Input-output Characteristics Research Articles

Efficient Estimation of Synthetic Indicators for the Assessment of Nonlinear Systems Quality

The availability of synthetic indicators of the degree and type of nonlinearity in systems is used in various fields to assess system quality or to highlight possible malfunctions. Different distortion or damage indexes are synthetic measures designed (and standardized) to evaluate the frequency trend of specific aspects resulting from the nonlinear behavior of the system under consideration. The different measures of deviation from linear behavior quantitatively consider the system and its nonlinearity characteristics; they were defined according to practically feasible measurement methodologies and the various aspects of the system’s nonlinearity that needed to be highlighted. In parallel, techniques for representing and modeling nonlinear systems have been defined, capable of describing the system in a more general way, attempting to capture its input–output characteristics by varying the level of stress to which the system is subjected. Numerous modeling techniques have been proposed, aimed at representing the nonlinear behavior of physical devices. In this paper, after an extensive description of the Hammerstein model identification technique based on swept sinusoidal signals, we show how the nonlinear model of the system can be used to obtain accurate estimates of the parameter aimed at describing the nonlinearity characteristics of the system. This extensive description makes it possible to point out that the same Hammerstein model can be obtained not only from a single type of excitation, but it is shown that the identification technique can be extended to input signals of different types. The description of the method also makes clear the motivation behind the introduction of the proposed original technique for estimating, from a single measurement, the model parameters of the nonlinear system—and from these the synthetic estimators—relative to multiple values of the input signal amplitude, thus enabling a considerable increase in the estimation efficiency of these parameters. The proposed technique is verified with both synthetic and laboratory experiments, demonstrating the effectiveness of the method in evaluating nonlinear system parameters, distortion estimates, and parameters defined for an early detection of defects of the structure.

Effect of friction layer thickness on energy conversion efficiency of traveling wave rotary ultrasonic motor

Abstract Due to its compact size and resistance to electromagnetic interference. Applications for traveling wave rotary ultrasonic motors (TRUM) include robotic joint drives and aerospace.The motor’s energy conversion efficiency is impacted by the characteristics of the friction layer, yet the underlying mechanism influencing the efficiency remains unreported. Using the previously created finite element model, the mechanism via which the amount of thickness within the friction layer influences the motor output characteristics is examined.Analysis is done on the variations in motor loss, energy conversion efficiency, and input-output characteristics. The computation results show that when the friction layer is less than 0.4 mm, the machine’s output speed increases as the friction layer’s thickness decreases.The results of the calculation indicate that when the friction layer thickness is between 0.05 and 0.4 mm, the motor’s output torque and speed would drop. The findings indicate that the output torque and motor torque decrease as the friction layer thickness increases when it is between 0.05 and 0.4 mm. Both the viscoelastic losses and the motor’s energy conversion efficiency rise.Research shows that the friction layer thickness has a huge impact on the motor and cannot be ignored, providing a reference for the application of motors in industries such as robot joints.

Effect of frame rate on image quality in cardiology evaluated using an indirect conversion dynamic flat-panel detector.

To verify the effect of the frame rate on image quality in cardiology, we used an indirect conversion dynamic flat-panel detector (FPD). We quantified the input-output characteristics, and determined the modulation transfer function (MTF) and normalized noise power spectrum (NNPS) of the equipment used in cardiology at 7.5, 10, 15, and 30 frames per second (fps). We also calculated the noise power spectrum for still images and videos at all frame rates and obtained the image lag correction factor r. The input-output characteristics and the MTF agreed even when the frame rate was varied. The NNPS tended to decrease uniformly as a function of frequency at increasing frame rates. The factor r decreased as a function of the frame rate, and its minimum value was 30 fps. Our results suggest that high-frame-rate imaging in cardiology using indirect conversion dynamic FPDs is affected by image lag.

