• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권7호
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• pp.1907-1928
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• 2024

In recent years, multi-focus image fusion has emerged as a prominent area of research, with transformers gaining recognition in the field of image processing. Current approaches encounter challenges such as boundary artifacts, loss of detailed information, and inaccurate localization of focused regions, leading to suboptimal fusion outcomes necessitating subsequent post-processing interventions. To address these issues, this paper introduces a novel multi-focus image fusion technique leveraging the Swin Transformer architecture. This method integrates a frequency layer utilizing Wavelet Transform, enhancing performance in comparison to conventional Swin Transformer configurations. Additionally, to mitigate the deficiency of local detail information within the attention mechanism, Convolutional Neural Networks (CNN) are incorporated to enhance region recognition accuracy. Comparative evaluations of various fusion methods across three datasets were conducted in the paper. The experimental findings demonstrate that the proposed model outperformed existing techniques, yielding superior quality in the resultant fused images.

• 대한의용생체공학회:의공학회지
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• 제41권2호
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• pp.101-106
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• 2020

Although benign gastric ulcers do not develop into gastric cancer, they are similar to early gastric cancer and difficult to distinguish. This may lead to misconsider early gastric cancer as gastric ulcer while diagnosing. Since gastric cancer does not have any special symptoms until discovered, it is important to detect gastric ulcers by early gastroscopy to prevent the gastric cancer. Therefore, we developed a Convolution Neural Network (CNN) model that can be helpful for endoscopy. 3,015 images of gastroscopy of patients undergoing endoscopy at Gachon University Gil Hospital were used in this study. Using ResNet-50, three models were developed to classify normal and gastric ulcers, normal and gastric cancer, and gastric ulcer and gastric cancer. We applied the data augmentation technique to increase the number of training data and examined the effect on accuracy by varying the multiples. The accuracy of each model with the highest performance are as follows. The accuracy of normal and gastric ulcer classification model was 95.11% when the data were increased 15 times, the accuracy of normal and gastric cancer classification model was 98.28% when 15 times increased likewise, and 5 times increased data in gastric ulcer and gastric cancer classification model yielded 87.89%. We will collect additional specific shape of gastric ulcer and cancer data and will apply various image processing techniques for visual enhancement. Models that classify normal and lesion, which showed relatively high accuracy, will be re-learned through optimal parameter search.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.214-222
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• 2022

Convolutional Neural networks (CNNs) are a category of deep learning networks that have proven very effective in computer vision tasks such as image classification. Notwithstanding, not much has been seen in its use for remote sensing image classification in developing countries. This is majorly due to the scarcity of training data. Recently, transfer learning technique has successfully been used to develop state-of-the art models for remote sensing (RS) image classification tasks using training and testing data from well-known RS data repositories. However, the ability of such model to classify RS test data from a different dataset has not been sufficiently investigated. In this paper, we propose a deep CNN model that can classify RS test data from a dataset different from the training dataset. To achieve our objective, we first, re-trained a ResNet-50 model using EuroSAT, a large-scale RS dataset to develop a base model then we integrated Augmentation and Ensemble learning to improve its generalization ability. We further experimented on the ability of this model to classify a novel dataset (Nig_Images). The final classification results shows that our model achieves a 96% and 80% accuracy on EuroSAT and Nig_Images test data respectively. Adequate knowledge and usage of this framework is expected to encourage research and the usage of deep CNNs for land cover mapping in cases of lack of training data as obtainable in developing countries.

• 산업경영시스템학회지
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• 제47권2호
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• pp.155-167
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• 2024

Distribution and logistics industries contribute some of the biggest GDP(gross domestic product) in South Korea and the number of related companies are quarter of the total number of industries in the country. The number of retail tech companies are quickly increased due to the acceleration of the online and untact shopping trend. Furthermore, major distribution and logistics companies try to achieve integrated data management with the fulfillment process. In contrast, small and medium distribution companies still lack of the capacity and ability to develop digital innovation and smartization. Therefore, in this paper, a deep learning-based demand forecasting & recommendation model is proposed to improve business competitiveness. The proposed model is developed based on real sales transaction data to predict future demand for each product. The proposed model consists of six deep learning models, which are MLP(multi-layers perception), CNN(convolution neural network), RNN(recurrent neural network), LSTM(long short term memory), Conv1D-BiLSTM(convolution-long short term memory) for demand forecasting and collaborative filtering for the recommendation. Each model provides the best prediction result for each product and recommendation model can recommend best sales product among companies own sales list as well as competitor's item list. The proposed demand forecasting model is expected to improve the competitiveness of the small and medium-sized distribution and logistics industry.

• 한국컴퓨터정보학회논문지
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• 제25권9호
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• pp.31-36
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• 2020

본 연구에서는 딥러닝 처리기술을 이용한 이미지 분석을 통하여 위치정보가 없는 사진의 위치를 사용자에게 제공하는 장소이미지 수집기술을 설계하였다. 본 서비스는 사용자가 생활 중에 관심 있는 장소의 이미지 사진을 서비스에 업로드하면 해당 장소의 이름과 위치뿐만 아니라 관련 주변 정보를 확인 할 수 있는 서비스 개발을 목적으로 설계되었다. 본 연구는 이미지에 해당하는 정보를 제공하고 그 위치 정보를 기반으로 사용자가 관심 있는 주변정보를 제공할 수 있는 서비스의 기반기술이다. 이를 통하여 다양한 서비스에 활용이 가능하다.

