Error Characteristics Research Articles

Розробка стенду для вимірювання метрологічних характеристик електродвигунів

The problems of accurate measurement of the forces transmitted to the rotating shafts of electric motors have been solved for more than one century. At the same time, they have not lost their relevance, which is due to the wide development and variety of electric motors and power plants, the specific operating conditions of which require the use of a wide arsenal of measuring instruments. An important aspect in the process of using high-precision electric motors in such areas as medicine, vehicles, military-industrial complex is the improvement of the metrological characteristics of measuring equipment. In such conditions, power measurements at high-speed installations, where in some cases conventional measurement systems are either unsuitable or have low accuracy, are of particular importance. At the same time, in the absence of means of accurate determination of the error, attempts are made to predict them, which makes it possible to determine the influence of subtle factors on the efficiency of power plants. Even under optimal conditions for the functioning of measuring instruments, due to the influence of a number of factors, gross errors may appear. Such errors are unpredictable, and their significance is difficult to predict. To prevent the impact of negative factors on the operation of measuring instruments, additional sensors are often used, for example, to identify unnecessary vibrations, vibrometers are used in parallel with strain gauges. In the absence of such additional measuring sensors, in order to identify gross errors in diagnosing the characteristics of the moment of force, it is advisable to use machine learning tools, factor analysis techniques, simulation modeling, and other forecasting tools. With the help of such methods, among the sampling of data obtained from strain gauges, or other sensors, for measuring the moment of force, it is possible to identify deviations from normal operation, through certain frequency patterns of such influences. Among the many works describing the characteristics of errors in measuring physical quantities, there are not many that are devoted to predicting the accuracy of measuring sensors. Thus, when the measured conditions change, gross errors occur that negate the process of controlling electric motors, which is often the cause of an emergency condition. To solve this problem, you need simple and affordable tools that will allow you to form a classification of deviations in measurement errors. However, taking into account a significant number of factors of influence on the measuring medium, this can be realized only under the condition of an individual approach to the construction of measuring instruments. For this purpose, a stand was developed for measuring the metrological characteristics of electric motors. It was tested in conditions of increased vibration. The results of such tests made it possible to determine the deviations from the nominal error value of the strain gauge sensor.And also highlight a number of features of such a deviation caused by the frequency characteristics of the pulse source. The structure of the software and technical characteristics of the proposed stand and its comparison with existing analogs, as well as the functional and electrical circuit are presented. As a result of testing the proposed stand, a classification of factors influencing the accuracy of strain gauges was developed.

Reweighted Discriminative Optimization for least-squares problems with point cloud registration

Optimization plays a pivotal role in computer graphics and vision. Learning-based optimization algorithms have emerged as a powerful optimization technique for solving problems with robustness and accuracy because it learns gradients from data without calculating the Jacobian and Hessian matrices. The key aspect of the algorithms is the least-squares method, which formulates a general parametrized model of unconstrained optimizations and makes a residual vector approach to zeros to approximate a solution. The method may suffer from undesirable local optima for many applications, especially for point cloud registration, where each element of transformation vectors has a different impact on registration. In this paper, Reweighted Discriminative Optimization (RDO) method is proposed. By assigning different weights to components of the parameter vector, RDO explores the impact of each component and the asymmetrical contributions of the components on fitting results. The weights of parameter vectors are adjusted according to the characteristics of the mean square error of fitting results over the parameter vector space at per iteration. Theoretical analysis for the convergence of RDO is provided, and the benefits of RDO are demonstrated with tasks of 3D point cloud registrations and multi-views stitching. The experimental results show that RDO outperforms state-of-the-art registration methods in terms of accuracy and robustness to perturbations and achieves further improvement than non-weighting learning-based optimization.

