Photobiomodulation (PBM) therapy using red- and near-infrared (NIR) light has shown beneficial regenerative effects on cell functionalities and consequently on health applications. Light parameter values, particularly...
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Background: Continuous monitoring of patient health statistics becomes a difficult task in hospitals. Manually, it is difficult to monitor the health of the patients in the hospital continuously. Older and unconscious...
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The adoption of container-based cloud computing services has been prevalent, especially with the introduction of Kubernetes, which enables the automated deployment, scaling, and administration of applications in conta...
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Cardiac arrhythmias pose a significant challenge to health care, requiring accurate and reliable detection methods to enable early diagnosis and treatment. However, traditional ECG beat classification methods often la...
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Despite the disadvantages of labeling cells, fluorescence imaging remains a cornerstone of biological and biomedical imaging. However, quantitative phase imaging (QPI) is increasingly being recognized in the biomedica...
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In this paper, we utilized machine learning (ML) algorithms to optimize Maximum Power Point Tracking (MPPT) in photovoltaic systems. Predicting the optimal voltage is important as, at that voltage, the system gains ma...
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ISBN:
(数字)9798331504847
ISBN:
(纸本)9798331504854
In this paper, we utilized machine learning (ML) algorithms to optimize Maximum Power Point Tracking (MPPT) in photovoltaic systems. Predicting the optimal voltage is important as, at that voltage, the system gains maximum energy output and efficiency. We predicted optimal voltage from irradiance and temperature datasets by applying ML algorithm. We evaluated several ML models, finding Linear Regression to perform best with an MSE (mean square error) of 0.0024 and RMSE (root mean square error) of 0.0489. The proposed solution integrates an ML (linear regression)-driven voltage predictor, a PID controller, and a DC-DC buck-boost converter in a MATLAB simulation environment. In MATLAB Simulink, we observed that the buck-boost converter produced the same voltage as the predicted value across the load.
1 Introduction onMultimodal Learning in Image Processing IP(Image processing),as a classical research domain in computer application technology,has been researched for *** is one of the most important research directi...
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1 Introduction onMultimodal Learning in Image Processing IP(Image processing),as a classical research domain in computer application technology,has been researched for *** is one of the most important research directions in computer vision,which is the basis for many current hotspots such as intelligent transportation/education/industry,*** image processing is the strongest link for AI(artificial intelligence)applying to real world application,it has been a challenging research field with the development of AI,from DNN(deep convolutional network),Attention/LSTM(long-short term memory),to Transformer/Diffusion/Mamba based GAI(generated AI)models,e.g.,GPT and Sora[1].Today,the description ability of single-model feature limits the performance of image *** comprehensive description of the image is required to match the computational performance of current large scale models.
Dysarthria is a motor speech disorder caused by neurological impairments that affect speech intelligibility. Extraction of acoustic features specific to dysarthria is very challenging. The acoustic models along with d...
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This paper presents a new automated unsupervised segmentation system to accurately delineate the pulmonary region in 3D computed tomography (CT) scans. It operates on a multi-dimensional joint probability mo...
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The semantic segmentation of 3D meshes is a critical component of 3D shape analysis, which involves assigning semantic labels to each face of a 3D mesh. Despite its significance, current methods often struggle to capt...
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