The cavitation in axial piston pumps threatens the reliability and safety of the overall hydraulic system. Vibration signal can reflect the cavitation conditions in axial piston pumps and it has been ...combined with machine learning to detect the pump cavitation. However, the vibration signal usually contains noise in real working conditions, which raises concerns about accurate recognition of cavitation in noisy environment. This paper presents an intelligent method to recognise the cavitation in axial piston pumps in noisy environment. First, we train a convolutional neural network (CNN) using the spectrogram images transformed from raw vibration data under different cavitation conditions. Second, we employ the technique of gradient‐weighted class activation mapping (Grad‐CAM) to visualise class‐discriminative regions in the spectrogram image. Finally, we propose a novel image processing method based on Grad‐CAM heatmap to automatically remove entrained noise and enhance class features in the spectrogram image. The experimental results show that the proposed method greatly improves the diagnostic performance of the CNN model in noisy environments. The classification accuracy of cavitation conditions increases from 0.50 to 0.89 and from 0.80 to 0.92 at signal‐to‐noise ratios of 4 and 6 dB, respectively.
This article presents and studies a two-level grad-div stabilized finite element discretization method for solving numerically the steady incompressible Navier–Stokes equations. The method consists ...of two steps. In the first step, we compute a rough solution by solving a nonlinear Navier–Stokes system on a coarse grid. And then, in the second step, we pass the coarse grid solution to a fine grid to linearize the nonlinear term, update the solution by solving a linearized problem based on Newton iterations. In both steps, a grad-div stabilization term is incorporated into the system to reduce the influence of pressure on the approximate velocity. We analyze stability and asymptotic convergence of the approximate solutions, derive explicit dependence of the solution errors on the grad-div stabilization parameter and viscosity. We perform also some numerical tests to validate the theoretical analysis and illustrate the efficiency of the proposed method. Compared with the standard two-level method without stabilizations, the grad-div stabilization term added in present method improves the accuracy of the approximate velocity, accelerates the convergence of the nonlinear iterations for the coarse mesh nonlinear system, and reduces the computational time.
•A two-level grad-div stabilized finite element discretization method for the incompressible Navier–Stokes equations is presented.•The method is easy to implement based on existing codes.•The method can yield much better solutions than the standard two-level discretization method with reduction in computational time when the viscosity is small.•Convergence results with respective to the mesh size, viscosity and stabilization parameter are derived.•Numerical results demonstrate the promise of the proposed method.
Introduction: Brain tumors are abnormal cell growths in the brain, posing significant treatment challenges. Accurate early detection using non-invasive methods is crucial for effective treatment. ...This research focuses on improving the early detection of brain tumors in MRI images through advanced deep-learning techniques. The primary goal is to identify the most effective deep-learning model for classifying brain tumors from MRI data, enhancing diagnostic accuracy and reliability. Methods: The proposed method for brain tumor classification integrates segmentation using K-means++, feature extraction from the Spatial Gray Level Dependence Matrix (SGLDM), and classification with ResNet50, along with synthetic data augmentation to enhance model robustness. Segmentation isolates tumor regions, while SGLDM captures critical texture information. The ResNet50 model then classifies the tumors accurately. To further improve the interpretability of the classification results, Grad-CAM is employed, providing visual explanations by highlighting influential regions in the MRI images. Result: In terms of accuracy, sensitivity, and specificity, the evaluation on the Br35H::BrainTumorDetection2020 dataset showed superior performance of the suggested method compared to existing state-of-the-art approaches. This indicates its effectiveness in achieving higher precision in identifying and classifying brain tumors from MRI data, showcasing advancements in diagnostic reliability and efficacy. Discussion: The superior performance of the suggested method indicates its robustness in accurately classifying brain tumors from MRI images, achieving higher accuracy, sensitivity, and specificity compared to existing methods. The method's enhanced sensitivity ensures a greater detection rate of true positive cases, while its improved specificity reduces false positives, thereby optimizing clinical decision-making and patient care in neuro-oncology.
