Time-varying Transfer Function Research Articles

BGOA-TVG: Binary Grasshopper Optimization Algorithm with Time-Varying Gaussian Transfer Functions for Feature Selection.

Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions have the characteristics of a fast convergence speed and a strong global search capability to convert a continuous search space to a binary one. The BGOA-TVG is tested and compared to S-shaped and V-shaped binary grasshopper optimization algorithms and five state-of-the-art swarm intelligence algorithms for feature selection. The experimental results show that the BGOA-TVG has better performance in UCI, DEAP, and EPILEPSY datasets for feature selection.

Experimental verification of a translation model of linear transfer function of ultrasonic vibrations with nonlinear wave modulation for detection of contact-type failures

Contact-type failures, such as the crack closure at the tip of a fatigue crack, closed delamination of composite materials, and loosening of bolted joints, are difficult to detect through nondestructive inspections using linear ultrasonic waves or vibrations. When low-frequency vibrations, such as environmental disturbances, operating vibrations, and vibrations excited by actuators, are applied to a structure with contact-type failure, the contact condition of the failure surface fluctuates in synchronization with the low-frequency vibration. The amplitude and phase of ultrasonic waves or vibrations through contact-type failure with fluctuating contact conditions are modulated in synchronization with low-frequency vibrations, that is, realizing nonlinear wave modulation. Previous reports have presented evaluation and localization methods of contact-type failure using amplitude and phase demodulation signals. In this study, amplitude and phase demodulations were affected by the change in the viscous damping of a structure. Therefore, the essence of nonlinear wave modulation was considered to be the fluctuation of the natural frequencies. In this study, a visualization experiment of the fluctuation of natural frequencies caused by nonlinear wave modulation was conducted. The time-varying transfer function was reconstructed by measuring the amplitude and phase demodulation signals with the change in the excitation frequency. Moreover, the translation model of the transfer function of the ultrasonic vibration with nonlinear wave modulation was evaluated.

Hybrid binary grey wolf naked mole-rat algorithm for fragment-type UWB antenna optimization using time-varying transfer functions

This paper introduces a novel hybrid binary variant of the Grey Wolf Optimizer (GWO) and the Naked Mole-Rat Algorithm (NMRA), called HbGNMR. The primary objective of this hybridization is to overcome the poor exploration and local optima stagnation problems of the basic NMRA as well as the poor exploitation of GWO. HbGNMR incorporates the global search strategy of GWO into the worker phase to enhance exploration, and a mating factor based on simulated annealing-inertia weight is added to the breeder phase to improve exploitation. Moreover, fourteen new time-varying binary transfer functions of two families: seven S-shaped and seven V-shaped functions have been analysed. Among these transfer functions, TVV5 was identified as the most effective in converting solutions from continuous search space to binary search space and maintaining a balance between exploration and exploitation phases. The hybrid performance of HbGNMR is evaluated on twenty-three CEC’05, thirty CEC’17, and eighty CEC’21 benchmark functions. The statistical results demonstrate that HbGNMR outperforms nine well-known state-of-the-art algorithms according to Friedman-test and Wilcoxon rank-sum test. To test the effectiveness of the proposed algorithm for real-world applications, HbGNMR is employed to optimize a large dimensional planar-monopole ultrawideband (UWB) fragment-type antenna structure of an 18 × 18 binary matrix cells. Experimental testing of the prototype is conducted on a vector-network-analyser-based experimental-testbed, which shows good agreement with the simulated results. Furthermore, the optimized structure achieves large bandwidth (3.1-12.6 GHz), excellent gain, and high directivity for the 10 × 15 mm2 compact size UWB antenna geometry. Therefore, the optimized antenna can be utilized for modern wireless applications.

EEG channel selection in CIT: a binary Harris Hawks optimization using a time-varying transfer function

EEG channel selection in CIT: a binary Harris Hawks optimization using a time-varying transfer function

A self-adaptive binary cat swarm optimization using new time-varying transfer function for gene selection in DNA microarray expression cancer data

A self-adaptive binary cat swarm optimization using new time-varying transfer function for gene selection in DNA microarray expression cancer data

Switch Capacitor-based Time-Varying Transfer Function for FCN and CNN MLSCA-resistant AES256 in 65nm CMOS

Switch Capacitor-based Time-Varying Transfer Function for FCN and CNN MLSCA-resistant AES256 in 65nm CMOS

Physical Time-Varying Transfer Function as Generic Low-Overhead Power-SCA Countermeasure

Physical Time-Varying Transfer Function as Generic Low-Overhead Power-SCA Countermeasure

Investigating the vibration response and modulation mechanism for health monitoring of wind turbine planetary gearboxes using a tribodynamics-based analytical model

In a wind turbine (WT) planetary gearbox system, the characteristics of incipient faults are difficult to recognize with increasing friction under a lack of lubrication condition. To this end, a translational–torsional coupled nonlinear tribodynamic model considering local faults is developed. A fitted time-varying transfer function is obtained using an impact experiment excited by a planet gear with a missing tooth and is further used to represent the modulation effect caused by the carrier. Using the proposed analytical model, the vibration response and modulation mechanism of the resultant signal under the coupling effect of tooth root crack and sliding friction are investigated. It is demonstrated that specific sidebands are greatly excited and the spectrum structure becomes extremely complex after the introduction of friction. Based on these results, sideband indicators are obtained to monitor the lubrication state and detect incipient faults. The experimental results based on an in-house WT drivetrain testbench are in good agreement with the simulation results.

