• Biomedical Engineering Letters
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• v.8 no.4
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• pp.365-371
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• 2018

Uninterrupted monitoring of multiple subjects is required for mass causality events, in hospital environment or for sports by medical technicians or physicians. Movement of subjects under monitoring requires such system to be wireless, sometimes demands multiple transmitters and a receiver as a base station and monitored parameter must not be corrupted by any noise before further diagnosis. A Bluetooth Piconet network is visualized, where each subject carries a Bluetooth transmitter module that acquires vital sign continuously and relays to Bluetooth enabled device where, further signal processing is done. In this paper, a wireless network is realized to capture ECG of two subjects performing different activities like cycling, jogging, staircase climbing at 100 Hz frequency using prototyped Bluetooth module. The paper demonstrates removal of baseline drift using Fast Fourier Transform and Inverse Fast Fourier Transform and removal of high frequency noise using moving average and S-Golay algorithm. Experimental results highlight the efficacy of the proposed work to monitor any vital sign parameters of multiple subjects simultaneously. The importance of removing baseline drift before high frequency noise removal is shown using experimental results. It is possible to use Bluetooth Piconet frame work to capture ECG simultaneously for more than two subjects. For the applications where there will be larger body movement, baseline drift removal is a major concern and hence along with wireless transmission issues, baseline drift removal before high frequency noise removal is necessary for further feature extraction.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.780-789
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• 2018

Nowadays, gun crimes occur very frequently not only in public places but in alleyways around the world. In particular, it is essential to detect a person armed by a pistol to prevent those crimes since small guns, such as pistols, are often used for those crimes. Because conventional works for armed person detection have treated an armed person as a single object in an input image, their accuracy is very low. The reason for the low accuracy comes from the fact that the gunman is treated as a single object although the pistol is a relatively much smaller object than the person. To solve this problem, we propose a novel algorithm called APDA(Armed Person Detection Algorithm). APDA detects the armed person using in a post-processing the positions of both wrists and the pistol achieved by the CNN-based human body feature detection model and the pistol detection model, respectively. We show that APDA can provide both 46.3% better recall and 14.04% better precision than SSD-MobileNet.

• Korean Journal of Heritage: History & Science
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• v.36
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• pp.69-86
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• 2003

This article introduce the manufacturing technique of Silla potteries based on the result excavated from Songogdong and Mulchunri site in Gyungju. As a result, we selected the kiln-site to produce Silla potteries and knew the feature which following to make them. 1. The Environmental elements to take a kiln-site were abundant fuel, plenty water and suitable soil. In particular, efficient usage of refracted winds and reserved space of forepart in the kiln-site were importantly applied to select place of kiln-site. 2. The structure of the kiln-body have been changing according to the time. It could be massproduced by produce-group from the middle and end of sixth centry which the fireplace-kiln was generalized. 3. The work center of equipments were related kiln-site. It consisted of mixed wheel, keepingpit and ditch. We knew that a look-out shed had been appeared according to utility purpose variously. 4. It sees as trimming trace of inner and outter aspects in excavated potteries and we knew that wheel had been turn to the contrast watch direction. For producing pottery of the good guality, various kiln-tools had been used already at Silla period and they used for the different purpose. 5. We intended to know method for laying the potteries in the kiln through the example of the adherent pottery to be melted. Finally, manufature and tomb-site are separated by the time through current situation of Songokdong and Mulchonri site. At the same time, we could know that group of Chounbuk kiln-site moved from the south to the north step by step.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• Imaging Science in Dentistry
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• v.52 no.3
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• pp.239-244
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• 2022

Purpose: Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer's knowledge and expertise. This study aimed to explore the possibility of automated sex determination using convolutional neural networks(CNNs) based on lateral cephalometric radiographs. Materials and Methods: Lateral cephalometric radiographs of 1,476 Iranian subjects (794 women and 682 men) from 18 to 49 years of age were included. Lateral cephalometric radiographs were considered as a network input and output layer including 2 classes(male and female). Eighty percent of the data was used as a training set and the rest as a test set. Hyperparameter tuning of each network was done after preprocessing and data augmentation steps. The predictive performance of different architectures (DenseNet, ResNet, and VGG) was evaluated based on their accuracy in test sets. Results: The CNN based on the DenseNet121 architecture, with an overall accuracy of 90%, had the best predictive power in sex determination. The prediction accuracy of this model was almost equal for men and women. Furthermore, with all architectures, the use of transfer learning improved predictive performance. Conclusion: The results confirmed that a CNN could predict a person's sex with high accuracy. This prediction was independent of human bias because feature extraction was done automatically. However, for more accurate sex determination on a wider scale, further studies with larger sample sizes are desirable.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.168-168
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• 2023

