Observation Interval Research Articles

INTERFERENCE RESISTANCE OF A CORRELATION RECEIVER AT A LIMITED OBSERVATION INTERVAL

The article considers the possibility of increasing the capability of detecting a useful radio signal against the background of noise and simultaneously reducing the possibility of mistaking noise fluctuations for a signal. Moreover, if it is absent then the Neumann-Pearson statistical test should be used. This criterion is a partial case from the class of criteria consisting in the calculation of the likelihood ratio and does not require knowledge of a priori probabilities of the presence or absence a useful signal while constructing an optimal receiver and Bayesian methods are used.
 The effectiveness of the detection procedure according to the Neumann-Pearson test is characterized by the probability of correct detection at a fixed false alarm probability. These probabilities depend on the signal-to-noise ratio at the output of the detection device. So, an important preparatory stage of the reception immunity analysis is the study of this parameter which is very important in the section of statistical radio engineering.
 It is known that the optimal detection of a signal against a background of white noise is reduced to the correlation integral calculation. This calculation can be done directly by using a multiplier and integrator (correlation method), by using a filter that is optimal for this signal (filter method) or by using a correlation-filter method.
 The presence of a priori uncertainty during signal reception makes it impossible to create an optimal (consistent) receiving path and therefore makes it impossible to obtain the required value of the signal-to-noise ratio at the output of the detection device. In this case, an autocorrelation algorithm is used which is resistant to a priori uncertainty of signal parameters and is “optimal” while detecting signals of unknown shape. The most general character for determining the signal/noise ratio has an expression for the case of an incoherent single-channel autocorrelation algorithm for detecting a noisy signal against a background of stationary white noise (interference).
 For example, quite a large number of scientific works are devoted to the interference immunity issue of the correlation reception method. They mainly consider the interference immunity of correlation receivers when a mixture of harmonic signal and noise passes through them. The circuits with the same average frequencies of multiplying processes and etc. are analyzed. There are also known works in which formulas relating the value of the signal-to-noise ratio at the output of the receiving path for a complex signal to the input signal-to-noise ratio are given.
 In the above works, idealized cases are considered, namely, when the observation is conducted on a fairly long observation interval but the real observation interval is quite limited. In this case, the averaging is carried out over a limited time interval and it is necessary to take into account the intercorrelation relations between the signal and noise, therefore, along with the signal-signal, noise-noise, signal-noise and noise-signal components, we will have the following mutual correlation moments between the combinations of signal and noise component.
 The issue of an autocorrelation receiver when passing an additive mixture of signal and white noise at a limited observation interval is investigated in which the immunity index is considered and a general solution of the signal/noise ratio based on the deviation criterion is obtained. Therefore, obtaining an analytical expression that establishes the relationship between the signal-to-noise ratio at the input and output of the receiver is very important and relevant.
 The purpose of the article is to obtain an analytical expression of the signal-to-noise ratio value at the threshold device input of an incoherent autocorrelation receiver at a limited observation interval.

Algorithm for estimating the energy distribution of radar signals scattering on acoustic disturbances created by UAVs

The task of estimating the energy distribution over the observation interval of radar signals scattered on atmospheric inhomogeneities, arising as a result of UAV operation, is considered. The solution to this problem is necessary to improve detection algorithms, to classify the detected UAVs according to additional informational features, to improve the resolution when detecting several devices located at the same range during the group application of UAVs, to clarify the time parameters of the evolution of the movement of UAVs in time and space. A similar problem arises due to the need to process useful radar signals with a low signal-to-noise ratio in order to achieve the maximum possible range of reliable UAV detection. Because of this, it becomes impossible to estimate directly the energy of useful signals by the method of comparison with reference physical quantities due to a large measurement error. Therefore, an evaluation algorithm is proposed, based on the methods of the theory of ordinal statistics, which provide, instead of comparing numerical realizations with a certain standard, to form a variational series from them under the condition of a priori knowledge of the distribution function of these realizations. At the same time, the fact is used that for certain distributions of a random variable, among which there are normal and all limited ones, the variance of the estimate in the form of a mathematical expectation of certain ordinal statistics is significantly less than the variance of a direct measurement at a low signal-to-noise ratio. In order to save time and computing resources during real-time processing of received signals, it is proposed to use pre-calculated arrays of numerical values of mathematical expectation and dispersion of ordinal statistics for various parameters of the density distribution of a random variable.