A 3-D Space-Time-Frequency Non-Stationary Model for Low-Altitude UAV mmWave and Massive MIMO Aerial Fading Channels

In this article, a 3-D geometry-based stochastic model (GBSM) is proposed for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) unmanned aerial vehicle (UAV) channels. The proposed model is the first mmWave massive MIMO UAV two-cylinder GBSM that enables to jointly model the channel space-time-frequency non-stationarity by a novel variable parameter-based method. In this novel method, key parameters of UAV channels are assumed to vary in space, time, and frequency domains, and the effect of the unique UAV-related parameters is further taken into account, such as the UAV’s altitude, velocity, and moving directions. Based on the proposed model, some statistical properties are derived, including the time-variant transfer function (TVTF), space-time-frequency correlation function (STF-CF), Doppler power spectral density (PSD), and the standard deviation of Doppler frequency on antenna arrays. Simulation results show that the channel non-stationarity in space, time, and frequency domains can be captured, and the aforementioned UAV channel-related parameters have a great impact on channel statistics. Finally, the utility of the proposed model is validated by the excellent agreement between simulation results and measurements.

Statistical Characteristics of 3D MIMO Channel Model for Vehicle-to-Vehicle Communications

Spatial and temporal characteristics of the propagation channel have a significant influence on multiantenna method applicability for fifth-generation- (5G-) enabled Internet of Things (IoT). In this paper, the statistical characteristics of a novel three-dimensional (3D) geometric-based stochastic model for next-generation vehicle-to-vehicle (V2V) multiple-input multiple-output (MIMO) communications under the nonisotropic scattering environment are investigated. In both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions, the proposed model investigates the spatial, frequency, and temporal domain statistical distribution of multipath received signals by using the time-variant transfer function for indoor environments. The probability density function (PDF) of separation distance between the transceiver antennas, angle-of-arrival (AoA), and angle-of-departure (AoD) in the azimuth and elevation planes is derived by using closed-form expressions. For the space, time, and frequency correlation function (STF-CF), a precise analytical expression is derived based on MIMO antenna system. We further determine the effects of several model parameters on the V2V channel performance, such as tunnel width, antenna array spacing, Ricean K -factor, and moving velocity. The statistical characteristics of the MIMO channel model are validated by simulation results, confirming the flexibility and effectiveness of our proposed model in the tunnel scenario.

Binary QUATRE Using Time-varying Transfer Functions

<p>QUasi-Affine TRansformation Evolutionary (QUATRE) is an excellent algorithm that generalized differential evolution algorithm to matrix form. There are two approaches of Binary QUATRE (BQUATRE) algorithm that convert the continuous search space to the binary one. The convergence process of meta-heuristic algorithms can be regarded as two stages: exploration and exploitation. Generally speaking, transfer function plays an important role in the binary metaheuristic algorithms. Therefore, four families of transfer functions are converted into the time-varying form to balance the exploration and exploitation. In particular, a new timevarying formula for the V-shaped, U-shaped and Z-shaped transfer functions is designed in this manuscript. Then, two new approaches of BQUATRE algorithm using the timevarying transfer functions are proposed. In addition, a wrapper feature selection algorithm is designed based on the proposed time-varying BQUATRE. In order to verify the performance of the proposed algorithms, the UCI repository is used in the experiment and the results show that the proposed algorithms are superior to the original BQUATRE and the state-of-the-art algorithms.</p> <p> </p>

Study on the Influence of a Soft Soil Interlayer on Spatially Varying Ground Motions

The existence of local soft interlayer can significantly amplify or attenuate the ground motion and thus might influence the lagged spatial coherency between spatially varying earthquake ground motions. A target site with a local soft interlayer was assumed first, and then two numerical examples were set. In example 1, linear soil behavior was considered and a large amount of quasi-stationary spatially varying earthquake ground motions were generated by combining the one-dimensional wave propagation theory and the classical spectral representation method. The influence regularity of varying shear wave velocity, buried depth, and thickness of the soft interlayer on the characteristics of lagged spatial coherency was investigated. In example 2, non-linear soil behavior was taken into account and fully non-stationary spatially varying earthquake ground motions were thus generated by using time-varying transfer function and spectral representation method. An overall evaluation was carried out to shed light on the differences of characteristics of spatial coherency between non-linear soil and linear soil cases. It showed that: (i) As the shear wave velocity of interlayer declines and as the buried depth and thickness increase, remarkable reduction of spatial coherency showed up; (ii) the reduction of lagged spatial coherency caused by varying buried depth may be more inclined to concentrate in the lower frequency range; (iii) the non-linear soil behavior can cause greater further reduction of lagged spatial coherency in comparison with linear soil behavior, especially in the higher frequency range; (iv) the troughs of lagged spatial coherency curve tend to be located in the variation range of vibration frequency of time-varying spectral ratio.

