Artificial Errors Research Articles

The Influence of the Spatial Co-Registration Error on the Estimation of Growing Stock Volume Based on Airborne Laser Scanning Metrics

Remote sensing (RS)-based forest inventories are becoming increasingly common in forest management. However, practical applications often require subsequent optimisation steps. One of the most popular RS-based forest inventory methods is the two-phase inventory with regression estimator, commonly referred to as the area-based approach (ABA). There are many sources of variation that contribute to the overall performance of this method. One of them, which is related to the core aspect of this method, is the spatial co-registration error between ground measurements and RS data. This error arises mainly from the imperfection of the methods for positioning the sample plots under the forest canopy. In this study, we investigated how this positioning accuracy affects the area-based growing stock volume (GSV) estimation under different forest conditions and sample plot radii. In order to analyse this relationship, an artificial co-registration error was induced in a series of simulations and various scenarios. The results showed that there were minimal differences in ABA inventory performance for displacements below 4 m for all stratification groups except for deciduous sites, where sub-metre plot positioning accuracy was justified, as site- and terrain-related factors had some influence on GSV estimation error (r up to 0.4). On the other hand, denser canopy and spatially homogeneous stands mitigated the negative aspects of weaker GNSS positioning capabilities under broadleaved forest types. In the case of RMSE, the results for plots smaller than 400 m2 were visibly inferior. The BIAS behaviour was less strict in this regard. Knowledge of the actual positioning accuracy as well as the co-registration threshold required for a particular stand type could help manage and optimise fieldwork, as well as better distinguish sources of statistical uncertainty.

Serial dependencies in motor targeting as a function of target appearance.

In order to bring stimuli of interest into our central field of vision, we perform saccadic eye movements. After every saccade, the error between the predicted and actual landing position is monitored. In the laboratory, artificial post-saccadic errors are created by displacing the target during saccade execution. Previous research found that even a single post-saccadic error induces immediate amplitude changes to minimize that error. The saccadic amplitude adjustment could result from a recalibration of the saccade target representation. We asked if recalibration follows an integration scheme in which the impact magnitude of the previous post-saccadic target location depends on the certainty of the current target. We asked subjects to perform saccades to Gaussian blobs as targets, the visuospatial certainty of which we manipulated by changing its spatial constant. In separate sessions, either the pre-saccadic or post-saccadic target was uncertain. Additionally, we manipulated the contrast to further decrease certainty, changing the spatial constant mid-saccade. We found saccade-by-saccade amplitude reductions only with a currently uncertain target, a previously certain one, and a constant target contrast. We conclude that the features of the pre-saccadic target (i.e., size and contrast) determine the extent to which post-saccadic error shapes upcoming saccade amplitudes.

Climate-readiness of fishery management procedures with application to the southeast US Atlantic

Abstract Global climate change threatens the assumption of stationarity inherent in many fisheries management decisions. This heightens the importance of developing management strategies that are robust to future uncertainty. Management strategy evaluation (MSE) is a framework in which management procedures (MPs) can be developed and tested using closed-loop simulation. We explored the performance of various model-based and empirical MPs with nonstationary future projections for three commercially and recreationally important fish stocks in the southeast US Atlantic. Using openMSE, we tested candidate MP performance across projections designed to emulate plausible future conditions, including regime shifts, nonstationarity, and observation error shifts in the survey index. Candidate MP performance was primarily measured based on its ability to maintain a healthy stock biomass. Results of this MSE demonstrate that several empirical MPs may be better able to adapt to regime shifts and nonstationary dynamics compared to traditional model-based MPs that employ full age-structured stock assessments, though empirical MPs struggle to maintain stock biomass when facing artificial index observation error shifts. Relative performance of model-based versus empirical MPs varied by stock and climate-change scenario. These findings highlight the value that adaptive MPs may hold for climate-ready fisheries management.

Estimation of heat source, specific heat, and thermal conductivity of insulation materials using modified conjugate gradient method

The use of insulation materials is crucial for minimizing thermal losses in various industries like automotive, building, solar, and aerospace. Plexiglass and Expanded Polystyrene (EPS) foam are popular choices due to their effective temperature regulation, lightweight nature, and cost-effectiveness. Accurate measurement of their thermal properties is vital for achieving conservation goals. While the parallel hot wire technique provides valuable insights, accurately determining heat source strength (HW) poses a challenge, impacting measurement accuracy. To address this, the modified Conjugate Gradient Method (CGM) is employed for simultaneous estimation of thermal conductivity (λ), specific heat (c) and HW of Plexiglass and EPS foam. The direct problem result has been validated with published experimental work that shows deviations less than 2 %. The study investigates the impact of sensor positioning relative to the hot-wire (rm) and introduces artificial Gaussian error with standard deviations (θ) of 0.01 °C, 0.02 °C and 0.1 °C. At θ = 0.1 °C the percentage relative errors in the estimation of λ, c and HW with rm = 5 mm for Plexiglass are 5 %, 1.442 %, and 4.651 % respectively. For EPS foam the corresponding errors are 2.44 %, 1.26 %, and 1.74 %. The modified CGM emerges as a reliable algorithm for the measurement of thermal properties of insulation materials.