Pedal Error Among Car Drivers: A Review on The Research Approach and Setup


 
 
 The movement of the driver’s foot from one pedal to another is a blind positioning movement. Variability of driver’s foot movement might lead to pedal error or pedal misapplication. In this paper, we reviewed the research focus in the pedal error study, who are the participants involved in the study, what kind of approach or method used to perform the pedal error study, how is the test setup, test condition, and flow of the study, and what are the factors influencing the pedal error accidents. The review searched relevant articles based on five keywords: pedal errors, pedal misapplication, sudden unintended acceleration (SUA), automotive foot pedals, and sudden acceleration accidents. Based on the review, the research focus is focusing on the factors causing the pedal error, understanding the fundamental principles of pedal misapplications, and characteristics of pedal errors among older drivers. The pedal error study has been conducted either using the naturalistic driving, driving simulation, or survey. The pedal error among ASEAN drivers is essential since there is no study related to this topic has been conducted in any ASEAN countries yet.
 
 

Uncertainty in remote sensing of streams using noncontact radars

Accounting for freshwater resources and monitoring floods are vital functions for societies throughout the world. Remote-sensing methods offer great prospects to expand stream monitoring in developing countries and to smaller, headwater streams that are largely ungauged worldwide. This study evaluates the potential to estimate discharge using eight radar units that have been installed over streams in diverse hydrologic and hydraulic settings across the United States. The research highlights error characteristics associated with the measurements of stage using pulsed wave radars, mean channel velocity from continuous wave Doppler radars, and their combined use to estimate discharge at sites that were collocated with conventional streamgauges. Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. A dry concrete, flume showed the temperature-dependent stage variations were no more than 2 cm. Surface velocity retrievals needed to be adjusted to represent the mean channel velocity when estimating discharge. Different approaches were evaluated and application of two different, depth-dependent adjustment factors was found to yield the most accurate estimates. This study found that it is possible to get accurate discharge estimates from noncontact radar measurements, providing cost-effective solutions for remote sensing of ungauged streams. Lastly, radar measurements of the raw variables (i.e., stage and surface velocity) can be used in an early alerting context to detect flash floods in ungauged streams.

Comprehensive error analysis of satellite precipitation estimates based on Fengyun-2 and GPM over Chinese mainland

The spatial-temporal error characteristics of four mainstream satellite precipitation products developed by China and United States, respectively, including the Fengyun-based (FY-2F and FY-2G) and the GPM-based (IMERG-Late and IMERG-Final) over Chinese mainland were comprehensively analyzed from January 2018 to December 2019. In general, both IMERG-Final and FY-2G perform better at the hourly and daily scales. While the FY-2F product has the relatively worst performance with lowest correlation coefficient (CC) and highest root mean square error (RMSE) values. In particular, FY-2F has considerable total bias and missed precipitation errors in summer when compared to other three precipitation products. As for winter, the IMERG product suites exhibit significantly overestimation in north-central region of China, while an opposite underestimation occurred in the Fengyun products. Among the four precipitation estimates, IMERG-Final and FY-2G have the lowest normalized-RMSE (NRMSE) at the elevation range of 100–300 m and 300–500 m at daily scale, respectively. The performance at the hourly scale was found to be similar for IMERG-Final and IMERG-Late, but both of which are slightly superior to FY-2G across the elevation ranges. In terms of the detectability for different rain intensities, the IMERG products performed best at higher rain rates, while the Fengyun-based precipitation estimates are superior to IMERG at relatively lower rain ones. The assessment results reported here will provide some valuable feedbacks for algorithm developers of Fengyun products, and enable data users to further understand the error characteristics and potential deficiencies of Fengyun precipitation estimates.

Spindle thermal error modeling method considering the operating condition based on Long Short-Term Memory

Thermal error has a significant effect on machine tools’ precision. Traditional methods usually establish the thermal error model by temperature. As the temperature only describes the instant state of the machine tool, it is inadequate to clarify the generation process of thermal error. To improve the description completeness of thermal error generation process, the operating condition data such as spindle speed, rotational number and power are employed besides the temperature. Furthermore, Long Short-Term Memory (LSTM) based thermal error model was proposed which combined the operating condition with temperature. This model not only considers the dynamic characteristics of the thermal error, but also selects and remembers the previous thermal error data information. To improve the accuracy for the whole speed range, the K-Means clustering analysis was adopted to obtain the optimal speed categorization, then different LSTM sub-models were established separately. Experiments demonstrated that the predicting accuracy of the proposed K-Means segmental LSTM method (K-S-LSTM) is over 85.0% under different operating conditions. Moreover, comparison experiments indicated that the predicting performance of K-S-LSTM is better than Elman and IRNN models. Finally, the compensation experiment showed that the thermal error can decrease from 49 μm to 12 μm after compensation in actual numerical control system.

Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling

Satellite precipitation estimates (SPEs) are promising alternatives to gauge observations for hydrological applications (e.g., streamflow simulation), especially in remote areas with sparse observation networks. However, the existing SPEs products are still biased due to imperfections in retrieval algorithms, data sources and post-processing, which makes the effective use of SPEs a challenge, especially at different spatial and temporal scales. In this study, we used a distributed hydrological model to evaluate the simulated discharge from eight quasi-global SPEs at different spatial scales and explored their potential scale effects of SPEs on a cascade of basins ranging from approximately 100 to 130,000 km2. The results indicate that, regardless of the difference in the accuracy of various SPEs, there is indeed a scale effect in their application in discharge simulation. Specifically, when the catchment area is larger than 20,000 km2, the overall performance of discharge simulation emerges an ascending trend with the increase of catchment area due to the river routing and spatial averaging. Whereas below 20,000 km2, the discharge simulation capability of the SPEs is more randomized and relies heavily on local precipitation accuracy. Our study also highlights the need to evaluate SPEs or other precipitation products (e.g., merge product or reanalysis data) not only at the limited station scale, but also at a finer scale depending on the practical application requirements. Here we have verified that the existing SPEs are scale-dependent in hydrological simulation, and they are not enough to be directly used in very fine scale distributed hydrological simulations (e.g., flash flood). More advanced retrieval algorithms, data sources and bias correction methods are needed to further improve the overall quality of SPEs.

Research on Real-Time Detection of Sprint Error Based on Visual Features and Internet of Things

The current sprint error detection methods do not consider the analysis of the visual characteristics of sprint error, which leads to low detection accuracy, long detection time, and poor detection stability. To overcome this defect, inspired by Internet of Things technology, a real-time sprint error detection method based on visual characteristics is proposed. Based on the basic principle of RFID action perception, the original phase data is preprocessed, the channel parameters are selected, and the tag layout is optimized to form the action-oriented feature. Based on the three-dimensional visual features, the three-dimensional coordinate points of the sports field are determined, and the movement features of the sprint are extracted and formally described. Based on the analysis of the visual characteristics of sprint errors, the block pheromones of single frame sprint motion edge contour are obtained for clustering, and the sprint errors’ feature information is obtained and filtered. Sift technology is used to obtain the boundary contour line and implement the corner characteristics. The Hessian matrix of contour wave domain edge detection is used to calculate the contour wave domain matrix of the image and draw the contour curve of the image of sprint error action to realize the detection of sprint error action. The experimental results show that the proposed method has good stability and can effectively improve the detection accuracy and shorten the detection time.

Frequencies and Neuropsychological Characteristics of Errors in the Clock Drawing Test.

Few studies have investigated associations between types of clock drawing test (CDT) errors and cognitive impairment. To explore associations of qualitative errors in the CDT with comprehensive neurocognitive assessment scores and clinical diagnosis. Outpatients at a memory clinic were enrolled. Frequencies of errors determined by Cahn's method were explored according to cognitive status (cognitively normal [CN] (n = 279), mild cognitive impairment [MCI] (n = 321), and Alzheimer's disease [AD]) (n = 575). Neuropsychological assessment scores were compared between participants with and without errors. Stimulus-bound response (SB) was relatively rare (6.8%) in the CN group but was markedly more common in the MCI (23.4%) and AD (33.2%) groups. Conceptual deficit (CD) was found in more than 20%of CN individuals, as well as about 50%of AD patients. Planning deficit (PD) frequencies were relatively similar among the groups. SB in both of CN and MCI individuals, and CD in both of CN and MCI individuals were associated with lower scores in several neuropsychological assessments. Meanwhile, PD was not associated with lower assessment scores in any of CN, MCI, or AD individuals. The frequencies of SB and CD increased from CN, MCI, to AD but showed somewhat different patterns. Both SB and CD were associated with lower cognition in all three cognitive stages.

