Error Self-correction Research Articles

Route Planning Method of UAV Patrol Based on High Precision Tower Model

The high-precision positioning technology of industrial Unmanned Aerial Vehicle (UAV) plays a vital role in intelligent trajectory planning in power inspection. First, the information on power facilities is collected and processed to form a planning grid to optimize the flight path and correct the trajectory of UAV. However, the preparatory work is more complex, and the algorithm model which needs more detailed and complex terrain database data is not universal enough. To improve the safety of UAV and the efficiency of path planning, we establish a three-dimensional model of the power tower, calculate the coordinate position of the inspection work according to the information of the high-voltage transmission tower, and then determine the corresponding relationship between the inspection point and the flight trajectory combined with the safe distance. Based on the set of random interference semaphores and model rasterization, the discrete error strategy and Euler method are introduced to improve the performance. In each iteration, the best solution is first updated using the execution strategy. We continue to calculate the spatial coordinates of all reference points and provide the coordinate position distribution for the flight mission. The solution of equipment inspection trajectory optimization is realized by using dynamic obstacle perception. The proposed discrete measurement error self-correction algorithm is deployed on the flight platform to guide UAV inspection tasks according to the point cloud coordinate model of transmission equipment in the power system and to correct the flight route in time. To verify the research results, the Algorithm Artemisinin optimization and Whale Optimization Algorithm test verify that the ability of the proposed algorithm to get rid of local optimization is improved by 8.94% and, 12.42% respectively compared with other optimization algorithms. The convergence accuracy is 12.4% and 7.9% higher than other schemes, and it has a better effect in solving numerical problems. The research results using the embedded system to complete the task deployment and unloading provide a reference for trajectory planning and task design in different application scenarios.

Improved Indoor Positioning Model Based on UWB/IMU Tight Combination with Double-Loop Cumulative Error Estimation

With the increasing applications of UWB indoor positioning technologies in industrial areas, to further enhance the positioning precision, the UWB/IMU combination method (UICM) has been considered as one of the most effective solutions to reduce non-line-of-sight (NLOS) errors. However, most conversional UICMs suffer from a high probability of positioning failure due to uncontrollable and cumulative errors from inertial measuring units (IMU). Hence, to address this issue, we improved the extended Kalman filter (EKF) algorithm of an indoor positioning model based on UWB/IMU tight combination with a double-loop error self-correction. Compared with conventional UICMs, this improved model consists of new modules for fixing time desynchronization, optimizing the threshold setting for UWB ranging, data fusion in NLOS, and double-loop error estimation, sequentially. Further, systematic error controllability analysis proved that the proposed model could satisfy the controllability of UWB indoor positioning systems. To validate this improved UICM, inevitable obstacles and atmospheric interferences were regarded as Gaussian white noises to verify its environmental adaptability. Finally, the experimental results showed that this proposed model outperformed the state-of-the-art UWB-based positioning models with a maximum deviation of 0.232 m (reduced by 83.93% compared to a pure UWB model and 43.14% compared to the conventional UWB/IMU model) and standard deviation of 0.09981 m (reduced by 88.35% compared to a pure UWB model and 22.21% compared to the conventional UWB-IMU model).

Gray-code fringe order jump error self-correction based on shifted phase encoding for phase measuring profilometry

Temporal phase unwrapping based on Gray-code method (TPU-GC) has prospective application in three-dimensional (3D) shape measurement due to its high-speed measurement and high-efficiency code. Currently, it is still an essential task to remove jump error that may be caused by edge ambiguity of Gray-code pattern or uneven reflectivity of the measured object. So, a Gray-code fringe order jump error self-correction method based on shifted phase encoding (FOSC-SP) is proposed. By changing the calculating sequences of N-step phase-shifting patterns, a series of wrapped phases with a shifted phase of 2π/N can be obtained to identify the 2π discontinuities uniquely. The fringe order error caused by the misalignment between the edges of fringe order and the 2π discontinuities can be located and then corrected accurately. Subsequently, the fringe order caused by uneven reflectivity can be identified by the difference map of wrapped phase and the primary unwrapped phase without edge fringe order error, and then eliminated by a statistical method. Experimental results demonstrate the feasibility and validity of the proposed method. It performs good anti-noise capability and robustness to correct the above two kinds of jump errors in TPU-GC and it is also a generalized correct method that can be used to others TPU, such as multi-frequency and phase-coded methods.

