Advanced Schemes Research Articles

Heat Flux Measurement in Shock Heated Combustible Gases and Clarification of Ignition Delay Time

Correct understanding of the ignition and combustion processes in the combustion chambers are critical for modeling advanced schemes of engines of high-speed aircraft and promising spacecraft. Moreover, experimental data on the ignition delay time are a universal basis for the development and testing of combustion kinetic models. Moreover, the higher the temperature of the fuel mixture, the smaller this time value and the more important its correct determination. The use of a thermoelectric detector allows to measure ignition delay times and record heat fluxes with a high time resolution (to tenths of μs) during ignition in propane–air mixtures. Due to the faster response time, the use of it allows refining the ignition delay time of the combustible mixture, and the detector itself can serve as a useful device that allows a more detailed study of the ignition processes.

Compact Triangular-Cavity Singlet-Based Filters in Stackable Multilayer Technologies

In this letter, triangular-cavity bandpass filters are investigated in stackable multilayer technologies in order to achieve highly compact designs with reduced fabrication complexity. The triangular-shaped cavities are first introduced in the form of singlets and then expanded on as a novel method for achieving a quasi-triplet filter response, where the filter's input and output irises are utilized as resonating means for two additional passband poles. Exploitation of this advanced singlet scheme exemplifies innovative use of resonant irises for achieving highly compact filters that can be manufactured with simple multilayer fabrication steps for use in future terahertz applications.

A chaos-based image compression and encryption scheme using fractal coding and adaptive-thresholding sparsification

With the wide application of digital images in the network, the problems of information security and transmission efficiency need to be solved urgently. Fractal encoding as an efficient compression method, its security is not guaranteed. To solve these problems, this paper proposes a new image compression and encryption scheme based on chaotic map and frequency division joint compression coding, which uses the combination of fractal encoding and adaptive-thresholding sparsification to compress images. Arnold scrambling and chaos-based plane scrambling diffusion are embedded in two compression processes respectively. In addition, an efficient encryption algorithm is designed based on the coupling-enhanced chaotic map, which encrypt the compressed data as a whole through simultaneous scrambling of parent blocks and sub-blocks and zigzag round-trip diffusion. The simulation results demonstrate that the proposed image compression and encryption scheme is suitable for gray and color images, and can achieve higher compression ratio and improve the efficiency of fractal compression. The designed encryption algorithm is reliable, and more efficient than several advanced image encryption schemes.

The Advancement of Solid-State Transformer Technology and Its Operation and Control with Power Grids: A Review

Solid-state transformer (SST) technology is one of the developing technologies that will be widely used in the future to integrate low-voltage and high-voltage networks with control circuitries and power electronics converters, facilitating renewables integration in smart grid applications. SST technology has crucial key advantageous features, including compact size and weight, low cost, and ease of connection in offshore applications. However, SST technology exhibits a few concerns, such as implementation, protection, economic, and communication compatibility, that need to be addressed. This paper aims to review SST technology with its advanced control schemes and provide future directions for research and development, applications, and prospects. In line with this, highly cited SST technology papers are examined to derive and summarize concerning issues related to its operation and control with further research development of power grids. Moreover, this review discusses the assessment and state-of-the-art technology of SSTs in different applications, focusing on configurations, control circuitry, and their drawbacks and benefits. Numerous issues and challenges of SST technology are explored to identify the existing knowledge gaps and potential future recommendations. All these critical analyses, information, and evaluations would benefit power engineers and researchers in developing and implementing advanced intelligent SST technologies for sustainable energy management in future power systems.

