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Background: We aimed to evaluate the existence of accommodative microfluctuations in eyes after cataract surgery. Methods: This retrospective observational cohort study included 1160 eyes of 713 patients (mean age: 72.5 ± 8.3 years) who underwent phacoemulsification, intraocular lens insertion, and an evaluation of accommodative microfluctuations with an autorefractometer. Patients with posterior segment disorders resulting in visual acuity impairment and those with unavailable medical information were excluded. High-frequency components (HFCs), between 1.0–2.3 Hz, based on fast Fourier transform analysis of the accommodative microfluctuation data were examined at postoperative 2–3 (2 M) and 6 months (6 M). The relationships between the HFCs and patient age, manifest refraction, and axial length were analyzed. Results: Increased HFC values (>65) were observed at a constant rate after cataract surgery, with prevalence rates of 33.4% at 2 M and 34.7% at 6 M. Postoperatively, at 2 M, increased HFC values were significantly more common for eyes with axial length ≥26 mm than for those with axial length <26 mm (p = 0.0056). However, they were not significantly correlated to age or postoperative manifest refraction. Conclusions: At 2 M postoperatively, increased HFC values presented more frequently in eyes with a greater axial length; hence, the precise detection and understanding of postoperative accommodative spasms in high myopia patients is important. [ABSTRACT FROM AUTHOR]

An increasing number of civil structures are being built near or across earthquake faults, making them more susceptible to severe seismic damage due to the presence of large velocity pulses in near-fault ground motions (NFGMs). Because of limited availability of NFGMs records, synthesized NFGMs are necessary for seismic designing of structures located around near-fault areas. Based on the far-field synthesis of ground motions (GMs), various methods for synthesizing NFGMs by superimposing high-frequency and low-frequency components in the time domain have been developed. However, such techniques suffer from wider variation in the response spectrum between the synthesized NFGMs and the target spectrum. To address this issue, a new approach for synthesizing NFGMs has been proposed. This approach investigates key parameters of the response spectrum for NFGMs based on a comprehensive database. Similarly, the shortcomings in existing NFGM methods have been analyzed by evaluating a couple of criteria for the suitability of the synthesizing method. Two adjustment strategies have been developed to attain consistency in the response spectrum. The spectral characteristics can be maintained during the spectrum adjustment process to overcome limitations of existing methods. Synthesized specimen of NFGMs has been utilized to validate the effectiveness of the proposed approach. • Fitting the values of key parameters of the response spectrum based on a more complete NF GM record database. • Adjusting the sequential steps of the fitting process to achieve highly agreement with the target response spectrum for final GMs. • An improved ground motion adjustment method maintaining the spectral characteristics of original GM. • Finally, this manuscript proposes an improved NF GMs fitting method. The results of examples show that the proposed method can effectively improve the shortcomings of existing methods. [ABSTRACT FROM AUTHOR]

Background: We aimed to evaluate the existence of accommodative microfluctuations in eyes after cataract surgery. Methods: This retrospective observational cohort study included 1160 eyes of 713 patients (mean age: 72.5 ± 8.3 years) who underwent phacoemulsification, intraocular lens insertion, and an evaluation of accommodative microfluctuations with an autorefractometer. Patients with posterior segment disorders resulting in visual acuity impairment and those with unavailable medical information were excluded. High-frequency components (HFCs), between 1.0–2.3 Hz, based on fast Fourier transform analysis of the accommodative microfluctuation data were examined at postoperative 2–3 (2 M) and 6 months (6 M). The relationships between the HFCs and patient age, manifest refraction, and axial length were analyzed. Results: Increased HFC values (>65) were observed at a constant rate after cataract surgery, with prevalence rates of 33.4% at 2 M and 34.7% at 6 M. Postoperatively, at 2 M, increased HFC values were significantly more common for eyes with axial length ≥26 mm than for those with axial length p = 0.0056). However, they were not significantly correlated to age or postoperative manifest refraction. Conclusions: At 2 M postoperatively, increased HFC values presented more frequently in eyes with a greater axial length; hence, the precise detection and understanding of postoperative accommodative spasms in high myopia patients is important.

