• The Korean Journal of Nuclear Medicine
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• v.35 no.5
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• pp.334-341
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• 2001

Purpose: Ascorbic acid us known to act as an antioxidant. Therefore, it can be used in increasing the efficiency of radiochemical labeling of Technetium-99m setamibi by inhibition of oxidation of $Sn^{2+}$ at low concentration. We intended to estimate the efficiency of radiochemical labeling and the stability of the newly formed formulation when ascorbic acid was added to a commercial kit. Materials and Methods: Synthesis of sestamibi was performed according to Dong-A's patent procedure (No.10-2001-0012877). First, we undertook a study to evaluate the efficiency of radiochemical labeling of sestamibi containing ascorbic acid. The stability of the vials was assessed using either $7.5{\mu}g\;or\;75{\mu}g$ of ascorbic acid, added to commercial vials under the accelerated condition(Temp : $40^{\circ}C{\pm}2^{\circ}C$, Relative humidity : $75{\pm}5%$). Results: Sestamibi was synthesized in overall 35-40% yield over 5 steps from a commercially available methallyl chloride as a starling material. When ascorbic acid was added, the efficiency of radiochemical labeling was maintained compared to the vial with no ascorbic acid. The accelerated test showed that the addition of ascorbic acid inhibited the oxidation of $Sn^{2+}$ ion by antioxidation mechanism. Also, the efficiency of radiochemical labeling of this vial after 9 months was nearly the same as the starting point. Therefore, the storage period of the kit is likely to be extended. Taken together, it suggests that the addition of ascorbic acid as a stabilizer is desirable. Conclusion: To increase the stability of a sestamibi cold kit, it is desirable to add ascorbic acid as a stabilizer to the commercial formulation.

• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.337-345
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• 2023

Estimating the rice heading date is one of the most crucial agricultural tasks related to productivity. However, due to abnormal climates around the world, it is becoming increasingly challenging to estimate the rice heading date. Therefore, a more objective classification method for estimating the rice heading date is needed than the existing methods. This study, we aimed to classify the rice heading stage from various images using a CNN classification model. We collected top-view images taken from a drone and a phenotyping tower, as well as slanted-view images captured with a RGB camera. The collected images underwent preprocessing to prepare them as input data for the CNN model. The CNN architectures employed were ResNet50, InceptionV3, and VGG19, which are commonly used in image classification models. The accuracy of the models all showed an accuracy of 0.98 or higher regardless of each architecture and type of image. We also used Grad-CAM to visually check which features of the image the model looked at and classified. Then verified our model accurately measure the rice heading date in paddy fields. The rice heading date was estimated to be approximately one day apart on average in the four paddy fields. This method suggests that the water head can be estimated automatically and quantitatively when estimating the rice heading date from various paddy field monitoring images.