Error Research Articles

First psychometric evaluation of the Mexican Spanish version of the 11-item BPFSC-11 and convergence with maladaptive self and interpersonal functioning.

The Borderline Personality Features Scale for Children (BPFSC-11) is a well-used, short, and easy-to-administer measure of borderline traits in young people. The aim of the current study was to evaluate the psychometric properties of the Mexican Spanish version of the BPFSC-11 in a large community-based sample of Mexican adolescents. In addition, we evaluated the convergence between the borderline construct as measured by the BPFSC-11 and general personality functioning consistent with more contemporary personality disorder formulations to assess the nomological net of the BPFSC-11 total score. A sample of 1212 adolescents (52.6% female, mean age = 15.11, SD = 1.74) were recruited from the community through schools in Mexico City and Michoacán area. Reliability of the BPFSC-11 was good (α = 0.805; ω = 0.806). A confirmatory factor analysis (CFA) model testing the unidimensional factor structure of the 11 items of the BPFSC-11 provided good fit (X2[44] = 137.96, p < 0.001; root mean square error of approximation (RMSEA) = 0.04 [90% CI: 0.03, 0.05]; comparative fit index (CFI) = 0.96; standardized root mean square residual (SRMR) = 0.03). Both configural and metric invariance for gender and age were demonstrated; however, only partial scalar invariance could be demonstrated with some items showing "bias" for gender and age. The Mexican Spanish BPFSC-11 also showed strong convergence with measures assessing contemporary conceptualization of general personality functioning. The Mexican Spanish BPFSC-11 appears to show similarly strong psychometric properties of other versions of the BPFSC-11; and the borderline construct as assessed with the BPFSC-11 shows good conversion with general personality functioning in adolescents.

Psychometric properties of a physiotherapy care satisfaction scale using telerehabilitation in caregivers of pediatric patients during the COVID-19 pandemic.

Evidence on the psychometric properties of satisfaction scales in telerehabilitation is limited, especially in specific populations such as caregivers of children. To determine the psychometric properties of a physiotherapy care satisfaction scale using telerehabilitation in caregivers of pediatric patients during the COVID-19 pandemic. A total of 155 caregivers were evaluated between June and December 2020. Approximately 91% (141) were women. Evidence of content validity for the scale was obtained through evaluation by three expert judges, who confirmed the adaptation of the University of Washington Telemedicine Patient Satisfaction Survey, in which the word "telemedicine" was replaced with "telerehabilitation" and "physician" with "physical therapist." For the confirmatory factor analysis, two models were tested. The first one-factor model with nine items did not fit satisfactorily based on the goodness-of-fit indices (χ 2/df = 13.96, comparative fit index [CFI] = 0.963, non-normed fit index [NNFI] = 0.951, root mean square error of approximation [RMSEA] = 0.290 [0.265, 0.316], and standardized root mean square residual [SRMR] = 0.178). In contrast, the second one-factor model, which involved respecification of Items 6 and 7, was considered acceptable (χ 2/df = 1.60, CFI = 0.998, NNFI = 0.998, RMSEA = 0.062 [0.021, 0.096], and SRMR = 0.057). Reliability was acceptable, with a value of 0.888. Additionally, network analysis confirmed the direct relationship between the items, with Item 7 showing the greatest strength centrality. The instrument demonstrated sufficient evidence of validity and reliability in the Peruvian context, supporting its use with pediatric patients.

Deterministic Weight Modification-based Extreme Learning Machine for Stock Price Prediction

Background: The prediction of the stock price is considered to be one of the most fascinating and important research and patent topics in the financial sector. Aims: Making more accurate predictions is a difficult and significant task because the financial industry supports investors and the national economy. Objectives: The DWM is used to adjust the connection weights and biases to enhance prediction precision and convergence rate. DWM was proposed as a method to reduce system error by changing the weights of various levels. The methods for predictable changes in weight were provided together with the computational difficulty. Methods: An extreme learning machine (ELM) is a fast-learning method for training a singlehidden layer neural network (SLFN). However, the model's learning process is ineffective or incomplete due to the randomly chosen weights and biases of the input's hidden layers. Hence, this article presents a deterministic weight modification (DWM) based ELM called DWM-ELM for predicting the stock price. Results: The calculated results showed that DWM-ELM had the best predictive performance, with RMSE (root mean square error) of 0.0096, MAE (mean absolute error) of 0.0563, 0.0428, MAPE (mean absolute percentage error) of 1.7045, and DS (Directional Symmetry) of 89.34. Conclusion: The experimental results showed that, in comparison to other well-known prediction algorithms, the suggested DWM+ELM prediction model offers better prediction performance.