There is potential for personal care products (PCPs) components and mixtures to induce hormesis. How hormesis is related to time and transmitted from components to mixtures are not clear. In this paper, we conducted determination of components in 16 PCP products and then ran frequent itemset mining on the component data. Five high-frequency components (HFCs), betaine (BET), 1,3-butanediol (BUT), ethylenediaminetetraacetic acid disodium salt (EDTA), glycerol (GLO), and phenoxyethanol (POE), and 14 mixtures were identified. For each mixture system, one mixture ray with the actual mixture ratios in the products was selected. Time-dependent microplate toxicity analysis was used to test the luminescence inhibition toxicity of five HFCs and 14 mixture rays to Vibrio qinghaiensis sp.-Q67 at 12 concentration gradients and eight exposure times. It is showed that BET, EDTA, POE, and 13 mixture rays containing at least one J-type component showed time-dependent hormesis. Characteristic parameters used to describe hormesis revealed that the absolute value of the maximum stimulatory effect (|E min |) generally increased with time. Notably, mixtures composed of POE and S-type components showed greater |E min | than POE alone at the same time. Importantly, the maximum stimulatory effective concentration, NOEC/the zero effective concentration point, and EC 50 remained relatively stable. Nine hormesis transmission phenomena were observed in different mixture rays. While all mixtures primarily exhibited additive action, varying degrees of synergism and antagonism were noted in binary mixtures, with no strong synergism or antagonism observed in ternary and quaternary mixtures. These findings offer valuable insights for the screening of HFCs and their mixtures, as well as the study of hormesis transmission in personal care products. [Display omitted] • Five high-frequency components and their 14 mixtures were identified by FIM. • All EC min , NOEC/ZEP, and EC 50 did not significantly change over time. • The maximum stimulation of J-type components and mixture rays increased over time. • There was no strong antagonism or synergism in ternary and quaternary mixtures. • Mixtures composed of POE and S-type components showed greater |E min | than POE alone. [ABSTRACT FROM AUTHOR]

The authors propose architectures that learn end‐to‐end mapping functions to improve the spatial resolution of the input natural images. The models are unique in forming non‐linear combinations of three image interpolation techniques using the convolutional neural network. Another proposed architecture uses a skip connection with nearest‐neighbour interpolation, achieving almost similar results. The architectures have been carefully designed to ensure that the reconstructed images lie precisely in the manifold of high‐resolution images, thereby preserving the high‐frequency components with fine details. They have compared with the state‐of‐the‐art and recent deep learning‐based natural image super‐resolution techniques and found that their methods can preserve the sharp details in the image, while also obtaining comparable or better peak‐signal‐to‐noise ratio values than them. Since their methods use image interpolations and a shallow convolutional neural network (CNN) with a fewer number of smaller filters, the computational cost is kept low. They have reported the results of the best two proposed architectures on five standard data sets for an upscale factor of 2. Their methods generalise well in most cases, which is evident from the better results obtained with increasingly complex data sets. For four times upscaling, they have designed similar architectures for comparing with other methods.

Short-duration high-strength human tourism activities (SHHTA) can result in more air pollution emissions owing to increase motor vehicle usage, energy consumption and cooking fume emissions. Because of the strong uncertainty of human tourism behaviour, it is difficult to accurately assess the impact of SHHTA on air quality of natural scenic spots. To overcome this difficulty, we propose a novel ensemble empirical mode decomposition and detrended cross-correlation analysis (EEMD-DCCA) model to assess the influence of short-duration high-strength human tourism activities (SHHTA) on air quality. Zhangjiajie in China was selected as the study area. Hourly concentrations of NO2 were analysed from 1 January 2016 to 31 December 2018 at two monitoring sites, in an urban area and a scenic spot. Through EEMD, the main modes of NO2 with short-duration high-frequency were obtained for both sites. The DCCA method was used to study the cross-correlation relationship between high-frequency modes of NO2 for the urban area and scenic spot. The results show that high-frequency modes of NO2 between the two sites displayed long-range cross-correlation at the 24-h time scale. Furthermore, the quantitative impacts of meteorological factors (e.g. precipitation, temperature, and wind speed) on the DCCA exponent for high-frequency modes of NO2 at the two sites were investigated. The novel model proposed in this study is not restricted by the uncertainty of pollution emission inventory. The relationship between meteorological factors and DCCA exponents corresponds to the hypothesis that NO2 pollution of the natural scenic spot mainly came from SHHTA. [ABSTRACT FROM AUTHOR]

