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1. Detecting the foreign matter defect in lithium-ion batteries based on battery pilot manufacturing line data analyses

Author:
Yue Pan; Xiangdong Kong; Yuebo Yuan; Yukun Sun; Xuebing Han; Hongxin Yang; Jianbiao Zhang; Xiaoan Liu; Panlong Gao; Yihui Li; Languang Lu; Minggao Ouyang
Source:
Energy 2023 v.262 pp. 125502
ISSN:
0360-5442
Subject:
algorithms,   etc   ; energy; lithium batteries;     Show all 3 Subjects
Abstract:
... Foreign matter defect introduced during lithium-ion battery manufacturing process is one of the main reasons for battery thermal runaway. Therefore, reliable detection of the foreign matter defect is needed for safe and long-term operation of lithium-ion batteries. It is favored to detect the defective battery during the battery manufacturing process before the battery is put into use. In this stu ...
DOI:
10.1016/j.energy.2022.125502

2. A multi-objective optimization strategy for building carbon emission from the whole life cycle perspective

Author:
Ruijun Chen; Yaw-Shyan Tsay; Ting Zhang
Source:
Energy 2023 v.262 pp. 125373
ISSN:
0360-5442
Subject:
algorithms,   etc   ; carbon; energy; models; prediction;     Show all 5 Subjects
Abstract:
... This research proposed an integrated strategy for building performance optimization from the whole life cycle perspective to explore the optimal building scheme. After the feature elimination, the ensemble learning model (ELM) was trained to obtain a high-precision model for predicting life cycle carbon emissions (LCCE), life cycle costs (LCC), and indoor discomfort hours (IDH). Then, the optimal ...
DOI:
10.1016/j.energy.2022.125373

3. State of health estimation of lithium-ion battery with improved radial basis function neural network

Author:
Ji Wu; Leichao Fang; Guangzhong Dong; Mingqiang Lin
Source:
Energy 2023 v.262 pp. 125380
ISSN:
0360-5442
Subject:
algorithms,   etc   ; data collection; energy; lithium batteries;     Show all 4 Subjects
Abstract:
... Accurate state of health (SOH) estimation for lithium-ion batteries is crucial to ensure the safety and reliability of electric vehicles. However, traditional neural network algorithms to estimate SOH often focus on fitting nonlinear fluctuation and is weak in the overall tracking trend. This paper thus proposes an improved radial basis function neural network (IRBFNN) to estimate the SOH with the ...
DOI:
10.1016/j.energy.2022.125380

4. Multivariate sparse Laplacian shrinkage for joint estimation of two graphical structures

Author:
Yuehan Yang; Siwei Xia; Hu Yang
Source:
Computational statistics & data analysis 2023 v.178 pp. 107620
ISSN:
0167-9473
Subject:
algorithms,   etc   ; covariance; data analysis; shrinkage; stock exchange;     Show all 5 Subjects
Abstract:
... Multivariate regression models are widely used in various fields for fitting multiple responses. In this paper, we proposed a sparse Laplacian shrinkage estimator for the high-dimensional multivariate regression models. We consider two graphical structures among predictors and responses. The proposed method explores the regression relationship allowing the predictors and responses derived from dif ...
DOI:
10.1016/j.csda.2022.107620

5. Tikhonov regularization stabilizes multi-parameter estimation of geothermal heat exchangers

Author:
Yufang Du; Min Li; Yong Li; Alvin CK. Lai
Source:
Energy 2023 v.262 pp. 125479
ISSN:
0360-5442
Subject:
algorithms,   etc   ; data collection; energy; heat; models; temperature;     Show all 6 Subjects
Abstract:
... Parameter estimation from thermal response tests (TRTs) becomes unreliable when testing time reduces or the number of estimated parameters increases because of low identifiability and ill-posed mathematical feature. To overcome this challenge, this paper reports an inversion algorithm integrating a short-time temperature response model and the zero-order Tikhonov regularization strategy. We applie ...
DOI:
10.1016/j.energy.2022.125479

