Types Of Input Signals Research Articles

Performance optimization and testing of a novel energy coupled actuated high-speed valve based on peak-and-hold driving strategy

High-speed on/off valve acts as critical enabling component of digital hydraulics, which has been challenging the traditional hydraulic system by providing efficient and effective hydraulic control solutions. The high-speed on/off valve that ideally fits the digital hydraulic system is expected to achieve fast response and long stroke with affordable energy consumption. This not only requires developing advanced actuation mechanism but also demands extensive study on the driving strategy. This study focuses on investigating the Peak-and-hold driving strategy applied to a novel high speed on/off valve-energy coupling actuated valve (ECAV), in order to achieve target performance with optimized energy consumptions. This work has developed an electro-mechanical coupled physics model based on commercial finite element solver COMSOL. This model helps to investigate the performance of ECAV under different peak-and-hold driving parameters and thereby recommend the optimal peak-and-hold driving strategy. Then, the experimental testing on the prototype ECAV has been conducted to validate the simulated performances. The measurement results showed that the prototype ECA using the optimal peak & hold solution demonstrated significant advantage in energy saving targeting at response of 10 ms for 1.5 mm stroke, especially compared to a typical step input signal.

Study of Short-Term and Long-Term Memories by Hodgkin–Huxley Memristor

Long-term memory (LTM ) and short-term memory (STM ) and their evolution from one to the other are important mechanisms to understand brain memory. We use the Hodgkin–Huxley (HH ) model, a well-tested and closest model to biological neurons and synapses, to shine some light on LTM and STM memorization mechanisms. The role of [Formula: see text] and [Formula: see text] ion channels playing in LTM and STM process is carefully examined by using three different types of input signals, namely, a step DC voltage, a positive part of sinusoidal wave and periodic square signal with read voltage. Results are analyzed based on first-order [Formula: see text] memristor and second-order [Formula: see text] memristor.

A Distributed Adaptive Algorithm Based on the Asymmetric Cost of Error Functions

In this paper, a family of novel diffusion adaptive estimation algorithms is proposed from the asymmetric cost function perspective by combining diffusion strategy and the linear–linear cost, quadratic-quadratic cost, and linear-exponential cost at all distributed network nodes, and named diffusion LLCLMS (DLLCLMS), diffusion QQCLMS (DQQCLMS), and diffusion LECLMS (DLECLMS), respectively. Then, the stability of mean estimation error and computational complexity of those three diffusion algorithms are analyzed theoretically. Finally, several experiment simulation results are designed to verify the superiority of those three proposed diffusion algorithms. Results show that DLLCLMS, DQQCLMS, and DLECLMS algorithms are more robust to the input signal and impulsive noise than the diffusion sign-error LMS, diffusion robust variable step-size least mean square (DRVSSLMS), and least mean logarithmic absolute difference algorithms. In brief, theoretical analysis and experiment results show that those proposed DLLCLMS, DQQCLMS, and DLECLMS algorithms perform better when estimating the unknown linear system under the changeable impulsive noise environments and different environments types of input signals.

Neural network for the estimation of LFP battery SOH cycled at different power levels

This work presents a method to quantify and estimate the degradation level of lithium-ion battery cell cycle aged at different power levels. Experimental results previously reported by the authors are analysed further, now with artificial intelligence workflows to establish a method to estimate the cells degradation rate, in term of State of Health. The neural network estimation capability is dependent on the type of input signal used for training, and the relative proportion of training vs. testing data. The development of a feedforward neural network which elaborates the information of voltage and current differences during sudden power changes significantly increased the predictive capability of the method, reaching State-of-Health estimation best-case errors lower than 1 %, in line with more complex Artificial Intelligence approaches found in literature. In addition, the results obtained with the feedforward neural network are then compared with a regression learning – based estimation function, trained and tested over the same dataset. As last test, both these two methods, trained with dataset coming from cycle aging experimental tests, are used to estimate the State of Health of a lithium cell aged due to calendar phenomena only.

