Maximum Root Mean Square Error Research Articles

A novel Kalman smoothing (Ks) − Long Short-Term Memory (LSTM) hybrid model for filling in short- and long-term missing values in significant wave height

Continuous recordings of significant wave height (SWH) are of significant importance to facilitate the development and application of wave energy assessments. However, SWH recording is often interrupted by a variety of factors, including severe weather, apparatus failure, etc. The presence of short-term intermittent and long-term continuous missing values is recognized as a significant challenge for the accurate analysis and energy assessment of wave data. To effectively fill in the short- and long-term missing values simultaneously, a hybrid Kalman smoothing (KS) − Long Short-Term Memory (LSTM) model, which is applied to fill in SWH for the first time, is proposed. Initially, the short-term intermittent missing values, with missing ratios of 10%-50%, are filled by using the KS method, achieving a root mean square error (RMSE) as low as 0.082. Based on short-term missing values addressed by KS, the LSTM model is introduced to effectively predict the long-term continuous missing values. The maximum RMSE reduction rate of KS-LSTM is reduced by 49.6%, 59.4%, and 57.8% compared to KS-ARIMA, LSTM, and ARIMA, respectively, within the short-term intermittent missing ratios of 10%-50%. The maximum reduction in mean square error (MSE) is observed to be 71.4%, 83.6%, and 82.1%. Similarly, the maximum reduction in mean absolute error (MAE) is 74.6%, 61.1%, and 55.5%. The lowest prediction RMSE of long-term missing values by KS-LSTM is only 0.091, which demonstrates that the effectiveness of filling in both short-term and long-term missing values simultaneously by the KS-LSTM.

A hybrid data-driven approach for state of health estimation in lithium-ion batteries

ABSTRACT The assessment of the state of health (SOH) in lithium-ion batteries is essential for ensuring safe operation and implementing scientific management practices. This paper proposes a novel hybrid data-driven approach for accurately estimating the SOH of lithium-ion cells. Initially, the sparrow search algorithm (SSA) was implemented to optimize the structural configuration of a hybrid long-short-term memory neural network and convolutional neural network model. Second, four health indicators, namely, constant current charging time (CCCT), constant current charging capacity (CCCC), discharging time (DT), and discharging capacity (DC), were identified as inputs to the estimation model by Pearson correlation analysis. The CALCE battery dataset is utilized for conducting experiments, yielding remarkably low average values of root mean square error (RMSE) and mean absolute error (MAE), standing at merely 0.6737% and 0.5286%, respectively. And comparing with GRU, TCN, CNN, LSTM, and PSO optimization models, the proposed method has higher SOH estimation accuracy, which is more than 11% better than that. The robustness of three additional kinds of batteries is tested to validate their performance under varying current rates and temperatures. The results demonstrate that the accurate estimation of SOH, and the estimated values’ relative errors for each cycle of the three different types of batteries are all basically within 1%, with their maximum average RMSE are only 0.4840%, 0.3609%, and 0.5681%, respectively, and their maximum average MAE are only 0.3900%, 0.2828%, and 0.4149%, respectively.

Integrating deep learning and regression models for accurate prediction of groundwater fluoride contamination in old city in Bitlis province, Eastern Anatolia Region, Türkiye

