• 구강회복응용과학지
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• 제38권4호
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• pp.196-203
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• 2022

목적: 본 연구는 79종의 치과 임플란트에 대해 딥러닝을 이용한 식별 모델의 정확도와 임상적 유용성을 평가하는 것을 목적으로 하였다. 연구 재료 및 방법: 2001년부터 2020년까지 30개 치과에서 임플란트 치료를 받은 환자들의 파노라마 방사선 사진에서 총 45396개의 임플란트 고정체 이미지를 수집했다. 수집된 임플란트 이미지는 18개 제조사의 79개 유형이었다. 모델 학습을 위해 EfficientNet 및 Meta Pseudo Labels 알고리즘이 사용되었다. EfficientNet은 EfficientNet-B0 및 EfficientNet-B4가 하위 모델로 사용되었으며, Meta Pseudo Labels는 확장 계수에 따라 두 가지 모델을 적용했다. EfficientNet에 대해 Top 1 정확도를 측정하고 Meta Pseudo Labels에 대해 Top 1 및 Top 5 정확도를 측정하였다. 결과: EfficientNet-B0 및 EfficientNet-B4는 89.4의 Top 1 정확도를 보였다. Meta Pseudo Labels 1은 87.96의 Top 1 정확도를 보였고, 확장 계수가 증가한 Meta Pseudo Labels 2는 88.35를 나타냈다. Top 5 정확도에서 Meta Pseudo Labels 1의 점수는 97.90으로 Meta Pseudo Labels 2의 97.79보다 0.11% 높았다. 결론: 본 연구에서 임플란트 식별에 사용된 4가지 딥러닝 알고리즘은 모두 90%에 가까운 정확도를 보였다. 임플란트 식별을 위한 딥러닝의 임상적 적용 가능성을 높이려면 더 많은 데이터를 수집하고 임플란트에 적합한 미세 조정 알고리즘의 개발이 필요하다.

• 대한의용생체공학회:의공학회지
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• 제42권4호
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

• Journal of Information Processing Systems
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• 제7권2호
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• pp.271-286
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• 2011

In this paper, we propose efficient algorithms for parallel prefix computation and sorting on a recursive dual-net. The recursive dual-net $RDN^k$(B) for k > 0 has $(2n_o)^{2K}/2$ nodes and $d_0$ + k links per node, where $n_0$ and $d_0$ are the number of nod es and the node-degree of the base-network B, respectively. Assume that each node holds one data item, the communication and computation time complexities of the algorithm for parallel prefix computation on $RDN^k$(B), k > 0, are $2^{k+1}-2+2^kT_{comm}(0)$ and $2^{k+1}-2+2^kT_{comp}(0)$, respectively, where $T_{comm}(0)$ and $T_{comp}(0)$ are the communication and computation time complexities of the algorithm for parallel prefix computation on the base-network B, respectively. The algorithm for parallel sorting on $RDN^k$(B) is restricted on B = $Q_m$ where $Q_m$ is an m-cube. Assume that each node holds a single data item, the sorting algorithm runs in $O((m2^k)^2)$ computation steps and $O((km2^k)^2)$ communication steps, respectively.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.406-412
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• 2022

Scene classification of very high-resolution (VHR) imagery can attribute semantics to land cover in a variety of domains. Real-world application requirements have not been addressed by conventional techniques for remote sensing image classification. Recent research has demonstrated that deep convolutional neural networks (CNNs) are effective at extracting features due to their strong feature extraction capabilities. In order to improve classification performance, these approaches rely primarily on semantic information. Since the abstract and global semantic information makes it difficult for the network to correctly classify scene images with similar structures and high interclass similarity, it achieves a low classification accuracy. We propose a VHR remote sensing image classification model that uses extracts the global feature from the original VHR image using an EfficientNet-V2L CNN pre-trained to detect similar classes. The image is then classified using a multilayer perceptron (MLP). This method was evaluated using two benchmark remote sensing datasets: the 21-class UC Merced, and the 38-class PatternNet. As compared to other state-of-the-art models, the proposed model significantly improves performance.

• 한국CDE학회논문집
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• 제9권3호
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• pp.203-211
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• 2004

In this paper, we present a method of extracting a regular 2-manifold triangular net from a triangular net including degenerate and self-intersected triangles. This method can be applied to obtaining an offset model without degenerate and self-intersected triangles. Then this offset model can be used to generate CL curves and extract machining features for CAPP The robust and efficient algorithm to detect valid triangles by growing regions from an initial valid triangle is presented. The main advantage of the algorithm is that detection of valid triangles is performed only in valid regions and their adjacent selfintersections, and omitted in the rest regions (invalid regions). This advantage increases robustness of the algorithm. As well as a k-d tree bucketing method is used to detect self-intersections efficiently.

