Compensation Approach Research Articles

Thermal Drifts of Capacitive Flow Meters: Analysis of Effects and Model-Based Compensation

Capacitive sensing has become a favorable measurement technology for flow metering in pneumatic conveying systems. Multielectrode sensing structures and tomographic signal evaluations enable spatially resolved flow parameter estimation, which is of particular interest for pneumatically conveyed solids due to inhomogeneous particle distributions within the pipeline. The noninvasive working principle of capacitive sensors is an important feature for the application in industrial processes with harsh environments. However, cross sensitivities of the capacitive probe cause effects, such as temperature drifts of the measurements. For a reliable operation of capacitive flow meters in harsh environments, induced drifts have to be compensated. In this article, we present the detailed analyses of thermal effects within capacitive sensors. Based on the findings, a model-based temperature compensation approach is developed within the Bayesian framework. The performance of the proposed compensation approach is analyzed by a measurement-based validation within a climate chamber and by a simulation-based uncertainty quantification. The capability to obtain temperature-independent estimates with calibration measurements acquired at room temperature is demonstrated.

Irradiance Restoration Based Shadow Compensation Approach for High Resolution Multispectral Satellite Remote Sensing Images.

Numerous applications are hindered by shadows in high resolution satellite remote sensing images, like image classification, target recognition and change detection. In order to improve remote sensing image utilization, significant importance appears for restoring surface feature information under shadow regions. Problems inevitably occur for current shadow compensation methods in processing high resolution multispectral satellite remote sensing images, such as color distortion of compensated shadow and interference of non-shadow. In this study, to further settle these problems, we analyzed the surface irradiance of both shadow and non-shadow areas based on a satellite sensor imaging mechanism and radiative transfer theory, and finally develop an irradiance restoration based (IRB) shadow compensation approach under the assumption that the shadow area owns the same irradiance to the nearby non-shadow area containing the same type features. To validate the performance of the proposed IRB approach for shadow compensation, we tested numerous images of WorldView-2 and WorldView-3 acquired at different sites and times. We particularly evaluated the shadow compensation performance of the proposed IRB approach by qualitative visual sense comparison and quantitative assessment with two WorldView-3 test images of Tripoli, Libya. The resulting images automatically produced by our IRB method deliver a good visual sense and relatively low relative root mean square error (rRMSE) values. Experimental results show that the proposed IRB shadow compensation approach can not only compensate information of surface features in shadow areas both effectively and automatically, but can also well preserve information of objects in non-shadow regions for high resolution multispectral satellite remote sensing images.

Novel Double Compensation for Impedance–Frequency Characteristics of Rotary Ultrasonic Machining via Multiobjective Genetic Algorithm

Rotary ultrasonic machining (RUM) is a superior technology to machine hard and brittle materials. Traditionally, the compensation optimization for the RUM system is limited to a single resonant frequency. This article presents a novel double compensation approach for the impedance and frequency regulations of RUM via multiobjective genetic algorithm (MOGA) aiming to achieve the system resonance and monitor the machining process in real time. For this, we first establish the impedance model of the rotary ultrasonic holder (RUH) by adopting the T-type circuit that includes the comprehensive electromagnetic parameters. The obtained impedance model reveals that both frequency mismatch and impedance mismatch exist in the RUM system, causing the low voltage gain and low vibration transmission. To obtain the optimal compensation to match both the frequency and impedance, an optimization model-based MOGA is developed to intelligently search the accurate capacitance values, where the Pareto frontier is employed to visualize the capacitance solution distribution. Moreover, the response feature of the RUH system is attained by using the state-space equation. Detailed comparisons of four compensation topologies show that the series–series (SS) topology offers the optimal performance, which can match both of the frequency and the impedance well, improving the output active power 7.634 times compared to the conventional one. Finally, the validity of the proposed optimization method is confirmed by using both simulations and experiments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by the transmission efficiency and the stability of ultrasonic vibration in the rotary ultrasonic machining (RUM). The matching issue is critical in RUM because the whole system is very complicated with multiple components such as electrical devices, piezoelectric rings, and mechanical horn. In the conventional RUM, the system matching is limited to a single resonant frequency between the piezoelectric transducer and the electrical driver. However, the impedance matching is also important to influence the ultrasonic vibration. The problem of the ultrasonic matching stems from empirical experiences of the engineers’ or simple heuristic rules instead of advanced approaches. On the other hand, optimization-based techniques are viewed as a great success in industrial and engineering applications. With the assistance of our proposed numerical model and the corresponding simulation, we found that the multiobjective genetic algorithm (MOGA) can intelligently search the optimal compensated parameters to achieve the double matching for both the resonant frequency and the impedance in the RUM system. Finally, our experiments prove that the MOGA optimization can attain optimal transmission efficiency for the electrical voltage and current compared with conventional approaches. This significantly benefits the application of the RUM technology in industry, especially for the precision machining of the hard and brittle materials.

