Security and Privacy Preservation for Data Communication Network

Kumar, Kamal; Kumar, Vijay; Seema, .

doi:10.1016/j.procs.2022.12.001

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…The ionic fluxes increase with increasing bias voltages. High-energy flux species reach the substrate, leading to rapid nucleation, suppressing the growth of columnar crystals and achieving dense polycrystalline or even nanocrystalline structures [50,[100][101][102][103][104][105][106][107][108]. As discussed in Section 2.1, during the growth process, highvalence state plasma also acquires kinetic energy from the bias voltage, disrupting the continuous growth of columnar crystals and even re-sputtering loose grains, ensuring coating densification.…”

Section: Micromorphologymentioning

confidence: 99%

Comparison of CrN Coatings Prepared Using High-Power Impulse Magnetron Sputtering and Direct Current Magnetron Sputtering

Bai,

Li,

Gao

et al. 2023

Materials

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Chromium Nitride (CrN) coatings have widespread utilization across numerous industrial applications, primarily attributed to their excellent properties. Among the different methods for CrN coating synthesis, direct current magnetron sputtering (DCMS) has been the dominant technique applied. Nonetheless, with the expanded applications of CrN coatings, the need for enhanced mechanical performance is concurrently escalating. High-power impulse magnetron sputtering (HiPIMS), an innovative coating deposition approach developed over the past three decades, is gaining recognition for its capability of yielding coatings with superior mechanical attributes, thereby drawing significant research interest. Considering that the mechanical performance of a coating is fundamentally governed by its microstructural properties, a comprehensive review of CrN coatings fabricated through both techniques is presented. This review of recent literature aims to embark on an insightful comparison between DCMS and HiPIMS, followed by an examination of the microstructure of CrN coatings fabricated via both techniques. Furthermore, the exploration of the underlying factors contributing to the disparities in mechanical properties observed in CrN coatings is revealed. An assessment of the advantages and potential shortcomings of HiPIMS is discussed, offering insight into CrN coating fabrication.

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Section: Micromorphologymentioning

confidence: 99%

Comparison of CrN Coatings Prepared Using High-Power Impulse Magnetron Sputtering and Direct Current Magnetron Sputtering

Bai,

Li,

Gao

et al. 2023

Materials

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“…The MCC utilizes the Gaussian kernel which is effective against both Gaussian and non-Gaussian noise signals. Many advanced versions of LMS, LMF and MCC, i.e., hyperbolic cosine LMS (HCLMS) [33], hyperbolic tangent function (HTF) [34], LMS-LMF [35], adaptive neuro-fuzzy LMS (ANF-LMS) [36], zero attracting normalized LMF (ZAN-LMF) [37], normalized MCC (NMCC) [38], fixed forward prediction (FFP) [39], adaptive kernel width sixth-order MCC (AKWSOMCC) [40], etc. The HC-LMS offers minimal dynamic oscillations, HTF acts as a higher-order filter, which offers reduced mean square error (MSE), combined LMS-LMF reduces the shortcomings of LMS control by switching between second and fourth-order filter, but the performance of both controls reduces during heavily tailed Gaussian noises due to fixed step-size.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Grasshopper Optimization Based MPPT and VSC Control of Grid-Tied PV-Battery System

et al. 2021

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This article presents the control of a three-phase three-wire (3P-3W) dual-stage grid-tied PV-battery storage system using a multi-objective grass-hopper optimization (MOGHO) algorithm. The voltage source converter (VSC) control of the presented system is implemented with adaptive kernel width sixth-order maximum correntropy criteria (AKWSOMCC) and maximum power point tracking (MPPT) control is accomplished using the variable step-size incremental conductance (VSS-InC) technique. The proposed VSC control offers lower mean square error and better accuracy, convergence rate and speed as compared to peer adaptive algorithms, i.e., least mean square (LMS), least mean fourth (LMF), maximum correntropy criteria (MCC), etc. The adaptive Gaussian kernel width is a function of the error signal, which changes to accommodate and filter Gaussian and non-Gaussian noise signals in each iteration. The VSS-InC based MPPT is provided with a MOGHO based modulation factor for better and faster tracking of the maximum power point during changing solar irradiation. Similarly, an optimized gain conventional PI controller regulates the DC bus to improve the power quality, and DC link stability during dynamic conditions. The optimized DC-link generates an accurate loss component of current, which further improves the VSC capability of fundamental load current component extraction. The VSC is designed to perform multi-functional operations, i.e., harmonics elimination, reactive power compensation, load balancing and power balancing at point of common coupling during diverse dynamic conditions. The MOSHO based VSS-InC, and DC bus performance is compared to particle swarm optimization (PSO) and genetic algorithm (GA). The proposed system operates satisfactorily as per IEEE519 standards in the MATLAB simulation environment.

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Detecting Routing Protocol Low Power and Lossy Network Attacks Using Machine Learning Techniques

Kareem,

Shaban,

Adil Nafea

et al. 2024

2024 21st International Multi-Conference on Systems, Signals &Amp;amp; Devices (SSD)

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Security and Privacy Preservation for Data Communication Network

Cited by 3 publications

References 2 publications

Comparison of CrN Coatings Prepared Using High-Power Impulse Magnetron Sputtering and Direct Current Magnetron Sputtering

Comparison of CrN Coatings Prepared Using High-Power Impulse Magnetron Sputtering and Direct Current Magnetron Sputtering

Multi-Objective Grasshopper Optimization Based MPPT and VSC Control of Grid-Tied PV-Battery System

Detecting Routing Protocol Low Power and Lossy Network Attacks Using Machine Learning Techniques

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