• 전기전자학회논문지
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• 제24권4호
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• pp.1141-1147
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• 2020

말벌 종은 모양이 매우 유사하기 때문에 비전문가가 분류하기 어렵고, 객체의 크기가 작고 빠르게 움직이기 때문에 실시간으로 탐지하여 종을 분류하는 것은 더욱 어렵다. 본 논문에서는 바운딩 박스를 이용한 딥러닝 알고리즘을 기반으로 말벌 종을 실시간으로 분류하는 시스템을 개발하였다. 훈련 영상의 레이블링 작업 시 바운딩 박스 안에 포함되는 배경 영역을 최소화하기 위하여 말벌의 머리와 몸통 부분만을 선택하는 방법을 제안한다. 또한 실시간으로 말벌을 탐지하고 그 종을 분류할 수 있는 최선의 알고리즘을 찾기 위하여 기존의 바운딩 박스 기반 객체 인식 알고리즘들을 실험을 통하여 비교한다. 실험 결과 컨볼루션 레이어의 활성함수로 mish 함수를 적용하고, 객체 검출 블록 전에 공간집중모듈(Spatial Attention Module, SAM)을 적용한 YOLOv4 모델을 사용하여 말벌 영상을 테스트한 경우 평균 97.89%의 정밀도(Precision)와 98.69%의 재현율(Recall)을 나타내었다.

• Smart Structures and Systems
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• 제30권6호
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• pp.601-612
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• 2022

The prediction of structural mechanical behaviors is vital important to early perceive the abnormal conditions and avoid the occurrence of disasters. Especially for underground engineering, complex geological conditions make the structure more prone to disasters. Aiming at solving the problems existing in previous studies, such as incomplete consideration factors and can only predict the continuous performance, the deep attention fused temporal convolution network (DATCN) is proposed in this paper to predict the spatial mechanical behaviors of structure, which integrates both the temporal effect and spatial effect and realize the cross-time prediction. The temporal convolution network (TCN) and self-attention mechanism are employed to learn the temporal correlation of each monitoring point and the spatial correlation among different points, respectively. Then, the predicted result obtained from DATCN is compared with that obtained from some classical baselines, including SVR, LR, MLP, and RNNs. Also, the parameters involved in DATCN are discussed to optimize the prediction ability. The prediction result demonstrates that the proposed DATCN model outperforms the state-of-the-art baselines. The prediction accuracy of DATCN model after 24 hours reaches 90 percent. Also, the performance in last 14 hours plays a domain role to predict the short-term behaviors of the structure. As a study case, the proposed model is applied in an underwater shield tunnel to predict the stress variation of concrete segments in space.

• 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.

• 감성과학
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• 제21권1호
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• pp.115-128
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• 2018

디자인 과정과 실제 공간을 사용함에 있어 공간감을 이해하는 것이 매우 중요한 역할을 하는데, 동일한 공간이라도 어떻게 디자인하느냐에 따라 공간의 깊이감에 대한 지각이 달라질 수 있다. 공간의 3차원적인 속성인 넓이, 깊이, 높이 중 넓이와 높이감에 대한 이론과 연구들에 비해 깊이감에 대한 이론이나 연구는 상대적으로 적은데 이는 넓이나 높이에 비해 공간의 깊이감이 초래하는 효과에 대한 관심이 부족했던 원인으로 보인다. 본 연구는 Computer Graphic으로 제작한 실내공간을 대상으로 시선추적장치를 이용하여 깊이감에 대한 지각과정을 이해하고자 하였다. 44명의 실내디자인 전공 학생들이 실험에 참가하여 마주보이는 벽면에 깊이감을 자극하는 구성요소를 달리한 3개의 이미지를 보며 가장 깊어보이는 공간을 탐색하였다. 그 결과 3개의 이미지간에 주시시간의 차이가 나타났고, 가장 깊어 보이는 공간에 대한 응답에 따라서도 주의집중에 통계적으로 유의미한 차이가 발견되었다. 본 연구는 깊이감에 영향을 끼치는 요인과 기존 정설에 대한 정량적인 타당성을 제시하고, 이상적인 공간의 깊이감을 얻을 수 있는 디자인 방법 개발 등에 도움이 될 것으로 본다.

• 한국실내디자인학회논문집
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• 제25호
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• pp.149-154
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• 2000

The concept of "place" has recently been given much attention by those who discuss problems of urban design and architecture. And we used the term "existential space" denote our concept or image of the environment. To create new space means to implement existential patterns in a given environment. So, this thesis explores the spatial meaning of correlation in Heideggers Existential Space and Situation of Fengshui.tuation of Fengshui.