Comprehensive Evaluations on the Error Characteristics of the State‐of‐the‐Art Gridded Precipitation Products Over Jiangxi Province in 2019

AbstractAccurate knowledge of the precipitation estimates with high quality and fine spatiotemporal resolutions is crucial to the precipitation science communities, especially over poorly gauged regions. In this study, we apply various validation metrics to comprehensively evaluate three latest model‐based and satellite‐based precipitation products, namely the fifth‐generation global atmospheric analysis data set of the European Center for Medium‐Range Weather Forecasts (ECMWF) (ERA5), ERA5‐Land and Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement (IMERG) Final Run (IMERG‐Final), against hourly rain gauge observations over Jiangxi province, south central China, in 2019. The main conclusions of this study include but not limited to: (a) considering the precipitation amount, both reanalysis precipitation data and satellite‐based precipitation products have similar spatial patterns and show overestimations; (b) IMERG‐Final generally outperforms ERA5 and ERA5‐Land at multiple temporal scales, especially in terms of CC, FAR and MFI; (c) ERA5 and ERA5‐Land precipitation products have similar spatiotemporal error characteristics, and ERA5‐Land performs better than ERA5; (d) in the respect of the capabilities of capturing precipitation events, the spatial characteristics of IMERG‐Final are the closest to those of rain gauges, while ERA5 and ERA5‐Land significantly overestimate the event duration and underestimate the mean event precipitation rate. These findings could help the state‐of‐the‐art model‐based and satellite‐based precipitation products improve the data quality in the future generations.

Characterization of DNA lesions associated with cell-free DNA by targeted deep sequencing

BackgroundRecently, a next-generation sequencing (NGS)-based method has been used for the successful detection of circulating tumor DNA (ctDNA) in various cancer types. Thus, the use of NGS on liquid biopsies will improve cancer diagnosis and prognosis. However, the low-allelic fraction of ctDNA poses a challenge for the sensitive and specific detection of tumor variants in cell-free DNA (cfDNA). To distinguish true variants from false positives, the characteristics of errors that occur during sample preparation and sequencing need to be elucidated.MethodsWe generated capture-based targeted deep sequencing data from plasma cfDNA and peripheral blood leucocyte (PBL) gDNA to profile background errors. To reveal cfDNA-associated DNA lesions, background error profiles from two sample types were compared in each nucleotide substitution class.ResultsIn this study, we determined the prevalence of single nucleotide substitutions in cfDNA sequencing data to identify DNA damage preferentially associated with cfDNA. On comparing sequencing errors between cfDNA and cellular genomic DNA (gDNA), we observed that the total substitution error rates in cfDNA were significantly higher than those in gDNA. When the substitution errors were divided into 12 substitution error classes, C:G>T:A substitution errors constituted the largest difference between cfDNA and gDNA samples. When the substitution error rates were estimated based on the location of DNA-fragment substitutions, the differences in error rates of most substitution classes between cfDNA and gDNA samples were observed only at the ends of the DNA fragments. In contrast, C:G>T:A substitution errors in the cfDNA samples were not particularly associated with DNA-fragment ends. All observations were verified in an independent dataset.ConclusionsOur data suggested that cytosine deamination increased in cfDNA compared to that in cellular gDNA. Such an observation might be due to the attenuation of DNA damage repair before the release of cfDNA and/or the accumulation of cytosine deamination after it. These findings can contribute to a better understanding of cfDNA-associated DNA damage, which will enable the accurate analysis of somatic variants present in cfDNA at an extremely low frequency.

Artificial Neural Network Assisted Error Correction for MLC NAND Flash Memory.

The multilevel per cell technology and continued scaling down process technology significantly improves the storage density of NAND flash memory but also brings about a challenge in that data reliability degrades due to the serious noise. To ensure the data reliability, many noise mitigation technologies have been proposed. However, they only mitigate one of the noises of the NAND flash memory channel. In this paper, we consider all the main noises and present a novel neural network-assisted error correction (ANNAEC) scheme to increase the reliability of multi-level cell (MLC) NAND flash memory. To avoid using retention time as an input parameter of the neural network, we propose a relative log-likelihood ratio (LLR) to estimate the actual LLR. Then, we transform the bit detection into a clustering problem and propose to employ a neural network to learn the error characteristics of the NAND flash memory channel. Therefore, the trained neural network has optimized performances of bit error detection. Simulation results show that our proposed scheme can significantly improve the performance of the bit error detection and increase the endurance of NAND flash memory.