A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting

Electricity load forecasting has seen increasing importance recently, especially with the effectiveness of deep learning methods growing. Improving the accuracy of electricity load forecasting is vital for public resources management departments. Traditional neural network methods such as long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) have been widely used in electricity load forecasting. However, LSTM and its variants are not sensitive to the dynamic change of inputs and miss the internal nonperiodic rules of series, due to their discrete observation interval. In this paper, a novel neural ordinary differential equation (NODE) method, which can be seen as a continuous version of residual network (ResNet), is applied to electricity load forecasting to learn dynamics of time series. We design three groups of models based on LSTM and BiLSTM and compare the accuracy between models using NODE and without NODE. The experimental results show that NODE can improve the prediction accuracy of LSTM and BiLSTM. It indicates that NODE is an effective approach to improving the accuracy of electricity load forecasting.

Fault detection for discrete-time fuzzy systems with measurement errors using interval observers

This paper presents an interval observer-based fault detection (FD) strategy for discrete-time T–S fuzzy systems with measurement errors. The system and measurement outputs are selected as the premise variables of plant and observer respectively. The bounds of mismatch items caused by the measurement errors are established by covering matched region, mismatched left adjacent region and right adjacent region. Piecewise Lyapunov function, taking full account of possible transitions, is employed to drive observer design condition. FD is turned into optimization problem with disturbance attenuation, fault sensitivity and nonnegativity constraints. The decision is implemented by judging whether zero is excluded from the residual interval. Finally, simulation is explored to verify the scheme.

Grasp-specific high-frequency broadband mirror neuron activity during reach-and-grasp movements in humans.

Broadly congruent mirror neurons, responding to any grasp movement, and strictly congruent mirror neurons, responding only to specific grasp movements, have been reported in single-cell studies with primates. Delineating grasp properties in humans is essential to understand the human mirror neuron system with implications for behavior and social cognition. We analyzed electrocorticography data from a natural reach-and-grasp movement observation and delayed imitation task with 3 different natural grasp types of everyday objects. We focused on the classification of grasp types from high-frequency broadband mirror activation patterns found in classic mirror system areas, including sensorimotor, supplementary motor, inferior frontal, and parietal cortices. Classification of grasp types was successful during movement observation and execution intervals but not during movement retention. Our grasp type classification from combined and single mirror electrodes provides evidence for grasp-congruent activity in the human mirror neuron system potentially arising from strictly congruent mirror neurons.

Modelling the Wind Turbine by Using the Tip-Speed Ratio for Estimation and Control

The development of dynamic models for control purposes is characterised by the challenge of finding a compromise between the minimum necessary information about the system dynamics contained in the model and a model with a low level of complexity such that the model-based control system design becomes comfortable. To achieve this balance, a modified dynamic model for the drivetrain of a wind turbine is proposed in this contribution. The main idea is to introduce the tip-speed ratio as a state variable so that an interval observer can be designed in such a way that its estimates can be used in the torque control during the partial load operation as well as for the estimation of the effective wind speed. During the runtime, the observer’s matrix gain is recalculated to adapt the behaviour to the current operational state, which changes all the time with the wind speed. Besides the theoretical formulation, a numerical example of a 20 MW reference wind turbine illustrates the utility of the method. The results show good control performance concerning the tip-speed ratio control loop and a satisfactory estimation of the effective wind speed.

Midterm safety and efficacy of elective drug-coated balloon angioplasty in comparison to drug-eluting stents for unrestrictive de novo coronary lesions: A single center retrospective study

