Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning

The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured signals.Although data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low interpretability.In response,this study integrates domain knowledge into deep learning to enhance the RUL prediction performance.We demonstrate accurate RUL prediction using only a single charging curve.First,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging data.The parameters inform a deep neural network(DNN)to predict RUL with high accuracy and efficiency.The trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged cases.Using data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,respectively.Besides its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and robustness.The inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.

A Hybrid Approach for Predicting the Remaining Useful Life of Bearings Based on the RReliefF Algorithm and Extreme Learning Machine

Accurately predicting the remaining useful life(RUL)of bearings in mining rotating equipment is vital for mining enterprises.This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features.This study proposes a hybrid predictive model to assess the RUL of rolling element bearings.The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features.The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm.Subsequently,the extreme learning machine(ELM)approach is applied to develop a predictive model of RUL based on the optimal features.The model is trained by optimizing its parameters via the grid search approach.The training datasets are adjusted to make them most suitable for the regression model using the cross-validation method.The proposed hybrid model is analyzed and validated using the vibration data taken from the public XJTU-SY rolling element-bearing database.The comparison is constructed with other traditional models.The experimental test results demonstrated that the proposed approach can predict the RUL of bearings with a reliable degree of accuracy.

Deep Learning-Based Classification of Rotten Fruits and Identification of Shelf Life

The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers.The impact of rotten fruits can foster harmful bacteria,molds and other microorganisms that can cause food poisoning and other illnesses to the consumers.The overall purpose of the study is to classify rotten fruits,which can affect the taste,texture,and appearance of other fresh fruits,thereby reducing their shelf life.The agriculture and food industries are increasingly adopting computer vision technology to detect rotten fruits and forecast their shelf life.Hence,this research work mainly focuses on the Convolutional Neural Network’s(CNN)deep learning model,which helps in the classification of rotten fruits.The proposed methodology involves real-time analysis of a dataset of various types of fruits,including apples,bananas,oranges,papayas and guavas.Similarly,machine learningmodels such as GaussianNaïve Bayes(GNB)and random forest are used to predict the fruit’s shelf life.The results obtained from the various pre-trained models for rotten fruit detection are analysed based on an accuracy score to determine the best model.In comparison to other pre-trained models,the visual geometry group16(VGG16)obtained a higher accuracy score of 95%.Likewise,the random forest model delivers a better accuracy score of 88% when compared with GNB in forecasting the fruit’s shelf life.By developing an accurate classification model,only fresh and safe fruits reach consumers,reducing the risks associated with contaminated produce.Thereby,the proposed approach will have a significant impact on the food industry for efficient fruit distribution and also benefit customers to purchase fresh fruits.

A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful Life

Accurate estimation of the remaining useful life(RUL)of lithium-ion batteries is critical for their large-scale deployment as energy storage devices in electric vehicles and stationary storage.A fundamental understanding of the factors affecting RUL is crucial for accelerating battery technology development.However,it is very challenging to predict RUL accurately because of complex degradation mechanisms occurring within the batteries,as well as dynamic operating conditions in practical applications.Moreover,due to insignificant capacity degradation in early stages,early prediction of battery life with early cycle data can be more difficult.In this paper,we propose a hybrid deep learning model for early prediction of battery RUL.The proposed method can effectively combine handcrafted features with domain knowledge and latent features learned by deep networks to boost the performance of RUL early prediction.We also design a non-linear correlation-based method to select effective domain knowledge-based features.Moreover,a novel snapshot ensemble learning strategy is proposed to further enhance model generalization ability without increasing any additional training cost.Our experimental results show that the proposed method not only outperforms other approaches in the primary test set having a similar distribution as the training set,but also generalizes well to the secondary test set having a clearly different distribution with the training set.The PyTorch implementation of our proposed approach is available at https://github.com/batteryrul/battery_rul_early_prediction.

