Measured Root Mean Square Research Articles

A miniaturized 35 GHz 65‐nm CMOS digital‐controlled differential variable gain amplifier

AbstractThis paper presents a 35 GHz differential variable gain amplifier (VGA) with 4‐bit digital‐controlled gain‐tuning using 65‐nm CMOS process. Using the proposed miniaturized differential matching inductors, a core size of the VGA is only 0.53 0.26 mm. The digital gain control mechanism is achieved using the digital version current steering topology. The measured gain control range of the VGA is from 17.4 to 9 dB at 35 GHz with 16 different linear‐in‐dB gain states. Thus, this work achieves the maximum peak gain per unit core size and maintains a much better gain control range per unit core size. The measured root mean square gain error is <0.29 dB and phase error is < from 34 to 36.5 GHz.

Investigation of acquisition time for harmonization of image quality between PET/CT and PET/MRI systems: A phantom study

The positron emission tomography (PET) system has technological differences between PET/computed tomography (CT) and PET/magnetic resonance imaging (MRI). It is important to compare image quality on acquisition time for equal diagnostic performance. Therefore, the aim of study was to investigate acquisition time as a factor for harmonizing the image quality. NEMA image quality phantom experiments were performed using Siemens Biograph PET/CT and PET/MRI. The phantom was filled with 18F-solution at a 10:1 of sphere-to-background ratio and scanned in the list-mode. PET images were reconstructed with increasing time frames of 1 min. To compare image quality and measure difference, the contrast recovery (CR), background variability (BV), and measure root mean square error (RMSE) of BVs were used. PET/CT and PET/MRI showed similar CR for spheres regardless of acquisition time, whereas the BV was higher with PET/MRI than with PET/CT at the same acquisition time. The BV decreased with increasing acquisition time. The difference of BV by RMSE showed small values at an acquisition time delay of 2 min for PET/MRI. The PET images on visual analysis were also comparable at the lowest RMSE values. In conclusion, the image quality between PET/CT and PET/MRI was harmonized at a PET/MRI acquisition time of PET/CT acquisition time plus 2 min

The adaptation and investigation of cone-beam CT reconstruction algorithms for horizontal rotation fixed-gantry scans of rabbits

Fixed-gantry radiation therapy has been proposed as a low-cost alternative to the conventional rotating-gantry radiation therapy, that may help meet the rising global treatment demand. Fixed-gantry systems require gravitational motion compensated reconstruction algorithms to produce cone-beam CT (CBCT) images of sufficient quality for image guidance. The aim of this work was to adapt and investigate five CBCT reconstruction algorithms for fixed-gantry CBCT images. The five algorithms investigated were Feldkamp–Davis–Kress (FDK), prior image constrained compressed sensing (PICCS), gravitational motion compensated FDK (GMCFDK), motion compensated PICCS (MCPICCS) (a novel CBCT reconstruction algorithm) and simultaneous motion estimation and iterative reconstruction (SMEIR). Fixed-gantry and rotating-gantry CBCT scans were acquired of 3 rabbits, with the rotating-gantry scans used as a reference. Projections were sorted into rotation bins, based on the angle of rotation of the rabbit during image acquisition. The algorithms were compared using the structural similarity index measure root mean square error, and reconstruction time. Evaluation of the reconstructed volumes showed that, when compared with the reference rotating-gantry volume, the conventional FDK algorithm did not accurately reconstruct fixed-gantry CBCT scans. Whilst the PICCS reconstruction algorithm reduced some motion artefacts, the motion estimation reconstruction methods (GMCFDK, MCPICCS and SMEIR) were able to greatly reduce the effect of motion artefacts on the reconstructed volumes. This finding was verified quantitatively, with GMCFDK, MCPICCS and SMEIR reconstructions having RMSE 17%–19% lower and SSIM 1% higher than a conventional FDK. However, all motion compensated fixed-gantry CBCT reconstructions had a 56%–61% higher RMSE and 1.5% lower SSIM than FDK reconstructions of conventional rotating-gantry CBCT scans. The results show that motion compensation is required to reduce motion artefacts for fixed-gantry CBCT reconstructions. This paper further demonstrates the feasibility of fixed-gantry CBCT scans, and the ability of CBCT reconstruction algorithms to compensate for motion due to horizontal rotation.