Ensemble Machine Learning for Predictive Accuracy and Experimental Corroboration on transition metal-based electrodes for Supercapacitor

Machine learning (ML) proves highly effective in refining the electrochemical performance of electrode materials through precise tuning of compositional parameters. This study employs a data-driven ensemble learning approach, integrating classification and regression techniques to investigate the electrochemical behavior of transition metal-based electrodes with varying Ni:Co stoichiometry. A resample filter is introduced to the dataset to enhance predictive accuracy, yielding closely aligned classification and regression predictions. ML predictions discern that Ni-rich electrodes are apt for high-capacity, while Co-rich excels in high-rate applications. The characterizations of developed electrode materials XRD, FT-IR, EDS, and XPS confirmed the formation of desired compositions. The XPS analysis indicates that nickel and cobalt are in variable Ni2+/Ni3+ and Co2+/Co3+ oxidation states, enabling a reversible charge storage mechanism, while phosphorus is present in the pentavalent state. The TEM micrograph suggests that the nanoparticle size is extremely small, in the range of 1-3 nm, which is significant for providing a high surface-to-volume ratio, thereby enhancing specific capacitance (Csp). Experimental findings highlight N1C2 as the superior electrode, exhibiting impressive capacitance (2247.6 F g−1 at 3 A g−1), robust rate retention (78.6% from 3 to 20 A g−1), and substantial capacitance retention (91.3% after 10,000 charge-discharge cycles), which is in line with ML prediction. The experimental validation highlights the excellent agreement (percentage error: 2.48 to 8.46%) between the capacitance that was predicted, and the capacitance achieved experimentally. Predicted rate and cyclic retention curves align closely with experimental results, with minimal errors (0.03 to 19.3% and 3.95 to 19.64%, respectively). Lastly, the introduction of reverse structural and operational feature engineering contributes to the optimization of electrode material performance.

Bitcoin volatility forecasting: a comparative analysis of conventional econometric models with deep learning models

The behavior of the Bitcoin market is dynamic and erratic, impacted by a range of elements including news developments and investor mood. One well-known aspect of bitcoin is its extreme volatility. This study uses both conventional econometric techniques and deep learning algorithms to anticipate the volatility of Bitcoin returns. The research is based on historical Bitcoin price data spanning October 2014 to February 2022, which was obtained using the Yahoo Finance API. In this work, we contrast the efficacy of generalized autoregressive conditional heteroskedasticity (GARCH) and threshold ARCH (TARCH) models with long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and multivariate Bi-LSTM models. Model effectiveness is evaluated by means of root mean squared error (RMSE) and root mean squared percentage error (RMSPE) scores. The multivariate Bi-LSTM model emerges as mostly effective, achieving an RMSE score of 0.0425 and an RMSPE score of 0.1106. This comparative scrutiny contributes to understanding the dynamics of Bitcoin volatility prediction, offering insights that can inform investment strategies and risk management practices in this quickly changing environment of finance.

A multivariable adaptive control method for aeroengine with H∞ performance considering engine output limitation protection based on fully adjustable Neural Network