Groundwater resources in Bitlis province and its surroundings in Türkiye’s Eastern Anatolia Region are pivotal for drinking water, yet they face a significant threat from fluoride contamination, compounded by the region’s volcanic rock structure. To address this concern, fluoride levels were meticulously measured at 30 points in June 2019 dry period and September 2019 rainy period. Despite the accuracy of present measurement techniques, their time-consuming nature renders them economically unviable. Therefore, this study aims to assess the distribution of probable geogenic contamination of groundwater and develop a robust prediction model by analyzing the relationship between predictive variables and target contaminants. In this pursuit, various machine learning techniques and regression models, including Linear Regression, Random Forest, Decision Tree, K-Neighbors, and XGBoost, as well as deep learning models such as ANN, DNN, CNN, and LSTM, were employed. Elements such as aluminum (Al), boron (B), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), phosphorus (Pb), lead (Pb), and zinc (Zn) were utilized as features to predict fluoride levels. The SelectKbest feature selection method was used to improve the accuracy of the prediction model. This method identifies important features in the dataset for different values of k and increases model efficiency. The models were able to produce more accurate predictions by selecting the most important variables. The findings highlight the superior performance of the XGBoost regressor and CNN in predicting groundwater quality, with XGBoost consistently outperforming other models, exhibiting the lowest values for evaluation metrics like mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) across different k values. For instance, when considering all features, XGBoost attained an MSE of 0.07, an MAE of 0.22, an RMSE of 0.27, a MAPE of 9.25%, and an NSE of 0.75. Conversely, the Decision Tree regressor consistently displayed inferior performance, with its maximum MSE reaching 0.11 (k = 5) and maximum RMSE of 0.33 (k = 5). Furthermore, feature selection analysis revealed the consistent significance of boron (B) and cadmium (Cd) across all datasets, underscoring their pivotal roles in groundwater contamination. Notably, in the machine learning framework evaluation, the XGBoost regressor excelled in modeling both the “all” and “rainy season” datasets, while the convolutional neural network (CNN) outperformed in the “dry season” dataset. This study emphasizes the potential of XGBoost regressor and CNN for accurate groundwater quality prediction and recommends their utilization, while acknowledging the limitations of the Decision Tree Regressor.

Rapid Estimation of Static Capacity Based on Machine Learning: A Time-Efficient Approach

With the global surge in electric vehicle (EV) deployment, driven by enhanced environmental regulations and efforts to reduce transportation-related greenhouse gas emissions, managing the life cycle of Li-ion batteries becomes more critical than ever. A crucial step for battery reuse or recycling is the precise estimation of static capacity at retirement. Traditional methods are time-consuming, often taking several hours. To address this issue, a machine learning-based approach is introduced to estimate the static capacity of retired batteries rapidly and accurately. Partial discharge data at a 1 C rate over durations of 6, 3, and 1 min were analyzed using a machine learning algorithm that effectively handles temporally evolving data. The estimation performance of the methodology was evaluated using the mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). The results showed reliable and fairly accurate estimation performance, even with data from shorter partial discharge durations. For the one-minute discharge data, the maximum RMSE was 2.525%, the minimum was 1.239%, and the average error was 1.661%. These findings indicate the successful implementation of rapidly assessing the static capacity of EV batteries with minimal error, potentially revitalizing the retired battery recycling industry.

Design and Experiment of an Autonomous Navigation System for a Cattle Barn Feed-Pushing Robot Based on UWB Positioning

The autonomous navigation system of feed-pushing robots is one of the key technologies for the intelligent breeding of dairy cows, and its accuracy has a significant influence on the quality of feed-pushing operations. Currently, the navigation methods of feed-pushing robots in the complex environment of cattle barns mainly include visual, LiDAR, and geomagnetic navigation, but there are still problems relating to low navigation accuracy. An autonomous navigation system based on ultra-wideband (UWB) positioning utilizing the dynamic forward-looking distance pure pursuit algorithm is proposed in this paper. First, six anchor nodes were arranged in the corners and central feeding aisle of a 30 × 86 m rectangular standard barn to form a rectangular positioning area. Then, utilizing the 9ITL-650 feed-pushing robot as a platform and integrating UWB wireless positioning technology, a global coordinate system for the cattle barn was established, and the expected path was planned. Finally, the pure pursuit model was improved based on the robot’s two-wheel differential kinematics model, and a dynamic forward-looking distance pure pursuit controller based on PID regulation was designed to construct a comprehensive autonomous navigation control system. Subsequently, field experiments were conducted in the cattle barn. The experimental results show that the static positioning accuracy of the UWB system for the feed-pushing robot was less than 16 cm under no-line-of-sight conditions in the cattle barn. At low speeds, the robot was subjected to linear tracking comparative experiments with forward-looking distances of 50, 100, 150, and 200 cm. The minimum upper-line distance of the dynamic forward-looking distance model was 205.43 cm. In the steady-state phase, the average lateral deviation was 3.31 cm, with an average standard deviation of 2.58 cm and the average root mean square error (RMSE) of 4.22 cm. Compared with the fixed forward-looking distance model, the average lateral deviation, the standard deviation, and the RMSE were reduced by 42.83%, 37.07%, and 42.90%, respectively. The autonomous navigation experiments conducted on the feed-pushing robot at travel speeds of 6, 8, and 10 m/min demonstrated that the maximum average lateral deviation was 7.58 cm, the maximum standard deviation was 8.22 cm, and the maximum RMSE was 11.07 cm, meeting the autonomous navigation requirements for feed-pushing operations in complex barn environments. This study provides support for achieving high-precision autonomous navigation control technology in complex environments.