Magnetostrictive bi-perceptive flexible sensor for tracking bend and position of human and robot hand

The sensor that simultaneously perceives bending strain and magnetic field has the potential to detect the finger bending state and hand position of the human and robot. Based on unique magneto-mechanical coupling effect of magnetostrictive materials, the proposed a bi-perceptive flexible sensor, consisting of the Co–Fe film and magnetic sensing plane coils, can realize dual information perception of strain/magnetic field through the change of magnetization state. The sensor structure and interface circuit of the sensing system are designed to provide high sensitivity and fast response, based on the input–output characteristics of the simulation model. An asynchronous multi-task deep learning method is proposed, which takes the output of the position task as the partial input of the bending state task to analyze the output information of the sensor quickly and accurately. The sensing system, integrating with the proposed model, can better predict the bending state and approach distance of human or robot hand.

An explainable neural network integrating Jiles-Atherton and nonlinear auto-regressive exogenous models for modeling universal hysteresis

The inherent nonlinear and memory-dependent input-output characteristics of piezoelectric actuators pose challenges to the precision of piezoelectric positioning systems. In order to solve this problem, this paper firstly transforms the Jiles-Atherton (JA) model into a neural network structure, designs the Jiles-Atherton neural network (JANN), and combines JANN with nonlinear autoregressive exogenous input (NARX) neural network. A hybrid JA-NARX neural network model is proposed for the first time. This model has the advantages of simple structure, high modeling accuracy, and good interpretability. The effectiveness of the proposed JA-NARX neural network model is validated through a series of experiments, specifically assessing its capacity to accurately capture rate-dependent and asymmetric hysteresis characteristics. The results show that although the proposed neural network model has fewer layers and relatively simple structure, it can realize the high-precision modeling of piezoelectric hysteresis dynamics at a lower computational cost. The experimental data shows that, under the excitation of 60 Hz input signal, the model's PV error only accounts for 0.82% of the full scale range, and the modeling performance is far superior to other models.

Improving photodetection response time of ReS2 devices through double-sided oxidation

Response time is a crucial factor limiting the performance of two-dimensional material-based photodetectors. The underlying mechanisms of response have recently garnered significant attention in the ongoing research. Supported ReS2 on substrates has been found to be predominantly governed by the photofloating gate effect, known to be slower compared to photoelectric effects. In this study, we present findings demonstrating suspended ReS2 devices. Removing the substrate results in a substantial enhancement in optical response by an order of magnitude compared to substrate-supported devices. Deep trap states induced by inherent defects are identified as the primary contributors to prolonged response times. Engineering these ReS2 trap states through defect manipulation can significantly improve response times. Here, we effectively modulate the response speed of ReS2 through gentle oxygen plasma treatments. The response speed of ReS2 is improved by two orders of magnitude. Under the optimal processing conditions of 50 W, 30 Pa, and 10 s, we observed rising and falling response times of 45 and 106 ms, respectively, under illumination at a wavelength of 637 nm. Additionally, we demonstrate that the input–output characteristic of photocurrent provides valuable insights into the underlying opto-physical processes responsible for generating photocurrent.

Physical reservoir application of random network of gold nanoparticles fabricated by multi-step immersion in gold colloidal solution

We fabricated a random network of gold nanoparticles (RN-GNPs) over 12 NiCr/Au electrodes by using a multi-step immersion method, where a sample was immersed in a gold colloid solution three times. Nonlinear current–voltage characteristics due to the Coulomb blockade were observed at 77 K. For demonstration of physical reservoir applications, input–output characteristics of the RN-GNPs were also measured in a one-input, nine-output terminal configuration. Distorted output voltage waveforms were obtained for a sinusoidal voltage input of 100 Hz. The higher-order harmonic components were confirmed in the frequency spectra of the outputs. The waveform reconstruction task and short-term storage capacity estimation were performed by an echo state network model with ridge regression and linear regression, respectively.