Aeroengines, being highly nonlinear systems subject to external disturbances, present a challenge for conventional control methods in achieving satisfactory performance. The radial basis function neural networks possess the capability to effectively model highly approximate nonlinear functions, making them suitable for implementation in aeroengine control. However, the radial basis function (RBF) neural network is a local approximation model that inadequately incorporates historical information. Additionally, the tracking error of neural networks can also impact control effectiveness. To address this issue, this paper proposes a multivariable control method for aeroengines with H∞ performance based on fully adjustable Long Short-term Memory-Radial Basis Function Neural Network (LSTM-RBFNN). Firstly, a factual mathematical model of an aeroengine is established based on the principles of aerodynamics and thermodynamics. Secondly, the paper proposed an LSTM-RBF neural network architecture to enhance prediction performance and proposed using a finite time perturbation estimator to gauge the approximate error of neural networks, thereby acquiring immeasurable approximative error information. Then, in order to mitigate the influence of neural network tracking error on the loop, the estimated errors are incorporated into the primary loop controller, and a static state disturbance full feedback H∞ controller is proposed. Then, leveraging the LSTM-RBF architecture proposed in this study, we further employ Taylor expansion of nodes to derive the adaptive law of the neural network and propose a novel neural network adaptive law that incorporates tracking error estimated by the disturbance observer into the adjustment mechanism to enhance tracking performance. The stability of this adaptive law has been proven based on Lyapunov function analysis. And considering the output limitation protection problem of aeroengine, a virtual command value based limitation protection method is proposed for the above control method, and it is proved that this limitation method does not damage the stability and design process of the original control method. Finally, numerical simulation and hardware in loop simulation experiments were conducted, and the results showed that under the same adaptive gain of the neural network, the performance indicators (IAE, IATE, σ) could be reduced by more than 50% compared to other methods, which demonstrates superior tracking and disturbance rejection performance achieved by our proposed method.

An Approach to Forecast Quality of Water Effectively Using Machine Learning Algorithms

Background:: The quality of water directly or indirectly impacts the health and environmental well-being. Data about water quality can be evaluated using a Water Quality Index (WQI). Computing WQI is a quick and affordable technique to accurately summarise the quality of water. Objective:: The objective of this study is to find strategies for data preparation to categorize a dataset on the water quality in two remote Indian villages in different geographic locations, to predict the quality of water, and to identify low-quality water before it is made accessible for human consumption. Methods:: To accomplish this task, four water quality features Nitrate, pH, Residual Chlorine, and Total Dissolved Solids which are crucial for human consumption, are considered to dictate the quality of water. Methods used in handling these features include five steps that are data preprocessing with min-max normalization, finding WQI, using feature correlation to identify parameter importance with WQI, application of supervised machine learning regression models such as Random Forest (RF), Multiple Linear Regression (MLR), Gradient Boosting (GB) and Support Vector Machine (SVM) for WQI prediction. Then, a variety of machine learning classification models, including K-Nearest Neighbour (KNN), Support Vector Classifier (SVC), and Multi-layer Perceptron (MLP), are ensembled with Logistic Regression (LR), acting as a meta learner, to create a stack ensemble model classifier to predict the Water Quality Class (WQC) more accurately. Results:: The examination of the testing model revealed that RF regression and MLR algorithms performed best in predicting the WQI with mean absolute error (MAE) of 0.003 and 0.001 respectively. Mean square error (MSE), root mean square error (RMSE), R squared (R2), and Explained Variance Score (EVS) findings are 0.002,0.005,0.988 and 0.998 respectively with RF while 0.001,0.031,0.999 and 0.999 respectively with MLR. Meanwhile, for predicting WQC, the stack model classifier showed the best performance with an Accuracy of 0.936, F1 score of 0.93, and Matthews Correlation Coefficient (MCC) of 0.893 for the dataset of Lalpura and Accuracy of 0.991, F1 Score of 0.991 and MCC of 0.981 respectively for the dataset of Heingang. Conclusion:: This study explores a method for predicting water quality that combines easy and feasible water quality measurements with machine learning. The stack model classifier performed best for multiclass classification, according to this study. To ensure that the highest quality of water is given throughout the year, information from this study will motivate researchers to look into the underlying root causes of the quality variations.

