• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2344-2353
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• 2018

A coupled recursive total least squares (CRTLS) algorithm is proposed for parameter estimation of permanent magnet synchronous machines (PMSMs). TLS considers the errors of both input variables and output ones, and thus achieves more accurate estimates than standard least squares method does. The proposed algorithm consists of two recursive total least squares (RTLS) algorithms for the d-axis subsystem and q-axis subsystem respectively. The incremental singular value decomposition (SVD) for the RTLS obtained by an approximate calculation with less computation. The performance of the CRTLS is demonstrated by simulation and experimental results.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.13-17
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• 2003

Requirements of a benchmarking for rapid prototyping systems and process usually include manufacturing time, cost (including system price), and dimensional accuracy. This paper deals with a benchmarking comparisons to investigate the functional requirements of RP system. A special designed IMS_T2 test part with dimensional. geometrical, and surface roughness features has been used in the inspection of RP processes. IMS_T2 test part was built on 5 commercially available RP machines which are relatively new model in Korea.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.746-754
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• 1996

Grading and sorting fresh cut flowers are time consuming process. In Korea, cut flowers are sorted mostly by human inspection due to the lack of adequate machinery. In this paper, quality evaluation factors of cut flowers are discussed, and types of sorting machines existing in the market are introduced . Aspects of computer image processing in evaluation the quality of cut flowers are also discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.276-282
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• 2015

This paper proposes a SMD (surface mounting device) classification method for the PCB assembly inspection machines. The package types of SMD components should be classified to create the job program of the inspection machine. In order to reduce the creation time of job program, we developed the automatic classification algorithm for the SMD packages. We identified the chip-type packages by color and edge distribution of the images. The input images are transformed into the HSI color model, and the binarized histroms are extracted for H and S spaces. Also the edges are extracted from the binarized image, and quantized histograms are obtained for horizontal and vertical direction. The neural network is then applied to classify the package types from the histogram inputs. The experimental results are presented to verify the usefulness of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.1
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• pp.59-65
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• 2016

As modern electronic devices get smaller and smaller, high-resolution, large Field-Of-View (FOV), fast, and cost-effective 3-dimensional (3-D) measurement is requested more and more. In particular, defect inspection machines using machine-vision technology nowadays require 3-D inspection as well as the conventional 2-D inspection. Phase Measuring Profilometry (PMP) is one of the fast non-contact 3-D shape measuring methods currently being extensively investigated in the electronic component manufacturing industry. The PMP system is well known and is successfully applied to measuring complex surface profiles with varying reflectance properties. However, for highly reflective surfaces, such as Ball Grid Arrays (BGAs), it has difficulty accurately measuring 3-D shapes. In this paper, we propose a new fast optical system that can eliminate the highly reflective saturated regions in BGA ball images. This is achieved by utilizing four Low Intensity Grating (LIG) images together with the conventional High Intensity Grating (HIG) images. Extensive experiments using BGA samples show a repeatability of under ${\pm}20um$ in standard deviation, which is suitable for most 3-D shape measurements of BGAs.

• Journal of the FoodService Safety
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• v.2 no.2
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• pp.103-110
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• 2021

This study was conducted to compare and evaluate the difference in washing and disinfection when the Hazard Analysis and Critical Control Point (HACCP) protocol was applied to foodservice operations. The results of the survey were as follows: Among the 116 foodservice operations surveyed, 67.2% were HACCP-compliant and 32.8% were not HACCP-compliant. Also, 62.9% served meals once daily, and 79.3% conducted food safety education once a month. Compared to HACCP non-compliant foodservice operations, the disinfection performance of HACCP-compliant operations was significantly better concerning worktables (p<0.001), food inspection tables (p<0.001), preparation tables for distribution (p<0.01), serving tables (p<0.01), overflow and trenches(p<0.05), sinks (p<0.05), and insect attracting lamps (p<0.01). Similarly, the disinfection performance of HACCP-compliant foodservice operations was significantly better for 18 cooking utensils and personal tools such as food slicers (p<0.001), multiple cooking machines (p<0.05), tray carts (p<0.001), stainless steel tools (p<0.001), rubber gloves (p<0.05). Worktables (45.1%), serving tables (29.6%), sinks (37.0%), and scales (21.6%) were most often disinfected 'at the end of each task', while food inspection tables (36.5%), food preparation tables for distribution (31.2%), dish machines (34.2%), overflow and trenches (25.7%), and floors (25.8%) were most often disinfected 'once a day'. All cooking utensils were most often disinfected 'at the end of each task'. 'Chemical disinfection' was most frequently used in all foodservice facilities. To improve the food safety management of foodservice operations, it is necessary to apply the HACCP protocol and comply with the washing and disinfection manual.