Peer-assisted learning to train high-school students to perform basic life-support

BACKGROUND: The inclusion of cardiopulmonary resuscitation(CPR) in formal education has been a useful approach to providing basic life support(BLS) services. However, because not all students have been able to learn directly from certified instructors, we studied the educational efficacy of the use of peer-assisted learning(PAL) to train high-school students to perform BLS services.METHODS: This study consisted of 187 high-school students: 68 participants served as a control group and received a 1-hour BLS training from a school nurse, and 119 were included in a PAL group and received a 1-hour CPR training from a PAL leader. Participants' BLS training was preceded by the completion of questionnaires regarding their background. Three months after the training, the participants were asked to respond to questionnaires about their willingness to perform CPR on bystander CPR and their retention of knowledge of BLS.RESULTS: We found no statistically significant difference between the control and PAL groups in their willingness to perform CPR on bystanders(control: 55.2%, PAL: 64.7%, P=0.202). The PAL group was not significantly different from the control group(control: 60.78±39.77, PAL: 61.76±17.80, P=0.848) in retention of knowledge about BLS services.CONCLUSION: In educating high school students about BLS, there was no significant difference between PAL and traditional education in increasing the willingness to provide CPR to bystanders or the ability to retain knowledge about BLS.

Delineation of an Urban Community Life Circle Based on a Machine-Learning Estimation of Spatiotemporal Behavioral Demand

Delineating life circles is an essential prerequisite for urban community life circle planning. Recent studies combined the environmental contexts with residents’ global positioning system(GPS) data to delineate the life circles. This method, however, is constrained by GPS data, and it can only be applied in the GPS surveyed communities. To address this limitation, this study developed a generalizable delineation method without the constraint of behavioral data. According to previous research, the community life circle consists of the walking-accessible range and internal structure. The core task to develop the generalizable method was to estimate the spatiotemporal behavioral demand for each plot of land to acquire the internal structure of the life circle, as the range can be delineated primarily based on environmental data. Therefore, behavioral demand estimation models were established through logistic regression and machine learning techniques, including decision trees and ensemble learning. The model with the lowest error rate was chosen as the final estimation model for each type of land. Finally, we used a community without GPS data as an example to demonstrate the effectiveness of the estimation models and delineation method. This article extends the existing literature by introducing spatiotemporal behavioral demand estimation models, which learn the relationships between environmental contexts, population composition and the existing delineated results based on GPS data to delineate the internal structure of the community life circle without employing behavioral data. Furthermore, the proposed method and delineation results also contributes to facilities adjustments and location selections in life circle planning, people-oriented transformation in urban planning, and activity space estimation of the population in evaluating and improving the urban policies.

Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review

Supercapacitors are appealing energy storage devices for their promising features like high power density,outstanding cycling stability,and a quick charge–discharge cycle.The exceptional life cycle and ultimate power capability of supercapacitors are needed in the transportation and renewable energy generation sectors.Hence,predicting the capacitance and lifecycle of supercapacitors is significant for selecting the suitable material and planning replacement intervals for supercapacitors.In addition,system failures can be better addressed by accurately forecasting the lifecycle of SCs.Recently,the use of machine learning for performance prediction of energy storage materials has drawn increasing attention from researchers globally because of its superiority in prediction accuracy,time efficiency,and costeffectiveness.This article presents a detailed review of the progress and advancement of ML techniques for the prediction of capacitance and remaining useful life(RUL)of supercapacitors.The review starts with an introduction to supercapacitor materials and ML applications in energy storage devices,followed by workflow for ML model building for supercapacitor materials.Then,the summary of machine learning applications for the prediction of capacitance and RUL of different supercapacitor materials including EDLCs(carbon based materials),pesudocapacitive(oxides and composites)and hybrid materials is presented.Finally,the general perspective for future directions is also presented.

Second Life in Language Learning: A Literature Review of Journals from 2012 to 2016

As a platform based on virtual reality(VR), Second Life(SL) is drawing much attention abroad because of its immersive environment. However, the number of research related to VR or SL assisted language learning is not large and this domain needs to be analyzed further.This paper aims to analyze the applications and research of SL in language learning, based on the journals in web of science from 2012 to 2016, and trying to find out hints for future research and implications for teachers and researchers with a summary of previous studies. By searching key words"Second Life"and"Language Learning"in web of science from2012 to 2016, 15 papers were selected finally. Mind maps and summary tables are used to demonstrate and analyze papers. It is found that most SL research is employed at universities, and research themes are varied from interaction to students' learning attitudes. Mixed research methods are used in both qualitative study and quantitative study. In terms of pedagogy, technical problems should be eliminated in order to optimize learning environment, and activity design is also important in SL for teachers to teach languages.

