Compensation Step Research Articles

RED FOX-BASED FRACTIONAL ORDER FUZZY PID CONTROLLER FOR SMART LED DRIVER CIRCUIT

Recently, traditional lighting has been replaced by high-power LED (HPLED) due to its significant developments, prolonged lifetime, high brightness, high reliability, and high energy efficiency. Even though conventional converters can precisely match each LED's current, they have some limitations when driving a long string of LEDs. To solve this issue, this paper presents a novel red fox optimized fractional order fuzzy PID Controller (RF-FOFPID) based high gain improved transformerless dc-dc (ITDC) boost converter for a smart LED driver circuit with optimal voltage regulation capability. The error bias is increased to zero by applying FOPID to the fuzzy logic-based compensation steps. The Red Fox optimization algorithm is used to adjust the control parameters of the fractional-level fuzzy PID controller with higher precision to solve limited problems with diverse search spaces. The Simulink model of the proposed converter was created using the MATLAB software tool Simulink and compared with controllers using fractional order PID, fuzzy PID, and conventional proportional integral derivative (PID). The results demonstrated that in terms of minimum overshoot, settling time, rise time, and steady-state error, the proposed converter based on RF-FOFPID outperforms current converters in voltage regulation.

EUROPEAN GROUND MOTION SERVICE FOR BRIDGE MONITORING: TEMPORAL AND THERMAL DEFORMATION CROSS-CHECK USING COSMO-SKYMED INSAR

Abstract. This paper elaborates on the potential of the deformation time-series provided by European Ground Motion Service (EGMS) data points for bridge monitoring, in terms of the response of bridge structures to temporal and thermal deformations. For this purpose, high-resolution Cosmo-SkyMed (CSK) Synthetic Aperture Radar (SAR) images have been utilized through Persistent Scatterer SAR Interferometry (PS-InSAR) to derive spatially detailed deformation time-series of two bridges located at the north of Lombardy region, Italy. Then, the Annual Displacement Velocity and Thermal Expansion Coefficient of the data points in both datasets are extracted based on the corresponding deformation time-series. The response parameters are post-processed through projection and compensation steps to become comparable. Cross-checking the results by comparing the response parameters along the bridges showed that the global (for L3 products) and local (for L2b products) differential behaviour and the variation of the values of these parameters from one side to the other side of each bridge are consistent between CSK and EGMS datasets. However, the EGMS L3 information shows a slightly lower magnitude in both displacement velocity and thermal expansion coefficient responses. According to the revealed capability of EGMS information in this work, it can be concluded that the EGMS data points, if spatially cover a bridge, can present a reasonable and sufficient insight into the temporal evolution of displacement and thermal structural response, suitable for structural health monitoring and condition assessment of bridges under operational and environmental loads.

Solid-State-LiDAR-Inertial-Visual Odometry and Mapping via Quadratic Motion Model and Reflectivity Information

This paper proposes a solid-state-LiDAR-inertial-visual fusion framework containing two subsystems: the solid-state-LiDAR-inertial odometry (SSLIO) subsystem and the visual-inertial odometry (VIO) subsystem. Our SSLIO subsystem has two novelties that enable it to handle drastic acceleration and angular velocity changes: (1) the quadratic motion model is adopted in the in-frame motion compensation step of the LiDAR feature points, and (2) the system has a weight function for each residual term to ensure consistency in geometry and reflectivity. The VIO subsystem renders the global map in addition to further optimizing the state output by the SSLIO. To save computing resources, we calibrate our VIO subsystem’s extrinsic parameter indirectly in advance, instead of using real-time estimation. We test the SSLIO subsystem using publicly available datasets and a steep ramp experiment, and show that our SSLIO exhibits better performance than the state-of-the-art LiDAR-inertial SLAM algorithm Point-LIO in terms of coping with strong vibrations transmitted to the sensors due to the violent motion of the crawler robot. Furthermore, we present several outdoor field experiments evaluating our framework. The results show that our proposed multi-sensor fusion framework can achieve good robustness, localization and mapping accuracy, as well as strong real-time performance.

