• Journal of Electrical Engineering and Technology
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• 제6권1호
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• pp.128-132
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• 2011

In this paper, two automatic mesh generation algorithms are presented. The methods seek to optimize mesh density with regard to geometries exhibiting both fine and coarse physical structures. When generating meshes, the algorithms attempt to satisfy the conditions on the maximum mesh spacing and the maximum grading ratio simultaneously. Both algorithms successfully produce non-uniform meshes that satisfy the requirements for finite-difference time-domain simulations of microwave components. Additionally, an algorithm successfully generates a minimum number of grid points while maintaining the simulation accuracy.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1993년도 Proceedings of International Conference for Agricultural Machinery and Process Engineering
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• pp.1292-1301
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• 1993

This study was intended to examine feasibility of sizing and color grading of Fuji apple with black/white image processing system , to develop a device with which the whole surface of an apple could be captured by one camera , to develop an algorithm for a high speed sorting , and to examine the effects of blurring on the performance of the experimental fruit grader.

• Journal of Biosystems Engineering
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• 제24권1호
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• pp.67-74
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• 1999

A prototype cut-flower sorter was developed and tested for its performance with five varieties of roses. Support plates driven by a chain mechanism transported the roses into an image inspection chamber. Color image processing algorithms were developed to evaluate the length, thickness, and straightness of stem and color, height, and maturity of bud. The average absolute errors of the system for the measurements of stem length, stem thickness, and height of bud were 19.7 mm, 0.5 mm, and 3.8 mm, respectively. The results of classification by the sorter were compared with those of a human inspector for straightness of stem and maturity of bud. The classification error for the straightness of stem was 8.6%, when both direct image and reflected image by a mirror were analyzed. The accuracy in classifying the maturity of bud varied among the varieties, the smallest for‘Nobless’(1.5%) and the largest for‘Rote Rose’(13.5%). The time required to process a rose averaged 2.06 seconds, equivalent to the capacity of 1,600 roses per hour.

• 전기학회논문지P
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• 제56권1호
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• pp.6-12
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• 2007

In this paper, an robust adaptive backstepping controller is proposed for the speed control of separately excited DC motor in pure electric vehicles. A general electric drive train of PEV is conceptually rearrange to major subsystems as electric propulsion, energy source, and auxiliary subsystem and the load torque is modeled by considering the aerodynamic, rolling resistance and grading resistance. Armature and field resistance, damping coefficient and load torque are considered as uncertainties and noise generated at applying load torque to motor is also considered. It shows that the backstepping algorithm can be used to solve the problems of nonlinear system very well and robust controller can be designed without the variation of adaptive law. Simulation results are provided to demonstrate the effectiveness of the proposed controller.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1993년도 추계학술대회
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• pp.27-30
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• 1993

Information about retinal blood vessels can be used in grading disease severity or as part of the process of automated diagnosis of diseases with ocular menifestations. In this paper, we address the problem of detecting retinal blood vessels and optic disk (papilla) in Eye-fundus images. We introduce an algorithm for feature extraction based on Fuzzy festering(FCM). The results ore compared to those obtained with other methods. The automatic detection of retinal blood vessels and optic disk in the Eye-fundus images could help physicians in diagnosing ocular diseases.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.198-204
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• 2021

Glioma is one of the common types of brain tumors starting in the brain's glial cell. These tumors are classified into low-grade or high-grade tumors. Physicians analyze the stages of brain tumors and suggest treatment to the patient. The status of the tumor has an importance in the treatment. Nowadays, computerized systems are used to analyze and classify brain tumors. The accurate grading of the tumor makes sense in the treatment of brain tumors. This paper aims to develop a classification of low-grade glioma and high-grade glioma using a deep learning algorithm. This system utilizes four transfer learning algorithms, i.e., AlexNet, GoogLeNet, ResNet18, and ResNet50, for classification purposes. Among these algorithms, ResNet18 shows the highest classification accuracy of 97.19%.

• Advances in concrete construction
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• 제15권4호
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• pp.269-286
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• 2023

In the present study, we aim to utilize the numerical solution frequency results of functionally graded beam under thermal and dynamic loadings to train and test an artificial neural network. In this regard, shear deformable functionally-graded beam structure is considered for obtaining the natural frequency in different conditions of boundary and material grading indices. In this regard, both analytical and numerical solutions based on Navier's approach and differential quadrature method are presented to obtain effects of different parameters on the natural frequency of the structure. Further, the numerical results are utilized to train an artificial neural network (ANN) using AdaGrad optimization algorithm. Finally, the results of the ANN and other solution procedure are presented and comprehensive parametric study is presented to observe effects of geometrical, material and boundary conditions of the free oscillation frequency of the functionally graded beam structure.

