The optical Vernier effect has garnered significant research attention and found widespread applications in enhancing the measurement sensitivity of optical fiber interferometric sensors. Typically, Vernier sensor int...
详细信息
The optical Vernier effect has garnered significant research attention and found widespread applications in enhancing the measurement sensitivity of optical fiber interferometric sensors. Typically, Vernier sensor interrogation involves measuring its optical spectrum across a wide wavelength range using a high-precision spectrometer. This process is further complicated by the intricate signal processing required for accurately extracting the Vernier envelope, which can inadvertently introduce errors that compromise sensing performance.
Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial...
详细信息
Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma *** study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation ***-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention *** powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range *** doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor *** rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 ***,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse ***,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset *** features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival *** model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing *** ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient ***,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.
Hybrid energy systems are increasingly critical in addressing the growing demand for sustainable and efficient power solutions. In this paper, a novel converter for a hybrid energy system with the capability to integr...
详细信息
Hybrid energy systems are increasingly critical in addressing the growing demand for sustainable and efficient power solutions. In this paper, a novel converter for a hybrid energy system with the capability to integrate two power sources of different characteristics, namely AC and DC is proposed. This paper aims to enhance the efficiency of hybrid energy systems that involve multiple power conversions and necessitate multiple power converters. The pivotal aspect of the proposed converter lies in its ability to connect photovoltaic (PV) and grid power sources. Diverging from conventional setups, this converter eliminates the need for a diode rectifier, streamlining the power conversion process and mitigating complexities associated with multiple stage conversions between DC and AC power stages. The proposed converter shows versatility by operating solely on grid power, solar power, or a combination of grid and solar power, and it is able to change its operating mode by adapting dynamically to varying power availability. The proposed converter with proposed flat-topped waveform has 5.69% voltage THD, which is 87.50% less than conventional system voltage waveform. Various regression models, such as trees, Gaussian processes regression (GPR), ensembles of trees, support vector machine, and neural network were trained and tested to forecast the PV power. Among these, the squared exponential GPR model outperforms other regression models, exhibiting the least root mean square error of 0.16745 and mean square error of 0.02841. The paper further analyses the behavior of the proposed converter in water pumping systems used for residential, commercial, and irrigation applications. The operating modes of the converter are determined by machine learning-based power predictions, influencing transitions between grid and solar power as well as the concurrent utilization of both sources. This research provides insights into the transient behaviors during these operational mode chan
In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the tran...
详细信息
In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.
Although brushless direct current motor (BLDCM) drives are gaining popularity in commercial and industrial applications, a few significant problems and research subjects still have not been conquered and need to be ad...
详细信息
We investigate feature selection problem for generic machine learning models. We introduce a novel framework that selects features considering the outcomes of the model. Our framework introduces a novel feature maskin...
详细信息
We investigate feature selection problem for generic machine learning models. We introduce a novel framework that selects features considering the outcomes of the model. Our framework introduces a novel feature masking approach to eliminate the features during the selection process, instead of completely removing them from the dataset. This allows us to use the same machine learning model during feature selection, unlike other feature selection methods where we need to train the machine learning model again as the dataset has different dimensions on each iteration. We obtain the mask operator using the predictions of the machine learning model, which offers a comprehensive view on the subsets of the features essential for the predictive performance of the model. A variety of approaches exist in the feature selection literature. However, to our knowledge, no study has introduced a training-free framework for a generic machine learning model to select features while considering the importance of the feature subsets as a whole, instead of focusing on the individual features. We demonstrate significant performance improvements on the real-life datasets under different settings using LightGBM and multilayer perceptron as our machine learning models. Our results show that our methods outperform traditional feature selection techniques. Specifically, in experiments with the residential building dataset, our general binary mask optimization algorithm has reduced the mean squared error by up to 49% compared to conventional methods, achieving a mean squared error of 0.0044. The high performance of our general binary mask optimization algorithm stems from its feature masking approach to select features and its flexibility in the number of selected features. The algorithm selects features based on the validation performance of the machine learning model. Hence, the number of selected features is not predetermined and adjusts dynamically to the dataset. Additionally, we openly s
The dual observation of periodic and aperiodic interferences cannot be balanced by traditional linear extended state observer (T-LESO). Therefore, the speed fluctuations caused by uncertain periodic and aperiodic inte...
详细信息
Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic ***,due to sensor limitations and imperfections during the image acq...
详细信息
Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic ***,due to sensor limitations and imperfections during the image acquisition and transmission phases,noise is introduced into the acquired image,which can have a negative impact on downstream analyses such as classification,target tracking,and spectral *** in hyperspectral images(HSI)is modelled as a combination from several sources,including Gaussian/impulse noise,stripes,and *** HSI restoration method for such a mixed noise model is ***,a joint optimisation framework is proposed for recovering hyperspectral data corrupted by mixed Gaussian-impulse noise by estimating both the clean data as well as the sparse/impulse noise ***,a hyper-Laplacian prior is used along both the spatial and spectral dimensions to express sparsity in clean image ***,to model the sparse nature of impulse noise,anℓ_(1)−norm over the impulse noise gradient is *** the proposed methodology employs two distinct priors,the authors refer to it as the hyperspectral dual prior(HySpDualP)*** the best of authors'knowledge,this joint optimisation framework is the first attempt in this *** handle the non-smooth and nonconvex nature of the generalℓ_(p)−norm-based regularisation term,a generalised shrinkage/thresholding(GST)solver is ***,an efficient split-Bregman approach is used to solve the resulting optimisation *** results on synthetic data and real HSI datacube obtained from hyperspectral sensors demonstrate that the authors’proposed model outperforms state-of-the-art methods,both visually and in terms of various image quality assessment metrics.
Objective: The purpose of this paper was to use Machine Learning (ML) techniques to extract facial features from images. Accurate face detection and recognition has long been a problem in computer vision. According to...
详细信息
Weather variability significantly impacts crop yield, posing challenges for large-scale agricultural operations. This study introduces a deep learning-based approach to enhance crop yield prediction accuracy. A Multi-...
详细信息
暂无评论