Dynamic Signal Model Research Articles

LC dynamic signal modelling and analysis of resonant frequency measurement error under rapid eye movements interference

Abstract LC resonant wireless passive intraocular pressure (IOP) sensors measure IOPs by mapping them to the LC resonance frequencies. However, during the sweeping acquisition process of every LC signal frame, possible rapid eye movement (REM) can interfere with the wireless mutual inductance coupling, leading to the signal distortion and resulting in the error of resonance frequency measuring. Currently, there is a lack of modelling analysis of the error generated by REM effect, resulting in absence of guidance for reducing the impact. Therefore here, we start from the perspective of the signal model and establish the LC dynamic signal model of sweeping acquisition. By means of the limit state analysis and Monte Carlo simulation, we analyze the influence of external REM parameters (including REM range and velocity) and internal parameters of the LC sensing system (including the quality coefficient of LC sensor, diameter, and number of turns of the reader coil, signal sweep acquisition speed and period) on the errors. We demonstrated theoretical relationship between the extreme errors with these parameters and verify it through a computer simulation. Based on the results, we propose to optimize the internal parameters of the LC sensing system to reduce the REMs effect on errors, safeguarding the quality of signal acquisition and improving the measurement accuracy.

Extraction of amplitude-domain characteristics from dynamic electric signals that cause large errors in electricity metering

Abstract Recent studies have shown that the power and current of high-power dynamic loads exhibit dynamic behaviors that cause large errors in electrical energy meters. Aimed at fast variations and large fluctuations in dynamic electric signals, this study examines a characteristic extraction method for small-time granularity and the impact of these characteristics on electrical energy meters. First, we constructed quasi-steady and dynamic model parameters for the dual-modal modulation models of current and power signals. Second, based on the two sets of signals sampled from the electrified railway traction substation and steelmaking arc furnace substation, we extracted quasi-steady and dynamic signal model parameters from fundamental active power signals. Third, we proposed three novel characteristic parameters by mapping the model parameter method, and extracted the four important characteristics. Finally, we designed a set of on-off-keying test signal experiments to determine the impact of the proposed characteristic parameters on the electric energy meter. The experimental results show that the electrical energy meter errors are sensitive to the proposed characteristic parameters, wherein the maximum impulse coefficient is the most sensitive characteristic of the EEM, which causes an error as large as −81.28%. The proposed characteristic parameters can be used as the input characteristic information of dynamic test signals for type testing and updating the test standards for electrical energy meters.

Dynamic phasor measurement algorithm based on high-precision time synchronization

Ensuring the swift and precise tracking of power system signal parameters, especially the frequency, is imperative for the secure and stable operation of power grids. In instances of faults within the distribution network, abrupt changes in frequency may occur, presenting a challenge for existing algorithms that struggle to effectively track such signal variations. Addressing the need for enhanced performance in the face of frequency mutations, this paper introduces an innovative approach—the Covariance Reconstruction Extended Kalman Filter (CREKF) algorithm. Initially, the dynamic signal model of electric power is meticulously analyzed, establishing a dynamic signal relationship based on high-precision time source sampling tailored to the signal model’s characteristics. Subsequently, the filter gain, covariance matrix, and variance iteration equation are determined based on the signal relationship among three sampling points. In a final step, recognizing the impact of the covariance matrix on algorithmic tracking ability, the paper proposes a covariance matrix reset mechanism utilizing hysteresis induced by output errors. Through extensive verification with simulated signals, the results conclusively demonstrate that the CREKF algorithm exhibits superior measurement accuracy and accelerated tracking speed when confronted with mutating signals.

