Measured Noise Parameters Research Articles

Single degree of freedom control based on deep reinforcement learning for underwater unmanned vehicle

This paper proposes a two-stage controller based on deep reinforcement learning for an unmanned underwater vehicle under conditions of parameter uncertainty and external measurement noise. The feedback linearization technique is first applied to linearize the dynamic model, and then this is used to design the control law, the integral term is added to expand the control law into a Proportional Integral Derivative (PID) control method, which is used as the first stage controller. The second stage controller is to realize parameter regulation, the Deep Deterministic Policy Gradient (DDPG) algorithm is used to train the agent in response to the controller’s need to adjust gain parameters in real time to cope with external interference. Moreover, in order to minimize unnecessary exploration at the beginning of the training process, the previous excellent manual experience is combined with the training process. Compared to the original DDPG algorithm, this algorithm exhibits a faster convergence rate. To verify the performance of Feedback Linearized DDPG PID Control (FLDDPGPIDC), three advanced methods were compared with the method. Results indicate that tracking performance is significantly improved in the presence of external measurement noise and dynamic parameter perturbations.

Applicability of Oculomics for Individual Risk Prediction: Repeatability and Robustness of Retinal Fractal Dimension Using DART and AutoMorph.

To investigate whether fractal dimension (FD)-based oculomics could be used for individual risk prediction by evaluating repeatability and robustness. We used two datasets: "Caledonia," healthy adults imaged multiple times in quick succession for research (26 subjects, 39 eyes, 377 color fundus images), and GRAPE, glaucoma patients with baseline and follow-up visits (106 subjects, 196 eyes, 392 images). Mean follow-up time was 18.3 months in GRAPE; thus it provides a pessimistic lower bound because vasculature could change. FD was computed with DART and AutoMorph. Image quality was assessed with QuickQual, but no images were initially excluded. Pearson, Spearman, and intraclass correlation (ICC) were used for population-level repeatability. For individual-level repeatability, we introduce measurement noise parameter λ, which is within-eye standard deviation (SD) of FD measurements in units of between-eyes SD. In Caledonia, ICC was 0.8153 for DART and 0.5779 for AutoMorph, Pearson/Spearman correlation (first and last image) 0.7857/0.7824 for DART, and 0.3933/0.6253 for AutoMorph. In GRAPE, Pearson/Spearman correlation (first and next visit) was 0.7479/0.7474 for DART, and 0.7109/0.7208 for AutoMorph (all P < 0.0001). Median λ in Caledonia without exclusions was 3.55% for DART and 12.65% for AutoMorph and improved to up to 1.67% and 6.64% with quality-based exclusions, respectively. Quality exclusions primarily mitigated large outliers. Worst quality in an eye correlated strongly with λ (Pearson 0.5350-0.7550, depending on dataset and method, all P < 0.0001). Repeatability was sufficient for individual-level predictions in heterogeneous populations. DART performed better on all metrics and might be able to detect small, longitudinal changes, highlighting the potential of robust methods.

An improved noise model of InP HEMT for millimeter wave application

AbstractA new temperature noise model, including the influence of gate‐drain series resistance Rgd on the noise performance for an InP HEMT, is presented in this article. An equivalent temperature Tgd of Rgd has been taken into account based on Pospieszalski's noise model. The corresponding extraction procedure of noise parameters is given. Good correlation between the simulated and measured noise parameters in the frequency range of 8–50 GHz for a wide range of bias points verify the validity of the improved noise model.

Environmental Services on the Public Green Open Space using Dpsir Approach: Study Case at Mataram City

The existence of public green open spaces provides a number of benefits for the community and the surrounding environment. This study aims to assess the environmental service function of public green spaces in Mataram City using several environmental analysis approaches. The research method used a quantitative descriptive approach with the research sample being Pagutan green space and Udayana green space. The research time was conducted from June to July 2023 with research instruments namely interviews, and observations. Some of the methods used are spatial analysis, ecosystem service analysis, and DPSIR analysis. Based on the DPSIR approach, the service function in public green spaces is triggered (Drivers) by the existence of several regulations that require urban areas to have 30% of their area used as green spaces. Pressures faced by public green spaces include an increase in the number of vehicles and population every year. Existing conditions (States) show differences in the value of the measurement of noise, temperature and air quality parameters in RTH Udayana and RTH Pagutan. The impact felt by the presence of RTH shows that some environmental parameters for noise range from 12.42-20.08 dB, temperature ranges from 0.83-1.07oC and air quality (TSP, PM10, SO2, CO, O3, Humidity) show a difference between RTH and non-RTH. Response to (drivers), (pressure), (states), and (impact) variables that have been formulated using SWOT analysis is to coordinate and cooperate all parties both government, private, and community related to planning, structuring and utilization of public green spaces, as well as regular monitoring to ensure the sustainability of environmental services produced by public green spaces.