A theoretical framework of information preservation method and its application to low-speed nonequilibrium gas flows

Simulations of nonequilibrium gas flows have garnered significant interest in modern engineering problems involving rarefied gas flow characteristics. Despite the popularity of the direct simulation Monte Carlo (DSMC) method in simulating such flows, its use in low-speed flows is limited by statistical noises. The information preservation (IP) method is a promising alternative known for its low noise properties. In this study, a new theoretical framework for the IP method based on kinetic theory is introduced to offer complete understanding for the transport properties of the preserved information. Specifically, we introduce a velocity-information joint distribution function (VIJDF) and derive its governing equation as well as the corresponding macroscopic transport equations. To ensure the accuracy of the IP method, the total stress/heat flux in IP, including information stress/heat flux generated during movement and collision steps and compensation stress/heat flux imposed in the compensation step, is matched to the molecular stress/heat flux in DSMC. To this end, a nonequilibrium model for the VIJDF is proposed to evaluate the compensation stress/heat flux. The parameters in the collision model of IP are theoretically determined by equating the transport coefficients associated with the preserved information to the coefficients of viscosity and thermal conductivity in DSMC. Numerical simulations for a variety of nonequilibrium gas flows, including low-speed Couette flow, Fourier flow, high-speed Couette flow, external force-driven Poiseuille flow, lid-driven cavity flow, and thermal creep flow, demonstrate that the IP method can achieve similar accuracy as the DSMC method with a much smaller sampling size.

A Monostatic Forward-Looking Staring Spotlight SAR Raw Data Generation and Hybrid-Domain Image Formation Modifications Based on Extended Azimuth Nonlinear Chirp Scaling Autofocusing

Forward-looking staring spotlight synthetic aperture radar (FSS-SAR) offers operational advantages over conventional squinted SAR counterparts. However, due to its complex observation configuration, pixel anomalies occur when processing raw data. For instance, in the forward-looking geometry, symmetrically distributed point scatterers along the flight path have the same Doppler history, resulting in azimuth ambiguities, whereas staring observation narrows and skews the 2D received spectrum, reducing signal orthogonality and causing spectral folding. As a result, not only is the Doppler frequency gradient too small to be precisely measured in the FSS-SAR mode, limiting the ability to achieve high azimuth angular resolution, but it also suffers from azimuth ambiguities when forming an image. To overcome such azimuth-variant characteristics known as inherent resolutional anomalies, a hybrid-domain image formation algorithm (IFA) based on azimuth scaling is modified here. As an approach, radiometric bias correction (RBC) techniques in conjunction with an extended azimuth nonlinear chirp scaling (ANCS) algorithm capable of equalizing range space variance in the azimuth direction, which benefits from an embedded autofocusing step, have been designed for raw data processing. Given the fact that the platform experiences trajectory deviations under a real-flight condition compared to an ideal trajectory, a pre-processing motion compensation (MOCO) step based on Kalman filtering is included within the IFA. The entire proposed IFA solution tackled the inherent resolutional anomalies of FSS-SAR and succeeded in reconstructing the focused image without azimuthal anomalies. To further assess the IFA, a modified Cartesian back-projection (BP) algorithm was developed along with other objective evaluation scenarios, all of which confirm that the proposed hybrid-domain extended ANCS algorithm is valid for the FSS-SAR application.

High-Resolution SAR Imaging with Azimuth Missing Data Based on Sub-Echo Segmentation and Reconstruction

Due to the substantial electromagnetic interference, radar interruptions, and other factors, the SAR system may fail to receive valid data in some azimuth areas. This phenomenon is known as Azimuth Missing Data (AMD). If classical SAR imaging algorithms are performed directly using AMD echo, the imaging results may be defocused or even display false targets, which seriously affects the accuracy of the image. Thus, we proposed a Sub-echo Segmentation and Reconstruction Azimuth Missing Data SAR Imaging Algorithm (SSR-AMDIA) to solve the problem of incomplete echo SAR imaging in this article. Instead of using the motion compensation step of the Polar Format algorithm (PFA) to recover the full echo from the AMD echo, the proposed SSR-AMDIA eliminates the effect of the planar approximation in PFA and expands the maximum depth of focus (DOF). The raw AMD echo was first subjected to range compression and Range Cell Migration Correction (RCMC), after which the AMD-RCMC echo was divided along the range direction. Then, we constructed a series of phase compensation functions based on the sub-segment AMD-RCMC echoes to guarantee the perfect recovery of the full RCMC echoes corresponding to the sub-scenes. Finally, we combined them to obtain the complete RCMC echo, and an excellent focused imaging result was then obtained via azimuth compression. Simulation and experimental data verified the effectiveness of the proposed algorithm. Furthermore, we derived the mathematical expressions for the two-dimensional maximum DOFs of the proposed algorithm. In contrast to the State-Of-the-Art (SOA) AMDIA, the SSR-AMDIA can obtain a superior imaging performance in a larger imaging scope under the conditions of most AMD cases.

