Changes In Control Parameters Research Articles

Investigating amplitude amplification in optimization-based control for a continuous stirred tank reactor

Quantum computers, which utilize quantum states called ‘qubits’ to process information, are becoming of increased interest in a variety of fields because they have the potential to outperform classical computers in certain situations. However, many challenges and hurdles remain, including the development of quantum algorithms that offer a speedup and can be applied to practical problems (current quantum algorithms that offer any sort of speedup are often only applicable in specific and sometimes contrived circumstances). Our previous works have studied the use of quantum computers and quantum algorithms for process control applications. Our prior work evaluated the use of a gate-based quantum amplitude amplification algorithm when applied to a model predictive control optimization problem, specifically evaluating a linear systems example. The results suggested that the algorithm may be suited for the specific linear problem studied (meaning that there is a high probability of obtaining the desired result when measuring the quantum state after the algorithm has been applied). However, the results cannot be generalized to a wider class of systems or to systems containing nonlinearities. To begin to gain an understanding of how the amplitude amplification algorithm might be generalized, this work evaluates the use of the algorithm for the optimization-based control for a nonlinear dynamic system (specifically, a continuous stirred tank reactor). We seek to understand aspects of the problem, such as the development of a solution space, the effects of changing process and control parameters, and the operation of the amplitude amplification algorithm itself. The results demonstrate the challenges that still exist, as well as a method involving inverse sampling transformations to potentially address these issues.

Impact of the FreeStyle Libre 2® System on Glycaemic Outcomes in Patients with Type 1 Diabetes-Preliminary Study.

We conducted a study of a cohort of 81 people with type 1 diabetes mellitus who used the FreeStyle Libre 2 (FSL2) sensor continuously for 3 months. Patients had not used a CGM before. The effectiveness of using the FSL2 system was assessed using AGP reports at two time points (3-4 weeks and 11-12 weeks of system use). Eight weeks after using FSL2, compared with results from 3-4 weeks of use, there were no differences in the glucose management indicator, time spent in range, above range and below range, or glucose variability. In the first month of FGM use, patients scanned the sensor significantly more often than in the following two months (p = 0.021). No significant differences were found in the change of the evaluated parameters when comparing patients by duration of diabetes and treatment method. Short-term use of FSL2 promotes a significant reduction in GMI in patients with more time spent in hyperglycaemia (especially > 250 mg/dL). In this short period of use, no other changes in glycaemic control parameters are observed.

Magnetron carbon structures obtained by high-frequency magnetron sputtering in Argon and Nitrogen

Purpose of research. Creation and characterization of carbon nanostructures by high-frequency magnetron sputtering from a carbon target in argon on a silicon substrate and in a reactive nitrogen environment, obtained on a Ni catalyst buffer layer. Methods. High-frequency magnetron sputtering on a silicon substrate with changes in control parameters: sputtering time power and working gas pressure Ar and N. Research was carried out using X-ray phase analysis, atomic force microscopy and holographic microscopy, Raman scattering. Results. The formation of carbon nanotubes, including single-walled ones, was confirmed by the method of Raman scattering of light along the lines ID 1363 and IG 1564 cm-1, as well as ωRDМ 308 and 227 cm–1. Using atomic force microscopic images, the fractal dimension of the nanofilms was calculated, which indicated their 3D nature. Based on X-ray phase analysis of magnetron nanofilms, the dimensions of the coherence region, texture, microdeformations and interplanar deformation distortions were determined. Conclusion. In carbon magnetron nanofilms, deformations of both signs occur: both compressive (∆a < 0) and tensile (∆a > 0). Carbon magnetron nanofilms are represented, among other things, by single-walled carbon nanotubes, the chirality of which in an argon environment is (6, 6), and in a reactive mixture of nitrogen and argon on a Ni buffer layer (7, 7). It was discovered that in high-frequency magnetron mode, silicon carbide is formed in both inert and reactive environments.

