• Journal of Animal Science and Technology
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• v.65 no.3
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• pp.638-651
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• 2023

The objective of this study was to quantitatively estimate the level of grazing area damage in outdoor free-range pig production using a Unmanned Aerial Vehicles (UAV) with an RGB image sensor. Ten corn field images were captured by a UAV over approximately two weeks, during which gestating sows were allowed to graze freely on the corn field measuring 100 × 50 m². The images were corrected to a bird's-eye view, and then divided into 32 segments and sequentially inputted into the YOLOv4 detector to detect the corn images according to their condition. The 43 raw training images selected randomly out of 320 segmented images were flipped to create 86 images, and then these images were further augmented by rotating them in 5-degree increments to create a total of 6,192 images. The increased 6,192 images are further augmented by applying three random color transformations to each image, resulting in 24,768 datasets. The occupancy rate of corn in the field was estimated efficiently using You Only Look Once (YOLO). As of the first day of observation (day 2), it was evident that almost all the corn had disappeared by the ninth day. When grazing 20 sows in a 50 × 100 m² cornfield (250 m²/sow), it appears that the animals should be rotated to other grazing areas to protect the cover crop after at least five days. In agricultural technology, most of the research using machine and deep learning is related to the detection of fruits and pests, and research on other application fields is needed. In addition, large-scale image data collected by experts in the field are required as training data to apply deep learning. If the data required for deep learning is insufficient, a large number of data augmentation is required.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.463-465
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• 2021

Currently, the inspection method printed on food packages and boxes is to sample only a few products and inspect them with human eyes. Such a sampling inspection has the limitation that only a small number of products can be inspected. Therefore, accurate inspection using a camera is required. This paper proposes a deep learning object recognition technology model, which is an artificial intelligence technology, as a method for detecting the defects of expiration date printed on the product packaging. Using the Faster R-CNN (region convolution neural network) model, the color images, converted gray images, and converted binary images of the printed expiration date are trained and then tested, and each detection rates are compared. The detection performance of expiration date printed on the package by the proposed method showed the same detection performance as that of conventional vision-based inspection system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.311-326
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• 2024

The rapid development of neural network technology promotes the neural network model driven by big data to overcome the texture effect of complex objects. Due to the limitations in complex scenes, it is necessary to establish custom template matching and apply it to the research of many fields of computational vision technology. The dependence on high-quality small label sample database data is not very strong, and the machine learning system of deep feature connection to complete the task of texture effect inference and speculation is relatively poor. The style transfer algorithm based on neural network collects and preserves the data of patterns, extracts and modernizes their features. Through the algorithm model, it is easier to present the texture color of patterns and display them digitally. In this paper, according to the texture effect reasoning of custom template matching, the 3D visualization of the target is transformed into a 3D model. The high similarity between the scene to be inferred and the user-defined template is calculated by the user-defined template of the multi-dimensional external feature label. The convolutional neural network is adopted to optimize the external area of the object to improve the sampling quality and computational performance of the sample pyramid structure. The results indicate that the proposed algorithm can accurately capture the significant target, achieve more ablation noise, and improve the visualization results. The proposed deep convolutional neural network optimization algorithm has good rapidity, data accuracy and robustness. The proposed algorithm can adapt to the calculation of more task scenes, display the redundant vision-related information of image conversion, enhance the powerful computing power, and further improve the computational efficiency and accuracy of convolutional networks, which has a high research significance for the study of image information conversion.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.4
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• pp.27-39
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• 2024