BackgroundThe safety and efficacy of elective drug-coated balloon (DCB) angioplasty for unrestrictive de novo coronary stenosis in daily practice is not fully understood, especially in comparison to those of drug-eluting stents (DESs). MethodsA total of 588 consecutive de novo coronary stenotic lesions electively and successfully treated with either DCB (n = 275) or DESs (n = 313) between January 2016 and December 2019 at our medical center were included. The primary safety endpoint was the incidence of target lesion failure (TLF), comprising cardiac death, non-fatal myocardial infarction, and target vessel revascularization. The secondary angiographic efficacy endpoint was angiographic restenosis frequency, defined as a follow-up percent diameter stenosis of >50. The endpoints were compared after baseline adjustment using propensity score matching. In addition, the frequency and predictors of late lumen enlargement (LLE), defined as minus late luminal loss, were examined in 201 crude angiographic follow-up lesions after DCB angioplasty. ResultsA total of 31 baseline parameters were adjusted to analyze 177 lesions in each group. The TLF frequencies (DCB group: 9.6 % during a mean observational interval of 789 ± 488 days vs. DES group: 10.2 %, 846 ± 484 days, p = 0.202) and cumulative TLF-free ratios of both groups were not significantly different (p = 0.892, log-rank test). The angiographic restenosis frequency in the DCB group (6.3 %, n = 128) was not significantly different from that of the DES group (10.1 %, n = 100, p = 0.593). LLE was observed in 45.3 % of entire lesions, and a type-A dissection was a significant predictor of LLE among 23 variables (odds ratio: 3.02, 95 % CI: 1.31–6.95, p = 0.010). ConclusionsThe present single-center retrospective study revealed statistically equivalent midterm clinical safety and angiographic efficacy among both elective DCB angioplasty and DESs placements in the treatment of unrestrictive de novo coronary lesions. In our daily practice environment, LLE was achieved in approximately half after DCB angioplasty.

Testing Predictions for Migration of Meandering Rivers: Fit for a Curvature‐Based Model Depends on Streamwise Location and Timescale

AbstractMany meandering rivers migrate, at rates that vary both along‐stream and inversely with the observation interval. Many numerical models have been developed to predict this migration; their success is usually evaluated statistically or by qualitative comparison to observations in map view. We propose a framework to test migration models that unites these statistical, spatial, and temporal perspectives. We measure model fit with a statistic that compares the magnitude and direction of migration between predictions and observations. Model fit is contextualized in space, using a dimensionless coordinate system based in the location along a half‐meander bend; and in time, using a dimensionless observation interval that accounts for channel scale and migration rate. We applied this framework to test predictions for a curvature‐driven model of channel migration, using data from seven rapidly migrating rivers in the Amazon Basin and 103 more slowly migrating rivers across the continental US, as reconstructed from a legacy data set. We find that across both datasets, channel migration rates peak slightly downstream of the bend apex. Migration rate underestimation/overestimation tends to occur when the observed rate is greater/less than its median along the channel. Predicted migration direction opposes observations for slowly migrating locations and upstream of the bend apex. Model forecasts break down if the channel migrates by more than its width. The analysis framework is portable to testing other models of channel migration, and can help improve predictions for the stability of infrastructure along rivers and for landscape change over geologic timescales.

A difference-algebraic interval observer design for perturbed discrete-time descriptor systems

This paper investigates a difference-algebraic (DA) interval observer (IO) design technique for perturbed discrete-time descriptor systems. The existence condition of the IO with a DA structure is first established, along with the canonical implementation of descriptor systems and its non-negativity criterion. Then, combined with such an IO, a triangular block transformation (TBT) is further introduced to reduce the universal IO design conservatism on cooperativity construction. Besides, the general and particular solutions to the TBT&DA-based IO are successively proposed not only to clearly show the available design parameters but also to achieve a convenient construction process. Three simulation results demonstrate the validity and superiority of the proposed methods.

Asymptotic convergence unknown input observer design via interval observer

In this paper, for a linear system with unknown input, a novelty unknown input observer (UIO) which can produce the asymptotic system state estimation and unknown input reconstruction simultaneously via interval observer is developed. Firstly, under the assumption that the boundary of the unknown input is known, the interval estimation of the output vector is obtained by designing an interval observer. Secondly, by using the upper and lower boundary estimations of the output vector, an algebraic relationship between the unknown input and the system state is set up. Thirdly, based on the algebraic relationship, a Luenberger-like state observer together with an algebraic unknown input reconstruction is designed to form a UIO. Moreover, a UIO is designed for a time-delay system with unknown input by applied the proposed UIO on the time-delay system directly, showing the advantages of the proposed UIO. Finally, simulation examples and some comparisons to the existing results are given to illustrate the effectiveness and advantages of the proposed method.

