Parameter Mismatch Research Articles

A novel method addressing NGS-based mappability bias for sensitive detection of DNA alterations.

A turning point in cancer research is the introduction of massively parallel sequencing technology which greatly reduced the cost and time for genome sequencing. This enhanced the scope for detecting and analyzing the role of structural alterations in cancer. However, certain bias exists in NGS-based approaches, which badly affects the CNV identification process. Moreover, DNA repeats existing in CNV regions need special attention as they will degrade the performance of majority of the existing CNV detection tools, even after applying generalized bias correction method. This motivated this work, where a novel method has been designed to address the issue of DNA repeats and thereby mappability bias existing in regions of CNV.The method consists of three phases, where the first phase computes the alignment information of uniquely mapped DNA reads, considering the base quality and base mismatch parameters at nucleotide level precision. The second and the third phase use a novel approach to allocate the non-uniquely mapped reads to an optimal region of the DNA repeats based on a probabilistic membership model. The proposed method is capable of identifying CNVs present in coding, as well as non-coding region of the DNA, and is also capable of detecting CNVs existing in DNA repeat regions. The methodology achieves a sensitivity greater than [Formula: see text] during the performed simulations, and on real data, the detected variants are validated with the database of genomic variants, where the percentage overlap is also greater than 95%, and has achieved much better breakpoint prediction, as compared with other popular bias correction CNV detection methods.

Noise and Parameter Mismatch in a Ring Network for Sensing Extremely Low Electric Fields

Over the past years, we have exploited the bistability features that are commonly found in many individual sensors to develop a network-based [ Acebrón et al. , 2003 ; Bulsara et al. , 2004 ; In et al. , 2003a ; In et al. , 2003b ; In et al. , 2005 ; In et al. , 2006 ; In et al. , 2012 ; Palacios et al. , 2005 ] approach to modeling, designing, and fabricating extremely sensitive magnetic- and electric-field sensors capable of resolving field changes as low as 150[Formula: see text]pT and 100[Formula: see text]fAmps, respectively. Higher sensitivity is achieved by exploiting the symmetry of the network to create infinite-period bifurcations that render the ensuing oscillations highly sensitive to symmetry-breaking effects from external signals. In this paper, we study the effects of noise on the response of a network-based electric-field sensor as well as the effects of parameter mismatch, which appear naturally due to material imperfections and noise. The results show that Signal-to-Noise Ratio (SNR) increases sharply near the onset of the infinite-period bifurcation, and they increase further as the coupling strength in the network increases while passing the threshold that leads to oscillatory behavior. Overall, the SNR indicates that the negative effects of highly contaminated signals are well-mitigated by the sensitivity response of the system. In addition, computer simulations show the network-based system to be robust enough to mismatches in system parameters, while the deviations from the nominal parameter values form regions where the oscillations persist. Noise has a smoothing effect over the boundaries of these regions.

Chaos synchronization of two coupled map lattice systems using safe reinforcement learning

When synchronizing two continuous typical chaotic systems, the state variables of the response system are usually bounded and satisfy the Lipschitz condition. This permits a universal synchronization method. In the synchronization of two high-dimensional discrete chaotic systems such as the coupled map lattice systems, the state variables of the response system often diverge, which makes it difficult to seek a universal synchronization method. To overcome this difficulty, bounded and hard constraints, which correspond to the concept of safety level III in reinforcement learning terms, on the state variables must be imposed, when the discrete response system is perturbed. We propose a universal method for synchronizing two discrete systems based on safe reinforcement learning (RL). In this method, the RL agent’s policy is used to reach the goal of synchronization and a safety layer added directly on top of the policy is used to satisfy safety level III. The safety layer consists of a one-step predictor for the perturbed response system and an action correction formulation. The one-step predictor, based on a next generation reservoir computing, is used to identify whether the next state of the perturbed system is within the chaotic domain, and if not, the action correction formula is activated to modify the corresponding perturbing force component to zero. In this way, the state of the perturbed system will remain in the bounded chaotic domain. We demonstrate that the proposed method succeeds in synchronization without divergence through a numerical example with two coupled map lattice systems, and the average synchronization time is 12.66 iteration steps over 1000 different initial conditions. The method works even when parameter mismatches and disturbances exist in the system. We compare the performance in both cases with and without the safety layer and find that successful synchronization is only possible in the former case, emphasizing the significance of the safety layer. The performance of the algorithm with optimal hyper-parameters obtained by Bayesian optimization is robust and stable.