Soil texture is determined by the proportions of sand, silt, and clay within the soil, which influence characteristics such as porosity, water retention capacity, electrical conductivity (EC), and pH. Traditional classification of soil texture requires significant sample preparation including oven drying to remove organic matter and moisture, a process that is both time-consuming and costly. This study aims to explore an alternative method by developing an AI model capable of predicting soil texture from images of pre-sorted soil samples using computer vision and deep learning technologies. Soil samples collected from agricultural fields were pre-processed using sieve analysis and the images of each sample were acquired in a controlled studio environment using a smartphone camera. Color distribution ratios based on RGB values of the images were analyzed using the OpenCV library in Python. A convolutional neural network (CNN) model, built on PyTorch, was enhanced using Digital Image Processing (DIP) techniques and then trained across nine distinct conditions to evaluate its robustness and accuracy. The model has achieved an accuracy of over 80% in classifying the images of pre-sorted soil samples, as validated by the components of the confusion matrix and measurements of the F1 score, demonstrating its potential to replace traditional experimental methods for soil texture classification. By utilizing an easily accessible tool, significant time and cost savings can be expected compared to traditional methods.

• 전자공학회논문지 IE
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• v.43 no.4
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• pp.1-7
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• 2006

In this paper, two types of blue organic light-emitting device were designed. We have analyzed the characteristics of Type I device without a hole blocking layer, and analyzed the characteristics of Type II device using a hole blocking layer of BCP or BAlq materials with 30 ${\AA}$ thickness. We obtained the ITO having the work function value of 5.02 eV using $N_2$ plasma treatment method with the plasma power 200 W. Type I device structure was ITO/2-TNATA/$\alpha$-NPD/DPVBi/$Alq_3$/LiF/Al:Li, and type II device structure was ITO/2-TNATA/$\alpha$-NPD/DPVBi/HBL/$Alq_3$/LiF/Al:Li. We have analyzed the characteristics of Type I and Type II device. The characteristics of the device were most efficiency on occasion of using a hole blocking layer of BAlq material with 30 ${\AA}$ thickness. Current density was 226.75 $mA/cm^2$, luminance was 10310 $cd/m^2$, Current efficiency was 4.55 cd/A, power efficiency was 1.43 lm/W at an applied voltage of 10V. The maximum EL wavelength of the fabricated blue organic light-emitting device was 456nm. The full-width at half-maximum (FWHM) for the EL spectra was 57nm. CIE color coordinates were x=0.1438 and y=0.1580, which was similar to NTSC deep-blue color with CIE color coordinates of x=0.14 and y=0.08.

• YAKHAK HOEJI
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• v.50 no.5
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• pp.313-321
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• 2006

There are three kinds of liquid petroleum marker which is extracted by the basic or acidic, and both developer. Korean marker, Unimark 1494 DB (marker) have been spectrophotometrically analysed by the determination of absorbance at 582 nm after base extraction by Unimark 1494 DB Developer C-5 (developer). Some blue dyes which have same reactive radical of marker and can be changed deep blue color in base developer extraction (BDE), may be increased absorbance at 582 nm, but dyes or markers which can be increased the absorbance, were not unclear. In this experiment, effects of three dyes or marker such as Orimax Green 151 (the mixture of CI Solvent Yellow 16 and CI Solvent Blue 70), quinizarin and Orimax Blue 2N (CI Solvent Blue 35), and two solvent such as topasol (P-250) and lubricant (P-8) on the absorbance were studied by HITACHI Recording Spectrophotometer U-3300. It shows that all of them increased absorbance at 582 nm after BDE. Absorbance at 582 nm can be showed 0.0544 by Orimax Green 151 at the concentration of 3.96 mg/l, quinizarin at the concentration of 1.38 mg/l, and Orimax Blue 2N at the concentration of 2.73 mg/l in the artificial petroleum (normal diesel oil: topasol: lubricant=2 : 4: 4), respectively. Absorbance, 0.0544 indicates that concentration of marker is 1.64 mg/l in the reference curves, respectively. And also these results can be showed that the artificial petroleum have about 10% cheep fuel such as kerosene which have marker (16.0 mg/l). Absorbance of P-250 was 0.01361-0.22842 depending upon the purchasing date, and that of P-8 was 0.05644. pH of developer was 14.83, and so this result indicates that Unimark 1494 DB is a base extractable petroleum marker, phenylazophenol (US Patent No. 5,252,106). In the BDE, the slight color of Orimax Blue 2N, Orimax Green 151 and quinizarin in artificial petroleum changed to deep bright blue color, respectively. These result indicate that the absorbance at 582 nm by BDE may be increased not only by azo, diazo, amine and ketone (anthraquinone, coumarin) dyes or markers, but also the contaminants of P-250 and P-8 which have same as reactive radical of dyes or markers.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.2
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• pp.138-145
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• 2007