Assessment of Geometrical Features of Internal Flaws with Artificial Neural Network

In nondestructive testing (NDT), geometrical features of a flaw embedded in the material such as its location, length, and orientation are critical factors to assess the severity of the flaw and make post-manufacturing decisions to improve the design. In this study, artificial intelligence (AI) based NDT approach was applied to the ultrasonic oscillograms obtained from virtual ultrasonic NDT to estimate geometrical features of a flaw. First, a numerical model of NDT specimen was constructed using acoustic finite element analysis (FEA) to produce the ultrasonic signals. The model was validated by comparing the simulated signals produced from the numerical model with the experimental data from actual NDT tests. Then, 750 numerical models containing flaws with different locations, lengths, and orientation angles were generated by FEA. Next, the oscillograms produced by the models were divided into 3 datasets: 525 for training, 113 for validation, and 112 for testing. Training inputs of the network were parameters extracted from ultrasonic signals by fitting them to sine functions. Lastly, to evaluate the network performance, outputs of the network including flaw’s location, length, and angle were compared with the desired values for all datasets. Deviations of the outputs from desired values were calculated by a regression analysis. Statistical analysis was also performed by measuring root mean square error (RMSE) and efficiency. RMSE in x-location, y-location, length, and angle estimations are 0.09 mm, 0.19 mm, 0.46 mm, and 0.75°, with efficiencies of 0.9229, 0.9466, 0.9140, and 0.9154, respectively for the testing dataset. Results suggest that the proposed AI-based method has the potential to interpret the oscillograms from ultrasonic NDT to estimate geometrical features of flaws embedded in the material.

Muscle Activation in Specific Regions of the Trapezius During Modified Kendall Manual Muscle Tests.

Manual muscle tests (MMTs) are often used when assessing shoulder injuries. For the trapezius, individual MMTs are used to selectively test the upper trapezius region (UTR), middle trapezius region (MTR), and lower trapezius region (LTR). The MMTs for each region are assumed to preferentially recruit the corresponding muscle fibers and produce a maximal contraction; however, whether this is true is unknown. To determine if maximal voluntary isometric contractions (MVICs) for the upper trapezius (UT-MVIC), middle trapezius (MT-MVIC), and lower trapezius (LT-MVIC), adapted from the Kendall MMTs, recruited the corresponding trapezius regions. Crossover study. Laboratory. A total of young, healthy individuals (10 men, 9 women, 1 not listed; age = 23.9 ± 1.7 years, height = 171.4 ± 9.6 cm, mass = 75.7 ± 11.6 kg). Participants performed 3 repetitions of each MVIC. High-density surface electromyography measurements were collected from the UTR, MTR, and LTR. Root mean square (excitation) of the UTR, MTR, and LTR. We observed an increase in UTR excitation during the LT-MVIC compared with the UT-MVIC (P = .016) and MT-MVIC (P < .001). The MTR excitation increased during the MT-MVIC (P = .001) and the LT-MVIC (P < .001) compared with the UT-MVIC. We also noted an increase in MTR excitation during the LT-MVIC compared with the MT-MVIC (P < .001). The LTR excitation increased during the MT-MVIC and LT-MVIC (P values < .001) compared with the UT-MVIC. The UT-MVIC and MT-MVIC did not necessarily recruit the corresponding trapezius regions more than the other MVICs did. Rather, the LT-MVIC appeared to produce the greatest excitation of all trapezius regions. Additional research is needed; however, clinicians should be aware that maximal contractions may not always recruit the desired muscle region.

Speech Scrambler Based on Discrete Cosine Transform and Novel Seven-Dimension Hyper Chaotic System

Because of their inherent features related to cryptography, the hyperchaotic system has strong potential in security systems. In this paper, a new cryptography scheme for speech scrambling has been proposed which gives it more security level. Also, it introduced a novel seven dimensions (7D) nonlinear hyperchaotic system in third-order which has twelve positive parameters. A chaotic behavior for the proposed novel system is analyzed. The hyperchaotic system is demonstrated that beside it appropriates for speech scrambler. PRNG (Pseudo Random Number Generated) is created based on the proposed 7D hyperchaotic result. The proposed scheme depends on this novel hyperchaotic system as PRNG to generate a random keystream bit. The input speech signal in this work is first squeezed by discrete cosine transformation (DCT) to minimize the residual intelligibility (R. I.). Increasing the number of system parameters accomplished a high key space, therefore, improves the security to resist several cryptographic attacks. The scheme is measured using various tests like. Peak Signal-to-Noise Ratio (PSNR), Signal to noise ratio (SNR), percentage of Difference (P. Diff.), Segmental Signal to Noise Ratio Measure (SSNR), Crest factor measure (CF), Correlation coefficient Measure (R_C), root mean square measure (RMS), histogram, key sensitivity, spectrogram, and waveform The results of simulation and comparison prove that the algorithm can achieve better performance in encryption that means the scheme is stronger and highly secure against various types of attacks than many recent similar audio encryption schemes. Simulated and implemented the result using Mathematica and MATLAB programs to simulation results, provided qualitatively, and figures.