Gate driver, snubber and circuit design considerations for fast‐switching series‐connected SiC MOSFETs

AbstractSeries connection of Silicon Carbide (SiC) Metal‐Oxide‐Semiconductor Field‐Effect Transistors (MOSFETs) is an interesting solution to design switches for voltages that are not yet commercially available or limited for single‐die devices. However, inherent static and dynamic voltage balancing must be achieved. Voltage imbalance is caused by the device parameters spread, whose impact is pronounced in low‐inductive circuit layouts. This study investigates the optimal design and tuning limits of resistor‐capacitor (RC)‐snubber circuits and non‐adaptive, standard, voltage‐source gate drivers for achieving the best balancing in transient and steady‐state voltage distributions among series‐connected discrete SiC MOSFETs operating at speeds up to . It has been shown that a larger parameter mismatch will lead to uneven switching energy losses and larger voltage imbalances. It was also experimentally shown that increasing the gate resistor to slow down the devices will not always improve balancing when their parameter spread is large. Thus, tuning recommendations for the RC‐snubber circuit and gate driver were developed based on these findings.

Sliding Mode Speed Control for PMSM Based on Model Predictive Current

To enhance the dynamic performance and disturbance rejection capability of the permanent magnet synchronous motor speed control system, a novel speed control method based on a novel sliding mode control (NSMC) and load torque observer is proposed on the basis of model predictive current control (MPCC) with a sliding mode disturbance observer. First, on the basis of MPCC, the influence of parameters such as resistance, inductance, and flux linkage on MPCC is analyzed. To address the aggregated disturbance caused by parameter mismatches, a piecewise square-root switching function sliding mode disturbance observer (SMDO) is designed to enhance the robustness of the parameters. To address the poor dynamic performance and inadequate robustness resulting from the proportional-integral-controller (PI) velocity loop control in the MPCC, a novel NSMC velocity control method is proposed. This method utilizes the hyperbolic sine function and fractional-order integral sliding mode surface, resolving the dilemma faced by traditional slide mode controllers (SMC) in balancing fast response and reduced vibration. Additionally, to enhance the system’s disturbance rejection capability, a sliding mode torque observer (SMTO) is designed to continuously update the observed load torque value into the NSMC controller, achieving speed compensation control. Finally, through comparative experiments among the proportional integral controller (PI), SMC, NSMC, and NSMC + SMTO, the results indicate that the proposed NSMC + SMTO exhibits the best speed response, steady-state characteristics, and disturbance rejection capability.

Quasi-projective synchronization of non-identical time-varying delayed quaternion-valued neural networks with interaction terms: Direct method

This article investigates the quasi-projective synchronization (QPS) of non-identical quaternion-valued neural networks (QVNNs) with time-varying delays and interaction terms. Due to parameter mismatches between the drive and response systems, complete projective synchronization is not possible. As a result, a novel idea, known as QPS, is explored up to a small error bound. The direct method is used to derive several general criteria to achieve QPS. The Lyapunov stability theory is employed to estimate the error bound. The numerical simulation results are presented graphically for various special cases to demonstrate the synchronization of time-varying delayed QVNNs with mismatched parameters and interaction terms, which confirm the efficiency of the presented research results. The second example showcases the application of QVNNs coupled with associative memory to illustrate its efficacy in accurately restoring the original color image patterns.

