• Journal of Periodontal and Implant Science
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• 제53권1호
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• pp.38-53
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• 2023

Purpose: The current Classification of Periodontal and Peri-Implant Diseases and Conditions, published and disseminated in 2018, involves some difficulties and causes diagnostic conflicts due to its criteria, especially for inexperienced clinicians. The aim of this study was to design a decision system based on machine learning algorithms by using clinical measurements and radiographic images in order to determine and facilitate the staging and grading of periodontitis. Methods: In the first part of this study, machine learning models were created using the Python programming language based on clinical data from 144 individuals who presented to the Department of Periodontology, Faculty of Dentistry, Süleyman Demirel University. In the second part, panoramic radiographic images were processed and classification was carried out with deep learning algorithms. Results: Using clinical data, the accuracy of staging with the tree algorithm reached 97.2%, while the random forest and k-nearest neighbor algorithms reached 98.6% accuracy. The best staging accuracy for processing panoramic radiographic images was provided by a hybrid network model algorithm combining the proposed ResNet50 architecture and the support vector machine algorithm. For this, the images were preprocessed, and high success was obtained, with a classification accuracy of 88.2% for staging. However, in general, it was observed that the radiographic images provided a low level of success, in terms of accuracy, for modeling the grading of periodontitis. Conclusions: The machine learning-based decision system presented herein can facilitate periodontal diagnoses despite its current limitations. Further studies are planned to optimize the algorithm and improve the results.

• 한국정보전자통신기술학회논문지
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• 제16권2호
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• pp.97-103
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• 2023

대부분의 정보는 네트워크를 통해 전송하기 때문에 제3자에 의한 도청, 가로채기 등이 발생할 수 있다. 네트워크에서 효과적이고, 안전한 비밀 통신을 위한 적절한 조치가 요구된다. 스테가노그래피는 비밀정보를 다른 매체에 숨기는 것을 제3자가 감지할 수 없도록 조치하는 기술이다. 구조적 취약점으로 인해 암호화와 스테가노그래피 기법에 의해 보호된 정보는 합법적이지 못한 그룹에게 쉽게 노출될 수 있다. 숨기는 방법의 단순성과 예측 가능성이 존재하는 LSB의 한계를 개선하기 위해 의사난수생성기와 재귀 함수에 기반하여 은닉하려는 메시지의 보안성을 향상시키는 기법을 제안한다. 보안성과 혼돈성을 강화하기 위해, 선택된 채널의 상위 비트에서 임의 비트를 선택한 결과와 RS-box에 의해 변형된 정보를 XOR 연산하였다. 제안된 방법의 성능을 확인하기 위해 PSNR과 SSIM을 이용하였다. 기준값에 비해 제안한 방법의 SSIM과 PSNR은 각각 0.9999, 51.366으로 정보를 숨기는데 적절함을 확인하였다.

• 천문학회보
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• 제45권1호
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• pp.38.2-38.2
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• 2020

Horizon Run 5 (HR5) is a cosmological hydrodynamical simulation which captures the properties of the Universe on a Gpc scale while achieving a resolution of 1 kpc. This enormous dynamic range allows us to simultaneously capture the physics of the cosmic web on very large scales and account for the formation and evolution of dwarf galaxies on much smaller scales. Inside the simulation box. we zoom-in on a high-resolution cuboid region with a volume of 1049 × 114 × 114 Mpc3. The subgrid physics chosen to model galaxy formation includes radiative heating/cooling, reionization, star formation, supernova feedback, chemical evolution tracking the enrichment of oxygen and iron, the growth of supermassive black holes and feedback from active galactic nuclei (AGN) in the form of a dual jet-heating mode. For this simulation we implemented a hybrid MPI-OpenMP version of the RAMSES code, specifically targeted for modern many-core many thread parallel architectures. For the post-processing, we extended the Friends-of-Friend (FoF) algorithm and developed a new galaxy finder to analyse the large outputs of HR5. The simulation successfully reproduces many observations, such as the cosmic star formation history, connectivity of galaxy distribution and stellar mass functions. The simulation also indicates that hydrodynamical effects on small scales impact galaxy clustering up to very large scales near and beyond the baryonic acoustic oscillation (BAO) scale. Hence, caution should be taken when using that scale as a cosmic standard ruler: one needs to carefully understand the corresponding biases. The simulation is expected to be an invaluable asset for the interpretation of upcoming deep surveys of the Universe.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.307-307
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• 2022