A New Fast Root-finder for Black Box Polynomials

Univariate polynomial root-finding has been studied for four millennia and very intensively in the last decades. Our black box root-finder involves no coefficients and works for a black box polynomial, defined by an oracle (that is, black box subroutine) for its evaluation. Such root-finders have various benefits, e.g., are particularly efficient where a polynomial can be evaluated fast, say, is a sum of a small number of shifted monomials (x−c)a. With incorporation of a fast algorithm by the first author for compression of a disc on the complex plane without losing roots, our root-finder approximates all d complex zeros of a dth degree polynomial p(x) (aka roots of equation p(x)=0) by using near-optimal Las Vegas expected number of bit-operations,1 that is, the root-finder is expected to run almost as fast as one accesses the coefficients with a precision required for the solution within a prescribed error bound. The only other known near-optimal polynomial root-finder was presented by the first author at ACM STOC 1995. It is quite involved and has never been implemented, while already in its initial implementation our new root-finder competed with user's choice package of root-finding subroutines MPSolve, according to extensive numerical experiments with standard test polynomials. Furthermore we readily extend our black box root-finder to approximation of the eigenvalues of a matrix in record expected bit operation time, while the root-finder of STOC 1995, using the coefficients of p(x), does not support such extension.

Towards optimal fillet portioning: a computer vision system for determining the fish fillet volume

Portioning large fish fillets for packaging is usually performed manually by skilled workers. Automating this process and obtaining packaged products with attractive shapes and affordable weights will be beneficial for promoting purchase decisions. Towards developing an automated fish fillet portioning system, this study investigated a computer vision approach for determining the fillet volume. A belt conveyor would transport a fish fillet to the image capture booth, where its cross-section areas would be calculated for volume determination. The developed system could be operated with a conveyor speed ranging from 7.5 to 30.6 mm/s. The system performance was evaluated at a conveyor speed of 7.5 mm/s using small catfish fillets from 142.2 to 225.4 cm3. A mean percent error of 9.2% was observed, and the smallest percent error of 3.8% was obtained with a 225.4 cm3 fillet. With minor measurement errors obtained for larger fillets, the proposed computer vision system showed great potential for developing a cost-effective automated system for customized fish fillet partitioning to accelerate purchase decisions and also for quality control and classification of the fish fillets.

Analytical Validation of Wrist-Worn Accelerometer-Based Step-Count Methods during Structured and Free-Living Activities.

Wrist-worn accelerometers can capture stepping behavior passively, continuously, and remotely. Methods utilizing peak detection, threshold crossing, and frequency analysis have been used to detect steps from wrist-worn accelerometer data, but it remains unclear how different approaches perform across a range of walking speeds and free-living activities. In this study, we evaluated the performance of four open-source methods for deriving step counts from wrist-worn accelerometry data, when applied to data from a range of structured locomotion and free-living activities. In addition, we assessed how modifying the parameters of these methods would affect their performance. Twenty-one participants (ages 20-33) wore an ActiGraph CentrePoint Insight Watch (Actigraph, LLC) on their non-dominant wrist while completing structured locomotion activities in a motion capture laboratory and during a free-living period in a mock apartment. Criterion step counts were determined from motion capture heel-strike events and from StepWatch 3 (Modus Health, LLC) during the free-living period. Four open-source methods implementing different algorithmic approaches were applied to CPIW data to derive step counts. The quantity and timing of method-derived and criterion steps during each type of activity were then compared. In terms of performance during structured locomotion, methods that relied on a single parameter, such as peak detection or threshold crossing, demonstrated the lowest bias among those investigated. Furthermore, three of the four investigated methods overestimated step counts during slow walking and underestimated step counts during fast walking, while the last method consistently underestimated at least half of the recorded steps across all speeds. During free-living activities, the method relying on frequency analysis exhibited the lowest percent error of all methods. Finally, we found that the incorporation of a locomotion classifier, wherein steps were only estimated during identified locomotion periods, reduced error for two methods when applied to data across structured and free-living settings. In studying the performance of different step-counting approaches across different settings, we found a tradeoff between performance during structured walking and that during free-living activities. These findings highlight the opportunity for novel, context-aware methods for accurate step counting across real-world settings.

