949 research outputs found
Monitoring the Interface of Carbon Fibre and Epoxy Microcomposites Using Raman Spectroscopy with Single Walled Carbon Nanotubes as Strain Sensors
A critical review of online battery remaining useful lifetime prediction methods.
Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining service life of lithium-ion batteries has become an important issue. This article reviews the methods for predicting the remaining service life of lithium-ion batteries from three aspects: machine learning, adaptive filtering, and random processes. The purpose of this study is to review, classify and compare different methods proposed in the literature to predict the remaining service life of lithium-ion batteries. This article first summarizes and classifies various methods for predicting the remaining service life of lithium-ion batteries that have been proposed in recent years. On this basis, by selecting specific criteria to evaluate and compare the accuracy of different models, find the most suitable method. Finally, summarize the development of various methods. According to the research in this article, the average accuracy of machine learning is 32.02% higher than the average of the other two methods, and the prediction cycle is 9.87% shorter than the average of the other two methods
Are you in a Masquerade? Exploring the Behavior and Impact of Large Language Model Driven Social Bots in Online Social Networks
As the capabilities of Large Language Models (LLMs) emerge, they not only
assist in accomplishing traditional tasks within more efficient paradigms but
also stimulate the evolution of social bots. Researchers have begun exploring
the implementation of LLMs as the driving core of social bots, enabling more
efficient and user-friendly completion of tasks like profile completion, social
behavior decision-making, and social content generation. However, there is
currently a lack of systematic research on the behavioral characteristics of
LLMs-driven social bots and their impact on social networks. We have curated
data from Chirper, a Twitter-like social network populated by LLMs-driven
social bots and embarked on an exploratory study. Our findings indicate that:
(1) LLMs-driven social bots possess enhanced individual-level camouflage while
exhibiting certain collective characteristics; (2) these bots have the ability
to exert influence on online communities through toxic behaviors; (3) existing
detection methods are applicable to the activity environment of LLMs-driven
social bots but may be subject to certain limitations in effectiveness.
Moreover, we have organized the data collected in our study into the
Masquerade-23 dataset, which we have publicly released, thus addressing the
data void in the subfield of LLMs-driven social bots behavior datasets. Our
research outcomes provide primary insights for the research and governance of
LLMs-driven social bots within the research community.Comment: 18 pages, 7 figure
Electrical Impedance Tomography with Deep Calder\'on Method
Electrical impedance tomography (EIT) is a noninvasive medical imaging
modality utilizing the current-density/voltage data measured on the surface of
the subject. Calder\'on's method is a relatively recent EIT imaging algorithm
that is non-iterative, fast, and capable of reconstructing complex-valued
electric impedances. However, due to the regularization via low-pass filtering
and linearization, the reconstructed images suffer from severe blurring and
underestimation of the exact conductivity values. In this work, we develop an
enhanced version of Calder\'on's method, using convolution neural networks
(i.e., U-net) via a postprocessing step. Specifically, we learn a U-net to
postprocess the EIT images generated by Calder\'on's method so as to have
better resolutions and more accurate estimates of conductivity values. We
simulate chest configurations with which we generate the
current-density/voltage boundary measurements and the corresponding
reconstructed images by Calder\'on's method. With the paired training data, we
learn the neural network and evaluate its performance on real tank measurement
data. The experimental results indicate that the proposed approach indeed
provides a fast and direct (complex-valued) impedance tomography imaging
technique, and substantially improves the capability of the standard
Calder\'on's method.Comment: 20 page
Recovery of Multiple Parameters in Subdiffusion from One Lateral Boundary Measurement
This work is concerned with numerically recovering multiple parameters
simultaneously in the subdiffusion model from one single lateral measurement on
a part of the boundary, while in an incompletely known medium. We prove that
the boundary measurement corresponding to a fairly general boundary excitation
uniquely determines the order of the fractional derivative and the polygonal
support of the diffusion coefficient, without knowing either the initial
condition or the source. The uniqueness analysis further inspires the
development of a robust numerical algorithm for recovering the fractional order
and diffusion coefficient. The proposed algorithm combines small-time
asymptotic expansion, analytic continuation of the solution and the level set
method. We present extensive numerical experiments to illustrate the
feasibility of the simultaneous recovery. In addition, we discuss the
uniqueness of recovering general diffusion and potential coefficients from one
single partial boundary measurement, when the boundary excitation is more
specialized.Comment: 28 page
Machine learning to predict electrochemical impedance spectra (EIS): Can EIS be replaced by constant current techniques?
Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence
Aesthetic quality prediction is a challenging task in the computer vision
community because of the complex interplay with semantic contents and
photographic technologies. Recent studies on the powerful deep learning based
aesthetic quality assessment usually use a binary high-low label or a numerical
score to represent the aesthetic quality. However the scalar representation
cannot describe well the underlying varieties of the human perception of
aesthetics. In this work, we propose to predict the aesthetic score
distribution (i.e., a score distribution vector of the ordinal basic human
ratings) using Deep Convolutional Neural Network (DCNN). Conventional DCNNs
which aim to minimize the difference between the predicted scalar numbers or
vectors and the ground truth cannot be directly used for the ordinal basic
rating distribution. Thus, a novel CNN based on the Cumulative distribution
with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic
score distribution of human ratings, with a new reliability-sensitive learning
method based on the kurtosis of the score distribution, which eliminates the
requirement of the original full data of human ratings (without normalization).
Experimental results on large scale aesthetic dataset demonstrate the
effectiveness of our introduced CJS-CNN in this task.Comment: AAAI Conference on Artificial Intelligence (AAAI), New Orleans,
Louisiana, USA. 2-7 Feb. 201
Laboratory evaluation of the residue of rubber-modified emulsified asphalt
Emulsified asphalt has been widely used in various surface treatment methods such as chip seal for low-volume road preservation. Using modified emulsified asphalt made it possible to use chip seal technology on medium-and even high-volume traffic pavements. The main objective of the study is to quantify the residue characteristics of rubber-modified emulsified asphalt and to assess the effectiveness of using crumb rubber to modify emulsified asphalt binder. The four emulsified asphalt residues used the distillation procedure. Then, the rheology characteristics of emulsified asphalt residue were evaluated. The Fourier transform infrared spectroscopy (FTIR) test analyzed the chemical change of emulsified asphalt during the aging procedure. The results indicate that the evaporation method cannot remove all the water in emulsified asphalt. The mass change during the rolling thin film oven (RTFO) process only represented the component change of emulsified asphalt binder residue. Both the high-temperature and low-temperature performance grade of the two emulsified asphalt binders with rubber were lower. The original asphalt binder adopted to emulsification had a crucial influence on the performance of emulsified asphalt. The rubber modification enhanced the property of the emulsified asphalt binder at low temperatures, and the improvement effect was enhanced as the rubber content in the emulsified asphalt was raised. The C=O band was more effective in quantifying the aging condition of the residue. The findings of this study may further advance the emulsified asphalt technology in pavement construction and maintenance
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