최근 기후변화와 댐 상류 토지이용 변화 등과 같은 다양한 원인에 의해 댐 유입량의 변동성이 증가하면서 댐 관리 및 운영조작 의사 결정에 어려움이 발생하고 있다. 따라서 이러한 댐 유입량의 변동 특성을 반영하여 댐 유입량을 정확하고 효율적으로 예측할 수 있는 방안이 필요한 실정이다. 머신러닝 기술이 발전하면서 Auto-ML(Automated Machine Learning)이 다양한 분야에서 활용되고 있다. Auto-ML은 데이터 전처리, 최적 알고리즘 선택, 하이퍼파라미터 튜닝, 모델 학습 및 평가 등의 모든 과정을 자동화하는 기술이다. 그러나 아직까지 수문 분야에서 댐 유입량을 예측하기 위한 모델을 개발하는데 있어서 Auto-ML을 활용한 사례는 부족하고, 특히 댐 유입량의 예측 정확성을 확보하기 위해 High-inflow and low-inflow 의 변동 특성을 고려한 하이브리드 결합 방식을 통해 Auto-ML 기반 앙상블 모델을 개발하고 평가한 연구는 없다. 본 연구에서는 Auto-ML의 패키지 중 Auto-sklearn을 통해 홍수기, 비홍수기 유입량 변동 특성을 반영한 하이브리드 앙상블 댐 유입량 예측 모델을 개발하였다. 소양강댐을 대상으로 적용한 결과, 하이브리드 Auto-sklearn 앙상블 모델의 댐 유입량 예측 성능은 R² 0.868, RMSE 66.23 m³/s, MAE 16.45 m³/s로 단일 Auto-sklearn을 통해 구축 된 앙상블 모델보다 전반적으로 우수한 것으로 나타났다. 특히 FDC (Flow Duration Curve)의 저수기, 갈수기 구간에서 두 모델의 유입량 예측 경향은 큰 차이를 보였으며, 하이브리드 Auto-sklearn 모델의 예측 값이 관측 값과 더욱 유사한 것으로 나타났다. 이는 홍수기, 비홍수기 구간에 대한 앙상블 모델이 독립적으로 구축되는 과정에서 각 모델에 대한 하이퍼파라미터가 최적화되었기 때문이라 판단된다. 향후 본 연구의 방법론은 보다 정확한 댐 유입량 예측 자료를 생성하기 위한 방안 수립뿐만 아니라 다양한 분야의 불균형한 데이터셋을 이용한 앙상블 모델을 구축하는데도 유용하게 활용될 수 있을 것으로 사료된다.

• International Journal of Computer Science & Network Security
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• 제24권1호
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• pp.226-234
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• 2024

The frequent handover problem and playing ping-pong effects in 5G (5th Generation) ultra-dense networking cannot be effectively resolved by the conventional handover decision methods, which rely on the handover thresholds and measurement reports. For instance, millimetre-wave LANs, broadband remote association techniques, and 5G/6G organizations are instances of group of people yet to come frameworks that request greater security, lower idleness, and dependable principles and correspondence limit. One of the critical parts of 5G and 6G innovation is believed to be successful blockage the board. With further developed help quality, it empowers administrator to run many systems administration recreations on a solitary association. To guarantee load adjusting, forestall network cut disappointment, and give substitute cuts in case of blockage or cut frustration, a modern pursuing choices framework to deal with showing up network information is require. Our goal is to balance the strain on BSs while optimizing the value of the information that is transferred from satellites to BSs. Nevertheless, due to their irregular flight characteristic, some satellites frequently cannot establish a connection with Base Stations (BSs), which further complicates the joint satellite-BS connection and channel allocation. SF redistribution techniques based on Deep Reinforcement Learning (DRL) have been devised, taking into account the randomness of the data received by the terminal. In order to predict the best capacity improvements in the wireless instruments of 5G and 6G IoT networks, a hybrid algorithm for deep learning is being used in this study. To control the level of congestion within a 5G/6G network, the suggested approach is put into effect to a training set. With 0.933 accuracy and 0.067 miss rate, the suggested method produced encouraging results.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.60-66
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• 2024

Host's data during transmission. Data tempering results in loss of host's sensitive information, which includes number of VM, storage availability, and other information. In the distributed cloud environment, each server (computing server (CS)) configured with Local Resource Monitors (LRMs) which runs independently and performs Virtual Machine (VM) migrations to nearby servers. Approaches like predictive VM migration [21] [22] by each server considering nearby server's CPU usage, roatative decision making capacity [21] among the servers in distributed cloud environment has been proposed. This approaches usage underlying server's computing power for predicting own server's future resource utilization and nearby server's resource usage computation. It results in running VM and its running application to remain in waiting state for computing power. In order to reduce this, a decentralized decision making hybrid model for VM migration need to be proposed where servers in decentralized cloud receives, future resource usage by analytical computing system and takes decision for migrating VM to its neighbor servers. Host's in the decentralized cloud shares, their detail with peer servers after fixed interval, this results in chance to tempering messages that would be exchanged in between HC and CH. At the same time, it reduces chance of over utilization of peer servers, caused due to compromised host. This paper discusses, an roatative decisive (RD) approach for VM migration among peer computing servers (CS) in decentralized cloud environment, preserving confidentiality and integrity of the host's data. Experimental result shows that, the proposed predictive VM migration approach reduces extra VM migration caused due over utilization of identified servers and reduces number of active servers in greater extent, and ensures confidentiality and integrity of peer host's data.