Comments on the H2 Norm of Discrete-Time Stochastic Jump Parameter Linear Systems

In this note, we consider a general class of stochastic, jump parameter systems and show that the so-called H2 norm is the Frobenius norm of the impulse response matrix of the system. We present a necessary and sufficient condition for the H2 norm to be the induced norm from the input to the output for a particular type of deterministic input signal. As expected in the context of time-varying systems, we cannot extend this result to more general deterministic input signals. Moreover, finite H2 norm do not prevent infinite norm output in response to a finite norm input, unless additional conditions are imposed on the system. Here, we give relatively mild conditions -mean square stability and jumps driven by a finite-dimensional Markov chain starting in equilibrium -and we show that finite H2 norm implies a bounded ratio between the norms of the output and the input, for certain suitable norms.

Comprehensive Learning Particle Swarm Optimized Fuzzy Petri Net for Motor-Bearing Fault Diagnosis

Petri net is a widely used fault-diagnosis algorithm. However, it presents poor fault-diagnosis effectiveness and accuracy caused by the parameter setting and adjustment, depending entirely on expert experience in a system with a single input signal type. To address this problem, a comprehensive learning particle swarm optimized fuzzy Petri net (CLPSO-FPN) algorithm is proposed for motor-bearing fault diagnosis. CLPSO is employed to obtain an adaptive system parameter set to reduce the fault-diagnosis error caused by human subjective factors. Moreover, a new proposed concept of the transition influence factor replaces the traditional transition confidence to improve the nonlinear expression ability of traditional Petri nets, which suppresses the space explosion problem of the fault-diagnosis model. Finally, experiments are implemented on a dataset of motor bearings. Compared with traditional faults diagnosis methods, the proposed method realized better performance in the fault location and prediction functions of motor bearings, which is beneficial for troubleshooting and motor maintenance.

COMPARATIVE STUDY OF WHITE GAUSSIAN NOISE REDUCTION FOR DIFFERENT SIGNALS USING WAVELET

The present work is an attempt to make a comparative study of the wavelet-based noise reduction algorithm. The algorithm has been developed and implemented in MATLAB GUI and then analyzed for different types of wavelets such as Coiflet, Daubechies, Symlet, and Biorthogonal with their different versions. This algorithm has been verified for different types of input signals from different domains. Various statistical aspects like Mean Absolute Error (MAE), Mean Squared Error (MSE), Signal to Noise Ratio (SNR), and Peak Signal to Noise Ratio (PSNR) are analyzed for this algorithm. It is observed that the developed algorithm for noise reduction using wavelet works very well for different types of wavelets.

Joint transmission design for IRS-assisted MISO SWIPT systems

In this paper, we investigate the transmission design for an intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system, where an IRS adjusts its reflection phase shifts to facilitate information transfer and energy harvesting. Two types of input signals, i.e., Gaussian signals and finite-alphabet signals, are both considered. Our goal is to maximize the mutual information between the access point (AP) and the information receiver (IR) by jointly optimizing the transmit beamforming at the AP and the phase shifts of the IRS under the constraints of transmit power at the AP and required harvested energy at the energy harvesting receiver (ER). We show that the transmission designs for these two types of signals actually can be unified into a common optimization problem. Although the formulated problem is an intractable non-convex problem, the solving frameworks in cases of perfect and imperfect channel state information (CSI) are provided, respectively. For the perfect case, an efficient algorithm, which combines semidefinite relaxation (SDR) and penalty-based manifold optimization (PMO), is proposed. For the imperfect case, we study the worst-case transmission design and develop an algorithm based on SDR. Numerical results show the superiority of our proposed algorithms.

A new DFT-based frequency estimation algorithm for protection devices under normal and fault conditions

Frequency is one of the essential parameters for monitoring, control, and protection in power systems. Some protection devices use frequency for deciding under various conditions of the power system. Therefore, it is important to estimate frequency rigorously and fast. In this paper, a DFT- based algorithm is introduced for frequency estimation in the presence of harmonics and decaying DC. Initially, According to the condition of the power system, the type of input signal (current or voltage) is determined. The current and voltage signals are the input to the proposed algorithm under fault and normal conditions, respectively. Based on the type of the input signal, the frequency estimation method (FEM) is selected and used. 2 FEMs are proposed to estimate the frequency based on solving a cubic equation. For both FEMs, an optimization-based filter is designed and applied to mitigate harmonics. The proposed algorithm is validated under various static and dynamic tests in MATLAB. The results show that the proposed algorithm is accurate and fast with a low computational burden.