Fast and Efficient Offset Compensation by Noise-Aware Pre-Charge and Operation of DRAM Bit Line Sense Amplifier

This brief proposes fast and efficient offset compensation for a DRAM bit-line sense amplifier. Precharging the sense amplifier under the same conditions as the initial offset compensation eliminates the deterministic offset leaving a small stochastic offset to be cured during the compensation step. The compensated offset is maintained throughout the subsequent charge sharing and sensing periods by keeping the noise environment constant as a compensation step to prevent an additional offset from being introduced after compensation. Fast and reliable offset compensation is confirmed by yield analysis in 256 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 288 DRAM cell array using extensive Monte Carlo simulation. Even when the offset compensation time is shortened to 2 ns, no yield drop is expected while with conventional amplifiers a sensing failure is estimated at 47.5%. An additional 1 ns reduction in charge sharing time is possible if a careful power delivery network is provided with the proposed sense amplifier.

An Extreme Learning Machine for the Simulation of Different Hysteretic Behaviors

Hysteresis is a non−unique phenomenon known as a multi−valued mapping in different fields of science and engineering. Accurate identification of the hysteretic systems is a crucial step in hysteresis compensation and control. This study proposes a novel approach for simulating hysteresis with various features that combines the extreme learning machine (ELM) and least−squares support vector machine (LS−SVM). First, the hysteresis is converted into a single−valued mapping by deteriorating stop operators, a combination of stop and play hysteresis operators. Then, the converted mapping is learned by a LS−SVM model. This approach facilitates the training steps and provides more accurate results in contrast to the previous experimental studies. The proposed model is evaluated for several hystereses with various properties. These properties include rate−independent or rate−dependent, congruent or non-congruent, and symmetric or asymmetric problems. The results indicate the efficiency of the newly developed technique in terms of accuracy, computational cost, and convergence rate.

Metformin induces mitochondrial remodeling and differentiation of pancreatic progenitor cells into beta-cells by a potential mechanism including suppression of the T1R3, PLCβ2, cytoplasmic Ca+2, and AKT.

The main goal of this study was to investigate the molecular changes in pancreatic progenitor cells subject to high glucose, aspartame, and metformin in vitro. This scope of work glucose, aspartame, and metformin were exposed to pancreatic islet derived progenitor cells (PID-PCs) for 10days. GLUT1's role in beta-cell differentiation was examined by using GLUT1 inhibitor WZB117. Insulin+ cell ratio was measured by flow cytometry; the expression of beta-cell differentiation related genes was shown by RT-PCR; mitochondrial mass, mitochondrial ROS level, cytoplasmic Ca2+, glucose uptake, and metabolite analysis were made fluorometrically and spectrophotometrically; and proteins involved in related molecular pathways were determined by western blotting. Findings showed that glucose or aspartame exposed cells had similar metabolic and gene expression profile to control PID-PCs. Furthermore, relatively few insulin+ cells in aspartame treated cells were determined. Aspartame signal is transmitted through PLCβ2, CAMKK2 and LKB1 in PID-PCs. The most obvious finding of this study is that metformin significantly increased beta-cell differentiation. The mechanism involves suppression of the sweet taste signal's molecules T1R3, PLCβ2, cytoplasmic Ca+2, and AKT in addition to the direct effect of metformin on mitochondria and AMPK, and the energy metabolism of PID-PCs is remodelled in the direction of oxidative phosphorylation. These findings are very important in terms of determining that metformin stimulates the mitochondrial remodeling and the differentiation of PID-PCs to beta-cells and thus it may contribute to the compensation step, which is the first stage of diabetes development.