An adaptive PID-type sliding mode learning compensation of torque ripple in PMSM position servo systems towards energy efficiency

In this paper, for periodic motion tasks, combining adaptive PID-type sliding mode control (APIDSMC), model reference adaptive control (MRAC) and periodic adaptive learning control (PALC), a novel APIDSMC-PALC compensation approach towards energy efficiency is proposed to suppress the influence of torque ripple in permanent magnet synchronous motor (PMSM) servo systems. Using particle swarm optimization (PSO) algorithm, the equivalent control gain of sliding mode control is optimized to achieve energy efficiency during long-term operation. The objective of the proposed ripple compensation algorithm is to accurately approximate two dominant harmonic amplitudes in the torque ripple and generate an additional control effort for ripple compensation. Simulation and testbed experimental results demonstrate that with the proposed ripple compensation algorithm, the objective of excellent position tracking performance is ensured, and the energy efficiency is improved.

I Know What You Mean: Information Compensation in an Authoritarian Country

How do people address information deficiency caused by rigid control of information in authoritarian regimes? We argue that there exists an internally oriented information compensation approach through which people can glean extra information from official messages domestically. This approach does not violate state regulations directly and allows people to retrieve information not explicitly publicized by the government. We delineate the circumstances of internally oriented information compensation using the case of China. We conduct trend and text analysis on the data of millions of individual-level actions of Chinese Internet search engines and social media users during a large anticorruption campaign that conspicuously claimed to crack down on influential corrupt leaders without naming who exactly. We show that some Chinese netizens were able to identify the unnamed high-ranking officials targeted by the campaign based on negative official reports about their family members. Some of the netizens even correctly predicted the downfall of the officials months before the government’s announcements. As the existing literature is increasingly concerned about the threat of digital authoritarianism on throttling the free flow of information, our findings indicate that some authoritarian citizens, instead of passively accepting the government’s information control, acquired their own arts of information self-salvation. This, though not directly challenging the government, constitutes an everyday politics under digital authoritarianism.

Consecutive Compensator in Station-Keeping of a Surface Vessel

This paper addresses the problem of station-keeping of a surface vessel by means of the consecutive compensator approach. The horizontal motion of the vessel is described by a dynamic model. The model is set up in vessel parallel coordinates, with three degrees of freedom: longitudinal, transverse and rotational motion. It is assumed that the vessel is fully actuated, i.e. there is a sufficient number and type of actuators and a thrust allocation system to ensure full manoeuvrability. Thus, the control can be designed with the assumption of three independent inputs and three output signals. The longitudinal motion can be considered separately, but a cross-coupling exists between the transverse and rotational kinetics. There is uncertainty both in parameters and signals, due to the vessel mass, inertia, and damping, as well as the unmeasured derivatives. The proposed control ensures station-keeping when the vessel is subjected to external disturbances. The consecutive compensator, which is based on high-gain feedback, provides robustness. Stability analysis is presented considering the cross-terms as limited disturbances. This allows proof of exponential stability. Experimental results are included from the Marine Cybernetics Laboratory (MC lab) at the Centre for Autonomous Marine Operations and Systems (A MOS) at the Norwegian University of Science and Technology (Norges teknisk-naturvitenskapelige universitet, NTNU ). Two scenarios are investigated: the scaled vessel is subjected to external disturbance, and the vessel executes the " four corner test". The experiments illustrate the applicability of the method.

