Improving the melting performance in a triple-pipe latent heat storage system using hemispherical and quarter-spherical fins with a staggered arrangement

This study aims to evaluate the melting characteristics of a phase change material (PCM) in a latent heat storage system equipped with hemispherical and quarter-spherical fins. A vertical triple-pipe heat exchanger is used as the PCM-based heat storage unit to improve the melting performance compared with a double-pipe system. Furthermore, the fins are arranged in inline and staggered configurations to improve heat transfer performance. For the quarter-spherical fins, both upward and downward directions are examined. The results of the system equipped with novel fins are compared with those without fins. Moreover, a fin is added to the heat exchanger’s base to compensate for the natural convection effect at the bottom of the heat exchanger. Considering similar fin volumes, the results show that the system equipped with four hemispherical fins on the side walls and an added fin on the bottom wall has the best performance compared with the other cases with hemispherical fins. The staggered arrangement of the fins results in a higher heat transfer rate. The downward quarter-spherical fins with a staggered configuration show the highest performance among all the studied cases. Compared with the case without fins, the heat storage rate improves by almost 78% (from 35.6 to 63.5 W), reducing the melting time by 45%

Reconstructing with geometric moments

International audienc

Spatio-Temporal Features Representation Using Recurrent Capsules for Monaural Speech Enhancement

Single-channel speech enhancement is important for modern communication systems and has received a lot of attention. A convolutional neural network (CNN) successfully learns feature representations from speech spectrograms but loses spatial information due to distortion, which is important for humans to understand speech. Speech feature learning is an important ongoing research to capture higher-level representations of speech that go beyond conventional techniques. By considering the hierarchical structure and temporal relationships within speech signals, capsule networks (CapsNets) have the potential to provide more expressive and context-aware feature representations. By considering the advantages of CapNets over CNN, this study presents a model for monaural speech enhancement that keeps spatial information in a capsule and uses dynamic routing to pass it to higher layers. Dynamic routing replaces the pooling recurrent hidden states to get speech features from the outputs of the capsule. Leveraging long-term contexts provides identification of the target speaker. Therefore, a gated recurrent layer, gated recurrent unit (GRU), or long-short-term memory (LSTM), is placed above the CNN module and next to the capsule module in the architecture. This makes it viable to extract spatial features and long-term temporal dynamics. The suggested convolutional recurrent CapNet performs better compared to the models based on CNNs and recurrent neural networks. The suggested speech enhancement produces considerably better speech quality and intelligibility. With the LibriSpeech and VoiceBank+DEMAND databases, the suggested speech enhancement improves the intelligibility and quality by 18.33% and (0.94) 36.82% over the noisy mixtures

Lightweight Real-Time Recurrent Models for Speech Enhancement and Automatic Speech Recognition

Traditional recurrent neural networks (RNNs) encounter difficulty in capturing long-term temporal dependencies. However, lightweight recurrent models for speech enhancement are important to improve noisy speech, while being computationally efficient and able to capture long-term temporal dependencies efficiently. This study proposes a lightweight hourglass-shaped model for speech enhancement (SE) and automatic speech recognition (ASR). Simple recurrent units (SRU) with skip connections are implemented where attention gates are added to the skip connections, highlighting the important features and spectral regions. The model operates without relying on future information that is well-suited for real-time processing. Combined acoustic features and two training objectives are estimated. Experimental evaluations using the short time speech intelligibility (STOI), perceptual evaluation of speech quality (PESQ), and word error rates (WERs) indicate better intelligibility, perceptual quality, and word recognition rates. The composite measures further confirm the performance of residual noise and speech distortion. With the TIMIT database, the proposed model improves the STOI and PESQ by 16.21% and 0.69 (31.1%) whereas with the LibriSpeech database, the model improves STOI by 16.41% and PESQ by 0.71 (32.9%) over the noisy speech. Further, our model outperforms other deep neural networks (DNNs) in seen and unseen conditions. The ASR performance is measured using the Kaldi toolkit and achieves 15.13% WERs in noisy backgrounds

Evaluation of an azocalix[4]arene derivative for the selective extraction of Pd(II) from hydrochloric acid solutions

International audienc

Viscous dissipation effect on amplitude and oscillating frequency of heat transfer and electromagnetic waves of magnetic driven fluid flow along the horizontal circular cylinder

