• Journal of IKEEE
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• v.24 no.4
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• pp.1141-1147
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• 2020

The hornet species are so similar in shape that they are difficult for non-experts to classify, and because the size of the objects is small and move fast, it is more difficult to detect and classify the species in real time. In this paper, we developed a system that classifies hornets species in real time based on a deep learning algorithm using a boundary box. In order to minimize the background area included in the bounding box when labeling the training image, we propose a method of selecting only the head and body of the hornet. It also experimentally compares existing boundary box-based object recognition algorithms to find the best algorithms that can detect wasps in real time and classify their species. As a result of the experiment, when the mish function was applied as the activation function of the convolution layer and the hornet images were tested using the YOLOv4 model with the Spatial Attention Module (SAM) applied before the object detection block, the average precision was 97.89% and the average recall was 98.69%.

• Smart Structures and Systems
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• v.30 no.6
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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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• v.18 no.4
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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.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.115-128
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• 2018

Understanding the process of spatial perception plays a significant role in the design process as well as in the use of actual spaces. The perception of spatial depth can vary according to the space composition and design even there is no change in the actual size of the space. The properties of 3-dimensional space are its width, height, and depth; however, compared to the perception of spatial width and height, little research and theories exist on spatial depth perception. The reasons may be there less interest lies in the effect of spatial depth perception than that of spaciousness or height of space. This study is an investigation of the process of spatial depth perception using an eye-tracking device with stimuli developed through Computer Graphics. A total of 44 interior design major students participated in the eye tracking experiment; and they looked at three images comprised of an identical room with only changes in the rear wall condition. The results show that a significant difference in the fixation duration per stimulus exists. In addition, a significant difference exists on the fixation duration per stimulus according to the participants' answer of the deepest space. The result of this study can help identify factors for spatial depth perception, validate the assumption on it, and provide knowledge on how to acquire desirable spatial depth by utilizing the research result.

• Korean Institute of Interior Design Journal
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• no.25
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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.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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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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• v.35 no.3
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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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• v.15 no.1
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• pp.35-52
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• 2007

Three-dimensional GIS, which provides spatial information through expression techniques of virtual reality close to the real world and the web, is one of the fields that attract a new attention. In particular, Open GIS Consortium(OGC) announced a topological relationship specification of spatial object which supports interoperability while interest in interoperability of spatial data is increasing. However, this specification is limited to two-dimensional spatial object. So this research established a topological relationship of three-dimensional spatial object in order to improve urban landscape and provide a foundation to use GIS. Based on this, this study proposes ways to visualize landscape information which is appropriate for new town's circumstances. It can be concluded that this research has a bigger meaning since it established a base of sharing information about realistic urban landscape that can be accessed regardless of place and time.

• Journal of Information Technology Services
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• v.18 no.1
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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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• v.4 no.6
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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.