Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering

State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and challenging.Most of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real application.This work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered.Fifteen features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding windows.Then,features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset selection.Multiple linear regression(MLR),Gaussian process regression(GPR),and support vector regression(SVR)were applied to estimate SOH,and root mean square error(RMSE)was used to evaluate and compare the estimation performance.The results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%,2.57%,and 2.82%for MLR,GPR and SVR respectively.It was demonstrated that the estimation based on partial charging profiles with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher accuracy.This work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.

A novel type of neural networks for feature engineering of geological data:Case studies of coal and gas hydrate-bearing sediments

The nature of the measured data varies among different disciplines of geosciences.In rock engineering,features of data play a leading role in determining the feasible methods of its proper manipulation.The present study focuses on resolving one of the major deficiencies of conventional neural networks(NNs)in dealing with rock engineering data.Herein,since the samples are obtained from hundreds of meters below the surface with the utmost difficulty,the number of samples is always limited.Meanwhile,the experimental analysis of these samples may result in many repetitive values and 0 s.However,conventional neural networks are incapable of making robust models in the presence of such data.On the other hand,these networks strongly depend on the initial weights and bias values for making reliable predictions.With this in mind,the current research introduces a novel kind of neural network processing framework for the geological that does not suffer from the limitations of the conventional NNs.The introduced single-data-based feature engineering network extracts all the information wrapped in every single data point without being affected by the other points.This method,being completely different from the conventional NNs,re-arranges all the basic elements of the neuron model into a new structure.Therefore,its mathematical calculations were performed from the very beginning.Moreover,the corresponding programming codes were developed in MATLAB and Python since they could not be found in any common programming software at the time being.This new kind of network was first evaluated through computer-based simulations of rock cracks in the 3 DEC environment.After the model’s reliability was confirmed,it was adopted in two case studies for estimating respectively tensile strength and shear strength of real rock samples.These samples were coal core samples from the Southern Qinshui Basin of China,and gas hydrate-bearing sediment(GHBS)samples from the Nankai Trough of Japan.The coal samples used in the experiments underwent nuclear magnetic resonance(NMR)measurements,and Scanning Electron Microscopy(SEM)imaging to investigate their original micro and macro fractures.Once done with these experiments,measurement of the rock mechanical properties,including tensile strength,was performed using a rock mechanical test system.However,the shear strength of GHBS samples was acquired through triaxial and direct shear tests.According to the obtained result,the new network structure outperformed the conventional neural networks in both cases of simulation-based and case study estimations of the tensile and shear strength.Even though the proposed approach of the current study originally aimed at resolving the issue of having a limited dataset,its unique properties would also be applied to larger datasets from other subsurface measurements.

A Construction Method of Online Course Portrait Based on Feature Engineering

With the emergence of massive online courses,how to evaluate the quality of courses with different qualities to improve the discrimination between courses and recommend personalized online course learning resources for learners needs to be evaluated from all aspects.In this paper,a method of constructing an online course portrait based on feature engineering is proposed.Firstly,the framework of online course portrait is established,the related features of the portrait are extracted by feature engineering method,and then the indicator weights of the portrait are calculated by entropy weight method.Finally,experiments are designed to evaluate the performance of the algorithms,and an example of the course portrait is given.

The Determination Method of Product Engineering Features Based on Linguistic Variables

To overcome the problem of imprecise and unclear information in the development of quality functions,a method for determining the priority of engineering features based on mixed linguistic variables is proposed.First,the evaluation member uses the determined linguistic variable to give the correlation strength evaluation matrix of customer requirements and engineering features.Secondly,the relative importance of the evaluation member and customer requirements are aggregated.Finally,the priority of engineering features is obtained by calculating the deviation.The feasibility and practicability of this method are proven by taking the design of a new product of a long bag low-pressure pulse dust collector as an example.