The significant importance of present research is to remove the extreme temperature along the magnetic driven horizontal circular cylinder. The induced electromagnetic field is applied around the surface of cylinder. The main novelty of current research is to control thermal and magnetic boundary layer in the presence of viscous dissipation and induced electromagnetic field. The dimensional mathematical form is developed with defined boundary conditions. The dimensional equations are transformed into dimensionless equations to generate physical factors. The primitive form is used to reduce dimensionless equations into convenient form for smooth algorithm. The finite difference method with Gaussian elimination technique is applied for numerical results in FORTRAN language tool. The velocity, temperature and electromagnetic field are sketched graphically with asymptotic sequence. The oscillatory shear stress, oscillating heat rate and periodical current density is plotted graphically and numerically. It is found that fluid velocity improves significantly as buoyancy force increases around each position. It is noticed that the increasing oscillations in heat transfer are sketched for maximum choice of Prandtl number. It is found that the maximum oscillations in current density are obtained for each Eckert parameter. It is noticed that the significant distribution in temperature profile is obtained in the presence of viscous dissipation and magnetic field

EL-RFHC: Optimized ensemble learners using RFHC for intrusion attacks classification

The extensive growth of mobile technology leads to magnifying the usage of digital gadgets around the world. This requires a fast-interconnecting communication medium to transfer the data between the devices. Meanwhile, the intruders attempt to make huge traffic in the network that leads to loss of data. To identify the intrusion attacks, ensemble Machine Learning (ML) classifiers are applied using the various feature variables importance. However, most of the transmitting data contains high dimensions with numerous variables leads to more execution time to classify the attacks. This study initiated the novel approach fusion of the Random Forest classifier and High Correlation (RFHC) feature selection approach to diminish the quantity of the variables. Also, the count of intrusion attacks class is lower than the normal class leads to generating an imbalanced dataset. Hence, Synthetic Minority Over-Sampling Technique (SMOTE) is suggested to create a balanced dataset for multi-class classification, and Un-upsampled data for binary-class classification respectively. The pre-processed dataset fed into the ensemble machine learners, and attention mechanism-based LSTM to classify as various intrusion attacks and normal data. This research work focused on reducing the CICIDS2017 dataset’s variable dimensions from 71 to 34 using RFHC. The performance results showed that RF classifier performed better with accuracy of 99.4 %, precision 99.4 %, average recall 99.2 % and average F1-score 99.6 % in binary-class classification, and Extreme Gradient Boosting (XGBoost) achieved better accuracy of 99.7 %, precision 98.7 %, average recall 99.5 % and average F1-score 99.2 % in multi-class classification

Intelligent Epileptic Seizure Detection and Classification Model Using Optimal Deep Canonical Sparse Autoencoder

Epileptic seizures are a chronic and persistent neurological illness that mainly affects the human brain. Electroencephalogram (EEG) is considered an effective tool among neurologists to detect various brain disorders, including epilepsy, owing to its advantages, such as its low cost, simplicity, and availability. In order to reduce the severity of epileptic seizures, it is necessary to design effective techniques to identify the disease at an earlier stage. Since the traditional way of diagnosing epileptic seizures is laborious and time-consuming, automated tools using machine learning (ML) and deep learning (DL) models may be useful. This paper presents an intelligent deep canonical sparse autoencoder-based epileptic seizure detection and classification (DCSAE-ESDC) model using EEG signals. The proposed DCSAE-ESDC technique involves two major processes, namely, feature selection and classification. The DCSAE-ESDC technique designs a novel coyote optimization algorithm (COA)-based feature selection technique for the optimal selection of feature subsets. Moreover, the DCSAE-based classifier is derived for the detection and classification of different kinds of epileptic seizures. Finally, the parameter tuning of the DSCAE model takes place via the krill herd algorithm (KHA). The design of the COA-based feature selection and KHA-based parameter tuning shows the novelty of the work. For examining the enhanced classification performance of the DCSAE-ESDC technique, a detailed experimental analysis was conducted using a benchmark epileptic seizure dataset. The comparative results analysis portrayed the better performance of the DCSAE-ESDC technique over existing techniques, with maximum accuracy of 98.67% and 98.73% under binary and multi-classification, respectively