Observation Interval Research Articles

A Practical Approach for Determining Multi-Dimensional Spatial Rainfall Scaling Relations Using High-Resolution Time–Height Doppler Data from a Single Mobile Vertical Pointing Radar

The rescaling of rainfall requires measurements of rainfall rates over many dimensions. This paper develops one approach using 10 m vertical spatial observations of the Doppler spectra of falling rain every 10 s over intervals varying from 15 up to 41 min in two different locations and in two different years using two different micro-rain radars (MRR). The transformation of the temporal domain into spatial observations uses the Taylor “frozen” turbulence hypothesis to estimate an average advection speed over an entire observation interval. Thus, when no other advection estimates are possible, this paper offers a new approach for estimating the appropriate frozen turbulence advection speed by minimizing power spectral differences between the ensemble of purely spatial radial power spectra observed at all times in the vertical and those using the ensemble of temporal spectra at all heights to yield statistically reliable scaling relations. Thus, it is likely that MRR and other vertically pointing Doppler radars may often help to obviate the need for expensive and immobile large networks of instruments in order to determine such scaling relations but not the need of those radars for surveillance.

Disturbance Interval Observer-Based Robust Constrained Control for Unmanned Aerial Vehicle Path Following

This work presents a robust constrained path-following control scheme for the unmanned aerial vehicle (UAV) under wind disturbances. Through appointing the projection from the UAV to the path, the Serret–Frenet frame is introduced to reduce the complexity of the path-following problem. Specifically, the disturbance interval observer is employed to generate the interval of the wind disturbances. Then, the path-following control design is presented based on the dynamic surface control technique, and the auxiliary system is adopted to deal with the command limitation during the design process. Accordingly, the stability of the closed-loop system is analyzed. The effectiveness of the developed control scheme is demonstrated using numerical simulations.

The state of cognitive functions in COVID-19 complicated by viral pneumonia in young and middle-aged patients in the period 2020–2022

BACKGROUND. Currently, the study of the cognitive sphere in COVID-19 is an urgent problem.
 AIM. To determine the presence of cognitive dysfunction using valid scales MoCA, FAB, clock drawing test and their subscales in patients in the acute period of coronavirus infection, to compare the data of cognitive impairment in different epidemiological waves.
 MATERIAL AND METHODS. Two groups of subjects were included in the present study. Groups No. 1 and No. 2 are patients infected with COVID-19 in the acute period, complicated by pneumonia. Group No. 1 was hospitalized in the period December 2020 March 2021. Group No. 2 was hospitalized in the period November 2021 January 2022. To study the neuropsychological status, the neuropsychological scales MoCA, FAB, the clock drawing test and their subscales were used. Screening for anxiety and depression and depression was performed using the valid HADS scale. Quantitative data on the normality of the distribution of the sample was tested using the ShapiroWilk test. The MannWhitney test was used to compare unrelated groups. Quantitative data are presented as median and interquartile range Me [P25; P75]. Differences were considered statistically significant at p 0.05.
 RESULTS. Statistically significant differences were found in the acute period of coronavirus infection complicated by pneumonia and cognitive impairment in both group No. 1 and group No. 2, respectively. In group No. 1 (observation period from December 2020 to March 2021), cognitive impairments were more severe than in group No. 2 (observation period from November 2021 to January 2022). When comparing the subscales of the MoCA test of groups No. 1 and No. 2 upon admission to the hospital, and at discharge in group No. 1, the results of MoCA testing were worse than in group No. 2 according to subscales: attention, speech, memory, indicators were statistically significant (p 0.05). According to the FAB data, in group No. 1, the indicators on admission to the hospital and at discharge were also worse than in group No. 2, highly significant differences were according to the data: fluency of speech, dynamic praxis, simple and complicated choice reaction, grasping reflex study (p 0.001). Anxiety and depression were not found in the study groups.
 CONCLUSION. With the help of such valid scales as: the clock drawing test, MoCA, FAB and their subscales, the presence of cognitive dysfunction in young and middle-aged patients at different time intervals of observation was proved; in the first group, the violations were more severe than in the group No. 2.

Maximal Effort Cytoreduction in Epithelial Ovarian Cancer: Perioperative Complications and Survival Outcomes from a Retrospective Cohort.

