or coating colors. Futhermore, the opacity (optical density) of the paper is changed at different process stages like wet pressing and calendering. The greatest advantage of using beta transmission method to measure paper formation is that it can be very reliably calibrated to measure true basis weight variation of all kinds of paper and board, independently on sample basis weight or paper grade. This gives us the possibility to measure, compare and judge papers made of different raw materials, different color, or even to measure heavily calendered, coated or printed papers. Scientific research of paper physics has shown that the orientation of the top layer (paper surface) fibers of the sheet paly the key role in paper curling and cockling , causing the typical practical problems (paper jam) with modern fax and copy machines, electronic printing , etc. On the other hand, the fiber orientation at the surface and middle layer of the sheet controls the bending stiffness of paperboard . Therefore, a reliable measurement of paper surface fiber orientation gives us a magnificent tool to investigate and predict paper curling and coclking tendency, and provides the necessary information to finetune, the manufacturing process for optimum quality. many papers, especially heavily calendered and coated grades, do resist liquid and gas penetration very much, bing beyond the measurement range of the traditional instruments or resulting invonveniently long measuring time per sample . The increased surface hardness and use of filler minerals and mechanical pulp make a reliable, nonleaking sample contact to the measurement head a challenge of its own. Paper surface coating causes, as expected, a layer which has completely different permeability characteristics compared to the other layer of the sheet. The latest developments in sensor technologies have made it possible to reliably measure gas flow in well controlled conditions, allowing us to investigate the gas penetration of open structures, such as cigarette paper, tissue or sack paper, and in the low permeability range analyze even fully greaseproof papers, silicon papers, heavily coated papers and boards or even detect defects in barrier coatings ! Even nitrogen or helium may be used as the gas, giving us completely new possibilities to rank the products or to find correlation to critical process or converting parameters. All the modern paper machines include many on-line measuring instruments which are used to give the necessary information for automatic process control systems. hence, the reliability of this information obtained from different sensors is vital for good optimizing and process stability. If any of these on-line sensors do not operate perfectly ass planned (having even small measurement error or malfunction ), the process control will set the machine to operate away from the optimum , resulting loss of profit or eventual problems in quality or runnability. To assure optimum operation of the paper machines, a novel quality assurance policy for the on-line measurements has been developed, including control procedures utilizing traceable, accredited standards for the best reliability and performance.

• Clean Technology
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• v.19 no.3
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• pp.264-271
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• 2013

In this study, we optimized the removal condition of contaminants attached on the scrap surface to recycle the scrap generated from the Zr alloy tube manufacturing process back to the nuclear grade. The main contaminant is remnant of watersoluble cooling lubricant that is used in the pilgering manufacture during the tube production, and it is assumed to be compressed and carbonized on the surface of tube. Zirlo alloy tube of ${\phi}9.50mm$, which has high occurrence frequency of scrap, was selected as the object to be cleaned, and cleaning abilities of reagents were evaluated by measuring the characteristics of contaminants remained and by analyzing the surface of the tube after cleaning process. For evaluation of each cleaning agent, we selected two types of sodium hydroxide series and three types of potassium hydroxide series. Furthermore, to confirm dependence on tempe-rature and ultrasonic intensities, cleaning at the room temperature, $40^{\circ}C$, and $60^{\circ}C$ was conducted, and results showed that higher the cleaning temperature and higher the ultrasonic intensity, better the cleaning effect. As a result of the bare-eye inspection, while the use of sodium hydroxide provided satisfactory condition on the tube surface, the use of potassium hydroxide series provided satisfactory condition on the tube surface only when the ultrasonic intensity was over 120 W. In the cleaning effect analysis using the gravimetric method, cleaning efficiency of sodium hydroxide series was as high as 97.6% ($60^{\circ}C$, 120 W), but since the tube surface condition was poor after the use of potassium hydroxide, the gravimetric method was not appropriate. In the analytical result of surface contaminants on the tube surface, C, O, Ca, and Zr were detected, and mainly C and O dominated the proportion of contaminants. It was also found that the degree of cleaning on the tube affected the componential ratio of C and O; if the degree of cleaning is high, or if cleaning is well-conducted, the proportion of C is decreased, and the proportion of O is increased. Based on these results, optimal cleaning for application in the industry can be expected by categorizing cleaning process into three steps of Alkali cleaning, Rinsing, and Drying and by adjusting cleaning parameters in each step.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.34 no.1
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• pp.7-12
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• 2001

To investigate difference of component changes in salt-fermented spring (SAS) and autumn (AAS) anchovy, Engraulis japonicus sauce during fermentation, various chemical properties were examined at $1.5\sim3$ months intervals during 18 months fermentation, The contents of total and amino nitrogen were higher in SAS than in AAS until 15.7 and 17.4 months fermentation, respectively, but there were no difference after that. The cross point of inosine (HxR) + hypoxanthine (Hx) and uric acid was faster in SAS with 10.6 months fermentation than in AAS with 11.5 months fermentation, After 18 months of fermentation, the SAS was rich in free amino acids, such as glutamic acid, alanine, aspartic acid, valine, lysine in that order, On the other hand, the AAS was rich in free amino acids, such as glutamic acid, leucine, alanine, lysine, isoleucine in that order. Absorbance at 453 nm were higher in SAS than in AAS, and increased gradually during fermentation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.29-37
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• 2019

Textile quality management targets the physical properties of fabrics and the subjective discriminations of color and fitting. Color is the most representative quality factor that consumers can use to evaluate quality levels without any instruments. For this reason, quantification using a color discrimination device has been used for statistical quality management in the textile industry. However, small and medium-sized domestic textile manufacturers use only visual inspection for color discrimination. As a result, color discrimination is different based on the inspectors' individual tendencies and work procedures. In this research, we want to develop a textile industry-friendly quality management method, evaluating the possibility of rapid color discrimination using a digital imaging device, which is one of the office-automation instruments. The results show that an imaging process-based color discrimination method is highly correlated with conventional color discrimination instruments ($R^2=0.969$), and is also applicable to field discrimination of the manufacturing process, or for different lots. Moreover, it is possible to recognize quality management factors by analyzing color components, ${\Delta}L$, ${\Delta}a$, ${\Delta}b$. We hope that our rapid discrimination method will be a substitute technique for conventional color discrimination instruments via elaboration and optimization.