Noise In Measured Data Research Articles

Improving the accuracy and robustness of RRAM-based in-memory computing against RRAM hardware noise and adversarial attacks

We present a novel deep neural network (DNN) training scheme and resistive RAM (RRAM) in-memory computing (IMC) hardware evaluation towards achieving high accuracy against RRAM device/array variations and enhanced robustness against adversarial input attacks. We present improved IMC inference accuracy results evaluated on state-of-the-art DNNs including ResNet-18, AlexNet, and VGG with binary, 2-bit, and 4-bit activation/weight precision for the CIFAR-10 dataset. These DNNs are evaluated with measured noise data obtained from three different RRAM-based IMC prototype chips. Across these various DNNs and IMC chip measurements, we show that our proposed hardware noise-aware DNN training consistently improves DNN inference accuracy for actual IMC hardware, up to 8% accuracy improvement for the CIFAR-10 dataset. We also analyze the impact of our proposed noise injection scheme on the adversarial robustness of ResNet-18 DNNs with 1-bit, 2-bit, and 4-bit activation/weight precision. Our results show up to 6% improvement in the robustness to black-box adversarial input attacks.

Mapping and distribution of noise using IDW interpolation algorithm in Thuan An city, Binh Duong province

The study is conducted to assess the level of noise pollution from traffic activities in urban areas (in the case of Thuan An city, Binh Duong province) with the specific goal of applying geographic information systems. (GIS) in building noise pollution distribution maps in the study area. The research team collected noise data at peak hours and normal hours on weekdays and weekends using noise meters at 61 survey points. Noise measurement data was then interpolated using IDW (Inverse Distance Weighting) method to assess the spatial distribution of noise in Thuan An city. In addition, the study digitizes traffic routes and special areas (hospitals, schools, churches - pagodas) in the study area to identify areas affected by sound noisy. The results show that the areas near the main road are most affected by noise during rush hour, while areas in the small lane are noise level within the permitted range. In addition, the results also show the difference in noise level and noise time between weekdays and weekends.

Semi-empiric noise modeling of a Cargo eVTOL UAV by means of system identification from flight noise measurement data

This publication shows the semi-empiric noise modeling of an electric-powered vertical takeoff and landing (eVTOL) unmanned aerial vehicle (UAV) by means of system identification from flight noise measurement data. This work aims to provide noise models with a compact analytical ansatz for horizontal and vertical flight which are suited for integration into a geographical information system. Therefore, flight noise measurement campaigns were conducted and evaluated. An existing noise model ansatz is adapted to the eVTOL UAV under consideration and noise models are computed from the measurement data using the output error method. The resulting models are checked for plausibility by comparing them to technical literature. The horizontal flight noise model is subjected to a correlation analysis and the influence of meteorological effects are examined. To achieve a higher level of accuracy in future noise modelings, an optimization of the microphone positions as well as the flight trajectory is carried out.

Joint damage detection of structures with noisy data by an effective deep learning framework using autoencoder-convolutional gated recurrent unit

Recent advances in chip and sensor technology allow a large amount of data to be collected for serving structural damage detection (SDD). However, how to efficiently utilize and transform these complex sensing data into useful engineering information has remained many challenges. One of the major challenges in SDD using sensing data is how to effectively extract features that are sensitive to damages from measured data under the effect of noise factors. In order to deal with this issue, this paper proposes a new deep learning (DL) framework using an autoencoder-convolutional gated recurrent unit (A-CGRU) for structural joint damage detection using noisy data. In the proposed approach, the autoencoder component is used for noise removal for the measured noise data and the output of the autoencoder is then fed into the convolutional component to automatically determine the important features. Finally, the latent features extracted from the convolutional component are fed into the gated recurrent unit to learn to predict the location and severity of damaged joints in structures. The performance and applicability of the proposed A-CGRU are validated through various joint damage scenarios in a two-story planar frame structure and a four-story planar frame structure. The outcomes achieved by the proposed method are compared with those of other methods in order to verify the accuracy and reliability of the proposed A-CGRU method. In addition, the influence of various parameters such as the number and location of sensors, the number of measured frequencies and mode shapes, as well as various noise levels on the capability and stability of the proposed method is also investigated. The obtained results show the accuracy, efficiency, and applicability of the proposed A-CGRU method in structural joint damage detection using noisy data.

