• Biomedical Engineering Letters
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• 제8권4호
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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.

• 방송공학회논문지
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• 제23권6호
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• pp.780-789
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• 2018

전 세계적으로 총기 사고는 인적이 드문 장소뿐만 아니라 사람들이 많이 모여 있는 공공장소에서도 빈번하게 일어난다. 특히, 권총과 같은 소형 총기 사고의 빈도수가 매우 높다. 그러므로 사람에 비해 상대적으로 매우 작은 크기의 객체인 권총을 가진 사람을 탐지하는 것은 사고의 피해를 최소화하는데 핵심적이다. '권총 든 사람'을 탐지하는 연구가 수행되고 있지만, 사람보다 권총은 상대적으로 크기가 작기 때문에 단일 객체만을 탐지하는 기존 객체 탐지 방법으로 '권총 든 사람'을 탐지하면 오류 발생 빈도수가 매우 높다. 이러한 문제점을 해결하기 위하여 권총으로 무장한 사람을 탐지하는 방법으로 APDA(Armed Person Detection Algorithm)를 제안한다. APDA는 입력 영상에서 합성곱신경망(Convolutional Neural Network, CNN) 기반의 인체 특징점 탐지 모델과 객체 탐지 모델을 병행하여 획득한 양 손목과 권총의 위치를 후처리 작업에서 이용하여 '권총 든 사람'을 탐지한다. APDA는 기존 방식보다 객관적 평가에서 재현율이 46.3% 향상되었고, 정밀도는 14.04% 향상되었다.

• 헤리티지:역사와 과학
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• 제36권
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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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• 제22권4호
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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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• 제52권3호
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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.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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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.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2021년도 추계학술대회
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• pp.290-293
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• 2021

본 논문에서는 공부하는 사용자의 상황을 감지하여, 학습의욕을 고취시키고 집중력 향상을 도와주기 위한 학습능률 확인 시스템을 구현하고자 한다. 이를 위해 실시간 카메라를 통해 사용자의 얼굴이나 몸의 움직임을 추출하여 학습 태도, 집중력에 대한 데이터를 측정한다. 실시간 임베디드 시스템 구현을 위해 Jetson 보드를 사용하였으며, 영상인식을 위한 CNN(Convolution Neural Network)를 구현하였다. CNN 을 이용해 대상의 특징 부분을 검출한 후 움직임 검파를 수행한다. 캡처한 영상을 PYQT5 로 작성된 GUI 에서 영상을 보여주며, 각각 방해되는 행동을 했을 때 푸시메시지를 보내며 데이터를 수집한다. 또한 GUI 로 만든 메인 화면에서 각각의 기능들을 실행 가능하며, 수집한 데이터를 산출해주는 통계그래프와 작업관리 목록, 화이트 노이즈 등의 기능을 수행한다. 구축된 학습능률 확인 시스템을 통해 대상의 데이터를 수집 및 분석을 비롯한 다양한 기능을 사용자에게 제공하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권10호
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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.

• 한국컴퓨터정보학회논문지
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• 제29권5호
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• pp.39-46
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• 2024

본 논문에서는 독거노인의 건강상태를 실시간 모니터링이 가능한 저가형 웨어러블 디바이스를 제안한다. 고령화가 가속되면서 노령 인구가 급증하고 있으며 독거노인의 사회적 고립으로 인해 신체, 정신적 어려움이 발생하고 고독사하는 노인들의 수가 증가해 사회적 문제가 되고 있다. 또한, 독거노인 관리를 위한 정부 인력은 한정되어 독거노인의 안전과 복지를 보장하는 데 현실적인 어려움이 있다. 이에 본 논문에서는 독거노인의 생체정보를 모니터링할 수 있는 복대형 웨어러블 기기를 제안한다. 제안하는 기기는 복대에 내장된 아두이노 기반의 센서를 통해 근전도, 심전도 및 체온 정보를 원격 서버에 전송한다. 전송된 정보는 실시간으로 웹 환경에서 모니터링 할 수 있으며, 적은 관리 인력으로 다수의 대상자를 원격 모니터링할 수 있는 특징을 가진다. 또한, 독거노인의 거주 환경에서 생체 데이터를 수집하고 일반 복대를 이용한 저가형 모델로 보급의 효율성을 가진다. 연구의 결과물은 독거노인의 고독사 상황을 사전에 감지할 뿐만 아니라 일상생활에서 발생할 수 있는 위험 상황에 즉각적인 대처를 통해 독거노인의 안전 및 복지 수준 향상에 기여할 것으로 기대된다.

• 핵의학기술
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• 제12권1호
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• pp.44-48
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• 2008

유방암은 FDG PET-CT에서 예민도는 80~96%, 특이도는 75~95% 정도이며, 유방촬영(mammography) 소견상 판단이 어려운 수진자에게 매우 유용하며, 검진으로서의 조기 암 검출을 충분히 기대할 수 있다. PET-CT에서 대부분의 검사는 앙와위(supine position)으로 검사되어 전신검사(whole body scan)에서 원형으로부터 변형된 유방영상을 획득된다. 반면에 보정구를 이용하여 복와위(prone position)으로 영상을 획득할 경우, 유방을 중력에 의존하여 보다 원형에 가깝게 표현함으로써 영상의 진단가치를 높일 수 있기에 그 유용성을 평가하였다. 유방암으로 의심되거나 확진 된 여성 환자 30명을 대상으로 하였으며, $^{18}F$-FDG 주사한 뒤 60분 후 supine position으로 whole body PET-CT scan 시행 후, 보정구를 이용하여 prone position으로 병변에 대한 추가검사(lesional scan)를 시행하였다. 각각 획득된 영상은 핵의학과 의사로부터 blind test를 하였고, PET-CT 영상의 SUV를 측정하여 분석하였다. 30명 중 27명의 환자에게서 prone자세가 진단 용이성과 정확한 판별률이 높았으며, 나머지 3명의 경우는 병소가 1 cm 이하의 크기로 인하여, MRI상에는 나타났지만 PET-CT에서는 진단에 의미있는 관찰을 할 수 없었다. supine position과 비교하여 prone position은 구조물의 중력에 의한 변형수치가 낮아, 병변의 구별이 곤란한 경우 명확하게 감별되었다. 각 자세에서 SUV분석은 유의수준 0.004로 유의하였다. Supine position을 통한 PET-CT 전신검사 후의 보정구를 이용한 prone position로 병변에 대한 lesional scan은 두 자세의 비교가 모두 가능하였으며, 경우에 따라 결정적으로 진단에 영향을 주었다. 두 가지 자세의 영상비교에 있어 prone position이 원발성(primary) 병변의 진단은 물론, 미세전이(micrometastasis)의 검출이 가능하였다. 이러한 prone position을 기반으로 구조적인 문제를 정량적 분석과 함께 극복한다면 병변의 진단에 있어 보다 유용한 진단적 정보를 얻을 수 있으리라 사료된다.