Background: Rates of maximal effort cytoreductive surgery in ovarian cancer patients increase gradually the last decade. The purpose of the present study is to evaluate factors that contribute to survival and morbidity outcomes in this group of patients. Methods: We retrospectively reviewed patient records of epithelial ovarian cancer patients with an intermediate and high Mayo Clinic surgical complexity score, operated between January 2010 and December 2018. Results: Overall, 107 patients were enrolled in the present study with a median age of 62 years (23-84) and a follow-up of 32 months (2-156). Thirteen Clavien-Dindo grade IIIa complications were documented in 10 patients (9.3%). Of all the investigated factors, only stage IVb (p = 0.027) and interval debulking surgery (p = 0.042) affected overall survival rates. Overall survival outcomes of patients operated on a primary setting started to differentiate compared to those that received neo-adjuvant chemotherapy after the 4th postoperative year. Conclusions: Maximal effort cytoreductive procedures should be considered feasible in the modern surgical era, as they are accompanied by acceptable rates of perioperative morbidity. Hence, every effort should be made to perform them in the primary setting, rather than following neoadjuvant chemotherapy as current evidence favor increased survival rates of patients that will likely surpass an interval of observation of more than 4 years.

Outlier-resistant interval observer design for multirate time-delayed systems under the adaptive event-triggered protocols

In this paper, an interval observer has been presented for a type of multirate time-delayed systems subject to measurement outliers under the adaptive event-triggered protocol (AETP). First of all, the multirate sampling system is studied to meet the practical requirements. Meanwhile, for relieving the communication burden, an AETP is considered to schedule the measurement information received by the observer. The augmented system is built at a consistent sampling/update rate by using the lifting technique. Moreover, the occurrence of measurement outliers would pose some serious threats to the reliability of the systems. For the addressed issue, an outlier-resistant interval observer is developed by means of saturation function to weaken the impact of the measurement outliers. A sufficient condition is proposed by using the positive system theory and Lyapunov stability theory to ensure that the interval observer is effective and the error system is input-to-state stable. Besides, the gain matrices of the interval observer are obtained by solving a set of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed interval observer is verified by a numerical simulation.

Interval Observer for Switching Discrete-Time Linear Systems with Unknown Switching Sequences

The present paper deals with interval observers in the discrete-time case for switching linear systems. When the discrete state is known, several interval observers have been proposed. In the present paper, such an observer is proposed in the case there is no assumption nor knowledge on the switching parameter, and when the switching system considered is affected by bounded perturbations.

PERIOD CHANGES OF FOUR CEPHEID VARIABLES

We have analysed the AAVSO (2023) photometric (UBVRI) observations of several dozens of Cepheids and W Vir variables. These observations allowed us to find the period changes of four Cepheid variables: ζ Gem, VX Cyg, V Lac, TT Aql. Mean phase light curves were obtained for 3-5 time intervals of observations using trigonometrical polynomial approximations of data in the V-band. VX Cyg shows the period decrease in 400 days interval that is also accompanied by the decrease of asymmetry. It was impossible to find regularities of period changes of other variables within error estimates due to the relatively small amount of observed cycles.

Wireless Technology Identification Employing Dynamic Mode Decomposition Modeling

Significant growth in broadband wireless services, as well as ever-increasing demand on the spectrum caused by the Internet of Things (IoT) have overstretched limited available spectrum space for wireless services. Heterogeneous wireless networks (HetNets)—wherein multiple wireless technologies (e.g., Wi-Fi, Bluetooth, Zigbee, LTE, and GSM) coexist and share spectrum—are a promising solution for enhancing spectrum sharing. An essential element in developing coexistence protocols is correctly identifying wireless technologies anticipated to share spectrum and to shift users between available wireless technologies in an effort to optimize spectrum usage and minimize interference. For the coexistence research reported in this paper, we analyzed the performance of our developed novel algorithm based on dynamic mode decomposition (DMD) mathematical modeling to identify and differentiate among various wireless technologies. More specifically, our technique identified GSM and LTE signals in the cellular domain, IEEE802.11n, ac, and ax in the Wi-Fi domain, as well as Bluetooth and Zigbee. The proposed DMD-based technique identifies the time domain signature of a signal by capturing embedded periodic features transmitted within the signal. Performance and accuracy were tested and validated using an experimental dataset collected for various time series, and raw-power measurements of the targeted technologies. Results showed that the developed DMD-based algorithm can differentiate and classify individual and coexisting wireless signals with high accuracy —greater than 90% for most cases. Furthermore, only a short time— less than one second—is required for identifying a signal and enabling implementation in real-time practical networks. The advantage of the developed technique over comparable techniques is lower complexity (i.e., shorter processing and training time, no channel estimation, no time/frequency synchronization, and no need for long observation-time intervals).