Fractional Order Modeling of PWR Pressurizer Dynamics and Fractional Order Nonlinear H∞ Controllers Design in LabVIEW

The novel fractional order intelligent transient dynamics and advanced fractional order nonlinear robust control synthesis scheme of the Pressurized Water Reactor (PWR) pressurizer are addressed in this research work. The Graphical User Interface (GUI) is designed for closed-loop model-based PWR pressurizer dynamical studies in LabVIEW. Based on the demand for power, the reactor power and turbine power are predicted using a fractional order backpropagation algorithm in an open loop configuration. Using turbine power and heater power as input variables, pressurizer level, pressurizer pressure and coolant average temperature as output variables, the open loop multi-input multi-output (MIMO) dynamic model of pressurizer is estimated using fractional order artificial intelligence in LabVIEW. Four fractional order robust nonlinear H∞ sub-controllers are designed for charging flow, spray flow, proportional heaters power and backup heaters power. All the dynamic controller models are in fractional order nonlinear H∞ framework and are designed in LabVIEW. The performance of the proposed design work is evaluated in closed loop configuration at 100 %, 75 % and 15 % in steady state conditions. Dynamic transient analysis is performed from 100% to 90% power reduction scenario and found satisfactory and within design limits and robust bounds.

A Comprehensive "Real-World Constraints"-Aware Requirements Engineering Related Assessment and a Critical State-of-the-Art Review of the Monitoring of Humans in Bed.

Currently, abnormality detection and/or prediction is a very hot topic. In this paper, we addressed it in the frame of activity monitoring of a human in bed. This paper presents a comprehensive formulation of a requirements engineering dossier for a monitoring system of a “human in bed” for abnormal behavior detection and forecasting. Hereby, practical and real-world constraints and concerns were identified and taken into consideration in the requirements dossier. A comprehensive and holistic discussion of the anomaly concept was extensively conducted and contributed to laying the ground for a realistic specifications book of the anomaly detection system. Some systems engineering relevant issues were also briefly addressed, e.g., verification and validation. A structured critical review of the relevant literature led to identifying four major approaches of interest. These four approaches were evaluated from the perspective of the requirements dossier. It was thereby clearly demonstrated that the approach integrating graph networks and advanced deep-learning schemes (Graph-DL) is the one capable of fully fulfilling the challenging issues expressed in the real-world conditions aware specification book. Nevertheless, to meet immediate market needs, systems based on advanced statistical methods, after a series of adaptations, already ensure and satisfy the important requirements related to, e.g., low cost, solid data security and a fully embedded and self-sufficient implementation. To conclude, some recommendations regarding system architecture and overall systems engineering were formulated.

Coupled and Synchronization Models of Rhythmic Arm Movement in Planar Plane.

Nonlinear dynamics have become a new perspective on model human movement variability; however, it is still a debate whether chaotic behavior is indeed possible to present during a rhythmic movement. This paper reports on the nonlinear dynamical behavior of coupled and synchronization models of a planar rhythmic arm movement. Two coupling schemes between a planar arm and an extended Duffing-Van der Pol (DVP) oscillator are investigated. Chaos tools, namely phase space, Poincare section, Lyapunov Exponent (LE), and heuristic approach are applied to observe the dynamical behavior of orbit solutions. For the synchronization, an orientation angle is modeled as a single well DVP oscillator implementing a Proportional Derivative (PD)-scheme. The extended DVP oscillator is used as a drive system, while the orientation angle of the planar arm is a response system. The results show that the coupled system exhibits very rich dynamical behavior where a variety of solutions from periodic, quasi-periodic, to chaotic orbits exist. An advanced coupling scheme is necessary to yield the route to chaos. By modeling the orientation angle as the single well DVP oscillator, which can synchronize with other dynamical systems, the synchronization can be achieved through the PD-scheme approach.

An Enhanced Z-Source Switched MLI Capacitor for Integrated Micro-Grid with Advanced Switching Pattern Scheme

In this paper, the Enhanced Z-Source Switched Capacitor Multi-Level Inverter (EZSC-MLI) is presented, which can generate a greater number of levels and magnitude in output. The proposed MLI has greater recognition due to its low harmonic profile, fewer switching components, compact size, low switch stress, isolated DC supply, high efficiency, and low cost. A high voltage boost factor is achieved by using the Z-Source. The Switched Capacitor module is used for charging all the capacitors to equal voltage magnitude based on a self-balanced scheme. The proposed topology for grid integration requires dual control loops, a primary voltage control loop, and a secondary current control loop. The performance of the proposed 7-level topology for grid integrated systems is verified with multi-carrier advanced modulation schemes and by simulations carried out in Matlab/Simulink.