Fractional-Order Modeling and Nonlinear Dynamic Analysis of Forward Converter

To accurately investigate the nonlinear dynamic characteristics of a forward converter, a fractional-order state-space averaged model of a forward converter in continuous conduction mode (CCM) is established based on the fractional calculus theory. And nonlinear dynamical bifurcation maps which use PI controller parameters and a reference current as bifurcation parameters are obtained. The nonlinear dynamic behavior is analyzed and compared with that of an integral-order forward converter. The results show that under certain operating conditions, the fractional-order forward converter exhibits bifurcations characterized by low-frequency oscillations and period-doubling as certain circuit and control parameters change. Under the same circuit conditions, there is a difference in the stable parameter region between the fractional and integral-order models of the forward converter. The stable zone of the fractional-order forward converter is larger than that of the integral-order one. Therefore, the circuit struggles to enter states of bifurcation and chaos. The stability domain for low-frequency oscillations and period-doubling bifurcations can be accurately predicted by using a small signal model and a predictive correction model of the fractional-order forward converter, respectively. Finally, by performing circuit simulations and hardware-in-the-loop experiments, the rationality and correctness of the theoretical analysis are verified.

Stackelberg game based optimal water allocation from the perspective of energy-water nexus: A case study of Minjiang River, China

Energy and water are interdependent. Water is essential for energy production, and energy is indispensable for water processes. This paper develops a Stackelberg game based bi-level multi-objective model for optimal water allocation under uncertainties from an energy-water nexus perspective, which considers energy-related water consumption and water-related energy production. It handles the hierarchical decision-making problem between the river basin authority and the subareas, as well as the tradeoffs between social equality and ecological damage. The upper level of the model makes schemes on water allocation for multiple subareas, and the lower level decides on water allocation for different types of power generation. To generate reasonable and practical strategies for water allocation, ɛ-constraint method and KKT conditions are used. A case study of Minjiang River, China is given to demonstrate the applicability and practicality. Scenarios analysis is also conducted to demonstrate the effectiveness of the model and the effects of the control parameter changes. The results indicate that the river basin authority would allocate larger water resources to the subareas with higher energy demand. It is concluded that the proposed allocation method is advantageous to the subareas with higher water utilization efficiency. The proposed bi-level model demonstrates its reasonableness and superiority to the single-level model. Policy implications are finally provided to support the sustainable development of energy-water nexus.

Pattern formation for a charge transfer model with cross-diffusion

This paper is devoted to a charge transfer model with cross-diffusion under Neumann boundary conditions. By applying the linear stability theory, sufficient conditions for the occurrence of Hopf bifurcation and Turing instability are successfully established. Moreover, the amplitude equations are derived near the critical value of Turing instability according to the standard multiple-scale analysis. Our research indicates that the entire pattern generated by this model is controlled by the cross-diffusion parameter d21. Dynamic pattern formation is described theoretically and numerically, such as changing control parameters for spot pattern, strip pattern, and spot-strip pattern.

Representation learning algorithms for inferring machine independent latent features in pedestals in JET and AUG

Variational autoencoder (VAE)-based representation learning algorithms are explored for their capability to disentangle tokamak size dependence from other dependencies in a dataset of thousands of observed pedestal electron density and temperature profiles from JET and ASDEX Upgrade tokamaks. Representation learning aims to establish a useful representation that characterizes the dataset. In the context of magnetic confinement fusion devices, a useful representation could be considered to map the high-dimensional observations to a manifold that represents the actual degrees of freedom of the plasma scenario. A desired property for these representations is organization of the information into disentangled variables, enabling interpretation of the latent variables as representations of semantically meaningful characteristics of the data. The representation learning algorithms in this work are based on VAE that encodes the pedestal profile information into a reduced dimensionality latent space and learns to reconstruct the full profile information given the latent representation. Attaching an auxiliary regression objective for the machine control parameter configuration, broadly following the architecture of the domain invariant variational autoencoder (DIVA), the model learns to associate device control parameters with the latent representation. With this multimachine dataset, the representation does encode density scaling with device size that is qualitatively consistent with Greenwald density limit scaling. However, if the major radius of the device is given through a common regression objective with the other machine control parameters, the latent state of the representation struggles to clearly disentangle the device size from changes of the other machine control parameters. When separating the device size as an independent latent variable with dedicated regression objectives, similar to separation of domain and class labels in the original DIVA publication, the latent space becomes well organized as a function of the device size.

Preoperative duration of type 2 diabetes mellitus and remission after Roux-en-Y gastric bypass: A single center long-term cohort study.