• Structural Monitoring and Maintenance
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• v.2 no.3
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• pp.181-197
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• 2015

Assessing the condition of paint on civil structures is an important but challenging and costly task, in particular when it comes to large and complex structures. Current practices of visual inspection are labour-intensive and time-consuming to perform. In addition, this task usually relies on the experience and subjective judgment of individual inspectors. In this study, hyperspectral imaging and classification techniques are proposed as a method to objectively assess the state of the paint on a civil or other structure. The ultimate objective of the work is to develop a technology that can provide precise and automatic grading of paint condition and assessment of degradation due to age or environmental factors. Towards this goal, we acquired hyperspectral images of steel surfaces located at long (mid-range) and short distances on the Sydney Harbour Bridge with an Acousto-Optics Tunable filter (AOTF) hyperspectral camera (consisting of 21 bands in the visible spectrum). We trained a multi-class Support Vector Machines (SVM) classifier to automatically assess the grading of the paint from hyperspectral signatures. Our results demonstrate that the classifier generates highly accurate assessment of the paint condition in comparison to the judgement of human experts.

• Korean Journal of Agricultural Science
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• v.48 no.3
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• pp.617-627
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• 2021

In the field of on-road motor vehicles, the level for autonomous driving technology is defined according to J3016, proposed by Society of Automotive Engineers (SAE) International. However, in the field of agricultural machinery, different standards are applied by country and manufacturer, without a standardized classification for autonomous driving technology which makes it difficult to clearly define and accurately evaluate the autonomous driving technology, for agricultural machinery. In this study, a method to classify the autonomy levels for autonomous agricultural machinery (ALAAM) is proposed by modifying the SAE International J3016 to better characterize various agricultural operations such as tillage, spraying and harvesting. The ALAAM was classified into 6 levels from 0 (manual) to 5 (full automation) depending on the status of operator and autonomous system interventions for each item related to the automation of agricultural tasks such as straight-curve path driving, path-implement operation, operation-environmental awareness, error response, and task area planning. The core of the ALAAM classification is based on the relative roles between the operator and autonomous system for the automation of agricultural machines. The proposed ALAAM is expected to promote the establishment of a standard to classify the autonomous driving levels of self-propelled agricultural machinery.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.125-140
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• 2018

As one of the processes in the manufacturing industry, quality inspection inspects the intermediate products or final products to separate the good-quality goods that meet the quality management standard and the defective goods that do not. The manual inspection of quality in a mass production system may result in low consistency and efficiency. Therefore, the quality inspection of mass-produced products involves automatic checking and classifying by the machines in many processes. Although there are many preceding studies on improving or optimizing the process using the data generated in the production process, there have been many constraints with regard to actual implementation due to the technical limitations of processing a large volume of data in real time. The recent research studies on big data have improved the data processing technology and enabled collecting, processing, and analyzing process data in real time. This paper aims to propose the process and details of applying big data for quality inspection and examine the applicability of the proposed method to the dairy industry. We review the previous studies and propose a big data analysis procedure that is applicable to the manufacturing sector. To assess the feasibility of the proposed method, we applied two methods to one of the quality inspection processes in the dairy industry: convolutional neural network and random forest. We collected, processed, and analyzed the images of caps and straws in real time, and then determined whether the products were defective or not. The result confirmed that there was a drastic increase in classification accuracy compared to the quality inspection performed in the past.

• The KIPS Transactions:PartB
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• v.15B no.5
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• pp.397-406
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• 2008

Despite the effectiveness of oriental medical practice in the diagnosis of symptoms and providing cure to it, the preferences in western medicinal values is socially prevalent. The diagnosis of a disease using western medicinal practices provides us with an objective diagnostic result, however, decisions by oriental doctors are based on their heuristic intuitions developed by practice and experience. Objective solutions for the cure of symptoms using oriental medical therapy can have a high impact on the world market. Therefore, development of diagnostic machines based on oriental therapy can enhance the Ocular Inspection which is evaluated as one of the best diagnostic treatment among Oriental Medical Science, is not researched much compared to other diagnoses. Because there is no color diagnosis rules for digital machines to analyze the actual color, looking at the person's face color is one of the most important components to diagnose the disease or illness. The thesis proposes the implementation of absolute observing a person's face color standards of the color settings for objective diagnosis. As a results, comparative digital color analysis for observing a person's face color can be the most effective rule based Color scheme system to diagnose disease. A standard solution for the researching conditions is suggested to reduce the variable which may occur depending on the differences between the researching conditions.