Luminous traffic safety mark is restricted to use only the place that has a thick fog, many night traffic accidents, limited field of view due to structure of road. Recently, LEDs are used for luminous traffic safety mark, but we propose an organic LED for a novel luminous traffic safety mark in the near future. The device structure was $ITO/2-TNATA(500{\AA})/{\alpha}-NPD(200{\AA})/DPVBi(300{\AA})/BCP(10{\AA})/Alq_3(200{\AA})/LiF(10{\AA})/Al:Li(1000{\AA})$. The characteristics of the device are most efficient on occasion of using $N_2$ gas plasma treatment. Current density is $240.71mA/cm^2$ luminance $10,550cd/m^2$, and current efficiency 3.53cd/A at an applied voltage of 10V. The maximum EL wavelength of the fabricated blue organic light-emitting device is 456nm. CIE color coordinates are x=0.1449 and y=0.1633, which is similar to NTSC deep-blue color with CIE color coordinates of x=0.14 and y=0.08.

• Journal of Korean Ophthalmic Optics Society
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• v.4 no.2
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• pp.123-128
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• 1999

This paper reports the preparation and characterization of colored coatings of $Al_2O_3$-CoO. Films of 25mol% CoO doped $Al_2O_3$, have been prepared on soda-lime-silica slide glasses by the sol-gel process from Al-alkoxide and Co-nitrate. The films have been characterized by a photospectroscopy and hardness tester. The color, spectral reflectance and spectral transmittance of the films was expressed in Lab color chart and on spectra plot. Microhardness of the films increased with increasing of the heating temperature. Transmittance and reflectance of the films decreased with increase of the heating temperature and coating times. The coating films showed various light-yellow, deep-yellow, greenish-yellow color as a function of the coating times and heating temperature.

• Culinary science and hospitality research
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• v.24 no.3
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• pp.196-203
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• 2018

This study was examined the quality characteristics of dough liquid according to the additional ratio of Doraji in seaweed snack manufacturing. Firstly, the results of Doraji type (dry & powder) were as follows: Carbohydrates 77.57~79.29, crude protein 9.10-9.25, crude fat 0.96~1.33 and calories 355~366 kcal, pH 5.42~5.45, sugar $3.53{\sim}3.96^{\circ}brix$, color 33.82~44.25 (L), 2.27~3.52 (B) and total free amino acids 2,200~2,699 mg/100 g. Total polyphenol contents had dry extracts 1,931.18 mg% and powder extract 1,382.43 mg%, DPPH and ABTs radical scavenging activities tended to increase with higher treatment concentration. Next, the results showed that dough liquid for seaweed snack manufacture which was added Doraji were as follows: Color became deep poppy red with increased addition of Doraji. The texture of adhesiveness, cohesiveness, chewiness, and brittleness tended to decrease with addition of Doraji. The springness showed the opposite tendency. Accordingly, these results suggest that 20% of dry Doraji extract is a proper proportion so that it can be added to the rice dough liquid to produce form Doraji (dry and powder) containing seaweed snacks.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.7 no.3
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• pp.73-77
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• 2004

The purpose of this study was to improve the ornamental value of Perilla frutescens var. acuta. The growth of Perilla frutescens var. acuta was significantly varied as according to light intensities. The plant height, crown width, petiole length, leaf length, leaf width, stem diameter, and chlorophyll content were the greatest with 30% shade treatment. All of growth characteristics decreased as increasing shading levels. The anthocyanin contents also decreased with 70% shading level. The leaf color turned from dark purplish red to deep yellowish green, and the growth rate and ornamental value were the lowest with 70% shading condition.