State estimation-based parameter identification for a class of nonlinear fractional-order systems

Parametric identification is an important part of system theory since knowledge of the parameters allows the analysis and control of the system. The aim of this paper is to propose a novel robust (against measurement noise) parameter identification method for a class of nonlinear fractional-order systems. In order to solve the parametric identification we carry out this problem to a state estimation problem, we introduce a Fractional Algebraic Identifiability (FAI) property which allows to represent the system parameters as a function of the inputs and outputs of the system, this parameter identification method provides an on-line identification process (while the system is operating), we also propose a fractional-order differentiator which allows to reduce the effect of measurement noise as well as to provide the estimation of a fractional-order derivative of the system output. Moreover, we use the Mittag–Leffler boundedness to demonstrate the convergence of this method, a different approach for this stability analysis method is given in this paper. Finally, we illustrate the accuracy and robustness of our proposed method by means of the parametric identification of two nonlinear fractional-order systems: a time-varying nonlinear fractional-order system and a nonlinear fractional-order mathematical model of a simple pendulum.

Production noise on an automated molding line in a foundry

The purpose of the work is to determine the nature of the noise generated during the operation of manual pneumatic rammers, an automated molding line and knockout grids in the foundry shop based on determining the levels of acoustic noise parameters in the mold manufacturing area and ways to reduce their level. To determine the noise parameters, a mode noise spectrum analyzer SL-5868F was used with subsequent digitization of the data obtained on the analyzer. Measurements of noise parameters were carried out at the site for the production of foundry molds with manual pneumatic rammers and an automatic molding line, as well as at the site for knocking out castings on knockout grids. It has been established that the noise generated in the areas of molding and knocking out molds, regardless of the molding method (manual, automatic) and the model of the knockout grid used, is broadband, and there is no homogeneity in the sound field of the generated noise, which is due to the generation of noise from sources characterized by differences in acoustic power levels and the nature of the noise spectrum. The noise from equipment or a technological operation with a shock nature is not constant and has a maximum sound power level in the region of medium and high frequencies generated by the equipment under study. It is possible to reduce the noise level in the foundry shop analyzed in the work by implementing a set of measures to modernize the molding and knocking out equipment or introducing into production an alternative method of manufacturing and knocking out foundry molds, in which, if it is possible to produce castings of a given nomenclature in the required volume with the required level of quality, the above mentioned are excluded and other sources of noise generation of unacceptable levels.

Ultrasonic array imaging of porosity defects with contrast enhancement based on dominant response subtraction

Porosity defects can be found in many engineering structures and their inspection remains a challenge in the field of ultrasonic non-destructive testing. In this paper, ultrasonic array imaging of porosity defects has been studied with the aim of improving the image quality in the “dead zone”, which is caused by the masking effects of the uppermost pores. The proposed approach first extracts contributions of the uppermost pores based on a single scattering model by adopting convolutional sparse coding. The extracted dominant contributions are then subtracted from the array data before forming an image, facilitating detection and localization of pores in the shadow zone. The performance of the proposed approach has been studied in simulation and experiments, and the mean localization errors of the pores are small (i.e., within 0.27 mm or 0.14λ). In addition, the effects of measurement noise and imaging parameters on robustness of the imaging result have been analyzed, providing guidelines for practical implementation of the proposed approach.