6. Hierarchical clustered multiclass discriminant analysis via cross-validation

Author:
Kei Hirose; Kanta Miura; Atori Koie
Source:
Computational statistics & data analysis 2023 v.178 pp. 107613
ISSN:
0167-9473
Subject:
algorithms,   etc   ; data analysis; data collection; discriminant analysis; prediction;     Show all 5 Subjects
Abstract:
... Linear discriminant analysis (LDA) is a well-known method for multiclass classification and dimensionality reduction. However, in general, ordinary LDA does not achieve high prediction accuracy when observations in some classes are difficult to be classified. A novel cluster-based LDA method is proposed that significantly improves prediction accuracy. Hierarchical clustering is adopted, and the di ...
DOI:
10.1016/j.csda.2022.107613

7. The application of machine learning based energy management strategy in multi-mode plug-in hybrid electric vehicle, part I: Twin Delayed Deep Deterministic Policy Gradient algorithm design for hybrid mode

Author:
Changcheng Wu; Jiageng Ruan; Hanghang Cui; Bin Zhang; Tongyang Li; Kaixuan Zhang
Source:
Energy 2023 v.262 pp. 125084
ISSN:
0360-5442
Subject:
algorithms,   etc   ; energy efficiency; issues and policy; torque; vehicles (equipment);     Show all 5 Subjects
Abstract:
... As the performance of Energy Management Strategy (EMS) is crucial for the energy efficiency of Hybrid Electric Vehicles (HEVs), a Deep Reinforcement Learning (DRL)-based algorithm, namely Twin Delayed Deep Deterministic Policy Gradient (TD3), is adopted to design EMS for the power Charge-Sustained (CS) stage of a multi-mode plug-in Hybrid Electric Vehicle (HEV). In addition, EMS is improved by com ...
DOI:
10.1016/j.energy.2022.125084

8. Detection of polycyclic aromatic hydrocarbons using a high performance-single particle aerosol mass spectrometer

Author:
Yao Zhang; Chenglei Pei; Jinwen Zhang; Chunlei Cheng; Xiufeng Lian; Mubai Chen; Bo Huang; Zhong Fu; Zhen Zhou; Mei Li
Source:
Journal of environmental sciences (China) 2023 v.124 pp. 806-822
ISSN:
1001-0742
Subject:
algorithms,   etc   ; aerosols; biomass; comparative study; mass spectrometry; spectrometers; China;     Show all 7 Subjects
Abstract:
... The real-time detection of the mixing states of polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs in ambient particles is of great significance for analyzing the source, aging process, and health effects of PAHs and nitro-PAHs; yet there is still few effective technology to achieve this type of detection. In this study, 11 types of PAH and nitro-PAH standard samples were analyzed using a high ...
DOI:
10.1016/j.jes.2022.02.003

9. Optimal allocation of tomato supply to minimize greenhouse gas emissions in major U.S. metropolitan markets

Author:
Eric Bell; Yuwei Qin; Arpad Horvath
Source:
Resources, conservation, and recycling 2023 v.188 pp. 106660
ISSN:
0921-3449
Subject:
algorithms,   etc   ; Monte Carlo method; climate; greenhouse gases; soil; tomatoes;     Show all 6 Subjects
Abstract:
... Our food system is very resource and emissions intensive and contributes to a broad range of environmental impacts. We have developed cradle-to-market greenhouse gas emissions estimates of supplying fresh tomatoes to 10 of the largest metropolitan areas in the United States and applied a linear optimization algorithm to determine the optimal tomato distribution scheme that will minimize tomato-rel ...
DOI:
10.1016/j.resconrec.2022.106660

10. A Markov decision process for response-adaptive randomization in clinical trials

Author:
David Merrell; Thevaa Chandereng; Yeonhee Park
Source:
Computational statistics & data analysis 2023 v.178 pp. 107599
ISSN:
0167-9473
Subject:
algorithms,   etc   ; data analysis; issues and policy; patients; statistics; utility functions;     Show all 6 Subjects
Abstract:
... In clinical trials, response-adaptive randomization (RAR) has the appealing ability to assign more subjects to better-performing treatments based on interim results. Traditional RAR strategies alter the randomization ratio on a patient-by-patient basis. An alternate approach is blocked RAR, which groups patients together in blocks and recomputes the randomization ratio in a block-wise fashion; pas ...
DOI:
10.1016/j.csda.2022.107599