Vibration Separation Methodology Compensated by Time-Varying Transfer Function for Fault Diagnosis of Non-Hunting Tooth Planetary Gearbox.

Due to planetary movement of planet gears, the vibration signal perceived by a stationary sensor is modulated and difficult to diagnose. This paper proposed a vibration separation methodology compensated by a time-varying transfer function (TVTF-VS), which is a further development of the vibration separation (VS) method in the diagnosis of non-hunting tooth planetary gearboxes. On the basis of VS, multi-teeth VS is proposed to extract and synthesize the meshing signal of a planet gear using a single transducer. Considering the movement regularity of a planetary gearbox, the time-varying transfer function (TVTF) is represented by a generalized expression. The TVTF is constructed using a segment of healthy signal and an evaluation indicator is established to optimize the parameters of the TVTF. The constructed TVTF is applied to overcome the amplitude modulation effect and highlight fault characteristics. After that, experiments with baseline, pitting, and compound localized faults planet gears were conducted on a non-hunting tooth planetary gearbox test rig, respectively. The results demonstrate that incipient failure on a planet gear can be detected effectively, and relative location of the local faults can be determined accurately.

Syn-STELLAR: An EM/Power SCA-Resilient AES-256 With Synthesis-Friendly Signature Attenuation

Mathematically secure cryptographic algorithms leak meaningful side-channel information in the form of correlated power and electromagnetic (EM) signals, leading to physical side-channel analysis (SCA) attacks. Circuit-level countermeasures against power/EM SCA include a current equalizer, IVR, non-linear LDOs, enhancing protection up to 10M traces, and current-domain signature attenuation (CDSA), and randomized NL-LDO cascaded with arithmetic countermeasures achieved protection up to >1B. This work embraces the concept of analog CDSA but makes it easily scalable over technology nodes with digital-friendly current sources, digital control loop, and digital bleed path to increase the MTD from 10M to 250M (25x improvement, using a single digital countermeasure). Ring oscillator (RO) used as the bleed path to bypass encryption-dependent leakage acts as local negative feedback (LNFB). Besides, based on RO oscillation frequency, AES node voltage can be tuned at startup, PVT, or frequency variation. Thus, RO acts as integrated LNFB and global feedback for the digital signature attenuation circuit (DSAC). Another circuit technique, namely, the time-varying transfer function (TVTF), removes the requirement of dc bias in the current-domain equalizer (best switch capacitor-based countermeasure till date) to make it digital and utilizes switch cap-based circuit for time-domain obfuscation to achieve enhanced security. This work, namely, Syn-STELLAR: SYNthesis-friendly Signature aTtenuation Embedded crypto with Low-Level metAl Routing, combines both DSAC and TVTF techniques to achieve an MTD>1.25B for both EM and power SCA, which is 25% higher than the existing state of the art. The 65-nm CMOS test chip contains unprotected and both the protected (DSAC and DSAC-TVTF) parallel AES-256 implementation. This implementation is the first synthesis-friendly countermeasure, which converges two analog-type strong protections (signature attenuation and switched cap current equalizer) in a digital-friendly solution and achieves >1.25B MTD with power and area overheads comparable to previous circuit-level countermeasures.

BHHO-TVS: A Binary Harris Hawks Optimizer with Time-Varying Scheme for Solving Data Classification Problems

Data classification is a challenging problem. Data classification is very sensitive to the noise and high dimensionality of the data. Being able to reduce the model complexity can help to improve the accuracy of the classification model performance. Therefore, in this research, we propose a novel feature selection technique based on Binary Harris Hawks Optimizer with Time-Varying Scheme (BHHO-TVS). The proposed BHHO-TVS adopts a time-varying transfer function that is applied to leverage the influence of the location vector to balance the exploration and exploitation power of the HHO. Eighteen well-known datasets provided by the UCI repository were utilized to show the significance of the proposed approach. The reported results show that BHHO-TVS outperforms BHHO with traditional binarization schemes as well as other binary feature selection methods such as binary gravitational search algorithm (BGSA), binary particle swarm optimization (BPSO), binary bat algorithm (BBA), binary whale optimization algorithm (BWOA), and binary salp swarm algorithm (BSSA). Compared with other similar feature selection approaches introduced in previous studies, the proposed method achieves the best accuracy rates on 67% of datasets.

