• YAKHAK HOEJI
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• v.52 no.3
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• pp.201-211
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• 2008

All pharmacists must provide the drug consultation whenever dispense drugs to patients by the Korean Pharmacy Law. Drug consultation is very important procedure for increasing pharmacotherapy. Because it maximizes the therapeutic effects or/and minimizes adverse drug reaction during the drug therapy. However, it is not easy to do because of the dynamic and hectic pharmacy environment. Especially, if someone has a disabling body function, they required more time and efforts to perform consultation by pharmacist. Currently several auxiliary labels for helping drug consultation are using in pharmacy practice but not for disabling patients. Therefore we developed the total 53 auxiliary labels with size of 0.7 cm (width) and 1 cm (length) by Braillewriter letters for blind patients. This research has been performed for total 12 months (Mar. 15ts, 2007$\sim$Feb. 25th, 2008) and the developing methods are consisted of 4 steps: 1) selection of essential informations, 2) simplification of information, 3) changing for Braillewriter letters, 4) application and revising by blindness patients. Also the labels are consisted of 12 for adverse reactions and precautions, 8 for directions, 2 for storages, 9 for duration, 9 for dosage forms, and 12 for common names. After developed those labels, we revised those labels by discussion with 2 blind people. In conclusion, the new auxiliary labels for blind patients can increase therapeutic effects and decrease risks from pharmacotherapy besides decreasing of pharmacist's work load in the future.

• Proceedings of the PSK Conference
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• 2003.10b
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• pp.72.1-72.1
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• 2003

The use of auxiliary labels in many advanced countries has shown that patients and caregivers understanding, safety and compliance with proper administration of medication is significantly increased. Although in Korea we give required information orally and written on the packaging when we dispense medication, many international studies have demonstrated a marked increase in full compliance when auxiliary labels were used. The pharmacist must insure that the patients understand how to take correctly in order to get the maximum effect of treatment. (omitted)

• Proceedings of the PSK Conference
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• 2004.10a
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• pp.154.1-154.1
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• 2004

• Smart Media Journal
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• v.12 no.9
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• pp.45-59
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• 2023

Weakly supervised object localization (WSOL) is a task of localizing an object in an image using only image-level labels. Previous studies have followed the conventional class activation mapping (CAM) pipeline. However, we reveal the current CAM approach suffers from problems which cause original CAM could not capture the complete defects features. This work utilizes a convolutional neural network (CNN) pretrained on image-level labels to generate class activation maps in a multi-scale manner to highlight discriminative regions. Additionally, a vision transformer (ViT) pretrained was treated to produce multi-head attention maps as an auxiliary detector. By integrating the CNN-based CAMs and attention maps, our approach localizes defective regions without requiring bounding box or pixel-level supervision during training. We evaluate our approach on a dataset of apple images with only image-level labels of defect categories. Experiments demonstrate our proposed method aligns with several Object Detection models performance, hold a promise for improving localization.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.185-207
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• 2022

Recently, with the development of deep learning technology, researches to apply a deep learning algorithm to analyze unstructured data such as text and images are being actively conducted. Text classification has been studied for a long time in academia and industry, and various attempts are being performed to utilize data characteristics to improve classification performance. In particular, a hierarchical relationship of labels has been utilized for hierarchical classification. However, the top-down approach mainly used for hierarchical classification has a limitation that misclassification at a higher level blocks the opportunity for correct classification at a lower level. Therefore, in this study, we propose a methodology for classifying hierarchical data using the autoencoder-based deeply supervised network that high-level classification does not block the low-level classification while considering the hierarchical relationship of labels. The proposed methodology adds a main classifier that predicts a low-level label to the autoencoder's latent variable and an auxiliary classifier that predicts a high-level label to the hidden layer of the autoencoder. As a result of experiments on 22,512 academic papers to evaluate the performance of the proposed methodology, it was confirmed that the proposed model showed superior classification accuracy and F1-score compared to the traditional supervised autoencoder and DNN model.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.11-20
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• 2024

Among the generative models in Artificial Intelligence (AI), especially Generative Adversarial Network (GAN) has been successful in various applications such as image processing, density estimation, and style transfer. While the GAN models including Conditional GAN (CGAN), CycleGAN, BigGAN, have been extended and improved, researchers face challenges in real-world applications in specific domains such as disaster simulation, healthcare, and urban planning due to data scarcity and unstable learning causing Image distortion. This paper proposes a new progressive learning methodology called Progressive Step Training (PST) based on the Auxiliary Classifier GAN (ACGAN) that discriminates class labels, leveraging the progressive learning approach of the Progressive Growing of GAN (PGGAN). The PST model achieves 70.82% faster stabilization, 51.3% lower standard deviation, stable convergence of loss values in the later high resolution stages, and a 94.6% faster loss reduction compared to conventional methods.

• International Journal of Contents
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• v.6 no.1
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• pp.6-11
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• 2010

Recently, there has been research to use portable digital camera to recognize objects in natural scene images, including labels or marks on a cylindrical surface. In many cases, text or logo in a label can be distorted by a structural movement of the object on which the label resides. Since the distortion in the label can degrade the performance of object recognition, the label should be rectified or restored from deformations. In this paper, a new method for label detection and restoration in digital images is presented. In the detection phase, the Hough transform is employed to detect two vertical boundaries of the label, and a horizontal edge profile is analyzed to detect upper-side and lower-side boundaries of the label. Then, the biquadratic transformation is used to restore the rectangular shape of the label. The proposed algorithm performs restoration of 3D objects in a 2D space, and it requires neither an auxiliary hardware such as 3D camera to construct 3D models nor a multi-camera to capture objects in different views. Experimental results demonstrate the effectiveness of the proposed method.