Advanced security model for multimedia data sharing in Internet of Things

AbstractSharing a file that contains multimedia data among the different peers of wireless Internet of Things (IoT) networks has several challenges. One of the main challenges is their centralized system, which leads to high‐security risk and low user reachability. One solution could be to simply change the system to a decentralized network by using the blockchain network to store these files. However, it may solve the low user reachability and security problem at the cost of low latency, longer response time, scalability and privacy issues. Therefore, this article uses the advanced blockchain scheme and distributes InterPlanetary File System. We also presented the system framework and its working. Finally, we do the security analysis of our proposed system and found that it has strong potential to solve most of the security challenges that traditional system faces. Moreover, our proposed approach can be applied to any file‐changing wireless IoT network that needs to exchange multimedia data such as healthcare data, IoT data in wearable devices, traffic data in smart cities, etc.

A New Numerical Approach for the Analysis of Variable Fractal and Fractional Order Differential Equations.

The variable fractional dimensions differential and integral operator overrides the phenomenon of the constant fractional order. This leads to exploring some new ideas in the proposed direction due to its varied applications in the recent era of science and engineering. The present papers deal with the replacement of the constant fractional order by variable fractional order in various fractal-fractional differential equations. An advanced numerical scheme is developed with the help of Lagrange three-point interpolation and further, it is employed for the solution of the proposed differential equations. However, the properties of these new operators are presented in detail. Finally, the error analysis is also conducted for the numerical scheme deployed. The results are validated by the suitable choice of applications to real-life problems. The well- known multi-step-Adams–Bashforth numerical scheme for classical differential equations is recovered when the non-integer order is one.

Development of a P1-filling process to increase the cell performance in the copper indium gallium Selenide photovoltaics by implementation of the inkjet technology

In this paper, the suitability of integrating a digital manufacturing tool i.e. inkjet technology is exemplified to increase the electrical performance of thin-film based Copper Indium Gallium Selenide (CIGS) photovoltaic (PV) modules. Typically, the series connection within the PV module is established conventionally by three adjacent scribing lines at different deposition stages (patterning P1, P2 and P3). The total width of these three scribes is typically in the order of several 100's of microns and is electrically inactive, so these regions are lost areas for current generation (up to 5% “dead area”). To reduce the line width of the first scribe (P1) without compromising on the electrical parameters, we have concentrated on accurately filling of the interconnect P1-trenches with insulating material using inkjet technology, as initial step to realize the concept for dead area reduction by developing a P1-filling process (< 80 μm printed filling width) for laser structured/patterned trenches of ~50 μm width. With the right combination of carefully selected material interface, inkjet printhead, deposition settings, pattern digitalization & other parameters, we are able to develop an industry relevant micro-filling process. And, show an increase in the performance of PV cells, as compared to the reference modules. The results lay the foundation for direct up-scaling to industrial-size substrate areas and open up the possibility to realize all kinds of advanced interconnect schemes for the next generation thin-film CIGS PV modules. • Digital manufacturing of P1-filling for the thin-film photovoltaics in conjunction with laser process. • Approach towards reduction of dead-area in photovoltaics. • Inkjet printing of functional material within μm range channels/trench (< 80 μm). • Increase in photovoltaic performance (fill factor & module efficiency). • P1-filling process developed under ambient condition, without any inert atmosphere.