Beyond the exposure phase: Microplastic depuration and experimental implications

Currently, most studies focus on the effect of microplastics (MPs) in the exposure phase, but pay limited attention to the depuration phase. Depuration is a promising practice to achieve safe aquaculture production, which is also helpful to understand the long-term impact of MPs. Therefore, investigating the post-exposure scenarios of MPs has great practical significance. In order to provide implications for future research, this work attempted to systematize the current findings and knowledge gaps regarding the depuration of MPs. More specifically, three methods, including direct fitting, one-compartment kinetic model and interval observation, for estimating the retention time of MPs to further determine the minimum depuration time were introduced, in which the one-compartment kinetic model could also be used to calculate the depuration rate constant and biological half-life of MPs. Moreover, the post-exposure effect of MPs generally presented three scenarios: incomplete reversal (legacy effect), return to control level (recovery) and stimulatory response (hormesis-like effect). In addition, the possible tissue translocation of MPs, the influence of food abundance and body shape on MPs egestion, and the potential interaction with environmental factors, have aroused great scientific concerns and need further exploration and clarification.

Optimal reduced‐order interval observer design for uncertain continuous‐time linear systems

AbstractThis article investigates the reduced‐order interval observer (R‐IO) design technique for continuous‐time linear systems with unknown external disturbances and measurement noises. First, we propose a coupled R‐IO structure with more design degrees of freedom, and it can be directly applied not only to solve the difficulty of the error system cooperativity construction but to relax the constraint on the sensor measurement noises. Second, the R‐IO existence condition is derived as a set of matrix equations (MEs), and a complete solution, explicitly showing the available design parameters, to such an R‐IO is further obtained by solving the MEs. Third, using the solution, an integrated optimization indicator of the R‐IO performance is built as the valid selection mechanism of these parameters. Finally, the efficiency of the obtained results is illustrated through a numerical example and a practical example.

Shortening the Time Interval for the Referral of Patients With Soft Tissue Sarcoma to Expert Centers Using Mobile Health: Retrospective Study.

According to guidelines, all patients with sarcoma must be managed from initial diagnosis at expert sarcoma centers. However, in everyday practice, the time interval to an expert center visit can be long, which delays presentation to an expert multidisciplinary tumor board and increases the risk of inappropriate management, negatively affecting local tumor control and prognosis. The advent of mobile health offers an easy way to facilitate communication and cooperation between general health care providers (eg, general practitioners and radiologists) and sarcomas experts. We developed a mobile app (Sar'Connect) based on the algorithm designed by radiologists from the French Sarcoma Group. Through a small number of easy-to-answer questions, Sar'Connect provides personalized advice for the management of patients and contact information for the closest expert center. This retrospective study is the first to assess this mobile app's potential benefits in reducing the time interval for patient referral to an expert center according to the initial clinical characteristics of the soft tissue tumor. From May to December 2021, we extracted tumor mass data for 78 patients discussed by the multidisciplinary tumor boards at 3 centers of the French Sarcoma Group. We applied the Sar'Connect algorithm to these data and estimated the time interval between the first medical description of the soft tissue mass and the referral to expert center. We then compared this estimated time interval with the observed time interval. We found that the use of Sar'Connect could potentially shorten the time interval to an expert center by approximately 7.5 months (P<.001). Moreover, for half (31/60, 52%) of the patients with a malignant soft tissue tumor, Sar'Connect could have avoided inappropriate management outside of the reference center. We did not identify a significant determinant for shortening the time interval for referral. Overall, promoting the use of a simple mobile app is an innovative and straightforward means to potentially accelerate both the referral and management of patients with soft tissue sarcoma at expert centers.

When is a Matrix of Dimension Three Similar to a Metzler Matrix? Application to Interval Observer Design

A simple, necessary, and sufficient condition ensuring that a real matrix of dimension three is similar to a Metzler matrix is exhibited. When this condition is satisfied, a construction of the transfer matrix is given. This construction is used to design an interval observer for a family of continuous-time systems. An example is provided with interval observer design for the so-called love dynamics in the case of limit cycles

Gas Migration Episodes Observed During Peridotite Alteration in the Samail Ophiolite, Oman.

Serpentinization and carbonation of mantle rocks (peridotite alteration) are fundamentally important processes for a spectrum of geoscience topics, including arc volcanism, earthquake processes, chemosynthetic biological communities, and carbon sequestration. Data from a hydrophone array deployed in the Multi-Borehole Observatory (MBO) of the Oman Drilling Project demonstrates that free gas generated by peridotite alteration and/or microbial activity migrates through the formation in discrete bursts of activity. We detected several, minutes-long, swarms of gas discharge into Hole BA1B of the MBO over the course of a 9month observation interval. The episodic nature of the migration events indicates that free gas accumulates in the permeable flow network, is pressurized, and discharges rapidly into the borehole when a critical pressure, likely associated with a capillary barrier at a flow constriction, is reached. Our observations reveal a dynamic mode of fluid migration during serpentinization, and highlight the important role that free gas can play in modulating pore pressure, fluid flow, and alteration kinetics during peridotite weathering.

