Benefiting from the rapid development of the modern new energy automobile industry, lithium-ion batteries as the core components of new energy vehicles, the demand is rising. For both industry and consumers, accurately predicting the remaining useful life of lithium-ion batteries is an urgent need to avoid accidents and reduce range anxiety. Therefore, we propose a new method to solve this problem, the ARMI-CNN-LSTM-AT model. The innovative use of pre-ordered ARIMA models to predict battery decay data enhances the data entry quality of the second stage. In the second stage, the attention mechanism was added to the output of CNN-LSTM to redistribute the weight of unbalanced input features, The network input and output through sliding Windows. In the case of 40 rounds of data training, the final average prediction result reached 0.0136 RMSE and 0.0079 MAE. We demonstrated the importance of adding predicted data to predict battery decay, and also experimentally demonstrated that the network can maintain good prediction results on different batteries.
Active noise control (ANC) is a technology that uses sound waves to reduce or eliminate unwanted ambient noise in a given environment. We approached ANC using a deep neural network consisting of convolutional and attention layers, followed by deconvolutional layers. The proposed deep learning model processes the acoustic signal samples in frames. A piece-wise non-linear function is employed to represent the secondary path and loudspeaker saturation non-linearies. To assess the efficacy of the proposed model, we conducted tests using filtered white noise across different frequency bands and various tonals from a diverse database. The proposed convolutional model with attention layers yields a normalized mean square error (NMSE) recuction of upto 3 dB more as compared to that achieved using just convolutional model. Experiment results are presented for both confined and anechoic environments, providing a comprehensive evaluation of the proposed ANC approach.
This paper presents FedCVD, a federated learning model designed for predicting cardiovascular disease (CVD) by employing logistic regression and Support Vector Machine (SVM) algorithms. FedCVD utilizes the privacy and scalability advantages offered by federated learning to facilitate collaborative model training using decentralized patient data, ensuring confidentiality. To evaluate the effectiveness of the proposed model, experiments were conducted using the 10-year risk of coronary heart disease Kaggle dataset. To address data imbalance challenges, three techniques—Random Over Sampling, Random Under Sampling, and Synthetic Minority Oversampling Technique (SMOTE)—were explored. The study demonstrates promising performance,For the federated logistic regression with SMOTE achieving an AUC value of 0.7048. Comparative analysis with a centralized logistic regression model shows competitive results, with an AUC value of 0.7081 using Random Over Sampling. For the federated SVM model, an AUC value of 0.7340 is achieved using Random Under Sampling. In comparison, a centralized machine learning approach utilizing SVM and Random Over Sampling achieves an AUC value of 0.6962. These findings highlight the effectiveness of the proposed federated learning approach, surpassing the performance of centralized machine learning models for CVD prediction.
In this article, a face recognition via thermal imaging: a comparative study of traditional and CNN-based approaches is proposed. The methodology comprises two distinct components: traditional face recognition and CNN-based face recognition. In the traditional face recognition, we employ Random Forest (RF) and Support Vector Machine Classifier (SVM) techniques. Conversely, CNN-based face recognition leverages Convolutional Neural Networks (CNN) to identify individuals. Our research involves a comprehensive evaluation conducted across different databases, including the PUCV Drunk Thermal Face (PUCV-DTF) and the UCH Thermal Temporal Face (UCH-TTF) datasets. To emulate real-life scenarios, we introduce elements such as glasses, face mask, and noise into the original thermal images during experimentation. The recognition rates of traditional and CNN-based methods for small size and middle size databases achieve 90% and 100%, respectively, under a challenging condition, wearing glasses and mask. Experimental results demonstrate the feasibility and effectiveness of our proposed method, showcasing its robustness in tackling various challenges.
This study outlines the creation of an object detection model utilizing YOLOv5 Small, designed to identify and categorize ambulances on the road, distinguishing them based on various types and characteristics. The research process included the assembly of a specific dataset of ambulance images from the Philippines, which comprised seven classes. The project made use of image augmentation, leading to a dissimilarity score of 0.1574 between the original and augmented images, signifying the successful introduction of variations. The process of hyperparameter tuning was carried out, with a batch size of 8 proving to be the most effective, resulting in a mAP@50 score of 93% for the detection and classification of ambulances and vehicles.
