Error Analysis Model Research Articles

Active Cross Pre-Compensation Decoupling Control of X-Y Linear Motor Platform Based on Contour Error Analysis Model

To improve the position accuracy of the X-Y linear motor motion platform, this paper derives a contour error analysis model (CEAM) and proposes an active cross pre-compensation decoupling controller (ACPDC). Three major factors affect the tracking performance of the X-Y motion platform are obtained from the derived CEAM: uniaxial-axis subsystem performance, dynamic compatibility between multiple axes, and disturbances. CEAM also shows that better contour control can be achieved by combining the unified modeling idea. Therefore, the ACPDC proposed in this paper integrates the inter-axis controller and single-axis subsystem by establishing a unified control law that combines active pre-compensation control with linear active disturbance rejection control. The theoretical analysis based on CEAM shows that ACPDC achieves improvements in the above three aspects. Finally, experimental results on an X-Y linear motor platform support the CEAM analysis that ACPDC has better contouring performance than other multi-axis controllers. Compared with these controllers, ACPDC has the best system robustness and uniaxial tracking performance.

ML-assisted QoT estimation: a dataset collection and data visualization for dataset quality evaluation

Machine learning (ML)-assisted solutions for quality of transmission (QoT) estimation or classification have received significant attention in recent years. However, due to the unavailability of large and well-structured datasets, individual research groups need to create and use their own datasets for validating their proposed solutions. Therefore, the reported results (obtained using different datasets) are difficult to reproduce and hardly comparable. Regardless of this limitation, the unavailability of a technique to be followed by different research groups for the explainability of the dataset makes it even harder to validate the developed ML-assisted solutions across different papers. In this work, we present a publicly available dataset collection to open the problem of data-driven QoT estimation to the ML community. The dataset collection allows various solutions presented by different research groups to be compared. Furthermore, we present techniques to visualize and evaluate datasets for QoT estimation. The presented visualizations can also deliver deep insight into the error analysis of ML models. We apply these new methods to evaluate an artificial neural network on different datasets. The results show the relevance of the presented visualizations for comparing different approaches and different datasets. The proposed methods enable the comparison and validation of different ML-based solutions and published datasets.

A case study of heavy PM2.5 secondary formation by N2O5 nocturnal chemistry in Seoul, Korea in January 2018: Model performance and error analysis

Heterogeneous hydrolysis of dinitrogen pentoxide (N2O5) plays an important role in nighttime nitrate (NO3−) formation in urban areas, and sometimes influences the occurrence of heavy PM2.5 pollution the next day in the Seoul Metropolitan Area (SMA), Korea. Here, we discuss the heavy PM2.5 wintertime episode of January 13–15, 2018, which was mainly induced by nighttime N2O5 heterogeneous reaction in the SMA. In our case, we confirmed that nighttime N2O5 hydrolysis is the most critical factor in the rapid formation of aerosol nitrate at high levels during the night, which prevailed in the morning of the next day. Our Integrated Process Rate (IPR) analysis showed that nighttime nitrate production in the episode was almost solely attributable to N2O5 chemistry, with hourly mean production rates of 0.8 ± 0.4 μg/m3 per hour in SMA, which is comparable to the daytime nitrate photochemical production rates of 0.9 ± 0.5 μg/m3 per hour. We also carried out a series of assessment of N2O5–driven nitrate formation sensitivity, and relevant errors were quantified by applying different N2O5 uptake coefficients in the WRF-CMAQ model. The potential errors of nighttime-average nitrate concentrations induced by N2O5 uptake process were assessed from a linear perspective for the planetary boundary layer (PBL) variances caused by four different PBL parameterization schemes: YSU, ACM2, MYJ, and QNSE. The potential error ranges by N2O5 uptake process were analyzed to be 2.3 to 3.5 μg/m3 (~10% relative to the nighttime-average), while biases of PBL simulations from 4 parameterization schemes were 2.3 ± 1.0 μg/m3, showing similar ranges in our episode. Although N2O5–driven heavy PM2.5 episodes do not occur often in SMA, our findings suggest the importance of N2O5 chemistry in vigorous wintertime nitrate formation and operational prediction errors of such PM2.5 episodes, under the premise of enhanced PBL simulation capabilities.