Estimation of Airspeed, Angle of Attack, and Sideslip for Small Unmanned Aerial Vehicles (UAVs) Using a Micro-Pitot Tube

Fixed and rotary-wing unmanned aircraft systems (UASs), originally developed for military purposes, have widely spread in scientific, civilian, commercial, and recreational applications. Among the most interesting and challenging aspects of small UAS technology are endurance enhancement and autonomous flight; i.e., mission management and control. This paper proposes a practical method for estimation of true and calibrated airspeed, Angle of Attack (AOA), and Angle of Sideslip (AOS) for small unmanned aerial vehicles (UAVs, up to 20 kg mass, 1200 ft altitude above ground level, and airspeed of up to 100 knots) or light aircraft, for which weight, size, cost, and power-consumption requirements do not allow solutions used in large airplanes (typically, arrays of multi-hole Pitot probes). The sensors used in this research were a static and dynamic pressure sensor (“micro-Pitot tube” MPX2010DP differential pressure sensor) and a 10 degrees of freedom (DoF) inertial measurement unit (IMU) for attitude determination. Kalman and complementary filtering were applied for measurement noise removal and data fusion, respectively, achieving global exponential stability of the estimation error. The methodology was tested using experimental data from a prototype of the devised sensor suite, in various indoor-acquisition campaigns and laboratory tests under controlled conditions. AOA and AOS estimates were validated via correlation between the AOA measured by the micro-Pitot and vertical accelerometer measurements, since lift force can be modeled as a linear function of AOA in normal flight. The results confirmed the validity of the proposed approach, which could have interesting applications in energy-harvesting techniques.

On the generation of entropy noise in a shock containing nozzle of high-performance aircraft at afterburner

High performance aircraft operate at high jet temperature and velocity. Measured noise data indicate that at afterburner condition their noise contain components not found in high temperature supersonic laboratory model jets. The combustion process inside an afterburner is highly unsteady and incomplete. Residual combustion takes place in the jet plume. This suggests that the flow from the afterburner into the jet nozzle would contain significant amount of hot and cold temperature blobs, generally referred to as entropy waves. It is known that when entropy waves move through a non-uniform mean flow, indirect combustion noise (also called entropy noise) would be generated. Military styled nozzles have a rather abrupt area change at and near the throat. This induces the formation of internal shocks inside the nozzle. This paper investigates the generation of entropy noise when strong entropy blobs or waves are convected through the internal shocks. It is found that the result of entropy blobs-shock interaction could lead to the radiation of intense entropy noise out of the nozzle exit into the jet plume as fast waves with a speed equal to sound speed plus jet flow speed. These fast waves create highly supersonic flow disturbances in the jet flow leading to strong Mach wave radiation. It is proposed that this Mach wave radiation is the source of a new noise component observed in the spectra of the F-22A aircraft. Detailed analysis of the F-22A noise data provides support for the proposal.

Mitigasi dampak kebisingan bandara terhadap kehidupan pemukiman sekitar bandara SSK II Pekanbaru

