• Journal of the Korean Society of Radiology
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• v.15 no.5
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• pp.585-590
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• 2021

Wrist braces are being used for patients with wrist trauma. Recently, many studies have been conducted to manufacture custom wrist braces using 3D printing technology. Such 3D printing customized orthosis has the advantage of reflecting various factors such as reflecting different shapes for each individual and securing breathability. In this paper, the stress on the orthosis by the number and position of Velcro bands that should be considered when manufacturing a 3D printing custom wrist brace was analyzed. For customized orthosis, 3D modeling of the bone and skin regions was performed using an automatic design software (Reconeasy 3D, Seeann Solution) based on CT images. Based on the 3D skin area, a wrist orthosis design was applied to suit each treatment purpose. And, for the elasticity of the brace, a wrist brace was manufactured with an FDM-type 3D printer using TPU material. To evaluate the effectiveness according to the number and position of the Velcro band of the custom 3D printed wrist brace, the stress distribution of the brace was analyzed by the finite element method (FEM). Through the finite element analysis of the wrist orthosis performed in this study, the stress distribution of the orthosis was confirmed, and the number and position of the orthosis production and Velcro bands could be confirmed. These experimental results will help provide quality treatment to patients.

• Journal of the Korea Organic Resources Recycling Association
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• v.27 no.2
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• pp.67-73
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• 2019

In this study, to optimize the production and utilization of biogas for organic waste resources, the precision monitoring of on-site facilities and the energy balance by facility were analyzed, and the solutions for field problems were investigated, and the design and operation guidelines for pretreatment facilities and generators were presented. Gas pre-treatment is required to solve frequent failures and efficiency degradation in operation of high quality refining facilities, and processing processes such as desulfurization, dehumidification, deoxidization, dust treatment, volatile organic compounds, etc. Since these processes are substances that are also eliminated from the high-quality process, quantitative guidelines are not presented in the gas pretreatment process, but are suggested to operate during the processing process as a qualitative guideline. In particular, dust, siloxane, and volatile organic compounds are the main cause of frequent failure of high-quality processes if they are not removed from the gas pretreatment process. Design of the biogas high-quality process. The operation guidelines provide quality standards [Methane content (including propane) of 95% or more] with 90% or more utilization of the total gas generation, two systems, and a margin of 10% or more. It also proposed installing gas equalization tank, installing thermal automatic control system for controlling equalization of auxiliary fuel, installing dehumidification device at the back of high quality for removing moisture generated in the process of gas compression, installing heat-resisting facilities to prevent freezing of facilities in winter and reducing efficiency, and installing membrane facilities in particular.

• 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.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.7
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• pp.1015-1026
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• 2019

Objective: The objective of this study was to evaluate three different degrees of white striping (WS) addressing their automatic assessment and customer acceptance. The WS classification was performed based on a computer vision system (CVS), exploring different machine learning (ML) algorithms and the most important image features. Moreover, it was verified by consumer acceptance and purchase intent. Methods: The samples for image analysis were classified by trained specialists, according to severity degrees regarding visual and firmness aspects. Samples were obtained with a digital camera, and 25 features were extracted from these images. ML algorithms were applied aiming to induce a model capable of classifying the samples into three severity degrees. In addition, two sensory analyses were performed: 75 samples properly grilled were used for the first sensory test, and 9 photos for the second. All tests were performed using a 10-cm hybrid hedonic scale (acceptance test) and a 5-point scale (purchase intention). Results: The information gain metric ranked 13 attributes. However, just one type of image feature was not enough to describe the phenomenon. The classification models support vector machine, fuzzy-W, and random forest showed the best results with similar general accuracy (86.4%). The worst performance was obtained by multilayer perceptron (70.9%) with the high error rate in normal (NORM) sample predictions. The sensory analysis of acceptance verified that WS myopathy negatively affects the texture of the broiler breast fillets when grilled and the appearance attribute of the raw samples, which influenced the purchase intention scores of raw samples. Conclusion: The proposed system has proved to be adequate (fast and accurate) for the classification of WS samples. The sensory analysis of acceptance showed that WS myopathy negatively affects the tenderness of the broiler breast fillets when grilled, while the appearance attribute of the raw samples eventually influenced purchase intentions.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.155-166
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• 2021

Recently, digital health care technology is spreading and developing in various fields. Therefore, in this paper, we realized that the field to which digital healthcare technology is not applied is the field of optometry, and implemented a digital healthcare optometry system for precise lens manufacturing. A device called Phoroptor is used to manufacture the lens, and this device sets the lens by measuring the visual acuity of the person who requested the glasses. And when the person to be measured wears glasses, a device called a PD meter is used to align the pupil center and lens focus. However, there is a limit to the convenience of precise lens production and optometry due to the absence of a database and program that can accumulate and analyze the PD measurement error, inconvenience and error due to manual control of the Phoroptor, and optometric information. Therefore, in this paper, PD meter design for more accurate PD measurement, Phoroptor design and Phoroptor control application design for automatic Phoroptor control, and a database and analysis program that automatically set lenses using optometry information for each subject had been designed. Based on this, ultimately, a digital healthcare optometry system using an optometry database has been implemented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.368-377
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• 2021

