Search Results (4)

State-of-charge (SOC) estimation is pivotal in optimizing lithium-ion battery management systems (BMSs), ensuring safety, performance, and longevity across various applications. This study introduces a novel SOC estimation framework that uniquely integrates Long Short-Term Memory (LSTM) neural networks with Hyperband-driven hyperparameter optimization, a combination not extensively explored in the literature. A comprehensive experimental dataset is created using data of LG 18650HG2 lithium-ion batteries subjected to diverse operational cycles and thermal conditions. The proposed framework demonstrates superior prediction accuracy, achieving a Mean Square Error (MSE) of 0.0023 and a Mean Absolute Error (MAE) of 0.0043, outperforming traditional estimation methods. The Hyperband optimization algorithm accelerates model training and enhances adaptability to varying operating conditions, making it scalable for diverse battery applications. Developing an intuitive, real-time user interface (UI) tailored for practical deployment bridges the gap between advanced SOC estimation techniques and user accessibility. Detailed residual and regression analyses confirm the proposed solution’s robustness, generalizability, and reliability. This work offers a scalable, accurate, and user-friendly SOC estimation solution for commercial BMSs, with future research aimed at extending the framework to other battery chemistries and hybrid energy systems. Full article

Traditional fundus image-based diabetic retinopathy (DR) grading depends on the examiner’s experience, requiring manual annotations on the fundus image and also being time-consuming. Wireless sensor networks (WSNs) combined with artificial intelligence (AI) technology can provide automatic decision-making for DR grading application. However, the diagnostic accuracy of the AI model is one of challenges that limited the effectiveness of the WSNs-aided DR grading application. Regarding this issue, we propose a WSN architecture and a parallel deep learning framework (HybridLG) for actualizing automatic DR grading and achieving a fundus image-based deep learning model with superior classification performance, respectively. In particular, the framework constructs a convolutional neural network (CNN) backbone and a Transformer backbone in a parallel manner. A novel lightweight deep learning model named MobileViT-Plus is proposed to implement the Transformer backbone of the HybridLG, and a model training strategy inspired by an ensemble learning strategy is designed to improve the model generalization ability. Experimental results demonstrate the state-of-the-art performance of the proposed HybridLG framework, obtaining excellent performance in grading diabetic retinopathy with strong generalization performance. Our work is significant for guiding the studies of WSNs-aided DR grading and providing evidence for supporting the efficacy of the AI technology in DR grading applications. Full article

A series of rhodamine B (RhB) encapsulated zeolitic imidazolate framework-8 (RhB@ZIF-8) composite nanomaterials with different concentrations of guest loadings have been synthesized and characterized in order to investigate their applicability to solid-state white-light-emitting diodes (WLEDs). The nanoconfinement of the rhodamine B dye (guest) in the sodalite cages of ZIF-8 (host) is supported by fluorescence spectroscopic and photodynamic lifetime data. The quantum yield (QY) of the luminescent RhB@ZIF-8 material approaches unity when the guest loading is controlled at a low level: 1 RhB guest per ~7250 cages. We show that the hybrid (luminescent guest) LG@MOF material, obtained by mechanically mixing a suitably high-QY RhB@ZIF-8 red emitter with a green-emitting fluorescein@ZIF-8 “phosphor” with a comparably high QY, could yield a stable, intensity tunable, near-white light emission under specific test conditions described. Our results demonstrate a novel LG@MOF composite system exhibiting a good combination of photophysical properties and photostability, for potential applications in WLEDs, photoswitches, bioimaging and fluorescent sensors. Full article

The object of this study was to demonstrate the ability of machine learning (ML) methods for the segmentation and classification of diabetic retinopathy (DR). Two-dimensional (2D) retinal fundus (RF) images were used. The datasets of DR—that is, the mild, moderate, non-proliferative, proliferative, and normal human eye ones—were acquired from 500 patients at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan. Five hundred RF datasets (sized 256 × 256) for each DR stage and a total of 2500 (500 × 5) datasets of the five DR stages were acquired. This research introduces the novel clustering-based automated region growing framework. For texture analysis, four types of features—histogram (H), wavelet (W), co-occurrence matrix (COM) and run-length matrix (RLM)—were extracted, and various ML classifiers were employed, achieving 77.67%, 80%, 89.87%, and 96.33% classification accuracies, respectively. To improve classification accuracy, a fused hybrid-feature dataset was generated by applying the data fusion approach. From each image, 245 pieces of hybrid feature data (H, W, COM, and RLM) were observed, while 13 optimized features were selected after applying four different feature selection techniques, namely Fisher, correlation-based feature selection, mutual information, and probability of error plus average correlation. Five ML classifiers named sequential minimal optimization (SMO), logistic (Lg), multi-layer perceptron (MLP), logistic model tree (LMT), and simple logistic (SLg) were deployed on selected optimized features (using 10-fold cross-validation), and they showed considerably high classification accuracies of 98.53%, 99%, 99.66%, 99.73%, and 99.73%, respectively. Full article