Analyzing the mechanical performance of fly ash-based geopolymer concrete with different machine learning techniques

Fly ash (FA)-based geopolymer concrete is considered as an alternative system with potentially lower environmental impact than Portland cement mixes. However, the prediction accuracy of compressive strength still needs to be improved. This study demonstrated the feasibility of predicting the 28-day strength of geopolymer concrete through mix proportions and pre-curing conditions by using three machine learning algorithms (backpropagation neural network (BPNN), support vector machine (SVM) and extreme learning machine (ELM)) and provided a comparison of their differences, highlighting variations in prediction accuracy. As a part of the evaluation of model performance and error analysis, the prediction accuracy differences of these three models in training, validation and testing sets were discussed, and the influence weight of each input parameter on results was analyzed by permutation feature importance (PFI) index. Results showed that all models revealed good prediction performance for the overall database. BPNN model had the largest number of instances where the error percentage was within ±20%. SVM model showed the highest generalization capability and most stable prediction accuracy among all three. Out of different variables investigated, SiO2 content in FA had the highest influence on strength, followed by Al2O3 content and activator content/concentration. These outcomes can enable reductions in experimental time, labor, materials and costs; and facilitate the adoption of alternative binders in the concrete industry.

Prediction of Grape Sap Flow in a Greenhouse Based on Random Forest and Partial Least Squares Models

Understanding variations in sap flow rates and the environmental factors that influence sap flow is important for exploring grape water consumption patterns and developing reasonable greenhouse irrigation schedules. Three irrigation levels were established in this study: adequate irrigation (W1), moderate deficit irrigation (W2) and deficit irrigation (W3). Grape sap flow estimation models were constructed using partial least squares (PLS) and random forest (RF) algorithms, and the simulation accuracy and stability of these models were evaluated. The results showed that the daily mean sap flow rates in the W2 and W3 treatments were 14.65 and 46.94% lower, respectively, than those in the W1 treatment, indicating that the average daily sap flow rate increased gradually with an increase in the irrigation amount within a certain range. Based on model error and uncertainty analyses, the RF model had better simulation results in the different grape growth stages than the PLS model did. The coefficient of determination and Willmott’s index of agreement for RF model exceeded 0.78 and 0.90, respectively, and this model had smaller root mean square error and d-factor (evaluation index of model uncertainty) values than the PLS model did, indicating that the RF model had higher prediction accuracy and was more stable. The relative importance of the model predictors was determined. Moreover, the RF model more comprehensively reflected the influence of meteorological factors and the moisture content in different soil layers on the sap flow rate than the PLS model did. In summary, the RF model accurately simulated sap flow rates, which is important for greenhouse grape irrigation.

Influence of Melt Ponds on the SSMIS-Based Summer Sea Ice Concentrations in the Arctic

As a long-term, near real-time, and widely used satellite derived product, the summer performance of the Special Sensor Microwave Imager/Sounder (SSMIS)-based sea ice concentration (SIC) is commonly doubted when extensive melt ponds exist on the ice surface. In this study, three SSMIS-based SIC products were assessed using ship-based SIC and melt pond fraction (MPF) observations from 60 Arctic cruises conducted by the Ice Watch Program and the Chinese Icebreaker Xuelong I/II. The results indicate that the product using the NASA Team (SSMIS-NT) algorithm and the product released by the Ocean and Sea Ice Satellite Application Facility (SSMIS-OS) underestimated the SIC by 15% and 7–9%, respectively, which mainly occurred in the high concentration rages, such as 80–100%, while the product using the Bootstrap (SSMIS-BT) algorithm overestimated the SIC by 3–4%, usually misestimating 80% < SIC < 100% as 100%. The MPF affected the SIC biases. For the high MPF case (e.g., 50%), the estimated biases for the three products increased to 20% (SSMIS-NT), 7% (SSMIS-BT), and 20% (SSMIS-OS) due to the influence of MPF. The relationship between the SIC biases and the MPF observations established in this study was demonstrated to greatly improve the accuracy of the 2D SIC distributions, which are useful references for model assimilation, algorithm improvement, and error analysis.