Amorphous B coated Mg nanopowder induces low angle grain boundaries and enhances J c of MgB2 wire

In this work, amorphous B coated Mg nanopowder (BCMN) is synthesized and transport property of MgB2 superconducting wire is significantly enhanced with different contents of BCMN. BCMN has high reactivity since it contains nanoscale Mg and amorphous B. It allows to obtain MgB2 nanocrystals at only 400 °C with the compression of a lattice parameter and expansion of c lattice parameter compared to MgB2 formed by micron-sized Mg mixed with amorphous B (Mg + B) powders. These MgB2 nanocrystals serve as crystal nuclei and promote the crystallization and growth of MgB2. The mismatch of different lattice parameters prepared using BCMN and M + B powders induces low angle grain boundaries (LAGBs) embedded in MgB2 grains. LAGBs act as plane defects, leading to a dominant surface pinning mechanism and an enhancement in the critical current density on the magnetic field (J c(H)). At 4.2 K in 6 T, transport critical current density (J ct) of wire with 20 wt.% BCMN is 6.7 × 104 A·cm−2, approximately 1.8 times wire with 0 wt.% BCMN.

Secure impulsive cluster synchronization control of complex dynamical networks with parameter mismatches under deception attacks

The issue of secure cluster synchronization (SCS) of complex dynamical networks (CDNs) with parameter mismatches under deception attacks is investigated in this article. Firstly, the deception attacks are supposed to inject false information into controller-to-actuator channels, while the occurrences of deception attack are always subject to Bernoulli distribution. Secondly, an improved pinning impulsive control strategy is designed. The impulsive gain in the control strategy is take from a new range. Moreover, we obtain the estimation of synchronization error upper bound and boundaries of secure synchronization region. The synchronization error will converge to a bounded ball that depends on attack energy at a exponential rate. The CDNs without deception attacks under pinning impulsive control can achieve complete cluster synchronization. Finally, two numerical examples with 20 nodes based on MATLAB software are presented to demonstrate the effectiveness of the theoretical results.

Model-free predictive flux vector control for N ∗ 3-Phase PMSM drives considering parameters mismatch

This paper develops a novel model free-predictive flux vector control method (MF-PFVC) for high-performance stator flux and torque monitoring of the multi-module three-phase PMSM (N ∗ 3-Phase PMSM) drive with parameters mismatch. The proposed MF-PFVC method is based on a convenient combination of direct predictive torque control (PTC) and model free predictive control techniques. First, the voltage source inverter (VSI) vector control of N ∗ 3-Phase PMSM is determined by the three-segment stator windings structure. Then, the parameter mismatch sensitivity of the N ∗ 3-Phase PMSM drive system is analyzed based on the conventional PTC. Furthermore, a novel MF-PFVC method with multivariable unknown disturbance integral terminal sliding mode observer (SMO) is proposed for N ∗ 3-Phase PMSM drive, which can effectively enhance robustness against the parameters mismatch. Finally, compared with the conventional PTC, the simulation and experimental results of MF-PFVC method illustrated improved stator flux control performance concerning lower stator flux ripple as well as reduced torque ripple while it can eliminate the influence of parameter mismatch.

Характеристики обнаружения когерентной системы при рассогласовании квадратурных каналов

