The growth of battery energy storage systems (BESS) is caused by the variability and intermittent nature of high demand and renewable power generation at the network scale. In the context of BESS, Lithium-ion (Li-ion)...
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The growth of battery energy storage systems (BESS) is caused by the variability and intermittent nature of high demand and renewable power generation at the network scale. In the context of BESS, Lithium-ion (Li-ion) battery occupies a crucial position, although it is faced with challenges related to performance battery degradation over time due to electrochemical processes. This battery degradation is a crucial factor to account for, based on its potential to diminish the efficiency and safety of electrical system equipment, thereby contributing to increased system planning costs. This implies that the health of battery needs to be diagnosed, particularly by determining remaining useful life (RUL), to avoid unexpected operational costs and ensure system safety. Therefore, this study aimed to use machine learning models, specifically extremegradientboosting (XGBoost) algorithm, to estimate RUL, with a focus on the temperature variable, an aspect that had been previously underemphasized. Utilizing XGBoost model, along with fine-tuning its hyperparameters, proved to be a more accurate and efficient method for predicting RUL. The evaluation of the model yielded promising outcomes, with a root mean square error (RMSE) of 90.1 and a mean absolute percentage error (MAPE) of 7.5 %. Additionally, the results showed that the model could improve RUL predictions for batteries within BESS. This study significantly contributed to optimizing planning and operations for BESS, as well as developing more efficient and effective maintenance strategies.
The current study on the synthesis problems of four-bar mechanism trajectories primarily relies on establishing a numerical atlas based on trajectory characteristics and employing neural networks to synthesize mechani...
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The current study on the synthesis problems of four-bar mechanism trajectories primarily relies on establishing a numerical atlas based on trajectory characteristics and employing neural networks to synthesize mechanism parameters. However, this approach has several shortcomings, including a vast database, inefficient retrieval, and challenges in maintaining accuracy. This paper presents a method for synthesizing a trajectory-generation mechanism that combines the extremegradientboosting (XGBoost) algorithm with a genetic algorithm (GA). The purpose is to synthesize, based on a particular trajectory, the dimensions and installation position parameters of a four-bar mechanism. The paper classifies the trajectories according to their shape features and geometric center placements, thereby improving the accuracy of the XGBoost model for synthesizing mechanisms. The XGBoost algorithm is employed to synthesize the basic dimensional parameters for the mechanism, with the relative slopes of trajectories as input features. The synthesized basic dimensional parameters are turned into parameters for the actual mechanism by researching the scaling, translation, and rotation relationships between mechanisms and the trajectories they generate. The accuracy of the generated trajectories from the synthesized mechanism can be improved by applying GA to optimize the mechanism parameters. Five comparative examples are provided in this research for the different scenarios of given trajectory curves and trajectory points. The effectiveness and accuracy of the proposed approach in this study are validated in comparison to existing research methods by comparing errors between the generated trajectories and the given trajectories. Graphical Abstract
Proton exchange membrane water electrolysis (PEMWE) powered by renewable energy stands out as a promising technology for the sustainable production of high-purity hydrogen. This study employed three machine learning (...
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Proton exchange membrane water electrolysis (PEMWE) powered by renewable energy stands out as a promising technology for the sustainable production of high-purity hydrogen. This study employed three machine learning (ML) algorithms, random forest (RF), support vector machine (SVM), and extremegradientboosting (XGBoost), to predict hydrogen production in PEMWE. Model performance was evaluated using root mean squared error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) metrics. The top-performing models, RF and XGBoost, were further refined through hyperparameter tuning. The final models demonstrated high reliability in predicting hydrogen production rates, with RF consistently outperforming XGBoost. The RF model achieved a predictive accuracy of R2 = 0.9898, RMSE = 19.99 mL/min, and MAE = 10.41 mL/min, while the XGBoost model achieved R2 = 0.9894, RMSE = 20.43 mL/min, and MAE = 11.50 mL/min. Partial dependency plots (PDPs) emphasized the critical role of optimizing both cell voltage and current to maximize hydrogen production in PEMWE. These insights provide valuable guidance for operational adjustments, ensuring optimal system performance for high efficiency and productivity. The study suggests further research on the impact of parameters like temperature and power density on hydrogen production, incorporating them for better optimization.