11. Smoothed tensor quantile regression estimation for longitudinal data

Author:
Baofang Ke; Weihua Zhao; Lei Wang
Source:
Computational statistics & data analysis 2023 v.178 pp. 107609
ISSN:
0167-9473
Subject:
algorithms,   etc   ; air quality; data analysis; data collection; regression analysis; China;     Show all 6 Subjects
Abstract:
... As extensions of vector and matrix data with ultrahigh dimensionality and complex structures, tensor data are fast emerging in a large variety of scientific applications. In this paper, a two-stage estimation procedure for linear tensor quantile regression (QR) with longitudinal data is proposed. In the first stage, we account for within-subject correlations by using the generalized estimating equ ...
DOI:
10.1016/j.csda.2022.107609

12. Ultrasensitive visual detection of miRNA-143 using a CRISPR/Cas12a-based platform coupled with hyperbranched rolling circle amplification

Author:
Wenyan Jiang; Zhongping Chen; Jin Lu; Xue Ren; Yue Ma
Source:
Talanta 2023 v.251 pp. 123784
ISSN:
0039-9140
Subject:
algorithms,   etc   ; biomarkers; diagnostic techniques; microRNA; nanogold; prostatic neoplasms; sequence homology;     Show all 7 Subjects
Abstract:
... MicroRNAs are proposed novel biomarker for noninvasive diagnosis of cancer. miRNA-143 is reported to be associated with the development of prostate cancer. However, detection of miRNAs is still challenging due to their unique characteristics, such as small size and high sequence homology among family members. We here developed a gold nanoparticle (AuNP)-based visual assay that combines with CRISPR ...
DOI:
10.1016/j.talanta.2022.123784

13. Genetic sizing optimization of residential multi-carrier energy systems: The aim of energy autarky and its cost

Author:
Fabian Schmid; Frank Behrendt
Source:
Energy 2023 v.262 pp. 125421
ISSN:
0360-5442
Subject:
algorithms,   etc   ; economic independence; economic performance; energy; hydrogen; latitude; models; solar energy;     Show all 8 Subjects
Abstract:
... The energy transition process fosters decentralized renewable energy generation and is characterized by an increased effort to achieve energy autarky. In this context, residential-size, photovoltaic-based multi-carrier energy systems using hydrogen as seasonal storage are analyzed as a possible solution to gain energy autarky. A high temporal (15 min) and long-term (10 a) power flow simulation app ...
DOI:
10.1016/j.energy.2022.125421

14. A fast and energy-efficient nonnegative least square-based optimal active battery balancing control strategy for electric vehicle applications

Author:
Arzu Turksoy; Ahmet Teke
Source:
Energy 2023 v.262 pp. 125409
ISSN:
0360-5442
Subject:
algorithms,   etc   ; batteries; electric vehicles; electronic circuits; energy efficiency; energy transfer; heat;     Show all 7 Subjects
Abstract:
... In an electric vehicle, a battery pack with many series-connected cells suffers from charge imbalances caused by manufacturing and varying operating conditions. The battery balancing system overcomes the cell imbalance while providing the required capacity maximization, prolonging the battery life, and safe operation. Various balancing topologies and control algorithms have been developed based on ...
DOI:
10.1016/j.energy.2022.125409

15. Adaptive Spectral Model for abnormality detection based on physiological status monitoring of dairy cows

Author:
Jelena Muncan; Mari Miyazaki; Shinichiro Kuroki; Kentarou Ikuta; Roumiana Tsenkova
Source:
Talanta 2023 v.253 pp. 123893
ISSN:
0039-9140
Subject:
algorithms,   etc   ; cows; milk; models; physiological state; spectral analysis; time series analysis; wavelengths;     Show all 8 Subjects
Abstract:
... This research study developed milk spectral data-driven approach, called Adaptive Spectral Model for Abnormality Detection - ASMAD, for detection of physiological abnormalities of individual dairy cows. The algorithm is based on the logic that milk spectra of each individual cow is highly animal-specific, which means it could be used as a respective individual marker for identification. When the a ...
DOI:
10.1016/j.talanta.2022.123893

16. Fast online reinforcement learning control of small lift-driven vertical axis wind turbines with an active programmable four bar linkage mechanism