A 21.5 ~ 25 GHz 7‐bit phase shifter with passive vector‐sum topology

AbstractA 7‐bit passive phase shifter with low‐phase error is presented in 65 nm CMOS technology. The vector‐sum topology is implemented to enable passive phase shifter the ability of flexible phase calibration. Therefore, the passive phase shifter with high resolution and low‐phase error is achieved. In order to achieve high linearity, attenuator based on resistor is implemented to scale the I/Q signals, and a vector synthesizer based on balun is utilized to sum the quadrature vectors. Measurement results show that the phase shifter achieves 7‐bit resolution. The measured root mean square (RMS) phase error is &lt;2.1°, and the RMS gain error is &lt;1.2 dB over 21.5 ~ 25 GHz. The measured input‐referred P1dB is &gt;10 dBm. The measured peak gain is about −14.3 dB, and the return loss is &gt;8 dB. The chip has no DC power consumption and occupies an area of 880 × 500 um. The measurement results demonstrate the proposed phase shifter is very suitable for high linearity and high‐angular resolution phased arrays in K‐band.

The Effect of Chalazion Excision on Corneal Aberrometric and Densitometric Values.

Objectives:The aim of this study was to investigate the effect of chalazion excision on corneal aberrations and corneal densitometry.Methods:Thirty-six patients with a chalazion in 1 eyelid and 40 healthy subjects were included in the study. Corneal aberration parameters of total root mean square (RMS), RMS high-order aberration (HOA), horizontal trefoil, oblique trefoil, horizontal coma, vertical coma, and spherical aberration values were measured using Scheimpflug corneal topography (Pentacam HR; Oculus Optikgeräte GmbH, Wetzler, Germany). Corneal densitometry values measured from 4 regions of the cornea (0–2, 2–6, 6–10, and 10–12 mm) and 4 corneal depths (anterior, central, posterior, and total) were recorded. Preoperative measurements of the patients (Group 1), postoperative first-month measurements (Group 2), and control group (Group 3) measurements were compared.Results:The total RMS measurement was 1.64±0.48 μm in Group 1, 1.35±0.32 μm in Group 2, and 1.17±0.38 μm in Group 3 (Group 1–2: p=0.007, Group 1–3: p<0.001, Group 2-3: p=0.173). The mean spherical aberration value was 0.183±0.057 μm in Group 1, 0.157±0.048 μm in Group 2, and 0.144±0.050 μm in Group 3 (Group 1–2: p=0.104, Group 1-3: p=0.004, Group 2–3: p=0.781). The total corneal densitometric measurement was 15.95±1.80 gray scale units (GSU) in Group 1, 14.76±1.76 GSU in Group 2, and 14.33±1.49 GSU in Group 3 (Group 1–2: p=0.01, Group 1–3: p<0.001, Group 2–3: p=0.804).Conclusion:It was observed that some corneal aberration and corneal densitometry values were higher in patients with a chalazion compared with those of healthy individuals, and there was a decrease in corneal aberration and densitometry values after surgical excision.

Predicting the Amount of Electric Power Transaction Using Deep Learning Methods

The most important thing to operate a power system is that the power supply should be close to the power demand. In order to predict the amount of electric power transaction (EPT), it is important to choose and decide the variable and its starting date. In this paper, variables that could be acquired one the starting day of prediction were chosen. This paper designated date, temperature and special day as variables to predict the amount of EPT of the Korea Electric Power company. This paper also used temperature data from a year ago to predict the next year. To do this, we proposed single deep learning algorithms and hybrid deep learning algorithms. The former included multi-layer perceptron (MLP), convolution neural network (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), support vector machine regression (SVR), and adaptive network-based fuzzy inference system (ANFIS). The latter included LSTM + CNN and CNN + LSTM. We then confirmed the improvement of accuracy for prediction using pre-processed variables compared to original variables We also assigned two years of test data during 2017–2018 as variable data to measure high prediction accuracy. We then selected a high-accuracy algorithm after measuring root mean square error (RMSE) and mean absolute percent error (MAPE). Finally, we predicted the amount of EPT in 2018 and then measured the error for each proposed algorithm. With these acquired error data, we obtained a model for predicting the amount of EPT with a high accuracy.