A fine acquisition algorithm based on fast three-time FRFT for dynamic and weak GNSS signals

As high-dynamics and weak-signal are of two primary concerns of navigation using Global Navigation Satellite System (GNSS) signals, an acquisition algorithm based on three-time fractional Fourier transform (FRFT) is presented to simplify the calculation effectively. Firstly, the correlation results similar to linear frequency modulated (LFM) signals are derived on the basis of the high dynamic GNSS signal model. Then, the principle of obtaining the optimum rotation angle is analyzed, which is measured by FRFT projection lengths with two selected rotation angles. Finally, Doppler shift, Doppler rate, and code phase are accurately estimated in a real-time and low signal to noise ratio (SNR) wireless communication system. The theoretical analysis and simulation results show that the fast FRFT algorithm can accurately estimate the high dynamic parameters by converting the traditional two-dimensional search process to only three times FRFT. While the acquisition performance is basically the same, the computational complexity and running time are greatly reduced, which is more conductive to practical application.

Dynamic Synchronous Phasor Measurement Algorithm Based on Compressed Sensing

The synchronous phasor measurement algorithm is the core content of the phasor measurement unit. This manuscript proposes a dynamic synchronous phasor measurement algorithm based on compressed sensing theory. First, a dynamic signal model based on the Taylor series was established. The dynamic power signal was preprocessed using a least mean square error adaptive filter to eliminate interference from noise and harmonic components. A Chirplet overcomplete dictionary was then designed to realize a sparse representation. A reduction of the signal dimension was next achieved using a Gaussian observation matrix. Finally, the improved orthogonal matching pursuit algorithm was used to realize the sparse decomposition of the signal to be detected, the amplitude and phase of the original power signal were estimated according to the best matching atomic parameters, and the total vector error index was used for an error evaluation. Chroma 61511 was used for the output of various signals, the simulation results of which show that the proposed algorithm cannot only effectively filter out interference signals, it also achieves a better dynamic response performance and stability compared with a traditional DFT algorithm and the improved DFT synchronous phasor measurement algorithm, and the phasor measurement accuracy of the signal is greatly improved. In practical applications, the hardware costs of the system can be further reduced.

Integrating protein localization with automated signaling pathway reconstruction

BackgroundUnderstanding cellular responses via signal transduction is a core focus in systems biology. Tools to automatically reconstruct signaling pathways from protein-protein interactions (PPIs) can help biologists generate testable hypotheses about signaling. However, automatic reconstruction of signaling pathways suffers from many interactions with the same confidence score leading to many equally good candidates. Further, some reconstructions are biologically misleading due to ignoring protein localization information.ResultsWe propose LocPL, a method to improve the automatic reconstruction of signaling pathways from PPIs by incorporating information about protein localization in the reconstructions. The method relies on a dynamic program to ensure that the proteins in a reconstruction are localized in cellular compartments that are consistent with signal transduction from the membrane to the nucleus. LocPL and existing reconstruction algorithms are applied to two PPI networks and assessed using both global and local definitions of accuracy. LocPL produces more accurate and biologically meaningful reconstructions on a versatile set of signaling pathways.ConclusionLocPL is a powerful tool to automatically reconstruct signaling pathways from PPIs that leverages cellular localization information about proteins. The underlying dynamic program and signaling model are flexible enough to study cellular signaling under different settings of signaling flow across the cellular compartments.

Impact of Synchrophasor Estimation Algorithms in ROCOF-Based Under-Frequency Load-Shedding

This paper investigates the impact of synchrophasor estimation algorithms in Under-Frequency Load-Shedding (UFLS) and Load-Restoration (LR) schemes, relying on frequency and Rate-of-Change-of-Frequency (ROCOF) measurements produced by Phasor Measurement Units (PMUs). We compare two consolidated window-based synchrophasor estimation algorithms, as representative approaches based on static and dynamic signal models, with a focus on the appropriateness of utilizing PMU-based ROCOF measurements. The performance of the proposed relaying scheme is assessed by means of a Real-Time Simulator implementing the time-domain full-replica dynamic model of the IEEE 39-Bus power system.