Analysis and compensation of the reflector antenna pointing error under wind disturbance

A large reflector antenna has been widely used in satellite communications, gravitational wave detection, galaxy origin observation and other fields due to its narrow beam and high gain. With the increase of the antenna aperture and the improvement of the working frequency, the requirements for the pointing accuracy of an antenna are also rising. However, the effect of environmental load on the deformation of the antenna structure, which in turn affects its beam pointing, has become a key problem to be solved urgently in the antenna engineering applications. The key issue to solving this problem involves accurately estimating the pointing error caused by the structural deformation and designing an effective controller that is based on the structural deformation. In this paper, we first establish a dynamic model for antenna structure based on the modal superposition method. The model is then modified by using modal characteristics and the dynamic displacement information of the sampling points to achieve the purpose of accurately estimating the structural deformation. Secondly, by considering the influence of the deformation of the rotating shaft and the reflector surface on the pointing accuracy, a control-oriented pointing error analysis model is established for estimating the pointing error caused by the environmental load in real time. Thirdly, based on considering the influence of the shaft deformation on the error compensation, the feedback error amount is decoupled and corrected to improve the accuracy of the compensation error. Finally, this paper analyzes and verifies the 65 m S/X-band dual reflector antenna with a numerical example. We consider a fluctuating wind with an average wind speed of 10 m s−1 as an example, which results in a maximum pointing error of 55.82″ as calculated by the antenna theoretical model, whereas the maximum pointing error as predicted by our model is 68.27″. The pointing error after compensating for the cause of the environmental load with the modified controller is reduced to 10.57″, which effectively improves the antenna pointing performance.

W-band radar observations for fog forecast improvement: an analysis of model and forward operator errors

Abstract. The development of ground-based cloud radars offers a new capability to continuously monitor fog structure. Retrievals of fog microphysics are key for future process studies, data assimilation, or model evaluation and can be performed using a variational method. Both the one-dimensional variational retrieval method (1D-Var) or direct 3D/4D-Var data assimilation techniques rely on the combination of cloud radar measurements and a background profile weighted by their corresponding uncertainties to obtain the optimal solution for the atmospheric state. In order to prepare for the use of ground-based cloud radar measurements for future applications based on variational approaches, the different sources of uncertainty due to instrumental, background, and forward operator errors need to be properly treated and accounted for. This paper aims at preparing 1D-Var retrievals by analysing the errors associated with a background profile and a forward operator during fog conditions. For this, the background was provided by a high-resolution numerical weather prediction model and the forward operator by a radar simulator. Firstly, an instrumental dataset was taken from the SIRTA observatory near Paris, France, for winter 2018–2019 during which 31 fog events were observed. Statistics were calculated comparing cloud radar observations to those simulated. It was found that the accuracy of simulations could be drastically improved by correcting for significant spatio-temporal background errors. This was achieved by implementing a most resembling profile method in which an optimal model background profile is selected from a domain and time window around the observation location and time. After selecting the background profiles with the best agreement with the observations, the standard deviation of innovations (observations–simulations) was found to decrease significantly. Moreover, innovation statistics were found to satisfy the conditions needed for future 1D-Var retrievals (un-biased and normally distributed).

The Efficacy of Two Teaching Methods on Minimizing the Grammatical Errors in Translating Persian Sentences into English

This study deals with spotting the grammatical errors committed by the Iranian students majoring in English translation while translating the Persian sentences into English and investigating the effect of two teaching methods, including Grammar-Translation Method (GTM) and Communicative Language Teaching Method (CLT), on minimizing these errors. For this purpose, the grammatical errors in the translation of thirty students were identified. These errors were analyzed and classified according to Keshavarz’s model of error analysis. The students were divided into two groups; the first group received the learning materials based on GTM, and in the second group CLT was applied. Afterward, a test which included English sentences extracted from Modern English 1 and 2 was designed based on the errors taken from the corpus compiled by the students’ translations. Then the frequency of the errors in both groups were analyzed by SPSS software to determine the significance of using these methods on minimizing the grammatical errors made by the students in their translations into English. To determine the level of significance, the probability value was calculated for raw errors, GTM and CLT errors. The analysis of Pearson correlation showed that both methods had their significance; however, the communicative method proved to be slightly more effective than the other. Undoubtedly, this does not mean to underestimate the efficacy of grammar translation method since it seems that it plays a complementary role in teaching environment, and achieving better pedagogical results cannot be prescribed on a single method.

