Deep learning enhanced lithium-ion battery nonlinear fading prognosis

With the assistance of artificial intelligence,advanced health prognosis technique plays a critical role in the lithium-ion(Li-ion) batteries management system.However,conventional data-driven early aging prediction exhibited dramatic drawbacks,i.e.,volatile capacity nonlinear fading trajectories create obstacles to the accurate multistep ahead prediction due to the complex working conditions of batteries.Herein,a novel deep learning model is proposed to achieve a universal and accurate Li-ion battery aging prognosis.Two battery datasets with various electrode types and cycling conditions are developed to validate the proposed approaches.Knee-point probability(KPP),extracted from the capacity loss curve,is first proposed to detect knee points and improve state-of-health(SOH) predictive accuracy,especially during periods of rapid capacity decline.Using one-cycle data of partial raw voltage as the model input,the SOH and KPP can be simultaneously predicted at multistep ahead,whereas the conventional method showed worse accuracy.Furthermore,to explore the underlying characteristics among various degradation tendencies,an online model update strategy is developed by leveraging the adversarial adaptationinduced transfer learning technique.This work gains new sights into the comprehensive Li-ion battery management and prognosis framework through decomposing capacity degradation trajectories and adversarial learning on the unlabeled samples.

Progress in wearable electronics/photonics—Moving toward the era of artificial intelligence and internet of things

The past few years have witnessed the significant impacts of wearable electronics/photonics on various aspects of our daily life,for example,healthcare monitoring and treatment,ambient monitoring,soft robotics,prosthetics,flexible display,communication,human-machine interactions,and so on.According to the development in recent years,the next-generation wearable electronics and photonics are advancing rapidly toward the era of artificial intelligence(AI)and internet of things(IoT),to achieve a higher level of comfort,convenience,connection,and intelligence.Herein,this review provides an opportune overview of the recent progress in wearable electronics,photonics,and systems,in terms of emerging materials,transducing mechanisms,structural configurations,applications,and their further integration with other technologies.First,development of general wearable electronics and photonics is summarized for the applications of physical sensing,chemical sensing,humanmachine interaction,display,communication,and so on.Then self-sustainable wearable electronics/photonics and systems are discussed based on system integration with energy harvesting and storage technologies.Next,technology fusion of wearable systems and AI is reviewed,showing the emergence and rapid development of intelligent/smart systems.In the last section of this review,perspectives about the future development trends of the next-generation wearable electronics/photonics are provided,that is,toward multifunctional,self-sustainable,and intelligent wearable systems in the AI/IoT era.

Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications

The era of artificial intelligence and internet of things is rapidly developed by recent advances in wearable electronics.Gait reveals sensory information in daily life containing personal information,regarding identification and healthcare.Current wearable electronics of gait analysis are mainly limited by high fabrication cost,operation energy consumption,or inferior analysis methods,which barely involve machine learning or implement nonoptimal models that require massive datasets for training.Herein,we developed low-cost triboelectric intelligent socks for harvesting waste energy from low-frequency body motions to transmit wireless sensory data.The sock equipped with self-powered functionality also can be used as wearable sensors to deliver information,regarding the identity,health status,and activity of the users.To further address the issue of ineffective analysis methods,an optimized deep learning model with an end-to-end structure on the socks signals for the gait analysis is proposed,which produces a 93.54%identification accuracy of 13 participants and detects five different human activities with 96.67%accuracy.Toward practical application,we map the physical signals collected through the socks in the virtual space to establish a digital human system for sports monitoring,healthcare,identification,and future smart home applications.

班纳特倍增器纳米发电机与其离子迁移谱仪的机器学习辅助挥发性有机物探测

离子迁移分析谱是识别气相化合物的一种常用分析技术,可实现气体的快速响应与多种成分辨识与分析.然而,传统的离子迁移谱仪因高压的电离导致系统的体积过大、离子腔体的工作温度高、测量精度差,不适用于可挥发性有机物(VOCs)的浓度的检测.为解决这类问题,本文报道了一种纳米发电机的高压实现冷场与常压放电的离子迁移谱分析仪,主要是基于电荷积累机制的多开关调控纳米发电机,可实现几百甚至几千伏的直流偏压.试验测试显示该系统具有良好的离子迁移重复性与特征,可用于VOCs种类与其浓度的辨识.为了进一步提高测试效果,作者采用了深度学习算法提取特征,极大提高了VOCs的检测能力.该方法为物联网的环境检测提供了一种响应速度快、功耗低的便携式VOCs检测与成分分析的解决方案.

Development of a masticatory robot using a novel cable-driven linear actuator with bidirectional motion

Masticatory robots are an effective in vitro performance testing device for dental material and mandibular prostheses.A cable-driven linear actuator(CDLA)capable of bidirectional motion is proposed in this study to design a masticatory robot that can achieve increasingly human-like chewing motion.The CDLA presents remarkable advantages,such as lightweight and high stiffness structure,in using cable amplification and pulley systems.This work also exploits the proposed CDLA and designs a masticatory robot called Southeast University masticatory robot(SMAR)to solve existing problems,such as bulky driving linkage and position change of the muscle’s origin.Stiffness analysis and performance experiment validate the CDLA’s efficiency,with its stiffness reaching 1379.6 N/mm(number of cable parts n=4),which is 21.4 times the input wire stiffness.Accordingly,the CDLA’s force transmission efficiencies in two directions are 84.5%and 85.9%.Chewing experiments are carried out on the developed masticatory robot to verify whether the CDLA can help SMAR achieve a natural human-like chewing motion and sufficient chewing forces for potential applications in performance tests of dental materials or prostheses.