• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권4호
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• pp.980-997
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• 2024

To improve the brightness of images and reveal hidden information in dark areas is the main objective of low-light image enhancement (LLIE). LLIE methods based on deep learning show good performance. However, there are some limitations to these methods, such as the complex network model requires highly configurable environments, and deficient enhancement of edge details leads to blurring of the target content. Single-scale feature extraction results in the insufficient recovery of the hidden content of the enhanced images. This paper proposed an edge detection-based multi-scale feature enhancement network for LLIE (EDMFEN). To reduce the loss of edge details in the enhanced images, an edge extraction module consisting of a Sobel operator is introduced to obtain edge information by computing gradients of images. In addition, a multi-scale feature enhancement module (MSFEM) consisting of multi-scale feature extraction block (MSFEB) and a spatial attention mechanism is proposed to thoroughly recover the hidden content of the enhanced images and obtain richer features. Since the fused features may contain some useless information, the MSFEB is introduced so as to obtain the image features with different perceptual fields. To use the multi-scale features more effectively, a spatial attention mechanism module is used to retain the key features and improve the model performance after fusing multi-scale features. Experimental results on two datasets and five baseline datasets show that EDMFEN has good performance when compared with the stateof-the-art LLIE methods.

• 방송공학회논문지
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• 제25권6호
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• pp.944-953
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• 2020

객체 검출 알고리즘은 자율주행 시스템 구현을 위한 핵심 요소이다. 최근 심층 합성곱 신경망 (Deep Convolutional Neural Network) 기반의 영상 인식 기술이 발전함에 따라 심층 학습을 이용한 객체 검출 관련 연구들이 활발히 진행되고 있다. 본 논문에서는 객체 검출에 가장 널리 사용되고 있는 Mask R-CNN의 경량화 모델을 제안하여 도로 내 다양한 객체들의 위치와 형태를 효율적으로 예측하는 방법을 제안한다. 또한, 주의 모듈(Attention Module)을 Mask R-CNN 내 각각 다른 역할을 수행하는 신경망 계층에 적용함으로써 특징 지도를 적응적으로 재교정(Re-calibration)하여 검출 성능을 향상시킨다. 실제 주행 영상에 대한 다양한 실험 결과를 통해 제안하는 방법이 기존 방법 대비 크게 감소된 신경망 매개변수만을 이용하여 고성능 검출 성능을 유지함을 보인다.

• Transactions on Electrical and Electronic Materials
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• 제18권1호
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• pp.30-34
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• 2017

The building integrated photovoltaic system (BIPV) attracts attention with regard to the future of the photovoltaic (PV) industry. It is because one of the promising national and civilian projects in the country. Since land area is limited, there is considerable interest in BIPV systems with a variety of angles and shapes of PV panels. It is therefore expected to be one of the major fields for the PV industry in the future. Since the irradiation is different from each installation angle, the output can be predicted by the angles. This is critical for a PV system to be operated at maximum power and use an efficient design. The development characteristics of tilted angles based on data results obtained via long-term monitoring need to be analyzed. The ratio of the theoretically available and actual outputs is compared with the installation angles of each PV module to provide a suitable PV system for the user.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.295-296
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• 2022

작물을 경작 해충과 질병은 오랫동안 주요 관심사였다. 농업에서 병해충을 탐지하기 위해 전통적인 방법을 사용하는 것은 더 이상 높은 효율성을 제공하지 않는다. 오늘날 과학과 인공 지능의 폭발적인 발달로 인해 농업분야의 연구원들은 병해충을 탐지하기 위해 딥 러닝을 적용하고 있다. 최근에 다양한 분야의 문제들을 해결하기 위해 수많은 모델들이 발표되었지만, 많은 병해충 진단 딥러닝을 사용한 방법들은 하드웨어 리소스를 낭비하고 실제 농장에서 사용하기 어렵다. 따라서 본 논문에서는 작물의 병해충을 분류하기 위해 Select Kernel Attention(SK Attention)을 Channel Attention Module 로 변경하여 Decoupling-and-Attention network (DANet)을 하드웨어 리소스 사용을 최소화한다.

• Smart Structures and Systems
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• 제29권1호
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• pp.1-16
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• 2022

Despite recent breakthroughs in deep learning and computer vision fields, the pixel-wise identification of tiny objects in high-resolution images with complex disturbances remains challenging. This study proposes a modified U-net for tiny crack segmentation in real-world steel-box-girder bridges. The modified U-net adopts the common U-net framework and a novel Self-Attention-Self-Adaption (SASA) neuron as the fundamental computing element. The Self-Attention module applies softmax and gate operations to obtain the attention vector. It enables the neuron to focus on the most significant receptive fields when processing large-scale feature maps. The Self-Adaption module consists of a multiplayer perceptron subnet and achieves deeper feature extraction inside a single neuron. For data augmentation, a grid-based crack random elastic deformation (CRED) algorithm is designed to enrich the diversities and irregular shapes of distributed cracks. Grid-based uniform control nodes are first set on both input images and binary labels, random offsets are then employed on these control nodes, and bilinear interpolation is performed for the rest pixels. The proposed SASA neuron and CRED algorithm are simultaneously deployed to train the modified U-net. 200 raw images with a high resolution of 4928 × 3264 are collected, 160 for training and the rest 40 for the test. 512 × 512 patches are generated from the original images by a sliding window with an overlap of 256 as inputs. Results show that the average IoU between the recognized and ground-truth cracks reaches 0.409, which is 29.8% higher than the regular U-net. A five-fold cross-validation study is performed to verify that the proposed method is robust to different training and test images. Ablation experiments further demonstrate the effectiveness of the proposed SASA neuron and CRED algorithm. Promotions of the average IoU individually utilizing the SASA and CRED module add up to the final promotion of the full model, indicating that the SASA and CRED modules contribute to the different stages of model and data in the training process.