In bridge engineering, void damage in rubber bearings refers to the phenomenon where gaps occur between the rubber bearing and the bridge structure due to factors such as aging, overloading, and earthquakes. Void dama...
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In bridge engineering, void damage in rubber bearings refers to the phenomenon where gaps occur between the rubber bearing and the bridge structure due to factors such as aging, overloading, and earthquakes. Void damage can lead to abnormal stress distribution, bearing deformation failure, and uneven load transfer, thereby affecting the overall stability and safety of the bridge. Consequently, long-term monitoring of void damage is essential. However, methods such as manual inspection and computer vision have disadvantages, including high costs, lengthy processes, and limited monitoring scope. To address these challenges in void damage detection, this paper proposes a novel method that combines the active sensing approach with the Bayesian optimization (BO)-optimized extremegradientboosting (XGBoost) algorithm. A total of 1200 sets of experimental data under different void conditions were obtained through the active sensing method, and a high-precision void damage detection model based on the BO-XGBoost algorithm was proposed. The study found that the proposed void damage detection model achieved an Accuracy value of 95.0% on the test set, indicating that the combination of the active sensing method with the BO-XGBoost algorithm can effectively solve the problem of void damage detection in laminated rubber bearings.
The inherent spatial heterogeneity of complex geological formations results in large differences in the ultimate bearing capacity of individual piles, and it is difficult to reliably quantify and assess the bearing ca...
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The inherent spatial heterogeneity of complex geological formations results in large differences in the ultimate bearing capacity of individual piles, and it is difficult to reliably quantify and assess the bearing capacity of individual piles. In this paper, based on the results of on-site static load test and sensitivity analysis method, eight sensitive factors are screened out, and the extreme gradient boosting algorithm (XGBoost) is used to predict the ultimate load carrying capacity of individual piles, however, the computed coefficient of determination is less than 0.9, and the prediction effect needs to be strengthened. On this basis, three kinds of swarm intelligent optimization algorithms are introduced to adaptively match the XGBoost hyper-parameters, and the results show that the HGS-XGBoost hybrid prediction model can more accurately calculate the ultimate bearing capacity of a single pile under the composite strata, and the prediction effect can satisfy the engineering requirements when using the HGS-XGBoost prediction model for the actual project.
The cement specific surface area is an important indicator of cement quality. The accurate prediction of the cement specific surface area aims to guide operators to control the cement grinding process to improve produ...
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The cement specific surface area is an important indicator of cement quality. The accurate prediction of the cement specific surface area aims to guide operators to control the cement grinding process to improve product quality while reducing system energy consumption. However, due to the complexity of the cement grinding process, the process variables have coupling, time-varying delay, nonlinear characteristics, and different sampling frequency. Herein, we proposed the specific surface area prediction model, which combined dual-frequency principal component analysis and extremegradientboosting (DF-PCA-XGB). In order to solve the problem of difficulty in modeling due to different sampling intervals of related data, this paper analyzes the low-frequency sampling data and high-frequency sampling data under multiple working conditions, and establishes prediction models respectively. Aiming at the data redundancy problem of high-frequency and low-frequency variable data in the introduced time window, a method based on the combination of principal component analysis (PCA) and extremegradientboosting (XGB) cross-validation is proposed to reduce data redundancy while retaining most of the characteristics of the data. The final specific surface area prediction results were obtained by weighting the high-frequency data model and the low-frequency data model. The simulation results showed that the prediction method in this paper can improve the prediction accuracy of the specific surface area of the finished cement product under multiple working conditions with high stability and has promising application in the cement manufacturing process.
As the core component of the electronic devices, the integrated circuit (IC) must be taken seriously with its security. The pre-silicon detection methods do not require gold chips, are not affected by process noise an...
