Average Mean Absolute Error Research Articles

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.

Crosswalking 4 Pain Impact Measures in a Nationally Representative Sample of Adults With Back Pain

ObjectiveTo generate crosswalk equations and tables for 4 pain impact measures: the Impact Stratification Score (ISS), Oswestry Disability Index (ODI), Roland-Morris Disability Questionnaire (RMDQ), and the Pain, Enjoyment of Life and General Activity Scale (PEG). DesignCross-sectional survey assessing demographics and pain impact. Crosswalks were developed using item-response theory (IRT) cocalibrations and linear regressions between the ISS, ODI, RMDQ, and PEG. SettingOnline panel. ParticipantsPopulation-based sample of United States adults aged 18 and older. Eligibility criteria were reporting current back pain, not reporting 2 fake health conditions, and having data for 2 or more pain measures (N=1530; 37% of sample). Crosswalks were developed (n=1030) and cross-validated in a subsample of 500 participants (n=125 randomly sampled from each ISS quartile). InterventionsNot applicable. Main Outcome MeasuresISS, ODI, RMDQ, and the PEG. ResultsAssociations of the ISS with the PEG and ODI met the criteria for IRT cocalibration. Other measure pairs were crosswalked using regression. Associations were strongest between the PEG and the ISS (r=0.87, normalized mean absolute error [NMAE]=0.38) and between the ODI and the ISS (r=0.85, NMAE=0.39). Associations were weakest between the PEG and the RMDQ (r=0.69, R2=0.48, NMAE: 0.55-0.58). Regression equations and IRT accounted for 48%-64% of the variance (NMAE: 0.38-0.58) in corresponding pain measures in the cross-validation sample. ConclusionsThe crosswalks between the ISS and common legacy pain measures created in this study of a nationally representative sample of United States adults with back pain can be used to estimate 1 pain impact measure from another. Further evaluation in clinical samples is recommended.

The Impact of the Weather Forecast Model on Improving AI-Based Power Generation Predictions through BiLSTM Networks

This study aims to comprehensively analyze five weather forecasting models obtained from the Open-Meteo historical data repository, with a specific emphasis on evaluating their impact in predicting wind power generation. Given the increasing focus on renewable energy, namely, wind power, accurate weather forecasting plays a crucial role in optimizing energy generation and ensuring the stability of the power system. The analysis conducted in this study incorporates a range of models, namely, ICOsahedral Nonhydrostatic (ICON), the Global Environmental Multiscale Model (GEM Global), Meteo France, the Global Forecast System (GSF Global), and the Best Match technique. The Best Match approach is a distinctive solution available from the weather forecast provider that combines the data from all available models to generate the most precise forecast for a particular area. The performance of these models was evaluated using various important metrics, including the mean squared error, the root mean squared error, the mean absolute error, the mean absolute percentage error, the coefficient of determination, and the normalized mean absolute error. The weather forecast model output was used as an essential input for the power generation prediction models during the evaluation process. This method was confirmed by comparing the predictions of these models with actual data on wind power generation. The ICON model, for example, outscored others with a root mean squared error of 1.7565, which is a tiny but essential improvement over Best Match, which had a root mean squared error of 1.7604. GEM Global and Gsf Global showed more dramatic changes, with root mean squared errors (RMSEs) of 2.0086 and 2.0242, respectively, indicating a loss in prediction accuracy of around 24% to 31% compared to ICON. Our findings reveal significant disparities in the precision of the various models used, and certain models exhibited significantly higher predictive precision.

Data-knowledge co-driven feature based prediction model via photoplethysmography for evaluating blood pressure

Background:Knowledge feature (KF) with clear physiological significance of photoplethysmography are widely used in predicting blood pressure. However, KF primarily focus on local information of photoplethysmography, which may struggle to capture the overall characteristics. Methods:Firstly, functional data analysis (FDA) was introduced to extract two types of data feature (DF). Furthermore, data-knowledge co-driven feature (DKCF) was proposed by combining FDA and constraints of KF. Finally, random forest, ada boost, gradient boosting, support vector machine and deep neural network were adopted, to compare the abilities of KF, DFs and DKCF in predicting blood pressure with two datasets (A published dataset and a self-collected dataset). Results:Under the premise of extracting only 9 features, the average mean absolute errors (MAE) of systolic blood pressure (SBP) and diastolic blood pressure (DBP) obtained by DKCF are both the smallest in dataset 1. In dataset 2, DKCF acquires the smallest MAE in predicting SBP and obtains the second smallest MAE in predicting DBP. Conclusions:The results demonstrate that low-dimensional DKCF of photoplethysmography is closely correlated with blood pressure, which may serve as an important indicator for health assessment.