Pseudorandom Dynamic Test Power Signal Modeling and Electrical Energy Compressive Measurement Algorithm

Abstract With the rapid construction of smart grid, many applications of the new generation and the large power dynamic loads are revolutionizing the electrical energy measurement of electricity meters. The dynamic measurement errors produced by electricity meters are intolerable. In order to solve the dynamic error measurement of electrical energy, firstly, this paper proposes a three-phase pseudorandom dynamic test power signal model to reflect the main characteristics of dynamic loads. Secondly, a compressive measurement algorithm is proposed by the means of steady-state optimization to accurately measure the electrical energy. The experimental results confirm the effectiveness of the three-phase pseudorandom dynamic test signal model, the maximum errors of compressive measurement algorithm are superior to 1×10-13, the high precision enables the algorithm to accurately measure the electrical energy under different dynamic conditions.

Noninvasive Fetal Heart Rate Monitoring: Validation of Phonocardiography-Based Fiber-Optic Sensing and Adaptive Filtering Using the NLMS Algorithm

Here we present the evaluation results of our novel noninvasive phonocardiographic-based fiber-optic sensor for fetal Heart Rate (fHR) detection using adaptive filtering and the NLMS Algorithm. The sensor uses two interferometric probes encapsulated inside a PolyDiMethylSiloxane (PDMS) polymer. Based on real data acquired from pregnant women in a suitable research laboratory environment, once they had given their written informed consents, we created a simplified dynamic signal model of the distribution of maternal and fetal heart sounds inside the maternal body. Building upon this signal model, we verified the functionality of our novel fiber-optic sensor and its associated adaptive filtering system using the NLMS Algorithm. The main reason why we chose this technology to develop our system was that it allows monitoring the fHR without exposing the fetus to any external energies or radiation (in contrast to the ultrasound-based Cardiotocography Method). We used objective criteria such as: Signal to Noise Ratios: SNR_in, SNR_out and Percentage Root-mean-square Difference (PRD) for our evaluations.

Rewarding Mediocrity? Optimal Regulation of R&D Markets with Reputation Concerns

In this paper, we consider a dynamic signaling model of an R&D market in which a researcher can choose either a safe project (exploitation) or a risky project (exploration) at each instance. We argue that there are substantial efficiency gains from rewarding minor innovations above their social value and further that it is indeed superior to rewarding major innovations directly, even when those minor innovations are intrinsically valueless in themselves. When only major innovations are rewarded, the R&D market eventually shuts down due to a version of the lemons problem. Rewarding minor innovations is actually conducive to major innovations as it induces self-sorting among researchers, which is essential in providing time and resources necessary for more productive ones to take riskier but more ambitious approaches. This result draws clear contrast to the static counterpart where such a scheme can never be optimal. Our model also exhibits reputation dynamics which capture a pervasive view in academia that “no publications are better than a few mediocre publications” at an early stage of one’s career.

Monitoring Financial Intermediaries with Subordinated Debt: A Dynamic Signal Model for Bank Risk

Dynamic Monitoring of Financial Intermediaries with Subordinated Debt by Gloria Gonzalez-Rivera University of California, Riverside Department of Economics Riverside, CA 92521 e-mail: gloria.gonzalez@ucr.edu phone: (951) 827 1470 fax: (951) 827 5685 David Nickerson Colorado State University Departments of Economics and Finance Fort Collins, CO 80523-1771 e-mail: david.nickerson@colostate.edu phone: (970) 491 5249

Franchise fairs: A relevant signal in franchise choice in social activity

Potential franchisees encounter difficulties in gaining knowledge about a franchise before embarking on their first start-up venture. For this reason, it is necessary to research which information signals help potential franchisees choose the franchise chains with which they wish to enter into business. Working within the framework of signaling theory, this study’s aim is to analyze the relationship between franchise choice and brand, price and participation in franchise fairs. The dynamic signaling model deployed to achieve the study´s aim draws on panel data methodology. This methodology allows us to analyze franchise chains over the period in which their parent franchises were using signaling to reveal information about their quality to potential franchisees. The results show that franchise fairs and up-front entry fees influence franchisee´s decisions. Therefore, it is concluded that potential franchisees prefer to garner information directly from franchise fairs, as opposed to heeding the other signals under study, and that when macroeconomic variables are exerting a strong influence on potential franchisees, up-front entry fees also constitute a signal that they consider. Finally, the managerial implications of the study are that franchise chains seeking franchisees should participate in franchise fairs to ensure that they are among the chosen franchises. Additionally, a franchisor should appropriately manage up-front entry fees as a signal, especially during periods of economic turmoil and recession.