Roux-en-Y gastric bypass (RYGB) has demonstrated excellent glycemic control and type 2 diabetes mellitus (T2DM) remission for patients with obesity and T2DM. Duration of T2DM is a consistent negative predictor of remission after RYGB. However, the exact timing to offer surgical intervention during the course of the disease is not well elucidated. We performed a retrospective cohort study between 2008-2020 to establish the exact association between duration of T2DM and remission after RYGB. We divided our cohort into quartiles of preoperative disease duration to quantify the change in remission rates for each year of delay between T2DM diagnosis and RYGB. We also compared the average time to remission and changes in glycemic control parameters. A total of 519 patients (67.2% female; age 53.4±10.7y; BMI 46.6±8.4kg/m2) with a follow-up period of 6.6±3.8 years were included. Remission was demonstrated in 51% of patients. Longer duration of T2DM was a significant negative predictor of remission with an estimated decrease in remission rates of 7% for each year of delay ([OR=0.931 (95% CI 0.892-0.971)]; P<0.001). Compared to patients with <3 years of T2DM, remission decreased by 37% for patients with 3-6 years, 64% for those with 7-12 years and 81% for patients with more than 12 years (P<0.001). Half of the patients reached T2DM remission after 0.5 and 1.1 years respectively for the first and second quartiles, while patients in the other quartiles never reached 50% remission. Lastly, we noted an overall improvement in all glycemic control parameters for all quartiles at last follow-up. Patients with a recent history of T2DM who undergo early RYGB experience significantly higher and earlier T2DM remission compared to patients with a prolonged history of preoperative T2DM, suggesting potential benefit of early surgical intervention to manage patients with obesity and T2DM.

Abduction/Adduction Assistance From Powered Hip Exoskeleton Enables Modulation of User Step Width During Walking.

As the first step to show its potential, the objective of this study was to investigate how human's step width reacted to hip exoskeleton's admittance control parameter changes during walking. Ten non-disabled individuals walked on a treadmill at a self-selected speed, while wearing our bilateral robotic hip exoskeleton. We used two equilibrium positions to define the direction of assistance. We studied the influence of multiple stiffness values in the admittance control on the participants' step width, step length, and electromyographic (EMG) activity of the gluteus medius. Step width were significantly modulated by the change of stiffness in exoskeleton control, while step length did not show significant changes. When the stiffness changed from zero to our studied stiffness values, the participants' step width started to modulate immediately. Within 4 consecutive heel strikes right after a stiffness change, the step width showed a significant change. Interestingly, EMG activity of the gluteus medius did not change significantly regardless the applied stiffness and powered direction. Tuning of stiffness in admittance control of a hip exoskeleton, acting in mediolateral direction, can be a viable way for controlling step width in normal walking. Unvaried gluteus medius activity indicates that the increase in step width were mainly caused by the assistive torque applied by the exoskeleton. Our study results pave a new way for future design and control of wearable robotics in enhancing mediolateral walking balance for various rehabilitation applications.

A Computer‐Aided Teleoperation System for Intuitively Controlling the Behavior of a Magnetic Millirobot within a Stomach Phantom

Untethered magnetic millirobots with a characteristic length of a few millimeters can be wirelessly controlled. They exhibit promising potential in a wide variety of applications, particularly for tasks in clinic workspaces. However, magnetically controlling these robots is counter‐intuitive and requires a steep learning curve, hindering their wide adoption. Herein, a computer‐aided teleoperation platform is developed to operate a soft millirobot, with its feedback control being conducted behind‐the‐scenes, bridging the user's inputs directly with the millirobot's actions to offer an intuitive control. This system enables untrained users to conveniently control the position and actions of the millirobot inside a human stomach phantom by pointing‐and‐clicking on a real‐time video monitor or using a keyboard. The platform automatically materializes the user's instructions by maneuvering a robotic arm with a tip‐mounted magnet to exert a magnetic field to induce the desired response from the millirobot. Experiments show that the system allows the user to intuitively operate the millirobot and deliver its cargo without splitting their attention to monitor the workspace or to calculate the constantly changing control parameters. This platform can lower the barrier for healthcare practitioners without engineering expertise to adopt miniature robotic systems into their workflow and realize these systems’ promising potential.

