Influence of Se and Te substitutions at Tl-site of Tl(Ba,Sr)CaCu2O7 superconductor on the AC susceptibility and electrical properties

The Sr and Ba bearing Tl-1212 phase, Tl(Ba,Sr)CaCu₂O₇ is an interesting superconductor. The Sr only bearing TlSr₂CaCu₂O₇ is not easily prepared in the superconducting form. The Ba only bearing TlBa₂CaCu₂O₇ on the other hand does not show improvement in the transition temperature with elemental substitution. In this work the influence of multivalent Se (non-metal) and Te (metalloid) substitutions at the Tl-site of Tl_1-xM_x(Ba,Sr)CaCu₂O₇ (M = Se or Te) superconductors for x = 0–0.6 was studied. The samples were prepared via the conventional solid-state reaction method. XRD patterns showed a single Tl-1212 phase for x = 0 and 0.1 Se substituted samples. The critical current density at the peak temperature, T_p of the imaginary (χ”) part of the AC susceptibility (χ = χ’ +χ”), J_c-inter(T_p) for all samples was between 15 and 21 A cm⁻². The highest superconducting transition temperature was shown by the x = 0.3 Se-substituted sample (T_c-onset = 104 K, T_c-zero = 89 K, T_cχ’ = 104 K and T_p = 80 K). Te suppressed the superconductivity of Tl-1212 phase. The order of highest transition temperatures are as follows: Tl_1-xTe_x(Ba,Sr)CaCu₂O₇<Tl(Ba,Sr)CaCu₂O₇<Tl_1-xSe_x(Ba,Sr)CaCu₂O₇. This work showed that Se was better than Te in improving the transition temperature and flux pinning of the Tl-1212 phase. The roles of ionic radius of Se and Te on the superconductivity of Tl(Ba,Sr)CaCu₂O₇ are discussed in this paper.     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

Temperature dependent negative permittivity in solid solutions Sr2Mn1-xSnxO4 (x = 0, 0.3, 0.5)

A few compositions of the system Sr₂Mn_1-xSn_xO₄ (x = 0.0, 0.3, 0.5) were synthesized in the air by the solid-state ceramic route. A change in the sign (positive to negative) of the permittivity above a particular temperature (T_C) is observed at all the measured frequencies. The negative permittivity was analyzed by the Drude-Lorentz model. It was found that negative permittivity is caused by the plasma oscillations of thermally excited free charge carriers. Analysis of XPS spectra confirmed the presence of mixed-valence states of both Mn (Mn⁴⁺ and Mn³⁺) and Sn (Sn⁴⁺ and Sn²⁺) ions. The UV–vis.-IR spectroscopy results indicated generation of a large number of defect states in the forbidden bandgap region of Sr₂MnO₄ on the substitution of Sn at Mn site. Synthesized samples are promising metamaterials for radio frequency (10 Hz -2 MHz) region applications due to the high-temperature plasmonic behavior.     

Optimal operating conditions to maximize the net power of polymer electrolyte membrane fuel cells: The stack-system interface of a commercial 18 kW module

A new, experimental method based on air flow rate rather than current is presented to optimize operating parameters for the stacks and systems of proton exchange membrane fuel cells (PEMFCs) for maximizing their net power. This approach is illustrated for a commercial 18 kW PEMFC module. The impact of contamination pressure drop across the cathode air filter is also investigated on the compressor behavior. It is further shown that a 4V reduction in the compressor voltage reduces its power consumption by 9.1%. Using the 3D graphs of the power-pressure-flow data, it is found that the stack pressure of 180 kPaa is superior to the higher tested pressures as it enhances the net power by 7.0 and 13.7% at different conditions. Application of the present study will lead to the development of PEMFCs with higher power output by optimizing stack pressure, stoichiometry and air flow to properly deliver the system design specifications.     

Finite-time adaptive fuzzy control for a class of output constrained nonlinear systems with dead-zone

The article investigates the finite-time adaptive fuzzy control for a class of nonlinear systems with output constraint and input dead-zone. First, by skillfully combining the barrier Lyapunov function, backstepping design method, and finite-time control theory, a novel adaptive state-feedback tracking controller is constructed, and the output constraint of the nonlinear system is not violated. Second, the fuzzy logic system is used to approximate unknown function in the nonlinear system. Third, the finite-time command filter is introduced to avoid the problem of “complexity explosion” caused by repeated differentiations of the virtual control signal in conventional backstepping control schemes. Meanwhile, a new saturation function is added in the compensating signal for filter error to improve control accuracy. Finally, based on Lyapunov stability analysis, all the signals of the closed-loop are proved to be semi-globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood region of the origin in a finite time. A simulation example is presented to demonstrate the effectiveness for the proposed control scheme.     