Numerical analysis of the Maxwell-Cattaneo-Vernotte nonlinear model

In the literature, one can find numerous modifications of Fourier’s law, the first of which is the Maxwell–Cattaneo–Vernotte heat equation. Although this model has been known for decades and successfully used to model low-temperature damped heat wave propagation, its nonlinear properties are rarely investigated. This paper aims to present the functional relationship between the transport coefficients and the consequences of their temperature dependence, particularly focusing on thermal conductivity. These are the consequences of the second law of thermodynamics. Furthermore, we introduce a particular implicit numerical scheme in order to solve such nonlinear heat equations reliably, free from artificial numerical errors. We investigate the scheme’s stability, dissipation, and dispersion attributes. We demonstrate the effect of temperature-dependent thermal conductivity on two different initial-boundary value problems, including time-dependent boundaries and heterogeneous initial conditions, for which we discuss the nontrivial constraints following irreversible thermodynamics.

BERT-Inspired Progressive Stacking to Enhance Spelling Correction in Bengali Text

Common spelling checks in the current digital era have trouble reading languages such as Bengali, which employ English letters differently. In response, we have created a better Bidirectional Encoder Representations from Transformers (BERT)–based spell checker that makes use of a convolutional neural network (CNN) sub-model (Semantic Network). Our novelty, which we term progressive stacking , concentrates on improving BERT model training while expediting the corrective process. We discovered that, when comparing shallow and deep versions, deeper models could require less training time. There is potential for improving spelling corrections with this technique. We categorized and utilized as a test set a 6,300-word dataset that Nayadiganta Mohiuddin supplied, some of which had spelling errors. The most popular terms were the same as those found in the Prothom-Alo artificial error dataset.

Potential roles of inter-chromosomal interactions in cell fate determination.

Mammalian genomic DNA is packed in a small nucleus, and its folding and organization in the nucleus are critical for gene regulation and cell fate determination. In interphase, chromosomes are compartmentalized into certain nuclear spaces and territories that are considered incompatible with each other. The regulation of gene expression is influenced by the epigenetic characteristics of topologically associated domains and A/B compartments within chromosomes (intrachromosomal). Previously, interactions among chromosomes detected via chromosome conformation capture-based methods were considered noise or artificial errors. However, recent studies based on newly developed ligation-independent methods have shown that inter-chromosomal interactions play important roles in gene regulation. This review summarizes the recent understanding of spatial genomic organization in mammalian interphase nuclei and discusses the potential mechanisms that determine cell identity. In addition, this review highlights the potential role of inter-chromosomal interactions in early mouse development.

Artificial neural network-based positioning error modeling and compensation for low-cost encoders of four-wheeled vehicles

Artificial neural network-based positioning error modeling and compensation for low-cost encoders of four-wheeled vehicles

Multi-scale terahertz image reconstruction

This paper proposes a novel terahertz (THz) image recovery algorithm and a new THz image dataset is publicly available. Because of transmission noise, artificial errors and problems with diffraction phenomena are among the main problems that cause degradation of THz images. The point spread function (PSF) in real transmission is first obtained using a terahertz imaging system. Experiments can generate degraded images and clean image datasets for supervised training of neural networks via terahertz PSF. A Depthwise Dense Instantiation Normalization Block (DwDIN Block) has been proposed to recover the datasets. We constructed a powerful multi-stage network (DwDINet) at each scale by utilizing the DwDIN module. For input and output, the results of each stage of the network and the original images are taken as input into the next layer in order to enhance the background information. DwDINet achieves state-of-the-art (SOTA) results on the two main THz datasets. To verify the generality of the model, we performed experiments on various image recovery tasks. According to the experimental results, the proposed model is of great potential for the field of low-level image clarification.