Introduction – The operation of an adaptive coherent detection system is considered. The structure of a processing device is presented, containing a series-connected shaper of quadrature signal components that convert high-frequency input radio signals to video frequency, an adaptive multi-channel coherent storage device with threshold devices. Goal – To study the influence of amplitude and phase mismatches of quadrature channel parameters, which are inevitable in practice, on the operation of an adaptive coherent detection system. Formulation of the problem – Determine the spectral-correlation characteristics of the processed signals when the parameters of the quadrature channels are mismatched. Assess the influence of amplitude and phase deviations of the parameters of quadrature channels on the characteristics of a coherent detection system. Method – The complex variable method is used, in which the input high-frequency signals are represented at the video frequency by a pair of complex conjugate real components, shifted in phase by ninety degrees. Results – A model of the processed signal is presented at the video frequency, taking into account possible mismatches in the parameters of quadrature channels in practice. Expressions are obtained and graphs of the spectral density of the complex signal are plotted separately for the amplitude and phase mismatch of the quadrature channels. It is shown that the mismatch of quadrature channels leads to the appearance in the spectrum of the output signal of phase detectors of a component at a frequency that is the mirror frequency of the signal, which leads to an increase in the probability of false detection. The results of calculations of the probability of false detection of a signal depending on the deviation of the transmission coefficients of the quadrature channels and the phase mismatch of the reference voltages of the quadrature channels are presented. Conclusion – The significant influence of amplitude and phase mismatches of the parameters of quadrature channels on the spectral-correlation characteristics of the complex signal is shown. The mismatch of the parameters of the quadrature channels leads to deterioration in the detection characteristics of the coherent processing system. The research methodology used allows us to develop requirements for the quality of tuning quadrature channels.

Composite control based on FNTSMC and adaptive neural network for PMSM system

In this paper, a novel fixed-time non-singular terminal sliding mode control (NFNTSMC) method with an adaptive neural network (ANN) is proposed for permanent magnet synchronous motor (PMSM) system to improve PMSM performance. For nominal PMSM system without disturbance, a novel fixed-time non-singular terminal sliding mode control is designed to achieve fixed-time convergence property to improve the dynamic performance of the system. However, parameters mismatch and external load disturbances generally exist in PMSM system, the controller designed by NFNTSMC requires a large switching gain to ensure the robustness of the system, which will cause high-frequency sliding mode chattering. Therefore, an adaptive radial basis function (RBF) neural network is designed to approximate the unknown nonlinear lumped disturbance including parameters mismatch and external load disturbances online, and then the output of the neural network can be compensated to the NFNTSMC controller to reduce the switching gain and sliding mode chattering. Finally, the fixed-time convergence property and stability of the system are proved by Lyapunov method. The simulation and experimental results show that the presented strategy possesses satisfactory dynamic performance and strong robustness for PMSM system. And the proposed control scheme also provides an effective and systematic idea of the controller design for PMSM.

Research on secure communication technology based on phase conjugate feedback chaotic injection system

This paper presents an experimental scheme using optical method instead of phase conjugate light. We have implemented a phase conjugate feedback semiconductor laser chaotic system based on the four-wave mixing principle through an established optical fiber experimental platform. Based on the high-dimensional wideband chaotic signals generated by this system, we propose a two-channel secure communication scheme based on phase conjugate feedback, and analyze its delay hiding mechanism and synchronization characteristics. The effects of parameter mismatch and injection strength on synchronization performance and communication quality are also considered. Our experimental results show that by adjusting the injection strength and frequency detuning parameters, the system can produce signals with time-delay signature completely suppressed, thus achieving high-quality and high-security communications.

A Common Grounded Voltage Quadrupler ASL/PSC Hybrid Converter With Reduced Voltage Stress

Active switched inductor (ASL) dc-dc converters have very high step-up voltage gain but practically they suffer from high voltage stress across switches because of the resonance caused by the mismatch among the inductor values and switch parameters. This paper presents a new hybrid dc-dc converter topology derived from ASL and passive switched capacitors (PSC). The proposed topology has high step up voltage gain over wide range of duty cycle. The resonance due to mismatch of parameters is eliminated. The proposed converter has low voltage stress, low current stress on circuit elements and common ground between input and output ports. The circuit configuration, operating principle and steady state analysis of the proposed converter are presented. The key performance parameters are compared with that of other high-gain dc-dc converters. A 300W laboratory prototype is developed and experimental verification is carried out for the voltage step-up from 30V to 400V. The closed-loop operation is also verified experimentally and results are presented.