• Communications for Statistical Applications and Methods
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• 제27권3호
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• pp.313-326
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• 2020

Deep Learning is the most important key to the development of Artificial Intelligence (AI). There are several distinguishable architectures of neural networks such as MLP, CNN, and RNN. Among them, we try to understand one of the main architectures called Recurrent Neural Network (RNN) that differs from other networks in handling sequential data, including time series and texts. As one of the main tasks recently in Natural Language Processing (NLP), we consider Neural Machine Translation (NMT) using RNNs. We also summarize fundamental structures of the recurrent networks, and some topics of representing natural words to reasonable numeric vectors. We organize topics to understand estimation procedures from representing input source sequences to predict target translated sequences. In addition, we apply multiple translation models with Gated Recurrent Unites (GRUs) in Keras on English-Korean sentences that contain about 26,000 pairwise sequences in total from two different corpora, colloquialism and news. We verified some crucial factors that influence the quality of training. We found that loss decreases with more recurrent dimensions and using bidirectional RNN in the encoder when dealing with short sequences. We also computed BLEU scores which are the main measures of the translation performance, and compared them with the score from Google Translate using the same test sentences. We sum up some difficulties when training a proper translation model as well as dealing with Korean language. The use of Keras in Python for overall tasks from processing raw texts to evaluating the translation model also allows us to include some useful functions and vocabulary libraries as well.

• 전기전자학회논문지
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• 제20권3호
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• pp.303-306
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• 2016

본 논문에서는 GPGPU를 활용하여 Convolutional neural network의 가속화 방법을 제안한다. Convolutional neural network는 이미지의 특징 값을 학습하여 분류하는 neural network의 일종으로 대량의 데이터를 학습해야하는 영상 처리에 적합하다. 기존의 Convolutional neural network의 convolution layer는 다수의 곱셈 연산을 필요로 하여 임베디드 환경에서 실시간으로 동작하기에 어려움이 있다. 본 논문에서는 이러한 단점을 해결하기 위하여 winograd convolution 연산을 통하여 곱셈 연산을 줄이고 GPGPU의 SIMT 구조를 활용하여 convolution 연산을 병렬 처리한다. 실험은 ModelSim, TestDrive를 사용하여 진행하였고 실험 결과 기존의 convolution 연산보다 처리 시간이 약 17% 개선되었다.

• 한국멀티미디어학회논문지
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• 제24권9호
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• pp.1224-1241
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• 2021

Recent development of deep learning techniques for image generation has led to straightforward generation of sophisticated deepfakes. However, as a result, privacy violations through deepfakes has also became increased. To solve this issue, a number of techniques for deepfake detection have been proposed, which are mainly focused on RGB channel-based analysis. Although existing studies have suggested the effectiveness of other color model-based analysis (i.e., Grayscale), their effectiveness has not been quantitatively validated yet. Thus, in this paper, we compare the effectiveness of Grayscale channel-based analysis with RGB channel-based analysis in deepfake detection. Based on the selected CNN-based models and deepfake datasets, we measured the performance of each color model-based analysis in terms of accuracy and time. The evaluation results confirmed that Grayscale channel-based analysis performs better than RGB-channel analysis in several cases.

• International Journal of Internet, Broadcasting and Communication
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• 제15권1호
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• pp.85-96
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• 2023

The number of traffic accidents caused by wet or icy road surface conditions is on the rise every year. Car crashes in such bad road conditions can increase fatalities and serious injuries. Historical data (from the year 2016 to the year 2020) on weather-related traffic accidents show that the fatality rates are fairly high in Korea. This requires accurate prediction and identification of hazardous road conditions. In this study, a forecasting model is developed to predict the chances of traffic accidents that can occur on roads affected by weather and road surface conditions. Multiple deep learning algorithms taking into account AlexNet and 2D-CNN are employed. Data on orthophoto images, automatic weather systems, automated synoptic observing systems, and road surfaces are used for training and testing purposes. The orthophotos images are pre-processed before using them as input data for the modeling process. The procedure involves image segmentation techniques as well as the Z-Curve index. Results indicate that there is an acceptable performance of prediction such as 65% for dry, 46% for moist, and 33% for wet road conditions. The overall accuracy of the model is 53%. The findings of the study may contribute to developing comprehensive measures for enhancing road safety.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.145-156
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• 2022

Children are the foundation and future of this society and understanding their impressions and behaviors is very important and the child's behavioral problems are a burden on the family and society as well as have a bad impact on the development of the child, and the early diagnosis of these problems helps to solve or mitigate them, and in this research project we aim to understand and know the behaviors of children, through artificial intelligence algorithms that helped solve many complex problems in an automated system, By using this technique to read and analyze the behaviors and feelings of the child by reading the features of the child's face, the movement of the child's body, the method of the child's session and nervous emotions, and by analyzing these factors we can predict the feelings and behaviors of children from grief, tension, happiness and anger as well as determine whether this child has the autism spectrum or not. The scarcity of studies and the privacy of data and its scarcity on these behaviors and feelings limited researchers in the process of analysis and training to the model presented in a set of images, videos and audio recordings that can be connected, this model results in understanding the feelings of children and their behaviors and helps doctors and specialists to understand and know these behaviors and feelings.