Contactless Core-temperature Monitoring by Infrared Thermal Sensor using Mean Absolute Error Analysis

Aims and Objectives: Usually, the increase in temperature of an individual indicates the possibility of being infected with a disease that might be risky to other people, such as coronavirus. Traditional techniques for monitoring body core-temperature require body contact either by oral, rectum, axillary, or tympanic means, which are unfortunately considered intrusive in nature as well as causes of contagion. Therefore, sensing human core-temperature non-intrusively and remotely is the objective of this research. Background: Nowadays, increasing the level of medical sectors is a necessary target for research operations, especially the development of integrated circuits, sensors, and cameras, to make life easier. Methods: The solution is proposed as an embedded system consisting of the Arduino microcontroller, which is trained with a model of Mean Absolute Error (MAE) analysis for predicting Contactless Core-Temperature (CCT), which is the actual body temperature. Results: The Arduino microcontroller was connected to an Infrared-Thermal sensor named MLX90614 as an input signal and was connected to the LCD to display the CCT. To evaluate the proposed system, experiments were conducted on 31 subjects, and contactless temperature from the three face sub-regions was sensed, including forehead, nose, and cheek. Conclusion: Experimental results demonstrated that CCT could be measured remotely from the human face, including three face sub-regions, among which the forehead region should be preferred (a smallest error rate of 2.3%), rather than nose and cheek regions (2.6 % and 3.2% error rate, respectively) for CCT measurement due to the lowest error rates achieved.

Exploring the Types and Sources of Iranian EFL University Students’ Writing Errors

Making errors is considered to be an integral part of language learning process. Therefore, it is of paramount importance that different types and sources of students’ errors be identified. The present study aimed to examine the types and sources of errors in a corpus of essays written by 40 Iranian university students. They were given a title and asked to write an essay having at least 150 words. Based on Keshavarz’s (2013) model for error analysis, the errors made by the students were identified and tabulated according to their frequency. Moreover, the errors were classified into intralingual and interlingual ones. The results of the study indicated that the participants made 404 different errors among them, 71.88% were caused by interlingual interference, and 23.84% of them were caused by intralingual factors. The results of the study can be informative for EFL teachers to be aware of the frequency and different types of learners’ errors in EFL writing classes and the reasons underlying them.

Evaluation of Distance Error with Bluetooth Low Energy Transmission Model for Indoor Positioning

Currently, an indoor positioning is a challenge application for location-based services (LBS) and proximity-based services (PBS). However, the indoor channel has dense multipath fading, causing more distance error than outdoor positioning. In this paper, the distance error analysis model is proposed for indoor positioning. The indoor channel is modeled as the sum of path loss model and multipath fading model. The path loss model is a linear regression model (LRM) based on Friis’ transmission formula, used for estimating the distance from received signal strength (RSS). The multipath fading is a Gaussian statistical model with zero mean, used for characterizing the multipath fading effect. The normalized distance error is evaluated and defined. The indoor channel with Bluetooth low energy (BLE) beacons is measured and compared with the proposed model. From the results, the normalized distance error obtained from the proposed model corresponds very well to measurement. This proposed model can be used as a tool for designing an indoor positioning system to obtain the specific distance error.

Management of Incorrect Public Works Cost Estimations for Japan’s Local Governments

Local governmental public works departments frequently engage in planning, cost estimation, ceiling price determination, bidding, contracting, construction, supervision, and inspection activities for various building projects. However, despite all this work, local governments in Japan often cancel bids for project design and construction due to incorrect cost estimations. Consequently, facilities are often not constructed as planned, resulting in adverse impacts on public services and decreased government employees’ motivation. While local governments have conducted internal investigations to prevent incorrect estimations, a firm diagnosis model and preventive measures have not been developed. This study analyses the various causes of incorrect cost estimations and subsequently examines the resulting social problems. We propose improved human resources development management and organisational management techniques using the V-mSELC model—an error analysis model designed to address incorrect cost estimations. The study aims to aid public works professionals and interested scholars.