Adversarial Resolution Enhancement for Electrical Capacitance Tomography Image Reconstruction.

High-quality image reconstruction is essential for many electrical capacitance tomography (CT) applications. Raw capacitance measurements are used in the literature to generate low-resolution images. However, such low-resolution images are not sufficient for proper functionality of most systems. In this paper, we propose a novel adversarial resolution enhancement (ARE-ECT) model to reconstruct high-resolution images of inner distributions based on low-quality initial images, which are generated from the capacitance measurements. The proposed model uses a UNet as the generator of a conditional generative adversarial network (CGAN). The generator’s input is set to the low-resolution image rather than the typical random input signal. Additionally, the CGAN is conditioned by the input low-resolution image itself. For evaluation purposes, a massive ECT dataset of 320 K synthetic image–measurement pairs was created. This dataset is used for training, validating, and testing the proposed model. New flow patterns, which are not exposed to the model during the training phase, are used to evaluate the feasibility and generalization ability of the ARE-ECT model. The superiority of ARE-ECT, in the efficient generation of more accurate ECT images than traditional and other deep learning-based image reconstruction algorithms, is proved by the evaluation results. The ARE-ECT model achieved an average image correlation coefficient of more than and an average relative image error about .

Field experimental investigation on broadband vibration mitigation using metamaterial-based barrier-foundation system

Both periodic barriers and periodic foundations can be used as passive isolation measures to reduce the unfavorable vibrations affecting the protected superstructure. Under a certain direction of excitation, the frequency band gaps provided by the periodic barriers and periodic foundations are usually distinguishable due to the different orientations of these subsurface structures. In this study, a series of field tests are conducted to assess the combined usage of the periodic barrier and periodic foundation in a manner to achieve better wave isolation effect. Both periodic barrier and periodic foundation used in this study are made of one-dimensional (1-D) layered periodic material. Using the state-of-art hydraulic mobile shaker (T-Rex), the excitation with various types of input signals can be applied in all the three orthogonal directions at the desired location. The arrangement of motion sensors allows for recording the response of the ground surface and the protected superstructure in all the three directions. The screening effectiveness, quantified as Frequency Response Function (FRF), of the periodic barrier and periodic foundation are discussed separately, followed by an overall evaluation of the wave isolation performance of the system composed of both periodic barrier and periodic foundation. The results show that the filtering capability of the periodic barrier and the periodic foundation is complementary to each other, and the combined usage of the periodic barrier and the periodic foundation is beneficial to mitigating the vibration of the protected superstructure. ∙ A novel large-scale field tests reported on wave isolation of periodic barriers and periodic foundations. ∙ State-of-art shaker (T-Rex) is adopted to apply triaxial excitation and triaxial soil velocity are collected. ∙ The composite isolation effect of periodic barrier-periodic foundation system is first reported. ∙ Various excitations lead to similar isolation results for the periodic barrier and periodic foundation. ∙ The addition of periodic foundation will not adversely affect the performance of periodic barrier.

Experimental study of the level-dependent softening of carbon particle stacks

Recently, it has been observed that a particle stack’s elastic modulus and damping change with incident levels. Hence, the measured normal incidence absorption coefficient also varies: i.e., the quarter-wave resonance peak shifts to a lower frequency and grows broader under progressively higher incident sound levels. Therefore, models with strain-dependent modulus and damping have been developed for sand particles based on a series of cyclic triaxial tests, while more recently, a velocity-dependent modulus was proposed to model the acoustically induced elastic softening for the hollow glass beads. To investigate the nature of this nonlinear behavior, the absorption coefficients of a 30 mm carbon particle stack were measured under four types of band-passed input signals (i.e., 500–1000 Hz, 500–2000 Hz, 500–4000 Hz, and 500–8000 Hz), each with 15 levels in steps of 1 dB. The input signals were designed to span the resonance frequency and to extend beyond to various degrees. The integrated sound pressure level, velocity, and displacement at the particle stack surface were calculated and were used to normalize the changing modulus and damping. It was found that only the total rms surface displacement can align the changing properties for all measurements.

