• 융합정보논문지
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• 제9권5호
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• pp.21-26
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• 2019

도심지에는 상 하수관로, 가스관, 수소관 등 필요에 따라 여러 가지 배관이 매설된다. 매설된 배관은 시간이 경과됨에 따라 균열 등으로 노후화되면서 폭발, 누수 등의 사고 발생 위험을 가지게 된다. 이러한 위험을 방지하기 위해 많은 노후 배관 수리, 교체되지만, 배관의 위치 또한 변경될 수 있다. 변경된 배관의 위치를 확인하지 못하면 배관을 건드려서 사고가 발생할 수 있다. 본 논문에서는 GPR을 사용하여 지하 단면 영상을 얻고, Faster R-CNN을 활용하여 지하 배관의 위치를 추정해보고, augmentation을 적용하여 부족한 데이터를 늘려서 실험을 진행하였다.

• 대한임베디드공학회논문지
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• 제17권4호
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• pp.217-228
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• 2022

Currently, there are increasing demands for applying deep neural networks (DNNs) in the embedded domain such as classification and object detection. The DNN processing in embedded domain often requires custom hardware such as NPU for acceleration due to the constraints in power, performance, and area. Processing DNN models requires a large amount of data, and its seamless transfer to NPU is crucial for performance. In this paper, we developed a cycle-accurate NPU simulator to evaluate diverse NPU microarchitectures. In addition, we propose a novel technique for reducing the number of memory accesses when processing convolutional layers in convolutional neural networks (CNNs) on the NPU. The main idea is to reuse data with memory interleaving, which recycles the overlapping data between previous and current input windows. Data memory interleaving makes it possible to quickly read consecutive data in unaligned locations. We implemented the proposed technique to the cycle-accurate NPU simulator and measured the performance with LeNet-5, VGGNet-16, and ResNet-50. The experiment shows up to 2.08x speedup in processing one convolutional layer, compared to the baseline.

• 한국산업융합학회 논문집
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• 제25권6_3호
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• pp.1285-1291
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• 2022

In this paper, we propose a low-cost, low-power embedded environment-based deep learning lightweight model for input images to recognize laundry management codes. Laundry franchise companies mainly use barcode recognition-based systems to record laundry consignee information and laundry information for laundry collection management. Conventional laundry collection management systems using barcodes require barcode printing costs, and due to barcode damage and contamination, it is necessary to improve the cost of reprinting the barcode book in its entirety of 1 billion won annually. It is also difficult to do. Recognition performance is improved by applying the VGG model with 7 layers, which is a reduced-transformation of the VGGNet model for number recognition. As a result of the numerical recognition experiment of service parts drawings, the proposed method obtained a significantly improved result over the conventional method with an F1-Score of 0.95.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2020년도 추계학술발표대회
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• pp.8-11
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• 2020

4 차 산업혁명 시대의 도래와 함께 AI, ICT 기술의 융합이 진행됨에 따라, 유저 레벨의 디바이스에서도 AI 서비스의 요청이 실현되었다. 이미지 처리와 관련된 AI 서비스는 피사체 판별, 불량품 검사, 자율주행 등에 이용되고 있으며, 특히 Deep Convolutional Neural Network (DCNN)은 이미지의 특색을 파악하는 데 뛰어난 성능을 보여준다. 하지만, 이미지의 크기가 커지고, 신경망이 깊어짐에 따라 연산 처리에 있어 낮은 데이터 지역성과 빈번한 메모리 참조를 야기했다. 이에 따라, 기존의 계층적 시스템 구조는 DCNN 을 scalable 하고 빠르게 처리하는 데 한계를 보인다. 본 연구에서는 DCNN 의 scalable 하고 빠른 처리를 위해 3 차원 메모리 구조의 Processing-In-Memory (PIM) 가속기를 제안한다. 이를 위해 기존 3 차원 메모리인 Hybrid Memory Cube (HMC)에 하드웨어 및 소프트웨어 모듈을 추가로 구성하였다. 구체적으로, Processing Element (PE)간 데이터를 공유할 수 있는 공유 캐시 및 소프트웨어 스택, 파이프라인화된 곱셈기 및 듀얼 프리페치 버퍼를 구성하였다. 이를 유명 DCNN 알고리즘 LeNet, AlexNet, ZFNet, VGGNet, GoogleNet, RestNet 에 대해 성능 평가를 진행한 결과 기존 HMC 대비 40.3%의 속도 향상을 29.4%의 대역폭 향상을 보였다.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제23권7호
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• pp.417-423
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• 2017

