Fast Ion Transport in Li-Rich Alloy Anode for High-Energy-Density All Solid-State Lithium Metal Batteries

All-solid-state Li batteries (ASSLBs) with solid-polymer electrolytes are considered promising battery systems to achieve improved safety and high energy density. However, Li dendrite formation at the Li anode under high charging current density/capacity has limited their development. To tackle the issue, Li-metal alloying has been proposed as an alternative strategy to suppress Li dendrite growth in ASSLBs. One drawback of alloying is the relatively lower operating cell voltages, which will inevitably lower energy density compared to cells with pure Li anode. Herein, a Li-rich Li₁₃In₃ alloy electrode (LiRLIA) is proposed, where the Li₁₃In₃ alloy scaffold guides Li nucleation and hinders Li dendrite formation. Meanwhile, the free Li can recover Li's potential and facilitate fast charge transfer kinetics to realize high-energy-density ASSLBs. Benefitting from the stronger adsorption energy and lower diffusion energy barrier of Li on a Li₁₃In₃ substrate, Li prefers to deposit in the 3D Li₁₃In₃ scaffold selectively. Therefore, the Li–Li symmetric cell constructed with LiRLIA can operate at a high current density/capacity of 5 mA cm⁻²/5 mAh cm⁻² for almost 1000 h.     

Association Rule Mining Frequent-Pattern-Based Intrusion Detection in Network

In the network security system, intrusion detection plays a significant role. The network security system detects the malicious actions in the network and also conforms the availability, integrity and confidentiality of data information resources. Intrusion identification system can easily detect the false positive alerts. If large number of false positive alerts are created then it makes intrusion detection system as difficult to differentiate the false positive alerts from genuine attacks. Many research works have been done. The issues in the existing algorithms are more memory space and need more time to execute the transactions of records. This paper proposes a novel framework of network security Intrusion Detection System (IDS) using Modified Frequent Pattern (MFP-Tree) via K-means algorithm. The accuracy rate of Modified Frequent Pattern Tree (MFPT)-K means method in finding the various attacks are Normal 94.89%, for DoS based attack 98.34%, for User to Root (U2R) attacks got 96.73%, Remote to Local (R2L) got 95.89% and Probe attack got 92.67% and is optimal when it is compared with other existing algorithms of K-Means and APRIORI.     

Throughput analysis in censoring-based cooperative cognitive radio network with energy harvesting

An energy harvesting (EH) and cooperative cognitive radio (CR) network (CRN) is studied in this paper where CR users transmit data through a primary user (PU) channel if the channel remains idle, else an optimal number CRs helps in transmission of PU. To achieve the optimum number of CRs (ONCR) involved in cooperation, a novel scheme based on a combination of channel censoring and total error is proposed. The performance of the proposed scheme is investigated under RF harvesting scenario. The EH is dependent on sensing decision and a CR source harvests energy from PU's RF signal. The harvested energy (HE) is split into two parts: One part is used by the CR network (CRN) for its own transmission, and the other part is used for supporting PU. The effect of the energy allocation factor on total throughput is also investigated. New expressions for optimal number of CRs and throughput are developed. The effect of network parameters such as sensing time, censoring threshold, and energy allocation parameter (EAP) on throughput is investigated. Impact of distance between nodes is also studied.     

Product beamforming and nested array in tandem for enhanced sonar performance

Multidimensional Systems and Signal Processing - High-performance sonar systems mostly combine a large sensor array and an efficient beamformer for enhancing the overall detection capabilities and...     

Design and analysis of text document clustering using salp swarm algorithm

The Journal of Supercomputing - In the technological era, exponential increase of unorganized text documents offers increased difficulties retrieving the most relevant data. The document clustering...     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.     

A new method using feature extraction for identifying paddy rice species for quality seed selection

The purpose of this work was to explore a new feature extraction method for classifying paddy seeds using a feature extraction algorithm to achieve the area ratio, horizontal–slant and front–rear angles and find whether the proposed features have high discriminating power. Another objective was to find the smallest feature set that can ensure highly accurate recognition of seeds. A total of a 100 image features were extracted, and features having significant discriminating power were identified based on the analysis of variance (ANOVA). From the 100 features, 14 features were found to have high discriminating power and from these features, six were selected as the proposed features. Experimental results show that the proposed features and removal of redundant features enhanced the discriminating power of the feature set, and that the proposed features have an excellent discriminating property for seeds. The presented features resulted in the highest classification accuracy (98.8%) when compared to other methods.     

Mixed-Mode Stress Intensity Factor Determination of Riveted Lap Joints Used in Aircraft Fuselage Structures

Journal of Failure Analysis and Prevention - In the present work, mixed-mode stress intensity factor (SIF) of multiple cracks in a riveted lap joint has been determined, with and without the...     

Myopic vs. far-sighted behaviours in a revenue-sharing supply chain with reference quality effects

In this paper, we consider a dynamic supply chain where a manufacturer produces and distributes a featured product through an exclusive retailer to end consumers. The manufacturer decides the product quality and wholesale price, while the retailer sets the retail price in the presence of a revenue-sharing contract and consumers’ reference quality effects. The objective of this study is to investigate the impacts of myopic behaviour regarding the reference quality on the product quality and pricing strategies and profits of both members. Our results suggest that the manufacturer’s myopia leads to a higher quality, higher price strategy and a higher quality–price ratio which benefits consumers. Meanwhile, relative to the far-sighted behaviour, myopia results in a more quality-sensitive but less price-sensitive market demand. What’s more, we find that the manufacturer is apt to act in a far-sighted way, but the retailer isn’t always willing to cooperate with a far-sighted manufacturer. Taking myopic strategies for both members is likely to gain a high profit of the whole supply chain for a relatively high marginal contribution of product quality on demand and a relatively low revenue-sharing proportion.     

ACO–EEOLSR: enhanced energy model based link stability routing protocol in mobile ad hoc networks

A mobile ad hoc network (MANET) has abundant mobile nodes that are free to communicate independently in many locations. Many existing energy models address the inadequacy of resources based on Ad hoc On-demand Multipath Distance Vector and Optimized Link State Protocol (OLSP) routing protocols for MANET along with various parameters. The architecture of energy-efficient routing mechanisms is a challenging problem in a MANET. In this work, a novel energy-aware routing model is introduced for MANET comprising an ant colony optimization (ACO) enhanced approach to energy-efficient-optimized link state routing (named ACO–EEOLSR). Initially, the route discovery is progressed by means of neighbor estimation and also with the authentication of link stability. Parameters such as energy, distance, and hop count are employed as willingness nodes, where both the energy and distance are entrenched through the OLSP. Consequently, the hop count is applied via the ACO system that is beneficial for link stability. After the acceptance of an acknowledgement, the hop count is authorized for further performance analysis. This approach increases the Quality of Service and also uses less energy compared to other energy models. The accomplished simulation upshot depicts that the ACO–EEOLSR outperforms the EEOLSR scheme with respect to the performance metrics of energy consumption, packet delivery ratio, total remaining time, average network lifetime, and a variance of energy.