The frequent occurrence of cyanobacterial harmful algal blooms (CHABs) in inland waters under climate change seriously damages the ecosystem and human health and is becoming a big problem in South Korea. Satellite remote sensing is suggested for effective monitoring CHABs at a larger scale of water bodies since the traditional method based on sparse in-situ networks is limited in space. However, utilizing a standalone variable of satellite reflectances in common CHABs dual-models, which relies on both chlorophyll-a (Chl-a) and phycocyanin or cyanobacteria cells (Cyano-cell), is not fully beneficial because their seasonal variation is highly impacted by surrounding meteorological and bio-environmental factors. Along with the development of Artificial Intelligence (AI), monitoring CHABs from space with analyzing the effects of environmental factors is accessible. This study aimed to investigate the potential application of AI in the dual-model strategy (Chl-a and Cyano-cell are output parameters) for monitoring seasonal dynamics of CHABs from satellites over Korean inland waters. The Sentinel-3 satellite was selected in this study due to the variety of spectral bands and its unique band (620 nm), which is sensitive to cyanobacteria. Via the AI-based feature selection, we analyzed the relationships between two output parameters and major parameters (satellite water-leaving reflectances at different spectral bands), together with auxiliary (meteorological and bio-environmental) parameters, to select the most important ones. Several AI models were then employed for modelling Chl-a and Cyano-cell concentration from those selected important parameters. Performance evaluation of the AI models and their comparison to traditional semi-analytical models were conducted to demonstrate whether AI models (using water-leaving reflectances and environmental variables) outperform traditional models (using water-leaving reflectances only) and which AI models are superior for monitoring CHABs from Sentinel-3 satellite over a Korean inland water body.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.290-293
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• 2021

In this paper, we implement a learning efficiency verification system to inspire learning motivation and help improve concentration by detecting the situation of the user studying. To this aim, data on learning attitude and concentration are measured by extracting the movement of the user's face or body through a real-time camera. The Jetson board was used to implement the real-time embedded system, and a convolutional neural network (CNN) was implemented for image recognition. After detecting the feature part of the object using a CNN, motion detection is performed. The captured image is shown in a GUI written in PYQT5, and data is collected by sending push messages when each of the actions is obstructed. In addition, each function can be executed on the main screen made with the GUI, and functions such as a statistical graph that calculates the collected data, To do list, and white noise are performed. Through learning efficiency checking system, various functions including data collection and analysis of targets were provided to users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2643-2657
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• 2023

Scene graphs are structured representations that can clearly convey objects and the relationships between them, but are often heavily biased due to the highly skewed, long-tailed relational labeling in the dataset. Indeed, the visual world itself and its descriptions are biased. Therefore, Unbiased Scene Graph Generation (USGG) prefers to train models to eliminate long-tail effects as much as possible, rather than altering the dataset directly. To this end, we propose Geometric and Semantic Improvement (GSI) for USGG to mitigate this issue. First, to fully exploit the feature information in the images, geometric dimension and semantic dimension enhancement modules are designed. The geometric module is designed from the perspective that the position information between neighboring object pairs will affect each other, which can improve the recall rate of the overall relationship in the dataset. The semantic module further processes the embedded word vector, which can enhance the acquisition of semantic information. Then, to improve the recall rate of the tail data, the Class Balanced Seesaw Loss (CBSLoss) is designed for the tail data. The recall rate of the prediction is improved by penalizing the body or tail relations that are judged incorrectly in the dataset. The experimental findings demonstrate that the GSI method performs better than mainstream models in terms of the mean Recall@K (mR@K) metric in three tasks. The long-tailed imbalance in the Visual Genome 150 (VG150) dataset is addressed better using the GSI method than by most of the existing methods.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.39-46
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• 2024

In this paper, we propose a low-cost wearable device that can monitor the health status of the elderly living alone in real-time. As aging is accelerating, the elderly population is rapidly increasing, and the social isolation of the elderly living alone is causing physical and mental difficulties and the number of elderly people dying alone is increasing, becoming a social problem. In this study, we propose a belly band-type wearable device that can monitor the biometric information of elderly living alone. The proposed device transmits electromyogram, electrocardiogram, and body temperature information to a remote server through an Arduino-based sensor built into the abdominal band. Transmitted information can be monitored in a web environment in real-time, and it has the feature of enabling remote monitoring of a large number of subjects with a small amount of management manpower. The research results will contribute to improving the safety and welfare of seniors living alone by not only detecting lonely deaths in advance but also responding immediately to dangerous situations that may occur in daily life.

• The Korean Journal of Nuclear Medicine Technology
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• v.12 no.1
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• pp.44-48
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• 2008

Purpose: In FDG-PET/CT of breast cancer, a sensitivity was 80~96% and a specificity was 75~95% commonly. It was valuable to identify a cancer in early stage been difficult in Mammography. Most of the PET/CT scans have been examined on supine position, so, the image of breast has been acquired by reconstructed whole body scan image. However, using prone position with a compensator, a shape of breast was reassembly shown to be real by gravity. Therefore, the purpose of this study was to evaluate diagnostic value of prone position in FDG PET-CT of breast cancer. Materials and Methods: 30 female patients with doubtful or positive breast cancer were examined. The PET-CT whole body scan was acquired at 60 minutes after $^{18}F$-FDG injection on Supine position. Then, regional breast spot scan was progressed on prone position using a compensator. Each image was evaluated by physicians blinded to patient's data, and statistical analysis did through SUVs measured in PET-CT images. Results: In 27 of 30 patients, prone position was shown accurate discrimination and diagnostic value, but in another 3 patients had a lesion 1cm below, PET-CT couldn't detect it, unlike MRI. Consequently, prone position distinguished a lesion better than Supine position, because of low degree of metamorphosis by gravity. The SUVs analysis of each position was significant (p value=0.004). Conclusion: In PET-CT of breast cancer, prone position could detect micrometastasis as well as primary lesion, better than supine position. Therefore, this study proposes that any technical change considered morphological feature like prone position can offer adequate and useful diagnostic information, together with complementary quantitative analysis.