A model smoothing formulation used to filter out the high-frequency components in the dynamical modeling process is developed and implemented in the framework of absolute nodal coordinate formulation (ANCF). Unlike the conventional method which employs stiff ordinary differential equation (ODE) solvers with numerical damping to calculate the dynamic responses of the ANCF model, a new formulation with the controlled high frequency is proposed in this paper. Using the average stress defined in a time interval to replace the instant stress in the virtual power of the internal force, the additional inertial and damping terms are introduced to the equations of motion of the ANCF element. It is proved that the highest frequency magnitude of the modified ANCF model can be adjusted by changing the length parameter of the time interval. As the high-frequency components are filtered out in the process of the model development, the traditional stiff problems of ANCF elements can be solved by using well-developed explicit integration algorithms, particularly in the very stiff and thin structures. Dynamic analysis of the classic examples including the pendulum beam and plate are performed to evaluate the performance of the model smoothing formulation of ANCF elements. Numerical results show that the proposed method can greatly improve the calculation efficiency of ANCF models, and the calculation accuracy can be also guaranteed. [ABSTRACT FROM AUTHOR]

Aiming at the problems of obscure clinical auscultation features of pulmonary hypertension associated with congenital heart disease and the complexity of existing machine-aided diagnostic algorithms, an algorithm based on the statistical characteristics of the high-frequency components of the second heart sound signal is proposed. Firstly, an endpoint detection adaptive segmentation method is employed to extract the second heart sounds. Subsequently, the high-frequency component of the heart sound is decomposed using the discrete wavelet transform. Statistical features including the Hurst exponent, Lempel-Ziv information and sample entropy are extracted from this component. Finally, the extracted features are utilized to train an extreme gradient boosting algorithm (XGBoost) classifier, which achieves an accuracy of 80.45% in triple classification. Notably, this method eliminates the need for a noise reduction algorithm, allows for swift feature extraction, and achieves effective multi-classification using only three features. It is promising for early screening of pulmonary hypertension associated with congenital heart disease.

The authors propose architectures that learn end‐to‐end mapping functions to improve the spatial resolution of the input natural images. The models are unique in forming non‐linear combinations of three image interpolation techniques using the convolutional neural network. Another proposed architecture uses a skip connection with nearest‐neighbour interpolation, achieving almost similar results. The architectures have been carefully designed to ensure that the reconstructed images lie precisely in the manifold of high‐resolution images, thereby preserving the high‐frequency components with fine details. They have compared with the state‐of‐the‐art and recent deep learning‐based natural image super‐resolution techniques and found that their methods can preserve the sharp details in the image, while also obtaining comparable or better peak‐signal‐to‐noise ratio values than them. Since their methods use image interpolations and a shallow convolutional neural network (CNN) with a fewer number of smaller filters, the computational cost is kept low. They have reported the results of the best two proposed architectures on five standard data sets for an upscale factor of 2. Their methods generalise well in most cases, which is evident from the better results obtained with increasingly complex data sets. For four times upscaling, they have designed similar architectures for comparing with other methods. [ABSTRACT FROM AUTHOR]

This work aims to enhance the quality of narrowband (0–4 kHz) voice signal in terms of frequency components, i.e. missing high‐frequency components in a range of 4–8 kHz. The proposed artificial bandwidth extension framework uses the H∞ optimisation. In this context, a signal model is used to get a better representation of wideband (0–8 kHz) information of a signal. The H∞ optimisation is used to obtain the synthesis filter for a given signal model, which is used to synthesise the high‐band (4–8 kHz) signal. The discrete Fourier transform addition is performed to add the narrowband signal and estimated high‐band signal for removing the leaked information from the synthesis filter and non‐ideal low pass filter. Gain adjustment is performed on the estimated high‐band signal to make its energy equal to the true high‐band signal. Non‐stationary characteristics of speech signals generate an assorted variety in synthesis filters and corresponding gain. For this, a deep neural network (DNN) is used to estimate the synthesis filter and gain by using the given narrowband information. The authors analyse the performances of the DNN model on two data sets. Objective and subjective analyses are carried out on these data sets. [ABSTRACT FROM AUTHOR]