Adaptive compensation of damping asymmetry in whole-angle hemispherical resonator gyroscope

Damping asymmetry is one of the most important factors that determines the performance of the hemispherical resonator gyroscope. In this paper, the error mechanism of damping asymmetry in the Whole Angle (WA) mode is investigated, and an adaptive compensation method is proposed to eliminate the angle drift induced by the damping asymmetry. This adaptive compensation approach adopts the recursive least-squares method to identify the damping asymmetry, and the optimal parameters can be calculated through simulation. Furthermore, a self-precession method is applied to make this compensation method available for more occasions. This compensation method is able to automatically complete the compensation process without any angular input or a priori information on damping distribution. According to the experimental results, the angle-dependent bias has been reduced from 0.0316 s−1 to 0.0051 s−1 by this method. The presented method is widely applicable for gyroscopes working in the WA mode, which may be useful for further research on inertial sensors with high accuracy.

Implementation of an Ultra Low Power Process-Insensitive Two Stage Complementary Metal Oxide Semiconductor Operational Amplifier with Enhanced Direct Current Gain at 45 nm Technology Node

This paper presents an ultra low power process-insensitive two stage CMOS OP-AMP employing bulk-biasing technique realised in a standard 45 nm CMOS technology. Bulk-Biasing technique has been employed to augment the DC gain of two stage CMOS OP-AMP without having any impact on its power dissipation and output swing. In this work, high gain-bandwidth product (GBW) with appropriate phase margin is achieved through pseudo-cascode compensation approach which overcomes the drawbacks of Miller compensation technique also. Furthermore, the effect of width scaling on performance metrics of proposed OP-AMP has been analysed. The designed OP-AMP exhibits enhanced DC gain of 94.2 dB, gain-bandwidth product (GBW) of 460 MHz and adequate phase margin of 80°; with fast settling response. Also, the proposed OP-AMP has power dissipation of 27 μW and leakage current of 6.4 pA only. The design and optimisation of proposed OP-AMP is carried out at a power supply of 0.7 V under room temperature in Cadence Virtuoso tool.

An integrated digital microfluidic bioreactor for fully automatic screening of microalgal growth and stress-induced lipid accumulation.

Algae are the promising feedstock of biofuel. The screening of competent species and proper fertilizer supply is of the most important tasks. To accelerate this rather slow and laborious step, we developed an integrated high-throughput digital microfluidic (DMF) system that uses a discrete droplet to serve as a microbioreactor, encapsulating microalgal cells. On the basis of fundamental understanding of various droplet hydrodynamics induced by the existence of different sorts of ions and biological species, incorporation of capacitance-based position estimator, electrode-saving-based compensation, and deterministic splitting approach, was performed to optimize the DMF bioreactor. Thus, it enables all processes (e.g.,nutrient gradient generation, algae culturing, and analyzing of growth and lipid accumulation) occurring automatically on-chip especially in a high-fidelity way. The ability of the system to compare different microalgal strains on-chip was investigated. Also, the Chlorella sp. were stressed by various conditions and then growth and oil accumulation were analyzed and compared, which demonstrated its potential as a powerful tool to investigate microalgal lipid accumulation at significantly lower laborites and reduced time.

Long Coherent Integration in Passive Radar Systems Using Super-Resolution Sparse Bayesian Learning

Maximizing the coherent processing interval (CPI) is crucial when performing passive radar detection on weak signal reflections. In practice, however, the CPI is limited by the target movement. In this work, the extent of the range and Doppler migration effects occurring when using a long CPI to integrate the returns from an L-band digital aeronautical communication system (LDACS) based passive radar is studied. In particular, our simulations underline the extensive Doppler migration effect that arises even for non-accelerating targets. To this end, the Keystone transform and fractional Fourier transform techniques are combined with the standard passive radar processing to enable the compensation of both range and Doppler migration effects. This non-model-based approach is, however, shown to have limitations, in particular for low signal-to-noise ratios and/or multitarget scenarios. To address these shortcomings, a novel model-based framework that allows to perform joint target detection and parameter estimation is developed. For this, a super-resolution sparse Bayesian learning approach is employed. This technique uses a multitarget observation model, which accurately accounts for the underlying range and Doppler migration effects and provides super-resolution estimation capabilities. This is particularly advantageous in the LDACS case since the narrow bandwidth generally limits the separation of closely spaced targets. The simulation experiments demonstrate the effectiveness of the algorithm and the advantages it provides when compared to the standard migration compensation approach.

