Accuracy Improvement Method Research Articles

Accuracy improvement method for dynamic substructuring models in vehicle systems

This study proposes an improved dynamic substructuring model using the estimated frequency response function information at coupling points between substructures. An assembled system generally consists of two or more substructures connected by a bolt. Individual substructure evaluation excluding the effects of other components is important in the development stage of a general mechanical system because the vibro-acoustic performance of the system depends on the specific combination of substructures. Therefore, this study predicted the final coupling system performance using information from the initial evaluation of the individual substructures. Accurate measurements of the joint properties are required to accurately estimate the dynamic assembled system characteristics; however, physical constraints typically limit such measurements at actual coupling points. Accordingly, a method that utilizes generalized coupling properties to estimate the dynamic characteristics of a new coupling system based on the characteristics of an original substructure is proposed. Virtual point transformation is then used to estimate accurate frequency response functions at the coupling points of the assembled system based on convenient measurements. The proposed method was validated using a vehicle suspension that was hard mounted in a test jig and onto an actual vehicle body to estimate the vibration characteristics of the assembled system. The findings of this study contribute to the accurate estimation of the dynamic properties of many real-world bolt-assembled systems.

Improving the Accuracy of Biosignal Analysis Using BCG by Applying a Signal-to-Noise Ratio and Similarity-Based Channel Selection Algorithm

As the socio-economic environment has changed, the number of single and elderly households has increased, and the demand for maintaining a long and healthy life has been increased by continuously monitoring and managing physical conditions against unexpected accidents that may occur when living alone at home. In response to these demands, it is necessary to develop services that provide personalized healthcare services by recording and analyzing living patterns and biometric information in an unconscious way. In this paper, we propose an accuracy improvement method using similarity and Signal-to-noise ratio analysis for BCG measurement method using piezo sensors and a system for continuous personal health monitoring in home without user awareness. Heart rate and respiration rate were derived using the acquired BCG data, and the biometric information is stored symmetrically for personal information security.

An Accuracy Improvement Method Based on Multi-Source Information Fusion and Deep Learning for TSSC and Water Content Nondestructive Detection in “Luogang” Orange

The objective of this study was to find an efficient method for measuring the total soluble solid content (TSSC) and water content of “Luogang” orange. Quick, accurate, and nondestructive detection tools (VIS/NIR spectroscopy, NIR spectroscopy, machine vision, and electronic nose), four data processing methods (Savitzky–Golay (SG), genetic algorithm (GA), multi-source information fusion (MIF), convolutional neural network (CNN) as the deep learning method, and a partial least squares regression (PLSR) modeling method) were compared and investigated. The results showed that the optimal TSSC detection method was based on VIS/NIR and machine vision data fusion and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the TSSC detection results were 0.8580 and 0.4276, respectively. The optimal water content detection result was based on VIS/NIR data and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the water content detection results were 0.7013 and 0.0063, respectively. This optimized method largely improved the internal quality detection accuracy of “Luogang” orange when compared to the data from a single detection tool with traditional data processing method, and provides a reference for the accuracy improvement of internal quality detection of other fruits.

Optimisation of micro W-bending process parameters using I-optimal design-based response surface methodology

There is an increasingly recognised requirement for high dimensional accuracy in micro-bent parts. Springback has an important influence on dimensional accuracy and it is significantly influenced by various process parameters. In order to optimise process parameters and improve dimensional accuracy, an approach to quantify the influence of these parameters is proposed in this study. Experiments were conducted on a micro W-bending process by using an I-optimal design method, breaking through the limitations of the traditional methods of design of experiment (DOE). The mathematical model was established by response surface methodology (RSM). Statistical analysis indicated that the developed model was adequate to describe the relationship between process parameters and springback. It was also revealed that the foil thickness was the most significant parameter affecting the springback. Moreover, the foil thickness and grain size not only affected the dimensional accuracy, but also had noteworthy influence on the springback behaviour in the micro W-bending process. By applying the proposed model, the optimum process parameters to minimize springback and improve the dimensional accuracy were obtained. It is evident from this study that the I-optimal design-based RSM is a promising method for parameter optimisation and dimensional accuracy improvement in the micro-bending process.