Sensor fault detection in a biohydrogen production process based on an interval observer and adaptive thresholds

In this paper, an interval observer-based fault detection strategy is proposed to deal with faults in the hydrogen flow rate sensor of a biohydrogen production process. A control Hinf-based robust interval observer is designed to (i) estimate the glucose and biomass concentrations from measurements of the hydrogen flow rate produced, (ii) attenuate the effect of the unknown inlet glucose concentration, and (iii) detect faults in the hydrogen flow rate sensor through adaptive thresholds. The gain is designed by solving a semi-definite optimization problem subject to LMIs. The feasibility of the proposed strategy is demonstrated by simulations.

Investigating the effect of observation interval on GPS, GLONASS, Galileo and BeiDou static PPP

GNSS observation intervals can be tuned from low rate to high rates (such as 300 to 1 s) for the specific applications. In this study, the effect of sampling intervals of 1, 5, 15, and 30 s on the convergence time and positioning accuracy of static precise point positioning is investigated using high-rate data from 26 IGS (International GNSS Service)-MGEX (Multi-GNSS Experiment) stations over a three-week period in 2020. Six different GNSS constellations – namely, GPS-only, GLONASS-only, Galileo-only, BeiDou-2-only, BeiDou-3-only, and multi-GNSS (GPS+GLONASS+Galileo+BeiDou-2+BeiDou-3) – are processed for static PPP. The results show that the use of higher rate of observation intervals significantly reduces the PPP convergence time for each GNSS constellation. Maximum improvements between 30 s and 1 s are found to be 55%, 60%, and 55% for north, east, and up components, respectively, for Galileo PPP. However, the results of positioning accuracy indicates that the use of higher rate of observation intervals slightly degrades the PPP converged positioning accuracy for each GNSS constellation except for BDS-3 and multi-GNSS PPP modes. The results demonstrate that the satellite clock interpolation error is mainly responsible for the degradation in accuracy at the higher rate of observation intervals compared with the orbit interpolation error.

Interval Observers for Discrete-Time Linear Systems with Uncertainties

In this paper, we consider the problem involved when designing the interval observer for the system described by a linear discrete-time model under external disturbances and measurement noises. To solve this problem, we used the reduced order model of the initial system, which is insensitive or has minimal sensitivity to the disturbances. The relations involved in designing the interval observer, which has minimal dimensions and estimates the prescribed linear function of the original system state vector, were obtained. The theoretical results were illustrated by a practical example.

Confidence Intervals for the Mean Function of a Compound Cyclic Poisson Process in the Presence of Power Function Trend

We consider the problem of estimating the mean function of a compound cyclic Poisson process in the presence of power function trend. The objectives of this paper are: (i) to construct confidence interval for the mean function of a compound cyclic Poisson process with significance level , (ii) to prove that the probability that the mean function contained in the confidence interval converges to , and (iii) to observe, using simulation study, that the probabilities of the mean function contained in the confidence intervals for bounded length of observation interval. The main results are a confidence interval for the mean function and a theorem about convergence of the probability that the mean function contained in confidence interval. The simulation study shows that the probability that the mean function contained in the confidence interval is in accordance with the theorem. The contribution of this study is to provide information for users regarding confidence interval for the mean function of a compound cyclic Poisson process in the presence of power function trend.

Functional interval observer design for linear time-varying systems with additive disturbances

This paper investigates the problem of the functional interval observer design for linear time-varying (LTV) systems with additive disturbances in both input and output channels. First, sufficient conditions for the existence of a functional interval observer for LTV systems are proposed. Based on the solution of a type of Sylvester matrix equations (generalized Sylvester equations, GSEs), completely parameterized expressions of functional interval observer coefficient matrices are established, yielding a simple and effective design approach for solving the interval estimation issue of the LTV system, while the free parameters in the expressions provide the design degrees of freedom that can be utilized to achieve additional system specifications. Furthermore, the developed observer may execute state estimation at the desired convergence rate, considerably improving estimate performance. Finally, a numerical example and a back-to-turn (BTT) aircraft control example are presented, with the results showing that the upper and lower bounds provided by the designed observer provide a better performance of interval estimation of the functional state variables, demonstrating the effectiveness of the proposed method.