This study aims to determine the level of noise caused by flight activities at Sultan Syarif Kasim II Airport Pekanbaru, analyze noise levels that occur due to flight activities at Sultan Syarif Kasim II Airport Pekanbaru and analyze efforts to control the negative impact of airport noise on the living environment of community settlements. around Sultan Syarif Kasim II Airport Pekanbaru. This research uses field observation method, which is making direct observations at the research location by looking at the condition of the location and the suitability of the location which is the sampling point of the study (the noise level boundary at Sultan Syarif Kasim II Airport). Observations were made for 16 hours (Ls) at an interval of 06.00 - 22.00. Measurement of sound pressure level is carried out on holidays (Sunday) and weekdays (Monday), which starts on November 1, November 2, November 8, and November 9, 2020, which is carried out in residential areas around Sultan Syarif Kasim II Airport Pekanbaru, which are spread across 6 measurement points where the measurement of sound pressure level is done in duplicate, namely: Jl. Kaswari (point 1), Jl. Rawa Indah II (Point 2), Jl. Rawa Indah III (Point 3), Jl. Cinnamon (Point 4), Jl. Pahlawan Kerja gg.Pala 49 (Point 5) and Jl. Nur Asiyah (Point 6) The results of the processing of noise measurement data were made of a mapping model using surfer 11 software and to clarify the noise description at the sampling location, the map of the results of surfer 11 software processing was plotted on the airport area map. Based on the results of measurements of noise levels around Sultan Syarif Kasim II airport, it is known that the location of point 1 (Jl. Rawa Indah II) experienced the highest noise exposure. These results indicate the location of point 1 should receive serious attention for the people who live around the airport, because the impact of airport noise has the potential to negatively affect the lives of residential communities. From the observations, it was also known that the level of noise attenuation was still low, both in terms of trees around the settlement and height, walls and fences were still not effective at reducing noise. Based on the results of measurements of noise levels around Sultan Syarif Kasim II airport, it is known that the location of point 1 (Jl. Rawa Indah II) experienced the highest noise exposure. These results indicate that the location of point 1 should receive serious attention for the people who live around the airport, because the impact of airport noise has the potential to negatively affect the lives of residential communities. From the observations, it was also known that the level of noise attenuation was still low, both in terms of trees around the settlement and height, walls and fences were still not effective at reducing noise. From the results of the research that has been done, several mitigation strategies can be formulated to reduce noise levels around Sultan Syarif Kasim II airport. Planting plants in accordance with the needs of controlling or reducing noise in human settlements. Tree categories suitable for planting in residential areas around the airport are: shady trees that can be planted tightly or with lots of leaves that can grow to a height of about 4 - 15 m (such as acacia, mahogany, flamboyant, ironwood or banyan trees, bamboo or cypress)

Аудиологические характеристики пациентов в зависимости от выраженности тиннитуса

The purpose of the work is to identify the existence of a relationship between the severity of ear noise according to THI (Tinnitus Handicap Inventory) and the results of audiological and neurophysiological studies. Materials and methods: As participants in the study, 100 patients from the main group with complaints of tinnitus were selected: 69 women and 31 men; the average age was 48.1 ± 9.3 years. Otolaryngological examination, audiological and neurophysiological (registration of trigeminal induced potentials) examination of 100 patients with complaints of tinnitus or in the head was carried out. Results: The correlation between THI and noise measurement data is undefined. The relationship between the degree of aggravation of ear noise and impedancebarometry data in the Toynbee and Valsalva maneuvers was revealed. Patients with medium tinnitus severity show a greater shift towards negative pressure compared to moderate patients. Conclusions: The degree of aggravation of tinnitus does not affect the indicators of noise measurement. Large pressure shifts during the Toynbee test in some patients are likely due to the effect on the function of the auditory tube muscles of certain forms of rhinitis. Secondary tension of the muscles of the auditory tube against the background of increased excitability of the trigeminal system in patients with more pronounced tinnitus is not excluded.

Comparison of Road Traffic Noise prediction models: CNOSSOS-EU, Nord2000 and TRANEX.

Road traffic noise is the most pervasive source of ambient outdoor noise pollution in Europe. Traffic noise prediction models vary in parameterisation and therefore may produce different estimates of noise levels depending on the geographical setting in terms of emissions sources and propagation field. This paper compares three such models: the European standard, Common Noise Assessment Methods for the EU Member States (hereafter, CNOSSOS), Nord2000 and Traffic Noise Exposure (TRANEX) model based on the UK methodology, in terms of their source and propagation characteristics. The tools are also compared by analysing estimated noise (LAeq) from CNOSSOS, Nord2000 (2006 version), and TRANEX for more than one hundred test cases (N=111) covering a variety of source and receiver configurations (e.g. varying source to receiver distance). The main aim of this approach was to investigate the potential pattern in differences between models' performance for certain types of configurations. Discrepancies in performance may thus be linked to the differences in parameterisations of the CNOSSOS, Nord2000, and TRANEX (e.g. handling of diffraction, refraction). In most cases, both CNOSSOS and TRANEX reproduced LAeq levels of Nord2000 (2006 version) within three to five dBA (CNOSSOS: 87%, TRANEX: 94%). The differences in LAeq levels of CNOSSOS, compared to Nord2000, can be related to several shortcomings of the existing CNOSSOS algorithms (e.g. ground attenuation, multiple diffractions, and mean ground plane). The analyses show that more research is required in order to improve CNOSSOS for its implementation in the EU. In this context, amendments for CNOSSOS proposed by an EU Working Group hold significant potential. Overall, both CNOSSOS and TRANEX produced similar results, with TRANEX reproducing Nord2000 LAeq values slightly better than the CNOSSOS. The lack of measured noise data highlights one of the significant limitations of this study and needs to be addressed in future work.