Implementation of Parallel Cascade Identification at Various Phases for Integrated Navigation System

Global navigation satellite systems (GNSS) are widely used for the navigation of land vehicles. However, the positioning accuracy of GNSS, such as the global positioning system (GPS), deteriorates in urban areas due to signal blockage and multipath effects. GNSS can be integrated with a micro-electro-mechanical system (MEMS)–based inertial navigation system (INS), such as a reduced inertial sensor system (RISS) using a Kalman filter (KF) to enhance the performance of the integrated navigation solution in GNSS challenging environments. The linearized KF cannot model the low-cost and small-size sensors due to relatively high noise levels and compound error characteristics. This paper reviews two approaches to employing parallel cascade identification (PCI), a non-linear system identification technique, augmented with KF to enhance the navigational solution. First, PCI models azimuth errors for a loosely coupled 2D RISS integrated system with GNSS to obtain a navigation solution. The experimental results demonstrated that PCI improved the integrated 2D RISS/GNSS performance by modeling linear, non-linear, and other residual azimuth errors. For the second scenario, PCI is utilized for modeling residual pseudorange correlated errors of a KF-based tightly coupled RISS/GNSS navigation solution. Experimental results have shown that PCI enhances the performance of the tightly coupled KF by modeling the non-linear pseudorange errors to provide an enhanced and more reliable solution. For the first algorithm, the results demonstrated that PCI can enhance the performance by 77% as compared to the KF solution during the GNSS outages. For the second algorithm, the performance improvement for the proposed PCI technique during the availability of three satellites was 39% compared to the KF solution.

Uncertainty modeling of wind power frequency regulation potential considering distributed characteristics of forecast errors

Large-scale integration of wind power generation decreases the equivalent inertia of a power system, and thus makes frequency stability control challenging. However, given the irregular, nonlinear, and non-stationary characteristics of wind power, significant challenges arise in making wind power generation participate in system frequency regulation. Hence, it is important to explore wind power frequency regulation potential and its uncertainty. This paper proposes an innovative uncertainty modeling method based on mixed skew generalized error distribution for wind power frequency regulation potential. The mapping relationship between wind speed and the associated frequency regulation potential is established, and key parameters of the wind turbine model are identified to predict the wind power frequency regulation potential. Furthermore, the prediction error distribution of the frequency regulation potential is obtained from the mixed skew model. Because of the characteristics of error partition, the error distribution model and predicted values at different wind speed sections are summarized to generate the uncertainty interval of wind power frequency regulation potential. Numerical experiments demonstrate that the proposed model outperforms other state-of-the-art contrastive models in terms of the refined degree of fitting error distribution characteristics. The proposed model only requires the wind speed prediction sequence to accurately model the uncertainty interval. This should be of great significance for rationally optimizing system frequency regulation resources and reducing redundant backup.

Error characteristic analysis of fiber optic current transformer with sine modulation

The optoelectronic circuit and modulation circuit of the fiber optic current transformer (FOCT) are easily affected by the complex environment in the converter station during operation, leading to abnormal operation and fault warning. In order to analyze the error characteristic on the output signal and harmonics of FOCT, the output signal models of FOCT with sine modulation in steady-state and error-state are established respectively in this paper. Combined with the output signal, the key parameters are solved, and it is pointed out that the change of photoelectric circuit may cause the ratio error. The Faraday phase shift and the change of the modulation circuit may cause the drift error. The error characteristics of the FOCT under different influencing factors are simulated and analyzed. The results show that output signal of FOCT is influenced by photoelectric circuit, Faraday phase shift circuit and modulation circuit. Changes in the photoelectric circuit will cause a proportional change in the FOCT output signal and harmonics. Both the Faraday phase-shifting and the increasing of modulation amplitude will reduce the minimum value of output signal. Changes in Faraday phase shift will cause an approximately proportional change in first harmonic. Changes in modulation signal amplitude will cause the harmonics to appear attenuate and oscillate. The modulation frequency is inversely proportional to the period of the output signal and harmonics. Both the first and second harmonics can be used as the monitoring state parameters of fault determination. Designed experiment verified the correctness of the conjecture. This article provides a theoretical basis for the follow-up status monitoring and fault diagnosis of FOCT.