Dynamic Quality Signaling with Hidden Actions

Asymmetric information is an important source of inefficiency when an asset (such as a firm) is transacted. The two main sources of this asymmetry are the unobserved idiosyncratic characteristics of the asset (such as future profitability) and unobserved idiosyncratic choices (like secret price cuts). Buyers may use noisy signals (such as sales) in order to infer actions and characteristics. In this situation, does the seller prefer to release information fast or slowly? Is it incentive compatible? When the market is pessimistic, is it better to give up or keep signaling? We introduce hidden actions in a dynamic signaling model in order to answer these questions. Separation is found to be fast in equilibrium when sending highly informative signals is more efficient than sending lowly informative signals. When the market is pessimistic about the quality of the asset, depending on the cost structure, the seller either “gives-up” by stopping signaling, or the seller “rushes-out” by increasing the informativeness of the signal. We find that the unobservability of the action causes equilibrium effort to be too low and the seller to stop signaling too early. The model can be applied to education where grades depend on students’ effort, which is endogenously related to their skills.

Dynamic Education Signaling with Dropout Risk, Third Version

This paper analyzes a dynamic education signaling model with dropout risk. Workers pay an education cost per unit of time and face an exogenous dropout risk before graduation. Since low-productivity workers’ cost of education is high, pooling with early dropouts helps them avoid a high education cost. In equilibrium, low-productivity workers choose to endoge- nously drop out over time, so the productivity of workers in college increases as the education process progresses. We find that the exogenous dropout risk generates rich dynamics in the endogenous dropout behavior of workers, and the maximum education length is decreasing in the prior about a worker being highly productive. We also extend the baseline model by allowing human capital accumulation.

Experimental design, validation and computational modeling uncover DNA damage sensing by DNA-PK and ATM.

Reliable and efficient detection of DNA damage constitutes a vital capability of human cells to maintain genome stability. Following DNA damage, the histone variant H2AX becomes rapidly phosphorylated by the DNA damage response kinases DNA-PKcs and ATM. H2AX phosphorylation plays a central role in signal amplification leading to chromatin remodeling and DNA repair initiation. The contribution of DNA-PKcs and ATM to H2AX phosphorylation is however puzzling. Although ATM is required, DNA-PKcs can substitute for it. Here we analyze the interplay between DNA-PKcs and ATM with a computational model derived by an iterative workflow: switching between experimental design, experiment and model analysis, we generated an extensive set of time-resolved data and identified a conclusive dynamic signaling model out of several alternatives. Our work shows that DNA-PKcs and ATM enforce a biphasic H2AX phosphorylation. DNA-PKcs can be associated to the initial, and ATM to the succeeding phosphorylation phase of H2AX resulting into a signal persistence detection function for reliable damage sensing. Further, our model predictions emphasize that DNA-PKcs inhibition significantly delays H2AX phosphorylation and associated DNA repair initiation.

Dynamic Quality Signaling with Hidden Actions, Second Version

Asymmetric information is an important source of inefficiency when an asset (such as a firm) is transacted. The two main sources of this asymmetry are the unobserved idiosyncratic characteristics of the asset (such as future profitability) and unobserved idiosyncratic choices (like secret price cuts). Buyers may use noisy signals (such as sales) in order to infer actions and characteristics. In this situation, does the seller prefer to release information fast or slowly? Is it incentive compatible? When the market is pessimistic, is it better to give up or keep signaling? We introduce hidden actions in a dynamic signaling model in order to answer these questions. Separation is found to be fast in equilibrium when sending highly informative signals is efficient. When the market is pessimistic about the quality of the asset, depending on the cost structure, the seller either “gives-up” by stopping signaling, or the seller “rushes-out” by increasing the informativeness of the signal. We find that the unobservability of the action causes equilibrium effort to be too low and the seller to stop signaling too early. The model can be applied to education where grades depend on students’ effort, which is endogenously related to their skills.