Method of undetermined coefficients for circuits and filters using Legendre functions

This article presents a new way to implement matching networks and filters using the method of undetermined coefficients. A method is proposed for approximating the transmission coefficient of the synthesized filter, taking into account the required amplitude-frequency characteristics. To synthesize the filter, an approximating function (AF) was used using orthogonal Legendre polynomials, which is a mathematical description using a system of equations. Filter properties whose implementation is based on modified Legendre approximating functions usually depend on the interval on which they are defined and have the property that they are orthogonal on this interval. An example of seventh order filter synthesis using modified Legendre approximating functions is given. The filter circuit is implemented, the elements of the filter circuit are calculated based on the selected approximating modified function. The criteria used were minimization of the unevenness of the group delay time (GDT) and minimization of the complex approximation error for given values of the AF parameters. As a result, the number of filter elements, the group delay value and the complex approximation error are significantly reduced.

Forecasting the number of rural migrant workers in Chongqing using the optimized grey N_Verhulst model

Scientific prediction of migrant worker numbers provides decision-making references for resolving rural talent supply issues. Based on the evolutionary patterns and data features of Chongqing’s migrant workers, a new grey prediction model is constructed. The new model is constructed by introducing fractional-order operators in the real domain. In this way, the accumulating order of the traditional N_Verhulst model is optimized. It expands from 1 to all real numbers, thus enhancing its capacity to mine approximately saturated S-shaped time-series data. When the new N_Verhulst model is applied to simulate and predict migrant worker numbers, after optimizing the accumulating order, the mean relative simulation percentage error of the N_Verhulst model reduces from 3.66 to 2.93%, the mean relative forecasting percentage error from 8.02 to 2.18%, and the comprehensive mean relative percentage error from 4.53 to 2.78%. This shows that the optimization boosts the simulation and prediction performance of the N_Verhulst model. The prediction results show that the number of migrant workers in Chongqing will experience an orderly growth, rising from 2.41 million in 2023 to 2.85 million in 2028, with an increase of 18.26% and an average annual growth rate of 3.41%.

Daily power generation forecasting for a grid-connected solar power plant using transfer learning technique

Deep learning is efficiently used for photovoltaic power generation forecasting to handle the intermittent nature of solar energy. However, big data are required for training deep networks which are not available for newly installed plants. Therefore, in this study, a novel strategy is proposed to train a deep learning model using a transfer learning technique to cop up with the unavailability of enough training datasets. A new 400 kWp solar power plant installed in the Himalayan region is considered as a case study to evaluate the proposed model. The proposed approach utilizes solar radiation data to train a deep neural network and then fine-tune the model using the power generation data from the plant. The network architecture is optimized using grey wolf optimizer to find the best suitable model for the data. The evaluation results show that the same model can achieve higher performance in generation forecasting with percentage error improved by 2% and R-value increased by 7.7% after applying transfer learning. Moreover, SHapley Additive exPlanation and Partial Dependence Plots are used to interpret the model behavior and showed that the model is mostly dependent on the previous generation values (up to 4 days) followed by the temperature and solar radiation.

Validating the Black Identity, Hair Product Use, and Breast Cancer Scale (BHBS) Among Black Breast Cancer Survivors

Personal care products containing toxic chemicals (e.g., endocrine-disrupting chemicals) may increase breast cancer risk, especially for Black women who use these products more than other racial groups. There are limited tools that examine the intersections of identity, behaviors, and attitudes surrounding product use, perceived safety, and breast cancer risk; thus, the Black Identity, Hair Product Use, and Breast Cancer Scale (BHBS) was developed to bridge this gap. While initial validations lacked diverse survivor representation, this study seeks to validate the BHBS among Black survivors. Methods: This study is a part of the Bench to Community Initiative (BCI), where respondents (n = 167) completed a 41-item survey including the BHBS between 2020 and 2022. The use of Principal Component Analysis (PCA) and confirmatory factor analysis (CFA) established the underlying component structures and model fit. CFA measures used to confirm component structures included the Root Mean Square Error of Approximation, the Comparative Fit Index, and the Tucker Lewis Index. Results: Black survivors on average were diagnosed with breast cancer before age 40 (37.41 ± 8.8) with Stage 1 (45%) disease. Sixty-three percent of the total variance resulted in a two-component structure. Subscale 1 (S1) measures the sociocultural perspectives about hair and identity (28% of the total variance; α = 0.73; 95% CI = 0.71–0.82). Subscale 2 (S2) can be used to assess perceived breast cancer risk related to hair product use (35% of the total variance; α = 0.86; 95% CI = 0.81–0.94). The two-component structure was confirmed with Root Mean Square Error of Approximation = 0.034, Comparative Fit Index = 0.93, and Tucker Lewis Index = 0.89. Discussion/Conclusions: The BHBS is a valid tool to measure identity, attitudes, and behaviors about product use and breast cancer risk among survivors. Hair is a significant cultural identity expression, and the health effects of styling products should be considered in future interventions.