• Journal of Biosystems Engineering
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• 제21권3호
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• pp.325-335
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• 1996

표고의 외관 특징들은 표고의 재배 시 생육상태의 정량적 측정을 위해서, 표고의 건조 시 건조 성능을 나타내는 정량적 지표로서, 그리고 건표고의 품질을 판정하는 요인으로서 중요한 역할을 한다. 본 논문에서는 컴퓨터 시각시스템 및 신경회로망 기술을 적용하여 표고의 갓 및 내피에 고루 분포되어 있는 외관특징을 정량적으로 추출하는 알고리즘을 개발하였다. 기존의 영상 처리 과정에서 유도되는 경험적 판정규칙 또는 명확한 수치적 판정조건에 의한 등급판정은 입력데이타의 결핍 또는 애매모호성에 따른 오차가 발생하기 쉽다. 신경회로망을 이용한 영상인식 기능을 도입함으로써 다양하고 애매모호한 표고의 외관 영상특징들을 효율적으로 처리하여 기존 영상처리 알고리즘에서 발생하는 오차를 개선하였다. 본 논문에서 제안하는 알고리즘은 표고의 갓과 내피면의 인식 및 특징 분할, 꼭지부의 검출, 제거 및 재생 등을 포함한다. 제안한 알고리즘에 의거하여 건표고의 등급판정에 주요한 품질인자들을 추출하고 정량화 하였다. 그리고 알고리즘의 개발은 흑백의 다치입력영상을 이용하여 수행하였다.

• 농업과학연구
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• 제47권4호
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• pp.1109-1122
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• 2020

The aim of this study was to develop a marbling classification and prediction model using small parts of sirloin images based on a deep learning algorithm, namely, a convolutional neural network (CNN). Samples were purchased from a commercial slaughterhouse in Korea, images for each grade were acquired, and the total images (n = 500) were assigned according to their grade number: 1++, 1+, 1, and both 2 & 3. The image acquisition system consists of a DSLR camera with a polarization filter to remove diffusive reflectance and two light sources (55 W). To correct the distorted original images, a radial correction algorithm was implemented. Color images of sirloins of Hanwoo (mixed with feeder cattle, steer, and calf) were divided and sub-images with image sizes of 161 × 161 were made to train the marbling prediction model. In this study, the convolutional neural network (CNN) has four convolution layers and yields prediction results in accordance with marbling grades (1++, 1+, 1, and 2&3). Every single layer uses a rectified linear unit (ReLU) function as an activation function and max-pooling is used for extracting the edge between fat and muscle and reducing the variance of the data. Prediction accuracy was measured using an accuracy and kappa coefficient from a confusion matrix. We summed the prediction of sub-images and determined the total average prediction accuracy. Training accuracy was 100% and the test accuracy was 86%, indicating comparably good performance using the CNN. This study provides classification potential for predicting the marbling grade using color images and a convolutional neural network algorithm.

• Journal of Animal Science and Technology
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• 제49권6호
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• pp.689-698
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• 2007

2007년 한․미 FTA 협상 체결로 미국산 쇠고기 수입이 재개된 현재 육질과 가격면에서 우수한 수입육이 늘어나고 있는 상황에서 한우의 경쟁력강화를 위해 많은 노력이 이루어지고 있다. 한우개량농가는 송아지 생산량의 빠른 증가와 송아지 폐사율의 감소로 사육기술이 향상되어지고 있으며, 1993년 이후부터 한우 경쟁력강화를 위해 육질개량에 중점을 두고 거세를 통한 장기 비육으로 1등급이상의 출현율이 점차 증가하는 추세를 보이고 있다. 소비자의 식육패턴도 양적인 측면에서 질적인 측면으로 변화하고 있다. 고급육의 생산은 품종(Course 등, 1989), 성별, 연령(Hunsley 등, 1971) 등에 의하여 결정될 수가 있다. 한우 도체형질에 관한 연구는 국내에서도 최근 박과 박(2002)과 노 등(2004)이 연구 보고하였으나 이는 후대검정우와 후대축들의 도체형질에 관한 연구였으며, 일반 한우 사육농가의 도체형질에 관한 연구는 이루어 지지 않고 있다. 환경효과의 경우 대부분의 연구가 출생년도－계절, 출생지역의 효과를 추정하였으며, 도축년도, 도축계절, 도축지역에 관한 연구는 이루어 지지 않고 있다. 본 연구에서는 일반 한우사육농가에서 출하하여 도축한 축산물등급판정소의 등급판정결과를 이용하여 도축년도, 도축계절, 성, 도축지역이 도체형질에 어떠한 영향을 미치는지 추정하고, 도체형질에 대해 REML(Restricted Maximum Likelihood) 방법으로 유전력 및 유전모수를 추정하여 일반 한우사육농가에서 출하한 개체의 도체형질에 관해 알아봄으로써 후대검정우나 후대축이 아닌 우리나라 전체 도축우에 대한 기초자료를 제공하고자 한다.