Detection and Analysis of Inter-Area Oscillations Through a Dynamic-Order DMD Approach

The paper deals with a novel method for detecting and analyzing inter-area oscillations on electric power transmission networks. The method starts from the modal analysis performed by the Dynamic Mode Decomposition algorithm, which is able to exploit the synchronized acquisitions of various measurement instruments to detect the mode of a dynamic system. Compared with the classical algorithm, the proposed method presents a fundamental improvement, which ensures its reliability even without having prior information on the type of input signal. In particular, the order of the DMD, i.e. the number of modes characterizing the acquired signal, is dynamically updated according to its energy content. The method has been tested with simulated signals, considering both single-oscillation signals and two-oscillation signals, varying the amplitude, frequency and damping of the oscillatory components. In this way, the improvement with respect to the classical DMD was highlighted and the performance in terms of deviation between the estimated and nominal parameters was evaluated. Furthermore, the assessment on real life acquired signals has been performed; the results confirmed the reliability and accuracy of the measurement method, even in the presence of noisy signals and ambient data.

Unknown Input Reconstruction from Temporal Activity Patterns of Thermosensitive Neuronal Ensembles using Reservoir Computing*

The paper addresses the problem of the unknown temperature reconstruction from the neuronal signal generated by networks of thermosensitive neurons modeled by the Hodgkin-Huxley formalism. We show that the instantaneous frequencies of the neurons do not provide sufficient information for the precise temperature reconstruction and that the temporal patterns of the instantaneous frequencies should be taken into account. For this purpose, we augment the considered network of thermosensitive neurons with a multi-layered artificial neural network (ANN) and train it to reconstruct the unknown piecewise-constant input temperature using a supervised learning technique. The input layer of the ANN receives the discretized (in time) trajectories of the instantaneous frequencies of neurons over a certain time interval. Interestingly more rich information containing the time evolution of membrane potentials in the input layer does not lead to any improvement of the unknown input reconstruction. This observation is in accordance with one of the fundamental principles of neuroscience stating that, except for a few highly specific contexts, information in neural systems is encoded in the temporal rather than voltage characteristics of action potentials. Finally, we benchmark the performance of the proposed reconstruction scheme against different types of input signals.

Kohonen Network-Based Adaptation of Non Sequential Data for Use in Convolutional Neural Networks.

Convolutional neural networks have become one of the most powerful computing tools of artificial intelligence in recent years. They are especially suitable for the analysis of images and other data that have an inherent sequence structure, such as time series data. In the case of data in the form of vectors of features, the order of which does not matter, the use of convolutional neural networks is not justified. This paper presents a new method of representing non-sequential data as images that can be analyzed by a convolutional network. The well-known Kohonen network was used for this purpose. After training on non-sequential data, each example is represented by so-called U-image that can be used as input to a convolutional layer. A hybrid approach was also presented, where the neural network uses two types of input signals, both U-image representation and the original features. The results of the proposed method on traditional machine learning databases as well as on a difficult classification problem originating from the analysis of measurement data from experiments in particle physics are presented.

Time Signature Detection: A Survey.

This paper presents a thorough review of methods used in various research articles published in the field of time signature estimation and detection from 2003 to the present. The purpose of this review is to investigate the effectiveness of these methods and how they perform on different types of input signals (audio and MIDI). The results of the research have been divided into two categories: classical and deep learning techniques, and are summarized in order to make suggestions for future study. More than 110 publications from top journals and conferences written in English were reviewed, and each of the research selected was fully examined to demonstrate the feasibility of the approach used, the dataset, and accuracy obtained. Results of the studies analyzed show that, in general, the process of time signature estimation is a difficult one. However, the success of this research area could be an added advantage in a broader area of music genre classification using deep learning techniques. Suggestions for improved estimates and future research projects are also discussed.

