• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.190-197
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• 2024

The O2O store model dissolves the boundaries between online and offline platforms, providing significant convenience to customers. To effectively operate such platforms, small business owners must provide necessary information in digital format. Specifically, the process of digitizing Korean menus manually can lead to multiple issues, and the use of OCR technology often results in high error rates due to the low accuracy in recognizing Korean. In response, this paper proposes an enhanced OCR model based on the popular EasyOCR framework, aimed at improving the recognition accuracy of Korean. The proposed model integrates the structural advantages of VGG and ResNet, and incorporates an attention mechanism to significantly improve the recognition performance of Korean. Moreover, experimental results indicate that the proposed model achieved approximately a 3.5% improvement in accuracy and around a 1% improvement in both confidence score and normalized edit distance compared to EasyOCR. Therefore, this demonstrates that the proposed method effectively addresses the existing challenges.

• Journal of the Korea Computer Graphics Society
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• v.28 no.1
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• pp.1-9
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• 2022

Non-small cell lung cancer (NSCLC) accounts for a high proportion of 85% among all lung cancer and has a significantly higher mortality rate (22.7%) compared to other cancers. Therefore, it is very important to predict the prognosis after surgery in patients with non-small cell lung cancer. In this study, the types of preoperative chest CT image patches for non-small cell lung cancer patients with tumor as a region of interest are diversified into five types according to tumor-related information, and performance of single classifier model, ensemble classifier model with soft-voting method, and ensemble classifier model using 3 input channels for combination of three different patches using pre-trained ResNet and EfficientNet CNN networks are analyzed through misclassification cases and Grad-CAM visualization. As a result of the experiment, the ResNet152 single model and the EfficientNet-b7 single model trained on the peritumoral patch showed accuracy of 87.93% and 81.03%, respectively. In addition, ResNet152 ensemble model using the image, peritumoral, and shape-focused intratumoral patches which were placed in each input channels showed stable performance with an accuracy of 87.93%. Also, EfficientNet-b7 ensemble classifier model with soft-voting method using the image and peritumoral patches showed accuracy of 84.48%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.399-401
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• 2022

A definitive diagnosis to classify the breast malignancy status may be achieved by microscopic analysis using surgical open biopsy. However, this procedure requires experts in the specializing of histopathological image analysis directing to time-consuming and high cost. To overcome these issues, deep learning is considered practically efficient to categorize breast cancer into benign and malignant from histopathological images in order to assist pathologists. This study presents a pre-trained convolutional neural network model architecture with a 100% fine-tuning scheme and Adagrad optimizer to classify the breast cancer histopathological images into benign and malignant using a 40× magnification BreaKHis dataset. The pre-trained architecture was constructed using the InceptionResNetV2 model to generate a modified InceptionResNetV2 by substituting the last layer with dense and dropout layers. The results by demonstrating training loss of 0.25%, training accuracy of 99.96%, validation loss of 3.10%, validation accuracy of 99.41%, test loss of 8.46%, and test accuracy of 98.75% indicated that the modified InceptionResNetV2 model is reliable to predict the breast malignancy type from histopathological images. Future works are necessary to focus on k-fold cross-validation, optimizer, model, hyperparameter optimization, and classification on 100×, 200×, and 400× magnification.

• Imaging Science in Dentistry
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• v.43 no.3
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• pp.171-177
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• 2013

Purpose: The purpose of this study was to compare the efficacy of two imaging modes in a cone beam computed tomography (CBCT) system in detecting root fracture in endodontically-treated teeth with fiber posts or screw posts by selecting two fields of view. Materials and Methods: In this study, 78 endodontically-treated single canal premolars were included. A post space was created in all of them. Then the teeth were randomly set in one of 6 artificial dental arches. In 39 of the 78 teeth set in the 6 dental arches, a root fracture was intentionally created. Next, a fiber post and a screw post were cemented into 26 teeth having equal the root fractures. High resolution (HiRes) and standard zoom images were provided by a CBCT device. Upon considering the reconstructed images, two observers in agreement with each other confirmed the presence or absence of root fracture. A McNemar test was used for comparing the results of the two modes. Results: The frequency of making a correct diagnosis using the HiRes zoom imaging mode was 71.8% and in standard zoom was 59%. The overall sensitivity and specificity in diagnosing root fracture in the HiRes mode were 71.79% and 46.15% and in the standard zoom modes were 58.97% and 33.33%, respectively. Conclusion: There were no significant differences between the diagnostic values of the two imaging modes used in the diagnosis of root fracture or in the presence of root canal restorations. In both modes, the most true-positive results were reported in the post space group.

