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... 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 ...
... 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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...