• Korean Journal of Clinical Laboratory Science
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• v.36 no.2
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• pp.193-198
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• 2004

PET/CT combines the functional information from a positron emission tomography (PET) exam with the anatomical information from a computed tomography (CT) exam into one single exam. A CT scan uses a combination of x-rays and computers to give the radiologist a non-invasive way to see inside your body. One advantage of CT is its ability to rapidly acquire two-dimensional pictures of your anatomy. Using a computer these 2-D images can be presented in 3-D for in-depth clinical evaluation. A PET scan detects changes in the cellular function - how your cells are utilizing nutrients like sugar and oxygen. Since these functional changes take place before physical changes occur, PET can provide information that enables your physician to make an early diagnosis. The PET exam pinpoints metabolic activity in cells and the CT exam provides an anatomical reference. When these two scans are fused together, your physician can view metabolic changes in the proper anatomical context of your body. PET/CT offers significant advantages including more accurate localization of functional abnormalities, and the distinction of pathological from normal physiological uptake, and improvements in monitoring treatment. A PET/CT scan allows physicians to measure the body's abnormal molecular cell activity to detect cancer (such as breast cancer, lung cancer, colorectal cancer, lymphoma, melanoma and other skin cancers), brain disorders (such as Alzheimer's disease, Parkinson's disease, and epilepsy), and heart disease (such as coronary artery disease).

• Journal of KIBIM
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• v.14 no.2
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• pp.13-24
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• 2024

This research suggests a novel visualization approach utilizing Generative AI to render photorealistic architectural alternatives images in the early design phase. Photorealistic rendering intuitively describes alternatives and facilitates clear communication between stakeholders. Nevertheless, the conventional rendering process, utilizing 3D modelling and rendering engines, demands sophisticate model and processing time. In this context, the paper suggests a rendering approach employing the text-to-image method aimed at generating a broader range of intuitive and relevant reference images. Additionally, it employs an Text-to-Image method focused on producing a diverse array of alternatives reflecting architects' styles when visualizing the exteriors of residential buildings from the mass model images. To achieve this, fine-tuning for architects' styles was conducted using the Low-Rank Adaptation (LoRA) method. This approach, supported by fine-tuned models, allows not only single style-applied alternatives, but also the fusion of two or more styles to generate new alternatives. Using the proposed approach, we generated more than 15,000 meaningful images, with each image taking only about 5 seconds to produce. This demonstrates that the Generative AI-based visualization approach significantly reduces the labour and time required in conventional visualization processes, holding significant potential for transforming abstract ideas into tangible images, even in the early stages of design.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.855-870
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• 2020

Windows are key components of building facades. Detecting windows, crucial to 3D semantic reconstruction and scene parsing, is a challenging task in computer vision. Early methods try to solve window detection by using hand-crafted features and traditional classifiers. However, these methods are unable to handle the diversity of window instances in real scenes and suffer from heavy computational costs. Recently, convolutional neural networks based object detection algorithms attract much attention due to their good performances. Unfortunately, directly training them for challenging window detection cannot achieve satisfying results. In this paper, we propose an approach for window detection. It involves an improved Faster R-CNN architecture for window detection, featuring in a window region proposal network, an RoI feature fusion and a context enhancement module. Besides, a post optimization process is designed by the regular distribution of windows to refine detection results obtained by the improved deep architecture. Furthermore, we present a newly collected dataset which is the largest one for window detection in real street scenes to date. Experimental results on both existing datasets and the new dataset show that the proposed method has outstanding performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2016-2034
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• 2011

Ubiquitous Healthcare (u-Healthcare) is the intelligent delivery of healthcare services to users anytime and anywhere. To provide robust healthcare services, recognition of patient daily life activities is required. Context information in combination with user real-time daily life activities can help in the provision of more personalized services, service suggestions, and changes in system behavior based on user profile for better healthcare services. In this paper, we focus on the intelligent manipulation of activities using the Context-aware Activity Manipulation Engine (CAME) core of the Human Activity Recognition Engine (HARE). The activities are recognized using video-based, wearable sensor-based, and location-based activity recognition engines. An ontology-based activity fusion with subject profile information for personalized system response is achieved. CAME receives real-time low level activities and infers higher level activities, situation analysis, personalized service suggestions, and makes appropriate decisions. A two-phase filtering technique is applied for intelligent processing of information (represented in ontology) and making appropriate decisions based on rules (incorporating expert knowledge). The experimental results for intelligent processing of activity information showed relatively better accuracy. Moreover, CAME is extended with activity filters and T-Box inference that resulted in better accuracy and response time in comparison to initial results of CAME.

• Journal of the Korean Society of Costume
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• v.53 no.3
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• pp.13-31
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• 2003

The purpose of this study is to analyze the formative style and aesthetic value of contemporary subcultural style with the understanding of characteristics of subcultural style in the sociocultural context. As for the research methodology, literary survey has been performed to study the concept and the history of subcultural styles from 1940s to 1980s. In addition, demonstrative studies on aesthetic images have been carried out through the analysis of pictures and photographs in order to categorize the subcultural styles since 1990s. In this study, subcultural styles since 1990s have been illustrated through the socio-cultural grounds. which are diversity of street culture, diffusion of moss culture or bubble-up phenomenon, pursuit of anti-social ideology among intellectual heads, club Cultures in the form of kitsch and pastiche and communal thinking as collective harmony. With the socio-cultural context examined above, subcultural styles in the contemporary fashion are categorized and deduced following aesthetic values ; Drag and Club style as the tendency of surrealists or artificial hedonists, Urban military style and Cyberpunks as pursuit of Bionic being, Sports casual style based upon, the pursuit of comfort and freedom, Vintage style and Retro dressing represented by the spirit of DIY (no It Yourself), Ecology style of New conservatives, and Mysticism style as the fusion of technology and shamanism (Technoshamanism).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.453-456
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• 2005