Improvement and uncertainty evaluation of a 1 MV lightning impulse measurement system

This paper focuses on development of an existing 1 MV lightning impulse (LI) measuring system as part of European project for i.a. LI calibration capabilities development. After carefully characterization of the original LI divider a new low voltage arm was designed and built with e.g. improved shielding and also RC circuits for compensating the detected drift in the step response. With the compensation step response was successfully corrected resulting in clear improvements in LI time parameter measurements. However, persistent high frequency noise was present in the step response complicating further fine-tuning of the response. The noise could not much be improved at least partly due to non-optimal damping resistor structure, which is difficult to improve afterwards. After the improvements an extensive uncertainty evaluation was carried out for the original system as well as for two versions of the improved system.

Learned modified perturbation backpropagation for fiber nonlinear equalization in high-symbol-rate transmission systems

A novel learned modified perturbation backpropagation (L-MPBP) algorithm for high-symbol-rate (HSR) coherent optical transmission system is proposed in this paper. As an extension of perturbation backpropagation (PBP), L-MPBP utilizes deep neural network to adjust the position of nonlinear compensation (NLC) in each compensation step and jointly optimize the perturbation coefficients, which greatly improves the performance of nonlinear equalization in HSR transmission systems. The proposed approach is applied to compensate nonlinear signal distortions in a 90-GBaud, PDM-16QAM coherent transmission system. The results show that the proposed L-MPBP achieves 1.75 dB effective SNR improvement compared with PBP while without inducing extra complexity. Our research indicates that the L-MPBP algorithm has great application potential for nonlinear compensation in HSR systems.

Image quality enhancement in variable-refresh-rate AMOLED displays using a variable initial voltage compensation scheme

In active matrix organic light emitting diode (AMOLED) displays, when a variable refresh rate is applied and the frame rate changes, the image’s color and luminance quality in AMOLED displays deteriorates. The frequency-dependent cognitive differences were experimentally demonstrated by using 6.76″ AMOLED displays. This phenomenon is dependent on the emission time and the data programming time on the frame rate. This degradation of the image quality during the frequency switch could be prevented by applying a variable initial voltage (VINI) to the OLED anode. For a frequency change between 60 and 120 Hz, the measured just noticeable color difference (JNCD) decreased from 7.50 to less than 1.00 in luminance, and from 2.34 to 0.02 in color. As our approach can prevent image quality distortion by utilizing an existing compensation pixel structure without additional compensation steps, it will be a promising technique for improving the picture quality in AMOLED displays.

A Novel Channel Inconsistency Estimation Method for Azimuth Multichannel SAR Based on Maximum Normalized Image Sharpness

For azimuth multi-channel synthetic aperture radar (SAR), unavoidable inconsistency errors between channels can degrade SAR image quality severely, leading to possible ghost targets and image defocusing, etc. To address this issue, a novel channel inconsistency estimation method is proposed based on maximum normalized image sharpness. First, channel amplitude and time delay errors are corrected in the coarse compensation step. Then images of each channel are attained by azimuth spectrum recovery and imaging processing. Next, range-variant channel phase errors are estimated via optimizing normalized image sharpness, which reaches the maximum value when the image is focused well or ghost targets are suppressed completely. The Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is employed to get the optimal solution based on the derived gradient of objective function. Finally, the ultimate image is formed through adding up phase compensated images of each channel. By optimizing the focused image quality, the proposed algorithm achieves high estimation accuracy. Simulated data and real multi-channel SAR data are processed to demonstrate the effectiveness of the proposed method.