Solvency II, Undertaking Specific Parameters (USPs) Validation, Generalization and Criticism

This paper provides a deeper actuarial insight in the mathematics and algorithms described in Delegate Act 35/2015 of Solvency II legislative framework with respect to the determination of Undertaking Specific Parameters (USPs). The numerical investigation is based on typical input signals used by control engineers in order to check the system response. It is finally revealed the close relationship between the USPs values and the values of the typical function of standard deviation. Finally, a generalization of the mathematical framework covering the special cases of the Pareto and Gamma distributions as inputs for the aggregate losses of an insurance company is provided.

Nonlinear Spatial Integration Underlies the Diversity of Retinal Ganglion Cell Responses to Natural Images.

How neurons encode natural stimuli is a fundamental question for sensory neuroscience. In the early visual system, standard encoding models assume that neurons linearly filter incoming stimuli through their receptive fields, but artificial stimuli, such as contrast-reversing gratings, often reveal nonlinear spatial processing. We investigated to what extent such nonlinear processing is relevant for the encoding of natural images in retinal ganglion cells in mice of either sex. We found that standard linear receptive field models yielded good predictions of responses to flashed natural images for a subset of cells but failed to capture the spiking activity for many others. Cells with poor model performance displayed pronounced sensitivity to fine spatial contrast and local signal rectification as the dominant nonlinearity. By contrast, sensitivity to high-frequency contrast-reversing gratings, a classical test for nonlinear spatial integration, was not a good predictor of model performance and thus did not capture the variability of nonlinear spatial integration under natural images. In addition, we also observed a class of nonlinear ganglion cells with inverse tuning for spatial contrast, responding more strongly to spatially homogeneous than to spatially structured stimuli. These findings highlight the diversity of receptive field nonlinearities as a crucial component for understanding early sensory encoding in the context of natural stimuli.SIGNIFICANCE STATEMENT Experiments with artificial visual stimuli have revealed that many types of retinal ganglion cells pool spatial input signals nonlinearly. However, it is still unclear how relevant this nonlinear spatial integration is when the input signals are natural images. Here we analyze retinal responses to natural scenes in large populations of mouse ganglion cells. We show that nonlinear spatial integration strongly influences responses to natural images for some ganglion cells, but not for others. Cells with nonlinear spatial integration were sensitive to spatial structure inside their receptive fields, and a small group of cells displayed a surprising sensitivity to spatially homogeneous stimuli. Traditional analyses with contrast-reversing gratings did not predict this variability of nonlinear spatial integration under natural images.

A Critical Analysis of Methodologies for Detection and Classification of Power Quality Events in Smart Grid

Recently, power quality (PQ) issues have drawn considerable attention of the researchers due to the increasing awareness of the customers towards power quality. The PQ issues maintain its pre-eminence because of the significant growth encountered in the smart grid technology, distributed generation, usage of sensitive and power electronic equipments with the integration of renewable energy resources. The IoT and 5G networks technologies have a number of advantages like smart sensor interfacing, remote sensing and monitoring, data transmission at high speed. Due to this, applications of these two are highly adopted in smart grid. The prime focus of the paper is to present an exhaustive survey of detection and classification of power quality disturbances by discussing signal processing techniques and artificial intelligence tools with their respective pros and cons. Further, critical analysis of automatic recognition techniques for the concerned field is posited with the viewpoint of the types of power input signal (synthetic/real/noisy), pre-processing tools, feature selection methods, artificial intelligence techniques and modes of operation (online/offline) as per the reported articles. The present work also elaborates the future scope of the said field for the reader. This paper provides valuable guidelines to the researchers those having interest in the field of PQ analysis and exploring the better methodologies for further improvement. Comprehensive comparisons have been presented with the help of tabular presentations. Although this critical survey cannot be collectively exhaustive, still this survey comprises the most significant works in the concerned paradigm by examining more than 300 research publications.

Nonlinearity Correction in Dynamic Measuring Devices Using Neural Network Models

A neural network compensator for the nonlinearity of a dynamic measuring instrument is proposed, which allows restoring the value of the measured input signal. The inverse model of a nonlinear dynamic measuring device is implemented based on a three-layer perceptron supplemented by delay lines of input signals. The properties of the proposed neural network compensator are studied through simulation computer modelling using various types of calibration input signals for the training of an artificial neural network.