Dynamic long-term streamflow probabilistic forecasting model for a multisite system considering real-time forecast updating through spatio-temporal dependent error correction

Long-term streamflow forecasting for a multisite river basin system can experience oscillation of spatio-temporal dependent forecast errors. Characterizing the dependent information and conducting spatio-temporal dependent forecast error correction can reduce the total uncertainty in real-time forecasting. On the basis of a single-site martingale model of forecast evolution (MMFE), which can simulate forecast uncertainty evolution over the temporal scale, this study developed a dynamic long-term streamflow probabilistic forecasting model that addresses spatio-temporal dependent error correction for a multisite system. Error characteristics and hybrid spatio-temporal forecast improvements with complex dependencies are captured by Copula function, which describes the systemwide evolution of forecast uncertainty. The proposed model was verified by forecasting the streamflow of the Hongze Lake–Luoma Lake system in China. The results revealed the following. 1) The spatio-temporal dependent error correction based on MMFE can refine the spatio-temporal dimension hybrid error information, which is helpful in improving forecast accuracy. 2) As the spatio-temporal dependence of forecast uncertainty is described using a Copula function, selection of the marginal distribution and connection function is flexible through combining statistical and informational characteristics of the errors, which is helpful in improving the fittedness of complex system error simulation. 3) Compared with the benchmark forecast model without correction, the proposed model greatly decreased forecast uncertainty by reducing the standard deviation of the errors, continuous ranked probability score (CRPS), and Brier score (BS) by nearly 40%, 11.4%, and 34.6%, respectively, and further reduce the CRPS by 2.1% on average (highest value: 3.7%) and reduce the BS by 1.2% on average (highest value: 2.0%) in comparison with only temporal correction. The results demonstrate that the proposed model improves the accuracy and reliability of probabilistic streamflow forecasting for complex water resource systems by reducing uncertainty.

Research on aeromagnetic three-component error compensation technology for multi-rotor UAV

Aeromagnetic three-component data contain a large amount of geomagnetic information, which can help to obtain a quantitative interpretation of a magnetic anomaly and to improve the identification accuracy of an abnormal body. However, aeromagnetic three-component data of an unmanned aerial vehicle (UAV) include the carrier fixed interference error, inductive interference error, eddy current interference error, and attitude error. Therefore, carrier interference compensation and attitude compensation are necessary to obtain three-component data in a geographical coordinate system. This paper analyzes the difference of six types of attitude conversion matrices for the mutual conversion between the geographic and carrier coordinate systems. Based on the analysis results, the yaw-pitch-roll sequence has the smallest error when calculating the attitude conversion, so the compensation sequence of the roll-pitch-yaw is selected for attitude compensation. The characteristics of the UAV carrier interference error are also analyzed. A low-pass filter is set up for data preprocessing according to the high-frequency characteristics of the carrier eddy current interference, and carrier fixed interference and induction interference compensation models are constructed based on the Tolles-Lawson model. The aeromagnetic three-component comprehensive compensation error objective function is established by integrating attitude compensation and carrier interference compensation. In addition, an adaptive step-size cuckoo algorithm is proposed to obtain the minimum value of the objective function to solve 12 compensation parameters so that the accurate three-component data can be obtained quickly, efficiently, and accurately. Finally, a comprehensive compensation experiment is performed on the three-component aeromagnetic data, and the mean error of the three-component data after compensation is reduced to less than 0.72 nT.