Laser tracking measurement accuracy improvement method for mega gear

Laser tracking measurement accuracy improvement method for mega gear

Polarization measurement accuracy analysis and improvement methods for the directional polarimetric camera.

The directional polarimetric camera (DPC) is a remote-sensing instrument for the characterization of atmospheric aerosols and clouds by simultaneously conducting spectral, angular, and polarimetric measurements. Polarization measurement accuracy is an important index to evaluate the performance of the DPC and mainly related to the calibration accuracy of instrumental parameters. In this paper, firstly, the relationship between the polarization measurement accuracy of DPC and the parameter calibration errors caused by the nonideality of the components of DPC are analyzed, and the maximum polarization measurement error of DPC in the central field of view and edge field of view after initial calibration is evaluated respectively. Secondly, on the basis of the radiometric calibration of the DPC onboard the GaoFen-5 satellite in an early companion paper [Opt. Express2813187 (2020)10.1364/OE.391078], a series of simple and practical methods are proposed to improve the calibration accuracy of the parameters-the diattenuation of the optics, absolute azimuth angle, and relative transmission corresponding to each pixel, thereby improving the polarization measurement accuracy of DPC. The calibration results show that, compared with the original methods, the accuracy of the diattenuation of the optics, relative azimuth angle, and relative transmission of three polarized channels obtained with the improved methods are improved from ±1%, 0.1 degree and ±2% to ±0.4%, 0.05 degree and ±0.2%, respectively. Finally, two verification experiments based on a non-polarized radiation source and a polarizing system were carried out in the laboratory respectively to verify the improvement of the parameters modified by the proposed methods on the polarization measurement accuracy of the DPC to be boarding the GaoFen-5 (02) satellite. The experimental results show that when the corrected parameters were employed, the average error in measuring the degree of linear polarization of non-polarized light source for all pixels in the three polarized bands and the maximum deviation of the degree of linear polarization between the values set by the polarizing system and the values measured by the DPC at several different field of view angles for each polarized spectral band are obviously reduced. Both the mean absolute errors and the root mean square errors of the degree of linear polarization obtained with the corrected parameters are much lower than those obtained with the original parameters. All of these prove the effectiveness of the proposed methods.

Accuracy improvement of time-of-flight depth measurement by combination of a high-resolution color camera.

Time-of-flight (ToF) cameras can acquire the distance between the sensor and objects with high frame rates, offering bright prospects for ToF cameras in many applications. Low-resolution and depth errors limit the accuracy of ToF cameras, however. In this paper, we present a flexible accuracy improvement method for depth compensation and feature points position correction of ToF cameras. First, a distance-error model of each pixel in the depth image is established to model sinusoidal waves of ToF cameras and compensate for the measured depth data. Second, a more accurate feature point position is estimated with the aid of a high-resolution camera. Experiments evaluate the proposed method, and the result shows the root mean square error is reduced from 4.38 mm to 3.57 mm.

A hybrid of Random Forest and Deep Auto-Encoder with support vector regression methods for accuracy improvement and uncertainty reduction of long-term streamflow prediction

Streamflow forecasting is an important component of water resources planning and management. Data-Driven Models (DDMs) are common approaches for streamflow prediction. DDMs try to obtain a mathematical relation between variables without any prior knowledge or assumption about the physical relation between them. Hence, these models are effective for complex process modeling. In this study, we use the Support Vector Regression (SVR) and Multiple Linear Regression (MLR) models for long-term streamflow prediction. Moreover, three approaches, Random Forest (RF), Deep Auto-Encoder (DAE), and Principle Component Analysis method (PCA) are employed as pre-processing methods and they are combined to SVR and MLR (six hybrid models produced) in order to improve the accuracy of prediction and reduce its uncertainty. Different evaluation criteria, including R2, RMSE, NSE, P-factor, and R-factor are computed to evaluate the efficiency of the hybrid models. The results of our study on Bookan dam indicate that the DAE-SVR model, in comparison with the PCA-MLR, RF-MLR, DAE-MLR, PCA-SVR hybrid models, provides a significant improvement in the runoff prediction accuracy and the reduction of its uncertainty. DAE-SVR and RF-SVR have the highest values in NSE (0.93 and 0.87, respectively), and the lowest values in RMSE (33.85 and 43.68 MCM, respectively) for the first forecast horizon. The uncertainty analysis of the streamflow prediction shows that DAE-SVR has the best performance at the 95% confidence level due to its P-factor and R-factor values (0.9 and 0.64, respectively). Furthermore, the DAE-SVR performance is likewise better than other hybrid models in the two, three, and four months forecast horizons.