A data-driven dynamic method of downhole rock characterisation for the vibro-impact drilling system

For the real-time characterisation of an inhomogeneous impact inhibiting constraint such as downhole rock layers, an unconventional method using machine learning (ML) and drill-bit vibrations is investigated. An impact oscillator with one-sided elastic constraint is employed in modelling the bit-rock impact actions. Measurable drill-bit dynamics, such as acceleration, were acquired and processed into features and 2D-images that were later used in developing ML models capable of predicting the stiffness of impacted rock constraint. Explored ML networks include Multilayer Perceptron (MLP), Convolutional Neural Network and Long Short-Term Memory Network. Both simulation and experimental studies have been presented to validate the proposed method while using coefficient of determination (R2) and normalised mean absolute error (NMAE) as the performance metrics of the ML models. Results showed that the feature-based models had better performances for both simulation and experiment compared to the raw signal and 2D-image based models. Aside being simple and computationally less expensive, the feature-based MLP models outperformed other models having R2 values > 0.7 and NMAE values < 0.2 for both simulation and experiment, thus presenting them as the preferred ML model for dynamic downhole rock characterisation. In general, this study presents a new modality to achieving logging-while-drilling during deep-hole drilling operations such as carried out in hydrocarbon, mineral and geothermal exploration.

An efficient hybrid approach for forecasting real-time stock market indices

The stock market’s volatility, noise, and information overload necessitate efficient prediction methods. Forecasting index prices in this environment is complex due to the non-linear and non-stationary nature of time series data generated from the stock market. Machine learning and deep learning have emerged as powerful tools for identifying financial data patterns and generating predictions based on historical trends. However, updating these models in real-time is crucial for accurate predictions. Deep learning models require extensive computational resources and careful hyperparameter optimization, while incremental learning models struggle to balance stability and adaptability. This paper proposes a novel hybrid bidirectional-LSTM (H.BLSTM) model that combines incremental learning and deep learning techniques for real-time index price prediction, addressing these scalability and memory challenges. The method utilizes both univariate time series derived from historical index prices and multivariate time series incorporating technical indicators. Implementation within a real-time trading system demonstrates the method’s effectiveness in achieving more accurate price forecasts for major stock indices globally through extensive experimentation. The proposed model achieved an average mean absolute percentage error of 0.001 across nine stock indices, significantly outperforming traditional models. It has an average forecasting delay of 2 s, making it suitable for real-time trading applications.

A two-step identification approach for an extended nonlinear double-capacitor model

Equivalent circuits are one of the most used models for Li-ion cells in the automotive area. However, it is a challenge to these models to be able to capture the cell discharge capacity under different loads, while still being accurate on both continuous charge and dynamic tests, fast to compute, and easy to parametrize from non-specialized data. To tackle this challenge, this paper proposes an extension of the nonlinear double capacitor model by increasing its order, parameter dependency with C-rate, and an identification procedure that exploits the pseudo-linear nature of the problem to find the parameter maps. An analogy between the parts of the circuit and the single particle model is also presented to reduce the search space of the identification algorithm and to enhance model interpretability. The performance of the proposed model extension is analyzed and compared to a state-of-the-art model on a challenging LiFePO4 dataset with different characteristics and validated on a realistic drive cycle, obtaining a mean absolute average error of around 20 mV for both training and validation tests.

Real-time prediction model of passenger thermal comfort for intelligent cabin

This article establishes a joint model of intelligent cabin and human thermal sensation, following experimental verification. And 12000 sets of datasets are obtained through joint model simulation under 120 random working conditions. Subsequently, a real-time prediction model of passenger thermal comfort is established and is trained using the Catboost algorithm based on the datasets. Finally, under various working conditions, the calculation results of the real-time prediction model were compared with these of the joint model and steady-state prediction model separately. The results indicate that the real-time prediction model can predict the passenger thermal comfort under unstable thermal environment, while the steady-state prediction model cannot. And compared to the accurate value calculated by joint model, the average mean absolute error (MAE) and mean absolute percent error (MAPE) of the real-time prediction model is 0.025 and 1.58 %, respectively. This indicates that the real-time prediction model established in this article has good accuracy.