Dynamic Education Signaling with Dropout, Second Version

We study students' dropout behavior and its consequences in a dynamic signaling model. Workers pay an education cost per unit of time and cannot commit to a fixed education length. Workers face an exogenous dropout risk before graduation. Since low-productivity students' cost is high, pooling with early dropouts helps them to avoid a high education cost. In equilibrium, low-productivity students choose to endogenously drop out over time, so the productivity of students in college increases along the education process. We find that the maximum education length is decreasing in the prior about a student being highly productive. We characterize the joint dynamics of returns to education and the dropout rate and provide an explanation of the declining dropout rate over the time students spend in school. We also extend the baseline model by allowing human capital accumulation and show that the dynamics of the dropout rate are helpful in decomposing the returns to education into the signaling effect and the human capital accumulation effect.

Dynamic Compressive Sensing of Time-Varying Signals Via Approximate Message Passing

In this work the dynamic compressive sensing (CS) problem of recovering sparse, correlated, time-varying signals from sub-Nyquist, non-adaptive, linear measurements is explored from a Bayesian perspective. While there has been a handful of previously proposed Bayesian dynamic CS algorithms in the literature, the ability to perform inference on high-dimensional problems in a computationally efficient manner remains elusive. In response, we propose a probabilistic dynamic CS signal model that captures both amplitude and support correlation structure, and describe an approximate message passing algorithm that performs soft signal estimation and support detection with a computational complexity that is linear in all problem dimensions. The algorithm, DCS-AMP, can perform either causal filtering or non-causal smoothing, and is capable of learning model parameters adaptively from the data through an expectation-maximization learning procedure. We provide numerical evidence that DCS-AMP performs within 3 dB of oracle bounds on synthetic data under a variety of operating conditions. We further describe the result of applying DCS-AMP to two real dynamic CS datasets, as well as a frequency estimation task, to bolster our claim that DCS-AMP is capable of offering state-of-the-art performance and speed on real-world high-dimensional problems.

A Hybrid Method for Power System Frequency Estimation

Power system frequency is a critical parameter of voltage and current measurements for many applications, such as power quality, monitoring, and protection. This paper presents a hybrid approach for frequency estimation based on Taylor series expansion and Fourier algorithm. The method is derived using a dynamic signal model with varying parameters. The changing envelope of a power signal within an observation data window is approximated with a second-order Taylor series. A Fourier algorithm-based method is proposed to compute the parameters of such signal model. The algorithm using the linear model approach aimed at alleviating the computational complexity is also presented. The comparison of the performance under various conditions between the two approaches is conducted. Inheriting from the use of Fourier algorithm, this hybrid algorithm is immune to power system harmonics. It achieves excellent performance for signals with dynamic variations. The performance is investigated and compared with other techniques through simulations for various scenarios observed in real power systems. Experimental studies demonstrate the advantages of the proposed algorithm.

Dynamic Education Signaling with Dropout

We study students’ dropout behavior and its consequences in a dynamic signaling model. Workers pay an education cost per unit of time and cannot commit to a xed education length. Workers face an exogenous dropout risk before graduation. Since low-productivity students’ cost is high, pooling with early dropouts helps them to avoid a high education cost. In equilibrium, low-productivity students choose to endogenously drop out over time, so the productivity of students in college increases along the education process. We nd that the maximum education length is decreasing in the prior about a student being highly productive. We characterize the joint dynamics of returns to education and the dropout rate and provide an explanation of the declining dropout rate over the time students spend in school. We also extend the baseline model by allowing human capital accumulation and show that the dynamics of the dropout rate are helpful in decomposing the returns to education into the signaling eect and the human capital accumulation eect.