Two Stage Prediction Model of Sunspots Monthly Value Based on CEEMDAN and Particle Swarm Optimization ELM

Sunspot number is the most basic parameter to describe the level of solar activity. The accurate prediction of sunspot number can reflect the electromagnetic disturbance level of the electromagnetic layer, ionosphere and the middle and high layers in the future in advance, so as to provide important reference information for navigation, positioning, communication and the prediction of orbital attenuation of LEO satellites. Aiming at the characteristics of sunspot time series such as non-stationary, chaotic and difficult to predict, this paper proposed a two-stage combined prediction model based on complete Ensemble Empirical mode decomposition with adaptive noise (CEEMDAN), particle swarm optimization (PSO) and extreme learning machine(ELM) network. In the first stage of prediction: firstly, the original sunspot monthly mean series is decomposed by CEEMDAN to reduce the non-linearity and non-stationary of the original series. Then, an ELM prediction model is established for the sub-sequences decomposed by CEEMDAN, and the input layer dimension, hidden layer dimension, input layer weight and hidden layer bias of ELM are optimized by PSO algorithm. Finally, the prediction results of the first stage are obtained by superimposing the prediction results of each sub-sequence. The second stage is the error self-correction stage. Firstly, the prediction error sequence of the first stage is obtained. Then, the CEEMDAN-PSO-ELM prediction model is used to self-correct the prediction error of the first stage. Finally, the prediction results of the first stage and the self-correction results of the second stage are superimposed to obtain the final prediction value of the monthly sunspot number.In this paper, CEEMDAN is used to reduce the non-linearity and non-stationary of the sunspot series, and PSO is used to determine the best parameters of ELM network, and the useful information in the prediction error is fully considered, which effectively improves the prediction accuracy of sunspot monthly mean series. The prediction experiment is carried out by using the measured sunspot monthly mean series, and the proposed model is compared with wavelet neural network (WNN), back propagation neural network (BPNN), ELM, CEEMDAN-ELM and CEEMDAN-PSO-ELM. The experimental results show that the prediction accuracy of the proposed two-stage prediction model has been significantly improved, and has better prediction stability.

A Novel Self-Correction Method for Linear Displacement Measurement Based on 2-D Synthesis Mechanism

With increased range, the accuracy of longer-range linear displacement measurement is difficult to guarantee. According to the analysis, it is inevitable that there will be a certain margin between the direction of the calibration grating and the direction of the reading head, which will impact the longer-range linear displacement measurement. In order to eliminate this measurement error, a self-correction method based on 2-D synthesis is herein proposed. First, we describe the principle of absolute linear displacement measurement based on image recognition. Then, the error model is established, which caused by the included angle between the calibration grating and the reading head. Third, a method is proposed for obtaining the vertical offset of the calibration grating by using the vertical image sensor. Finally, we reach an error self-correction method based on 2-D synthesis. In order to test the feasibility of the proposed method, a linear displacement measuring device with a range of 200 mm was developed. After experiment, the maximum absolute error was reduced from 4.34 to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.03~\mu \text{m}$ </tex-math></inline-formula> with the proposed error correction in the long range of 200 mm. The proposed algorithm does not depend on the assembly and installation position, and can realize self-correction of errors. So the method could lay the groundwork for improving larger range linear displacement measurement technology.

Iterative Error Self-Correction for Robust Fault Diagnosis of Mechanical Equipment With Noisy Label

Deep neural network (DNN) is an effective technology for machinery fault diagnosis. The good performance of DNN is based on the assumption that all labels are completely correct. However, mislabeled data is common in actual industrial applications, which will cause severe performance degradation. This paper explores the performance of DNN under noisy labels and the reasons for its performance degradation. Furthermore, a novel iterative error self-correction (IESC) algorithm based on the maximum-activation of softmax is proposed. During the training process, the label is dynamically optimized, and it is no longer fixed. IESC automatically models the distribution of correct labels, gradually identifies incorrect labels, and automatically corrects incorrect labels. In addition, a noise-tolerant loss is introduced to enhance the network&#x2019;s noise robustness. Experiments on two real machinery fault diagnosis cases prove that our method has excellent label correction and fault diagnosis performance. It significantly improves the performance of DNNs and promotes its practical application potential.