An artificial neural network based approach for predicting the proton beam spot dosimetric characteristics of a pencil beam scanning technique

Utilising Machine Learning (ML) models to predict dosimetric parameters in pencil beam scanning proton therapy presents a promising and practical approach. The study developed Artificial Neural Network (ANN) models to predict proton beam spot size and relative positional errors using 9000 proton spot data. The irradiation log files as input variables and corresponding scintillation detector measurements as the label values. The ANN models were developed to predict six variables: spot size in the x-axis, y-axis, major axis, minor axis, and relative positional errors in the x-axis and y-axis. All ANN models used a Multi-layer perception (MLP) network using one input layer, three hidden layers, and one output layer. Model performance was validated using various statistical tools. The log file recorded spot size and relative positional errors, which were compared with scintillator-measured data. The Root Mean Squared Error (RMSE) values for the x-spot and y-spot sizes were 0.356 mm and 0.362 mm, respectively. Additionally, the maximum variation for the x-spot relative positional error was 0.910 mm, while for the y-spot, it was 1.610 mm. The ANN models exhibit lower prediction errors. Specifically, the RMSE values for spot size prediction in the x, y, major, and minor axes are 0.053 mm, 0.049 mm, 0.053 mm, and 0.052 mm, respectively. Additionally, the relative spot positional error prediction model for the x and y axes yielded maximum errors of 0.160 mm and 0.170 mm, respectively. The normality of models was validated using the residual histogram and Q-Q plot. The data over fit, and bias were tested using K (k = 5) fold cross-validation, and the maximum RMSE value of the K fold cross-validation among all the six ML models was less than 0.150 mm (R-Square 0.960). All the models showed excellent prediction accuracy. Accurately predicting beam spot size and positional errors enhances efficiency in routine dosimetric checks.

Neural network emulation of the human ventricular cardiomyocyte action potential for more efficient computations in pharmacological studies.

Computer models of the human ventricular cardiomyocyte action potential (AP) have reached a level of detail and maturity that has led to an increasing number of applications in the pharmaceutical sector. However, interfacing the models with experimental data can become a significant computational burden. To mitigate the computational burden, the present study introduces a neural network (NN) that emulates the AP for given maximum conductances of selected ion channels, pumps, and exchangers. Its applicability in pharmacological studies was tested on synthetic and experimental data. The NN emulator potentially enables massive speed-ups compared to regular simulations and the forward problem (find drugged AP for pharmacological parameters defined as scaling factors of control maximum conductances) on synthetic data could be solved with average root-mean-square errors (RMSE) of 0.47 mV in normal APs and of 14.5 mV in abnormal APs exhibiting early afterdepolarizations (72.5% of the emulated APs were alining with the abnormality, and the substantial majority of the remaining APs demonstrated pronounced proximity). This demonstrates not only very fast and mostly very accurate AP emulations but also the capability of accounting for discontinuities, a major advantage over existing emulation strategies. Furthermore, the inverse problem (find pharmacological parameters for control and drugged APs through optimization) on synthetic data could be solved with high accuracy shown by a maximum RMSE of 0.22 in the estimated pharmacological parameters. However, notable mismatches were observed between pharmacological parameters estimated from experimental data and distributions obtained from the Comprehensive in vitro Proarrhythmia Assay initiative. This reveals larger inaccuracies which can be attributed particularly to the fact that small tissue preparations were studied while the emulator was trained on single cardiomyocyte data. Overall, our study highlights the potential of NN emulators as powerful tool for an increased efficiency in future quantitative systems pharmacology studies.