Author:
He Shen; Alexis Ruiz; Ni Li
Source:
Energy 2023 v.262 pp. 125350
ISSN:
0360-5442
Subject:
algorithms,   etc   ; energy; issues and policy; power generation; system optimization; urban areas; wind;     Show all 7 Subjects
Abstract:
... Maintaining high power generation for small lift-driven vertical axis wind turbines in a changing wind environment has not been well studied yet, due to the challenges inherited from the unpredictable turbulent flow-blade interaction and complex blade interferences. Herein, a fast online reinforcement learning pitch control using an active programmable four bar linkage mechanism is proposed, makin ...
DOI:
10.1016/j.energy.2022.125350

17. Impacts of meteorology and precursor emission change on O3 variation in Tianjin, China from 2015 to 2021

Author:
Jing Ding; Qili Dai; Wenyan Fan; Miaomiao Lu; Yufen Zhang; Suqin Han; Yinchang Feng
Source:
Journal of environmental sciences (China) 2023 v.126 pp. 506-516
ISSN:
1001-0742
Subject:
algorithms,   etc   ; air; humidity; loess; meteorology; ozone; pollution; summer; temperature; time series analysis; China;     Show all 11 Subjects
Abstract:
... Deterioration of surface ozone (O₃) pollution in Northern China over the past few years received much attention. For many cities, it is still under debate whether the trend of surface O₃ variation is driven by meteorology or the change in precursors emissions. In this work, a time series decomposition method (Seasonal-Trend decomposition procedure based on Loess (STL)) and random forest (RF) algor ...
DOI:
10.1016/j.jes.2022.03.010

18. Spatial expansion paths of urban heat islands in Chinese cities: Analysis from a dynamic topological perspective for the improvement of climate resilience

Author:
Zhi Qiao; Yingshuang Lu; Tong He; Feng Wu; Xinliang Xu; Luo Liu; Fang Wang; Zongyao Sun; Dongrui Han
Source:
Resources, conservation, and recycling 2023 v.188 pp. 106680
ISSN:
0921-3449
Subject:
algorithms,   etc   ; climate; climate change; ecological resilience; land use; summer; surface temperature; topology; China;     Show all 9 Subjects
Abstract:
... Urban ecological resilience enhancement is important for the mitigation of climate change risks; however, urban heat islands (UHIs) have negative impacts on urban resilience. A UHI expansion index (UHIEI) was developed to identify new UHI patches produced by including infilling, edge expansion, and leapfrogging. In this study, we used a simplified urban extent (SUE) algorithm to estimate new UHI p ...
DOI:
10.1016/j.resconrec.2022.106680

19. Discrimination method of biomass slagging tendency based on particle swarm optimization deep neural network (DNN)

Author:
Yingxin Bi; Chunxiang Chen; Xiaodong Huang; Haokun Wang; Guangsheng Wei
Source:
Energy 2023 v.262 pp. 125368
ISSN:
0360-5442
Subject:
algorithms,   etc   ; ash content; biofuels; biomass; combustion; combustion efficiency; data collection; energy; neural networks;     Show all 9 Subjects
Abstract:
... The slagging problem that occurs during the combustion of biomass fuels is difficult to deal with, affecting the safe operation of the boiler and reducing the combustion efficiency. Predicting the slagging tendency of biomass combustion can guide the selection of fuel, reduce the experimental cost and improve the production efficiency of the boiler. In this paper, 114 kinds of biomass are selected ...
DOI:
10.1016/j.energy.2022.125368

20. A multivariable hybrid prediction model of offshore wind power based on multi-stage optimization and reconstruction prediction

Author:
Hao Wang; Jingzhen Ye; Linxuan Huang; Qiang Wang; Haohua Zhang
Source:
Energy 2023 v.262 pp. 125428
ISSN:
0360-5442
Subject:
algorithms,   etc   ; Canis lupus; Passeriformes; data collection; energy; forests; hybrids; neural networks; prediction; wind power;     Show all 10 Subjects
Abstract:
... Offshore wind power prediction is the basis for safe operation and grid dispatch. However, it is difficult due to the high volatility. Aiming at the three shortcomings of current methods: lack of analysis of the impact of multiple variables; the reconstruction method of decomposition components often adopts the summation method; the traditional machine learning prediction methods are not accurate ...
DOI:
10.1016/j.energy.2022.125428