Cloud Botany: Shallow Cumulus Clouds in an Ensemble of Idealized Large‐Domain Large‐Eddy Simulations of the Trades

AbstractSmall shallow cumulus clouds (&lt;1 km) over the tropical oceans appear to possess the ability to self‐organize into mesoscale (10–100 km) patterns. To better understand the processes leading to such self‐organized convection, we present Cloud Botany, an ensemble of 103 large‐eddy simulations on domains of 150 km, produced by the Dutch Atmospheric Large Eddy Simulation model on supercomputer Fugaku. Each simulation is run in an idealized, fixed, larger‐scale environment, controlled by six free parameters. We vary these over characteristic ranges for the winter trades, including parameter combinations observed during the EUREC4A (Elucidating the role of clouds–circulation coupling in climate) field campaign. In contrast to simulation setups striving for maximum realism, Cloud Botany provides a platform for studying idealized, and therefore more clearly interpretable causal relationships between conditions in the larger‐scale environment and patterns in mesoscale, self‐organized shallow convection. We find that any simulation that supports cumulus clouds eventually develops mesoscale patterns in their cloud fields. We also find a rich variety in these patterns as our control parameters change, including cold pools lined by cloudy arcs, bands of cross‐wind clouds and aggregated patches, sometimes topped by thin anvils. Many of these features are similar to cloud patterns found in nature. The published data set consists of raw simulation output on full 3D grids and 2D cross‐sections, as well as post‐processed quantities aggregated over the vertical (2D), horizontal (1D) and all spatial dimensions (time‐series). The data set is directly accessible from Python through the use of the EUREC4A intake catalog.

Impact of the COVID-19 pandemic on the outcomes of Indonesian chronic disease management program

&lt;b&gt;Background: &lt;/b&gt;The&lt;i&gt; &lt;/i&gt;Indonesian Government launched chronic disease management program (PROLANIS) with the aim of improving clinical outcomes and preventing disease complications of patients with type 2 diabetes (T2D). During the coronavirus disease 2019 (COVID-19) pandemic, the overwhelmed healthcare system shifted resources away from non-communicable diseases in the attempt to mitigate it. Thus, the implementation of PROLANIS during the COVID-19 pandemic might not be as optimal as before the pandemic era, leading to worse clinical outcomes. This pilot study aims to evaluate the impact of the COVID-19 pandemic on PROLANIS in rural areas by analyzing the changes of metabolic control and renal function parameters.&lt;br /&gt; &lt;b&gt;Methods:&lt;/b&gt; This study used data from three PROLANIS groups report in rural areas in East Java Province, Indonesia. Study population was PROLANIS participants who came for six-month-evaluation in December 2019 (T0), June 2020 (T1), and December 2020 (T2). Evaluated metabolic control parameters were body mass index (BMI), blood pressure, hemoglobin A1C (HbA1C), total cholesterol (TC), high-density lipid, low-density lipid, and triglyceride (TG), whereas evaluated renal function parameters were blood urea nitrogen, serum creatinine, and urinary albumin. Independent t-test and Wilcoxon signed-rank test were used for statistical analyses. p-value &amp;lt;0.05 was considered statistically significant.&lt;br /&gt; &lt;b&gt;Results:&lt;/b&gt; Among 52 PROLANIS participants included in the analyses, four metabolic control parameters (BMI, blood pressure, TC, and TG) and all renal function parameters significantly worsened right after the pandemic started but improved 6 months afterwards. Meanwhile, HbA1C continuously worsened throughout the study period, albeit statistically insignificant.&lt;br /&gt; &lt;b&gt;Conclusions: &lt;/b&gt;The metabolic control and renal function parameters in our study population deteriorates especially in the beginning of the COVID-19 pandemic.