• 한국군사과학기술학회지
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• 제24권2호
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• pp.175-186
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• 2021

In the field of automatic target recognition(ATR) with synthetic aperture radar(SAR) imagery, it is usually impractical to obtain SAR target images covering a full range of aspect views. When the database consists of SAR target images with limited angular diversity, it can lead to performance degradation of the SAR-ATR system. To address this problem, this paper proposes a deep learning-based method where channel attention modules(CAMs) are inserted to a convolutional neural network(CNN). Motivated by the idea of the squeeze-and-excitation(SE) network, the CAM is considered to help improve recognition performance by selectively emphasizing discriminative features and suppressing ones with less information. After testing various CAM types included in the ResNet18-type base network, the SE CAM and its modified forms are applied to SAR target recognition using MSTAR dataset with different reduction ratios in order to validate recognition performance improvement under the limited angular diversity condition.

• 정보교육학회논문지
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• 제21권2호
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• pp.191-198
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• 2017

본 연구의 목적은 SW 교육의 초보학습자들을 위한 모듈 카드 활용 협동학습을 설계하고 적용하여 그 효과와 인식을 분석하는 것이다. 30가지의 모듈 카드를 활용한 협동학습은 3단계의 활동으로 이루어져 있으며, 이러한 협동학습의 효과를 검증하기 위해 초등학생들을 대상으로 적용해 보았다. 연구 분석을 위해 SW 교육에 대한 사전 사후 학습동기와 본 협동학습에 대한 만족도, 흥미수준, 단계별 인식에 대하여 분석하였다. 분석 결과, SW 교육의 학습동기에 대해서는 주의 집중, 관련성, 자신감, 만족감의 모든 영역에서 유의미한 향상을 보였다. 또한 학생들은 모듈 카드를 활용한 협동학습의 2단계 활동은 가장 흥미롭고 3단계 활동은 가장 도움이 되었다고 응답하였다.

• 문화기술의 융합
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• 제6권2호
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• pp.349-355
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• 2020

현대 사회의 사회적 문제를 위한 대안으로 소셜로봇이 주목받고 있으며 소셜로봇의 효율적 관리를 위해 모듈화의 필요성이 제기되었다. 본 연구는 '개인용 소셜 서비스 로봇'의 개념을 재정립하고 이에 적용하기 위한 새로운 모듈화 방식을 제안하는 것을 목적으로 한다. 이를 위해 소셜 로봇과 서비스 로봇의 정의와 로봇의 모듈화에 대한 문헌연구를 수행하였다. 기존 서비스 로봇의 모듈화 사례를 분석하여 소셜 서비스 로봇의 모듈화에 적용하기 위한 고려사항을 도출하고 이를 바탕으로 새로운 모듈화 방안을 제안하였다. 본 연구는 모듈의 전기/전자적 부품 여부에 따라 액티브 모듈과 패시브 모듈로 구분하고, 액티브 모듈은 다시 로봇의 기본형과 교체형에 따라 기본 모듈, 추가 모듈로 분류했다. 이러한 방안이 적용된 실제 로봇의 프로토타입을 제작하는 것으로 모듈화를 검증하였다.

• 전기학회논문지P
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• 제67권3호
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• pp.137-142
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• 2018

Energy harvesting technology is drawing attention as a means of collecting various eco-friendly energy and accumulating residual energy. Recently, differential power processing (DPP) is being developed as part of energy harvesting. This is being studied as a solution to the loss of power generation between power modules and the problems caused by module small losses depending on the size of power production. In this paper, we propose the necessity of the DPP by comparing and analyzing energy harvesting related module integration system and power supply efficiency of DPP. The power efficiency of the converter and the power difference between the wind power and the photovoltaic power supply have been changed to demonstrate the effectiveness of the proposed system.

• International Journal of Internet, Broadcasting and Communication
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• 제13권1호
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• pp.90-99
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• 2021

Recently, studies on wearable devices in ubiquitous computing environments have increased and the technology collecting user's activities to provide services has received great attention. We have compared the step counting methods according to sensor molding methods in case of counting steps by using the piezoelectric sensor. We have classified the cases which could result from the course of molding the internal module of a smart shoe as follows: (i) the module is unmolded, (ii) molded but only to the extent that a sensor is fixed or (iii) molded to the extent that a sensor is not moved. Moreover, we have made comparison to verify which algorithm should be used to increase the accuracy of counting steps by the respective cases. Based on the comparison result, we have confirmed that the accuracy of counting steps is higher when using gradient value rather than when using threshold value. In the case of no molding and small molding under the condition of using gradient value, it was turned out to be 100% accuracy for step counting.