SMOOTHING IN NEURAL NETWORK FOR UNIVARIAT TIME SERIES DATA FORECASTING

Time series data is interesting research material for many people. Not a few models have been produced, but very optimal accuracy has not been obtained. Neural network is one that is widely used because of its ability to understand non-linear relationships between data. This study will combine a neural network with exponential smoothing to produce higher accuracy. Exponential smoothing is one of the best linear methods is used for data set transformation and thereafter the new data set will be used in training and testing the Neural Network model. The resulting model will be evaluated using the standard error measure Root Mean Square Error (RMSE). Each model was compared with its RMSE value and then performed a T-Test. The proposed ES-NN model proved to have better predictive results than using only one method.

Deep learning-based urban big data fusion in smart cities: Towards traffic monitoring and flow-preserving fusion

ObjectiveIn the last few years, several techniques and models are used for retrieving significant information from urban big data of smart cities. This research work aims at developing a data fusion-based traffic congestion control system in smart cities using a deep learning model. MethodologyA hybrid model based on the convolution neural network (CNN) and long short term memory (LSTM) architectures are used for region-based traffic flow predictions in smart cities. CNN is used for the classification of spatial data while LSTM for temporal data. ConclusionThe experiments used the CityPulse Traffic and CityPulse Pollution datasets, and measured root mean square error (RMSE), time consumption and accuracy. A small RMSE value of 49 and highest accuracy of 92.3% compared to other baseline models depicts the applicability of the proposed model in the region-based traffic flow prediction problems in the smart cities.

Voltage Quality Enhancement of Grid-Integrated PV System Using Battery-Based Dynamic Voltage Restorer

The advancement of power electronic-based sensitive loads drives the power utilities’ devotion to power quality issues. The voltage disturbance could be happening due to fault conditions, switching of loads, energizing of transformers, or integration of highly intermittent energy sources such as PV systems. This research work attempts to enhance the voltage fluctuation of a sensitive load connected to a grid-integrated PV system using a battery-based dynamic voltage restorer (DVR). The proposed battery energy storage-based DVR has two separate controlling stages that are implemented at the DC–DC buck/boost converter of the battery and voltage source converter (VSC) system. Charging and discharging of the battery is operated based on the state-of-charge (SOC) value of the battery and the measured root mean square (RMS) voltage at the point of common coupling (PCC). The VSC of the DVR detection and reference generation control is done appropriately. In the detection control of the VSC, a combination of RMS and dq0 measurement techniques is used, whereas in the reference generation control, pre-fault strategy is implemented to restore both phase jump and magnitude distortions. Symmetrical and asymmetrical voltage sags scenarios are considered and the compensation demonstration is carried out using power system computer-aided design (PSCAD/EMTDC) software.

Reduced Heart Rate Variability in Patients With Medically Unexplained Physical Symptoms: A Meta-Analysis of HF-HRV and RMSSD.

Medically unexplained physical symptoms (MUPS) and related syndromes are common and place a substantial burden on both patients and society. Chronic psychological distress and dysregulation of the autonomic nervous system may be common factors associated with MUPS, although previous studies have reported mixed results. The aims of this meta-analysis are to provide an updated synthesis of studies investigating heart rate variability (HRV) indices associated with autonomic nervous system functioning in three common MUPS syndromes and to explain inconsistencies in previous study findings. Literature search yielded 58 studies comparing HRV indices of reduced parasympathetic activity of healthy individuals with those of patients with chronic fatigue syndrome (npatients = 271), irritable bowel syndrome (npatients = 1005), and fibromyalgia (npatients = 534). Separate random-effects meta-analyses were conducted on studies measuring root mean square of successive differences (RMSSD) and high-frequency HRV (HF-HRV). Regardless of syndrome type, patients had significantly lower RMSSD (k = 22, Hedges g = -0.37 [-0.53 to -0.21], p < .001) and HF-HRV (k = 52, Hedges g = -0.69 [-1.03 to -0.36], p < .001) than did healthy individuals. Sample age and publication year explained a substantial variation in RMSSD, whereas controlling for confounders in statistical analyses explained variation in HF-HRV. Lower RMSSD and HF-HRV in patients with MUPS versus healthy controls indicates that autonomic nervous system dysregulation, particularly lower parasympathetic activity, may play a role in patients with these conditions. This conclusion may have important implications for the underlying mechanisms and treatment of MUPS and related syndromes.