Optimizing photovoltaic power plant forecasting with dynamic neural network structure refinement

Reliable prediction of photovoltaic power generation is key to the efficient management of energy systems in response to the inherent uncertainty of renewable energy sources. Despite advances in weather forecasting, photovoltaic power prediction accuracy remains a challenge. This study presents a novel approach that combines genetic algorithms and dynamic neural network structure refinement to optimize photovoltaic prediction. This methodology dynamically adjusts the neural network parameters during training, including the number of neurons, transfer functions, weights, and biases, to minimize the root mean square error. Evaluation was performed on twelve representative days using annual, monthly, and seasonal data, and a comparison was made with multiple linear regression and nonlinear autoregressive neural network models, demonstrating the approach’s effectiveness. Evaluation metrics such as mean square error, R-value, and mean percentage error reveal promising prediction accuracy. MATLAB is used for modeling, training, and testing, and a real 4.2 kW PV plant is used for validation. The results indicate significant improvements, with mean square errors as low as 20 W on cloudy days and 175 W on sunny days. The proposed methodology achieves prediction versus target regressions consistency, with R values ranging from 0.95824 to 0.99980, highlighting its efficiency in providing reliable predictions of PV power generation.

Investigation of the dynamic behavior of hybrid composites using finite element and experimental method

ABSTRACT Composite materials are used extensively nowadays because of their excellent strength-to-weight ratio, high hardness, and good tensile strength. The characteristics of the composite material are a function of the type of the fiber, i.e. natural or synthetic, size, a resin used and so on. Most industries are using hybrid composites nowadays to have the desired properties. In the present analysis, four different fibers, i.e. Carbon, E-Glass, Flax and Bamboo, are used, their composites hybrid composites have been created with ASTM D3039, and their mechanical behavior is investigated using Finite Element Analysis. The eigenvalues and vibrational behavior of the hybrid composites are analyzed through Finite Element Analysis. The eigenvalues and mechanical behavior are also determined through experimental testing and compared with the same obtained through Finite Element Analysis. The results obtained from the two methods agree well; therefore, the analytical model is validated. The optimum hybrid composite material is decided based on the mechanical properties, natural frequencies and damping ratio. The six hybrid composites show the natural frequency varies from 17 hz to 32 hz. Similar frequencies are predicted from Finite Element Analysis, and the maximum percentage error of experimental and Finite Element Analysis models varies up to 4%.

Temperature Compensation of Low-cost Sensors for Accurate Temperature Measurement

Evapotranspiration (ET) is a critical process in agricultural system. ET states the combined water loss from the soil and plant surfaces. Predicting ET is vital for effective water resource management. Accurate temperature and humidity measurements is instrumental in calculating Et. Temperature measurement plays a key role in various industrial, scientific, and agricultural applications. The study investigates the performance of three temperature humidity sensors. DHT11, DHT22 and LM35. The sensors are analyzed under different conditions, including compensation, and with a temperature compensation algorithm, and with proportional P and proportional-integral-derivative PID controllers. Sensors are interfaced with a Raspberry Pi 4B model. The sensors were tested in controlled environments with temperatures ranging from 10 degrees to 100 degrees C, and their accuracy was evaluated by comparing their outputs with the reference values. Results revealed that the error percentages for all sensors exceeded permissible limits when used outside their specified ranges. Incorporating temperature compensation algorithm and P and PID controllers significantly improved measurement accuracy, reducing error percentages and root mean square errors (RMSE) across all sensors. Out of the three sensors, LM35 demonstrated a 5-fold reduction in error percentage at higher temperatures compared to other methods. The improved sensor accuracy has significant implications for agricultural applications such as ET calculation, where precise temperature data is necessary. Reducing error enhances better agricultural water management strategies and overall productivity.