Integrated Parameter Identification Based on a Topological Structure for Servo Resonance Suppression

In the dual-inertia servo system, the servo resonance in which the characteristics are closely related to the mechanical parameters is usually suppressed by adding a notch filter. As a result, accurate mechanical parameter identification facilitates adjusting the notch parameters and avoiding the failed servo resonance suppression caused by the mechanical parameter mismatch. In this paper, a topological structure expanded by five improved Sliding-Mode Observers (ISMOs) is proposed to simultaneously identify the load speed, the motor inertia, the load inertia, the stiffness coefficient, and the load disturbance, where the load speed and the load disturbance are considered as intermediate variables to provide sufficient load-side information to ensure the identification accuracy of the mechanical parameters. Furthermore, according to the natural knowledge of the dual-inertia servo system, a time-varying interconnected gain with an equal derivative to the identification parameter in each ISMO is designed to promote the global convergence of the topological structure. Finally, the identified mechanical parameters are utilized to adjust the notch parameters for the servo resonance suppression. Simulation and experimental results are presented to indicate the superiority of the topological structure and the effectiveness of the servo resonance suppression.

A New Sliding-Mode Observer-Based Deadbeat Predictive Current Control Method for Permanent Magnet Motor Drive

This article proposes a new deadbeat predictive current control (DPCC) method based on a sliding-mode observer (SMO), which is applied in the field of permanent magnet motor control. A novel DPCC control method based on SMO is proposed according to the inherent issues of DPCC, which can effectively suppress internal parameter mismatch disturbances and external disturbances in the current loop. The mathematical model and derivation process of the proposed method are introduced. A simulation model is built and the effectiveness of the proposed method is verified. An experimental platform is built and the superiority of the proposed method is verified based on comparative experiments. Experimental results show that the proposed algorithm has strong robustness to the motor parameter mismatch. Compared with extended state observer (ESO) and adaptive observer (AO), the proposed algorithm has faster response speed and higher steady-state accuracy.

Random Numbers Generated Based on Dual-Channel Chaotic Light

This paper presents a chaotic system based on novel semiconductor nanolasers (NLs), systematically analyzing its chaotic region and investigating the influence of key parameters on the unpredictability of chaotic output. This study found that under optical feedback conditions, NLs generate chaos across a wide range of feedback parameters, with the highly unpredictable region completely overlapping with the chaotic region. Further injection into the slave lasers enhances the chaotic output, expanding the range of unpredictability. Additionally, we analyzed the impact of internal parameter mismatch on the complexity of chaotic signals and found it to be similar to the scenario when parameters are matched. Using this chaotic system as an entropy source, we constructed a random number generator (RNG) and investigated the effects of internal parameters mismatch and differences in the injection parameters on the generator’s performance. The simulation results show that the RNG performs well under different parameter settings, and the generated random sequences pass all random number tests successfully. Therefore, this chaotic system can yield a high-complexity chaotic light source with appropriate parameter selection, and when combined with effective post-processing, it can generate high-quality random numbers. This is crucial for advancing the realization of small-sized, high-randomness RNGs.

Hybrid impulses for almost sure quasi-synchronization of stochastic complex networks: an indefinite Lyapunov function method

This article studies quasi-synchronization of stochastic complex networks under hybrid impulses. With parameter mismatches, different from the previous quasi-synchronization results in the sense of mean square on stochastic complex networks, almost sure quasi-synchronization is investigated, in which noises have a positive effect on the synchronization. By utilizing the Lyapunov method, stochastic analysis theory, average impulsive interval, and average impulsive gain, we formulate the almost sure quasi-synchronization criteria when the average impulsive interval z 0 satisfies z 0 < + ∞ and z 0 = + ∞ , respectively, where the synchronizing impulses and the desynchronizing impulses are simultaneously considered. It is worth noticing that in our criteria of quasi-synchronization, the mutual restraints among the average impulsive interval, average impulsive gain, and noise intensity are given. Moreover, for the criteria in this article, the piecewise continuous scalar functions cannot only replace the constant coefficient of the upper bound estimation for the diffusion operator of a Lyapunov function in available literature but also even be unbounded, which has wider applications than some existing works. Our corollaries regarding complete synchronization and analyses of the synchronization for stochastic complex networks with aperiodic intermittent noises provide sufficient conditions in closed form. Finally, the theoretical results are applied to a coupled chaotic Lorentz system, and several numerical examples are presented to demonstrate the advantages of the theoretical results.