Plum is a typical fruit that is consumed processed rather than raw. In this study, we manufactured one of the processed foods, viz., plum Jangachi. In this process, the manpower-dependent seed separation and flesh cutting operations were automated by mechanizing, thereby altering the manufacturing process. Quality and Safety were assessed through microbial evaluation, analysis of color, and detection of preservatives in the plum Jangachi. Preference factors were identified through sensual evaluation. When compared with other plum Jangachi currently available in the market, our product was determined to contain 2.7±0.1 Log CFU/g total aerobic bacteria, which is slightly higher than the average of other products. This was not surprising, as the figures are due to the inherent characteristics, which were determined to be lower as compared to other commercial plum Jangachi. Other coliforms, tar dyes, and preservatives were undetected, thereby conferring satisfactory Quality and Safety. In general, there was no statistical difference in the sensual evaluation and appearance; overall, our product received better feedbacks than products on the market. Taken together, our results provide a foundation for applying the mechanization of plum-processed foods, thereby promoting the local economy in the main production area, and overall characteristics obtained are regarded sufficient in terms of market competitiveness.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.34-45
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• 2021

Weather is one of the important factors in the agricultural industry as it affects the price, production, and quality of crops. Upland crops are directly exposed to the natural environment because they are mainly grown in mountainous areas. Therefore, it is necessary to provide accurate weather for upland crops. This study examined the effectiveness of 12 forest soil factors to interpolate the weather in mountainous areas. The daily temperature and precipitation were collected by the Korea Meteorological Administration between January 2009 and December 2018. The Generalized Additive Model (GAM), Kriging, and Random Forest (RF) were considered to interpolate. For evaluating the interpolation performance, automatic weather stations were used as training data and automated synoptic observing systems were used as test data for cross-validation. Unfortunately, the forest soil factors were not significant to interpolate the weather in the mountainous areas. GAM with only geography aspects showed that it can interpolate well in terms of root mean squared error and mean absolute error. The significance of the factors was tested at the 5% significance level in GAM, and the climate zone code (CLZN_CD) and soil water code B (SIBFLR_LAR) were identified as relatively important factors. It has shown that CLZN_CD could help to interpolate the daily average and minimum daily temperature for upland crops.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.193-201
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• 2021

The welding has a high proportion of the production and drying of ships or offshore plants. Non-destructive testing is carried out to verify the quality of welds in Korea, radiography test (RT) is mainly used. Currently, most shipyards adopt analog-type techniques to print the films through the shoot of welding parts. Therefore, the time required from radiography test to pass or fail judgment is long and complex, and is being manually carried out by qualified inspectors. To improve this problem, this paper covers a platform for scanning and digitalizing RT films occurring in shipyards with high resolution, accumulating them in management servers, and applying artificial intelligence (AI) technology to detect welding defects. To do this, we describe the process of designing and developing RT film scanning equipment, welding inspection information integrated management platform, fault reading algorithms, visualization software, and testing and verification of each developed element in conjunction.

• Journal of Korean Society of Archives and Records Management
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• v.22 no.1
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• pp.43-59
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• 2022

Electronic public records are classified simultaneously as production, a preservation period is granted, and after a certain period, they are transferred to an archive and preserved. This study intends to find a way to improve the efficiency in classifying transferred records and maintain consistent standards. To this end, the current record classification work process carried out by the National Archives of Korea was analyzed, and problems were identified. As a way to minimize the manual work of record classification by converging the required improvement, the process of identifying outlier candidates based on a list consisting of classified information of the transferred records was proposed and systemized. Furthermore, the proposed outlier discrimination process was applied to the actual records transferred to the National Archives of Korea. The results were standardized and constructed as a training data format that can be used for machine learning in the future.

• Resources Recycling
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• v.31 no.4
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• pp.3-11
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• 2022

Owing to the increasing demand for electric vehicles (EVs), appropriate management of their waste batteries is required urgently for scrapped vehicles or for addressing battery aging. With respect to technological developments, data-driven diagnosis of waste EV batteries and management technologies have drawn increasing attention. Moreover, robot-based automatic dismantling technologies, which are seemingly interesting, require industrial verifications and linkages with future battery-related database systems. Among these, it is critical to develop and disseminate various advanced battery diagnosis and assessment techniques to improve the efficiency and safety/environment of the recirculation of waste batteries. Incorporation of lithium-related chemical substances in the public pollutant release and transfer register (PRTR) database as well as in-depth risk assessment of gas emissions in waste EV battery combustion and their relevant fire safety are some of the necessary steps. Further research and development thus are needed for optimizing the lifecycle management of waste batteries from various aspects related to data-based diagnosis/classification/disassembly processes as well as reuse/recycling and final disposal. The idea here is that the data should contribute to clean design and manufacturing to reduce the environmental burden and facilitate reuse/recycling in future production of EV batteries. Such optimization should also consider the future technological and market trends.