The original signal of rolling bearing fault contains a large number of phase coupling components and is easily submerged in the background noise, which make the fault information difficult to be extracted accurately. Aiming at the above problems, a method of fault feature extraction for rolling bearing is proposed, which combines empirical mode decomposition (EMD) with a 1.5-dimensional spectrum. The original signal is decomposed by EMD to obtain the intrinsic modal function (IMF) of different scales. The IMF is selected by the size of a correlation coefficient and a kurtosis value to eliminate the high-frequency components, which is reconstructed to achieve the purpose of noise reduction. The reconstructed Hilbert envelope signal is analysed by the 1.5-dimensional spectrum to extract the nonlinear characteristic of two phase couplings, so that the fault characteristic frequency of the bearing is obtained. By analysing the signal of actual rolling bearings, the fault characteristic frequency of bearing inner and outer rings can be effectively extracted, and the validity and feasibility of the method are proved.

A new method of bearing fault diagnosis based on multi-masking empirical mode decomposition (MMEMD) and extreme machine learning optimised by differential evolution algorithm (DE_ELM) is proposed in this study. MMEMD is an improvement of empirical mode decomposition (EMD). By adding masking signals to the signals to be decomposed in different levels, MMEMD can restrain low-frequency components from mixing in high-frequency components effectively in the sifting process and then suppress the mode mixing. Differential evolution algorithm is applied to determine the parameters of ELM for improving the classification accuracy. The four parameters are determined at one time by uniformly coded as the individuals of the differential evolution algorithm. To achieve the bearing fault diagnosis, the fault signals are first decomposed into different intrinsic mode functions (IMFs) and the sample entropy of each IMF was calculated as the fault feature. Then the fault feature was divided into training set and testing set. Input the training set to the DE_ELM to obtain the fault classification model. Finally, the testing set was put into the model for fault diagnosis. The experiment and wind turbine bearing fault diagnosis results show that the method could identify the different bearing faults with high reliability and accuracy.

Due to the scarcity of near-fault records with forward-directivity effects, synthetic near-fault pulse-like ground motions through superimposing pulse models and high-frequency components are commonly used in the earthquake engineering. However, in existing studies, the cross-over frequency in generating high-frequency components is usually empirical and unclear. In this article, a hybrid decomposition and resynthesis method is developed to quantify the cross-over frequency, in which the wavelet decomposition and high-pass filter are, respectively, used to get the pulse and high-frequency components for near-fault records. Using the 30 near-fault pulse-like records, the distribution of cross-over frequencies is obtained. It is interestingly found that the cross-over frequency is inversely proportional to the pulse period. [ABSTRACT FROM AUTHOR]

The massive installation and deployment of electric vehicle (EV) charging infrastructures requires designing efficient and convenient testing tools for the field testing. This paper presents a novel testing system for the EV off‐board charger, in an application such as field testing and maintenance. The authors discuss the design theory and hardware implementation of the system in first. Two typical charger testing scenarios using the proposed tester are discussed. In addition, the modular structure of the system provides the user with a high degree of flexibility, which allows them to configure their own testing systems according to their applications. Three digital processing approaches for constraining high‐frequency components and harmonics are introduced. The authors demonstrate that the digitalised traditional filter has an overwhelming advantage than the statistical filters in the experimental results. They also validate the function of the proposed testing system with a DC 60 kW charger, by finishing testing items such as the regulation accuracy, current imbalance and extreme temperature testing. Finally, they present a comparative study of recent EV charger testing systems. [ABSTRACT FROM AUTHOR]

A new method of bearing fault diagnosis based on multi-masking empirical mode decomposition (MMEMD) and extreme machine learning optimised by differential evolution algorithm (DE_ELM) is proposed in this study. MMEMD is an improvement of empirical mode decomposition (EMD). By adding masking signals to the signals to be decomposed in different levels, MMEMD can restrain low-frequency components from mixing in high-frequency components effectively in the sifting process and then suppress the mode mixing. Differential evolution algorithm is applied to determine the parameters of ELM for improving the classification accuracy. The four parameters are determined at one time by uniformly coded as the individuals of the differential evolution algorithm. To achieve the bearing fault diagnosis, the fault signals are first decomposed into different intrinsic mode functions (IMFs) and the sample entropy of each IMF was calculated as the fault feature. Then the fault feature was divided into training set and testing set. Input the training set to the DE_ELM to obtain the fault classification model. Finally, the testing set was put into the model for fault diagnosis. The experiment and wind turbine bearing fault diagnosis results show that the method could identify the different bearing faults with high reliability and accuracy. [ABSTRACT FROM AUTHOR]