Mechanical–electrical‐grid model for the doubly fed induction generator wind turbine system considering oscillation frequency coupling characteristics

AbstractWith the evolution of renewable energies, many doubly fed induction generators (DFIGs) are being connected to the power grid, whose operation and grid‐connection stability have a major impact on the power grid. Currently, most studies focus on either modeling the mechanical–electrical section or the electrical‐grid section, and discussions have been limited to shaft oscillation or frequency coupling problems. In this study, a mechanical–electrical‐grid model of a DFIG was established to examine the impacts of wind speed and system control parameters on electrical damping and grid‐connection stability. The accuracy of the proposed model and validity of the analyses were verified using simulations. The following were observed: (1) In the case of changing wind speeds, the wind speed and the applied control model determine the shaft oscillation of DFIG, whereas the grid‐connected impedance on the rotor side is dependent on the wind speed. (2) At a constant wind speed, changes in control parameters under different control modes affect the dynamic characteristics of the drive train differently, whereas the grid‐connected impedance on the rotor side is primarily determined by the proportional gain of the inner/outer loop of the control system. The conclusions drawn from this study can further improve the safe and stable operation of DFIG wind power generation systems as well as their connection to the power grid.

Effect of outer-cylinder rotation on the radially heated Taylor–Couette flow

A Taylor–Couette setup with radial heating is considered where a Boussinesq fluid is sheared in the annular region between two concentric, independently rotating cylinders maintained at different temperatures. Linear stability analysis is performed to determine the Taylor number for the onset of instability. Two radius ratios corresponding to wide and thin gaps with several rotation rate ratios are considered. The rotation of the outer cylinder is found to have a general stabilizing effect on the stability threshold as compared to pure inner-cylinder rotation, with a few exceptions. The radial heating sets up an axial flow which breaks the reflection symmetry of isothermal Taylor–Couette flow in the axial coordinate. This symmetry breaking separates linear stability thresholds, and we find the fastest growing modes with both positive and negative azimuthal numbers for different parameters. Another important finding of the current study is the discovery of unstable modes in the Rayleigh-stable regime. Furthermore, closed disconnected neutral curves (CDNCs) are observed for both wide and thin gaps which can separate from or merge into open neutral–stability curves. Alternatively, CDNCs can also morph into open neutral stability curves as the rotation rate ratio is changed. CDNCs are observed to be sensitive to changes in control parameters, and their appearance/disappearance is shown to induce discontinuous jumps in the critical Taylor number. For both wide and thin gaps, the fastest-growing modes found in the pure corotation case are shown to have their origins in the instability islands at smaller values of rotation rate ratios.

Research and Performance Optimization of Jump-Takeoff in Autogyros

The main focus of this article is on the jump-takeoff method for autogyros. On the basis of a high-confidence autogyro model, we design a jump-takeoff simulation experiment to study and optimize jump-takeoff performance. Using a simplified version of blade element theory, we conduct secondary development on the YASim dynamics library in FlightGear software and construct a highly accurate auto-rotation rotor model. The implementation of jump-takeoff requires appropriate control parameters for collective angle and pre-rotation speed. We explore the minimum collective angle condition and minimum pre-rotation speed condition to obtain the jump-takeoff envelope, and we investigate the effect of changes in control parameters within the jump envelope on jump-takeoff performance. Furthermore, we optimize the jump-takeoff performance by varying the rotor diameter and blade tip weighting. Through this study of jump-takeoff performance, we are able to determine appropriate control parameters and rotor parameters for jump-takeoff schemes, establish parameter settings for simulations of jump-takeoff tests, and thereby lay the foundation for future experimental investigations of jump-takeoff of actual autogyros.

Chaotic Complex Hashing: A simple chaotic keyed hash function based on complex quadratic map

In the last two decades, the use of chaotic iterative maps in cryptographic systems, especially chaotic hash functions, has been expanding. Sensitivity to small changes in initial conditions and control parameters in chaotic systems is the best feature for designing a chaotic hash function. This paper introduces a new generalized chaotic map based on a complex quadratic map, which in addition to the chaotic behavior in the real and imaginary parts, also has a high key length, which can guarantee the security of a cryptographic system. Various analyses of dynamical systems, such as the bifurcation diagram and the Lyapunov exponent, show that the proposed chaotic map has the characteristics of a chaotic dynamical system. The foundation of a secure and efficient algorithm for designing and developing a new hash function lies in how to use this new chaotic map. Also, statistical analysis, collision analysis, key space analysis, and speed analysis use to prove the security and efficiency of the proposed method. The results of these analyses and their comparison with similar methods show that the proposed method can be a reliable and efficient method for practical applications in information technology.