Event-Triggered Control Under Unknown Input and Unknown Measurement Delays Using Interval Observers

We provide a new input-to-state stabilizing event-triggered feedback design for linear systems with unknown input delays, unknown measurement delays, and unknown additive disturbances. Our trigger times are computed using only the matrices defining the system and time-lagged sampled state values. We use the theory of positive systems, interval observers, and a vector version of Halanay’s inequality. We illustrate our method using a marine robotic model.

Onboard Observation Planning for Sequential Image-based Planetary Landing Navigation in Unknown Environments

Autonomous navigation based on sequential images is widely accepted as an effective method to guarantee pinpoint landing and precise obstacle avoidance in planetary landings. Observing a large number of landmarks and processing a large number of images places a computational burden on landers with limited computational resources, but existing studies lack observation planning methods for landmarks in unknown environments that can be operated autonomously on landers. In this paper, we analyze the observability of a vision-aided inertial navigation (VAIN) system and propose an observation planning strategy. The minimum number of landmarks and the minimum number of observations are obtained from the observability analysis, which is the boundary condition for observation planning. The landmark configuration planning in the observation planning strategy optimizes only univariate convex functions to efficiently select a small number of landmarks in unknown environments with high navigation accuracy, and the observation interval planning adaptively reduces the number of observations without significantly affecting the navigation accuracy. Simulation results verify the correctness of the observability analysis results, and it is found that the proposed observation planning strategy outperforms traditional observability degrees in terms of both improved navigation accuracy and computational speed when observing a small number of landmarks, and can effectively reduce the number of observations.

Power Generation Control Algorithm for the Participation of Photovoltaic Panels in Network Stability

The push for renewable energy and sustainable development has led to an ever-increasing integration of grid-tied photovoltaic (PV) systems. To maximize revenue, this resource generally operates in maximum power point trackers (MPPT) mode. However, to ensure grid stability and reliability, system operators will continue to introduce new requirements, eventually forcing PV plants to adhere to primary frequency regulation. To perform this task, PV plants will have to be capable of operating outside the MPP and varying their power production, to maintain an active power reserve, according to grid request. This article presents an innovative model-based (MB) tracking algorithm devoted to supporting power network regulation. Due to the updated formulation, the algorithm can vary the power curtailment according to a reduction factor given by the power system operators. Results show the remarkable performance and accuracy of the new algorithm, providing power regulation capability in the range 20%–100% of the maximum available power. Moreover, the impact of the employment of constant reduction coefficients on the algorithm performances has been evaluated. Validation has been performed based on the data collected over an observation interval of more than six months. Due to its flexibility, this could be the basis for the participation of PV systems to frequency regulation.

Piecewise linear regression model of hemostasis dynamics in donors and patients with schizophrenia

This work is to develop and use a new approach to the analysis of differences between the velocity profiles of registered light signals in groups of patients with schizophrenia and healthy donors. The present study involved 62 patients (all female) with schizophrenia or schizoaffective disorders in the acute period (observational study - 2016-2017). 44 patients were diagnosed with paranoid schizophrenia with an attack-progredient type of course (F20.01 according to ICD-10) or with a continuous type of course (F20.02). The fibrinodynamics test (FD) was performed on the T-2 thrombodynamics device (Hemacore LLC, Moscow), which makes it possible to monitor the processes of coagulation and fibrinolysis in the cuvette channels filled with fresh blood plasma. The result of the test are the brightness profiles of the clot. In the proposed approach, each profile is characterized by a vector of distances between it and other analyzed profiles. At that the distances between the profiles are calculated as the sum of the modules of differences at different points of the observation interval. This method makes it possible to overcome a certain loss of information that occurs when using a standard method based on the calculation of several parameters that characterize the analyzed velocity profiles. The method includes the construction of all kinds of statistically significant piecewise linear regression models that link the distances to two profiles. The velocity profiles corresponding to the points lying on opposite sides of the break point of such models have significantly different shapes. In practice, it turns out that for many piecewise linear regression models, the break points also significantly separate the velocity profiles for the compared groups of donors and patients with schizophrenia. Statistically significant deviations at the level p≤0.01 exist for 16.1% of 3143 statistically significant piecewise linear models. The developed method makes it possible to visually establish the relationship between the shape of the velocity profile and the presence of the disease.