The original signal of rolling bearing fault contains a large number of phase coupling components and is easily submerged in the background noise, which make the fault information difficult to be extracted accurately. Aiming at the above problems, a method of fault feature extraction for rolling bearing is proposed, which combines empirical mode decomposition (EMD) with a 1.5-dimensional spectrum. The original signal is decomposed by EMD to obtain the intrinsic modal function (IMF) of different scales. The IMF is selected by the size of a correlation coefficient and a kurtosis value to eliminate the high-frequency components, which is reconstructed to achieve the purpose of noise reduction. The reconstructed Hilbert envelope signal is analysed by the 1.5-dimensional spectrum to extract the nonlinear characteristic of two phase couplings, so that the fault characteristic frequency of the bearing is obtained. By analysing the signal of actual rolling bearings, the fault characteristic frequency of bearing inner and outer rings can be effectively extracted, and the validity and feasibility of the method are proved. [ABSTRACT FROM AUTHOR]

This study presents an adaptive data‐transition decision feedback equaliser (DT‐DFE) with a sign3 least‐mean‐square (LMS) tap adaptation. Commonly used data‐state (DS) DFE suffers from reduced vertical eye‐opening when consecutive 1's or 0's are encountered. The proposed DT‐DFE performs DFE only when a data transition is detected. It boosts the eye‐opening of the high‐frequency components of data without attenuating the low‐frequency components of data whereas DS‐DFE boosts the eye‐opening of the high‐frequency components of data at the expense of the attenuated low‐frequency components of data. The reference voltages of DS‐DFE is tap‐dependent whereas those of DT‐DFE are tap‐independent and are obtained by conveying consecutive 1's and 0's to the channel in a training phase. The proposed DT‐DFE utilises loop unrolling to detect the occurrence of data transition. The performance of the proposed DT‐DFE is compared with that of DS‐DFE using two 5 Gbps backplane serial links designed in a TSMC 65 nm CMOS technology. Simulation results demonstrate that the eye‐opening of the link with DT‐DFE is 1.54 times that with DS‐DFE. The vertical eye‐opening of the link with DT‐DFE activating tap adaptation on two consecutive state transitions of opposite polarities is 1.2 times that that activates tap adaptation on single state transition. The proposed DT‐DFE is less sensitive to process uncertainty whereas DS‐DFE is prone to process uncertainty with severely deteriorating performance. [ABSTRACT FROM AUTHOR]

Aiming at meeting the requirement of balancing the fluctuating wind power, this study proposed an optimal control strategy for wind power hybrid energy storage system considering output power amendment. Firstly, fluctuant wind power is decomposed into low-frequency, sub-high frequency and high-frequency components. Low-frequency part is used as the expected value of wind power integration. Sub-high frequency and high-frequency components are used as the target value of output power of storage batteries and ultra-capacitor energy storage system. Secondly, a double power amendment is proposed. In the first-time power amendment, the amendment coefficient is calculated by fuzzy controller whose inputs are the target value of power and state of charge (SOC) of the energy storage system. On the other hand, complement of batteries and super-capacitors are utilised in the second amendment. Through simulation analysis, it can be indicated that the configuration of the hybrid energy storage system and the control system design are feasible. This system can be widely used in wind farm, undertaking the task of balancing the fluctuating wind power.

To faithfully recover the clean images corrupted by additive white Gaussian noise (AWGN) and impulse noise (IN), a novel edge preserving image denoising algorithm is proposed. The low- and high-frequency components of the image are restored separately. The high-frequency components of the images are restored based on nonlocal self-similarity (NSS) learning from natural images. An energy minimization function is developed to combine the low- and high-frequency components into one model. Experiments demonstrate that the proposed method outperforms existing mixture noise removal methods in peak signal-to-noise ratio (PSNR), edges preservation and visual performance. [ABSTRACT FROM AUTHOR]