Features of Tropical Volcanic Rock and Soil of Jakarta-Bandung HSR and Engineering Countermeasures

The geological features of three types of tropical volcanic rock and soil distributed along Jakarta-Bandung high-speed railway(HSR),including pozzolanic clayey soil,mud shale and deep soft soil,are studied through field and laboratory tests.The paper analyzes the mechanism and causes of engineering geological problems caused by tropical volcanic rock and soil and puts forward measures to control subgrade slope instability by rationally determining project type,making side slope stability control and strengthening waterproofing and drainage.The“zero front slope”tunneling technology at the portal,the simplified excavation method of double-side wall heading and the cross brace construction method of arch protection within the semi-open cut row pile frame in the“mountainside”eccentrically loaded soft soil stratum are adopted to control the instability of tunnel side and front slopes,foundation pits and working faces;CFG or pipe piles shall be used to reinforce soft and expansive foundation or replacement measures shall be taken,and the scheme of blind ditch+double-layer water sealing in ballastless track section shall be put forward to prevent arching deformation of foundation;the treatment measures of CFG pile,pipe pile and vacuum combined piled preloading are adopted to improve the bearing capacity of foundation in deep soft soil section and solve the problems of settlement control and uneven settlement.These engineering countermeasures have been applied during the construction of Jakarta-Bandung HSR and achieved good results.

Mesh representation matters:investigating the influence of different mesh features on perceptual and spatial fidelity of deep 3D morphable models

Background Deep 3D morphable models(deep 3DMMs)play an essential role in computer vision.They are used in facial synthesis,compression,reconstruction and animation,avatar creation,virtual try-on,facial recognition systems and medical imaging.These applications require high spatial and perceptual quality of synthesised meshes.Despite their significance,these models have not been compared with different mesh representations and evaluated jointly with point-wise distance and perceptual metrics.Methods We compare the influence of different mesh representation features to various deep 3DMMs on spatial and perceptual fidelity of the reconstructed meshes.This paper proves the hypothesis that building deep 3DMMs from meshes represented with global representations leads to lower spatial reconstruction error measured with L_(1) and L_(2) norm metrics and underperforms on perceptual metrics.In contrast,using differential mesh representations which describe differential surface properties yields lower perceptual FMPD and DAME and higher spatial fidelity error.The influence of mesh feature normalisation and standardisation is also compared and analysed from perceptual and spatial fidelity perspectives.Results The results presented in this paper provide guidance in selecting mesh representations to build deep 3DMMs accordingly to spatial and perceptual quality objectives and propose combinations of mesh representations and deep 3DMMs which improve either perceptual or spatial fidelity of existing methods.

Feature engineering methodology for congestion forecasting

Short-term traffic forecasting is a key element in proactive traffic management,e.g.,mitigating the negative effect of impending congestion through appropriate capacity allocation at signalized intersections.In this study,we develop a data-driven methodology for reliably and robustly predicting impending stable congestion.By incorporating feature engineering techniques into an iterative machine learning process,we develop a prediction model that can be intuitively understood by traffic experts and is amenable to diagnostics during implementation.Our iterative machine learning process combines the embedded and filter approaches for feature selection with the use of expert knowledge to create aggregative input variables.The embedded approach is represented by application of a decision tree algorithm,while the filter approach is reflected in use of the mean decrease in accuracy output of a random forest algorithm for identifying expressive variables.We tested the methodology by applying it to field data from a sub-network in Tel Aviv.We demonstrated a reduction in the number of decision tree input variables from 66 raw variables to the five most effective aggregative ones,while achieving statistically significant improvement in all performance indicators.The identification rate of stable congestion increased from 65%to 74%while the robustness of the results was enhanced:the standard deviations of the identification and false alarm rates fell from 8%to 3%,respectively,to 5%and 2%.

Forecasting NDVI in multiple complex areas using neural network techniques combined feature engineering

NDVI(Normalized difference vegetation index)is a critical variable for monitoring climate change,studying ecological balance,and exploring the pattern of regional phenology.Traditional neural network models only consider image features in time series prediction,while historical data and its changes play an important role in time series forecasting.For this study,we proposed convolutional neural networks(CNN)combined feature engineering forecasting model(SF-CNN),which integrated both the advantages of image characteristics learned from CNN and statistic characteristics calculated by historical data related to the forecast period to improve the accuracy of NDVI predictions in the next 3 months with 30-day interval at multiple complex areas.To intuitively show the performance of SF-CNN,it was compared with CNN using the same parameters.Results mainly showed that(1)in terms of visual analysis,the texture,pattern,and structure of predicted NDVI using SF-CNN are similar to the observed NDVI,and SF-CNN exhibits strong generalization ability;(2)in terms of quantitative assessment,SF-CNN generally outperforms CNN,and it can improve the reliability and robustness for predicting NDVI through simple statistical characteristics while reducing the uncertainties;(3)SF-CNN can learn seasonal and sudden changes in four different and complex study areas with considerable accuracy and without extra data.