Neural network emulation of the human ventricular cardiomyocyte action potential for more efficient computations in pharmacological studies

Computer models of the human ventricular cardiomyocyte action potential (AP) have reached a level of detail and maturity that has led to an increasing number of applications in the pharmaceutical sector. However, interfacing the models with experimental data can become a significant computational burden. To mitigate the computational burden, the present study introduces a neural network (NN) that emulates the AP for given maximum conductances of selected ion channels, pumps, and exchangers. Its applicability in pharmacological studies was tested on synthetic and experimental data. The NN emulator potentially enables massive speed-ups compared to regular simulations and the forward problem (find drugged AP for pharmacological parameters defined as scaling factors of control maximum conductances) on synthetic data could be solved with average root-mean-square errors (RMSE) of 0.47 mV in normal APs and of 14.5 mV in abnormal APs exhibiting early afterdepolarizations (72.5% of the emulated APs were alining with the abnormality, and the substantial majority of the remaining APs demonstrated pronounced proximity). This demonstrates not only very fast and mostly very accurate AP emulations but also the capability of accounting for discontinuities, a major advantage over existing emulation strategies. Furthermore, the inverse problem (find pharmacological parameters for control and drugged APs through optimization) on synthetic data could be solved with high accuracy shown by a maximum RMSE of 0.22 in the estimated pharmacological parameters. However, notable mismatches were observed between pharmacological parameters estimated from experimental data and distributions obtained from the Comprehensive in vitro Proarrhythmia Assay initiative. This reveals larger inaccuracies which can be attributed particularly to the fact that small tissue preparations were studied while the emulator was trained on single cardiomyocyte data. Overall, our study highlights the potential of NN emulators as powerful tool for an increased efficiency in future quantitative systems pharmacology studies.

Evaluating the Impact of the Recent Combined and Satellite-Only Global Geopotential Model on the Gravimetric Geoid Model

Geoid represents Earth’s surface, ocean, and gravitational field, which influence the elevations, shape, and mass distribution of the geopotential surface, a hypothetical surface that is perpendicular to the direction of gravity at every point. This geopotential surface serves as a reference for measuring elevations and is used as a fundamental reference surface for geodetic and surveying purposes. In this study, the Least Squares Modification of Stokes Formula (LSMS) with Additive Corrections (AC), also known as the KTH method, is used to generate a new gravimetric geoid model for Peninsular Malaysia. The KTH method was developed at the Royal Institute of Technology (KTH) in Stockholm-Sweden. The dataset used is the most recent global digital elevation model, Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global, generated by the National Aeronautics and Space Administration (NASA) and the National Imagery and Mapping Agency (NIMA). In addition to this elevation data, the dataset includes the Global Geopotential Model (GGM), which is composed of the XGM2016, XGM2019e, Tongji_GGMG2021S, and Tongji-Grace02k models. Furthermore, it incorporates sets of regional gravity data, including terrestrial gravity, airborne gravity, and marine gravity anomalies, all of which are derived from the Technical University of Denmark (DTU 21). The actual 45 Global Navigation Satellite System (GNSS)-levelling points data have been compared to the gravimetric geoid model developed in this study and the geoid acquired from Department of Survey and Mapping Malaysia (DSMM). According to the statistical results, NXGM2019e provides better accuracy, with the Root Mean Square Error (RMSE) geoid model errors of ±0.033 m, compared to the deviations in free-air anomalies, XGM2019e, which has the minimum RMSE of 10.291 mGal. Meanwhile, Tongji-GMMG2021S has the maximum RMSE of 14.792 mGal. The geoid is derived from the XGM2019e model and has maximum and minimum values of 0.032 m and 0.147 m, respectively, with mean residuals of 0.089 m. In conclusion, the XGM2019e has the potential to determine a precise local geoid model for Peninsular Malaysia

Research on Time Series Monitoring of Surface Deformation in Tongliao Urban Area Based on SBAS-PS-DS-InSAR.