최근, 딥러닝을 사용 가능한 임베디드 디바이스가 상용화됨에 따라 임베디드 시스템 영역에서도 딥러닝 활용에 대한 다양한 연구가 진행되고 있다. 그러나 임베디드 시스템을 고성능 PC 환경과 비교하면 상대적으로 저사양의 CPU/GPU 프로세서와 메모리를 탑재하고 있으므로 딥러닝 기술의 적용에 있어서 많은 제약이 있다. 본 논문에서는 다양한 최신 딥러닝 네트워크들을 임베디드 디바이스에 적용했을때의 성능을 시간과 전력이라는 관점에서 실험적으로 평가한다. 또한, 호스트 CPU와 GPU 디바이스간의 메모리를 공유하는 임베디드 시스템들의 아키텍처적인 특성을 이용하여 메모리 복사를 줄임으로써 실시간 성능과 저전력성을 높이는 방법을 제시한다. 제안된 방법은 대표적인 공개 딥러닝 프레임워크인 Caffe를 수정하여 구현되었으며, 임베디드 GPU를 탑재한 NVIDIA Jetson TK1에서 성능평가 되었다. 실험결과, 대부분의 딥러닝 네트워크에서 뚜렷한 성능향상을 관찰할 수 있었다. 특히, 메모리 사용량이 높은 AlexNet에서 약 33%의 이미지 인식 속도 단축과 50%의 소비 전력량 감소를 관찰할 수 있었다.

• 한국멀티미디어학회논문지
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• 제23권4호
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• pp.539-548
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• 2020

Deep learning technology is currently being used and applied in many different fields. Convolution neural network (CNN) is a method of artificial neural networks in deep learning, which is commonly used for analyzing different types of images through classification. In the conventional classification of histopathology images of prostate carcinomas, the rating of cancer is classified by human subjective observation. However, this approach has produced to some misdiagnosing of cancer grading. To solve this problem, CNN based classification method is proposed in this paper, to train the histological images and classify the prostate cancer grading into two classes of the benign and malignant. The CNN architecture used in this paper is based on the VGG models, which is specialized for image classification. However, color normalization was performed based on the contrast enhancement technique, and the normalized images were used for CNN training, to compare the classification results of both original and normalized images. In all cases, accuracy was over 90%, accuracy of the original was 96%, accuracy of other cases was higher, and loss was the lowest with 9%.

• 대한의용생체공학회:의공학회지
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• 제41권1호
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• pp.48-54
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• 2020

In this paper, we deal with a liver fibrosis classification problem using ultrasound B-mode images. Commonly representative methods for classifying the stages of liver fibrosis include liver biopsy and diagnosis based on ultrasound images. The overall liver shape and the smoothness and roughness of speckle pattern represented in ultrasound images are used for determining the fibrosis stages. Although the ultrasound image based classification is used frequently as an alternative or complementary method of the invasive biopsy, it also has the limitations that liver fibrosis stage decision depends on the image quality and the doctor's experience. With the rapid development of deep learning algorithms, several studies using deep learning methods have been carried out for automated liver fibrosis classification and showed superior performance of high accuracy. The performance of those deep learning methods depends closely on the amount of datasets. We propose an enhanced U-net architecture to maximize the classification accuracy with limited small amount of image datasets. U-net is well known as a neural network for fast and precise segmentation of medical images. We design it newly for the purpose of classifying liver fibrosis stages. In order to assess the performance of the proposed architecture, numerical experiments are conducted on a total of 118 ultrasound B-mode images acquired from 78 patients with liver fibrosis symptoms of F0~F4 stages. The experimental results support that the performance of the proposed architecture is much better compared to the transfer learning using the pre-trained model of VGGNet.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.175-182
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• 2022

