• 한국산업융합학회 논문집
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• 제21권6호
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• pp.301-307
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• 2018

This paper deals with the development of the high frequency thawing machine. The fishery products caught over the world are kept frozen to maintain freshness. These fishery products require thawing before they are sold to customers as food. However, the thawing process can cause freshness reduction, drip coming out, quality deterioration, discharging polluted water, as well as a lot of space and time. The high frequency thawing machine developed to solve this problem has a narrow space, a short thawing time and a small drip. The developed high frequency thawing machine can be used in many fields such as fish processing plant, livestock processing plant. This paper describes the design of the high frequency thawing machine by developing the high frequency generator, development of the controller, and the design of mechanism, and shows the superiority of the high frequency thawing machine by the performance evaluation.

• 대한안전경영과학회지
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• 제18권1호
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• pp.35-43
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• 2016

The past five years, industrial accidents due to food processing machinery have been occurred 725 cases, injury by the food cutter occurred 390 cases in its. On this study, to prevent food cutter injury, an attempt is made to present the improved documentation of fundamental safety of the food cutter workplace through the injury analysis of food cutter injury and surveys on band saw machine business field. Analyzing the result of 390 cases on food cutter injury, amputation, cut, puncture occupied 75.1 percent (293 cases), compressed occupied 23.3% (91 cases), also it showed constant component without reference to gender, age, scale of work place, service period. In the survey, lack of concentration for workers have been pointed out as the biggest factor in the cause of band saw machine injury. Meanwhile, such as the EU and Japan, whereas presents safety standards about band saw machines that are tailored to each country, on the other hand, South Korea doesn't provide the standards. To prevent the food cutter injury, safety standards need to be established in consideration of amputation, cut, puncture, compressed injury and financial support is required to procure protective equipment at each place of business.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2020년도 추계학술발표대회
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• pp.315-318
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• 2020

반려동물을 키우는 사람들 중 1인 가구 증가로 인하여 주인의 부재 시 반려동물에게 배식하는데 어려움이 있어 자동급식기가 고안되었다. 본 논문에서는 아두이노 플랫폼을 이용한 사료 배급뿐만 아니라 약 배급 기능을 갖춘 IoT 약, 사료 혼합 자동급식기를 제안한다. 제안한 지동급식기에서는 사용자가 웹사이트를 통해 급식 서비스에 접근할 수 있고, 두 재료의 혼합 자동 배식 시스템을 통하여 편리하며 확장된 배식 기능을 경험 할 것 이라고 기대된다.

• Journal of Biosystems Engineering
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• 제42권3호
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• pp.227-233
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• 2017

Purpose: Morphological properties of soybean roots are important indicators of the vigor of the seed, which determines the survival rate of the seedlings grown. The current vigor test for soybean seeds is manual measurement with the human eye. This study describes an application of a machine vision technique for rapid measurement of soybean seed vigor to replace the time-consuming and labor-intensive conventional method. Methods: A CCD camera was used to obtain color images of seeds during germination. Image processing techniques were used to obtain root segmentation. The various morphological parameters, such as primary root length, total root length, total surface area, average diameter, and branching points of roots were calculated from a root skeleton image using a customized pixel-based image processing algorithm. Results: The measurement accuracy of the machine vision system ranged from 92.6% to 98.8%, with accuracies of 96.2% for primary root length and 96.4% for total root length, compared to manual measurement. The correlation coefficient for each measurement was 0.999 with a standard error of prediction of 1.16 mm for primary root length and 0.97 mm for total root length. Conclusions: The developed machine vision system showed good performance for the morphological measurement of soybean roots. This image analysis algorithm, combined with a simple color camera, can be used as an alternative to the conventional seed vigor test method.

• 농업과학연구
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• 제47권3호
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• pp.645-655
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• 2020

Bacteria are a very common cause of food poisoning. Moreover, bacteria form biofilms to protect themselves from harsh environments. Conventional detection methods for foodborne bacterial pathogens including the plate count method, enzyme-linked immunosorbent assays (ELISA), and polymerase chain reaction (PCR) assays require a lot of time and effort. Hyperspectral imaging has been used for food safety because of its non-destructive and real-time detection capability. This study assessed the feasibility of using hyperspectral imaging and machine learning techniques to detect biofilms formed by Escherichia coli. E. coli was cultured on a high-density polyethylene (HDPE) coupon, which is a main material of food processing facilities. Hyperspectral fluorescence images were acquired from 420 to 730 nm and analyzed by a single wavelength method and machine learning techniques to determine whether an E. coli culture was present. The prediction accuracy of a biofilm by the single wavelength method was 84.69%. The prediction accuracy by the machine learning techniques were 87.49, 91.16, 86.61, and 86.80% for decision tree (DT), k-nearest neighbor (k-NN), linear discriminant analysis (LDA), and partial least squares-discriminant analysis (PLS-DA), respectively. This result shows the possibility of using machine learning techniques, especially the k-NN model, to effectively detect bacterial pathogens and confirm food poisoning through hyperspectral images.