Measuring Noise Parameters Using an Open, Short, Load, and λ/8-Length Cable as Source Impedances

Noise parameters are a set of four measurable quantities which determine the noise performance of a radio-frequency device under test. The noise parameters of a 2-port device can be extracted by connecting a set of 4 or more source impedances at the device's input, measuring the noise power of the device with each source connected, and then solving a matrix equation. However, sources with high reflection coefficients cannot be used due to a singularity that arises in entries of the matrix. Here, we detail a new method of noise parameter extraction using a singularity-free matrix that is compatible with high-reflection sources. We show that open, short, load and an open cable ("OSLC") can be used to extract noise parameters, and we detail a practical measurement approach. The OSLC approach is particularly well-suited for low-noise amplifier measurement at frequencies below 1 GHz, where alternative methods require physically large apparatus.

Coupling coefficient observer based on Kalman filter for dynamic wireless charging systems

&lt;span lang="EN-US"&gt;In the dynamic wireless charging system for electric vehicles, the transfer efficiency reaches the maximum value when the load impedance is equal to the optimal impedance value. However, the optimal impedance value depends on the coupling coefficient, which varies with the electric vehicle's position. Therefore, to track the optimal impedance as well as to improve the transfer efficiency, it is necessary to know the coupling coefficient. This paper proposes a coupling coefficient observer method based on the Kalman filter.&lt;a name="_Hlk112085491"&gt;&lt;/a&gt; Then, on the secondary side, an optimal impedance controller acts on the active rectifier to improve efficiency. &lt;a name="_Hlk112099041"&gt;&lt;/a&gt;The results show that the estimation method achieves high accuracy. The estimated error of the mutual inductance is less than 5% in both the case of impact measurement noise and change system parameters. System efficiency improved by 3.2% compared with the conventional estimation method.&lt;/span&gt;

Applying Spanish Acoustic Regulations to mechanical ventilation with heat recovery systems. Case study.

Due to recent changes in IAQ (Indoor Air Quality) and energy efficiency requirements, MVHR (Mechanical Ventilation with Heat Recovery) has become increasingly common in Spanish new build dwellings. Regarding acoustics, Spanish Decree RD 1367/2007 sets out limits for sound pressure levels due to building services and describes the noise measurement and assessment procedure, which applies also to ventilation noise. The assessment procedure consists of a series of measurements of different noise parameters and then the calculation of corrections due to tonal, low frequency and impulsive components. This paper shows an example of a MVHR fan unit installed in the ceiling of a common dwelling, where some measurements were performed. Based on these measurements, this paper discusses the noise measurement and assessment procedures and proposes some improvements for the Spanish acoustic regulations. In addition, it also analyses the available calculation methods (ASHRAE and VDI 2081) and their application to comply with Spanish requirements.

Impulse noise measurement in view of noise hazard assessment and use of hearing protectors

Experience shows the occurrence of situations when the measurements of impulse noise parameters are made with measurement equipment unsuitable for such conditions. The results of using such equipment were compared with the results of using equipment with a sufficiently large upper limit of the measurement range. The analysis was carried out on the example of noise generated during shots from a Mossberg smooth-bore shotgun and AKM rifle, as well as produced in the forge. The use of the unsuitable equipment allowed to indicate the exceeding of the exposure limit value of the peak value of the signal (LC peak), but this is not always possible when determining the energy properties of the signal (L EX,8h). While the inadequate properties of the measurement equipment will generally not prevent the conclusion that noise in a particular workplace is hazardous to hearing, the results of measurements cannot be used to select hearing protectors.

Identification of Frequencies and Track Irregularities of Railway Bridges Using Vehicle Responses: A Recursive Bayesian Kalman Filter Algorithm

On-board monitoring of track irregularities and bridge dynamic characteristics based on vehicle vibration responses provides basic data for the condition assessment of high speed railway bridges. However, the identification process inevitably introduces estimation uncertainty because of measurement noise and system parameter uncertainty. Here, in a probability framework, we propose a recursive Bayesian Kalman filtering (RBKF) algorithm for quantifying the identification uncertainty of the track irregularities and bridge natural frequencies. A nonlinear state-space model with measurement noise and process noise was first established for vehicle-bridge (VB) systems. Then the RBKF algorithm was formulated using a nonlinear state-space model, and the identification uncertainty was quantified in terms of estimation variances. A numerical study of two high speed railway bridges validated the RBKF algorithm. This study may help develop new approaches for on-board monitoring and condition assessment of high speed railway bridges.