Proposal of Analysis Accuracy Improvement Method in Modified Williamson-Hall Method

Proposal of Analysis Accuracy Improvement Method in Modified Williamson-Hall Method

A Robust Fusion Method for Motion Artifacts Reduction in Photoplethysmography Signal

Robustness of estimating cardiorespiratory parameters from photoplethysmography (PPG) signal is highly dependent on the quality of the signal, which is heavily affected by motion artifacts. To increase the estimation accuracy of cardiorespiratory parameters, this article describes a novel fusion method to efficiently and effectively reduce the motion artifacts from the acquired PPG signal. The proposed fusion technique requires simultaneously acquiring data from a PPG sensor and accelerometer. To filter out the frequencies associated with motion, the method uses stopband filters with a central rejection frequency and bandwidth determined by the output signal of the accelerometer. Under such conditions, the proposed method to remove the motion artifacts does not depend on the quality of the reference signal and has almost no impact on the nature of PPG signals (i.e., amplitude, baseline, and periodicity). The effectiveness of the proposed method in the suppression of in-band and out-of-band frequencies of motion is numerically and experimentally evaluated. It is shown that the filtered PPG signal has sufficient information to estimate different cardiac parameters such as heart- (HR), respiration rate (RR), and blood oxygen saturation (SpO2). The motion artifact-free PPG signal obtained using our proposed method can estimate HR, RR, and SpO2 with an accuracy of above 95%. This level of accuracy confirms the usefulness of the proposed fusion method for accuracy improvement of cardiorespiratory parameters monitored by the filtered PPG signal.

Geometric Accuracy Improvement Method for High-Resolution Optical Satellite Remote Sensing Imagery Combining Multi-Temporal SAR Imagery and GLAS Data

With the widespread availability of satellite data, a single region can be described using multi-source and multi-temporal remote sensing data, such as high-resolution (HR) optical imagery, synthetic aperture radar (SAR) imagery, and space-borne laser altimetry data. These have become the main source of data for geopositioning. However, due to the limitation of the direct geometric accuracy of HR optical imagery and the effect of the small intersection angle of HR optical imagery in stereo pair orientation, the geometric accuracy of HR optical imagery cannot meet the requirements for geopositioning without ground control points (GCPs), especially in uninhabited areas, such as forests, plateaus, or deserts. Without satellite attitude error, SAR usually provides higher geometric accuracy than optical satellites. Space-borne laser altimetry technology can collect global laser footprints with high altitude accuracy. Therefore, this paper presents a geometric accuracy improvement method for HR optical satellite remote sensing imagery combining multi-temporal SAR Imagery and GLAS data without GCPs. Based on the imaging mechanism, the differences in the weight matrix determination of the HR optical imagery and SAR imagery were analyzed. The laser altimetry data with high altitude accuracy were selected and applied as height control point in combined geopositioning. To validate the combined geopositioning approach, GaoFen2 (GF2) optical imagery, GaoFen6 (GF6) optical imagery, GaoFen3 (GF3) SAR imagery, and the Geoscience Laser Altimeter System (GLAS) footprint were tested. The experimental results show that the proposed model can be effectively applied to combined geopositioning to improve the geometric accuracy of HR optical imagery. Moreover, we found that the distribution and weight matrix determination of SAR images and the distribution of GLAS footprints are the crucial factors influencing geometric accuracy. Combined geopositioning using multi-source remote sensing data can achieve a plane accuracy of 1.587 m and an altitude accuracy of 1.985 m, which is similar to the geometric accuracy of geopositioning of GF2 with GCPs.