• 대한임베디드공학회논문지
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• 제17권5호
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• pp.265-272
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• 2022

As the risk of lung cancer has increased, early-stage detection and treatment of cancers have received a lot of attention. Among various medical imaging approaches, computer tomography (CT) has been widely utilized to examine the size and growth rate of lung nodules. However, the process of manual examination is a time-consuming task, and it causes physical and mental fatigue for medical professionals. Recently, many computer-aided diagnostic methods have been proposed to reduce the workload of medical professionals. In recent studies, encoder-decoder architectures have shown reliable performances in medical image segmentation, and it is adopted to predict lesion candidates. However, localizing nodules in lung CT images is a challenging problem due to the extremely small sizes and unstructured shapes of nodules. To solve these problems, we utilize atrous spatial pyramid pooling (ASPP) to minimize the loss of information for a general U-Net baseline model to extract rich representations from various receptive fields. Moreover, we propose mixed-up attention mechanism of reverse, boundary and convolutional block attention module (CBAM) to improve the accuracy of segmentation small scale of various shapes. The performance of the proposed model is compared with several previous attention mechanisms on the LIDC-IDRI dataset, and experimental results demonstrate that reverse, boundary, and CBAM (RB-CBAM) are effective in the segmentation of small nodules.

• The Journal of Korean Physical Therapy
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• 제35권3호
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• pp.64-70
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• 2023

Purpose: This study investigated the influence of attention-demanding tasks on gait and measured differences in the temporal, spatial and kinematic characteristics between young healthy adults and elderly healthy adults. Methods: We recruited 16 healthy young adults and 15 healthy elderly adults in this study. All participants performed two cognitive tasks: a subtraction dual-task (SDT) and working memory dual-task (WMDT) during gait plus one normal gait. Using the LEGSys+ system, knee and hip-joint kinematic data during stance and swing phase and spatiotemporal parameter data were assessed in this study. Results: In the elderly adult group, attention-demanding tasks with gait showed a significant decrease in hip-joint motion during the stance phase, compared to the normal gait. Step length, stride length and stride velocity of the elderly adult group were significantly decreased in WMDT gait compared to normal gait (p<0.05). In the young adult group, kinematic data did not show any significant difference. However, stride velocity and cadence during SDT and WMDT gaits were significantly decreased compared to those of normal gait (p<0.05). Conclusion: We determined that attention-demanding tasks during gait in elderly adults can induce decreased hip-joint motion during stance phase and decreased gait speed and stride length to maintain balance and prevent risk of falling. We believe that understanding the changes during gait in older ages, particularly during attention-demanding tasks, would be helpful for intervention strategies and improved risk assessment.

• Spatial Information Research
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• 제15권1호
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• pp.35-52
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• 2007

실세계에 가까운 가상현실의 표현기술과 웹을 통해 공간정보를 제공하는 3차원 GIS는 새로이 주목받는 분야 중에 하나이다. 특히, 공간데이터의 상호운용성에 대한 관심이 높아지고 있는 가운데, OGC는 상호운용을 지원하는 공간객체의 위상관계 명세를 발표하였다. 그러나 이 명세서는 2차원 공간객체에 국한되어있다. 이에 본 연구에서는 도시의 경관개선과 GIS 활용 기반 조성의 측면에서 3차원 공간객체의 위상관계를 구축하였다. 나아가 이를 토대로 신도시의 실정에 맞는 경관정보 가시화 방안을 제시하였다. 결과적으로 장소에 구애받지 않고 상시접속이 가능한 현실감 있는 도시경관에 대한 정보공유의 기틀을 마련하였다는 점에서 보다 큰 의의가 있다고 할 것이다.

• 한국IT서비스학회지
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• 제18권1호
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• pp.131-140
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• 2019

This study compares and analyzes the spatial distribution of people in two cities using location information in twitter data. The target cities were selected as Paris, a traditional tourist city, and Dubai, a tourist city that has recently attracted attention. The data was collected over 123 days in 2016 and 125 days in 2018. We compared the spatial distribution of two cities according to the two periods and residence status. In this study, we have found a hot place using a spatial statistical model called dart-shaped space division and estimated the urban area by reflecting the distribution of tweet population. And we visualized it as a CDF (cumulative distribution function) curve so that the distance between all the tweets' occurrence points and the city center point can be compared for different cities.

• Current Optics and Photonics
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• 제4권6호
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• pp.530-539
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• 2020

Hyperspectral images feature a relatively narrow band and are easily disturbed by noise. Accurate estimation of the types and parameters of noise in hyperspectral images can provide prior knowledge for subsequent image processing. Existing hyperspectral-noise estimation methods often pay more attention to the use of spectral information while ignoring the spatial information of hyperspectral images. To evaluate the noise in hyperspectral images more accurately, we have proposed a spectral-spatial cooperative noise-evaluation method. First, the feature of spatial information was extracted by Gabor-filter and K-means algorithms. Then, texture edges were extracted by the Otsu threshold algorithm, and homogeneous image blocks were automatically separated. After that, signal and noise values for each pixel in homogeneous blocks were split with a multiple-linear-regression model. By experiments with both simulated and real hyperspectral images, the proposed method was demonstrated to be effective and accurate, and the composition of the hyperspectral image was verified.