This paper presents a new technique for very high speed detection of fault during power swing on a hybrid transmission line consisting of an overhead line and an underground cable. The wavelet packet transform with the mother wavelet db1 is utilised to segregate the high‐frequency components from three‐phase current signals recorded at the relay point and a fault detection index is calculated using these components to detect faults during power swing. The proposed method is tested on a 400 kV double circuit transmission system and a 400 kV 3‐machine 9‐bus Western System Coordinating Council system simulated in electromagnetic transient program software for both symmetrical and asymmetrical faults, different fault resistances, load angles, and fault inception times at random locations on the transmission line. Its performance is also analysed for close‐in faults, high dc offset current, and the faults on the series compensated transmission line. The method is very fast to detect faults during power swing irrespective of the variations of fault conditions, and a comparative assessment with the existing methods justifies its merit. [ABSTRACT FROM AUTHOR]

For renewable energy penetration, a frequency control strategy is proposed to assure the multi‐area hybrid power system stable operation. The load frequency control (LFC) and the battery energy storage system (BESS) are used together to design the frequency control strategy. Furthermore, in order to improve the control accuracy and reduce the battery capacity, the area control error and active power disturbance are divided into low‐frequency components and high‐frequency components. Therefore, LFC is designed by using sliding mode algorithm for the low‐frequency deviation and BESS is adjusted by using the designed disturbance observer for the high‐frequency disturbance. Finally, the effectiveness of the proposed frequency control strategy is proved by the real‐time digital simulator which can optimise the frequency quality and reduces the capacity of the BESS. [ABSTRACT FROM AUTHOR]

Aiming at meeting the requirement of balancing the fluctuating wind power, this study proposed an optimal control strategy for wind power hybrid energy storage system considering output power amendment. Firstly, fluctuant wind power is decomposed into low-frequency, sub-high frequency and high-frequency components. Low-frequency part is used as the expected value of wind power integration. Sub-high frequency and high-frequency components are used as the target value of output power of storage batteries and ultra-capacitor energy storage system. Secondly, a double power amendment is proposed. In the first-time power amendment, the amendment coefficient is calculated by fuzzy controller whose inputs are the target value of power and state of charge (SOC) of the energy storage system. On the other hand, complement of batteries and super-capacitors are utilised in the second amendment. Through simulation analysis, it can be indicated that the configuration of the hybrid energy storage system and the control system design are feasible. This system can be widely used in wind farm, undertaking the task of balancing the fluctuating wind power. [ABSTRACT FROM AUTHOR]

This study presents an illumination pre‐processing method termed as 'Discrete wavelet transform enhanced contrast limited adaptive histogram equalisation' (DWT E‐CLAHE) to recognise the front view facial images in the difficult light conditions. A recent image enhancement method CLAHE‐DWT motivates to combine the two‐dimensional discrete wavelet transform (2D DWT) and CLAHE. The DWT E‐CLAHE is implemented as follows: The original image is enhanced using the Gamma intensity correction (GIC); then split into low‐frequency and high‐frequency components using 2D DWT; finally, to the low‐frequency components, the logarithmic transform, GIC and CLAHE are applied in the sequential order. The face recognition of DWT E‐CLAHE is made using Gabor magnitude features. The face recognition of CLAHE‐DWT is implemented for the first time. The experimental results of DWT E‐CLAHE in the various face databases prove the following: (i) The proper selection of parameters of DWT E‐CLAHE improves the brightness and contrast of the image. (ii) The recognition accuracy of DWT E‐CLAHE in the Carnegie Mellon University (CMU) Pose, Illumination, and Expression (PIE) and Extended Yale B databases are extremely good, since the brightness and contrast are improved significantly. (iii) The performance comparison of DWT E‐CLAHE outperforms CLAHE‐DWT and state‐of‐the‐art face recognition methods. (iv) DWT E‐CLAHE recognises the varying facial expressions. [ABSTRACT FROM AUTHOR]

• The components from two separated adaptive transfer functions are combined. • Regression models are used to obtain the adaptive transfer functions. • The shape and high-frequency components of aortic pressure waveform are improved. • Two-level adaptive transfer function strategy is practical. Existing studies cannot estimate the high-frequency components of the aortic pressure waveform (APW) practically and reliably. To solve this problem, a two-level adaptive transfer function (ATF) strategy was proposed. Two ATFs were derived from raw and normalized brachial and aortic pressure waveforms. The APW was reconstructed with the amplitude and pulsatility information from the two ATFs. Experimental results showed that our method achieved lower errors for total waveform, diastolic blood pressure, the pressures at the start-systolic and dicrotic-notch points, and three high-frequency-dependent indices including the pressure at inflection point, augmentation index and the time interval from foot to inflection points (3.11 versus 4.24 mmHg, 1.74 versus 1.94 mmHg, 2.56 versus 2.75 mmHg, 6.94 versus 7.47 mmHg, 3.35 versus 3.80 mmHg, 9.56 versus 10.48 % and 24.64 versus 27.73 ms, respectively) compared with traditional generalized transfer function. The proposed method can accurately estimate both the shape and high-frequency components of APW. [ABSTRACT FROM AUTHOR]