As urban economies flourish and populations become increasingly concentrated, urban surface deformation has emerged as a critical factor in city planning that cannot be overlooked. Surface deformation in urban areas can lead to deformations in structural supports of infrastructure such as road bases and bridges, thereby posing a serious threat to public safety and creating significant safety hazards. Consequently, research focusing on the monitoring of urban surface deformation holds paramount importance. Interferometric synthetic aperture radar (InSAR), as an important means of earth observation, has all-day, wide-range, high-precision, etc., characteristics and is widely used in the field of surface deformation monitoring. However, traditional solitary InSAR techniques are limited in their application scenarios and computational characteristics. Additionally, the manual selection of ground control points (GCPs) is fraught with errors and uncertainties. Permanent scatterers (PS) can maintain high interferometric coherence in man-made building areas, and distributed scatterers (DS) usually show moderate coherence in areas with short vegetation; the combination of DS and PS solves the problem of manually selecting GCPs during track re-flattening and regrading, which affects the monitoring results. In this paper, 45 Sentinel-1B data from 16 February 2019 to 14 December 2021 are used as the data source in the urban area of Horqin District, Tongliao City, Inner Mongolia Autonomous Region, for example. A four-threshold (coherence coefficient threshold, FaSHPS adaptive threshold, amplitude divergence index threshold, and deformation velocity interval) GCPs point screening method for PS-DS, as well as a Small Baseline Subset-Permanent Scatterers-Distributed Scatterers-Interferometric Synthetic Aperture Radar (SBAS-PS-DS-InSAR) method for selecting PS and DS points as ground control points for orbit refinement and re-flattening, are proposed. The surface deformation results obtained using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the SBAS-PS-DS-InSAR proposed in this paper were comparatively analysed and verified. The maximum cumulative line-of-sight settlements were -90.78 mm and -83.68 mm, and the maximum cumulative uplifts are 74.94 mm and 97.56 mm, respectively; the maximum annual average line-of-sight settlements are -35.38 mm/y and -30.38 mm/y, and the maximum annual average uplifts are 25.27 mm/y and 27.92 mm/y. The results were evaluated and analysed in terms of correlation, mean absolute error (MAE), and root mean square error (RMSE). The deformation results of the two InSAR methods were evaluated and analysed in terms of correlation, MAE, and RMSE. The errors show that the Pearson correlation coefficients between the vertical settlement results obtained using the SBAS-PS-DS-InSAR method and the GPS monitoring results were closer to 1. The maximum MAE and RMSE were 13.7625 mm and 14.8004 mm, respectively, which are within the acceptable range; this confirms that the monitoring results of the SBAS-PS-DS-InSAR method were better than those of the original SBAS-InSAR method. SBAS-InSAR method, which is valid and reliable. The results show that the surface deformation results obtained using the SBAS-InSAR, SBAS-PS-DS-InSAR, and GPS methods have basically the same settlement locations, extents, distributions, and temporal and spatial settlement patterns. The deformation results obtained using these two InSAR methods correlate well with the GPS monitoring results, and the MAE and RMSE are within acceptable limits. By comparing the deformation information obtained using multiple methods, the surface deformation in urban areas can be better monitored and analysed, and it can also provide scientific references for urban municipal planning and disaster warning.

EASS: An automatic steering system for agricultural wheeled vehicles using fuzzy control

Automatic steering is a crucial technology for achieving automatic navigation and unmanned driving of agricultural machinery. In this research, agricultural wheeled vehicles with the HPAS (Hydraulic Power Assistant Steering) system were selected as the research object and the EASS (Electric Automatic Steering System) was proposed by using fuzzy control to realize effective steering instead of human drivers. The PID (Proportional Integral Differential) algorithm was used to process the difference between the desired and actual steering angles and to realize automatic control of rotation of directional wheels. Fuzzy control was adopted to achieve self-adjusting of PID parameters and to optimize working state of the steering motor in real-time for different agricultural wheeled vehicles under various operating conditions. Fuzzy control rules were explored and formulated by analyzing variation of the steering angle under different resistive torque values. For evaluation of its feasibility, the newly developed EASS was applied in automatic navigation of a rice transplanter, a tractor, and a high-clearance sprayer. Field testing results showed that the maximum path tracking lateral error and heading error were 4.39 cm and 2.13°, respectively. And the maximum RMS (Root Mean Square) errors were 3.75 cm and 1.02°, respectively. Therefore, the fuzzy control-based the EASS in this research was applicable to various agricultural wheeled vehicles.