Correction efficiency and error characteristics for cosmic-ray soil moisture on mountainous terrain

A Cosmic-Ray Neutron Probe (CRNP) is a promising tool for obtaining reliable intermediate scale soil moisture. Although there are sound ways of correcting neutron counts and calibrating soil moisture, it is difficult to obtain all the data required for corrections and calibration in a complex environment. In this study, three major correcting variables (i.e., absolute humidity, air pressure, incoming neutron flux intensity) were evaluated to determine their impacts on the neutron counts in the Seolmacheon experimental site (SMC) located in mountainous terrain. Efficiency of correction factors for atmospheric water vapor (fwv), air pressure (fp,), and incoming neutron flux intensity (fi) was independently assessed through comparisons between the variables and a inversely calculated N0 (N0,inv). Particular attention was given to the expected biases and errors in estimated soil moisture (SMCRNP) with and without each correction factor, especially at the early stage of the calibration period. Air pressure had the greatest impact on neutron count at the SMC site, followed by absolute humidity. The dependency of neutron count on air pressure (absolute humidity) was found from the inverse relationship with N0,inv that is not corrected for air pressure (absolute humidity), with correlation coefficient −0.88 (−0.78). When fp (fwv) was applied, the dependency of neutron count on the corresponding variable was significantly reduced, with a correlation coefficient 0.06 (−0.07). On the other hand, the effect of applying fi was contrary, slightly degrading the result. When neutron count was not corrected for air pressure (absolute humidity), the performance of SMCRNP decreased, with a correlation coefficient of 0.74 (0.90) and Root Mean Square Error (RMSE) of 0.030 m3/m3 (0.026 m3/m3) at best. The best result was obtained when both of them were corrected (r = 0.95, RMSE = 0.014 m3/m3). As data accumulates, error metrics in terms of bias and variability converged at approximately one periodic cycle, implying at least one seasonal cycle is necessary for recalibration. However, in a case where a variable with large intra-seasonal variability (e.g., air pressure in this study) is missing, it is recommended to adopt a multi-point N0 method.The procedures suggested in this study allow independent evaluations of the factors for which neutron count is corrected, thus providing a basis for judging which factors are appropriate and which are not. In addition, N0 can be more efficiently tuned according to the characteristics of the errors in SMCRNP. Based on these analyses, CRNP stations will be able to selectively apply favorable correction factors under various kinds of local characteristics, making it easier to achieve target accuracy and precision.

Effect of dual aircraft formation spacing on target position tracking on measurement conversion by monopulse radar

Aimed at the current problem of insufficient analysis of the influence of the diversified formation of dual aircraft on monopulse radar position tracking, the dual aircraft echo equivalent two point source jamming model is built. We designed the dual aircraft formation penetration movement scenario and three different formation spacings of 150, 250, and 350 m and extracted the dynamic radar cross section sequences of dual aircraft under different formation spacings. We established the dual aircraft formation equivalent scenario and calculated the dynamic signal-to-noise ratio and distance measurement accuracy of the radar received echo signals with different formation spacings. We fully considered the randomness characteristics of the phase difference of the actual dual aircraft echo and built the random angle error model of dual aircraft echo equivalent jamming. The distribution and random numerical characteristics of the random angular error are simulated and analyzed, and we combined the radar measurement conversion target position tracking algorithm to analyze the influence of different formation spacings on the monopulse radar tracking target position and position tracking accuracy. The simulation results show that a formation spacing of 350 m produces a greater randomness of angular error for dual targets, resulting in a greater dispersion of radar measurement traces, and the final average position mean square error is 132.8 m, which improves the position error by 15.6% compared with 150 m spacing.

Analysis and evaluation of short-term wind power interval forecast error based on K-means clustering

The intermittency, volatility and randomness of wind power will inevitably affect the accuracy of wind power prediction. In order to improve the accuracy of wind power interval prediction and evaluation, a method of wind power interval prediction and evaluation based on K-means clustering is proposed. First, use K-means clustering to classify wind power prediction error data; second, combine the climate type to more accurately divide and process historical data, and apply multiple linear regression models to establish preliminary prediction models corresponding to each subset; Finally, according to the distribution characteristics of forecast errors, the results of wind power forecasting are analyzed and evaluated with interval coverage and interval average bandwidth as indicators. Comparing the K-means clustering algorithm with the BP neural network algorithm, the results show that the method proposed in this paper can better capture the characteristics of wind power error data, and can obtain more accurate wind power interval prediction and evaluation results.