Structural design and coupling analysis of multimode variable coupling parallel mobile robots

Purpose There is coupling between the branches of mobile parallel robots, similar to traditional parallel mechanisms, but there is currently relatively little research on the coupling problem between the branches of mobile parallel robots. Design/methodology/approach This study optimizes the coupling analysis method of traditional parallel mechanisms, treats the mobile parallel mechanism as a whole, takes the motion of the active pair as input and the overall motion of the mobile parallel mechanism as output and analyzes the input–output characteristics of the mobile parallel mechanism. Moreover, this study applies this theory to a mobile parallel mechanism, designs control logic and finally conducts simulation and physical verification. Findings This study proposes a coupling analysis method suitable for parallel mobile robots and designs the control logic of their active pair based on the results of their coupling analysis. This study designs a multimode variable coupling parallel mobile robot, which can change the coupling of the mechanism by changing its own branch chain structure, so that it can switch between different coupling configurations to meet the different needs brought by different terrains. Originality/value The work presented in this paper propose a method for analyzing the coupling of mobile parallel robots and simplify their control logic by applying coupling theory to the design of mobile parallel robots. This study conducts simulation and physical experiments, thereby filling the gap in the coupling analysis of parallel mobile robots and laying the foundation for the research of uncoupled parallel mobile robots.

Functional health prognosis approach for multistate manufacturing systems based on quality stochastic flow manufacturing network

Ensuring the predictability of the reliability of the final product is the functional goal of multistate manufacturing system health prognosis. However, the existing studies of basic reliability-oriented health state evaluation ignore the functional input–output characteristics of the dynamic operational manufacturing system. Therefore, a novel functional health prognosis approach considering functional fitness quality based on a quality stochastic flow manufacturing network (QSFMN) is proposed. First, to achieve the specified functional goal, the functional degradation mechanism is expounded from the system’s operational perspective, and based on this, the connotation of functional health considering product fitness quality is further provided. Second, from the perspective of functional fitness quality, through introducing a buffer to reflect dynamic production rhythm, a QSFMN model is proposed based operational quality data to expound the functional relationship between functional requirements, processing machines performance, and output quality. Third, a functional health prognosis method of multistate manufacturing systems based on QSFMN is proposed. Finally, a case study of subway flow receiver manufacturing systems is proposed to verify the effectiveness and advantage of the proposed approach.

Effects of tunable circuit parameters on pulsed discharge and input-output characteristics of a XeCl excimer laser

High-pressure, pulsed discharge-pumped XeCl excimer lasers are operating in the ultraviolet range at a wavelength of 308 nm. In this paper, a one-dimensional fluid model coupled to a magnetic pulse compression (MPC) excitation circuit and a laser gain model is established for describing the discharge and laser output characteristics of XeCl excimer lasers. The effects of the peaking capacitor (the terminal of the MPC circuit) on the discharge properties and input-output characteristics are explored. The results reveal that plays a key role in suppressing discharge pulse oscillation, which in turn influences the peak value and duration of the laser pulse. By optimizing the capacitance, a maximum excimer formation efficiency of 6.6% and a maximum laser output efficiency of 5% (output energy of 1.26 J) are obtained when , where is the storage capacitor in the MPC circuit.

Nonlinear signal conversion in gold nanoparticle random array for physical reservoir applications

We present nonlinear electrical characteristics of a random array of gold nanoparticles (GNPs). We fabricated a GNP random array with six electrodes by using dielectrophoresis. The Coulomb blockade appeared on current–voltage characteristics at 4.2 K. Input–output characteristics of the GNP random array were also measured for a configuration with one-input and three-output terminals. For a sinusoidal voltage input of 100 Hz, distorted output voltage waveforms were acquired at the three output terminals. Higher harmonic components were confirmed on the FFT spectra of the outputs. Wave reconstruction tasks for a triangle and a cosine waveform were performed via an echo-state-network model with ridge regression, which demonstrated that nonlinear signal conversion in a GNP random array had potential for a physical reservoir device.

An embedded machine learning strategy for analyzing interfacial characteristics in impact welding of dissimilar alloys

ABSTRACT In this study, a novel embedded machine learning (ML) framework was employed to analyze interfacial characteristics in dissimilar-alloy impact welding. While keeping the substrates constant, i.e., stainless steel and Ti alloy, the influence of interlayer types on the resultant morphology of impact-welded bonds was also highlighted. The dataset was meticulously generated through numerical simulations, utilizing materials properties and processing parameters as primary inputs. These inputs were used to predict various interfacial features, including the extent of wave formation (L), specific welding zone area (S), average waveform amplitude (A), and maximum interface temperature (Tmax). Impressively, exceptional predictive capabilities were demonstrated by the ML model, with determination coefficients of 0.938, 0.924, 0.967, and 0.909 for S, L, A, and Tmax, respectively. Based on the results, strong dependencies between interfacial features and the weight functions of materials properties and processing parameters were unveiled. Intriguingly, as the weight function of materials properties for a specific output objective increased, a corresponding decrease in the weight function of processing parameter inputs was observed. These findings emphasize the complex connection between input parameters and output characteristics, revealing previously unexplored intricacies in the impact welding process, facilitated by our advanced ML methodology.