We propose a novel algorithm to segment a low depth-of-field (DOF) image into its focused region-of-interest (ROI) and defocused background using adaptive second-order statistics (ASOS). Most previous methods depend on the assump -tion that the images are in noise-free conditions, which leads to high false positive rates in noisy images. In this letter, we introduce a novel image segmentation algorithm for noisy low-DOF images. Specifically, we propose a novel feature transform method, called ASOS, which indicates the spatial distribution of the high-frequency components in the face of noisy low-DOF images. Experimental results demonstrate that the proposed method is effective for image segmentation in noisy images compared to several state-of-the-art methods proposed in the literature. [ABSTRACT FROM AUTHOR]

The growing need for high-precision measurement instruments in modern industrial facilities leads to new measurement principles to reduce sensor costs while maintaining accuracy. A novel measurement principle suited for these applications is based on radar, which originally is associated with measurements in free space. For high precision, guided waves can be used to prevent disturbing reflections from the environment. Another advantage of a waveguide-based measurement is that the electromagnetic wave can be guided in a circle to measure rotational motions, which are in particular relevant in machine tools. This paper describes an FMCW radar sensor in the K-band for angle measurements. The sensor principle is described in detail, including its high-frequency components and the mechanical constraints to achieve a full angle range of 360°. The propagation characteristics of the radar signal in a curved waveguide with an adapted feeding structure using a Riblet coupler are presented. In addition to simulations, measurements with a prototype setup are presented to verify the radar-based angle measurement principle. The achieved accuracy is ±0.05° which corresponds to an accuracy in distance of ±125 μm. [ABSTRACT FROM PUBLISHER]

This study proposes a novel H∞‐based approach called advanced frequency control (AFC) to integrate storage into frequency control. The objective is to split the task of balancing frequency deviations introduced by renewable energy source (RES) and load variations according to the capabilities of storage and generators. Hence, via frequency dependent weighting functions, a so‐called frequency separation objective is achieved: the conventional generators mainly balance the low‐frequency components of the RES and load variations while the energy storage devices compensate the high‐frequency components. First, a state‐space structure‐preserving model of the power system is derived, which is needed for the design of the controller. In order to enable the controller design for storage devices located at buses with no generators, a model for the frequency at such a bus is developed. Then, AFC controllers are synthesised through decentralised static output feedback to reduce the complexity in the practical implementation of such controllers. An existing linear matrix inequality algorithm is improved and employed to solve the involved H∞ problem. Finally, the WECC 9‐bus test system is used to verify the performance of the AFC design. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, two improved constrained learning algorithms that are able to guarantee to obtain better generalization performance are proposed. These two algorithms are substantially on-line learning ones. The cost term for the additional functionality of the first improved algorithm is selected based on the first-order derivatives of the neural activation at hidden layers, while the one of the second improved algorithm is selected based on second-order derivatives of the neural activation at hidden layers and output layer. In the course of training, the cost terms selected from these additional cost functions can penalize the input-to-output mapping sensitivity or high-frequency components included in training data. Finally, theoretical justifications and simulation results are given to verify the efficiency and effectiveness of the two proposed learning algorithms. [Copyright &y& Elsevier]

High-frequency components in ECG during global ischaemia were studied in isolated guinea-pig hearts perfused ad modum Langendorf. Electrocardiograph recordings were carried out from the epicardial surface both in normo- and low-flow perfusion. After bandpass filtering (5–500 Hz), signal-averaging, was undertaken. The high-frequency components either increased or decreased after low-flow perfusion was instituted. Root-mean-square voltage (RMS) of the depolarization signal correlated poorly with the signal amplitude, but highly with the first and second derivative, i.e. the velocity and the acceleration of the signal. It is concluded that high-frequency components are not pathological phenomena per se, but reflect the shape of the original electrocardiographic signal. [ABSTRACT FROM AUTHOR]