An Advanced Echo Separation Scheme for Space-Time Waveform-Encoding SAR Based on Digital Beamforming and Blind Source Separation

To achieve high-resolution and wide-swath (HRWS) imaging, a space-time waveform-encoding (STWE) spaceborne synthetic aperture radar (SAR) system is adopted. In rugged terrain, the beam-pointing mismatch problem will appear when the traditional digital beamforming (DBF) technique is used to separate the received echoes. This problem leads to decreasing the received echo’s gain, deteriorating the quality of the image product and making the interpretation of SAR image difficult. To address this problem, an advanced echo separation scheme for STWE spaceborne SAR based on the DBF and blind source separation (BSS) is put forward in this paper. In the scheme, the echoes are transmitted within the adjacent pulse repetition intervals to simulate multiple beams, and the scattered echoes are received by the sixteen-channel antennas in elevation simultaneously. In post-processing, a detailed flow is adopted. In the method, the DBF is firstly performed on received echoes. Due to the error caused by terrain undulation, the degree of echo separation is not enough. Then, the BSS is performed on the multiple echoes obtained after the DBF processing. Finally, the highly separated echo signal can be obtained. In this scheme, there is no need to perform the direction of arrival (DOA) estimation before the DBF processing, which saves valuable computing resources. In addition, to verify the proposed scheme, point target and distributed target simulations based on the 16-channel data of an elevation X-band DBF-SAR system are carried out. The results of point targets indicate that the residual echo caused by rough terrain can be reduced by more than 14 dB using the proposed scheme. The proposed scheme can be directly implemented into existing SAR systems; thus, it does not increase the complexity of the system design. The scheme has the potential to be applied to future spaceborne SAR missions.

A High-Precision Elastic Reverse-Time Migration for Complex Geologic Structure Imaging in Applied Geophysics

High-precision elastic reverse-time migration (ERTM) imaging has always been one of the trends in the development of geophysics. However, current wavefield simulations using time-domain finite-difference (FD) approaches in ERTM have second-order temporal accuracy, resulting in travel time changes and waveform distortion in wavefield propagation with large time steps, i.e., temporal dispersion. Errors caused by the temporal dispersion can lead to erroneous imaging locations and out-of-focus diffraction events. A new ERTM and its workflow are established here using temporal and spatial high-order FD accuracy wavefields and the vector-based imaging condition. Our method computes elastic vector-based wavefields by solving a P- and S-wave decomposition form of a quasi-stress–velocity equation. An advanced finite-difference scheme is employed in the wavefield solution to achieve simulation with temporal fourth-order accuracy and spatial arbitrary even-order accuracy. The normalized dot-product imaging condition of the source and receiver P/S wavefields is then applied to generate high-quality images. The elastic wavefield simulation and ERTM numerical examples presented here reveal that the anti-dispersion workflow can improve modeling and imaging accuracy. In addition, the field data application shows that our method can achieve reasonable and reliable ERTM images. This method can integrate the most advanced imaging techniques into this computational framework to improve imaging accuracy.

Properties of ultrathin molybdenum films for interconnect applications

The structural and electrical properties of Mo thin films with thicknesses between 3 and 50 nm, deposited by physical vapor deposition, have been evaluated in order to assess the potential of Mo as an alternative to Cu or W for nano-interconnect applications. Mo films deposited on SiO2/Si (100) were polycrystalline with randomly oriented grains close to the interface and the progressive formation of a (110) texture above 5 nm film thickness. Adhesion between Mo and low-κ dielectrics was strong with adhesion energies above 5 J/m2. The films showed intrinsic tensile stress of ∼1 GPa, which decreased with increasing thickness. The Mo resistivity showed a weaker thickness dependence than Cu, which rendered Mo competitive with conventional TaN/Cu/TaN metallization below metal thicknesses of 8 nm. Semiclassical resistivity modeling found that the thin film resistivity was limited by grain boundary scattering with a reflection coefficient of R = 0.46 ± 0.03. Furthermore, the effects of Mo deposition on different dielectric and metallic substrates and of post-deposition annealing up to 950ºC were investigated. Crystallinity, impurity concentration, and resistivity were all affected by the substrate. Post-deposition annealing in H2 led to continuous grain growth, followed by recrystallization at 860ºC. Furthermore, annealing at 650°C led to compressive stress in the films. By contrast, annealing in H2/N2 led to the incorporation of N and ultimately to Mo nitride formation above 500ºC. The results indicate that Mo can be a promising candidate for advanced interconnect metallization schemes if surface oxidation as well as impurity incorporation can be kept under control.

