An advanced electrical vehicle charging station using adaptive hybrid particle swarm optimization intended for renewable energy system for simultaneous distributions

Deep convolutional neural network based hyperspectral brain tissue classification

BACKGROUND

Hyperspectral brain tissue imaging has been recently utilized in medical research aiming to study brain science and obtain various biological phenomena of the different tissue types. However, processing high-dimensional data of hyperspectral images (HSI) is challenging due to the minimum availability of training samples.

OBJECTIVE

To overcome this challenge, this study proposes applying a 3D-CNN (convolution neural network) model to process spatial and temporal features and thus improve performance of tumor image classification.

METHODS

A 3D-CNN model is implemented as a testing method for dealing with high-dimensional problems. The HSI pre-processing is accomplished using distinct approaches such as hyperspectral cube creation, calibration, spectral correction, and normalization. Both spectral and spatial features are extracted from HSI. The Benchmark Vivo human brain HSI dataset is used to validate the performance of the proposed classification model.

RESULTS

The proposed 3D-CNN model achieves a higher accuracy of 97% for brain tissue classification, whereas the existing linear conventional support vector machine (SVM) and 2D-CNN model yield 95% and 96% classification accuracy, respectively. Moreover, the maximum F1-score obtained by the proposed 3D-CNN model is 97.3%, which is 2.5% and 11.0% higher than the F1-scores obtained by 2D-CNN model and SVM model, respectively.

CONCLUSION

A 3D-CNN model is developed for brain tissue classification by using HIS dataset. The study results demonstrate the advantages of using the new 3D-CNN model, which can achieve higher brain tissue classification accuracy than conventional 2D-CNN model and SVM model.

Optimize and analysis of drilling PMSM using hybrid technique based on BN, FFT, FCE, and FMA for risk assessment

A failure risk assessment must be carried out and potential drilling equipment failure risks must be promptly addressed in order to prevent drilling fluid pollution during offshore oil drilling. The qualitative, comprehensive, and quantitative failure risks for Drilling Permanent Magnetic Synchronous Motors (DPMSM) are examined in this article using a hybrid methodology. First, the Drilling PMSM using Failure Mode Analysis (FMA) method is combined with the Risk Matrix (RM) approach to analyse the risk levels of risk factors individually. Next, the Borda number is introduced to compare the risk levels exactly. To execute a Fuzzy Comprehensive Evaluation (FCE) of the system failure risk, a fuzzy relation matrix of risk factors is generated, and the weight of each risk component is calculated using importance analysis. The failure rate is then determined using fuzzy inference, and the Fault Tree (FT) is then built based on the risk variables. Fault tree analysis is used to compute the system failure rate, and the significance of the bottom event is evaluated. The Bayesian network (BN) is used to depict the Fuzzy Fault Tree (FFT) analysis. By utilizing Bayesian forward causal inference and reverse diagnostic inference to calculate the leaf node failure rate and root node posterior probability, the system’s weak points and potential failure causes are determined.

Attention aware fully convolutional deep learning model for retinal blood vessel segmentation

Recent reports indicate a rise in retinal issues, and automatic artery vein categorization offers data that is particularly instructive for the medical evaluation of serious retinal disorders including glaucoma and diabetic retinopathy. This work presents a competent and precise deep-learning model designed for vessel segmentation in retinal fundus imaging. This article aims to segment the retinal images using an attention-based dense fully convolutional neural network (A-DFCNN) after removing uncertainty. The artery extraction layers encompass vessel-specific convolutional blocks to focus the tiny blood vessels and dense layers with skip connections for feature propagation. Segmentation is associated with artery extraction layers via individual loss function. Blood vessel maps produced from individual loss functions are authenticated for performance. The proposed technique attains improved outcomes in terms of Accuracy (0.9834), Sensitivity (0.8553), and Specificity (0.9835) from DRIVE, STARE, and CHASE-DB1 datasets. The result demonstrates that the proposed A-DFCNN is capable of segmenting minute vessel bifurcation breakdowns during the training and testing phases.

AI Topology based sensor fault diagnosis in induction motor drive

Recently, Induction motor (IM) become the most prevalent machine type and finds an applications in many fields such as industries, electric cars etc., A typical IMD system includes IM, power controller, converter and measurement sensors. The effective performance of the IM indirectly depends upon the sensors connected with IMD. Recently, sensor fault diagnosis plays a vital role in IMD control. Thus, this work formulated a unique methodology using current vector determined from the stator currents of IM to identify sensor failures. ANN topology is incorporated to detect the Sensor failure. MATLAB software is utilized to verify the efficacy of the suggested topology. To demonstrate the practicality of this technology, experimental verification is carried out. The efficiency of the proposed approach for IM drives is demonstrated by both simulation and experimental findings. From the obtained results, it is proven that this technique detects the failure of the sensors within less time duration (about 0.25 ms). Hence, it can be effectively utilized in automobile industry.