On the feasibility of simultaneous identification of a material property of a Timoshenko beam and a moving vibration source

This paper presents a computational study for investigating the feasibility of simultaneous identification of a material property of a Timoshenko continuous beam and a moving vibration source on the beam by using the data of measured vibrations on it. This work employs the finite element method to solve the wave equations of a Timoshenko beam subject to a moving vibrational source. It uses the Genetic Algorithm (GA) as an inversion solver to identify the values of targeted control parameters that characterize a material property of the beam and a moving vibration source on it. The numerical results show that, first, the presented inversion method can detect the characteristics of a moving wave source as well as the spatial variation of the elastic modulus of a Timoshenko-beam continuous bridge model, which is set to be piece wisely homogeneous in this work. Second, the GA-based joint inversion is effective even when the moving vibrational source’s moving velocity is not constant over time. Third, the detrimental effect of noise in measurement data on the accuracy of the inversion becomes more significant as the number of control parameters increases. By using the presented method, engineers can take advantage of vehicle-induced ambient vibrations on bridges measured by modern sensors for the sake of passive wave source-based structural health monitoring (SHM).

A Noise Monitoring System Used for Substation Boundary: Part 1 -- Devices and Synchronized Measurement Method

Due to the interference of background noise, it is necessary to measure multi-point noise synchronously in the noise measurement of substation boundary. This measurement method not only increases the work intensity of measurement personnel, but also affects the reliability and accuracy of measurement data. In the long-term continuous noise monitoring task, there are still some problems, such as the data does not support online transmission, or cannot guarantee multi-point synchronous monitoring, or the standby time is insufficient. In order to solve the problem mentioned above, this paper provides a wide area synchronous noise measurement method and a continuous noise monitoring device. By deploying multiple sets of noise monitoring devices at the boundary of the substation and conducting wide area synchronous measurement, it can significantly improve the reliability of noise measurement data and improve the monitoring efficiency.

Investigations on Low Frequency Noises of On-Shore Wind Turbines

The expansion of renewable energy usage is one of the major social tasks in Europe and therefore requires acceptance and support from the population. In the case of onshore wind turbines, the complaints of local residents are often interpreted as infrasound disturbances conceivably caused by wind turbine operation. To improve the acceptance for wind energy projects, national standards and regulations need to incorporate such low frequency effects. This contribution presents long-term acoustic measurement data of low frequency noise recorded directly near wind turbines (emission) and inside of residential buildings (immission) with the objectives to identify the signal characteristics and main influential parameters. Different locations (wind farm and individual turbine), wind conditions, and time ranges are evaluated. It is shown that various frequency content below 150 Hz (harmonics of blade passing frequency, etc.) is connected to the rotation of the rotor blade and the operation of the generator. Furthermore, stable atmospheric conditions are determined to be of high importance for the transmission of the characteristic signals. For future research, this work also serves as an example for low frequency sound pressure data during operation and shutdown of wind turbines.