In this paper, we propose method to improve recognition performance using the most effective algorithm selectively after clustering various face data, because recognition performance of each algorithm according to facial attribute is change. The proposed face recognition is divided into two steps. First step is the clustering integrated various data to be optimized in algorithm. Second is that classify input image by a similar cluster, select suitable algorithm and recognize the target. This thesis takes the first step towards the creation of a synthetic classifier fusiontesting environment. The effects of data correlation on three classifier fusion techniques were examined. We proposed fusion method for each recognition algorithm's result. This research explores how the degree of correlation in classification data affects the degree of accuracy in a fusion context.

• Journal of Urban Science
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• v.7 no.2
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• pp.53-60
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• 2018

Digital Twin is a technology that creates a photocopy of real-world objects on a computer and analyzes the past and present operational status by fusing the structure, context, and operation of various physical systems with property information, and predicts the future society's countermeasures. In particular, 3D rendering technology (UAS, LiDAR, GNSS, etc.) is a core technology in digital twin. so, the research and application are actively performed in the industry in recent years. However, UAS (Unmanned Aerial System) and LiDAR (Light Detection And Ranging) have to be solved by compensating blind spot which is not reconstructed according to the object shape. In addition, the terrestrial LiDAR can acquire the point cloud of the object more precisely and quickly at a short distance, but a blind spot is generated at the upper part of the object, thereby imposing restrictions on the forward digital twin modeling. The UAS is capable of modeling a specific range of objects with high accuracy by using high resolution images at low altitudes, and has the advantage of generating a high density point group based on SfM (Structure-from-Motion) image analysis technology. However, It is relatively far from the target LiDAR than the terrestrial LiDAR, and it takes time to analyze the image. In particular, it is necessary to reduce the accuracy of the side part and compensate the blind spot. By re-optimizing it after fusion with UAS and Terrestrial LiDAR, the residual error of each modeling method was compensated and the mutual correction result was obtained. The accuracy of fusion-based 3D model is less than 1cm and it is expected to be useful for digital twin construction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3729-3749
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• 2021

At present, deep convolution network-based salient object detection (SOD) has achieved impressive performance. However, it is still a challenging problem to make full use of the multi-scale information of the extracted features and which appropriate feature fusion method is adopted to process feature mapping. In this paper, we propose a new adjacency auxiliary network (AANet) based on multi-scale feature fusion for SOD. Firstly, we design the parallel connection feature enhancement module (PFEM) for each layer of feature extraction, which improves the feature density by connecting different dilated convolution branches in parallel, and add channel attention flow to fully extract the context information of features. Then the adjacent layer features with close degree of abstraction but different characteristic properties are fused through the adjacent auxiliary module (AAM) to eliminate the ambiguity and noise of the features. Besides, in order to refine the features effectively to get more accurate object boundaries, we design adjacency decoder (AAM_D) based on adjacency auxiliary module (AAM), which concatenates the features of adjacent layers, extracts their spatial attention, and then combines them with the output of AAM. The outputs of AAM_D features with semantic information and spatial detail obtained from each feature are used as salient prediction maps for multi-level feature joint supervising. Experiment results on six benchmark SOD datasets demonstrate that the proposed method outperforms similar previous methods.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.505-518
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• 2023

3D point cloud semantic segmentation is a computer vision task that involves dividing the point cloud into different objects and regions by predicting the class label of each point. Existing 3D semantic segmentation models have some limitations in performing sufficient fusion of multi-modal features while ensuring both characteristics of 2D visual features extracted from RGB images and 3D geometric features extracted from point cloud. Therefore, in this paper, we propose MMCA-Net, a novel 3D semantic segmentation model using 2D-3D multi-modal features. The proposed model effectively fuses two heterogeneous 2D visual features and 3D geometric features by using an intermediate fusion strategy and a multi-modal cross attention-based fusion operation. Also, the proposed model extracts context-rich 3D geometric features from input point cloud consisting of irregularly distributed points by adopting PTv2 as 3D geometric encoder. In this paper, we conducted both quantitative and qualitative experiments with the benchmark dataset, ScanNetv2 in order to analyze the performance of the proposed model. In terms of the metric mIoU, the proposed model showed a 9.2% performance improvement over the PTv2 model using only 3D geometric features, and a 12.12% performance improvement over the MVPNet model using 2D-3D multi-modal features. As a result, we proved the effectiveness and usefulness of the proposed model.

• Journal of the Korea Convergence Society
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• v.8 no.5
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• pp.275-285
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• 2017

The architectural site exists in the context of the surrounding environment as the background of architectural design, and the architecture exists in the connection between the site and the surrounding environment therefore, the plan of the building starts from the analysis of the site, and its consequences too meaning can be found in the relation with the surrounding environment. Ironically, however, there are no direct papers describing site analysis with this meaning. Therefore, this paper categorizes the site analysis items around the four environments of Land, Water, Fire, and Wind that constitute the environment of the ecosystem, furthermore, in order to apply the result of the fusion information to the architectural design by synthesizing the organic correlation between the inside and the surrounding environment, i created the '9sectors system', which is a combination of 'horizontal 3 and vertical 3' to provide a more rational and efficient architectural plan. Through the progress of the project using this system, it is considered that it is a more applicable method of the site analysis data than the existing site analysis method.