In our daily life, we come across different types of information, for example in the format of multimedia and text. We all need different types of information for our common routines as watching/reading the news, listening to the radio, and watching different types of videos. However, sometimes we could run into problems when a certain type of information is required. For example, someone is listening to the radio and wants to listen to jazz, and unfortunately, all the radio channels play pop music mixed with advertisements. The listener gets stuck with pop music and gives up searching for jazz. So, the above example can be solved with an automatic audio classification system. Deep Learning (DL) models could make human life easy by using audio classifications, but it is expensive and difficult to deploy such models at edge devices like nano BLE sense raspberry pi, because these models require huge computational power like graphics processing unit (G.P.U), to solve the problem, we proposed DL model. In our proposed work, we had gone for a low complexity model for Audio Event Detection (AED), we extracted Mel-spectrograms of dimension 128×431×1 from audio signals and applied normalization. A total of 3 data augmentation methods were applied as follows: frequency masking, time masking, and mixup. In addition, we designed Convolutional Neural Network (CNN) with spatial dropout, batch normalization, and separable 2D inspired by VGGnet [1]. In addition, we reduced the model size by using model quantization of float16 to the trained model. Experiments were conducted on the updated dataset provided by the Detection and Classification of Acoustic Events and Scenes (DCASE) 2020 challenge. We confirm that our model achieved a val_loss of 0.33 and an accuracy of 90.34% within the 132.50KB model size.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.80-80
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• 2022

Estimation of heading date of paddy rice is laborious and time consuming. Therefore, automatic estimation of heading date of paddy rice is highly essential. In this experiment, deep learning classification models were used to classify two difference categories of rice (vegetative and reproductive stage) based on the panicle initiation of paddy field. Specifically, the dataset includes 444 slanted view images belonging to two categories and was then expanded to include 1,497 images via IMGAUG data augmentation technique. We adopt two transfer learning strategies: (First, used transferring model weights already trained on ImageNet to six classification network models: VGGNet, ResNet, DenseNet, InceptionV3, Xception and MobileNet, Second, fine-tuned some layers of the network according to our dataset). After training the CNN model, we used several evaluation metrics commonly used for classification tasks, including Accuracy, Precision, Recall, and F1-score. In addition, GradCAM was used to generate visual explanations for each image patch. Experimental results showed that the InceptionV3 is the best performing model in terms of the accuracy, average recall, precision, and F1-score. The fine-tuned InceptionV3 model achieved an overall classification accuracy of 0.95 with a high F1-score of 0.95. Our CNN model also represented the change of rice heading date under different date of transplanting. This study demonstrated that image based deep learning model can reliably be used as an automatic monitoring system to detect the heading date of rice crops using CCTV camera.

• 한국인터넷방송통신학회논문지
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• 제24권1호
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• pp.9-16
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• 2024

알약 구매 후 조제약 봉투나 포장지를 잃어버린 경우 약의 효능을 알기 어렵고 많은 사람들이 시중에서 판매하는 약을 구매하여 복용한 후 보관 시 용도를 구분해서 보관하지 않는 경우가 많다. 또한 알약 부작용에 대한 정보의 접근성이 낮아 약을 오남용하는 상황이 발생하기도 한다. 기존의 알약 정보를 검색하여 정보를 알려 주는 대부분의 서비스 및 어플리케이션들은 사용자가 알약의 상세정보를 직접 입력하거나 선택해야 하는 번거로움이 존재한다. 본 논문에서는 이러한 불편한 상황들은 개선하고 보다 나은 서비스를 제공하기 위해 약학정보원에서 제공하는 알약 22,000종의 사진에 대해 제형과 색상을 학습시킨 딥러닝 모델을 구축하였다. 구축된 정보를 활용하여 촬영된 사진으로 알약을 검색하고 알약 정보를 알려주며 사용자의 약 복용을 관리해주는 기능을 갖는 시스템을 개발하였다.