The Validity Of A Smartphone-based Seated Postural Control Assessment In Non-ambulatory Adults

Individuals who rely on wheeled mobility have unique fall risk factors (e.g. seated postural control) but recommended fall risk screening tools are predominately designed for ambulatory individuals. Consequently, most non-ambulatory adults do not undergo comprehensive fall risk screening or receive targeted fall prevention strategies. PURPOSE: To examine the validity of smartphone-based postural control assessments in non-ambulatory adults. METHODS: Eleven participants (age: 35.4 ± 17.9) completed three clinical tests: The Trunk Control Test, Function in Sitting Test (FIST), and Tee-Shirt Test, as well as, four instrumented balance tasks in a standardized order: eyes open, eyes closed, functional reach, and functional stability boundary. During the balance tasks, participants held a smartphone and research-grade accelerometer to their chest. These devices measured root mean square (RMS) acceleration in the medial-lateral (ML), anterior-posterior, and vertical axes during all tasks. A median split of FIST scores differentiated participants with better and worse postural control. Spearman rank-order correlations between the two devices’ measurements were conducted, and receiver operating characteristic (ROC) and the area under the curves (AUC) were constructed to distinguish participants with better and worse postural control. RESULTS: Participant scores from the FIST differed between those with better and worse postural control (p = 0.020). There were significant moderate to strong correlations between measures derived from the smartphone and measures derived from the research-grade accelerometer during the balance tasks (ρ = 0.636-1.000; p < 0.01-0.035). The AUC for ROC plots were significant for RMS ML sway during the eyes open task and functional stability boundary (p=0.045 and 0.018, respectively). CONCLUSION: This pilot study illustrated that smartphone technology may be able to provide a valid assessment of seated postural control and have the ability to distinguish between those with better and worse postural control in the ML direction. Leveraging this form of technology could provide easily accessible and objective fall risk assessments for non-ambulatory adults. Funded by: The National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR)

Automatic intelligibility assessment of dysarthric speech using glottal parameters

Objective intelligibility assessment of dysarthric speech can assist clinicians in diagnosis of speech disorders as well as in medical treatment. This study investigates the use of glottal parameters (i.e. parameters that describe the acoustical excitation of voiced speech, the glottal flow) in the automatic intelligibility assessment of dysarthric speech. Instead of directly predicting the intelligibility of dysarthric speech using a single-stage system, the proposed method utilizes a two-stage framework. In the first stage, two-class severity classification of dysarthria is performed using support vector machines (SVMs). In the second stage, intelligibility estimation of dysarthric speech is computed using a linear regression model. Two sets of glottal parameters are explored: (1) time-domain and frequency-domain parameters and (2) parameters based on principal component analysis (PCA). Acoustic parameters proposed in a similar intelligibility prediction study by Falk et al. (2012) are used as baseline features. Evaluation results show that the two-stage framework leads to improvement in the intelligibility assessment measures (correlation and root mean square error) compared to the single-stage framework. The combination of the glottal parameters sets results in better performance in the severity classification and intelligibility estimation tasks compared to the baseline features.

A 32-GHz Nested-PLL-Based FMCW Modulator With 2.16-GHz Bandwidth in a 65-nm CMOS Process

This article presents a 32-GHz frequency modulated continuous wave (FMCW) modulator based on the phase-locked loop (PLL) with nested sub-PLL structure in a 65-nm CMOS process. With the sub-PLL, the low-pass effect in phase domain is realized, reducing the noise folding effect, quantization noise, and spurs due to the delta sigma modulator (DSM). To achieve good stability and phase noise performance, both the phase-domain model and the phase noise model are analyzed and simulated. Based on these models, the chirp linearity is discussed and simulated, which helps to determine the design parameters and verifies the linearity improvement. The measurement results illustrate that in fractional-N mode, the nested-PLL achieves the phase noise of -91 dBc/Hz at 1-MHz offset frequency and the fractional spurs of less than -54 dBc at 30.78-GHz output frequency. In FMCW mode, the proposed modulator achieves a triangular chirp with 1.08-2.16-GHz bandwidth at about 32-GHz center frequency. In addition, the measured root mean square (rms) frequency errors of 400 and 770 kHz are achieved with the ramp slopes of 1.08 GHz/93 μs and 2.16 GHz/93 μs, respectively. Measurement results prove the improvements of the phase noise and chirp linearity with the sub-PLL. Including all pads, the chip occupies a silicon area of 1.5 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and consumes 62-mW dc power.

An Unsupervised Data-Driven Model to Classify Gait Patterns in Children with Cerebral Palsy.