Anomalous Network Behaviour Detection in Interoperable Health Systems using Machine Learning in Resource Limited Areas

The connection of devices in distributed environments produces and shares a vast amount of data useful for different organisational decision-making. In healthcare service organisations, for example, multiple e-health systems from different departments or facilities connect and share health data and information. During sharing, proper management is important to ensure the information is secure against intruders. Machine learning as a non-conventional security technique can be used along conventional techniques like firewalls, antivirus and intrusion detection systems to predict future network threats and other anomalies using historical backgrounds and other features. However, some machine learning algorithms have complex computation thus requiring resourceful systems in terms of network bandwidth, CPU power, memory, and storage capacity. In resource-constrained environments, therefore, special consideration is needed to ensure that the analysis of the big data is successful and that the benefits associated with them are effectively obtained. In this paper, a Machine Learning algorithm was selected among four algorithms whose performance was compared through various performance metrics. Classification accuracy, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Relative Absolute Error (RAE) among other performance metrics were used to compare the ANN, Random forest, Decision trees, and Naïve byes classification algorithms using an extract from CICDDOS2019 dataset. Using the Weka version 3.8.6, the algorithms were compared to choose the best one to classify the data. By using three computers with different resources, the experiments were carried out to determine the performance of those machine learning algorithms. The result revealed that the random forest produced a good average classification performance in resource-limited systems since it surpassed other algorithms in classifying the data at an average of 99 per cent with a low average mean absolute error of 0.0001. Furthermore, as an ensemble that classifies with multiple decision trees algorithm, it likewise uses reasonable time to build and test the model therefore recommended for resource-limited systems.

Gated recurrent unit and temporal convolutional network with soft thresholding and attention mechanism for tool wear prediction

Predicting tool wear significantly improves cutting efficiency and quality in intelligent manufacturing. Traditional recurrent neural networks suffer from vanishing or exploding gradients and need to be improved prediction accuracy. Therefore, this paper proposes a gated recurrent unit and temporal convolutional network with soft thresholding and attention mechanism (TCN-BiGRU-SA). Monotonicity, robustness, and trendability indicators are used to assess time and frequency domain features. Then, evaluation results of these three indicators are weighted to identify the sensitive features with the degradation trend. The attention mechanism is proposed to adaptively adjust the soft thresholding, and the SA is added to TCN-BiGRU to remain helpful features. Finally, the proposed model is validated using an experimental dataset. Compared to the TCN-BiGRU model without the SA mechanism, the predicted results show that the average mean absolute error and root mean square error are reduced by 48.29 % and 36.39 %, respectively.

Elucidating and forecasting the organochlorine pesticides in suspended particulate matter by a two-stage decomposition based interpretable deep learning approach

Accurately predicting the concentration of organochlorine pesticides (OCPs) presents a challenge due to their complex sources and environmental behaviors. In this study, we introduced a novel and advanced model that combined the power of three distinct techniques: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), and a deep learning network of Long Short-Term Memory (LSTM). The objective is to characterize the variation in OCPs concentrations with high precision. Results show that the hybrid two-stage decomposition coupled models achieved an average symmetric mean absolute percentage error (SMAPE) of 23.24 % in the empirical analysis of typical surface water. It exhibited higher predictive power than the given individual benchmark models, which yielded an average SMAPE of 40.88 %, and single decomposition coupled models with an average SMAPE of 29.80 %. The proposed CEEMDAN-VMD-LSTM model, with an average SMAPE of 13.55 %, consistently outperformed the other models, yielding an average SMAPE of 33.53 %. A comparative analysis with shallow neural network methods demonstrated the advantages of the LSTM algorithm when coupled with secondary decomposition techniques for processing time series datasets. Furthermore, the interpretable analysis derived by the SHAP approach revealed that precipitation followed by the total phosphorus had strong effects on the predicted concentration of OCPs in the given water. The data presented herein shows the effectiveness of decomposition technique-based deep learning algorithms in capturing the dynamic characteristics of pollutants in surface water.

