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

• 산업경영시스템학회지
• /
• 제43권3호
• /
• pp.228-242
• /
• 2020

In recent years, interest in the welfare of soldiers is increasing. More specifically, soldiers enlisted to fulfill their military service obligations live a group life that is controlled by group life, unlike military officers selected by their will. Therefore, this study aims to verify whether there is an effect of improving the morale of soldiers by increasing the satisfaction of military service meals along with the trend of increasing morale through enhancement of soldier welfare. In order to achieve the purpose of this study, we surveyed 145 army soldiers working in the front and rear areas and used 137 valid samples for analysis. The results of this study are as follows. First, both SERVQUAL (Responsiveness·Assurance, Tangibles) and Servicescape (Spatial Environment, Spatial Design) had a positive (+) effect on military meal satisfaction. Second, military service satisfaction and emotional commitment have a positive (+) effect on morale. Third, satisfaction with military meals has a positive effect on emotional commitment. This research has also shown that improvements in SERVQUAL (Responsiveness·Assurance, Tangibles) and Servicescape (Spatial Environment, Spatial Design) improve military service satisfaction and emotional engagement. Military food service SERVQUAL (Responsiveness·Assurance, Tangibles) and Servicescape (Spatial Environment, Space Design) must pay attention to increase military morale.

• Current Research on Agriculture and Life Sciences
• /
• 제32권4호
• /
• pp.203-209
• /
• 2014

This study investigated characteristics of rainfall and water quality in Saemangeum area with attention to temporal and spatial distributions. A high variability in rainfall was noted during July and August. The temporal analysis of water quality data indicated that DO and TN as well as BOD, COD and SS were within national standards except for increased concentrations during spring and summer, unlike TP values that indicated poor water quality. Standard deviation showed a high variability in SS among the seasons most especially during summer. The high dispersion indicated variability in the chemical composition of pollutants where the temporal and spatial variations caused by polluting sources and/or seasonal changes were most evident for BOD and COD during winter and spring. The box plots and bar charts showed steadily low concentrations of BOD, COD, TN and TP except within Iksan and notable significant variations in SS concentrations among the monitoring stations. Thus, high pollution levels requiring intervention were identified in Mangyeong river basin with particular concern for areas represented by Iksan station. It was noted that Iksan received a considerable amount of rainfall which meant high runoff which could explain the significant pollution levels revealed in the water quality spatial distribution. Major pollution contributing pollutants within Saemangeum area were identified as SS, BOD, COD and TN. Therefore the present results could be used as a guideline for the temporal and spatial distributions analysis of both rainfall and water quality in Saemangeum watershed.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권7호
• /
• pp.2131-2153
• /
• 2022

To achieve accurate detection of tuberculosis (TB) areas in chest radiographs, we design a chest X-ray TB area detection algorithm. The algorithm consists of two stages: the chest X-ray TB classification network (CXTCNet) and the chest X-ray TB area detection network (CXTDNet). CXTCNet is used to judge the presence or absence of TB areas in chest X-ray images, thereby excluding the influence of other lung diseases on the detection of TB areas. It can reduce false positives in the detection network and improve the accuracy of detection results. In CXTCNet, we propose a channel attention mechanism (CAM) module and combine it with DenseNet. This module enables the network to learn more spatial and channel features information about chest X-ray images, thereby improving network performance. CXTDNet is a design based on a sparse object detection algorithm (Sparse R-CNN). A group of fixed learnable proposal boxes and learnable proposal features are using for classification and location. The predictions of the algorithm are output directly without non-maximal suppression post-processing. Furthermore, we use CLAHE to reduce image noise and improve image quality for data preprocessing. Experiments on dataset TBX11K show that the accuracy of the proposed CXTCNet is up to 99.10%, which is better than most current TB classification algorithms. Finally, our proposed chest X-ray TB detection algorithm could achieve AP of 45.35% and AP50 of 74.20%. We also establish a chest X-ray TB dataset with 304 sheets. And experiments on this dataset showed that the accuracy of the diagnosis was comparable to that of radiologists. We hope that our proposed algorithm and established dataset will advance the field of TB detection.