UWB Based Relative Planar Localization with Enhanced Precision for Intelligent Vehicles

Along with the rapid development of advanced driving assistance systems for intelligent vehicles, essential functions such as forward collision warning and collaborative cruise control need to detect the relative positions of surrounding vehicles. This paper proposes a relative planar localization system based on the ultra-wideband (UWB) ranging technology. Three UWB modules are installed on the top of each vehicle. Because of the limited space on the vehicle roof compared with the ranging error, the traditional triangulation method leads to significant positioning errors. Therefore, an optimal localization algorithm combining homotopy and the Levenberg–Marquardt method is first proposed to enhance the precision. The triangular side lengths and directed area are introduced as constraints. Secondly, a UWB sensor error self-correction method is presented to further improve the ranging accuracy. Finally, we carry out simulations and experiments to show that the presented algorithm in this paper significantly improves the relative position and orientation precision of both the pure UWB localization system and the fusion system integrated with dead reckoning.

Error self-correction method for phase jump in multi-frequency phase-shifting structured light.

Among 3D measurement approaches, multi-frequency phase-shifting structured light has advantages such as high resolution and high sampling rate owing to its point-to-point calculation method. However, there is always phase jump in the measurement process, which greatly reduces measurement accuracy. This paper proposes an error self-correction method for phase jump based on the multi-frequency heterodyne approach. The method uses redundant measurement data to implement self-correction and does not require additional data acquisition steps. We perform both simulations and experiments using the proposed error self-correction method and the classical heterodyne approach to compare the results. The experiment results verify both the accuracy and suitability of the proposed method.

Design and Optimization of FBG Implantable Flexible Morphological Sensor to Realize the Intellisense for Displacement.

The measurement accuracy of the intelligent flexible morphological sensor based on fiber Bragg grating (FBG) structure was limited in the application of geotechnical engineering and other fields. In order to improve the precision of intellisense for displacement, an FBG implantable flexible morphological sensor was designed in this study, and the classification morphological correction method based on conjugate gradient method and extreme learning machine (ELM) algorithm was proposed. This study utilized finite element simulations and experiments, in order to analyze the feasibility of the proposed method. Then, following the corrections, the results indicated that the maximum relative error percentages of the displacements at measuring points in different bending shapes were determined to be 6.39% (Type 1), 7.04% (Type 2), and 7.02% (Type 3), respectively. Therefore, it was confirmed that the proposed correction method was feasible, and could effectively improve the abilities of sensors for displacement intellisense. In this paper, the designed intelligent sensor was characterized by temperature self-compensation, bending shape self-classification, and displacement error self-correction, which could be used for real-time monitoring of deformation field in rock, subgrade, bridge, and other geotechnical engineering, presenting the vital significance and application promotion value.

An analysis of EFL learners’ error self-correction attitudes

&#x0D; &#x0D; &#x0D; During language teaching and learning, the focus is on how correct or wrong the input is reflected in the output. Accuracy first approaches reject errors due to the risk of interference whereas the communicative approaches use them as signs of learning. So, errors play a vital role in foreign language learning. It is viewed as improvement in different language learning approaches. This paper after reviewing the literature about error and error correction in English language teaching examines the types of errors the students can self-correct. The procedure is as follows: Twenty erroneous sentences from examination papers were chosen as student errors and learners were asked to correct any kind of errors in the sentences. The results were categorised, analysed and commented. The results show that students are better at correcting the structural errors and worst in discourse ones. Under these findings, it is suggested that learner training should focus more on contextual learning.&#x0D; Keywords: Error correction, EFL, self-correction, accuracy, exam papers.&#x0D; &#x0D; &#x0D;

Intelligent Self-Adaptation Data Behavior Control Inspired by Speech Acts

Wireless sensor networks (WSNs) are a promising technology for collecting information by utilizing various types of small-sized sensors. Implementing efficient data acquisition and transmission is important for real-time monitoring and data analysis, owing to massive and heterogeneous data with low precision. In this work, the data behavior model inspired by speech acts is built, including the data correlation model and the data comment model. Intelligent self-adaptation data behavior control is then proposed, of which the main idea is to make sensor nodes (SNs) process data intelligently. In the proposed data behavior control, the temporal compression behavior is achieved through variable-cycle transmission and the multivariate spatial compression behavior is motivated by compressed sensing (CS) theory. The multivariate spatial-temporal compression composed of the above two compression behaviors can adjust itself through data comment behaviors including the large-cycle error self-correction behavior based on the node credibility and the large-cycle event self-adaptation behavior based on the information granularity. The data behavior control presented has been validated by the experiments in the Shanghai metro tunnel. The experiment results show that these intelligent data behaviors inspired by speech acts can make WSNs more effective and intelligent with no change in the existing network structure.