First measurement of interplanetary scintillation with the ASKAP radio telescope: Implications for space weather

We report on a measurement of interplanetary scintillation (IPS) using the Australian Square Kilometre Array Pathfinder (ASKAP) radio telescope. Although this proof-of-concept observation utilised just 3 s of data on a single source, this is nonetheless a significant result, since the exceptional wide field of view of ASKAP, and this validation of its ability to observe within 10 degrees of the Sun, mean that ASKAP has the potential to observe an interplanetary coronal mass ejection (CME) after it has expanded beyond the field of view of white light coronagraphs, but long before it has reached the Earth. We describe our proof of concept observation and extrapolate from the measured noise parameters to determine what information could be gleaned from a longer observation using the full field of view. We demonstrate that, by adopting a ‘Target Of Opportunity’ (TOO) approach, where the telescope is triggered by the detection of a CME in white-light coronagraphs, the majority of interplanetary CMEs could be observed by ASKAP while in an elongation range <30°. It is therefore highly complementary to the colocated Murchison Widefield Array, a lower-frequency instrument which is better suited to observing at elongations >20°.

Sensor Accuracy Analysis on Incubator Analyzer to Measure Noise and Airflow Parameters

Infant incubators are equipment to maintain a stable body temperature for premature babies. Premature babies need room conditioning that is close to conditions in the womb. Room conditioning is carried out in a baby incubator by providing a stable temperature, relative humidity, and measured air flow. This parameter must be controlled so as not to exceed the threshold that will harm the baby. Periodic calibration should be applied to the infant incubator to monitor its function. To ensure the availability of baby incubators according to service standards, it is necessary to conduct test (calibrate) using an incubator analyzer. The purpose of this study is to conduct further research on the incubator analyzer that focuses on discussing the accuracy of noise and airflow sensors with the gold standard. In this study, an experiment was carried out for the sensitivity level of several sensors that had been treated by giving treatment to sensors to choose sensors with good sensitivity to be assembled into one in the incubator analyzer module. The noise sensors (KY-037 and Analog Sound Sensor V2.2) were further compared with the values ​​on the sound level meter and the airflow sensor (D6F-V03A1) was compared with the anemometer. Sensors whose values ​​are close to the comparison values ​​were selected to be integrated into the incubator analyzer module. The incubator analyzer module used Arduino Mega2560 as a data processor and was equipped with an SD Card for the data storage. The built incubator analyzer module was also compared to the Fluke INCU II gold standard for data analysis. The results showed that the Analog Sound Sensor V2.2 had the highest error value (-4.6%) at 32°C and the D6F-V03A1 had the ability to measure sensitivity, where the results were more accurate than INCU II. Based on the error value of the noise sensor, the V2.2 sensor can be applied to measure noise in the baby incubator and the D6F-V03A1 airflow sensor produced an accuracy of up to 3 digits behind the comma which is more accurate than the standard module. The results of the INCU analyzer from this study can be used to calibrate the baby incubator, so that the certainty of the feasibility of the baby incubator is guaranteed. This research can be used as a reference for other researchers who will develop research on incubator analyzers in the future.

Partial Extended Observability Certification and Optimal Design of Moving-Horizon Estimators

This article addresses the observability analysis and the optimal design of observation parameters in the presence of noisy measurements and parametric uncertainties. The notion of almost <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -observability is introduced and a systematic procedure to assess its satisfaction for a given system with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> known measurement noise statistics and parameter discrepancy is sketched. Moreover, the concept of observation-target quantities is introduced to analyze the precision with which specific chosen expressions of the state and the parameters can be reconstructed. The overall framework is exposed and validated through an illustrative example.

Crack identification of functionally graded beam using distributed piezoelectric sensor

The present paper addresses development of a procedure for crack detection in functionally graded Timoshenko beam using a distributed piezoelectric sensor. Crack is represented by a pair of translational and rotational springs of stiffness calculated from its depth. A piezoelectric layer bonded to the beam is employed as a distributed sensor. Adopting the double beam model for the beam with the sensor, governing equations are conducted and utilized for establishing a database to propose a procedure for detecting a single crack in a functionally graded beam using the sensor output charge. The modal sensor charge provides a novel indicator more efficient for crack identification in functionally graded beams than natural frequencies. The effect of measurement noise, sensor thickness, and material parameters on crack identification has been investigated for beams with different boundary conditions.