Mispronunciation and Substitution of Mid-high Front and Back Hausa Vowels by Yorùbá Native Speakers

The mid short vowels: /e/ and /o/ are among the vowels shared between Hausa and Yorùbá but differ in Hausa mid-high long, front and back vowels: /e:/ and /o:/. The phonemic differences in the two languages have caused learning difficulties among the Yorùbá native speakers to achieve their second language learning desire and competence. Yorùbá-Hausa learners mispronounce certain disyllabic Hausa words due to the substitution of vowels in the first and second syllables. Thus, both lexical and grammatical meanings of the Hausa words are affected. This study examined the production of the 12 Hausa vowels by level 1 and level 3 students who were learning Hausa as a second language to determine if there was a significant difference in how level 1 and level 3 students pronounced the short and long mid-high, front and back Hausa vowels. 88 Yorùbá native speakers were recruited using purposive sampling. Twenty-four different wordlists extracted from Bargery's (1934) Hausa-English dictionary and prepared in carrier phrases were audio-recorded. It was a mixed-method, and the results were discussed within the theoretical framework of Flege and Bohn's (2020) Revised Speech Learning Model and Corder's (1967) 'Error Analysis Model'. The results of the Mann-Whitney U test revealed that participants in level 1 generally performed lower than level 3 participants in the pronunciation of mid-Hausa vowels due to substitutions. Such errors have pedagogical implication in learning Hausa as a second language, and if not addressed accordingly, the standard of Hausa will continue to fall at an undesirable and alarming rate.

Investigating new CT contrast agents: a phantom study exploring quantification and differentiation methods for high-Z elements using dual-energy CT

To develop a dual-energy CT method for differentiating and quantifying high-Z contrast elements and to evaluate the limitations based on element concentration and atomic number by using an anthropomorphic phantom study. Mass spectrometry standards for iodine, barium, gadolinium, ytterbium, tantalum, gold, and bismuth were diluted from 10.0 to 0.3 mg/mL, placed inside 7-mL vials, and scanned with dual-energy CT using an abdominal phantom and cylindrical water-filled insert. This procedure was repeated with all seven high-Z elements at six isoattenuating values from 250 to 8 HU. Quantification accuracy was measured using a linear regression model and residual error analysis with 90% limits of agreement. The limit of detection for each element was evaluated using the limit of blank of water. Pairwise differentiation of isoattenuating vials was evaluated using AUC values and the difference in fit angles between the two elements. Each high-Z element had a unique concentration vector in a two-dimensional plot of Compton scattering versus photoelectric effect attenuations. Mean quantification values were within ± 0.1 mg/mL of the true values for each element with no proportional bias. Limits of detection ranged from 0.35 to 0.56 mg/mL. Pairwise differentiations were proportional to the isoattenuating HU and the angle between the linear fits with mean AUC values increasing from 0.61 to 0.98 at 8 to 250 HU, respectively. Dual-energy CT can differentiate and quantify isoattenuating high-Z elements. The high-attenuation characteristics and unique concentration vectors of ytterbium, tantalum, gold, and bismuth are well suited for new dual-energy CT contrast agents especially when simultaneously imaged with iodine, barium, or gadolinium. • Dual-energy CT can accurately quantify high-Z contrast elements and readily differentiate iodine, barium, and gadolinium from ytterbium, tantalum, gold, and bismuth. • The differentiation and quantification capabilities for high-Z contrast elements are largely unaffected by phantom size and transaxial location within the phantom. • Potential benefits of new CT contrast agents based on these high-Z elements include alternatives for patients with iodine sensitivity, high conspicuity at both 120 and 140 kVp, simultaneous imaging of two contrast agents, and reduced injection volume.

Prediction of the Number of Pure Electric Vehicles Based on the Extended GM(1,1) Model

The gray system theory is used to study the changes in the number of pure electric vehicles nationwide. According to the data on the number of pure electric vehicles in the country from 2016 to 2020, the original time series prediction model is established based on the GM(1,1) model. Using MATLAB to establish the original GM(1,1), new information GM(1,1) and metabolic GM(1,1), and compare them, and select the metabolic GM(1,1) with the smallest sum of squared errors for model performance Inspection and error analysis showed that the degree of fit was up to standard, and the predicted results were obtained. The prediction results show that the number of pure electric vehicles in my country will increase year by year from 2021 to 2023, and the number of pure electric vehicles in my country will exceed 10 million by the end of 2023.