An Environmental Learning Support System Incorporating the Life Cycle Concept

The need for environmental education, which incorporates the life cycle concept into the learning program, will become increasingly greater all over the world. In the present study, an e-learning system, which is made up of 3 parts including text-based learning materials, quizzes to review the content of the learning materials and CO2 emission simulation, was designed and developed with the purpose of supporting environmental learning. Targeting a wide range of people, the operation period of this system was 1 month. Based on the results of questionnaire survey for users, it was evident that the quiz function and the simulation function of CO2 emission contributed to the efficiency in environmental learning, and the format of the e-learning system was effective and helpful for environmental learning. Additionally, with the users’ awareness related to environmental conservation before and after using the system, significant changes in awareness were seen in areas such as behavioral intention, sense of urgency and sense of connection. Furthermore, as it was revealed that 62% of the total access numbers were from mobile devices, it was effective to prepare an interface optimized for mobile devices enabling users to use the system from their smartphones and tablet PCs.

The Perceived Effect of Quality of Life on College Learning Among Chinese Students

Nowadays, the competitiveness of college student recruitment and the increasing challenge of college fund-raisinghave made students＇ quality of life （QoL） a priority in many universities, in addition to their pursuit of high qualityeducation. This study investigates the effect of QoL on college learning among the Chinese university students atthe National Quemoy University, Taiwan, using a questionnaire and interview techniques. The quantitative datacollected reveals that the 311 randomly selected college participants perceived a 70.5% influence of QoL on theirlearning and academic growth on a scale of 0 to 100. Furthermore, the qualitative data collected shows that thestudents perceived QoL as crucial to their learning and academic achievements, and that multiple life facets--suchas quality of sleep or diet, peer relationships, or time management--interacted in influencing their learning. It isthus suggested that educational programs, resources, and relevant decisions are made to further advance thewell-being of said college students. This study has significant implications for classroom teaching practice andhigher education administration.

Application of Machine Learning Techniques for Okra Shelf Life Prediction

The ability of machine learning techniques to make accurate predications is increasing. The aim of this work is to apply machine learning techniques such as Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression, and K-Nearest Neighbour algorithms to predict the shelf life of Okra. Predicting the shelf life of Okra is important because Okra becomes harmful for human consumption if consumed after its shelf life. Okra parameters such as weight loss, firmness, Titrable Acid, Total Soluble Solids, Vitamin C/Ascorbic acid content, and PH were used as inputs into these machine learning techniques. Support Vector Machine, Naïve Bayes and Decision Tree each accurately predicted the shelf life of Okra with accuracies of 100%. However, the Logistic Regression and K-Nearest Neighbour achieved 88.89% and 88.33% accuracies, respectively. These results showed that machine learning techniques especially Support Vector Machine, Naïve Bayes and Decision Tree can be effectively applied for the prediction of Okra shelf life.

A TimeImageNet Sequence Learning for Remaining Useful Life Estimation of Turbofan Engine in Aircraft Systems

Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under consideration.Sensor data of all possible states of a system are used for building machine learning models.These models are further used to predict the possible downtime for proactive action on the system condition.Aircraft engine data from run to failure is used in the current study.The run to failure data includes states like new installation,stable operation,first reported issue,erroneous operation,and final failure.In the present work,the non-linear multivariate sensor data is used to understand the health status and anomalous behavior.The methodology is based on different sampling sizes to obtain optimum results with great accuracy.The time series of each sensor is converted to a 2D image with a specific time window.Converted Images would represent the health of a system in higher-dimensional space.The created images were fed to Convolutional Neural Network,which includes both time variation and space variation of each sensed parameter.Using these created images,a model for estimating the remaining life of the aircraft is developed.Further,the proposed net is also used for predicting the number of engines that would fail in the given time window.The current methodology is useful in avoiding the health index generation for predicting the remaining useful life of the industrial components.Better accuracy in the classification of components is achieved using the TimeImagenet-based approach.