Assessing the influence of sugarcane bagasse ash for the production of eco-friendly concrete: Experimental and machine learning approaches

The innovative utilization of sugarcane bagasse ash (SCBA) in producing lightweight concrete (LWC) needs to be explored experimentally and quantification with machine learning approaches. Therefore, this study aims to investigate fresh, mechanical and microstructural characteristics and machine learning modeling of incorporating SCBA as a partial substitute of cement. In this investigation, OPC was replaced partially by the weight of SCBA in the proportion of 0 %, 5 %, 10 %, 15 %, and 20 %. The influences of SCBA on the fresh characteristics of concrete were investigated through tests of the slump, compacting factor, Kelly ball, k-slump, density, and air content. In addition, for hardened properties compressive, splitting tensile, flexural strength, and static modulus of elasticity were tested at 7 and 28 days. Furthermore, a microstructural characteristic was carried out for varying content of SCBA. Different machine learning (ML) predictive models were performed utilizing artificial neural network (ANN) and random forest (RF), to predict the fresh and mechanical attributes of the LWC. It was noticed that the workability value of fresh concrete was enhanced almost by 40 % to 50 % with the incorporation of SCBA content. The hardened test results revealed that a mixture having SCBA up to 10 % content SCBA resulted in nearly 10 % to 30 % better performance to satisfy the requirement of LWC. Regarding the sustainability assessment, the less embodied CO2 (eCO2) is produced in SCBA mixes, whereas it is also economically beneficial to produce SCBA mixes. Models' efficacy was evaluated including mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2). The maximum R2 and minimum RMSE came across for RF (0.989 and 1.393, respectively) which provides the well estimation of the model.

Development of machine-learning-based models for identifying the sources of nitrate and fluoride in groundwater and predicting their human health risks

AbstractThis research aimed to identify the main sources of groundwater pollution and assess the non-carcinogenic human health risk resulting from nitrate and fluoride contamination. These goals were achieved by employing unsupervised and supervised machine algorithms, including principal component analysis (PCA) and multilayer perceptron artificial neural networks (MLP-ANN). Thirty-seven groundwater samples were analyzed for twelve physical and chemical parameters, including pH, EC, TDS, TH, Cl, F, SO4, NO3, Ca, Mg, Na, and HCO3, and the initial investigation indicated that except for Cl, F, Ca, and Mg, all the parameters are above the guidelines of the World Health Organization (WHO). PCA indicated that mineral dissolution is the main source of F, while high NO3 concentration primarily resulted from agricultural operation due to extensive use of nitrogen and calcium-based fertilizers. Consequently, the non-carcinogenic human health risk (HHR) for children and adults is evaluated based on NO3 and F. The conventional approach for assessing HHR is time-consuming and often associated with errors in calculating hazard quotients (HQ) and hazard indices (HI). In this research, MLP-ANN is suggested to overcome these limitations. In the MLP-ANN modeling, the data were divided into two parts training (80%) and validation (20%), with NO3 and F concentration as inputs and HQ and HI as outputs. The performance of the resulting models was tested using root mean square error (RMSE) and coefficient of determination (R2). The model provided a satisfactory result with a maximum RMSE of 4% and R2 higher than 97% for training and validation. As a result, obtained HIs suggested that 97.3% of the groundwater samples in the study area are suitable for human consumption. The non-carcinogenic HHR is successfully assessed using machine learning algorithms, and the results have led to the conclusion that this approach is highly recommended for effectively managing groundwater resources.

Revolutionizing spatial data analysis: unveiling a cutting-edge approach for batch coordinate transformation

Spatial data have become indispensable across various disciplines and provide crucial insights. These data are associated with coordinates and different coordinate systems. However, the diversity of geospatial data formats and disparate coordinate systems present challenges in harmonizing them for analysis. This study addresses the pressing need for an improved approach to the batch transformation of commonly used coordinate systems in Sri Lanka. First, we examine different coordinate transformation systems and identify their limitations. Subsequently, we present a comprehensive procedure for seamless coordinate transformations between various systems. To demonstrate the practical applications of our approach, we have developed a user-friendly desktop application capable of simultaneously converting input coordinates into multiple systems. This application streamlines the process for users unfamiliar with sophisticated geographic information system (GIS) applications and datum transformations. We validate the output coordinates transformed using our application by comparing them with those obtained from established applications such as ArcGIS and epsg.io. The results, which have been assessed based on the root mean squared error (RMSE) and mean absolute error (MAE), indicate high levels of accuracy, with a maximum RMSE of approximately 0.013 and a maximum MAE of approximately 0.008. A performance evaluation reveals that our approach is exceptionally efficient, outperforming ArcGIS and epsg.io by 40x and 60x, respectively. Moreover, the proposed pipeline holds potential as an infrastructure for developing web applications, mobile applications, and plugins for popular GIS platforms such as ArcGIS and QGIS.