Ankle and foot orthoses are commonly prescribed to children with cerebral palsy (CP). It is unclear whether 3D gait analysis (3DGA) provides sufficient and reliable information for clinicians to be consistent when prescribing orthoses. Data-driven modeling can probe such questions by revealing non-intuitive relationships between variables such as 3DGA parameters and gait outcomes of orthoses use. The purpose of this study was to (1) develop a data-driven model to classify children with CP according to their gait biomechanics and (2) identify relationships between orthotics types and gait patterns. 3DGA data were acquired from walking trials of 25 typically developed children and 98 children with CP with additional prescribed orthoses. An unsupervised self-organizing map followed by k-means clustering was developed to group different gait patterns based on children’s 3DGA. Model inputs were gait variable scores (GVSs) extracted from the gait profile score, measuring root mean square differences from TD children’s gait cycle. The model identified five pathological gait patterns with statistical differences in GVSs. Only 43% of children improved their gait pattern when wearing an orthosis. Orthotics prescriptions were variable even in children with similar gait patterns. This study suggests that quantitative data-driven approaches may provide more clarity and specificity to support orthotics prescription.

GaAs growth rates of 528 μm/h using dynamic-hydride vapor phase epitaxy with a nitrogen carrier gas

We demonstrate record GaAs growth rates approaching 530 μm/h using nitrogen carrier gas and 400 μm/h using hydrogen carrier gas in a dynamic-hydride vapor phase epitaxy reactor. We measured root mean square surface roughness below 1 nm using a 1 μm × 1 μm atomic force microscopy scan for GaAs growth rates up to 483 μm/h using a nitrogen carrier gas and 400 μm/h using a hydrogen carrier gas. We performed computational fluid dynamics modeling to study the effect of the carrier gas choice on the thermal profile within the reactor and how that influences the degree of AsH3 decomposition. The modeling suggests that the lower thermal conductivity of the nitrogen carrier gas minimizes the amount of AsH3 that thermally decomposes before reaching the wafer surface and the heavier atomic mass decreases the likelihood that AsH3 will reach the heated reactor walls, leading to a growth rate enhancement relative to the hydrogen carrier case in a hydride-enhanced growth regime.

Precise Positioning of Linear Motor Mover Directly From the Phase Difference Analysis

This article presents a new visual detection method with high precision, robustness, and processing rate to estimate the linear motor displacements. Subpixel level location is achieved by using the phase-difference matrix and a structured aperiodic fence stripe placed on the mover. Based on Fourier shifted properties, this subpixel method builds a linear relation between the mover's spatial displacements and the direct frequency-domain phase difference (not the phase correlation matrix) to calculate the translational shift in a series of stripe images; thereby, precisely determining the mover location of linear motor. Unlike the traditional phase correlation algorithm, the proposed method converts 2-D stripe images into 1-D signals via the projection technique and then uses the least squares method to linearly fit the phase difference slope, thereby acquiring subpixel shifts with less time complexity. Due to the motion parameter that is directly estimated from the phase difference, this method is also insensitive to the gray-scale change with certain anti-interference capability, thereby making it suitable for measuring the moving position of the mover. Experimental results show that the proposed vision-based detection method is efficient and applicable for real-time accurate positioning of linear motor, and the measuring root mean square error is about 0.05 mm.

A New Perspective of Wind Power Grid Codes Under Unbalanced and Distorted Grid Conditions

Increasing penetration of wind power has led power system operators worldwide to develop new grid codes for integration of a wind power plant (WPP) onto the grid. According to the grid codes issued by Federal Energy Regulatory Commission (FERC) in the US, a WPP must have low voltage ride through (LVRT) capability, power factor design criteria, and supervisory control and data acquisition (SCADA) system to ensure power system reliability. Fast Fourier transform (FFT) is frequently used to measure root mean square (RMS) voltage, power factor, and for supervisory data acquisition in order to verify that a WPP conforms to the grid code requirements. However, FFT inherently assumes signal is periodic in nature, and it provides misleading results under unbalanced and distorted grid conditions. To overcome these issues, this work proposes a new method for wind power grid codes based on time-frequency analysis technique. Unlike FFT, it provides accurate result both in steady-state and transient conditions. The efficacy of the proposed method is verified by applying it to computer simulated and real-world cases provided by National Renewable Energy Laboratory (NREL) in the US. Time-frequency analysis is performed utilizing Time-Frequency Toolbox (TFTB) in MATLAB ® developed for the analysis of non-stationary signals.