• Journal of electromagnetic engineering and science
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• 제6권2호
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• pp.103-109
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• 2006

This paper suggests an efficient and comprehensive algorithm of the protection ratio derivation and illustrates some calculated results applicable to the initial planning of frequency coordination in the fixed wireless access networks. The net filter discrimination associated with Tx spectrum mask and overall Rx filter characteristic has been also examined to show the effect of the adjacent channel interference. The calculations for co-channel and adjacent channel protection ratios are performed for the current microwave frequency band of 6.7 GHz including Tx spectrum mask and Rx filter response. According to results, fade margin and co-channel protection ratio reveal 41.4 and 75.2 dB, respectively, for 64-QAM and 60 km at BER $10^{-6}$. It is shown that the net filter discrimination with 40 MHz channel bandwidth provides 28.9 dB at the first adjacent channel, which yields 46.3 dB of adjacent channel protection ratio. In addition, the protection ratio of 38 GHz radio relay system is also reviewed for millimeter wave band applications. The proposed method gives some advantages of an easy and systematic extension for protection ratio calculation and is also applied to frequency coordination in fixed millimeter wave networks.

• ETRI Journal
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• 제46권2호
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• pp.263-276
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• 2024

Different diseases occur in the brain. For instance, hereditary and progressive diseases affect and degenerate the white matter. Although addressing, diagnosing, and treating complex abnormalities in the brain is challenging, different strategies have been presented with significant advances in medical research. With state-of-art developments in artificial intelligence, new techniques are being applied to brain magnetic resonance images. Deep learning has been recently used for the segmentation and classification of brain images. In this study, we classified normal and pathological brain images using pretrained deep models through transfer learning. The EfficientNet-B5 model reached the highest accuracy of 98.39% on real data, 91.96% on augmented data, and 100% on pathological data. To verify the reliability of the model, fivefold cross-validation and a two-tier cross-test were applied. The results suggest that the proposed method performs reasonably on the classification of brain magnetic resonance images.

• 한국전자파학회:학술대회논문집
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• 한국전자파학회 2005년도 종합학술발표회 논문집 Vol.15 No.1
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• pp.125-130
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• 2005

This paper suggests an efficient method of protection ratio calculation and shows some calculated results applicable to frequency coordination in microwave relay system networks, and the net filter discrimination (NFD) associated with Tx spectrum mask and overall Rx filter characteristics has been examined to obtain the adjacent channel protection ratio. The protection ratio comprises several factors such as C/N of modulation scheme, noise-to-interference ratio, multiple interference allowance, fade margins of multi-path and rain attenuation, and NFD. According to computed results for 6.7 GHz, 64-QAM, and 60 km at BER $10^{-6}$, fade margin and co-channel protection ratio are 41.1 and 75.2 dB, respectively, In addition, NFD for channel bandwidth of 40 MHz reveals 28.9 dB at the first adjacent channel, which results in adjacent channel protection ratio of 46.3 dB. The proposed method provides some merits of an easy calculation, systematic extension, and applying the same concept to frequency coordination in millimeter wave relay system networks.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.655-657
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• 2021

인공지능 알고리즘을 이용한 유방암의 조기진단에 관련된 연구는 최근들어 활발하게 진행되고 있으나, 사용자의 목적에 맞는 처리속도 및 정확도 등에 다양한 한계점을 보인다. 이러한 문제를 해결하기 위해, 본 논문에서는 ImageNet에서 학습된 ResNet 모델을 현미경 기반 암세포 이미지에서 활용이 가능한 다단계 전이 학습을 제안하고, 이를 다시 전이 학습하여 초음파 유방암 영상을 양성 및 악성으로 분류하는 실험을 진행하였다. 제안된 다단계 전이 학습 알고리즘은 초음파 유방암 영상을 분류하였을 때 96% 이상의 정확도를 보였으며, 향후 암 세포주 및 실시간 영상처리 등의 추가를 통해 보다 높은 활용도와 정확도를 보일 것으로 기대한다.

• 대한전자공학회논문지TC
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• 제43권1호
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• pp.133-142
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• 2006

본 논문에서는 고정 무선중계 시스템의 주파수 조정을 위한 효율적인 보호비 산출 방법을 제안하고 계산된 결과를 제시한다. 제안된 보호비는 변조방식의 C/N, 잡음 대 간섭의 비(N/I), 다중간섭허용, 다중경로 및 강우감쇠의 페이드 마진, 통합필터 변별도의 함수로 표현된다. 실제 고정 무선중계 주파수에 대해 페이드 마진, 변조방식, 거리 및 간섭을 고려한 보호비를 산출하였으며, 계산 결과에 의하면 BER 10-6 기준으로 6.2GHz, 64-QAM, 거리 60km에 대해 페이드 마진과 동일채널의 보호비는 각각 41.1 dB 와 74.9 dB가 됨을 알 수 있었다. 제안된 방법은 보호비 정정인자를 통해 다양한 변수에 대해 보호비를 체계적 그리고 용이하게 구할 수 있으며, 또한 동일한 개념을 밀리미터파 대역의 무선중계 시스템 주파수 조정을 위한 보호비 산출에도 적용할 수 있는 장점을 지닌다.