Energy Theft Detection in Smart Grids with Genetic Algorithm-Based Feature Selection

As big data,its technologies,and application continue to advance,the Smart Grid(SG)has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology(ICT)and cloud computing.As a result of the complicated architecture of cloud computing,the distinctive working of advanced metering infrastructures(AMI),and the use of sensitive data,it has become challenging tomake the SG secure.Faults of the SG are categorized into two main categories,Technical Losses(TLs)and Non-Technical Losses(NTLs).Hardware failure,communication issues,ohmic losses,and energy burnout during transmission and propagation of energy are TLs.NTL’s are human-induced errors for malicious purposes such as attacking sensitive data and electricity theft,along with tampering with AMI for bill reduction by fraudulent customers.This research proposes a data-driven methodology based on principles of computational intelligence as well as big data analysis to identify fraudulent customers based on their load profile.In our proposed methodology,a hybrid Genetic Algorithm and Support Vector Machine(GA-SVM)model has been used to extract the relevant subset of feature data from a large and unsupervised public smart grid project dataset in London,UK,for theft detection.A subset of 26 out of 71 features is obtained with a classification accuracy of 96.6%,compared to studies conducted on small and limited datasets.

METHOD TO EXTRACT BLEND SURFACE FEATURE IN REVERSE ENGINEERING

A new method of extraction of blend surface feature is presented. It contains two steps: segmentation and recovery of parametric representation of the blend. The segmentation separates the points in the blend region from the rest of the input point cloud with the processes of sampling point data, estimation of local surface curvature properties and comparison of maximum curvature values. The recovery of parametric representation generates a set of profile curves by marching throughout the blend and fitting cylinders. Compared with the existing approaches of blend surface feature extraction, the proposed method reduces the requirement of user interaction and is capable of extracting blend surface with either constant radius or variable radius. Application examples are presented to verify the proposed method.

Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,etc.Still,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during pregnancy.Several researchers have proposed various methods for classifying the status of fetus growth.Manual processing of CTG data is time-consuming and unreliable.So,automated tools should be used to classify fetal health.This study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal health.Various strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also illustrated.This study easily provides valuable interpretations for healthcare professionals in the decision-making process.

A Low Complexity ML-Based Methods for Malware Classification

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware identification.Using an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware classification.This technique optimizes the model’s performance and reduces computational requirements.The proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature vector.Through the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate classification.The evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced features.This demonstrates the method’s ability to classify malware samples accurately while minimizing processing time.The method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and prediction.The new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and efficiency.The research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained devices.Novelty and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

Diabetes Prediction Using Derived Features and Ensembling of Boosting Classifiers

Diabetes is increasing commonly in people’s daily life and represents an extraordinary threat to human well-being.Machine Learning(ML)in the healthcare industry has recently made headlines.Several ML models are developed around different datasets for diabetic prediction.It is essential for ML models to predict diabetes accurately.Highly informative features of the dataset are vital to determine the capability factors of the model in the prediction of diabetes.Feature engineering(FE)is the way of taking forward in yielding highly informative features.Pima Indian Diabetes Dataset(PIDD)is used in this work,and the impact of informative features in ML models is experimented with and analyzed for the prediction of diabetes.Missing values(MV)and the effect of the imputation process in the data distribution of each feature are analyzed.Permutation importance and partial dependence are carried out extensively and the results revealed that Glucose(GLUC),Body Mass Index(BMI),and Insulin(INS)are highly informative features.Derived features are obtained for BMI and INS to add more information with its raw form.The ensemble classifier with an ensemble of AdaBoost(AB)and XGBoost(XB)is considered for the impact analysis of the proposed FE approach.The ensemble model performs well for the inclusion of derived features provided the high Diagnostics Odds Ratio(DOR)of 117.694.This shows a high margin of 8.2%when compared with the ensemble model with no derived features(DOR=96.306)included in the experiment.The inclusion of derived features with the FE approach of the current state-of-the-art made the ensemble model performs well with Sensitivity(0.793),Specificity(0.945),DOR(79.517),and False Omission Rate(0.090)which further improves the state-of-the-art results.