Data-driven discovery of the governing equations of dynamical systems via moving horizon optimization

Discovering the governing laws underpinning physical and chemical phenomena entirely from data is a key step towards understanding and ultimately controlling systems in science and engineering. Noisy measurements and complex, highly nonlinear underlying dynamics hinder the identification of such governing laws. In this work, we introduce a machine learning framework rooted in moving horizon nonlinear optimization for identifying governing equations in the form of ordinary differential equations from noisy experimental data sets. Our approach evaluates sequential subsets of measurement data, and exploits statistical arguments to learn truly parsimonious governing equations from a large dictionary of basis functions. The proposed framework reduces gradient approximation errors by implicitly embedding an advanced numerical discretization scheme, which improves robustness to noise as well as to model stiffness. Canonical nonlinear dynamical system examples are used to demonstrate that our approach can accurately recover parsimonious governing laws under increasing levels of measurement noise, and outperform state of the art frameworks in the literature. Further, we consider a non-isothermal chemical reactor example to demonstrate that the proposed framework can cope with basis functions that have nonlinear (unknown) parameterizations.

A methodology for improved TSO-DSO coordination in grid operation planning

The pan-European electric grid evolves towards a network with a wide diversity of (micro-)energy systems spread throughout all voltage levels. At the transmission level, this poses new challenges for cross-border energy exchange. At the distribution level small-scale generation, storages, and controllable loads offer flexibility available for provision of ancillary services. Meeting the EU energy supply policy while optimally operating the network under these conditions needs advanced TSO-DSO collaboration schemes. Taking this into account, this paper proposes a methodology which combines application of local flexibility management and control with central grid operation planning in order to meet planning criteria for future network operation. We discuss motivation and methodology process and present results of a proof-of-concept where the methodology was applied to a synthetic benchmark grid model.

Effect of dust on rainfall over the Red Sea coast based on WRF-Chem model simulations

Abstract. Water is the single most important element of life. Rainfall plays an important role in the spatial and temporal distribution of this precious natural resource, and it has a direct impact on agricultural production, daily life activities, and human health. One of the important elements that govern rainfall formation and distribution is atmospheric aerosol, which also affects the Earth's radiation balance and climate. Therefore, understanding how dust compositions and distributions affect the regional rainfall pattern is crucial, particularly in regions with high atmospheric dust loads such as the Middle East. Although aerosol and rainfall research has garnered increasing attention as both an independent and interdisciplinary topic in the last few decades, the details of various direct and indirect pathways by which dust affects rainfall are not yet fully understood. Here, we explored the effects of dust on rainfall formation and distribution as well as the physical mechanisms that govern these phenomena, using high-resolution WRF-Chem simulations (∼ 1.5 km × 1.5 km) configured with an advanced double-moment cloud microphysics scheme coupled with a sectional eight-bin aerosol scheme. Our model-simulated results were realistic, as evaluated from multiple perspectives including vertical profiles of aerosol concentrations, aerosol size distributions, vertical profiles of air temperature, diurnal wind cycles, and spatio-temporal rainfall patterns. Rainfall over the Red Sea coast is mainly caused by warm rain processes, which are typically confined within a height of ∼ 6 km over the Sarawat mountains and exhibit a strong diurnal cycle that peaks in the evening at approximately 18:00 local time under the influence of sea breezes. Numerical experiments indicated that dust could both suppress or enhance rainfall. The effect of dust on rainfall was calculated as total, indirect, and direct effects, based on 10-year August-average daily-accumulated rainfall over the study domain covering the eastern Red Sea coast. For extreme rainfall events (domain-average daily-accumulated rainfall of ≥ 1.33 mm), the net effect of dust on rainfall was positive or enhancement (6.05 %), with the indirect effect (4.54 %) and direct effect (1.51 %) both causing rainfall increase. At a 5 % significance level, the total and indirect effects were statistically significant whereas the direct effect was not. For normal rainfall events (domain-average daily-accumulated rainfall &lt; 1.33 mm), the indirect effect enhanced rainfall (4.76 %) whereas the direct effect suppressed rainfall (−5.78 %), resulting in a negative net suppressing effect (−1.02 %), all of which were statistically significant. We investigated the possible physical mechanisms of the effects and found that the rainfall suppression by dust direct effects was mainly caused by the scattering of solar radiation by dust. The surface cooling induced by dust weakens the sea breeze circulation, which decreases the associated landward moisture transport, ultimately suppressing rainfall. For extreme rainfall events, dust causes net rainfall enhancement through indirect effects as the high dust concentration facilitates raindrops to grow when the water vapor is sufficiently available. Our results have broader scientific and environmental implications. Specifically, although dust is considered a problem from an air quality perspective, our results highlight the important role of dust on sea breeze circulation and associated rainfall over the Red Sea coastal regions. Our results also have implications for cloud seeding and water resource management.