Accuracy analysis and improvement method for continuous web-based precision assembly

This article proposes and investigates an assembly accuracy control method for assembly processes where the partners are joined during a feeding motion and where a web-driven process kinematic couples the accuracy variables. The inappropriateness of available methods arises from their restriction that only individual, non-dynamic reference points are considered instead of multiple, dynamically moving reference points. The theoretical elaboration in this article approaches the assembly kinematics as a multi-input-multi-output system in a dynamic context. A case study applies this to a novel continuous folding process for Lithium-Ion-Batteries. Further applications are discussed to illustrate the generic relevance of the method.

Artificial Intelligence Based Methods for Accuracy Improvement of Integrated Navigation Systems During GNSS Signal Outages: An Analytical Overview

The limitations of Kalman filter (KF) have motivated researchers to consider alternative methods of integrating inertial navigation systems (INS) and global navigation satellite systems (GNSS), predominantly based on artificial intelligence (AI). Over the past two decades, a great number of research gained in order to validate the possibility of using AI methods in the field of integrated navigation systems. Different approaches have been proposed for combining AI modules with the other parts of the INS/GNSS system. The article suggests a new classification of the resulting schemes based on the functionality of AI modules in the INS/GNSS system. The paper also provides a brief explanation of each scheme with its advantages and disadvantages. Some aspects that need to be considered in future research in this field are also highlighted.

New Algorithms for Autonomous Inertial Navigation Systems Correction with Precession Angle Sensors in Aircrafts.

This paper presents new algorithmic methods for accuracy improvement of autonomous inertial navigation systems of aircrafts. Firstly, an inertial navigation system platform and its nonlinear error model are considered, and the correction schemes are presented for autonomous inertial navigation systems using internal information. Next, a correction algorithm is proposed based on signals from precession angle sensors. A vector of reduced measurements for the estimation algorithm is formulated using the information about the angles of precession. Finally, the accuracy of the developed correction algorithms for autonomous inertial navigation systems of aircrafts is studied. Numerical solutions for the correction algorithm are presented by the adaptive Kalman filter for the measurement data from the sensors. Real data of navigation system Ts-060K are obtained in laboratory experiments, which validates the proposed algorithms.

Output Voltage Accuracy Improvement Method for Inverter to which Asynchronous PWM Applied

Output Voltage Accuracy Improvement Method for Inverter to which Asynchronous PWM Applied

A Simple, Low-Cost Micro-Coating Method for Accuracy Improvement and Its Application in Pressure Sensors.

The demand for high-accuracy pressure sensors has increased with the advancement of technology in a wide variety of applications. However, it is generally difficult and expensive to improve the accuracy of the pressure sensor because it usually depends on the sensing principle and the internal physical structure of the pressure sensor, varying with its material and production process. Thus, a simple, low-cost, and generally applied post-processing method is proposed to improve the accuracy of pressure sensors. In this method, a micro-coating is cladded on the surface of the sensor, which effectively isolates the adverse effect of the external environment, similar to applying a “micro-protective clothing” on the pressure sensor. Experiments on seven pressure sensors are conducted, in which the micron-thin parylene polymer is utilized as the surface-deposited coating layer to demonstrate the improvement of accuracy. Results show that the accuracy was improved, with an average increase of approximately 62.54% than before cladding, while the sensitivity was almost unchanged. The principle of improving the accuracy of this method was also analyzed. The proposed simple, efficient, and low-cost method of cladding micro-coating for enhancing the accuracy of sensors can be widely applied in various fields of industrial automatic control.

Accuracy Improvement Method of Energy Storage Utilization with DC Voltage Estimation in Large-Scale Photovoltaic Power Plants

In regard to electric devices, currently designed large-scale distributed generation systems require a precise prediction strategy based on the composition of internal component owing to an environmental fluctuating condition and forecasted power variation. A number of renewable resources, such as solar or marine based energies, are made up of a low voltage direct current (DC) network. In addition to actively considering a power compensation plan, these generation systems have negative effects, which can be induced to a connected power system. When a storage is connected to a DC-based generation system on an inner network along with other generators, a precise state analysis plan should back the utilization process. This paper presents a cooperative operating condition, consisting of the shared DC section, which includes photovoltaic (PVs) and energy storage devices. An active storage management plan with voltage-expectation is introduced and compared via a commercialized electro-magnetic transient simulation tool with designed environmental conditions. Owing to their complexity, the case studies were sequentially advanced by dividing state analysis verification and storage device operation.