Machine Learning Prediction of Fetal Health Status from Cardiotocography Examination in Developing Healthcare Contexts

Reducing neonatal mortality is a critical global health objective,especially in resource-constrained developing countries.This study employs machine learning(ML)techniques to predict fetal health status based on cardiotocography(CTG)examination findings,utilizing a dataset from the Kaggle repository due to the limited comprehensive healthcare data available in developing nations.Features such as baseline fetal heart rate,uterine contractions,and waveform characteristics were extracted using the RFE wrapper feature engineering technique and scaled with a standard scaler.Six ML models—Logistic Regression(LR),Decision Tree(DT),Random Forest(RF),Gradient Boosting(GB),Categorical Boosting(CB),and Extended Gradient Boosting(XGB)—are trained via cross-validation and evaluated using performance metrics.The developed models were trained via cross-validation and evaluated using ML performance metrics.Eight out of the 21 features selected by GB returned their maximum Matthews Correlation Coefficient(MCC)score of 0.6255,while CB,with 20 of the 21 features,returned the maximum and highest MCC score of 0.6321.The study demonstrated the ability of ML models to predict fetal health conditions from CTG exam results,facilitating early identification of high-risk pregnancies and enabling prompt treatment to prevent severe neonatal outcomes.

Predicting the device performance of the perovskite solar cells from the experimental parameters through machine learning of existing experimental results

The performance of the metal halide perovskite solar cells(PSCs)highly relies on the experimental parameters,including the fabrication processes and the compositions of the perovskites;tremendous experimental work has been done to optimize these factors.However,predicting the device performance of the PSCs from the fabrication parameters before experiments is still challenging.Herein,we bridge this gap by machine learning(ML)based on a dataset including 1072 devices from peer-reviewed publications.The optimized ML model accurately predicts the PCE from the experimental parameters with a root mean square error of 1.28%and a Pearson coefficientr of 0.768.Moreover,the factors governing the device performance are ranked by shapley additive explanations(SHAP),among which,A-site cation is crucial to getting highly efficient PSCs.Experiments and density functional theory calculations are employed to validate and help explain the predicting results by the ML model.Our work reveals the feasibility of ML in predicting the device performance from the experimental parameters before experiments,which enables the reverse experimental design toward highly efficient PSCs.

REVERSE MODELING FOR CONIC BLENDING FEATURE

A novel method to extract conic blending feature in reverse engineering is presented. Different from the methods to recover constant and variable radius blends from unorganized points, it contains not only novel segmentation and feature recognition techniques, but also bias corrected technique to capture more reliable distribution of feature parameters along the spine curve. The segmentation depending on point classification separates the points in the conic blend region from the input point cloud. The available feature parameters of the cross-sectional curves are extracted with the processes of slicing point clouds with planes, conic curve fitting, and parameters estimation and compensation, The extracted parameters and its distribution laws are refined according to statistic theory such as regression analysis and hypothesis test. The proposed method can accurately capture the original design intentions and conveniently guide the reverse modeling process. Application examples are presented to verify the high precision and stability of the proposed method.

基于机器学习的公共卫生数据可靠性评估系统的研究与设计

随着信息技术的快速发展,卫生健康数据不断增多,数据的可靠性评估是数据实践应用、科学研究的前提和保障。传统的人工、统计学数据可靠性评估方法难以适用于海量数据的可靠性评估,而且造成人力资源的浪费。本研究构建一种基于机器学习的公共卫生数据可靠性评估系统,首先对数据进行存储、标注和规则性质控,并对数据进行特征工程处理,然后选取部分数据由机器学习算法自主训练数据特征并形成数据可靠性评估模型,通过模型来评估其他数据的可靠性,最后进行数据量检验并综合评价数据的可靠性。由此形成数据可靠性评估的新方法、新模式,有助于弥补现有评估方法的不足,提升数据可靠性评估的准确率和效率。

