Strain sensors with good stability are vital to the development of wearable healthcare monitoring systems.However,the design of strain sensor with both duration stability and environmental stability is still a challen...Strain sensors with good stability are vital to the development of wearable healthcare monitoring systems.However,the design of strain sensor with both duration stability and environmental stability is still a challenge.In this work,we propose an ultra-stable and washable strain sensor by embedding a coupled composite film of carbon nanotube(CNT)and Ti_(3)C_(2)T_(x) MXene into polydimethylsiloxane(PDMS)matrix.The composite strain sensor with embedded microstructure and uneven surface makes it conformal to skin,while the CNT/MXene sensing layer exhibits a resistance sensitive to strain.This sensor shows reliable responses at different frequencies and with long-term cycling durability(over 1,000 cycles).Meanwhile,the CNT/MXene/PDMS composite strain sensor provides the advantages of superior anti-interference to temperature change and water washing.The results demonstrate less than 10%resistance changes as the temperature rises from-20 to 80℃or after sonication in water for 120 min,respectively.The composite sensor is applied to monitor human joint motions,such as bending of finger,wrist and elbow.Moreover,the simultaneous monitoring of the electrocardiogram(ECG)signal and joint movement while riding a sports bicycle is demonstrated,enabling the great potential of the as-fabricated sensor in real-time human healthcare monitoring.展开更多
In winter,wind turbines are susceptible to blade icing,which results in a series of energy losses and safe operation problems.Therefore,blade icing detection has become a top priority.Conventional methods primarily re...In winter,wind turbines are susceptible to blade icing,which results in a series of energy losses and safe operation problems.Therefore,blade icing detection has become a top priority.Conventional methods primarily rely on sensor monitoring,which is expensive and has limited applications.Data-driven blade icing detection methods have become feasible with the development of artificial intelligence.However,the data-driven method is plagued by limited training samples and icing samples;therefore,this paper proposes an icing warning strategy based on the combination of feature selection(FS),eXtreme Gradient Boosting(XGBoost)algorithm,and exponentially weighted moving average(EWMA)analysis.In the training phase,FS is performed using correlation analysis to eliminate redundant features,and the XGBoost algorithm is applied to learn the hidden effective information in supervisory control and data acquisition analysis(SCADA)data to build a normal behavior model.In the online monitoring phase,an EWMA analysis is introduced to monitor the abnormal changes in features.A blade icing warning is issued when themonitored features continuously exceed the control limit,and the ambient temperature is below 0℃.This study uses data fromthree icing-affected wind turbines and one normally operating wind turbine for validation.The experimental results reveal that the strategy can promptly predict the icing trend among wind turbines and stably monitor the normally operating wind turbines.展开更多
The methods of Traditional Chinese Medicine(TCM)’s diagnosis and treatment have undergone several changes.It is crucial to build a proper model which is capable to modernize TCM into a both standardized and individua...The methods of Traditional Chinese Medicine(TCM)’s diagnosis and treatment have undergone several changes.It is crucial to build a proper model which is capable to modernize TCM into a both standardized and individualized treatment.Tong xiao-lin proposed the state-target strategy to build a bridge for the integration of Chinese and Western medicine.It is a model based on modern medical disease concepts and using the method of TCM to balance the pathological states and adopting the achievements of pharmacology of Chinese medicine to focus on the disease targets,symptom targets,and biochemical indicator targets.The reconstruction of TCM diagnosis and treatment system for diabetes is a good example to demonstrate this theory.It could improve the clinical efficacy,support the scientific research,and reinforce the standardization of TCM.展开更多
基金supported by the National Natural Science Foundation of China(No.61804185)the National Key Research and Development Program of China(No.2017YFA0206600)+3 种基金the Natural Science Foundation of Hunan Province(No.2019JJ50804)the Science and Technology Innovation Program of Hunan Province(No.2020RC4004)the Special Funding for the Construction of Innovative Provinces in Hunan Province(No.2020GK2024)Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing(No.GXKL06200208).
文摘Strain sensors with good stability are vital to the development of wearable healthcare monitoring systems.However,the design of strain sensor with both duration stability and environmental stability is still a challenge.In this work,we propose an ultra-stable and washable strain sensor by embedding a coupled composite film of carbon nanotube(CNT)and Ti_(3)C_(2)T_(x) MXene into polydimethylsiloxane(PDMS)matrix.The composite strain sensor with embedded microstructure and uneven surface makes it conformal to skin,while the CNT/MXene sensing layer exhibits a resistance sensitive to strain.This sensor shows reliable responses at different frequencies and with long-term cycling durability(over 1,000 cycles).Meanwhile,the CNT/MXene/PDMS composite strain sensor provides the advantages of superior anti-interference to temperature change and water washing.The results demonstrate less than 10%resistance changes as the temperature rises from-20 to 80℃or after sonication in water for 120 min,respectively.The composite sensor is applied to monitor human joint motions,such as bending of finger,wrist and elbow.Moreover,the simultaneous monitoring of the electrocardiogram(ECG)signal and joint movement while riding a sports bicycle is demonstrated,enabling the great potential of the as-fabricated sensor in real-time human healthcare monitoring.
基金This research was funded by the Basic Research Funds for Universities in Inner Mongolia Autonomous Region(No.JY20220272)the Scientific Research Program of Higher Education in InnerMongolia Autonomous Region(No.NJZZ23080)+3 种基金the Natural Science Foundation of InnerMongolia(No.2023LHMS05054)the NationalNatural Science Foundation of China(No.52176212)We are also very grateful to the Program for Innovative Research Team in Universities of InnerMongolia Autonomous Region(No.NMGIRT2213)The Central Guidance for Local Scientific and Technological Development Funding Projects(No.2022ZY0113).
文摘In winter,wind turbines are susceptible to blade icing,which results in a series of energy losses and safe operation problems.Therefore,blade icing detection has become a top priority.Conventional methods primarily rely on sensor monitoring,which is expensive and has limited applications.Data-driven blade icing detection methods have become feasible with the development of artificial intelligence.However,the data-driven method is plagued by limited training samples and icing samples;therefore,this paper proposes an icing warning strategy based on the combination of feature selection(FS),eXtreme Gradient Boosting(XGBoost)algorithm,and exponentially weighted moving average(EWMA)analysis.In the training phase,FS is performed using correlation analysis to eliminate redundant features,and the XGBoost algorithm is applied to learn the hidden effective information in supervisory control and data acquisition analysis(SCADA)data to build a normal behavior model.In the online monitoring phase,an EWMA analysis is introduced to monitor the abnormal changes in features.A blade icing warning is issued when themonitored features continuously exceed the control limit,and the ambient temperature is below 0℃.This study uses data fromthree icing-affected wind turbines and one normally operating wind turbine for validation.The experimental results reveal that the strategy can promptly predict the icing trend among wind turbines and stably monitor the normally operating wind turbines.
文摘The methods of Traditional Chinese Medicine(TCM)’s diagnosis and treatment have undergone several changes.It is crucial to build a proper model which is capable to modernize TCM into a both standardized and individualized treatment.Tong xiao-lin proposed the state-target strategy to build a bridge for the integration of Chinese and Western medicine.It is a model based on modern medical disease concepts and using the method of TCM to balance the pathological states and adopting the achievements of pharmacology of Chinese medicine to focus on the disease targets,symptom targets,and biochemical indicator targets.The reconstruction of TCM diagnosis and treatment system for diabetes is a good example to demonstrate this theory.It could improve the clinical efficacy,support the scientific research,and reinforce the standardization of TCM.