Artificial neural network-based geometry compensation to improve the printing accuracy of selective laser melting fabricated sub-millimetre overhang trusses

Abstract Selective laser melting processes deposit and join metal powders to near net shape in a layer-by-layer manner. The process of melting and re-solidification of several layers of deposited material can result in geometric deviations, and the impact is particularly significant for sub-millimetre structures oriented at a wide range of overhang angles with respect to the building platform. This paper assesses and benchmarks the capabilities of a neural network-based geometric compensation approach for truss lattice structures with circular cross-sections. The neural network method is capable to generate free-form cross-sections with enhanced geometric freedom for compensation compared to more established analytical compensation approaches limited to predefined geometric shapes. For neural network training, lattice dome structures composed of trusses with different overhang angles were designed and printed by selective laser melting and measured via X-ray computed tomography, resulting in point cloud data sets containing more than 20,000 data points for each overhang angle. For experimental validation, neural network-compensated dome structures were benchmarked against dome structures with elliptical parameter compensation. Results show that the neural network compensated lattice trusses achieve higher printing dimensional accuracy compared to the uncompensated structures and those compensated based on elliptical parameter estimates.

An improved spatial subsidy approach for ecological compensation in coastal seascapes for resilient land-sea management

Human activities are considered a critical impact factor for decision-making in coupled human-nature systems, such as conservation of coastal systems. Identifying key human activities that cause significant habitat degradation for coastal species remains challenging. We improved the spatial subsidy approach to identify and prioritize control strategies for human-caused distribution shifts of marine species. We applied this method to a threatened Indo-Pacific humpback dolphin (Sousa chinensis) in Xiamen Bay, China. Our results indicate that (1) a significant distribution shift for humpback dolphins from existing nature reserves to peripheral waters occurred from 2011 to 2014; (2) coastal tourism and industrial and urban construction had more significant negative impacts on humpback dolphins than maritime transportation and reclamation; and (3) proactive management should be implemented for maritime transportation and reclamation, while reactive management should be implemented for coastal tourism and industrial and urban construction. Human impact analysis, combined with spatially explicit modeling, contributes to determining the spatial alternatives for conservation planning. In response to possible ecological damage caused by human activities, the improved spatial subsidy results help provide knowledge and platforms for ecological compensation.

CASH-RAM: Enabling In-Memory Computations for Edge Inference Using Charge Accumulation and Sharing in Standard 8T-SRAM Arrays

Machine Learning (ML) workloads being memory- and compute-intensive, consume large amounts of power running on conventional computing systems, restricting their implementations to large-scale data centers. Transferring large amounts of data from the edge devices to the data centers is not only energy expensive, but sometimes undesirable in security-critical applications. Thus, there is a need for building domain-specific hardware primitives for energy-efficient ML processing at the edge. One such approach - in-memory computing , eliminates frequent and unnecessary data-transfers between the memory and the compute units, by directly computing the data where it is stored. However, the analog nature of computations introduces non-idealities, which degrades the overall accuracy of neural networks. In this paper, we propose an in-memory computing primitive for accelerating dot-products within standard 8T-SRAM caches, using charge-sharing. The inherent parasitic capacitance of the bitlines and sourcelines is used for accumulating analog voltages, which can be sensed for an approximate dot product. The charge sharing approach involves a self-compensation technique which reduces the effects of non-idealities, thereby reducing the errors. Our results for ternary weight neural networks show that using the proposed compensation approaches, the accuracy degradation is within 1% and 5% of the baseline accuracy, for the MNIST and CIFAR-10 dataset, respectively, with an energy-delay product improvement of $38\times $ over the standard von-Neumann computing system. We believe that this work can be used in conjunction with existing mitigation techniques, such as re-training approaches, to further enhance system performance.