Simultaneous estimation of heat source strength of hot wire and thermal conductivity of material

The hot-wire (HW) technique is a widely used method for measuring the thermal conductivity (k) of insulation materials. However, there are some limitations to the applicability. For instance, inaccuracies may arise when the wire is assumed to be infinitely thin and long, or when the sample material is assumed to be an infinite medium. Additionally, the accuracy of k measurements relies on accurately specifying the heat source strength (QW ) generated by the HW. The modified conjugate gradient method (CGM) provides a solution to these limitations of the HW technique. In the modified CGM technique, a scaling factor based on the Levenberg-Marquardt method is utilized to address the low derivative and divergence issues of the traditional CGM. The current work simultaneously estimates QW generated by the HW and k of the sample materials by using modified CGM. The investigation looked into the effect of sensor positioning relative to the HW and the introduction of artificial Gaussian error (noise) with a standard deviation of 0.01 °C, 0.02 °C and 0.1 °C into the simulated temperature measurements. The modified CGM estimates the unknown parameters accurately compared to the traditional CGM. The measurement location should be closer to the HW to obtain accurate estimation results. The noise level of 0.1 °C results in low accuracy of estimation of k and QW for Plexiglas with one sensor, which can be enhanced by employing two sensors. Overall, the modified CGM algorithm proved to be a robust inverse algorithm that could be employed to estimate unknown parameters.

Classifying Coal Mine Pillar Stability Areas with Multiclass SVM on Ensemble Learning Models

Pillars are key structural components in coal mining. The safety requirements of underground coal mines are non-negotiable. Accurately classifying the areas of pillar stability helps ensure safety in coal mines. This study aimed to classify new pillar stability categories and their stability areas. The multiclass support vector machine (SVM) method was implemented with two types of kernel functions (polynomial and radial basis function (RBF) kernels) on pillar stability data with four new categories: failed or intact, either with or without an appropriate safety factor. This classification uses three basic ensemble learning models: Artificial Neural Network-Backpropagation Rectified Linear Unit, Artificial Neural Network-Backpropagation Exponential Linear Unit, and Artificial Neural Network-Backpropagation Gaussian Error Linear Unit. The results with four data proportions and ten experiments had an average accuracy and standard deviation of 92.98% and 0.56%-1.64% respectively. The accuracies of the multiclass SVM method using the polynomial kernel and the RBF kernel with Bayesian parameter optimization to classify the areas of pillar stability were 91% and 92%, respectively. The multiclass SVM method with the RBF kernel captured 96.6% of potentially dangerous pillars. The visualization of classification areas showed that areas with intact pillars may also have failed pillars.

Evaluation of plastic properties and equi-biaxial residual stress via indentation and ANN

This study presents a method for evaluating plastic properties and equi-biaxial residual stress (RS) simultaneously using the images of generated imprints in indentation tests and an artificial neural network (ANN). The ANN model is trained with data from finite element analysis (FEA) for establishing the relationship between material properties and the radial (ur) and vertical (uz) displacements resulting from indentation tests. To deal with the inherent noise in experimental data, an artificial random noise was added to the FEA data used for training the ANN model. By inputting parameters with artificial errors into the ANN model, the robustness of predicted values against potential testing errors was examined. Also, the accuracy of the predicted residual stresses and material properties from ANN are validated using tensile tests and indentation tests on the stress-induced specimens.

ARABIC SOFT SPELLING CORRECTION WITH T5

Spelling correction is considered a challenging task for resource-scarce languages. The Arabic language is one of these resource-scarce languages, which suffers from the absence of a large spelling correction dataset, thus datasets injected with artificial errors are used to overcome this problem. In this paper, we trained the Text-to-Text Transfer Transformer (T5) model using artificial errors to correct Arabic soft spelling mistakes. Our T5 model can correct 97.8% of the artificial errors that were injected into the test set. Additionally, our T5 model achieves a character error rate (CER) of 0.77% on a set that contains real soft spelling mistakes. We achieved these results using a 4-layer T5 model trained with a 90% error injection rate, with a maximum sequence length of 300 characters.

Optical flow-based vertical angular rate fault detection on UAVs

This paper discusses optical flow-based vertical angular rate fault detection based-on real fight IMU data and camera image sequences. The fight test platform utilizes a gimbal stabilized downward looking camera that is why only the angular rate around the inertial vertical axis can be estimated. Improvements relative to the previous work of the authors are the application of forward-backward optical flow to filter outliers, the direct detection of angular rate faults without the need for Euler angles or GPS velocities and tuning and verification on real images and fight data. After presenting the optical flow equations and the method for image-based vertical angular rate estimation the fight test scenarios and video processing steps are introduced. Four different Artificial errors are added to the IMU measurements and the fault detection applies residual thresholding and up-down counters. Tuning is based on false alarm and missed detection rates and the application of receiver operating characteristics. Finally, the estimation results and possible further improvements are presented.

