As one of the most important tumor-associated antigens of colorectal adenocarcinoma, the carcinoembryonic antigen （CEA） threatens human health seriously ali over the globe. Fast electrical and highly sensitive detec...As one of the most important tumor-associated antigens of colorectal adenocarcinoma, the carcinoembryonic antigen （CEA） threatens human health seriously ali over the globe. Fast electrical and highly sensitive detection of the CEA with A1GaN/GaN high electron mobility transistor is demonstrated experimentally. To achieve a low detection limit, the Au-gated sensing area of the sensor is functionalized with a CEA aptamer instead of the corresponding antibody. The proposed aptasensor has successfully detected different concentrations （ranging from 50picogram/milliliter （pg/ml） to 50 nanogram/milliliter （ng/ml）） of CEA and achieved a detection limit as low as 50pg/ml at Vas = 0.5 V. The drain-source current shows a c/ear increase of 11.5μA under this bias.展开更多
Magnetic skyrmions are recognized as potential information carriers for building the next-generation spintronic memory and logic devices.Towards functional device applications,efficient electrical detection of skyrmio...Magnetic skyrmions are recognized as potential information carriers for building the next-generation spintronic memory and logic devices.Towards functional device applications,efficient electrical detection of skyrmions at room temperature is one of the most important prerequisites.展开更多
With the application of the advanced measurement infrastructure in power grids,data driven electricity theft detection methods become the primary stream for pinpointing electricity thieves.However,owing to anomaly sub...With the application of the advanced measurement infrastructure in power grids,data driven electricity theft detection methods become the primary stream for pinpointing electricity thieves.However,owing to anomaly submergence,which shows that the usage patterns of electricity thieves may not always deviate from those of normal users,the performance of the existing usage-pattern-based method could be affected.In addition,the detection results of some unsupervised learning algorithm models are abnormal degrees rather than“0-1”to ascertain whether electricity theft has occurred.The detection with fixed threshold value may lead to deviation and would not be sufficiently flexible to handle the detection for different scenes and users.To address these issues,this study proposes a new electricity theft detection method based on load shape dictionary of users.A corresponding strategy for tunable threshold is proposed to optimize the detection effect of electricity theft,and the efficacy and applicability of the proposed adaptive electricity theft detection method were verified from numerical experiments.展开更多
Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic accidents.To effectively monitor compliance,the utilization of target detection algorithms through traffic cam...Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic accidents.To effectively monitor compliance,the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles.However,manual enforcement by traffic police is time-consuming and labor-intensive.Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques.This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles,addressing these challenges.The proposedmodel improves uponYOLOv8n by deepening the network structure,incorporating weighted connections,and introducing lightweight convolutional modules.These modifications aim to enhance the precision of small target recognition while reducing the model’s parameters,making it suitable for deployment on low-performance devices in real traffic scenarios.Experimental results demonstrate that the model achieves an mAP@0.5 of 91.8%,showing an 11.5%improvement over the baselinemodel,with a 16.2%reduction in parameters.Additionally,themodel achieves a frames per second(FPS)rate of 58,meeting the accuracy and speed requirements for detection in actual traffic scenarios.展开更多
[FeNi（3 nm）/Zn1-xCoxO（3 nm）]2/ZnO（d nm）/[Zn1-xCoxO（3 nm）/Co（3 nm）]2 （d=3 and 10） semiconductor junctions were prepared by magnetron sputtering system and photolithography. The spin valve effect was observe...[FeNi（3 nm）/Zn1-xCoxO（3 nm）]2/ZnO（d nm）/[Zn1-xCoxO（3 nm）/Co（3 nm）]2 （d=3 and 10） semiconductor junctions were prepared by magnetron sputtering system and photolithography. The spin valve effect was observed in these junctions because the utility of the ferromagnetic composite layers acted as soft and hard magnetic layers. The electrical detection was performed by measuring the magnetoresistance of these junctions to investigate the current spin polarization asc in the ZnO layer and the spin injection efficiency η of spin-polarized electrons. asc was reduced from 11.7% （and 10.5%） at 90 K to 7.31% （and 5.93%） at room temperature for d=3 （and d=10）. And η was reduced from 39.5% （and 35.5%） at 90 K to 24.7% （and 20.0%） at room temperature for d=3 （and d=10）.展开更多
Effective probing current-induced magnetization switching is highly required in the study of emerging spin-orbit torque(SOT)effect.However,the measurement of in-plane magnetization switching typically relies on the gi...Effective probing current-induced magnetization switching is highly required in the study of emerging spin-orbit torque(SOT)effect.However,the measurement of in-plane magnetization switching typically relies on the giant/tunneling magnetoresistance measurement in a spin valve structure calling for complicated fabrication process,or the non-electric approach of Kerr imaging technique.Here,we present a reliable and convenient method to electrically probe the SOT-induced in-plane magnetization switching in a simple Hall bar device through analyzing the MR signal modified by a magnetic field.In this case,the symmetry of MR is broken,resulting in a resistance difference for opposite magnetization orientations.Moreover,the feasibility of our method is widely evidenced in heavy metal/ferromagnet(Pt/Ni_(20)Fe_(80) and W/Co_(20)Fe_(60)B_(20))and the topological insulator/ferromagnet(Bi_(2)Se_(3)/Ni_(20)Fe_(80)).Our work simplifies the characterization process of the in-plane magnetization switching,which can promote the development of SOT-based devices.展开更多
One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which make...One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.展开更多