A Baseband Wireless VNA for the Characterization of Multiport Distributed Systems

Frequency characterization of spatially large structures has become increasingly required, mostly in the fields of structural health monitoring (SHM) and communication systems based on nonconventional media. When the ports of the system under measurement are far apart, methods based on traditional wired instruments become unattractive for field applications, due to the increased complexity, cost, and signal integrity-related issues. Aiming toward removing the wired connection from the ports of the system under test and the elaboration unit (EU), the main issue to be dealt with is the time synchronization of measurements at the ports. This contribution proposes a solution to such an issue by presenting a Wireless Vector Network Analyzer (WVNA), suitable for the characterization of distributed systems. For this purpose, a wireless synchronization scheme is proposed, which is based on the disciplining of the signal sampling clock from the 1-pulse/s reference signal. The proposed synchronization method reduces clock jitter at different ports at 1.13 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> over a 300-s observation interval. The hardware and software implementation of the system are detailed and experimental results proving its operation are provided.

Interval estimation methods of fault estimation for discrete-time switched systems

&lt;p style='text-indent:20px;'&gt;This paper investigates the fault estimation problem for discrete-time switched systems. Firstly, the sensor fault is regarded as an augmented state, and then the original system is converted into a singular system. After that, based on the obtained singular system, a robust augmented state observer is designed using &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ L_{\infty} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; technique. Given above robust augmented state observer, an interval observer based on the zonotope method is established to complete the fault interval estimation. Finally, a circuit system is simulated to illustrate the efficiency of the proposed method.&lt;/p&gt;

Functional Interval Observer for Discrete-Time Nonlinear Lipschitz Systems

This paper considers discrete-time nonlinear Lipschitz systems with unknown but bounded disturbances. Thanks to the existence of bounding decomposition functions for mixed monotone mappings and the Lyapunov approach, we present a functional interval observer to achieve stable upper and lower bounds for a linear function of the system state. The simulation on a numerical example is given to illustrate the theoretical results.

Safety Monitoring and Alert for Neural Network-Enabled Control Systems*

This paper considers the safety monitoring and enhancement for neural network-enabled control systems with disturbance and measurement noise. A robustly stable interval observer is designed to generate sound lower and upper bounds of the system state. The obtained interval is used to monitor the runtime system state and predict the one-step ahead future system trajectory, providing system safety monitoring and alert. The simulation results of a numerical example and an adaptive cruise control system demonstrate efficacy of the observer in runtime system monitoring and its potentials in detecting sensor faults and enhancing system safety.

Weak Fault Detection of Rotor Winding Inter-Turn Short Circuit in Excitation System Based on Residual Interval Observer

Weak Fault Detection of Rotor Winding Inter-Turn Short Circuit in Excitation System Based on Residual Interval Observer

Distributed interval estimation for continuous-time linear systems: A two-step method⋆

This paper proposes a two-step distributed interval estimation method for continuous-time linear time-invariant (LTI) systems subject to unknown but bounded disturbance and measurement noise. First, a network of local observers is designed to obtain point-wise estimation. Second, interval estimation is obtained based on the point-wise estimation and peak-to-peak analysis. The conditions of the observer design and the peak-to-peak analysis are formulated as linear matrix inequalities, which can be solved efficiently. Compared with the distributed interval observers designed using the monotone system theory, the proposed method has less restrictive design conditions and hence broader application scopes. Moreover, the parameter matrices in the proposed method can be optimized to obtain tight interval estimation. A numerical simulation is given to illustrate the performance of the proposed method.

Interval Velocity Estimation for Unmanned Underwater Vehicles

This letter addresses the problem of interval velocity estimation for unmanned underwater vehicles subject to multiple model uncertainties. We extend our previous works to the case of large initial bounds on uncertainties by introducing an adaptive mechanism. For improving the accuracy of interval estimation, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> approach is used in the design of interval observer to attenuate the effect of uncertainties. Sufficient design conditions are formulated as a set of linear matrix inequalities. Simulation results demonstrate the effectiveness of the proposed method.

Limiting State Estimation of Switched Interval Systems with Metzler-Takagi-Sugeno Models

The paper deals with interval observer design for fuzzy switched positive systems. The systems are represented by the Takagi-Sugeno fuzzy models, with premise variables depending on a measurable part of the state vector. Stability conditions for the proposed interval observer structure are formulated via linear matrix inequalities to ensure nonnegative system state estimation. The proposed method allows to compute the lower and upper bounds of the system state under the assumption that the system disturbance are bounded. The properties of proposed approach are illustrated in numerical example.