Photovoltaic modules fault detection, power output, and parameter estimation: A deep learning approach based on electroluminescence images

Accurately detecting faults in photovoltaic modules/cells and estimating their effective power output and parameters of the equivalent circuit representation of photovoltaic modules is becoming increasingly critical for both the reliability of associated systems and the efficiency of electricity production from renewable energy sources. Existing studies often work with datasets containing photovoltaic cells that exhibit one fault at a time, leading to the classification of photovoltaic cells with multiple faults as “mixed” faults. Moreover, factors such as cell alignment and specific fault types, collectively called “cell level features”, are not considered in current studies estimating the power output of a photovoltaic module. Therefore, this paper focuses on a comprehensive deep-learning pipeline to separately detect three types of faults in photovoltaic modules/cells using electroluminescence images. Furthermore, it addresses the estimation of the output power of photovoltaic modules and the series resistance of their equivalent circuit, considering the cell-level characteristics extracted from the electroluminescence images. The proposed model demonstrates its ability to detect “black core”, “crack”, and “edge” faults with global accuracies of 0.93, 0.868, and 0.95, respectively. Furthermore, the proposed model estimates the power output of photovoltaic modules with a normalized mean absolute error of 0.03547 and a normalized root mean squared error of 0.04892. This outperforms the base model that relies solely on non-pre-processed detected faults and significantly larger models adept at extracting features from the electroluminescence images. Moreover, the VGG16-based model estimates the series resistance in the equivalent circuit representation of photovoltaic modules with a normalized mean absolute error of 0.04472 and a normalized root mean squared error of 0.0622.

Distance-aware network for physical-world object distribution estimation and counting

Crowd counting has become popular due to its applications in congested scenes. Current methods excel with specialized datasets but often ignore the density distribution affected by perspective. To investigate the true distribution of crowd density in the physical world, this paper introduces a framework leveraging head size variations to estimate real crowd distribution and count crowds. First, a convolutional neural network is designed to generate predicted density maps and corresponding bounding boxes across different datasets. Second, a filter kernel is employed to identify the most crowded area in the input image based on the obtained boxes. Finally, the vanishing point is computed by calculating the intersection of the two generated lines, which is then used to obtain the inverse perspective of the predicted density map. The Grid Mean Relative Error (GMRE) metric is proposed for evaluating transformation accuracy. In comparison with the Grid Average Mean Absolute Error (GAME), GMRE is distance-aware and more suitable for evaluating differences between distinct coordinate systems. Additionally, extensive experiments are conducted to validate the counting capabilities of the proposed network. Experimental results show the network’s competitive counting ability and reduced transformation error.

Supply Chain Management Optimization and Cost Reduction Based on Data Mining Algorithm

Supply Chain Management (SCM) is an evolving industry that constantly evolves, but one constant is the significance of effective inventory control in optimizing profits and customer satisfaction. The primary objective of the research is to identify the most effective method for a business to supply several products while maintaining customer service and keeping inventory costs low. The main aim of this paper's Stochastic Gradient Descent (SGD)-enhanced Support Vector Machine (SVM) approach is to predict ideal order and lack quantities, considering into account historical demand, level of inventory, vendor performance, and cost data. The key goal is to reduce the total cost of inventory (which includes holding, placing orders, and supply-chain costs) while maintaining excellent customer service. In order to accomplish this, the proposed system combines SGD's efficiency to enhance predictions and SVM's predictability. A consumer products business chain's real-world dataset was employed to train and test the algorithm. The dataset provides three years' worth of revenue, stock levels, vendors, and costs. Parameters like Mean Absolute Error (MAE), Inventory Turnover Ratio (ITR), and Service Level Achievement (SLA) have been employed to test the methodology, and parameters were optimized using a 5-fold cross-validation. Based on the study findings, the simulation model obtains an average MAE of 1.81 following 100 training epochs, substantially boosting ITR and SLA. As it effectively and accurately balances inventory levels with customer needs, the findings demonstrate that the SVM model with SGD is an excellent match for IMO.

Rapid prediction of wall shear stress in stenosed coronary arteries based on deep learning.

There is increasing evidence that coronary artery wall shear stress (WSS) measurement provides useful prognostic information that allows prediction of adverse cardiovascular events. Computational Fluid Dynamics (CFD) has been extensively used in research to measure vessel physiology and examine the role of the local haemodynamic forces on the evolution of atherosclerosis. Nonetheless, CFD modelling remains computationally expensive and time-consuming, making its direct use in clinical practice inconvenient. A number of studies have investigated the use of deep learning (DL) approaches for fast WSS prediction. However, in these reports, patient data were limited and most of them used synthetic data generation methods for developing the training set. In this paper, we implement 2 approaches for synthetic data generation and combine their output with real patient data in order to train a DL model with a U-net architecture for prediction of WSS in the coronary arteries. The model achieved 6.03% Normalised Mean Absolute Error (NMAE) with inference taking only 0.35s; making this solution time-efficient and clinically relevant.

Transformer-Based Deep Learning Prediction of 10-Degree Humphrey Visual Field Tests From 24-Degree Data.