Intelligent Dual Active Protocol Stack Handover Based on Double DQN Deep Reinforcement Learning for 5G mmWave Networks

The recently proposed dual active protocol stack (DAPS) handover (HO) is one of the mobility enhancements that can effectively reduce the handover interruption time (HIT) in 5G networks. By using a DAPS solution, users can be connected to both the source cell and target cell during the HO execution phase, and thereby 0 ms of HIT becomes possible. However, the DAPS HO procedure may fail in 5G networks due to the channel characteristics of millimeter-wave (mmWave) signals. Since mmWave links are vulnerable to blockages, the received signal quality may degrade suddenly, which gives rise to an abrupt outage before DAPS HO can be completed. In this paper, a novel learning-based DAPS HO technology named intelligent DAPS (I-DAPS) HO is proposed to avoid sudden radio link failure (RLF) while providing a high data rate. The proposed I-DAPS HO uses a double deep Q-network (DDQN) deep reinforcement learning (DRL) framework, where blockage predictions are made based on past received signal data such that RLFs can be actively avoided. The performance evaluation demonstrates that the proposed I-DAPS HO scheme can effectively avoid RLF and improve the throughput performance compared to advanced 5G HO schemes.

An Advanced and Efficient Cluster Key Management Scheme for Agriculture Precision IoT Based Systems

Things that connect to other devices &amp; systems via Internet or communication networks are called IoT. It can also be said as a network of wireless sensors connected to a cloud and controlled by embedded devices. Considering the large framework of IoT, it becomes a little difficult to maintain security at each sensor node especially with limited information regarding hardware and deployment capabilities. Therefore, management of keys has become a point of concern peculiarly taking account of node capturing attack. This paper proposes an advanced cluster key management scheme for agriculture precision which involves EBS constructor and Chinese remainder theorem together. Once the data is collected from the nodes and a list is created, it is sent from the Cluster Head to the Backend Server, which filters it for hostile IDs and ignore the unauthentic sensor, returning filtered list with preloaded keys, &amp; an authentication code to Cluster Head for use. To ensure added security, in this scheme encryption of data is done twice. Upon comparing the proposed scheme with others, it has been observed that we have achieved higher delivery ratio and reduced the energy consumption and packet drop rate to a great extent.

Extra Proximal-Gradient Network with Learned Regularization for Image Compressive Sensing Reconstruction.

Learned optimization algorithms are promising approaches to inverse problems by leveraging advanced numerical optimization schemes and deep neural network techniques in machine learning. In this paper, we propose a novel deep neural network architecture imitating an extra proximal gradient algorithm to solve a general class of inverse problems with a focus on applications in image reconstruction. The proposed network features learned regularization that incorporates adaptive sparsification mappings, robust shrinkage selections, and nonlocal operators to improve solution quality. Numerical results demonstrate the improved efficiency and accuracy of the proposed network over several state-of-the-art methods on a variety of test problems.