State of Charge Estimation for Batteries Based on Common Feature Extraction and Transfer Learning

The state of charge (SOC) of a battery is a key parameter of electrical vehicles (EVs). However, limited by the lack of computing resources, the SOC estimation strategy used in vehicle-mounted battery management systems (V-BMS) is usually simplified. With the development of the new energy vehicle big data platforms, it is possible to obtain the battery SOC through cloud-based BMS (C-BMS). In this paper, a battery SOC estimation method based on common feature extraction and transfer learning is proposed for C-BMS applications. Considering the diversity of driving cycles, a common feature extraction method combining empirical mode decomposition (EMD) and a compensation strategy for C-BMS is designed. The selected features are treated as the new inputs of the SOC estimation model to improve the generalization ability. Subsequently, a long short-term memory (LSTM) recurrent neural network is used to construct a basic model for battery SOC estimation. A parameter-based transfer learning method and an adaptive weighting strategy are used to obtain the C-BMS battery SOC estimation model. Finally, the SOC estimation method is validated on laboratory datasets and cloud platform datasets. The maximum root-mean-square error (RMSE) of battery SOC estimation with the laboratory dataset is 2.2%. The maximum RMSE of battery pack SOC estimation on two different electric vehicles is 1.3%.

A robust approach for the derivation of rating curves using minimum gauging data

Rating curves are robust tools for continuously evaluating streamflows by gauging water levels. This paper introduces a procedure to derive rating curves by combining a stage-discharge relationship proposed by Maghrebi et al. and the inverse-distance weighting (IDW) method. In order to determine the appropriate value of the α parameter in IDW, a trial-and-error procedure using different values of α from 0 to 5 is implemented. To this end, 2000 subsamples in different sizes selected randomly from the observed data of four cross-sections are generated, and the corresponding rating curves are delivered. The mean absolute percentage error (MAPE) and the root mean square error (RMSE) are used to evaluate the results. The results from four cross-sections demonstrated that α = 0 corresponds to the least favorable results, while α = 1 is the most appropriate value. The Maximum MAPE and RMSE, observed in the Trent River section, were 22.5 % and 13.1 m3/s using Maghrebi’s relation, respectively. These values decreased to 13.5 % and 8.6 m3/s when applying the proposed method using 7 points. Additional cross-sections from the Tiber River are used to validate the method and to compare it with the classical model. The results demonstrated that MAPE and RMSE values are reduced with increasing sample size, and the proposed model outperforms the classical one for small sample sizes. RMSE values declined from 62, 27.4, 46, 76.7 m3/s to 32.8, 14.4, 23.5, 33.9 m3/s using seven observed points in Ponte Nuovo, Santa Lucia, Ponte Felcino, and Ponte Rosciano cross-sections, respectively. The proposed model illustrated a notable decline in the uncertainty bands, which diminished significantly with increasing sample sizes. The proposed method is advantageous in constructing rating curves in new sites, specifically in the case of limited funding, and building more reliable rating curves in gauged sites.