Action research of an error self-correction intervention: Examining the effects on the spelling accuracy behaviors of fifth-grade students identified as at-risk

ABSTRACTIn this action research case study, the researchers used a multiple baseline across two student pairs design to investigate the effects of the error self-correction method on the spelling accuracy behaviors for four fifth-grade students who were identified as being at risk for learning disabilities. The dependent variable was the participants' spelling skills, which were measured using weekly spelling tests. The study took place in an integrated co-taught classroom, over a 15-week period with one spelling test per week. The results indicated that error self-correction was an effective intervention to improve the spelling accuracy behaviors of students at risk for learning disabilities. The participants' mean weekly spelling test scores improved at least 24% over baseline levels during the intervention phase. A discussion of the results and suggestions for implementation are included.

Method of investigating the elastohydrodynamic contact in slide bearings with compressible and incompressible lubrication

An algorithm of solving the problem of elastohydrodynamic contact is presented in the paper. It is used to calculate characteristics of journal and thrust bearings with oil or gas lubrication. The algorithm is based on simultaneous solution of the problem of lubrication flow in the gap created by the bearing sliding surfaces and the problem of specifying the change in the gap shape due to the shift of position and deformations of sliding surfaces caused by lubrication pressure in the gap. Modifications of Reynolds equation describing incompressible (oil) and compressible (gas) lubrication flow in journal and thrust bearing are used for the calculation of bearing characteristics. The finite-element method is used to solve the Reynolds equation. Lagrange multipliers are used to ensure closedness of the rectangular region for a journal bearing.&nbsp; A step-by-step process with error self-correction at each step is applied for solving the nonlinear Reynolds equation for compressible lubrication. The shape of the gap in bearings with incompressible lubrication is defined by calculating the equilibrium pad position in a bearing taking into account the deformations of siding surfaces and bearing parts under the action of lubrication pressure. The shape of the gap in gas bearings is defined by the deformations of elastic foils. These deformations are calculated when solving the problem of deformation and contact interaction of the foils with each other and with the bearing housing under the action of lubrication pressure. The presented method of calculation of elastohydrodynamic contact in slide bearings allows taking into account the design features of physical products for the analysis of rotor supports characteristics.

Method of investigating the elastohydrodynamic contact in slide bearings with compressible and incompressible lubrication

An algorithm of solving the problem of elastohydrodynamic contact is presented in the paper. It is used to calculate characteristics of journal and thrust bearings with oil or gas lubrication. The algorithm is based on simultaneous solution of the problem of lubrication flow in the gap created by the bearing sliding surfaces and the problem of specifying the change in the gap shape due to the shift of position and deformations of sliding surfaces caused by lubrication pressure in the gap. Modifications of Reynolds equation describing incompressible (oil) and compressible (gas) lubrication flow in journal and thrust bearing are used for the calculation of bearing characteristics. The finite-element method is used to solve the Reynolds equation. Lagrange multipliers are used to ensure closedness of the rectangular region for a journal bearing. A step-by-step process with error self-correction at each step is applied for solving the nonlinear Reynolds equation for compressible lubrication. The shape of the gap in bearings with incompressible lubrication is defined by calculating the equilibrium pad position in a bearing taking into account the deformations of siding surfaces and bearing parts under the action of lubrication pressure. The shape of the gap in gas bearings is defined by the deformations of elastic foils. These deformations are calculated when solving the problem of deformation and contact interaction of the foils with each other and with the bearing housing under the action of lubrication pressure. The presented method of calculation of elastohydrodynamic contact in slide bearings allows taking into account the design features of physical products for the analysis of rotor supports characteristics.

RFID Indoor Localization Algorithm Based on Adaptive Self-correction

With the rapid development of wireless communication and embedded system, wireless positioning systems are paid more and more attention to. Radio Frequency Identification (RFID) localization system is getting more important, due to its own advantages, such as no contact, non-line-of-sight nature, promising transmission range and cost-effectiveness. To improve the accuracy of active RFID indoor location system, some traditional RFID indoor localization systems were studied, such as LANDMARC. On this basis, an adaptive selfcorrection location algorithm was presented, which uses a positioning correction value to correct the positioning result. N minimum errors and position results are obtained by using adaptive K-nearest neighbor algorithm N times. The positioning correction value calculated with N minimum errors in weighted way. The sum of the positioning average value and the positioning correction value would be the final positioning results. Experimental results show that compared with adaptive K-nearest neighbor algorithm and error self-correction algorithm, the proposed method provides a higher accuracy and stability.

