• 방송공학회논문지
• /
• 제23권5호
• /
• pp.669-681
• /
• 2018

본 논문에서는 고해상도 자궁경부암 세포사진을 CNN(Convolution Neural Network)을 통해 효과적으로 인식 및 분류하는 방법을 소개한다. 이때 고려되는 세포의 종류는 Ascus, Inflammation, RCC, Normal 로 네 가지가 있다. 본 논문에서는 먼저 기존의 고해상도 이미지를 분류하는 알고리즘을 소개하고, 이 방법을 이용하여 고해상도 세포사진을 분류하는 과정에서 어떤 정보의 손실이 발생하는지 분석한 후, 이를 해결하기 위한 방법을 제시한다. 이를 위해서 본 논문에서 제안하는 학습 모델에서는 dilated convolution을 이용하여 고해상도 사진의 정보의 손실을 최소한으로 줄임과 동시에 학습속도 빠르게 하는 알고리즘을 제시한다. 또한 이미지 전처리 과정으로 임계치를 사용함으로써 암세포를 판단하는데 혼란을 줄 수 있는 부분을 제거함으로써 인식률을 향상시킨다. 본 논문에서 제시되는 실험 결과를 통해, 제안한 알고리즘이 기존 기술보다 높은 인식률을 제공하는 것을 확인할 수 있었다.

• ETRI Journal
• /
• 제44권2호
• /
• pp.208-219
• /
• 2022

Crime amount prediction is crucial for optimizing the police patrols' arrangement in each region of a city. First, we analyzed spatiotemporal correlations of the crime data and the relationships between crime and related auxiliary data, including points-of-interest (POI), public service complaints, and demographics. Then, we proposed a crime amount prediction model based on 2D convolution and long short-term memory neural network (2DCONV-LSTM). The proposed model captures the spatiotemporal correlations in the crime data, and the crime-related auxiliary data are used to enhance the regional spatial features. Extensive experiments on real-world datasets are conducted. Results demonstrated that capturing both temporal and spatial correlations in crime data and using auxiliary data to extract regional spatial features improve the prediction performance. In the best case scenario, the proposed model reduces the prediction error by at least 17.8% and 8.2% compared with support vector regression (SVR) and LSTM, respectively. Moreover, excessive auxiliary data reduce model performance because of the presence of redundant information.

• 한국인공지능학회지
• /
• 제11권3호
• /
• pp.17-22
• /
• 2023

In this paper, we propose a black ice detection system using Convolutional Neural Networks (CNNs). Black ice poses a serious threat to road safety, particularly during winter conditions. To overcome this problem, we introduce a CNN-based architecture for real-time black ice detection with an encoder-decoder network, specifically designed for real-time black ice detection using thermal images. To train the network, we establish a specialized experimental platform to capture thermal images of various black ice formations on diverse road surfaces, including cement and asphalt. This enables us to curate a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Additionally, in order to enhance the accuracy of black ice detection, we propose a multi-scale dilation convolution feature fusion (MsDC-FF) technique. This proposed technique dynamically adjusts the dilation ratios based on the input image's resolution, improving the network's ability to capture fine-grained details. Experimental results demonstrate the superior performance of our proposed network model compared to conventional image segmentation models. Our model achieved an mIoU of 95.93%, while LinkNet achieved an mIoU of 95.39%. Therefore, it is concluded that the proposed model in this paper could offer a promising solution for real-time black ice detection, thereby enhancing road safety during winter conditions.

• 한국방송∙미디어공학회:학술대회논문집
• /
• 한국방송∙미디어공학회 2021년도 추계학술대회
• /
• pp.221-222
• /
• 2021

본 연구는 Convolution Neural Network에서 사용되는 Convolution 연산기를 Systolic Array를 이용하여 구현한다. 두 개의 층으로 나뉜 연산기에 고정 소수점 값을 가지는 커널 값과 연속적인 입력을 넣고 정확한 출력이 나오는지 확인한다. 연산기 구현은 Verilog HDL로 하였으며 대조 연산은 Python에서 진행하였다.

• 문화기술의 융합
• /
• 제3권4호
• /
• pp.171-175
• /
• 2017

최근 CNN(Convolutional Neural Network)의 구조가 복잡해지고 신견망의 깊이가 깊어지고 있다. 이에 따라 신경망의 학습에 요구되는 연산량 및 학습 시간이 증가하게 되었다. 최근 GPGPU 및 FPGA를 이용하여 신경망의 학습 속도를 가속화 하는 방법에 대한 연구가 활발히 진행되고 있다. 본 논문에서는 NVIDIA GPGPU를 제어하는 CUDA를 이용하여 CNN의 특징추출부와 분류부에 대한 연산을 가속화하는 방법을 제시한다. 특징추출부와 분류부에 대한 연산을 GPGPU의 블록 및 스레드로 할당하여 병렬로 처리하였다. 본 논문에서 제안하는 방법과 기존 CPU를 이용하여 CNN을 학습하여 학습 속도를 비교하였다. MNIST 데이터세트에 대하여 총 5 epoch을 학습한 결과 제안하는 방법이 CPU를 이용하여 학습한 방법에 비하여 약 314% 정도 학습 속도가 향상된 것을 확인하였다.