Efficient Sandstorm Image Enhancement Using the Normalized Eigenvalue and Adaptive Dark Channel Prior

A sandstorm image has features similar to those of a hazy image with regard to the obtaining process. However, the difference between a sand dust image and a hazy image is the color channel balance. In general, a hazy image has no color cast and has a balanced color channel with fog and dust. However, a sand dust image has a yellowish or reddish color cast due to sand particles, which cause the color channels to degrade. When the sand dust image is enhanced without color channel compensation, the improved image also has a new color cast. Therefore, to enhance the sandstorm image naturally without a color cast, the color channel compensation step is needed. Thus, to balance the degraded color channel, this paper proposes the color balance method using each color channel’s eigenvalue. The eigenvalue reflects the image’s features. The degraded image and the undegraded image have different eigenvalues on each color channel. Therefore, if using the eigenvalue of each color channel, the degraded image can be improved naturally and balanced. Due to the color-balanced image having the same features as the hazy image, this work, to improve the hazy image, uses dehazing methods such as the dark channel prior (DCP) method. However, because the ordinary DCP method has weak points, this work proposes a compensated dark channel prior and names it the adaptive DCP (ADCP) method. The proposed method is objectively and subjectively superior to existing methods when applied to various images.

Generalized bias compensated pseudolinear Kalman filter for colored noisy bearings-only measurements

Bias compensated pseudolinear Kalman filter (BC-PLKF) has been shown to solve the bias problem of pseudolinear Kalman filter (PLKF) and outperform Extended Kalman Filter (EKF) and many others in bearings-only target estimation applications with a low computational cost. However, BC-PLKF assumes that measurement noise is white, which is not a valid approximation for some applications such as weather-vane-used guided missiles where wind disturbance appears as a strongly time-correlated measurement noise, estimators performing high-frequency measurement updates, or cascaded Kalman filter-based algorithms. When the well-known noise augmentation method is applied to BC-PLKF, no straightforward solution for the bias compensation is available. First, process noise and observer matrix become coupled leading to unique bias. Second, the measurement autocovariance turns into zero whose inverse is used at the bias compensation step of BC-PLKF. Therefore, a bias analysis is performed for PLKF where the measurement noise is colored. Moreover, the generalized BC-PLKF algorithm (GBC-PLKF) for colored noise-corrupted measurements is derived. Simulations are performed to compare performances of GBC-PLKF, EKF, Cubature Kalman Filter (CKF), BC-PLKF, and colored noise augmented EKF and CKF (C-EKF and C-CKF) with typical air-to-surface missile engagement scenarios. Results verify that GBC-PLKF outperforms all comparison filters with a low computational cost for bearings-only estimation applications.

High-Efficiency Ion-Exchange Doping of Conducting Polymers.

Molecular doping-the use of redox-active small molecules as dopants for organic semiconductors-has seen a surge in research interest driven by emerging applications in sensing, bioelectronics, and thermoelectrics. However, molecular doping carries with it several intrinsic problems stemming directly from the redox-active character of these materials. A recent breakthrough wasa doping technique based on ion-exchange, which separates the redox and charge compensation steps of the doping process. Here, the equilibrium and kinetics of ion exchange doping in a model system, poly(2,5-bis(3-alkylthiophen-2-yl)thieno(3,2-b)thiophene) (PBTTT) doped with FeCl3 and an ionic liquid, is studied, reaching conductivities in excess of 1000 S cm-1 and ion exchange efficiencies above 99%. Several factors that enable such high performance, including the choice of acetonitrile as the doping solvent, which largely eliminates electrolyte association effects and dramatically increases the doping strength of FeCl3 , are demonstrated. In this high ion exchange efficiency regime, a simple connection between electrochemical doping and ion exchange is illustrated, and it is shown that the performance and stability of highly doped PBTTT is ultimately limited by intrinsically poor stability at high redox potential.

Solution of the Analysis Problem of a Machine Assembly Distributed System with Time-Dependent Heat Transfer Coefficient

It is well known that temperature is a factor that significantly influences the accuracy of machine tools. Compensation enables machine errors to be reduced, even for a moderately accurate machine tool. The first step in compensation is to estimate the thermal characteristics of the machine part. Thermal models with distributed parameters provide high accuracy of estimation. In these thermal models, the environmental thermal fluctuations influencing the temperature may be taken into account as the time-dependent heat-transfer coefficient. The finite elements method facilitates simulation of the machine system geometry, but is computationally expensive. One approach is to use the simplified thermal model at an early stage of development, which allows the investigation of the temperature field and the possible influence of the environment at any point of the model. In this article, it is proposed to use the spectral method based on the expansion of the temperature function in a Fourier series to analyze the thermal model distributed along the axial coordinate presented in PDE form. To maintain the similarity of thermal processes and the model, the dimension parameters of the model should be chosen such that the Biot and Fourier coefficients would be the same for the model and the machine part. The proposed method allows the PDE to be represented as an indefinite system of linear algebraic equations for the coefficients of the Fourier series, which are the amplitudes of the space–time modes of the temperature function. The solution has the advantage of an analytical solution because it provides information about the model’s temperature at any point.