PREDICTION OF BLAST-INDUCED FLYROCK BY USING NEURAL-IMPERIALIST COMPETITIVE METHOD (CASE STUDY: SUNGUN COPPER MINE)

This research focuses on conducting studies that predict the distance of blast-induced flyrock, which is an undesirable environmental phenomenon in open-pit mines. While there are experimental methods available for predicting blastinduced flyrock, the complex process of assessing the distance of flyrock has reduced the efficiency of these approaches. This study employs artificial intelligence methods and statistical techniques to forecast the flyrock distance in the Sungun copper mine. Thus, an Artificial Neural Network (ANN-MLP) and a new hybrid model of Artificial Neural Network (ANN) optimized by the Imperialist Competitive Algorithm (ICA), known as (ICA-ANN), are used to predict the flyrock distance, considering crucial parameters such as the number of holes, hole spacing, burden, total charge, specific drilling, charge per hole and specific charge. The results showed that the Artificial Neural Network, with RMSE, MAE, and R2 error values of 9.31 m, 7.10 m, and 0.81, respectively, was able to predict the flyrock distance well compared to the measured data in the test phase. However, the implementation of the imperialist competitive algorithm optimizer in the neural network enhanced the prediction of the flyrock distance, yielding RMSE, MAE, and R2 values of 5.66 m, 4.60 m, and 0.89, respectively. Finally, by performing sensitivity analysis on the input parameters of the flyrock distance, it was determined that the amount of explosive consumption and the number of holes have the greatest impact on the blastinduced flyrock distance.

Thermal displacement prediction of variable preload motorized spindles based on speed reduction experiments and IABC-BP optimization models

The thermal error caused during the high-speed operation of a high-speed motorized spindle has a significant impact on machine tool precision. In this paper, we firstly built, constructed an experimental platform for variable voltage preloaded motorized spindle, used the natural deceleration experimental method to ascertain the spindle's thermal displacement and temperature information when bearing preload is 1400 N, 1450 N, 1550 N and 1700 N, and combined with the thermoelasticity theoretical analysis, the thermal displacement of affected by the heat generation of motor and bearing was separated, and a new artificial bee colony algorithm-optimized BP neural network-based thermal error prediction model has been presented. The thermal displacements of the spindles are separated and a BP neural network optimization of a novel thermal error prediction model based on Improved Artificial Bee Colony Algorithm (IABC-BP) is proposed for transformer preloaded spindles. Combined with the experimental data of bearing preload of 1400 N, the thermal displacements of the spindle at preloads of 1450 N, 1550 N and 1700 N are predicted, and the evaluation indexes obtained from the simulation of the BP, ABC-BP, and IABC-BP models are compared. absolute error (MAE) and root-mean-square error (RMSE), higher goodness-of-fit (R2), and 20.37 % lower mean absolute error and 26.83 % lower root-mean-square error compared with the traditional thermal error prediction model. The IABC-BP prediction model proposed in this paper provides a more accurate compensation method for transformer preloaded spindles.

Improve the capability of physical model for runoff and sediment yield modelling with a hybrid artificial intelligence-based error updating system

Uncertainty of physical models is caused by the model's complexity as well as the accuracy of input variables. In present study, an error update model was developed based on artificial intelligence, namely Wavelet Multilayer Perceptron (WMLP), Wavelet Support Vector Machine (WSVM), Wavelet Multilayer Perceptron-Genetic Algorithm (WMLP-GA), and Wavelet Support Vector Machine-Genetic Algorithm (WSVM-GA). Sensitivity findings demonstrate that groundwater recession factor (ALPHA_BF), plant uptake compensation factor (EPCO), and curve number were the most sensitive for runoff in the Shivnath River basin, India, as well as four distinctparameters were shown to be highly sensitive to sediment yield at the Sigma station. During the calibration and validation phase, the SWAT model overpredicts runoff at the Simga and Pathardhi stations while underpredicting it at the Kotni station. SWAT model performance for runoff modelling is enhanced via the use of statistical indicators like as Percent of Bias (PBIAS) decreased from −35.355 to −20.503, Root Mean Square Error (RMSE) decreased from 319.905 to 25.191 m3/sec, and coefficient of determination increase from 0.712 to 0.904. At Simga station, the Nash–Sutcliffe efficiency (NSE) increase from 0.315 to 0.745, the PBIAS decreased from −39.502 to –22.946, the RMSE decreased from 186.711 to 24.432 m3/sec, and the coefficient of determination increased from 0.733 to 0.996. Further, NSE increased from 0.305 to 0.985 at Kotni station and PBIAS reduced from −114.026 to −9.214, RMSE reduced from 82.205 to 31.082 m3/sec, coefficient of determination increased from 0.779 to 0.976, NSE increased from 0.523 to 0.927 at Pathardhi station after applying an error update model. For sediment yield prediction, similar outcomes to runoff were obtained. The WSVM-GA model was reported to be the optimum error update model both for runoff and sediment yield modelling. It was recommended that the SWAT model might be applied for runoff and sediment yield simulation in the future.

