• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.393-400
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• 2015

As the development and distribution of the intelligent transport system is spreading recently and some of the services are commercialized through a pilot project, interest in DSRC with high utilization is increasing and antennas for roadside and on board equipment are being studied. A single patch was used for a vehicle antenna due to the requests of miniaturization of size, but there was performance degradation in most cases due to miniaturization. In addition, some methods to improve performance have been used in the antennas that were previously researched using the arrays, but they have the disadvantages of bulkiness in size of the antennas when using the arrays. Therefore, in this paper, the CPW fed microstrip patch antenna with the simple structure of being compact and easy to produce, which can be used in the OBU of DSRC, was designed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.245-261
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• 2024

The COVID-19 pandemic has led to the widespread adoption of contactless transactions, resulting in a noticeable increase in the trend towards fully unmanned stores. In such stores, all operational processes are automated, primarily using artificial intelligence (AI) technology. However, this AI technology has several security vulnerabilities, which can be critical in the environment of fully unmanned stores. This paper analyzes the security vulnerabilities that AI-based fully unmanned stores may face, focusing particularly on the object detection model YOLO, demonstrating that Hiding Attacks and Altering Attacks using adversarial patches are possible. It is confirmed that objects with adversarial patches attached may not be recognized by the detection model or may be incorrectly recognized as other objects. Furthermore, the paper analyzes how Data Augmentation techniques can mitigate security threats by providing a defensive effect against adversarial patch attacks. Based on these results, we emphasize the need for proactive research into defensive measures to address the inherent security threats in AI technology used in fully unmanned stores.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.705-705
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• 2006

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.700-700
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• 2006

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.04a
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• pp.361-362
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• 2009

• 대한핵의학회:학술대회논문집
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• 1995.05a
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• pp.202-202
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• 1995

• 한국지구물리탐사학회:학술대회논문집
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• 2011.10a
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• pp.177-178
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• 2011

• Structural Engineering and Mechanics
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• v.24 no.2
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• pp.269-273
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• 2006

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1273-1283
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• 2019

This paper proposes an advanced crop classification model that combines a procedure for weighted combination of spatial features extracted from multi-scale input images with a conventional convolutional neural network (CNN) structure. The proposed model first extracts spatial features from patches with different sizes in convolution layers, and then assigns different weights to the extracted spatial features by considering feature-specific importance using squeeze-and-excitation block sets. The novelty of the model lies in its ability to extract spatial features useful for classification and account for their relative importance. A case study of crop classification with multi-temporal Landsat-8 OLI images in Illinois, USA was carried out to evaluate the classification performance of the proposed model. The impact of patch sizes on crop classification was first assessed in a single-patch model to find useful patch sizes. The classification performance of the proposed model was then compared with those of conventional two CNN models including the single-patch model and a multi-patch model without considering feature-specific weights. From the results of comparison experiments, the proposed model could alleviate misclassification patterns by considering the spatial characteristics of different crops in the study area, achieving the best classification accuracy compared to the other models. Based on the case study results, the proposed model, which can account for the relative importance of spatial features, would be effectively applied to classification of objects with different spatial characteristics, as well as crops.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.33 no.1 s.60
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• pp.23-28
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• 2007

Ascorbic acid (vitamin C, AsA) has been known as a strong reducing agent and is supposed to retard the synthesis of melanin pigment. A main problem that arose in using vitamin C in cosmetic formulation was its poor stability and low skin permeability, which result in low lightening efficacy in clinical trials. In this study, iontophoretic gel patch with flexible thin layer battery was employed in order to enhance skin permeation of vitamin c derivative (ascorbyl glucoside, AsAG) and to increase its lightening efficacy. in vitro iontophoretic skin permeation and stability of AsAG, safety and clinical lightening efficacy of iontophoretic patch containing 2% AsAG solution were examined. A optimun current of ionthophoretic patch for korean women was 0.1 mA, considering the skin permeability and skin irritation of consumers. We suggest that iontophoretic gel patch could be a safe system for enhancing the skin permeation of AsAG and lightning efficacy.