To predict 10-2 Humphrey visual fields (VFs) from 24-2 VFs and associated non-total deviation features using deep learning. We included 5189 reliable 24-2 and 10-2 VF pairs from 2236 patients, and 28,409 reliable pairs of macular OCT scans and 24-2 VF from 19,527 eyes of 11,560 patients. We developed a transformer-based deep learning model using 52 total deviation values and nine VF test features to predict 68 10-2 total deviation values. The mean absolute error, root mean square error, and the R2 were evaluation metrics. We further evaluated whether the predicted 10-2 VFs can improve the structure-function relationship between macular thinning and paracentral VF loss in glaucoma. The average mean absolute error and R2 for 68 10-2 VF test points were 3.30 ± 0.52dB and 0.70 ± 0.11, respectively. The accuracy was lower in the inferior temporal region. The model placed greater emphasis on 24-2 VF points near the central fixation point when predicting the 10-2 VFs. The inclusion of nine VF test features improved the mean absolute error and R2 up to 0.17 ± 0.06dB and 0.01 ± 0.01, respectively. Age was the most important 24-2 VF test parameter for 10-2 VF prediction. The predicted 10-2 VFs achieved an improved structure-function relationship between macular thinning and paracentral VF loss, with the R2 at the central 4, 12, and 16 locations of 24-2 VFs increased by 0.04, 0.05 and 0.05, respectively (P < 0.001). The 10-2 VFs may be predicted from 24-2 data. The predicted 10-2 VF has the potential to improve glaucoma diagnosis.

Koreksi Bias Data Hujan Satelit Persiann Sebagai Alternatif Pos Stasiun Hujan dengan Pendekatan Quantile Mapping di Sub Das Brantas Hulu

The rainfall data is usually obtained from meteorological stations, which many sill observed manually in Indonesia and can lead to an observation error. Remote sensing rainfall data observed by satellite offer interesting alternatives to overcome the limitation of existing meteorological stations in Indonesia. It offers accuracy, spatial coverage, timeliness and cost effectiveness. However it contains bias if compared to land based station. This research will evaluate rainfall data observed in PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) in Brantas Hulu Watershed. The rainfall data of 13 years from 12 stations is evaluated to obtain area averaged rainfall using Thiessen Polygon method. Three datasets from PERSIANN, PERSIANN CCS and PERSIANN CDR are compared statistically with the area averaged area for daily, monthly and yearly rainfall. Bias correction is performed using Quantile Mapping to match the cumulative distribution curve from the area averaged rainfall. Verification of bias correction is performed using 2 years data from 2019 to 2020. Finally, the extreme rainfall analysis is performed to test the corrected data for design rainfall calculations. The correlation of daily data shows poor agreement for all three datasets with correlation coefficient below 0,40. The monthly and yearly data gives better correlation with coefficient above 0,80. It shows that for time series event, the satellite data gives poor correlation with the area averaged rainfall. However, quantile mapping correction gives consistent cumulative distribution agreement for correction and model verification. The verification analysis shows an increase in coefficient correlation from 0.387 to 0.43 (daily) and 0.887 to 0.915 (monthly). In addition, there is a decrease in normalized root mean square error (NRMSE) from 31.03% to 16.91% (daily) and 46.03% to 14.03% (monthly). A decrease in normalized mean absolute error (NMAE) occurred from 16.12% to 9.74% (daily) and 31.67% to 10.07% (monthly). Beside that, the results of extreme rainfall analysis produce values that are very close to the rain station. So it is found that the PERSIANN CCS quantile mapping regression model is the best in improving data quality.

An AI-Based Method for Estimating the Potential Runout Distance of Post-Seismic Debris Flows