Multivariable robust MPC design for neutralisation plant: Experimental analysis

The paper provides an extensive experimental analysis of offset-free multivariable robust model predictive control (MPC) of a neutralisation plant. The behaviour of the neutralisation plant was nonlinear and asymmetric. The controlled variable was a pH value of the outlet stream, and the manipulated variables were the inlet volumetric flow rates of the acid and base. The observer was designed to estimate the system states. Operating conditions make the tuning of the robust controller a challenging task. First, the conventional robust MPC was designed. The control setup was tuned and experimentally analysed in various operating conditions. Based on the measured data, the novel approach of the real-time compensation of the asymmetric behavior was investigated. Implementation of the proposed control strategy leads to improved control performance. The real-time compensation of asymmetric behaviour can be considered for the control of other systems with nonlinear behaviour.

Control of Zeros and Poles in Problems of Synthesis of Regulation Systems. Part I. Compensation Approach

A highly important place in the theory and practice of automatic systems occupy the problems of synthesis of automatic regulation systems (ARS) based on the requirements for the dynamic quality of regulation processes. For class of linear stationary ARS such requirements are imposed upon the type and parameters of its transition characteristic which is uniquely determined by its transfer function (TF). In this connection the setting of problem of ARS synthesis with desired TF, corresponding to the given amplification coefficient, zeros and poles of the synthesized system is regularity. The given problem is worth while to call as the control problem of zeros and poles of ARS. In the present paper consists into two parts, the questions of control of zeros and poles ARS, which are important for engineering applications are considered. A critical analysis of the known compensation and compensation-modal regulation schemes is given. And also the new circuit solutions combining the functionality possibilities of compensation and modal approaches are proposed. The first part of the article the effect of compensation of zeros and poles of control objects in ARS is analyzed. The understanding of this effect gives the representation of the system of canonical structure of R. Kalman, according to which the compensation of zeros and poles of object does not mean their physical liquidation. As a result of compensation, they become the poles of its unobservable and unmanageable parts, which will be tell upon the regulation processes in the conditions of disturbances of the state of control object. The given effect and its negative results are clearly detected in the classical method of constructing of controllers on a priori given (desired, reference) TF of closed ARS. The influence of the factor of non-minimal phase zeros on the dynamics of control systems is studied. The effect of negative ejection in the transition characteristic of the system is described and its quantitative assessment is given for the case of a single real right zero. In the second part of the article the well-known methods for ARS synthesis with desired TF, based on use of polynomial calculus apparatus are considered and analyzed. New compensatory modal methods which may be of interest in engineering applications are proposed.

High-accuracy atmospheric turbulence compensation based on a Wirtinger flow algorithm in an orbital angular momentum-free space optical communication system

In this study, high-accuracy atmospheric turbulence compensation based on a Wirtinger flow (WF) algorithm in an orbital angular momentum-free space optical system is demonstrated. An adaptive optics compensation approach based on the WF sensing part is introduced. In addition, the configuration complexity of the wavefront sensing part is reduced to one input. The WF algorithm can be considered as a gradient descent method with two steps, initialisation and iteration, which effectively prevent a fall into the local optimal solution. The compensation performance of the WF algorithm is numerically simulated. The simulation results demonstrate that the compensation can effectively eliminate the impact of atmospheric turbulence. The performance parameters, namely, root-mean-square (RMS) error, mode purity, crosstalk and bit error rate, are significantly improved. In particular, the RMS error reduces to 10−15 compared with 10−2 for the conventional Gerchberg–Saxton algorithm. The performance of the simulations indicates the high effectiveness and accuracy of the WF algorithm.