This work studies a parallel grad-div stabilized finite element algorithm for the damped Stokes equations. In this algorithm, in the light of a fully overlapping domain decomposition technique, we ...solve a global grad-div stabilized problem to compute a local solution in an intersecting subdomain on a global composite mesh, which is fine in the subdomain and rough elsewhere, making the proposed algorithm easy to implement based on an available sequential solver. We derive error bounds of the approximate solutions from our presented algorithm by the theoretical tool of local a priori estimate for the grad-div stabilized finite element solution. Numerical results verify the validity of the theoretical analysis and demonstrate the benefits of the proposed algorithm. On the one hand, compared with the counterpart one excluding grad-div stabilization, this algorithm can reduce significantly the effect of pressure on the approximate velocities, and hence, yields much better approximate velocities in the case of small viscosities. On the other hand, it takes much less computational time in getting approximate solutions with a comparable accuracy than the standard grad-div stabilization method.
Our study presents MozzieNet, a customized CNN model aimed at improving the identification of malaria parasites in blood smear microscopic images. By optimizing hyperparameters and incorporating ...techniques like data augmentation, batch normalization, and dropout, our model enhances robustness and generalization, addressing overfitting issues. Using the open‐source NIH malaria dataset with 27,558 images, we achieve a classification accuracy of 96.73%, recall rate of 97.90%, precision of 95.67%, area under the curve (AUC) of 99.35%, and F1 score of 96.77%. We performed feature maps and Grad‐CAM analysis on our proposed MozzieNet model to visualize and examine the targeted regions that are crucial for accurate predictions. Statistical analysis shows that the proposed architecture achieves promising performance and is superior to pre‐trained models and existing methods for malaria detection. MozzieNet is designed for cloud and low‐end smartphones, enabling malaria diagnosis in remote areas, thereby assisting physicians in informed malaria diagnosis and decision‐making.
Autori u radu donose preliminarne rezultate arheoloških istraživanja lokaliteta Stari
grad Ljubuški koje je Studij arheologije Filozofskog fakulteta Sveučilišta u Mostaru
proveo u dvije istraživačke ...kampanje, tijekom lipnja 2020. i ožujka 2021. godine. Arheološka
istraživanja i konzervatorsko-restauratorski radovi obavljeni su u okviru dva EU projekta prekograničnih suradnji: FORT-NET i Heritage REVIVED. Iako je riječ o manjim istraživanjima, ona su polučila značajne rezultate koji u mnogome već sada mijenjaju dosadašnje spoznaje, osobito one o vremenu izgradnje i korištenja prostora Staroga grada Ljubuški.
Automatic accent classification is an active research field concerning speech processing. It can be useful to identify a speaker's region of origin, which can be applied in police investigations ...carried out by Law Enforcement Agencies, as well as for the improvement of current speech recognition systems. This paper presents a novel descriptor called Grad-Transfer, extracted using the Gradient-weighted Class Activation Mapping (Grad-CAM) method based on convolutional neural network (CNN) interpretability. Additionally, we propose a methodology for accent classification that implements Grad-Transfer, which is based on transferring the knowledge acquired by a CNN to a classical machine learning algorithm. The paper works on two hypotheses: the coarse localization maps produced by Grad-CAM on spectrograms are able to highlight the regions of the spectrograms that are important for predicting accents, and Grad-Transfer descriptors computed from audios represent distinctive descriptions of the target accents. These hypotheses were demonstrated experimentally, clustering the generated Grad-Transfer descriptors according to the original accent of the audios using Birch and <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>-means algorithms. We carried out experiments on the Voice Cloning Toolkit dataset, seeing an increase of macro average accuracy, and unweighted average recall in the results obtained by a Gaussian Naive Bayes classifier up to <inline-formula><tex-math notation="LaTeX">23.00\%</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">23.58\%</tex-math></inline-formula>, respectively, compared to a model trained with spectrograms. This demonstrates that Grad-Transfer is able to improve the performance of accent classification models and opens the door to new implementations in similar tasks.
A neural network solving the Grad-Shafranov equation constrained with measured magnetic signals to reconstruct magnetic equilibria in real time is developed. The database created to optimize the ...neural network's free parameters contains off-line EFIT results as the output of the network from 1118 KSTAR experimental discharges of two different campaigns. Input data to the network constitute magnetic signals measured by a Rogowski coil (plasma current), magnetic pick-up coils (normal and tangential components of magnetic fields) and flux loops (poloidal magnetic fluxes). The developed neural networks fully reconstruct not only the poloidal flux function but also the toroidal current density function with the off-line EFIT quality. To preserve the robustness of the networks against missing input data, an imputation scheme is utilized to eliminate the required additional training sets with a large number of possible combinations of the missing inputs.
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