Medical images are valuable for clinical diagnosis and decision making. Image modality is an important primary step, as it is capable of aiding clinicians to access required medical image in retrieval systems. Traditional methods of modality classification are dependent on the choice of hand-crafted features and demand a clear awareness of prior domain knowledge. The feature learning approach may detect efficiently visual characteristics of different modalities, but it is limited to the number of training datasets. To overcome the absence of labeled data, on the one hand, we take deep convolutional neural networks (VGGNet, ResNet) with different depths pre-trained on ImageNet, fix most of the earlier layers to reserve generic features of natural images, and only train their higher-level portion on ImageCLEF to learn domain-specific features of medical figures. Then, we train from scratch deep CNNs with only six weight layers to capture more domain-specific features. On the other hand, we employ two data augmentation methods to help CNNs to give the full scope to their potential characterizing image modality features. The final prediction is given by our voting system based on the outputs of three CNNs. After evaluating our proposed model on the subfigure classification task in ImageCLEF2015 and ImageCLEF2016, we obtain new, state-of-the-art results—76.87% in ImageCLEF2015 and 87.37% in ImageCLEF2016—which imply that CNNs, based on our proposed transfer learning methods and data augmentation skills, can identify more efficiently modalities of medical images.

The heat loss and cooling modes of a permanent magnet synchronous motor (PMSM) directly affect the its temperature rise. The accurate evaluation and prediction of stator winding temperature is of great significance to the safety and reliability of PMSMs. In order to study the influencing factors of stator winding temperature and prevent motor insulation ageing, insulation burning, permanent magnet demagnetization and other faults caused by high stator winding temperature, we propose a computer model for PMSM temperature prediction. Ambient temperature, coolant temperature, direct-axis voltage, quadrature-axis voltage, motor speed, torque, direct-axis current, quadrature-axis current, permanent magnet surface temperature, stator yoke temperature, and stator tooth temperature are taken as the input, while the stator winding temperature is taken as the output. A deep neural network (DNN) model for PMSM temperature prediction was constructed. The experimental results showed the prediction error of the model (MAE) was 0.1515, the RMSE was 0.2368, the goodness of fit (R²) was 0.9439 and the goodness of fit between the predicted data and the measured data was high. Through comparative experiments, the prediction accuracy of the DNN model proposed in this paper was determined to be better than other models. This model can effectively predict the temperature change of stator winding, provide technical support to temperature early warning systems and ensure safe operation of PMSMs.

As a new insulating material, nanoscale thin dielectric films have been widely used in variable frequency motors, electron devices and other fields. Dielectric loss is a key performance parameter of this material. Currently, studies on the dielectric properties of polymer matrix nanocomposite films are based mostly on experiments that are costly and time-consuming. In this article, an integrated method that combines experiment and ensemble learning is applied. In situ polymerization is employed to prepare 32 polyimide matrix nanocomposite films that have different weight ratios, sizes and thicknesses and that are doped with different inorganic nanoscale particles. The dielectric losses of these 32 prepared films are measured as well. Ten multilayer perceptrons are integrated into a random forest and multilayer perceptron (RF-MLP) model using the random forest (RF) method. As shown in the experimental results, under the 10-fold cross validation, the correlation coefficient, the mean absolute error, the root mean squared error and the root relative squared error of the RF-MLP model are 0.9447, 0.0007, 0.0013 and 32.0972%, respectively. The deviation between the predicted value and the measured value is small. The RF-MLP model has a better prediction performance than other single models, such as linear regression, backpropagation neural network, radial basis function neural network, support vector regression and multilayer perceptron as well as other ensemble learning methods, such as bagging, boosting and RF-decision stump. Therefore, the RF-MLP model is a fast and reliable method applicable to predicting the properties of the new nano-dielectric material and other materials.

The starting and synchronization process of high-voltage permanent magnetic synchronous motors (HVPMSM) is a complex electromechanical transient problem. This paper proposes a multi-objective five-dimensional optimization method for starting characteristics of HVPMSM based on quadratic response surface models (QRSM) combined with Cuckoo search algorithm. Eighty-nine sets of transient finite element models were developed. To obtain starting current, starting time, starting torque and torque ripple, the transient process of HVPMSM is calculated with different cage bar width, cage bar depth, cage bar conductivity, and slot wedge conductivity. Steady-state and locked rotor experiments are conducted to verify the accuracy of the FEA. Then, the Cuckoo search method is employed to optimize design parameters achieved by the finite element models to obtain the minimum starting current, the maximum starting torque, the shortest starting time, and the minimum torque ripple, while maintaining high output performance and low cost. The results demonstrate that QRSM combined with Cuckoo search algorithm accurately describe the non-linear relationship between the structural characteristics, material properties and output performance of the HVPMSM. The best solutions are consistent with the results of the finite element analysis. In addition, the theoretical method is verified by experiments.