相移干涉测量中相移误差的自修正

The reasons for the phase shifting errors in a phase shifting interferometric measurement were analyzed,and a method to correct the phase shifting errors was researched based on the least square method.The calculation and correction for the relative phase step amount between arbitrary two interferograms were implemented.With the combination of optical,mechanical,electrical and calculating subsystems,more than three step interferometric images with arbitrary intervals were achieved.Then,the iterative least-square algorithm was used to calculate the relative phase step amount between arbitrary two interferograms.The phase shift error was feedbacked to the hardware system and the phase step interval was corrected in self-adaption until the phase step interval was equal to the characteristic value of the phase shift.A phase shift error correction system based on iterative least-square algorithm was constructed to achieve phase step error self-correction on a phase shifting interferometry.The real measurement of optical surface was conducted to verify the correction of algorithm and the feasibility of phase step error self-correction system.The results show that the relative phase step error is less than 5%,and the root mean square error(RMS) for measurement repeatability of the optical surface is less than λ/1 000 with the help of the proposed phase step self-correction process.

Error self-correction in the tri-node indirect teleportation of a qutrit state with two partially entangled qutrit states

Teleportation of a qutrit state with two different non-maximally entangled qutrit states as quantum channels linking three nodes of an intending quantum network is studied via an ancilla method. With this method a global accumulation approach (GAA) different from the usual node progression approach (NPA) is proposed. Comparisons between two approaches from the four aspects of quantum resource consumption, operation complexity, success probability and efficiency indicate that the GAA is much better than the NPA overall. The average fidelities in both methods are calculated and compared, it is shown that the average fidelity in the GAA is higher than that in the NPA. The error self-correction in the GAA is confirmed and its efficiency relative to the channel-state coefficients is explicitly given.

Two-chicane compressed harmonic generation of soft x rays

Seeding an electron bunch prior to compression simultaneously shifts the laser modulation to shorter wavelengths while decreasing the required modulation amplitude. The final x-ray wavelength is then tunable by controlling the compression factor with the rf phase. In this paper we describe a two-chicane scheme that allows for large modulation amplitudes, extending the method to photocathode beams with significant uncorrelated energy spreads. The downside of such compressed seeding is the need to maintain bunching across an extended accelerator region. We present analytical estimates and computer simulations to study tolerances for a sample lattice. We also note that transportation of the fine compressed modulation structure is helped by error self-correction in the second chicane, an effect that may be of more general interest.

A New Noncontact Flatness Measuring System of Large 2-D Flat Workpiece

The flatness measurement of a large 2D flat workpiece such as a satellite solar panel substrate is a key problem in its manufacturing process. Based on the current measuring methods and the actual engineering project, a new noncontact high-precision, low-cost large 2-D flat workpiece flatness measuring system is developed, in which the techniques of oblique optical triangle measuring structure, virtual measurement datum plane, and error self-correction are adopted. By means of the oblique optical triangle measuring structure that is first proposed, the measuring area and resolution of the measuring system can be greatly increased. Meanwhile, on the basis of the techniques of virtual measurement datum plane and error self-correction, the noncontact high-precision, low-cost flatness measurement can be realized on a nonprecise large metallic platform. Experimental results on the satellite solar panel substrate indicate the correctness and effectiveness of the proposed noncontact flatness-measuring system of the large 2-D flat workpiece. Although the specific application of the satellite solar panel substrate is presented in this paper, the proposed measurement techniques and measuring system could be applied independent of the nature or use of the large 2-D flat workpieces.

Error self-correction and spelling: improving the spelling accuracy of secondary students with disabilities in written expression

In order to improve the spelling performance of high school students with deficits in written expression, an error self-correction procedure was implemented. The participants were two tenth-grade students and one twelfth-grade student in a program for individuals with learning disabilities. Using an alternating treatments design, the effect of error self-correction was compared with a more traditional method of spelling practice. The intervention and follow-up phases were implemented over a 6-week period with maintenance checks conducted 4 and 8 weeks after the termination of instruction. Results indicated that the error self-correction procedure was superior to the traditional method of review during the intervention and follow-up phases, but some gains were lost during the maintenance phase.