Visible/near Infrared Reflection Spectrometer and Electronic Nose Data Fusion as an Accuracy Improvement Method for Portable Total Soluble Solid Content Detection of Orange

The visible/near infrared (VIS/NIR) spectrometer and electronic nose (E-nose) are two commonly used portable and nondestructive detection apparatuses which have a promising application for the quick acquisition of fruit’s internal quality in both the orchard and market. However, the accuracy of these instruments is sometimes unsatisfactory, especially for thick peeled fruit like the ‘Aiyuan 38’ orange, which was investigated in this research. The objective of this research was to find a method to improve the accuracy for the detection of an orange’s total soluble solid content (TSS) using a VIS/NIR spectrometer and E-nose. Different spectrum detection positions and conventional feature extraction methods are compared to get the optimal data fusion parameters. The detection model was then built up based on the obtained fusion data under the optimal parameters. Partial least squares regression (PLSR) and mutual information theory (MIT) were applied for feature extraction, and PLSR and principal component analysis (PCA)-back propagation neural network (BPNN) were applied for modeling and detection. PLSR results showed that the sampling reflection spectrum at the position of the calyx results in a better orange TSS detection than other sampling positions. For VIS/NIR reflection spectrum feature extraction, PLSR and MIT results showed that the optimal data process + feature extraction method is Savitzky-Golay + 763 features, when their mutual information values between the feature and TSS value were larger than 0.74. For E-nose feature extraction, PLSR and MIT results showed that the combined feature (combination of 75 s value, average value, average of differential value, integral value, and maximum value) is the optimal feature extraction method, and all features are retained for modeling. The PLSR detection ability of orange TSS based on fusion data is better than the single detection method, with the detection ability of the single detection methods being unsatisfactory. PCA-BPNN has better orange TSS detection ability than PLSR. The R2, RMSE, and slope from the calibration set for PCA-BPNN detection were 0.9695, 0.1895, and 0.9665, respectively, and from the validation set for PCA-BPNN detection were 0.8872, 0.4709, and 1.0871, respectively, indicating that this method can detect orange TSS efficiently.

Measured accuracy improvement method of velocity and displacement based on adaptive Kalman filter

PurposeThis paper aims to investigate the effect of unknown noise parameters of Kalman filter on velocity and displacement and to enhance the measured accuracy using adaptive Kalman filter with particle swarm optimization algorithm.Design/methodology/approachA novel method based on adaptive Kalman filter is proposed. Combined with the displacement measurement model, the standard Kalman filtering algorithm is established. The particle swarm optimization algorithm fused with Kalman is used to obtain the optimal noise parameter estimation using different fitness function.FindingsThe simulations and experimental results show that the adaptive Kalman filter algorithm fused with particle swarm optimization can improve the accuracy of the velocity and displacement.Originality/valueThe adaptive Kalman filter algorithm fused with particle swarm optimization can serve as a new method for optimal state estimation of moving target.

A decision tree‐based NLOS detection method for the UWB indoor location tracking accuracy improvement

SummaryAmong existing wireless technologies, ultra‐wideband (UWB) is the most promising solution for indoor location tracking. UWB has a great multipath fading immunity; however, great multipath resolvability alone does not eliminate the effect of non‐line‐of‐sight (NLOS) and multipath propagation. NLOS and multipath propagation in indoor environments can easily produce meters of UWB ranging error. This condition gives an enormous impact on the accuracy of indoor location tracking data. To address this problem, we propose an NLOS detection method using recursive decision tree learning. Using the UWB channel quality indicators information, we develop our model with the Gini index and altered priors splitting criteria. We then validate the constructed model using the 10‐fold cross‐validation method. Our experiment shows that the constructed model has correctly detected 90% of both line‐of‐sight (LOS) and NLOS cases on the seven different indoor environments. The result of this work can be used for the UWB indoor location tracking accuracy improvement.