• Korean Journal of Radiology
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• 제22권1호
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• pp.97-105
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• 2021

Objective: The present study aimed to investigate the association between myocardial blood flow (MBF) quantified by dynamic CT myocardial perfusion imaging (CT-MPI) and the increments in heart rate (HR) after stress in patients without obstructive coronary artery disease. Materials and Methods: We retrospectively included 204 subjects who underwent both dynamic CT-MPI and coronary CT angiography (CCTA). Patients with more than minimal coronary stenosis (diameter ≥ 25%), history of myocardial infarction/revascularization, cardiomyopathy, and microvascular dysfunction were excluded. Global MBF at stress was measured using hybrid deconvolution and maximum slope model. Furthermore, the HR increments after stress were recorded. Results: The median radiation dose of dynamic CT-MPI plus CCTA was 5.5 (4.5-6.8) mSv. The median global MBF of all subjects was 156.4 (139.8-180.4) mL/100 mL/min. In subjects with HR increment between 10 to 19 beats per minute (bpm), the global MBF was significantly lower than that of subjects with increment between 20 to 29 bpm (153.3 mL/100 mL/min vs. 171.3 mL/100 mL/min, p = 0.027). This difference became insignificant when the HR increment further increased to ≥ 30 bpm. Conclusion: The global MBF value was associated with the extent of increase in HR after stress. Significantly higher global MBF was seen in subjects with HR increment of ≥ 20 bpm.

• 대기
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• 제34권2호
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• pp.153-176
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• 2024

At 0843 UTC 30 May 2021, a commercial aircraft encountered severe turbulence at z = 11.5 km associated with the rapid development of Mesoscale Convective System (MCS) in the Gyeonggi Bay of Korea. To investigate the generation mechanisms of Near-Cloud Turbulence (NCT) near the MCS, Weather Research and Forecasting model was used to reproduce key features at multiple-scales with four nested domains (the finest ∆x = 0.2 km) and 112 hybrid vertical layers. Simulated subgrid-scale turbulent kinetic energy (SGS TKE) was located in three different regions of the MCS. First, the simulated NCT with non-zero SGS TKE at z = 11.5 km at 0835 UTC was collocated with the reported NCT. Cloud-induced flow deformation and entrainment process on the downstream of the overshooting top triggered convective instability and subsequent SGS TKE. Second, at z = 16.5 km at 0820 UTC, the localized SGS TKE was found 4 km above the overshooting cloud top. It was attributed to breaking down of vertically propagating convectively-induced gravity wave at background critical level. Lastly, SGS TKE was simulated at z = 11.5 km at 0930 UTC during the dissipating stage of MCS. Upper-level anticyclonic outflow of MCS intensified the environmental westerlies, developing strong vertical wind shear on the northeastern quadrant of the dissipating MCS. Three different generation mechanisms suggest the avoidance guidance for the possible NCT events near the entire period of the MCS in the heavy air traffic area around Incheon International Airport in Korea.

• IMMUNE NETWORK
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• 제21권3호
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• pp.23.1-23.16
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• 2021

Chemokines are key factors that influence the migration and maintenance of relevant immune cells into an infected tissue or a tumor microenvironment. Therefore, it is believed that the controlled administration of chemokines in the tumor microenvironment may be an effective immunotherapy against cancer. Previous studies have shown that CCL3, also known as macrophage inflammatory protein 1-alpha, facilitates the recruitment of dendritic cells (DCs) for the presentation of tumor Ags and promotes T cell activation. Here, we investigated the role of CCL3 in regulating the tumor microenvironment using a syngeneic mouse tumor model. We observed that MC38 tumors overexpressing CCL3 (CCL3-OE) showed rapid regression compared with the wild type MC38 tumors. Additionally, these CCL3-OE tumors showed an increase in the proliferative and functional tumor-infiltrating T cells. Furthermore, PD-1 immune checkpoint blockade accelerated tumor regression in the CCL3-OE tumor microenvironment. Next, we generated a modified CCL3 protein for pre-clinical use by fusing recombinant CCL3 (rCCL3) with a non-cytolytic hybrid Fc (HyFc). Administering a controlled dose of rCCL3-HyFc via subcutaneous injections near tumors was effective in tumor regression and improved survival along with activated myeloid cells and augmented T cell responses. Furthermore, combination therapy of rCCL3-HyFc with PD-1 blockade exhibited prominent effect to tumor regression. Collectively, our findings demonstrate that appropriate concentrations of CCL3 in the tumor microenvironment would be an effective adjuvant to promote anti-tumor immune responses, and suggest that administering a long-lasting form of CCL3 in combination with PD-1 blockers can have clinical applications in cancer immunotherapy.

• 한국간담췌외과학회지
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• 제28권1호
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• pp.14-24
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• 2024

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.