• Journal of Information Processing Systems
• /
• 제18권4호
• /
• pp.575-586
• /
• 2022

Uneven light in real-world causes visual degradation for underexposed regions. For these regions, insufficient consideration during enhancement procedure will result in over-/under-exposure, loss of details and color distortion. Confronting such challenges, an unsupervised low-light image enhancement network is proposed in this paper based on the guidance of the unpaired low-/normal-light images. The key components in our network include super-resolution module (SRM), a GAN-based low-light image enhancement network (LLIEN), and denoising-scaling module (DSM). The SRM improves the resolution of the low-light input images before illumination enhancement. Such design philosophy improves the effectiveness of texture details preservation by operating in high-resolution space. Subsequently, local lightness attention module in LLIEN effectively distinguishes unevenly illuminated areas and puts emphasis on low-light areas, ensuring the spatial consistency of illumination for locally underexposed images. Then, multiple discriminators, i.e., global discriminator, local region discriminator, and color discriminator performs assessment from different perspectives to avoid over-/under-exposure and color distortion, which guides the network to generate images that in line with human aesthetic perception. Finally, the DSM performs noise removal and obtains high-quality enhanced images. Both qualitative and quantitative experiments demonstrate that our approach achieves favorable results, which indicates its superior capacity on illumination and texture details restoration.

• 대한원격탐사학회지
• /
• 제37권5_1호
• /
• pp.1061-1070
• /
• 2021

본 연구에서는 국내 농업지역에 대한 작물재배지역의 분류를 위하여 FC-DenseNet 모델에 attention gate를 적용하여 딥러닝 모델의 성능을 향상시키고자 하였다. Attention gate는 특징맵의 공간/분광적 중요도에 따른 가중치를 추가적으로 학습하여 딥러닝 모델의 학습을 용이하게 하고, 모델의 성능을 향상시킬 수 있다. Attention gate를 FC-DenseNet의 스킵 연결 부분에 추가한 딥러닝 모델을 이용하여 양파 및 마늘 지역의 작물분류를 수행하였다. PlanetScope 위성영상을 이용하여 훈련자료를 제작하였으며, 훈련자료의 불균형 문제를 해결하기 위하여 전처리 과정을 적용하였다. 다양한 평가자료를 이용하여 작물재배분류 결과를 평가한 결과, 제안된 딥러닝 모델은 기존의 FC-DenseNet과 비교하여 효과적으로 양파 및 마늘 지역을 분류할 수 있는 것을 확인하였다.

• 지적과 국토정보
• /
• 제46권2호
• /
• pp.269-281
• /
• 2016

최근 공간 정보 분야에서 소셜 미디어와 같은 공간 빅 데이터의 분석과 처리에 많은 관심이 집중되고 있다. 본 연구에서는 공간 빅 데이터 분석의 한 사례로서 트윗 데이터가 가지고 있는 위치 정보와 시간 정보를 바탕으로 시간대별로 공간분포를 분석하고 그 특성을 파악하였다. 부산시 지역의 트윗 데이터를 수집하고, 시간대별 공간분석을 통하여 그 특성을 파악하여, 그 지역의 토지이용 특성과 비교하였다. 부산시 지역의 트윗 데이터를 시간대에 따라 평일 주간, 평일 야간, 휴일 주간, 휴일 야간으로 구분하고, 각 시간대별로 공간적 분포 특성을 파악하여, 공간적으로 집중된 지역의 토지이용 특성과 비교하였다. 본 연구의 결과 트윗 데이터는 시간대에 따라 공간분포가 다르게 나타나고 있으며, 이는 그 지역의 일상생활 패턴과 토지이용 특성을 어느 정도 반영하고 있었다. 본 연구에서는 공간정보 분야에서 트윗 데이터와 같은 소셜 미디어 자료의 분석을 통한 활용 가능성을 제시하였다. 향후 토지 계획이나 도시 계획 등의 분야에서 다양한 소셜 미디어 자료를 활용할 수 있을 것으로 전망된다.