State of Charge Estimation of Lithium-ion Battery in Electric Vehicles Using the Smooth Variable Structure Filter: Robustness Evaluation against Noise and Parameters Uncertainties

State of Charge (SoC) of Lithium-ion battery is a key parameter in battery management systems for electric vehicles. This paper uses the fundamental theory of the smooth variable structure filter (SVSF) and proposes a SoC estimation algorithm for a Manganese Cobalt (NMC) cell with a nominal capacity of 20 Ah. Several tests are conducted considering different types of noise and parameters variation. A nonrandom Gaussian noise is first added to the battery voltage. The maximum root mean square error (RMSE) of the estimated SoC is about for a standard deviation of the noise set to P.U. The same noise is applied to the battery current and the maximum RMSE of the SoC is obtained as 1.36%. Moreover, an EMI noise is added to the battery voltage and the obtained RMSE of the SoC is about 1.73% for a peak amplitude of the noise set to 0.07 P.U. The convergence of the algorithm is also confirmed under battery parameters variation due to the temperature change. However, its accuracy degrades considerably. Finally, a comparative study is carried out with the extended Kalman filter and shows the superiority of SVSF in terms of accuracy and robustness against measurement noise.

An Ionospheric Total Electron Content Model with a Storm Option over Japan Based on a Multi-Layer Perceptron Neural Network

Ionospheric delay has a severe effect on reducing the accuracy of positioning and navigation of single-frequency receivers. Therefore, it is necessary to construct a precise regional ionospheric model for real-time Global Navigation Satellite System (GNSS) applications. The total electron contents (TECs) of 839 GNSS stations affiliated with the GPS Earth Observation Network were used to build a Japanese ionospheric model (JIM) based on a multi-layer perceptron neural network. During quiet space conditions, the correlation coefficient between the targets and the predictions of the JIM was about 0.98, and the root-mean square error (RMSE) of TEC residuals was ~1.5TECU, while under severe space events, the correlation coefficient increased to 0.99, and the corresponding RMSE dropped to 0.96 TECU. Moreover, the JIM model successfully reconstructed the two-dimensional (time vs latitude) TEC maps, and the TEC maps had evident hourly and seasonal variations. Most of TEC residuals accumulated between universal time 01–06 with an averaged magnitude of 1-2TECU. Furthermore, the JIM model had a perfect prediction performance under various kinds of complex space environments. In the quiet days, the prediction accuracy of the JIM was nearly equal to the global ionosphere map (GIM), and in some moments, the JIM was more competitive than the GIM. In the disturbed days, the RMSEs of TEC residuals were proportional to the solar wind speed and were inversely proportional to the geomagnetic Dst value. The maximum RMSE of the JIM was lower than 2TECU, while the corresponding RMSEs for the IRI and TIE-GCM exceeded 5TECU.

Demonstration of a Fizeau Directly-Imaging Sparse-Aperture Telescope with Pointing and Tracking Capabilities

At present, the majority of sparse-aperture telescopes (SATs) are unable to observe moving targets. In this paper, we describe the construction of and present the results obtained using a Fizeau directly-imaging sparse-aperture telescope (FDISAT) that permits pointing and the tracking of moving targets. The telescope comprises three sub-apertures, each of which is equipped with a Risley prism system that permits a maximum tracking range of 5° and has independent boresight adjustment capability. On targets in various positions, experiments with pointing and tracking are conducted. The maximum root-mean-square error (RMSE) of pointing in the sub-apertures was found to be 8.22 arcsec. When considering a target moving at 0.01°/s for approximately 320 s, the maximum RMSE of tracking in the sub-apertures was found to be 4.23 arcsec. The images obtained from the focal plane detector exhibit clear interference fringes while tracking. The experimental results demonstrate that the system can effectively track moving targets, providing a method for SAT observation of moving targets.

Predicting Centrifugal Pumps’ Complete Characteristics Using Machine Learning

The complete characteristics of centrifugal pumps are crucial for the modeling of hydraulic transient phenomena occurring in pipe systems. However, due to the effort required to obtain these curves, pump manufacturers typically only provide basic information, particularly when the pump operates under normal conditions. To acquire the full characteristic curves based on the manufacturer’s normal performance curve, a machine learning (ML) model is proposed to predict full, complete Suter curves using a pump’s specific speed with the known parts of the Suter curve. The training data for the model are sourced from the available Suter curves from laboratory experiments. Subsequently, the proposed ML model combines several types of regression models in an attempt to find the most accurate prediction in terms of the root mean square error (RMSE). The result proved highly efficient, as the experiments attained a maximum RMSE value of 0.032 across the three categories of centrifugal pumps based on their specific speeds, hence demonstrating the potential of machine learning in the study of pump characteristic curves.