Regression-based compensation of part inaccuracies in incremental sheet forming at elevated temperatures

Incremental sheet forming is a sheet forming process for small lot sizes due to its dieless principle. One of its process variants includes local heating of the sheet to counteract some of the process restrictions (formable materials, forming forces, achievable deformations). Although forming at elevated temperatures provides various advantages, the geometric accuracy of the formed part remains low due to shrinking effects caused by local heating and cooling. This publication presents a data-driven approach where process data is gathered and used in regression learning to predict the geometric accuracy resulting from the shrinking effects. To successfully apply regression learning, a big amount of process data is needed covering a wide range of possible process states. Therefore, a specific experimental series, consisting of 54 individual forming experiments, is designed and carried out. Based on the 3D digitization of the formed parts, a process database is built up comprising 408,296 records, each representing a toolpath point. This process database is used to train 19 different regression models. The performance of their ability to predict the geometric deviations is investigated. A compensation approach is presented that improves the geometric accuracy through a prediction-based modification of the toolpath. Validation experiments demonstrate the improvement of the geometric accuracy of the formed part and the generalizability of the approach.

Visual Sensor Fusion With Error Compensation Strategy Toward a Rapid and Low-Cost 3D Scanning System for the Lower Residual Limb

Rapid and low-cost 3D scanning system becomes a common solution for prosthetic designers and engineers to acquire accurate 3D geometrical model of the involved patient. Multiple Kinect sensor fusion solutions have been proposed but still, there is a lack of fusion scheme within a single workstation and a systematic reconstruction error analysis. The objective of the present study was to develop a new Kinect sensor fusion scheme running on a single workstation and an error compensation strategy. Architecture of a 3D scanning system for the lower residual limb was proposed. Four Kinect sensors were used to develop the fusion scheme. An error compensation approach based on linear regression method was performed. Then, an iterative rigid multi-set registration method was proposed. Different primitive geometries (cylindrical objects, box) with well-known physical dimensions were designed, fabricated and used for the evaluation process. Obtained results showed that the error compensation strategy leads to a reduction of around 40% of 3D geometrical reconstruction error for the residual stump-like structure. The total scanning and processing time is 25.4 ± 2.5 seconds. Moreover, our novel iterative rigid multi-set registration method leads to an improvement of accuracy of 60% compared to conventional registration method. Our proposed 3D scanning system using multiple Kinect cameras with error compensation strategy is expected to reduce the design cost and improve the design accuracy. As perspective, this 3D scanning system with fusion scheme will be integrated into a Computer-Aided Design (CAD) system with soft tissue deformation feedbacks for lower limb prostheses.

A weighted distributed predictor-feedback control synthesis for interconnected time delay systems

The paper investigates the control design of interconnected time delay systems by means of distributed predictor-feedback delay compensation approaches and event-triggered mechanism. The idea behind delay compensation is to counteract the negative effects of delays in the control-loop by feeding back future predictions of the system state. Nevertheless, an exact prediction of the overall system state vector cannot be obtained providing that each system has only knowledge of their local data regarding the system model and state variables. Consequently, predictor-feedback delay compensation may lose effectiveness if the coupling between subsystems is sufficiently strong. To circumvent this drawback, the proposed distributed predictor-feedback control incorporates extra degree of freedom for control synthesis by introducing new weighting factors for each local prediction term. The design of the weighting factors is addressed, together with the event-triggered parameters, by an algorithm based on Linear Matrix Inequalities (LMI) and the Cone Complementarity Linearization (CCL). Simulation results are provided to show the achieved improvements and validate the effectiveness of the proposed method, even in the case that other control strategies fail to stabilize the closed-loop system.

An adaptive computer-aided path planning to eliminate errors of contact probes on free-form surfaces using a 4-DOF parallel robot CMM and a turn-table

Nowadays, digitizing precise parts consisting of free-form surfaces is essential in geometrical inspections and reverse engineering. Despite the ease of use and high speed of non-contact techniques, contact methods have higher precision. However, using touch probes confront a few difficulties. Various compensation approaches that are often post-processing techniques have been introduced for contact probe errors consisting of ball-tip-radius error, stylus tilt, pre-travel, and probe lobbing. Moreover, in precise applications, achieving satisfying results is difficult and time-consuming.The current paper proposes a computer-aided planning method by implementing a new 4-DOF parallel robot and a cost-effective linear contact probe. This parallel structure exploits three translational DOF and one rotational DOF. Furthermore, the robot is equipped with a precise turn-table. A measuring algorithm for free-form surfaces by normal-to-surface contact has been developed. Evaluating the simulation and the practical measuring results shows excellent feasibly of the method on elimination or great reduction of errors.