Position control for an autonomous underwater vehicle under noisy conditions

Position tracking of an autonomous underwater vehicle is not trivial as its accuracy is affected by process noise, measurement noise, and uncertain hydrodynamic parameters. Thus, in this article, we studied the position control of an autonomous underwater vehicle that operates under largely unbounded system uncertainties and large noise levels and proposed a method that reduces the interference and thereby enhances the overall autonomous underwater vehicle position estimation. Our technique extended the ensemble Kalman filter by combining it with a time-delay estimator to compensate against extended uncertainty of the system dynamics which cannot be solved by the traditional ensemble Kalman filter. Our synthetic simulations demonstrated the effectiveness of the proposed controller, highlighting its appealing position-control accuracy under simultaneous noise and uncertain hydrodynamics parameters. In addition, our simulation results showed that the proposed controller outperforms the conventional time-delay controller by a percentage range of approximately 30.8%–92.6% in terms of root-mean-square error and requires on average less than 88.2% calculation time than the conventional model predictive control.

WiSen: Zero-Knowledge Passive Human Tracking Using a Single WiFi Link

Some pioneer WiFi-based passive human tracking systems have achieved sub-meter level accuracy. However, their key limitation is that the location of WiFi devices must be known in advance. To this end, we propose WiSen, a novel system that estimates the location of a passive user in an indoor environment without any prior knowledge about the location of the WiFi devices. Specifically, we first exploit a signal power model for human-induced reflection signal extraction and multi-dimensional parameter estimation. Due to measurement noise and low-resolution parameter estimates, we further design a confidence-aware-based path pruning method that combines the distribution of path parameters from successive windows to select reliable human-induced reflection paths. Before that, we also adopt a novel data augmentation method to increase the number of available path parameters for better learning parameter distribution. These path parameters after path pruning are then employed to statistically estimate the relative location of the transmitter with respect to the receiver and finally localize the passive user. We implement WiSen on commodity WiFi devices and validate its performance in real indoor environments. The experimental results show that WiSen can realize the sub-meter level accuracy for passive human tracking and device localization with only a single WiFi link.

Hydrogen flow rate control using an adaptive output feedback second-order sliding mode control for hydrogen production by PEM EL

This paper deals with the problem of controlling the hydrogen flow rate of the proton exchange membrane electrolyzer (PEM EL through the nominal PEM EL voltage. To achieve this objective an interleaved buck converter (IBC) has been chosen to supply the PEM EL from the DC bus. Therefore, the main objective of this paper is to design an adaptive output feedback control of the IBC- PEM EL system using an adaptive observer-based extended Kalman filter (EKF) and super-twisting sliding mode control (ST-SMC). In the first part, an adaptive observer-based EKF has been developed to estimate the inductors current under measurement noise and unknown PEM EL parameters. In the second part developed an ST-SMC to ensure tight regulation of the electrolyzer voltage to its reference value, equal current sharing between the three parallel legs of the interleaved buck converter, achieve finite-time convergence of electrolyzer voltage, and current, and guarantees the asymptotic stability of closed-loop system. The theoretical analysis and controller performances have been validated using numerical simulation.

Time-Frequency Sparse Reconstruction of Non-Uniform Sampling for Non-Stationary Signal

We address the problem of the reconstruction of time-frequency characteristics of sparse multi-band signals by using the discrete multi-coset sampling (DMCS) model. In this article, the signal is characterized by complicated time-variable components. According to the feature of the short-time Fourier transform (STFT) analysis method, we obtain the rewritten matrix form of the discrete STFT. Then, an analysis method of the multi-coset sliding window (MCSW) is proposed, and the discrete multi-coset sampling sequence is locally windowed. The sparse signal reconstruction algorithm is used to obtain the optimal time-frequency reconstruction value of the original signal. Numerical simulations show the feasibility of this method. We simulate the effects of measurement noise, sampling rate, and time-frequency analysis parameters on the reconstruction accuracy. Our method can carry out time-frequency reconstruction for undersampled signals obtained from multi-coset sampling to ensure the time-varying feature of the original signals, which can well highlight the characteristics of signals. The method is of great significance to the research and development of the sub-Nyquist sampling technique.