The widely distributed sediments following an earthquake presents a continuous threat to local residential areas and infrastructure. These materials become more easily mobilized due to reduced rainfall thresholds. Before establishing an effective management plan for debris flow hazards, it is crucial to determine the potential reach of these sediments. In this study, a deep learning-based method—Dual Attention Network (DAN)—was developed to predict the runout distance of potential debris flows after the 2022 Luding Earthquake, taking into account the topography and precipitation conditions. Given that the availability of reliable precipitation data remains a challenge, attributable to the scarcity of rain gauge stations and the relatively coarse resolution of satellite-based observations, our approach involved three key steps. First, we employed the DAN model to refine the Global Precipitation Measurement (GPM) data, enhancing its spatial and temporal resolution. This refinement was achieved by leveraging the correlation between precipitation and regional environment factors (REVs) at a seasonal scale. Second, the downscaled GPM underwent calibration using observations from rain gauge stations. Third, mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were employed to evaluate the performance of both the downscaling and calibration processes. Then the calibrated precipitation, catchment area, channel length, average channel gradient, and sediment volume were selected to develop a prediction model based on debris flows following the Wenchuan Earthquake. This model was applied to estimate the runout distance of potential debris flows after the Luding Earthquake. The results show that: (1) The calibrated GPM achieves an average MAE of 1.56 mm, surpassing the MAEs of original GPM (4.25 mm) and downscaled GPM (3.83 mm); (2) The developed prediction model reduces the prediction error by 40 m in comparison to an empirical equation; (3) The potential runout distance of debris flows after the Luding Earthquake reaches 0.77 km when intraday rainfall is 100 mm, while the minimum distance value is only 0.06 km. Overall, the developed model offers a scientific support for decision makers in taking reasonable measurements for loss reduction caused by post-seismic debris flows.

Fate and transport of chlorine dioxide: Modeling chlorine dioxide in water distribution systems.

This study aims to conduct a comprehensive analysis of switching disinfectants from sodium hypochlorite bleach to chlorine dioxide (ClO2) in the water distribution system of Geyikbayiri, Antalya. For this purpose, bulk decay rates of ClO2 at various water temperatures were determined in laboratory studies. The study revealed ClO2 bulk decay rates of 0.12639 day-1, 0.17848 day-1, and 0.19621 day-1 at temperatures 15°C, 20°C, and 30°C, respectively. The EPANET, a widely employed computer program for simulating the extended-period behavior of hydraulic and water quality in pressurized pipes, was utilized for the analysis of the fate and transport of ClO2. A hydraulic model was first developed, calibrated, and verified using distinct data sets. The Hazen-Williams friction coefficient of the PSA was determined to be 120 by the trial-and-error method with a mean absolute error (MAE) of 0.408 m. A ClO2 model was then integrated with the calibrated and verified hydraulic model, revealing a wall decay rate of 0.01 m/day and an average MAE of 0.034 mg/l. After calibration and verification of the ClO2 model, several management scenarios were developed, and ClO2 dosing rates were determined. The study showed that ClO2 dosing rates of 0.40 mg/l and 0.45 mg/l should be applied to keep ClO2 concentrations within certain limits. PRACTITIONER POINTS: Disinfectants must maintain a sufficient residual in water distribution systems. Chlorine dioxide requires less contact time and is not affected by pH fluctuations. Modeling serves as a decision-making tool for the management of disinfectants. Bulk and wall decay rates of chlorine dioxide are crucial for management strategies. Chlorine dioxide is a good alternative as a disinfectant in such systems.

Joint estimation of State of Charge (SOC) and State of Health (SOH) for lithium ion batteries using Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Long Sort Term Memory Network (LSTM) models

This paper proposes a data-driven method for jointly estimating the State of Charge (SOC) and State of Health (SOH) of batteries, addressing the impact of battery aging on SOC estimation. Initially, Support Vector Machine (SVM) is employed to estimate the SOH of the battery, using the constant voltage charging time and constant voltage discharging time of lithium-ion batteries as inputs, and SOH as the output. By training the SVM model, accurate SOH estimation is achieved. Subsequently, the rated capacity of the battery is adjusted based on the estimated SOH to obtain the current maximum available capacity. This adjustment allows for the coupling of SOH and SOC, resulting in SOC estimation that accounts for aging factors. Leveraging the advantages of Convolutional Neural Networks (CNN) in feature extraction and Long Short-Term Memory (LSTM) neural networks in handling long-term sequential data, a CNN-LSTM model is utilized for SOC estimation. The proposed method utilizes the Oxford Battery Dataset (Cells 1–8) and the NASA Battery Dataset (B0005–B0007) for SOH estimation, and the Oxford Battery Dataset (Cell 8) and the NASA Battery Dataset (B0007) for SOC estimation. The SOH estimation results demonstrate that the Root Mean Square Error (RMSE) is less than 0.81 % and the Mean Absolute Error (MAE) is less than 0.65 % for Cells 1–8, while for B0005 to B0007, the RMSE is less than 1.81 % and the MAE is less than 1.29 %. For SOC estimation, the results show that the average RMSE over the entire lifecycle of Cell 8 is 0.3923 % and the average MAE is 0.3339 %, whereas for B0007, the average RMSE over the entire lifecycle is 0.6123 % and the average MAE is 0.4976 %.