NVH Optimization Analysis of Permanent Magnet Synchronous Motor by Rotor Slotting

With the development of new energy vehicles, the NVH (Noise, Vibration, Harshness) performance of the permanent magnet synchronous motors (PMSM) for vehicles has attracted more and more attention. The rotor slotting optimization analysis is the critical issue in the NVH performance study of PMSM. In this paper, the theoretical formula of the PMSM radial electromagnetic force is presented. Based on which, the spatial order and frequency order characteristics of the radial force of a 6-pole 36-slot PMSM are analyzed. Firstly, electromagnetic simulation of the motor is carried out, and the specific force components, which may cause an NVH problem, is extracted. Secondly, the harmonics are calculated with a method of freezing the relative magnetic permeability. Thirdly, the motor is optimized by rotor slotting to reduce the amplitudes of these harmonics, and the effect on electromagnetic noise is analyzed in theory and simulation. Finally, the NVH test in original state and after rotor slotting state was performed in the semi-anechoic chamber. The accuracy of the theory and simulation was verified based on the comparison of the measured noise data. This paper provides a new NVH optimization idea, this method can quickly locate the fundamental problem location of electromagnetic NVH, provide a fast channel for electromagnetic NVH optimization from simulation to verification, and improve the optimization efficiency of NVH.

Smart dampers-based vibration control – Part 1: Measurement data processing

Exploiting smart dampers (SmDs) based on data-driven models have been seen as an appropriate approach for many applications such as vehicle suspension system. Reality has shown that the error of SmDs’ identification due to noise in the measured data (MD) sets as well as uncertainty related to the mathematical tools selected to describe control systems reduces control efficiency. To overcome this issue we are interested in finding effective solutions for online filtering noise in MD, selecting and building data-driven models of SmDs, and seeking an appropriate approach to reduce the model errors. To undertake these, we divide the research into two parts; part 1 and part 2. In this current part, we focus on the filtering of the noise by proposing two new filters. Deriving from a discovered optimal data screening threshold (ODST), the first one is an ODST-based filter (ODSTbF) for dealing with random and impulse noise (IN). The second one named combined filter (CoFilter) is a combination of the ODSTbF and the median smoother to extend the filtering capability. To determine the ODST of a data source, a new algorithm for estimating the ODST named AfODST is proposed via an offline process. Many surveys using MD coming from a magnetorheological damper (MRD) are performed to evaluate positive effects of the proposed method.

Decreasing Pediatric PACU Noise Level and Alarm Fatigue: A Quality Improvement Initiative to Improve Safety and Satisfaction

PurposeTo describe how a pediatric postanesthesia care unit used a two-phased approach of bundled interventions to reduce unit noise levels and improve staff perceptions of their work environment. DesignPre/post design. MethodsPostanesthesia care unit sound levels and monitor alarms were measured at baseline and after implementing both project phases. Nursing staff members were surveyed at baseline and after completion of the project. FindingsMonitor alarms were reduced by more than 50% after Phase I. However, noise measurement data did not reflect a difference in sound levels between baseline and intervention phases. Despite this, staff perceived the unit as much quieter. ConclusionsThe reduction in monitor alarms did not cause an appreciable change in sound levels as measured by noise dosimeters in either intervention phase. Despite these findings, nurses perceived a quieter and more pleasant workplace. These impressions might have resulted from subjective expectations versus actual volume levels, or they might owe to the reduction in incidence of alarms themselves, which they had viewed as nuisance sounds.

The Effect of Measurement Limitations on High-Frequency Radar-Derived Spectral Energy Fluxes

AbstractThe ocean’s inverse cascade of energy from small to large scales has been confirmed from satellite altimetry for scales larger than 100 km. However, measurements of the direct energy cascade to smaller scales have remained difficult to obtain. Here, the possibility of estimating these energy transfers to smaller scales from observations by high-frequency radars is investigated using numerical simulations. Synthetic measurements are first extracted from a quasigeostrophic simulation of freely decaying turbulence for which the reference energy flux is characterized by the transition from positive to negative values. Fluxes obtained from synthetic data are compared to this reference flux in order to assess the robustness to various measurement limitations. The geometry of the observational domain (nonperiodicity, domain size, and aspect ratio) affects mostly large scales, while the spatial resolution of the instruments affects mostly small scales. In contrast, measurement noise and missing data affect both large and small scales. Despite resulting in significant biases in the amplitude of the fluxes, the transition scale between the positive and negative fluxes is relatively robust to measurement limitations. These results are also confirmed using a simulation from a primitive-equations model in a realistic coastal geometry.