Functionalized magnetic metal organic framework nanocomposites for high throughput automation extraction and sensitive detection of antipsychotic drugs in serum samples

Due to the complexity of biological sample matrix, the automated and high-throughput pretreatment technology is urgently needed for monitoring the antipsychotic drugs for mental patients. In this study, functionalized magnetic zirconium-based organic framework nanocomposites (Fe3O4@SiO2@Zr-MOFs) were successfully designed and synthesized by the layer-by-layer growth. Among them, Fe3O4@SiO2@UiO-67-COOH showed the best adsorption performance, and at the same time it exhibited excellent water dispersibility, high thermal stability, chemical stability and high hydrophobicity. Results of adsorption kinetics, isotherm and FT-IR showed that the adsorption process was dominated by chemical adsorption (hydrogen bond, electrostatic interaction, π-π interaction) and monolayer adsorption. Moreover, the smaller pore size improved the protein exclusion rate which reached 98.9–99.8%. Based on the above result, the synthesized magnetic nanoparticles were introduced to 96-well automatic extractor, antipsychotic drugs in 96 serum samples were automatically extracted within 9 min, which most greatly saved the time and labor costs and avoided artificial errors. By further integrating with ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), antipsychotic drugs can be detected in the range of 0.2–3.0 ng mL−1 with a quantitative limit of 0.06–0.9 ng mL−1. The recoveries of antipsychotic drugs and their metabolites in serum ranged from 95.7% to 112.3% within 1.4–6.5% of RSD. These features indicate that the proposed method is promising for high throughput and sensitively monitoring of drugs and other hazardous substances.

Machine‐learning‐based morphological analyses of leaf epidermal cells in modern and fossil ginkgo and their implications for palaeoclimate studies

AbstractLeaf stomata form an essential conduit between plant tissue and the atmosphere, thus presenting a link between plants and their environments. Changes in their properties in fossil leaves have been studied widely to infer palaeo‐atmospheric‐CO2 in deep time, ranging from the Palaeozoic to the Cenozoic. Epidermal cells of leaves, however, have often been neglected for their usefulness in reconstructing past‐environments, as their irregular shape makes the manual analyses of epidermal cells a challenging and error‐prone task. Here, we used machine‐learning (using the U‐Net architecture, which evolved from a fully convolutional network) to segment epidermal cells automatically, to efficiently reduce artificial errors. We furthermore applied minimum bounding rectangles to extract length‐to‐width ratios (RL/W) from the irregularly shaped cells. We applied this to a dataset including over 21 000 stomata and 170 000 epidermal cells in 114 Ginkgo leaves from 16 locations spanning three climate zones in China. Our results show negative correlations between the RL/W and specific climatic parameters, suggesting that local temperature and precipitation conditions may have affected the RL/W of epidermal cells. We subsequently tested this methodology and the observations from the modern dataset on 15 fossil ginkgoaleans from the Lower to the Middle Jurassic (China). It suggested that the RL/W values of fossil ginkgo generally had a similar negative response to warmer climatic backgrounds as modern G. biloba. The automated analyses of large palaeo‐floral datasets provide a new direction for palaeoclimate reconstructions and emphasize the importance of hidden morphological characters of epidermal cells in ginkgoaleans.

Determining the Sparking Voltage of Working Electrolytes

Sparking voltage is one of the vital parameters of the working electrolytes for aluminum electrolytic capacitors, which determines the operating voltage of the capacitors. However, the existing methods for measuring the sparking voltage suffer from low accuracy, bad reproducibility, high artificial error, etc. In this paper, we reported a versatile approach to determining the sparking voltage of the electrolytes. By a linear fitting procedure, the voltage-time data acquired from the test experiments were transformed into a correlation coefficient-time curve. Since the abrupt correlation coefficient change due to the occurrence of sparking could be more readily identified in the correlation coefficient-time curve, the limit voltage of the electrolyte was accurately determined. Thus, this voltage was subtracted from the IR drop in the solution to get the true sparking voltage of the electrolyte. This method prevented artificial errors arising from the direct observation or listening of sparking features, by which reliable and reproducible results can be gained by a computer program. This study provided active support for the development of high-performance working electrolytes.