Optimized ANN for LiFePO_(4) battery charge estimation using principal components based feature generation

Electric vehicles(EVs)have gained prominence in the present energy transition scenario.Widespread adoption of EVs necessitates an accurate State of Charge estimation(SoC)algorithm.Integrating predictive SoC estimations with smart charging strategies not only optimizes charging efficiency and grid reliability but also extends battery lifespan while continuously enhancing the accuracy of SoC predictions,marking a crucial milestone in sustainable electric vehicle technology.In this research study,machine learning methods,particularly Artificial Neural Networks(ANN),are employed for SoC estimation of LiFePO4 batteries,resulting in efficient and accurate estimation algorithms.The investigation first focuses on developing a custom-designed battery pack with 12V,4 Ah capacity with a facility for real-time data collection through a dedicated hardware setup.The voltage,current and open-circuit voltage of the battery are monitored with computerized battery analyzer.The battery temperature is sensed with a DHT22 temperature sensor interfaced with Raspberry Pi.Principal components are derived for the collected battery data set and analyzed for feature engineering.Three principal components were generated as input parameters for the developed ANN.Early Stopping for the ANN was also implemented to achieve faster convergence of the ANN.While considering eleven combinations for ten different optimizers loss function is minimized.Comparative analysis of hyperparameter tuning and optimizer selection revealed that the Adafactor optimizer with specific settings produced the best results with an RMSE value of 0.4083 and an R2 Score of 0.9998.The proposed algorithm was also implemented for two different types of datasets,a UDDS drive cycle and a standard cell-level dataset.The results obtained were in line with the results obtained with the ANN model developed based on the data collected from the developed experimental setup.

An Early Warning Model of Telecommunication Network Fraud Based on User Portrait

With the frequent occurrence of telecommunications and networkfraud crimes in recent years, new frauds have emerged one after another whichhas caused huge losses to the people. However, due to the lack of an effectivepreventive mechanism, the police are often in a passive position. Usingtechnologies such as web crawlers, feature engineering, deep learning, andartificial intelligence, this paper proposes a user portrait fraudwarning schemebased on Weibo public data. First, we perform preliminary screening andcleaning based on the keyword “defrauded” to obtain valid fraudulent userIdentity Documents (IDs). The basic information and account information ofthese users is user-labeled to achieve the purpose of distinguishing the typesof fraud. Secondly, through feature engineering technologies such as avatarrecognition, Artificial Intelligence (AI) sentiment analysis, data screening,and follower blogger type analysis, these pictures and texts will be abstractedinto user preferences and personality characteristics which integrate multidimensionalinformation to build user portraits. Third, deep neural networktraining is performed on the cube. 80% percent of the data is predicted basedon the N-way K-shot problem and used to train the model, and the remaining20% is used for model accuracy evaluation. Experiments have shown thatFew-short learning has higher accuracy compared with Long Short TermMemory (LSTM), Recurrent Neural Networks (RNN) and ConvolutionalNeural Network (CNN). On this basis, this paper develops a WeChat smallprogram for early warning of telecommunications network fraud based onuser portraits. When the user enters some personal information on the frontend, the back-end database can perform correlation analysis by itself, so as tomatch the most likely fraud types and give relevant early warning information.The fraud warning model is highly scaleable. The data of other Applications(APPs) can be extended to further improve the efficiency of anti-fraud whichhas extremely high public welfare value.

Prediction of users online purchase behavior based on selective ensemble learning

A probabilistic multi-dimensional selective ensemble learning method and its application in the prediction of users' online purchase behavior are studied in this work.Firstly, the classifier is integrated based on the dimension of predicted probability, and the pruning algorithm based on greedy forward search is obtained by combining the two indicators of accuracy and complementarity.Then the pruning algorithm is integrated into the Stacking ensemble method to establish a user online shopping behavior prediction model based on the probabilistic multi-dimensional selective ensemble method.Finally, the research method is compared with the prediction results of individual learners in ensemble learning and the Stacking ensemble method without pruning.The experimental results show that the proposed method can reduce the scale of integration, improve the prediction accuracy of the model, and predict the user's online purchase behavior.