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08b
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• pp.1745-1748
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• 2007

First, thin-film photodevices are evaluated, and a p/i/n thin-film phototransistor (TFPT) is recommended because the photo-induced current is relatively high and independent of the applied voltage. Next, an artificial retina is developed using the p/i/n TFPTs, and it is found that it can detect photo illuminance profile with sensitivity control.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.255-256
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• 2015

The increasing penetration of renewable energy based distributed generation(DG) sources in low-voltage grid feeders has been receiving increased attention. High penetration of renewable energy generation in a distribution system can cause power quality and efficiency problem. In this paper, the operating plan for ESS and the efficiency analysis on RES(Renewable energy source) concentrated distribution system.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2295-2303
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• 2021

To investigate wall-thinning impact on axial load resistance of Zircaloy-4 cladding rods after a LOCA transient, axial tensile samples have been machined on as-received tubes with reduced thicknesses between 370 and 580 ㎛. After high temperature oxidation under steam at 1200 ℃ with measured ECR ranging from 10 to 18% and water quenching, machined samples were axially loaded until fracture. These tests were modeled using a fracture mechanics approach developed in a previous study. Fracture stresses are rather well predicted. However, the slightly lower fracture stress observed for wall-thinned samples is not anticipated by this modeling approach. The results from this study confirm that characterizing the axial load resistance using semi-integral tests including the creep and burst phases was the best option to obtain accurate axial strengths describing accurately the influence of wall-thinning at burst region.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.211-221
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• 2024

Pakistan is a top producer and exporter of high-quality rice, but traditional methods are still being used for detecting rice diseases. This research project developed an automated rice blast disease diagnosis technique based on deep learning, image processing, and transfer learning with pre-trained models such as Inception V3, VGG16, VGG19, and ResNet50. The modified connection skipping ResNet 50 had the highest accuracy of 99.16%, while the other models achieved 98.16%, 98.47%, and 98.56%, respectively. In addition, CNN and an ensemble model K-nearest neighbor were explored for disease prediction, and the study demonstrated superior performance and disease prediction using recommended web-app approaches.

• 한국정보디스플레이학회:학술대회논문집
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• 2005.07b
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• pp.1071-1074
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• 2005

TAC film is an indispensable optical component that protects the polarizing PVA (polyvinyl alcohol) film from being deteriorated and gives high durability, due to its unique features. The newly developed technology of controlling the birefringence of TAC film, together with the coating technology of a discotic material layer, enables excellent viewing angle characteristics and a cost-effective roll-to-roll polarizer manufacturing process.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2011.06a
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• pp.7-10
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• 2011

According to the requirement of Res.MSC.285(86) for natural gas-fueled engine installations in ships, pump and compressor rooms should be fitted with effective mechanical ventilation system of the under pressure type, providing a ventilation capacity of at least 30 air changes per hour. It generally considered that gas leakage is more likely from a Fueled Gas Supply System(FGS) room as compared to other places, where installed in many kind of machinery or equipments like gas supply high-pressure pipes, valves, flanges and etc. Furthermore, leaked gas may be dispersed in a short time in an enclosed space, especially a FGS room, due to high pressure. However, the present requirement in Res.MSC.285(86) just considers the ventilating capacity of air changes per hour but the capacity of leaked gas. Hence, the current requirements may not meet effectively when enforcing the new propulsion systems as marine fuel. This study is conducted for the purpose of safety evaluation about the dispersion and ventilation efficiency with estimated leakage scenario. Numerical analysis predictions as the result of this paper are explained to know the features of flow pattern and the diffusion of natural gas concentration.