• 한국정보전자통신기술학회논문지
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• 제11권2호
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• pp.161-168
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• 2018

본 논문은 곡물의 수확후 이력관리를 위하여 미곡종합처리장(RPC, Rice processing complex)에서 곡물의 반입, 건조, 저장 및 가공공정에서의 변화하는 중량 및 함수율 정보를 실시간으로 측정하고 모니터링이 가능한 IT기반의 이력관리시스템을 개발하였다. 곡물의 반입, 건조, 저장 및 가공 등 수확후 처리공정에서 반입 및 배출과정에서 중량 및 함수율 정보를 실시간으로 수집하고 이를 기반으로 수율 및 감모와 같은 품질정보를 DB sever에 저장하였다. 이 때의 Lot No를 생성하여 품질 및 이력정보를 연계하여 이력관리가 가능한 시스템을 구축할 수 있도록 설계하였다. 또한, MMI(Man Machine Interface)기반의 자동제어시스템과 자동수율 및 재고관리 시스템(YICS)과 TCP/IP통신을 이용하여 곡물을 대상으로 한 IT 기반의 이력관리시스템을 구축하였으며, 미곡종합처리장에서 시뮬레이션을 통해 검증을 수행하였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 추계학술발표대회
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• pp.581-584
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• 2019

Food consumption is growing worldwide every year owing to a growing population. Hence, the increasing population needs the production of sufficient and good quality food products. Strawberry is one of the world's most famous fruit. To obtain the highest strawberry output, we worked with three strawberry varieties supplied with three kinds of nutrient water in a greenhouse and with the outcome of the strawberry production, the highest yielding strawberry variety is detected. This Study uses the nutrient water consumed every day by the highest yielding strawberry variety. The atmospheric temperature, humidity and CO2 levels within the greenhouse are identified and used for the prediction, since the water consumption by any plant depends primarily on weather conditions. Machine learning techniques show successful outcomes in a multitude of issues including time series and regression issues. In this study, daily nutrient water consumption of strawberry plants is predicted using machine learning algorithms is proposed. Four Machine learning algorithms are used such as Linear Regression (LR), K nearest neighbour (KNN), Support Vector Machine with Radial Kernel (SVM) and Gradient Boosting Machine (GBM). Gradient Boosting System produces the best results.

• Journal of Biosystems Engineering
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• 제39권4호
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• pp.324-329
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• 2014

Purpose: The finite element method (FEM) is advantageous because it can save time and cost by reducing the number of samples and experiments in the effort to identify design factors. In computational problem-solving it is necessary that the exact material properties are input for achieving a reliable analysis. However, in the case of fiber boards, it is difficult to measure their cross-directional material properties because of their small thickness. In previous research studies, the Poisson's ratio was measured by analyzing ultrasonic wave velocities. Recently, the Poisson's ratio was measured using a high-speed digital camera. In this study, we measured the transverse strain of a fiber board and calculated its Poisson's ratio using a high-speed digital camera in order to apply these estimates to a FEM analysis of a fiber board, a corrugated board, and a corrugated box. Methods: Three different fiber board samples were used in a uniaxial tensile test. The longitudinal strain was measured using the Universal Testing Machine. The transverse strain was measured using an image processing method. To calculate the transverse strain, we acquired images of the fiber board before the test onset and before the fracture occurred. Acquired images were processed using the image processing program MATLAB. After the images were converted from color to binary, we calculated the width of the fiber board. Results: The calculated Poisson's ratio ranged between 0.2968-0.4425 (Machine direction, MD) and 0.1619-0.1751 (Cross machine direction, CD). Conclusions: This study demonstrates that measurement of the transverse properties of a fiber board is possible using image processing methods. Correspondingly, these processing methods could be used to measure material properties that are difficult to measure using conventional measuring methodologies that employ strain gauge extensometers.

• International journal of advanced smart convergence
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• 제6권1호
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• pp.39-43
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• 2017

As the industry grows, the amount of data grows exponentially, and data analysis using these serves as a predictable solution. As data size increases and processing speed increases, it has begun to be applied to new fields by combining artificial intelligence technology as well as simple big data analysis. In this paper, we propose a method to quickly apply a machine learning based algorithm through efficient resource allocation. The proposed algorithm allocates memory for each attribute. Learning Distinct of Attribute and allocating the right memory. In order to compare the performance of the proposed algorithm, we compared it with the existing K-means algorithm. As a result of measuring the execution time, the speed was improved.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.681-684
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• 2021

In this paper, we study image clustering without labeling using machine learning techniques. We proposed an unsupervised machine learning technique to design an image clustering model that automatically categorizes images into groups. Our experiment focused on inception convolutional neural networks (inception V3) with k-mean methods to cluster images. For this, we collect the public datasets containing Food-K5, Flowers, Handwritten Digit, Cats-dogs, and our dataset Rice Germination, and the owner dataset Palm print. Our experiment can expand into three-part; First, format all the images to un-label and move to whole datasets. Second, load dataset into the inception V3 extraction image features and transferred to the k-mean cluster group hold on six classes. Lastly, evaluate modeling accuracy using the confusion matrix base on precision, recall, F1 to analyze. In this our methods, we can get the results as 1) Handwritten Digit (precision = 1.000, recall = 1.000, F1 = 1.00), 2) Food-K5 (precision = 0.975, recall = 0.945, F1 = 0.96), 3) Palm print (precision = 1.000, recall = 0.999, F1 = 1.00), 4) Cats-dogs (precision = 0.997, recall = 0.475, F1 = 0.64), 5) Flowers (precision = 0.610, recall = 0.982, F1 = 0.75), and our dataset 6) Rice Germination (precision = 0.997, recall = 0.943, F1 = 0.97). Our experiment showed that modeling could get an accuracy rate of 0.8908; the outcomes state that the proposed model is strongest enough to differentiate the different images and classify them into clusters.