Front Cover: Transitions of Collective Motions Driven by Phase Resetting (ChemPhysChem 12/2023)

The Front Cover describes the collective motion transformations of the active-gel-induced step changes of control parameters (light intensity in this case), inspired by biological populations. Such sudden changes of systematic or environmental parameters are also widely present in nature, such as the multimodal motions of fish schools induced by predator hunting. More information can be found in the Research Article by Irving R. Epstein, Qingyu Gao and co-workers.

Generative and discriminative training of Boltzmann machine through quantum annealing

A hybrid quantum-classical method for learning Boltzmann machines (BM) for a generative and discriminative task is presented. BM are undirected graphs with a network of visible and hidden nodes where the former is used as the reading site. In contrast, the latter is used to manipulate visible states’ probability. In Generative BM, the samples of visible data imitate the probability distribution of a given data set. In contrast, the visible sites of discriminative BM are treated as Input/Output (I/O) reading sites where the conditional probability of output state is optimized for a given set of input states. The cost function for learning BM is defined as a weighted sum of Kullback-Leibler (KL) divergence and Negative conditional Log-likelihood (NCLL), adjusted using a hyper-parameter. Here, the KL Divergence is the cost for generative learning, and NCLL is the cost for discriminative learning. A Stochastic Newton-Raphson optimization scheme is presented. The gradients and the Hessians are approximated using direct samples of BM obtained through quantum annealing. Quantum annealers are hardware representing the physics of the Ising model that operates on low but finite temperatures. This temperature affects the probability distribution of the BM; however, its value is unknown. Previous efforts have focused on estimating this unknown temperature through regression of theoretical Boltzmann energies of sampled states with the probability of states sampled by the actual hardware. These approaches assume that the control parameter change does not affect the system temperature; however, this is usually untrue. Instead of using energies, the probability distribution of samples is employed to estimate the optimal parameter set, ensuring that the optimal set can be obtained from a single set of samples. The KL divergence and NCLL are optimized for the system temperature, and the result is used to rescale the control parameter set. The performance of this approach, as tested against the theoretically expected distributions, shows promising results for Boltzmann training on quantum annealers.

Optimisation of Imaging Confocal Microscopy for Topography Measurements of Metal Additive Surfaces

Additive manufactured surfaces, especially metal powder bed fusion surfaces, present unique challenges for measurement because of their complex topographies. To address these measurement challenges, optimisation of the measurement process is required. Using a statistical approach, sensitivity analyses were performed on measurement settings found on a commercial programmable array scanning confocal microscope. The instrument measurement process parameters were compared by their effects on three quality indicators: the areal surface texture parameter Sa, measurement noise, and number of non-measured points. An analysis was performed using a full factorial design of experiments for both the top and side surfaces of test surfaces made from Inconel 718 and Ti-6Al-4V using powder bed fusion. The results indicated that measurements of metal additive surfaces are robust to changes in the measurement control parameters for Sa, with variations within 5% of the mean parameter value for the same objective, surface, and measured area. The number of non-measured points and the measurement noise were more varied and were affected by the choice of measurement control parameters, but such changes could be predicted by the statistical models. The contribution offered by this work is an increased understanding of imaging confocal microscopy measurement of metal additive surfaces, along with the establishment of good practice guidance for measurements.

Parallel control strategy of energy storage interface converter with virtual DC machine

To improve the inertia and damping properties of the energy storage units (ESUs) interface converters in DC microgrids, an enhanced virtual DC machine (VDCM) control technique based on traditional P-U droop control is proposed in this paper. The control scheme actively introduces additional inertia and damping to the converter by equivalently analogizing the P-U droop control of the energy storage converter with the speed control of the DC machine. With the proposed control scheme, the operation stability of the DC microgrid can be improved effectively. Due to the problem that the energy storage interface converter under VDCM control cannot achieve power distribution, a coordinated control method of power proportional distribution of parallel energy storage converter is proposed. A small signal model is established to analyze the influence of control parameter changes on system performance. The effectiveness of the proposed method is verified by the simulation, the results show that the proposed strategy can actively introduce greater inertia and damping for the converters to improve the inertia and damping characteristics of the energy storage converter. At the same time, it can play a dynamic adjustment effect when the energy storage interface converters are connected in parallel, which can make each converter distribute power according to the set proportion in the three working modes of charging, discharging and charging and discharging switching.