Is Short-Term Memory Made of Two Processing Units? Clues from Italian and English Literatures down Several Centuries

We propose that short-term memory (STM), when processing a sentence, uses two independent units in series. The clues for conjecturing this model emerge from studying many novels from Italian and English Literature. This simple model, referring to the surface of language, seems to describe mathematically the input-output characteristics of a complex mental process involved in reading/writing a sentence. We show that there are no significant mathematical/statistical differences between the two literary corpora by considering deep-language variables and linguistic communication channels. Therefore, the surface mathematical structure of alphabetical languages is very deeply rooted in the human mind, independently of the language used. The first processing unit is linked to the number of words between two contiguous interpunctions, variable Ip, approximately ranging in Miller’s 7 ± 2 range; the second unit is linked to the number of Ip’s contained in a sentence, variable MF, ranging approximately from 1 to 6. The overall capacity required to process a sentence fully ranges from 8.3 to 61.2 words, values that can be converted into time by assuming a reading speed, giving the range 2.6∼19.5 s for fast-reading and 5.3∼30.1 s for the average reader. Since a sentence conveys meaning, the surface features we have found might be a starting point to arrive at an information theory that includes meaning.

A New Short-Circuit Current Calculation and Fault Analysis Method Suitable for Doubly Fed Wind Farm Groups.

The transient characteristics of wind farms in groups are quite different; in addition, there is a strong coupling between the wind farms and the grid, and these factors make the fault analysis of the grid with wind farm groups complicated. In order to solve this problem, a mathematical model of the converter is established based on the input-output external characteristics of the converter, and a transient model of a doubly fed wind turbine (DFIG) is presented considering the influence of the low-voltage ride-through control (LVRT) of the converter, and the effect mechanism of the LVRT strategy on the short-circuit current is analyzed. Finally, a short-circuit current calculation model of a doubly fed wind turbine with low-voltage crossing control is established. The interaction mechanism between wind farms during the fault is analyzed, and a short-circuit current calculation method of doubly fed wind farm groups is proposed. RTDS is used to verify the accuracy of the proposed short-circuit current calculation method for doubly fed field groups. On this basis, a method of power grid fault analysis after doubly fed field group access is discussed and analyzed.

Thermoelectrical comprehensive analysis and optimization of multi-stack solid oxide fuel cell system

Solid oxide fuel cell (SOFC) is one of the most promising power generation technologies for its high efficiency, low emission, and fuel adaptability. The power of a single SOFC stack is usually limited to about 1 kW to guarantee its reliability, and stacks are therefore integrated in series or parallel to meet load power demand. However, the performance of a multi-stack SOFC system predominantly depends on the configuration (electrical and gas path structure) of the stack module. Consequently, it is important for the development of SOFC to explore the influence of stack integration structure on system performance and thermal safety, and to further grasp the multi-stack design principles by analyzing the input-output characteristics of the system. The existing studies mainly focus on the electric performance-related parameters analysis and optimization of multi-stack, without considering the thermal safety, the laws of the integration structure influence on system performance and comprehensive optimization on system level. In this work, general multi-stack SOFC systems with four gas distribution structures constructed by four validated stacks are considered and their thermoelectrical performance is systematically investigated and optimized under operational constraints based on a completed multi-SOFC system model. Moreover, the input-output characteristics, thermoelectrical performance and operation rules of the multi-stack SOFC system analysis are conducted, and finally, the stack module design principles and optimal stack topology are obtained. The results show that the parallel connection of two serial stack branches is preferred and can reach the maximum net system efficiency of 42.90 % and maintain the maximum temperature gradient within 6.04 K/cm at a low cost.