Prediction of Foundation Pit Dewatering Water Level Based on Neural Network and Improved Kriging Interpolation

Based on a foundation pit project in Shenyang, a large number of water level detection data were collected. The research focus of this paper is to use these data to build a depth neural network model to predict the water level of foundation pit dewatering. The Spatio-temporal convolution neural network has two components for capturing temporal correlation and spatial correlation of nodes, which have strong capturing ability of spatio-temporal correlation. The construction of spatio-temporal convolution neural network model can realize the time domain prediction of water level at all measuring points at the same time. The Kriging interpolation model optimized by Dupuit dewatering formula is superior in the spatial prediction of dewatering water level in the whole study area. Compared with OK method, IDW method and SP method, the prediction performance of improved Kriging interpolation is improved by 33.02%, 38.26% and 44.96% respectively Based on STGCN-Kriging model, the average error is 0.71 m and the error percentage is 2.55%. Combining with STGCN network and improved Kriging interpolation, the discrete prediction results in time domain are mapped to the spatial distribution results of water level buried depth in time domain, and the spatial distribution of foundation pit dewatering water level is predicted in time domain.

Assessing moisture content in XLPE power cables using frequency-dependent tangent delta measurements

Abstract The reliability of cross-linked polyethylene (XLPE)-insulated power cables, mostly used for electricity distribution and transmission in urban areas, is often compromised due to the lack of effective condition-based monitoring systems. This leads to difficulties in early fault detection and increased risk of unscheduled outages, and failures, particularly in cable end terminations. This research aims to develop a robust and accurate methodology for estimating moisture content in XLPE cable end terminations to enhance condition-based monitoring and ensure reliable power delivery. The study employed frequency domain spectroscopy to examine the dielectric properties of XLPE cable samples with varying moisture content levels. A novel predictive formula was derived to estimate moisture content using dielectric frequency response (DFR) supported by validation through sweep frequency response analysis (SFRA) tests. We conduct an experiment to estimate the moisture content and observe satisfactory agreement between the experimental and theoretical values. The proposed method achieved a high accuracy of 96%; the percentage error variation was minimal in moisture estimation, outperforming existing techniques. This study provides a practical and reliable approach for monitoring XLPE cable condition, particularly at end terminations. The proposed methodology ensures accurate moisture content estimation, enabling early fault detection and reducing the risk of unscheduled outages. This advancement has major implications for enhancing the reliability and efficiency of urban power systems.

Ultrasonic Sensor Modeling with Support Vector Regression

This study proposes a novel approach for predicting the output behaviors of the Pepperl+Fuchs 3RG6232-3JS00-PF ultrasonic sensor. The sensor, integrated into the Festo MPS-PA Didactic System, serves to monitor the water level in a tank, facilitating water extraction to bottles delivered via a conveyor belt. This modeling approach represents the initial phase in the creation of a digital twin of the physical sensor, providing the capability for users to observe the sensor’s response and forecast its life cycle for maintenance objectives. This study utilizes the Festo MPS-PA Compact Didactic System and support vector regression (SVR) for data acquisition (DAQ), preprocessing, and model training with hyperparameter optimization. The objective of this modeling approach is to establish a digital framework for transition towards Industry 4.0. It holds the potential for creating a digital counterpart of the entire MPS-PA System when combining the proposed sensor modeling technique with computer-assisted design (CAD) software such as Siemens NX in the future. This would enable users to oversee the entire process in a three-dimensional visualization engine, such as Tecnomatix Plant Simulation. This research significantly contributes to the comprehension and application of digital twins in the realm of mechatronics and sensor systems technology. It also underscores the importance of digital twins in enhancing the efficiency and predictability of sensor systems. The method used in this paper involves predicting the rate of change (RoC) of the water level and then integrating this rate to estimate the actual water level, providing a robust approach for sensor data modeling and digital twin creation. The result shows a promising 6.99% error percentage.