An adaptive bi-limit homogeneous sliding mode control for 3-DOF stabilized system of ship propulsion-assisted sail with prescribed performance

Sail-assisted ships have garnered widespread interest as a sustainable technology. The three-degree-of-freedom (3-DOF) stabilized system of ship propulsion-assisted sail is used to track the optimal azimuth of the sail and isolate the swaying of the ship's hull. In this paper, the problem of tracking control for the system with prescribed performance is investigated. By using an appointed-time prescribed performance function (APPF), a novel bi-limit homogeneous sliding surface with expected trajectory is proposed to improve the convergence rate, accuracy, and overshoot. Considering the external disturbance, a wind resistance model is established and compensated to reduce the conservatism and chattering. Adaptive extreme learning machines (AELMs) are employed to estimate the mismatch parameters caused by Reynolds number fluctuations and unmodelled dynamics. Finally, an adaptive bi-limit homogeneous sliding mode controller is constructed, by which the system can track the 3-DOF target signals with prescribed performance and suppress disturbances. The fixed-time reachability of the system is verified by Lyapunov analysis and fixed-time theory. To validate the effectiveness of the presented controller, numerical simulations and principle prototype experiments are conducted. The results are compared with other algorithms and show that regardless of the control performance or energy-saving benefits, the proposed method has a better effect.

Explicit Model Predictive Cascade-Free Direct Force and Torque Control of Quadrotors

High-performance control of quadrotors' force and torques is the basis for achieving excellent trajectory tracking. However, the conventional method indirectly controls the force and torques by controlling four permanent magnet synchronous motors' speeds, resulting in poor dynamic performance and static deviations when pneumatic parameters mismatch. Dedicated to achieving high-dynamic and high-accuracy control of force and torques, an explicit model predictive (EMP) cascade-free direct force and torque control method is proposed. Firstly, defining force current and torque currents, whereby a new structure is proposed. Then, dynamics of the quadrotor and motor are fused to design the linear robust predictive models of force and torques. Accordingly, the EMP controller is constructed to replace the traditional cascaded speed solution module and four speed controllers uniformly. The EMP controller generates the optimal current commands directly from the force and torque commands, realizing direct control without the speed loop. The new structure makes it possible to solve the pneumatic parameter mismatch problem with an auxiliary extended state observer, obtaining offset-free control. The proposed method optimizes the dynamic performance and steady-state accuracy of force and torques and improves the system's robustness. Finally, the proposed method's feasibility is verified by experiments.

Model-Free Predictive Current Control of SPMSM Based on Enhanced Extended State Observer

Conventional model-free predictive current control (MFPCC) using an extended state observer (ESO) based on ultra-local model significantly decreased the sensitivity to partial motor parameters. However, the performance and stability of ESO will be degraded in the face of amplified high-frequency (HF) perturbation caused by inductance parameter mismatch. To solve the above problem, an enhanced ESO-based MFPCC method is proposed in this article. On the basis of constructing the discrete equation of ESO, an accurate and fast calculation of inductance parameter is proposed, which is real-time convergence to the actual value by deadbeat control principle, and fed back to the lumped disturbance and current observation calculation. It effectively eliminates the effects of HF perturbation on ESO. Simultaneously, the inductance and compensated lumped disturbance obtained from ESO are provided to the MFPCC command voltage calculation module, further improving the parameter robustness of MFPCC. The stability of the proposed ESO is ensured by the design of observer gain. Experiments are carried out on 1kW surfaced permanent magnet synchronous machines (SPMSM) test bench. Compared with the conventional ESO-based MFPCC method, the proposed method is superior in dynamic response, steady-state performance, and parameter robustness.