Prediction and Sound Quality Analysis of Tire Pattern Noise Based on System Identification by Utilizing an Optimal Adaptive Filter

Identifying the cause of vehicle noise is a basic requirement for the development of low-noise vehicles. The tire pattern noise depends on the tire itself and causes complex and unpredictable sounds. In pneumatic tire pattern design, the prediction technology of the tire pattern noise according to pattern shape design is important. The conventional method of predicting tire pattern noise is to simply scan the pattern shape of tire and to analyze its spectrum. However, this method has limitations because it does not consider the transfer function and precise mechanism of tire pattern noise. In this study, adaptive filter theory was applied to identify the transfer function between the grooves of patterns and measured acoustic data. To predict the waveform of an actual pattern noise in the time domain, the impulse response of this transfer function was convolved by the scanned pattern input of tires. The predicted waveform of pattern noise was validated with the waveforms of measured noise data. Finally, a sound quality index (SQI) of tire pattern noise was developed using the measured pattern noises and was applied to estimate the sound quality of pattern noise. Eventually, using the prediction method from this study, we hope to reduce the time and cost spent on tire pattern design and verification.

Constrained Robust Unscented Kalman Filter for Generalized Dynamic State Estimation

Due to physical or control-related limitations, some dynamic state variables are constrained, such as the regulated voltage, the exciter reference voltage, etc. In addition, there exists a set of algebraic constraints that exactly confines the evolution of the system states. However, a systematic way to fully consider all inequality and equality constraints is lacking in the existing dynamic state estimation (DSE) literature. This paper proposes a general constrained robust DSE framework that is able to deal with various equality and inequality constraints. The project operator is integrated with the pseudo-measurements formulation in a unique manner to address the inequality and equality constraints, respectively, within the unscented Kalman filter (UKF) framework, which relies on the unscented transformation and is derivative-free. To this end, the derivative-free robust UKF is extended to address all the constraints while maintaining its robustness to measurement noise and bad data. Numerical results on the IEEE 39-bus system show that the proposed method exhibits the following benefits: first, improved accuracy of the state estimates in the presence of measurement noise; second, enhanced convergence speed and ability to address weakly observed dynamic state variables; and third, enhanced capability to deal with bad data and cyber attacks.

Electrochemical noise measurement of a lithium iron(II) phosphate (LiFePO4) rechargeable battery

The electrochemical noise of rechargeable lithium iron(II) phosphate (LiFePO4) battery was measured for the first time during discharge using a constant value resistor. A continuous data registration technique was applied to collect the noise measurement data. The standard deviation values were calculated for different state of charge values. Power spectral density frequency dependencies were obtained as well. The results showed that the standard deviation and spectrum slope absolute values increased at the end of the discharge process and may be used as indicators of over-discharge. Electrochemical impedance spectra were collected for the various discharge values and their properties were shown to correlate with the noise evolution during the discharge process.

Damage detection in a novel deep-learning framework: a robust method for feature extraction

Extracting damage features precisely while overcoming the adverse interferences of measurement noise and incomplete data is a problem demanding prompt solution in structural health monitoring (SHM). In this article, we present a deep-learning-based method that can extract the damage features from mode shapes without utilizing any hand-engineered feature or prior knowledge. To meet various requirements of the damage scenarios, we use convolutional neural network (CNN) algorithm and design a new network architecture: a multi-scale module, which helps in extracting features at various scales that can reduce the interference of contaminated data; stacked residual learning modules, which help in accelerating the network convergence; and a global average pooling layer, which helps in reducing the consumption of computing resources and obtaining a regression performance. An extensive evaluation of the proposed method is conducted by using datasets based on numerical simulations, along with two datasets based on laboratory measurements. The transferring parameter methodology is introduced to reduce retraining requirement without any decreases in precision. Furthermore, we plot the feature vectors of each layer to discuss the damage features learned at these layers and additionally provide the basis for explaining the working principle of the neural network. The results show that our proposed method has accuracy improvements of at least 10% over other network architectures.