Efficient Nonparametric ISAR Autofocus Algorithm Based on Contrast Maximization and Newton’s Method

Phase autofocus is a significant step in translational motion compensation for inverse synthetic aperture radar (ISAR) imaging. Among the existing autofocus methods, contrast maximization-based algorithms are superior in accuracy and robustness. However, the existing maximum contrast autofocus methods are parametric, which significantly affects their convergence speed and, in some cases, limits their applicability. In this paper, a novel non-parametric maximum contrast ISAR autofocus algorithm based on Newton’s method is proposed to inherit the high level of accuracy and robustness of contrast-based algorithms and, at the same time, achieve faster convergence. The simplified Newton’s method, the modified Newton’s method, and the secant method are used to obtain a higher computational efficiency. The proposed method is then compared to a well-established non-parametric method, namely the minimum entropy autofocus method. It will be shown that the proposed method can improve the computational efficiency by a factor from 5 to 10 times in typical scenarios. Both simulated and real data will be used to test the proposed algorithm, particularly in terms of computational effectiveness.

Characterization of idiopathic Parkinson’s disease subgroups using quantitative gait analysis and corresponding subregional striatal uptake visualized using 18F-FP-CIT positron emission tomography

BackgroundGait disturbance is one of the most common symptoms among patients with idiopathic Parkinson’s disease (IPD). Nevertheless, Parkinson’s disease subtype clustering according to gait characteristics has not been thoroughly investigated. Research questionThe aim of this study was to identify subgroups according to gait pattern among patients with IPD. MethodsThis study included 88 patients with IPD who underwent 18F-fluorinated-N-3-fluoropropyl-2-β-carboxymethoxy-3-β-4-iodophenyl-nortropane positron emission tomography (18F-FP-CIT PET) and three-dimensional gait analysis (3DGA) between January 1, 2014 and December 31, 2016. We performed cluster analysis using temporal-spatial gait variables (gait speed, stride length, cadence, and step width) and divided patients into four subgroups. The kinematic and kinetic gait variables in 3DGA were compared among the four subgroups. Furthermore, we compared the uptake patterns of striatum among the four subgroups using 18F-FP-CIT PET. ResultsThe patients were clustered into subgroups based on gait hypokinesia and cadence compensation. Group 1 had decreased stride length compensating with increased cadence. Group 2 had decreased stride length without cadence compensation and wider step width. Group 3 had relatively spared stride length with decreased cadence. Group 4 had spared stride length and cadence. The uptake of posterior putamen was significantly decreased in Group 3 compared with Group 4. SignificanceGait hypokinesia and cadence can help to classify gait patterns in IPD patients. Our subgroups may reflect the different gait patterns in IPD patients.

3-D Imaging Using Synthetic Aperture Radar With a Frequency Diverse Array

In this letter, a 3-D synthetic aperture radar with a frequency diverse array (3-D-FDA-SAR) is proposed for 3-D imaging. By applying an FDA to the 3-D-SAR system, the proposed 3-D-FDA-SAR can achieve real 3-D imaging using a simple hardware structure and a simple imaging algorithm. Specifically, the bandwidth needed for the range resolution is dispersed in space by the FDA, and the spatial resources (large aperture) needed for the along-track resolution are replaced by time resources. Therefore, the transmission and processing of wideband signals can be avoided, so the imaging algorithm only comprises a phase compensation step based on the back projection (BP) imaging algorithm. A resolution analysis of the range, along-track and across-track is presented to illustrate the rationality of the bandwidth dispersion and resource replacement. The simulation and experimental results verify the effectiveness of the proposed strategy.