A fast simulation method for analysis of SEE in VLSI

The transistor simulation tools (e.g. TCAD and SPICE) are widely used to simulate single event effects (SEE) in industry. However, due to the variances of the physical parameters in practical design, e.g. the nature of the particle, linear energy transfer and circuit characteristics would have a large impacts on the final simulation accuracy, which will significantly increase the complexity and cost in the workflow of the transistor level simulation for large scale circuits. Therefore, a new SEE simulation scheme is proposed to offer a fast and cost-efficient method to evaluate and compare the performance of large scale circuits in the effects of radiation particles. In this work, we have combined both the advantages of transistor and hardware description language (HDL) simulations, and proposed accurate SEE digital error models for high-speed error analysis in the large scale circuits. The experimental results show that the proposed scheme is able to handle SEE simulations for more than 40 different circuits with the sizes varied from 100 transistors to 100 k transistors.

Analysis of Land Development Drivers Using Geographically Weighted Ridge Regression

Land development processes are driven by complex interactions between socio-economic and spatial factors. Acquiring an understanding of such processes and the underlying procedures helps urban and regional planners, environmental scientists, and policy makers to base their decisions on valid and profound information. In this work, remote-sensing-derived land-cover data were used to characterize the patterns of land development from the beginning of 1985 to the beginning of 2015, in the state of West Virginia (WV), US. We applied spatial pattern analysis, ridge regression, and Geographically Weighted Ridge Regression (GWRR) to examine the impact of population, energy resources, existing land developments dynamics, and economic status on land transformation. We showed that in presence of multicollinearity of explanatory variables, how penalizing regression models in both local and global levels lead to a better fit and decreases the model’s variance. We used geographical error analysis of regression models to visualize the difference between the model estimates and actual values. The findings of this research indicate that because of shifting geography of opportunities, the patterns and processes of land development in the studied region are unstable. This leads to fragmented land developments and prevents formation of large communities.

High-Precision Automatic Calibration Modeling of Point Light Source Tracking Systems.

To realize high-precision and high-frequency unattended site calibration and detection of satellites, automatic direction adjustment must be implemented in mirror arrays. This paper proposes a high-precision automatic calibration model based on a novel point light source tracking system for mirror arrays. A camera automatically observes the solar vector, and an observation equation coupling the image space and local coordinate systems is established. High-precision calibration of the system is realized through geometric error calculation of multipoint observation data. Moreover, model error analysis and solar tracking verification experiments are conducted. The standard deviations of the pitch angle and azimuth angle errors are 0.0176° and 0.0305°, respectively. The root mean square errors of the image centroid contrast are 2.0995 and 0.8689 pixels along the x- and y-axes, respectively. The corresponding pixel angular resolution errors are 0.0377° and 0.0144°, and the comprehensive angle resolution error is 0.0403°. The calculated model values are consistent with the measured data, validating the model. The proposed point light source tracking system can satisfy the requirements of high-resolution, high-precision, high-frequency on-orbit satellite radiometric calibration and modulation transfer function detection.

Using a System-Based Monitoring Paradigm to Assess Fatigue during Submaximal Static Exercise of the Elbow Extensor Muscles.

Current methods for evaluating fatigue separately assess intramuscular changes in individual muscles from corresponding alterations in movement output. The purpose of this study is to investigate if a system-based monitoring paradigm, which quantifies how the dynamic relationship between the activity from multiple muscles and force changes over time, produces a viable metric for assessing fatigue. Improvements made to the paradigm to facilitate online fatigue assessment are also discussed. Eight participants performed a static elbow extension task until exhaustion, while surface electromyography (sEMG) and force data were recorded. A dynamic time-series model mapped instantaneous features extracted from sEMG signals of multiple synergistic muscles to extension force. A metric, called the Freshness Similarity Index (FSI), was calculated using statistical analysis of modeling errors to reveal time-dependent changes in the dynamic model indicative of performance degradation. The FSI revealed strong, significant within-individual associations with two well-accepted measures of fatigue, maximum voluntary contraction (MVC) force () and ratings of perceived exertion (RPE) (), substantiating the viability of a system-based monitoring paradigm for assessing fatigue. These findings provide the first direct and quantitative link between a system-based performance degradation metric and traditional measures of fatigue.