生活场所的学习(Lifeplace learning)对中国教育发展的启示

生活场所的学习(Lifeplace learning)是近年来西方学习科学研究的一个热点,虽然其概念还存在争议,但所有的定义都强调了这种学习代表着一种来自学习者的动机和兴趣的一种学习方式,通过对环境的利用来提升自己。它是对传统教育的一种补充,而非是对传统校园学习的替代,它代表着一种自由学习和选择来实现学习的理想。深入探讨生活场所学习的意义与过程,对于反思和改进中国教育具有重大价值。

Life’s Language Lessons Ghanaian students look toward learning Chinese as a way to find opportunities

While some struggle to find their path in life. for others, nature aligns things for them. This is what happened to Edward .loseph Yawson, a 26-year-old teaching assistant at the Chinese Section of the Department of Modern Languages, Uni versity of Ghana, Legon, who trusted his intuition when a Chinese language course was offered to him.

U-Learning与终身学习的时代契合——浅谈U-Learning环境下的终身学习

随着现代移动技术和无线网络技术的不断发展,U-Learning时代的到来将极大地推进终身学习。本文在介绍"U-Learning"普适技术及其特点的基础上,探讨普适计算技术在当前终身学习领域的应用现状,展望发展的前景。

以终身学习为导向的E-Learning系统的设计

发展E-Learning是实现教育终身化的有效途径,设计以终身学习为导向的E-Learning时应当遵循系统性、层次性、实用性、高效性、安全性和发展性等主要原则。

基于E-Learning的终身教育网络平台构建研究

分析了当前终身教育与网络教学系统的研究现状.重点研究了采用J2EE技术,基于MVC模式,构建了包含学习者模型、教学模型、知识库模型三大模块的终身教育网络服务平台,以实现教育资源的最大化、教学方法的多样化、学习方式的个别化、教育对象的大众化和教学效果的最优化,对我国深化人才培养模式改革,提升在职从业人员技能、提升社会公民素质具有一定的理论意义和实践价值.

论高校图书馆E-learning网站的构建

E-learning已经成为国内外知名企业员工培训和远程教育的重要方式。文章论述了高校图书馆员终身教育的必要性、有利条件、学习类型和存在的问题,以及E-learning在员工培训中的独特优势,之后对高校图书馆构建E-learning网站的类型、基本教学模块架构和实施措施进行了全面阐述。

State-of-the-art and future directions of machine learning for biomass characterization and for sustainable biorefinery

Machine learning(ML)has emerged as a significant tool in the field of biorefinery,offering the capability to analyze and predict complex processes with efficiency.This article reviews the current state of biorefinery and its classification,highlighting various commercially successful biorefineries.Further,we delve into different categories of ML models,including their algorithms and applications in various stages of biorefinery lifecycle,such as biomass characterization,pretreatment,lignin valorization,chemical,thermochemical and biochemical conversion processes,supply chain analysis,and life cycle assessment.The benefits and limitations of each of these algorithms are discussed in detail.Finally,the article concludes with a discussion of the limitations and future prospects of ML in the field of biorefineries.

基于LEARNS模式的健康教育对PCI术后病人自我管理能力及生活质量的影响

目的:探讨应用LEARNS健康教育模式对经皮冠状动脉介入治疗(PCI)术后病人自我管理能力及生活质量的影响。方法:选取某三级甲等医院2022年1月—6月80例PCI术后病人为研究对象,按照随机数字表法将其分为观察组和对照组,每组40例。对照组采用术后常规健康教育模式,观察组在对照组基础上使用LEARNS健康教育模式对病人进行健康宣教,在病人干预前、后分别使用中国心血管病人生活质量评定问卷和冠心病自我管理行为量表对病人进行评估。结果:观察组病人自我管理能力和生活质量得分高于对照组(P<0.05)。结论:LEARNS健康教育模式在PCI术后病人中的应用效果显著,能够明显提高病人的自我管理能力和生活质量。