• 한국수학교육학회지시리즈E:수학교육논문집
• /
• 제37권2호
• /
• pp.277-297
• /
• 2023

합성곱(convolution)은 인공지능(artificial intelligence)에서 컴퓨터 비전(computer vision), 심층학습(deep learning) 등의 분야를 이해하고 응용하려면 알아야 하는 중요한 수학적 연산이다. 그러나 현재의 공학수학 교과과정의 합성곱 내용은 독립적인 주제가 아니라 단편적으로 다루어지고 있어서 그 의미를 충분히 전달하지 못하고 있다. 이에 본 논문에서는 공학수학에서 인공지능 교육과 연계할 수 있도록 개발한 합성곱 교수·학습 자료를 제시한다. 먼저 기존 공학과 인공지능 기술의 통합적 관점에서 합성곱에 대한 배경지식과 응용 사례를 정리하고, 코딩을 이용한 교육이 가능하도록 파이썬(Python)/SageMath 코드를 개발하여 제공한다. 또한 합성곱 지식이 인공지능에서 어떻게 활용되는지 보여주는 구체적인 예시로, 이미지 분류에 사용되는 합성곱신경망(Convolutional Neural Network, CNN)을 개발된 코드와 함께 제공한다. 본 교수·학습자료는 합성곱 개념을 쉽고 효과적으로 교육할 수 있도록 공학수학의 보충 자료로 활용가능하며, 학습자는 코딩을 통해 합성곱을 배우고 본인의 전공과 관련된 인공지능 기술을 학습하는 데 이를 이용할 수 있다.

• Journal of Information Processing Systems
• /
• 제17권2호
• /
• pp.411-425
• /
• 2021

In this paper, a vehicle recognition algorithm based on deep convolutional neural network and compression dictionary is proposed. Firstly, the network structure of fine vehicle recognition based on convolutional neural network is introduced. Then, a vehicle recognition system based on multi-scale pyramid convolutional neural network is constructed. The contribution of different networks to the recognition results is adjusted by the adaptive fusion method that adjusts the network according to the recognition accuracy of a single network. The proportion of output in the network output of the entire multiscale network. Then, the compressed dictionary learning and the data dimension reduction are carried out using the effective block structure method combined with very sparse random projection matrix, which solves the computational complexity caused by high-dimensional features and shortens the dictionary learning time. Finally, the sparse representation classification method is used to realize vehicle type recognition. The experimental results show that the detection effect of the proposed algorithm is stable in sunny, cloudy and rainy weather, and it has strong adaptability to typical application scenarios such as occlusion and blurring, with an average recognition rate of more than 95%.

• Journal of Platform Technology
• /
• 제6권1호
• /
• pp.3-8
• /
• 2018

This paper proposes a technique for recognizing online handwritten cursive data obtained by tracing a motion trajectory while a user is in the 3D space based on a convolution neural network (CNN) algorithm. There is a difficulty in recognizing the virtual character input by the user in the 3D space because it includes both the character stroke and the movement stroke. In this paper, we divide syllable into consonant and vowel units by using labeling technique in addition to the result of localizing letter stroke and movement stroke in the previous study. The coordinate information of the separated consonants and vowels are converted into image data, and Korean handwriting recognition was performed using a convolutional neural network. After learning the neural network using 1,680 syllables written by five hand writers, the accuracy is calculated by using the new hand writers who did not participate in the writing of training data. The accuracy of phoneme-based recognition is 98.9% based on convolutional neural network. The proposed method has the advantage of drastically reducing learning data compared to syllable-based learning.

• 한국멀티미디어학회논문지
• /
• 제20권2호
• /
• pp.126-133
• /
• 2017

The conventional hand-crafted features used to track objects have limitations in object representation. Convolutional neural networks, which show good performance results in various areas of computer vision, are emerging as new ways to break through the limitations of feature extraction. CNN extracts the features of the image through layers of multiple layers, and learns the kernel used for feature extraction by itself. In this paper, we use the feature map extracted from the convolution layer of the convolution neural network to create an outline model of the object and use it for tracking. We propose a method to adaptively update the outline model to cope with various environment change factors affecting the tracking performance. The proposed algorithm evaluated the validity test based on the 11 environmental change attributes of the CVPR2013 tracking benchmark and showed excellent results in six attributes.

• 센서학회지
• /
• 제29권6호
• /
• pp.427-432
• /
• 2020

The most lethal foreign body in meat is a needle, and X-ray images are used to detect it. However, because the difference in thickness and fat content is severe depending on the type of meat and the part of the meat, the shade difference and contrast appear severe. This problem causes difficulty in automatic classification. In this paper, we propose a method for generating training patterns by efficient pre-processing and classifying needles in meat using a convolution neural network. Approximately 24000 training patterns and 4000 test patterns were used to verify the proposed method, and an accuracy of 99.8% was achieved.