• Journal of Forest and Environmental Science
• /
• 제33권3호
• /
• pp.161-171
• /
• 2017

Global environmental changes have the capacity to make dramatic alterations to floral and faunal composition, and elucidation of the mechanism is important for predicting its outcomes. Studies on global climate change have traditionally focused on statistical summaries within relatively wide scales of spatial and temporal changes, and less attention has been paid to variability in microclimates across spatial and temporal scales. Microclimate is a suite of climatic conditions measured in local areas near the earth's surface. Environmental variables in microclimatic scale can be critical for the ecology of organisms inhabiting there. Here we examine the effect of spatial and temporal changes in microclimates on those of carabid beetle communities in Hyangnobong, Korea. We found that climatic variables and the patterns of annual changes in carabid beetle communities differed among sites even within the single mountain system. Our results indicate the importance of temporal survey of communities at local scales, which is expected to reveal an additional fraction of variation in communities and underlying processes that has been overlooked in studies of global community patterns and changes.

• 한국지리정보학회지
• /
• 제27권3호
• /
• pp.99-114
• /
• 2024

최근 지역 간 격차 해소와 지역 특성 반영을 통한 균형발전 및 지속 가능한 개발에 대한 기대로 생활권 개념이 주목받고 있다. 생활권 계획에서는 일상생활을 지원할 수 있는 필수적인 생활인프라 시설에 대한 접근성이 중요한 요소로 다루어지며, 이러한 배경에서 본 연구에서는 생활권계획에서 설정한 생활인프라 시설 및 접근범위를 기반으로 시설로부터의 접근성을 분석하고 생활권계획 및 공간적 군집성과 연계하여 접근성이 집중되는 지역과 떨어지는 지역에 대한 공간적 접근성 불평등 지역을 분석하였다. 부산광역시를 대상으로 접근성을 분석한 결과, 시설에 따라 접근성 범위가 다양하게 존재하였으며 생활권계획과 연계하여 살펴보면 중생활권인 동래권, 원도심권, 해운대권은 공간적 접근성이 높게, 강서권과 기장권은 공간적 접근성이 낮게 나타났다. 공간적 군집성과 연계하여 분석한 결과 강동권, 원도심권, 동래권, 해운대권에서 핫스팟 지역이, 강서권과 기장권은 콜드스팟 지역이 많이 분포하는 지역적 불평등이 나타났으며, 같은 생활권내에서도 핫스팟과 콜드스팟이 동시에 존재하는 공간적 불평등이 나타났다. 이러한 공간적 특성을 고려하면 소생활권 단위에서 부족한 시설에 대한 세밀한 계획과 정책이 요구되며, 분석 결과는 최근 추진하는 균형발전이라는 도시정책을 실현하는 데 의미가 있을 것으로 판단된다.

• East Asian mathematical journal
• /
• 제40권2호
• /
• pp.267-286
• /
• 2024

This study investigates the perceptions toward prospective elementary teachers regarding the revised 2015 elementary mathematics curriculum. The aim is to understand the importance and implementation of the revised curriculum and provide implications for curriculum improvement in elementary teacher education institutions, using Interpretative Phenomenological Analysis (IPA). The research findings are as follows: Firstly, prospective elementary teachers perceived that the areas of the revised 2015 elementary mathematics curriculum that require particular focus are number and operations and data and probability. Secondly, they identified the specific elements within these areas that demand dedicated attention as follows: numbers up to four digits in number and operations, mixed calculations with natural numbers, shapes of solid figures, spatial sense of solid figures, comparison of quantities in measurement, etc. These findings can inform the improvement of the curriculum in elementary teacher education institutions.