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ISBN:
(纸本)9781728128719
As the core component of the electronic devices, the integrated circuit (IC) must be taken seriously with its security. The pre-silicon detection methods do not require gold chips, are not affected by process noise and are suitable for the safe detection of a very large scale integration (VLSI). Therefore, more and more researchers are paying attention to the presilicon detection method. In this paper, we propose a machine-learning-based hardware-Trojans detection method in gatelevel. First, by the analysis of the Trojan circuits, we put forward new Trojan-net features. After that, we use the scoring mechanism of the extremegradientboosting (XGBoost) to set up a new effective feature set of 49 out of 56 features. Finally, the hardware-Trojan classifier was trained based on the effective feature set. The experimental results show that the proposed method can obtain the average Recall of 89.84%, the average F-measure of 87.75% and the average Accuracy of 99.83%. Furthermore, through the comparison experiments, it is proved that the features proposed in this paper can further improve the performance of detection.
The Three-Dimensional High Efficiency Video Coding standard is a video compression standard developed based on the two-dimensional video coding standard HEVC and used to encode multi-view plus depth format video. This...
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The Three-Dimensional High Efficiency Video Coding standard is a video compression standard developed based on the two-dimensional video coding standard HEVC and used to encode multi-view plus depth format video. This paper proposes an algorithm based on extremegradientboosting to solve the problem of high inter-frame coding complexity in 3D-HEVC. Firstly, explore the correlation between the division depth of the inter-frame coding unit and the texture features in the map, as well as the correlation between the coding unit division structure between each map and each viewpoint. After that, based on the machine learning method, a fast selection mechanism for dividing the depth range of the inter-frame coding tree unit based on the extreme gradient boosting algorithm is constructed. Experimental results show that, compared with the reference software HTM-16.0, this method can save an average of 35.06% of the coding time, with negligible degradation in terms of coding performance. In addition, the proposed algorithm has achieved different degrees of improvement in coding performance compared with the related works.
The prediction of particles less than 2.5 micrometers in diameter(PM2.5)in fog and haze has been paid more and more attention,but the prediction accuracy of the results is not *** prediction algorithms based on tradit...
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The prediction of particles less than 2.5 micrometers in diameter(PM2.5)in fog and haze has been paid more and more attention,but the prediction accuracy of the results is not *** prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of *** order to improve the effects of prediction,this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated *** Redundancy Maximum Relevance method is used to extract low-level features of haze,and deep confidence network is utilized to extract high-level *** gradientboostingalgorithm is adopted to fuse low-level and high-level features,as well as predict *** PM2.5 concentration pollution grade classification index,and grade the forecast *** expert experience knowledge is utilized to assist the optimization of the pre-warning *** experiment results show the presented algorithm can get better prediction effects than the results of Support Vector Machine(SVM)and Back Propagation(BP)widely used at present,the accuracy has greatly improved compared with SVM and BP.
Induction motors (IMs) have been commonly applied to industrial fields since the past decades;thus, developing advanced fault diagnosis methods becomes vital for IM applications. This study proposed an online fault di...
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Induction motors (IMs) have been commonly applied to industrial fields since the past decades;thus, developing advanced fault diagnosis methods becomes vital for IM applications. This study proposed an online fault diagnosis system for IMs based on the Random Forest (RF) and extremegradientboosting (XGBoost) algorithms to reduce the additional repair costs and prevent unexpected downtime. It focused on detecting healthy three-phase IMs and five common fault conditions of the IMs, involving broken rotor bars, rotor unbalance, and composite faults with short-circuited stator windings that combined two or three types of the faults, for practical purposes. The experimental results show that the model performance improved by 15% over the default model when train-test split ratios, feature selection, and hyperparameter optimization, notably in XGBoost, are considered. The proposed XGBoost model enables a high accuracy of 96.06% for RF to perform a motor fault diagnosis under six different motor conditions. Furthermore, the execution time required by the proposed fault diagnosis system is 57% less than the time required by existing motor fault diagnosis methods. These results successfully demonstrate the effectiveness of the methods proposed in this study for online motor diagnosis.
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