Improving Flower Pollination Algorithm for Solving 0–1 Knapsack Problem

Binary knapsack problem has received considerable attention in combinational optimization. Various meta-heuristic algorithms are dedicated to solve this problem in the literature. Recently, a binary flower pollination algorithm (BFPA) was proposed, which has been successfully applied to solve 0-1 knapsack problem. In this paper, two new time-varying transfer functions are proposed to improve the exploration and exploitation capability of the BFPA with the best solution and short computing time. Based on small, medium, and high-dimensional scales of the knapsack problem, the computational results reveal that the proposed time-varying transfer functions not only to find the best possible solutions but also to have less computational time. Compared to the standard transfer functions, the efficiency of the proposed time-varying transfer functions is superior, especially in the high-dimensional scales.

Transient Analysis of the Set-Membership LMS Algorithm

Adaptive filtering algorithms, which adopt the set-membership strategy, are able to attain good steady-state performance with low computational burden. In general, such advantages are obtained by defining a bounded-error. This specification translates into a time-variant step size, chosen in each iteration according to a nonlinear function of the instantaneous error. Unfortunately, this type of nonlinear behaviour hampers the stochastic modelling of these algorithms. This work devises a novel transient analysis of the Set-Membership Least Mean Squares algorithm. Additionally, a new interpretation is advanced about the implicit optimization problem solved by the algorithm. This explanation is important since it can contribute to the design of new adaptive algorithms. The proposed theoretical analysis provides predictions for: (i) steady-state performance; (ii) transient performance; and (iii) evolution of the update probability. It is noteworthy that the latter influences the computational complexity of the algorithm. Furthermore, we perform a novel comprehensive transient analysis of a set-membership algorithm. In addition, both time-variant transfer functions and deficient-length configurations are addressed. The resulting theoretical estimates are confirmed by simulations.

A hybrid feature selection method based on Binary Jaya algorithm for micro-array data classification

Micro-array technology generates high-dimensional data. The high dimensionality of data hampers the learning capability of machine learning algorithms. Dimensionality can be reduced using feature selection (FS) techniques, which is an important and essential pre-processing step to process high dimensional data. In this work, a hybrid filter–wrapper approach is proposed for feature selection. The multi-attribute decision-making method called Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used as a filter for informative feature extraction. Further, Binary Jaya algorithm with time-varying transfer function is proposed as a wrapper feature selector to find the optimal subset of features. The proposed approach is tested on 10 benchmark micro-array datasets and compared with state-of-the-art methods. Experimental results suggest that the proposed approach performs better in terms of classification accuracy and it is 10 times faster than existing approaches.

Vibration fault features of planetary gear train with cracks under time-varying flexible transfer functions

Due to the multiple gear-pair meshes in planetary gear train, fault features of tooth root crack are weak and complicated in vibration signals. Influenced by the time-varying transfer paths of vibration signals, the fault features of crack are further weakened, which becomes hard to diagnose. To reveal the origination of crack fault features, a lumped-parameter-model of planetary gear train is firstly built to solve the excitation forces that generate vibration signals. Gear mesh stiffness that drives the excitation forces is used to equivalently reflect the crack characteristics. Secondly, a finite-element-model of flexible housing is used to calculate the time-varying transfer functions that determine the amplitude characteristics of vibration signals. Based on the signal convolution theorem, the full-band vibration signal is deduced by the convolution of excitation forces and flexible transfer functions. Simulations and experimental tests on two typical types of planetary gear trains show good consistency at crack fault frequency features, which effectively validates the proposed models. Significant fault features locating at resonance modulation sidebands are summarized as effective indicators for crack diagnosis.

Channel modelling for vehicle‐to‐vehicle MIMO communications in geometrical rectangular tunnel scenarios

This study presents a multiple-input multiple-output (MIMO) statistical channel model for vehicle-to-vehicle (V2V) line-of-sight (LOS) and non-LOS mobile communication system in a rectangular tunnel environment. In order to improve the V2V communication system performance, the authors introduced the elevation and azimuth angles in three-dimensional modelling based on the original literature. In the proposed reference channel model, the single-bounced scattering propagation path of electromagnetic signals is considered and it is assumed that the scatterers are randomly distributed on the rectangular tunnel wall. Starting from the statistical physical channel model, the time-variant transfer functions are derived. Thereafter, the analytical expressions of the space–time–frequency cross-correlation function, the space cross-correlation function, the temporal autocorrelation function, the frequency correlation function, and MIMO channel capacity are derived. The influence of model parameters on the performance of the V2V communication system is also analysed. This work provides an innovative approach to channel modelling in a rectangular tunnel mobile communication environment.