Controller design of robot nonlinear system based on the improved fuzzy sliding mode control

In this paper, the input-output finite time stability of fuzzy sliding mode control in nonlinear robot systems is discussed. Specifically, the system considers more realistic factors, such as uncertainty, nonlinearity, interference, and state delay. An improved fuzzy sliding mode control (FSMC) scheme is developed, which is considered in most results of FSMC schemes. Based on the input-output finite-time stability characteristics and the proposed control scheme, the objective of this work is to reduce the effects of uncertainty, nonlinearity, disturbance, and state delay. Finally, the effectiveness and superiority of the proposed method are verified by simulation and comparison with other reference methods.

Hybrid Analog Computer for Modeling Nonlinear Dynamical Systems: The Complete Cookbook

This paper describes a design process for a universal development kit based on an analog computer concept that can model the dynamics of an arbitrarily complex dynamical system up to the fourth order. The constructed development kit contains digital blocks and associated analog-to-digital and digital-to-analog converters (ADCs and DAC), such that multiple-segmented piecewise-linear input-output characteristics can be used for the synthesis of the prescribed mathematical model. Polynomial input-output curves can be implemented easily by four-quadrant analog multipliers. The proposed kit was verified through several experimental scenarios, starting with simple sinusoidal oscillators and ending with generators of continuous-time robust chaotic attractors. The description of each individual part of the development kit is accompanied by links to technical documentation, allowing skilled readers in the construction of electronic systems to replicate the proposed functional example. For this purpose, the electrical scheme of the hybrid analog computer and all important source codes are available online.

Resource reallocation strategies for sustainable efficiency improvement of retail chains

Sustainability is an important agenda for retail businesses in the present times. To create long-term value for customers, retail businesses are required to adopt approaches that can enable them to integrate operational efficiency with concerns for environmental and social sustainability. Optimal deployment of resources among the several stores run by a retail chain, with an objective of maximizing sustainable efficiency, is an enabling step in this direction. This research finds three main contributions to the body of knowledge: (1) resource reallocation models are proposed based on data envelopment analysis (DEA), integrating sustainability in the efficiency measurement model and aiming for improvement in the aggregated efficiency, (2) the proposed models integrate selective centralized and decentralized reallocation decisions according to the input-output characteristics, and (3) different scenarios are explored depending on whether the total resources are to be kept the same or reduced through reallocations according to the management's objectives. The application of the proposed models is illustrated with the case study of a supermarket retail chain.

Data-driven evaluation of abnormal vibration of cables during typhoon In-Fa

The input–output characteristics of cables of a long-span cable-stayed bridge in time and frequency domains were studied during the whole process of typhoon In-Fa landfall based on monitoring data. According to the signal characteristics of the time series of wind speed and direction, a new adaptive extraction method of quasi-steady wind speed and direction was developed, fusing wavelet transform and the certain-length time series averaging method. The central tendency and fluctuation about the wind speed and direction were assessed and the influences of wind and rainfall on cable abnormal vibration were analysed. Using the short-time Fourier transform, the characteristics of the evolution of vibrational energy in the time–frequency domain for abnormal cable vibration during the typhoon were reproduced. Slice analysis of the power spectrum in the frequency domain was performed at typical moments. The results of this work revealed the following. The extremely high-amplitude non-Gaussian section of abnormal cable vibrations in this typhoon event was caused by the simultaneous action of wind and rain. A high wind speed or a wind direction with low fluctuation will more likely excite high-amplitude abnormal cable vibration. Sufficient damping may help cables suppress rain-wind-induced high-amplitude vibration.

Monetary Policy Shocks and Input–Output Characteristics of Production Networks

This paper revisits the production network’s role in transmitting monetary policy shocks. The study uses macroeconomic data for multiple OECD economies, for which it estimates the time-varying impulse response functions of GDP to monetary shocks. In contrast to recent macroeconomics papers focusing on upstreamness or downstreamness, the paper studies measures from the input–output literature, like average propagation length or fields of influence. When looking at the relationship between the production network measures and the impact of monetary policy shocks on GDP, measures like average propagation length or rows’ fields of influence, amplify the negative impact of the monetary policy shocks, while the forward linkage dampens them.