Secure multi-keyword search for outsourced cloud data has gained popularity, especially for scenarios involving multiple data owners. This work proposes a method for secure multi-keyword searches across encrypted clou...
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One of the most affordable methods for improving the performance of radial distribution networks is through the deployment of capacitors. However, the optimal allocation of capacitors is a serious issue that must be r...
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ISBN:
(纸本)9781665474450
One of the most affordable methods for improving the performance of radial distribution networks is through the deployment of capacitors. However, the optimal allocation of capacitors is a serious issue that must be resolved. This paper proposed a novel dingo optimization algorithm (DOA) to tackle the problem of the allocation of capacitors. A single objective function, subjected to several equality and inequality constraints, was formulated and solved using the proposed DOA for capacitor allocation. The performance of the network was assessed using voltage profile, real power, and reactive power losses as the performance metrics. The results indicated a significant improvement in the performance of the network as real power as well as reactive power losses were greatly reduced by 34.44 and 35.20%, respectively, and the overall network voltage profile was enhanced. Thus, DOA proved effective in the allocation of capacitors in radial distribution networks.
Optimal operation modeling plays an important role in wave rotor refrigeration process;however, considering covariance among multiple variables and high nonlinearity in wave rotor refrigeration process, it becomes mor...
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Optimal operation modeling plays an important role in wave rotor refrigeration process;however, considering covariance among multiple variables and high nonlinearity in wave rotor refrigeration process, it becomes more and more difficult to establish an accurate operation modeling using first-principles methods. This study pro-posed a novel modeling algorithm for the temperature parameter of the wave rotor refrigeration process based on elastic net and dingooptimization deep belief network (Enet-DOA-DBN). Firstly, to determine the correlation between the input variables and reduce the dimension of the input variables, the elastic net (Enet) algorithm is used to select the input variables that are irrelevant to the temperature parameter of the wave rotor refrigeration process. In this way, the covariance between multiple variables is eliminated and the model structure is simplified. Secondly, in order to improve the generalization of the temperature parameter model, a deep belief network (DBN) deep learning is proposed for modeling the temperature parameter of the wave rotor refrigeration process. Considering that the numerous hyperparameters of DBN algorithm have a great impact on the training and prediction results, the hyperparameters are optimized by the dingo optimization algorithm (DOA). The proposed Enet-DOA-DBN algorithm is validated by simulation using the benchmark data sets and the wave rotor refrigeration industrial process data sets. The simulation results show that the proposed Enet-DOA-DBN algo-rithm has good generalization ability, meanwhile it can effectively implement variable selection and simplify the model structure.
Macular edema (ME) is a primary cause of blindness and loss of vision in people with visual retinal disorders. Deep learning (DL) algorithms benefit significantly from extensive and diverse datasets during training, b...
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Macular edema (ME) is a primary cause of blindness and loss of vision in people with visual retinal disorders. Deep learning (DL) algorithms benefit significantly from extensive and diverse datasets during training, but obtaining a sufficient amount of labeled data for macular edema is challenging. An insufficient dataset may result in overfitting, reducing the network's capability to generalize the diverse cases. An optical coherence tomography (OCT) framework is utilized to solve the problem of diabetic macular edema (DME). Due to the complex nature of this condition and the saturation of healthcare in affluent nations, it is among the main factors that induce blindness. In this paper, a novel DIO-RegNet was introduced for the early recognition of the ME using DL techniques. The input OCT images are pre-processed by a Gaussian adaptive bilateral filter to enhance the image quality. The noise-free images are fed to the Modified DeepLabV3 + to segment the Macular area in retinal images. Then, the segmented Macular region is fed into deep learning-based RegNet for extracting the structural feature. Finally, the dingooptimization (DIO) algorithm is applied for the feature selection and classify the cases of macular edema. The proposed DIO-RegNet achieves a detection accuracy of 99.44 % for macular edema. Compared to Dense Net, Alex Net, and ResNet, RegNet achieves an accuracy rate of 96.72 %, 92.89 %, and 97.11 %, respectively. The DIO-RegNet improves overall accuracy by 2.44 %, 5.04 %, and 4.34 % over CNN, faster R-CNN, and VGG-16 CNN, respectively.
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