Comprehensive numerical investigations on the thermal performance of heat recirculating micro combustor with pin fins for micro-thermal voltaic system applications

Aiming at improving the relatively low energy output and energy conversion efficiency of the micro-thermal voltaic (MTPV) system, an innovative heat recirculating micro combustor with pin fins is designed. The effects of pin fins arrangement, hydrogen/air equivalent ratio on the energy output and performance of CHMC, HMCP and HMCI are compared and investigated. The result shows that when the V_in is 6 m/s and Φ is 1.0, the emitter power of CHMC is 72.76W, and that of HCMP and HCMI micro combustor are 75.99W and 76.35W. and the emitter efficiency of CHMC, HCMP and HCMI is 41.93%, 43.26% and 44.01%. HMCI has better energy output capability compared with CHMC and HMCP. Even though, HMCI brings a higher pressure drop, it is within the acceptable range. When the V_in is 6 m/s, the pressure drop from the pin fins only accounts for 26.4% of the total pressure drop for HMCI. Through the study of equivalent ratio, it is found that HMCI has good adaptability in different equivalent ratio range. This work provides new ideas for the development of MTPV system in the future.     

Full AC analysis of value of DER to the grid and optimal DER scheduling

This paper presents part of the work ComEd and Quanta Technology have performed to quantify the locational and temporal value of DER to avoid distribution grid upgrade investments. It focuses on the formulation of a robust and efficient algorithm for DER optimal dispatch on a distribution feeder to mitigate the violation of current and voltage limits using the allocated cost of capacity and locational marginal value of real and reactive DER injection/withdrawal.     

Adaptive compensation of persistent actuator failures of nonlinear systems

In this article, adaptive compensation designs are developed for nonlinear systems with uncertainties from the system functions and persistent actuator failures of characterizations that (i) some unknown system inputs are stuck at some unknown fixed or varying values at unknown time instants and (ii) the failure pattern always switches from one to another and the switching does not stop. Such a controlled plant is described by an uncertain time-varying nonlinear system, and some robust adaptive feedback linearization based failure compensation results are studied for closed-loop system stabilization and bounded output tracking for some specific conditions. To improve the tracking performance in the presence of persistent actuator failures, a new adaptive control scheme is developed, using the failure indicator function which contains the failure pattern and failure time in the formulation. Detailed stability and tracking performance are shown. Simulation results are shown to verify the effectiveness of the proposed adaptive actuator failure compensation method.     

Scalable Clock Synchronization Analysis: A Symmetric Noncooperative Output Feedback Tubes-MPC Approach

In the cyber-physical environment, the clock synchronization algorithm is required to have better expansion for network scale. In this paper, a new measurement model of observability under the equivalent transformation of minimum mean square error (MMSE) is constructed based on basic measurement unit (BMU), which can realize the scaled expansion of MMSE measurement. Based on the state updating equation of absolute clock and the decoupled measurement model of MMSE-like equivalence, which is proposed to calculate the positive definite invariant set by using the theoretical-practical Luenberger observer as the synthetical observer, the local noncooperative optimal control problem is built, and the clock synchronization system driven by the ideal state of local clock can reach the exponential convergence for synchronization performance. Different from the problem of general linear system regulators, the state estimation error and state control error are analyzed in the established affine system based on the set-theory-in-control to achieve the quantification of state deviation caused by noise interference. Based on the BMU for isomorphic state map, the synchronization performance of clock states between multiple sets of representative nodes is evaluated, and the scale of evaluated system can be still expanded. After the synchronization is completed, the state of perturbation system remains in the maximum range of measurement accuracy, and the state of nominal system can be stabilized at the ideal state for local clock and realizes the exponential convergence of the clock synchronization system.      

基于峰谷分时电价的虚拟电厂日前申报及运行优化

鉴于分布式能源的出力性质为间歇性和波动性,影响电网安全高效运行,而虚拟电厂可解决此问题。对虚拟电厂进行申报-调度两阶段建模,以包含分布式电源(光电、风电和燃气轮机)和储能系统(储能电池)两大类分布式能源的虚拟电厂为例,基于光照强度及风速等不确定因素,研究峰谷分时电价下日前市场虚拟电厂的出力申报和运行调度策略并利用风险价值模型进行风险管理。结果表明,申报出力时,当电价激励足够高时储能电池方会参与出力,成功起到削峰填谷的作用;运行调度时,平衡出力偏差方面,储能电池发挥主要作用,且虚拟电厂运行调度策略具有一定的经济效益。