Effective detection of abnormal electricity users and analysis of the spatial distribution and influencing factors of abnormal electricity consumption in urban areas have positive effects on the quality of electricity...Effective detection of abnormal electricity users and analysis of the spatial distribution and influencing factors of abnormal electricity consumption in urban areas have positive effects on the quality of electricity consumption by customers,safe operation of power grids,and sustainable development of cities.However,current abnormal electricity consumption detection models do not consider the time dependence of time-series data and rely on a large number of training samples,and no study has analyzed the spatial distribution and influencing factors of abnormal electricity consumption in urban areas.In this study,we use the Seasonal-Trend decomposition procedure based on Loess(STL)based time series decomposition and outlier detection to detect abnormal electricity consumption in the central city of Pingxiang,and analyze the relationship between spatial variation and urban functions through Geodetector.The results show that the degree of abnormal electricity consumption in urban areas is related to geographic location and has spatial heterogeneity,and the abnormal electricity users are mainly located in areas with highly mixed residential,commercial and entertainment functions in the city.The results obtained from this study can provide a reference basis and a theoretical foundation for the detection of abnormal electricity consumption by users and the arming of electricity theft devices in the power grid.展开更多
With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft detection.However,the imbalance of electricity consumption data is vio...With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft detection.However,the imbalance of electricity consumption data is violent,which makes the training of detection model challenging.In this case,this paper proposes an electricity theft detection method based on ensemble learning and prototype learning,which has great performance on imbalanced dataset and abnormal data with different abnormal level.In this paper,convolutional neural network(CNN)and long short-term memory(LSTM)are employed to obtain abstract feature from electricity consumption data.After calculating the means of the abstract feature,the prototype per class is obtained,which is used to predict the labels of unknown samples.In the meanwhile,through training the network by different balanced subsets of training set,the prototype is representative.Compared with some mainstream methods including CNN,random forest(RF)and so on,the proposed method has been proved to effectively deal with the electricity theft detection when abnormal data only account for 2.5%and 1.25%of normal data.The results show that the proposed method outperforms other state-of-the-art methods.展开更多
the power network fault detection system can only analyze all kinds of fault signal in stationary sequence： the internal grid and external disturbance presents degeneration, tiny, signal intensity changes randomly fl...the power network fault detection system can only analyze all kinds of fault signal in stationary sequence： the internal grid and external disturbance presents degeneration, tiny, signal intensity changes randomly fluctuate, that will cause the system to detect the fault isolation ability of confusion and small fault signal is not strong; The article propose a method of power network fault detection for based on GSM, Through the underlying sensing equipment acquisition abnormal information of current, voltage power in the network and GSM networking scheme can filter the interference factors in extraction of fault information from and attribute value. The embedded gateway take STM32 chip as the core to monitoring data processing, to achieve a unified data management and user remote access, realizing method of system software are given, to construct the monitor management information platform. The actual test system show that, identification diagnosis ability of fault signal separation ability and small signal increases 17%, also meet the requirements.展开更多
Timely detection of abnormal electricity consumption behaviors plays a key role in saving energy.However,the detection of abnormal electricity consumption faces many problems.Imbalanced data are important challenges i...Timely detection of abnormal electricity consumption behaviors plays a key role in saving energy.However,the detection of abnormal electricity consumption faces many problems.Imbalanced data are important challenges in this field.When the normal data are much more than the abnormal data,the network can hardly recognize the features of the minority class data,which generates low detection efficiency.Therefore,in this paper,we employ adaptive synthetic sampling(ADASYN)to achieve effective expansion of the minority class data.In addition,we adopt gated recurrent units to complete the classification of electricity consumption data.We conduct detailed experiments to verify this proposed method.Experimental results show that this method is more effective than other methods.展开更多
A multi-technique approach to prove the preparation of poly(3,4-ethylenedioxythiophene/cucurbit[7]uril)pseudorotaxanes(PEDOT∙CB7-PPs)is reported.Molecular docking simulation and matrix-assisted laser desorption/ioniza...A multi-technique approach to prove the preparation of poly(3,4-ethylenedioxythiophene/cucurbit[7]uril)pseudorotaxanes(PEDOT∙CB7-PPs)is reported.Molecular docking simulation and matrix-assisted laser desorption/ionization mass spectrometry(MALDI MS)validate the complexation ability of the CB7 molecule towards 3,4-ethylenedioxythiophene(EDOT),which leads to the EDOT∙CB7 inclusion complex.Oxidative polymerization of EDOT∙CB7 enabled the synthesis of PEDOT∙CB7-PPs.The water-soluble part of PEDOT∙CB7-PPs was selected,freeze-dried,and chemically characterized.Furthermore,dynamic light scattering(DLS)has been used to study the particle size and z-potential(ZP-ζ)of PEDOT∙CB7-PPs.The ZP-ζvalue(35 mV)evidenced that the PEDOT∙CB7-PPs formed stable water dispersion.By combining the emerging nanopore resistive pulse sensing technique(Np-RPS)and computational modeling,we identified strong interactions of PEDOT∙CB7-PPs with the aerolysin(Ael)nanopore.PEDOT∙CB7-PPs behave as positive charged species,and thus trans negative bias promotes its interactions with the Ael nanopore.The computational modeling results are fully consistent with the Np-RPS detection,which also reveals strong interactions between PEDOT∙CB7-PPs and the Ael nanopore.With this study,we hope to provide new insights and a better understanding of the interactions between supramolecular complexes based on CB7 and biological entities,which is instrumental for future applications in the field of nanobiotechnology.展开更多
CONSPECTUS:Chiral optoelectronics,which utilize the unique interactions between circularly polarized(CP)light and chiral materials,open up exciting possibilities in advanced technologies.These devices can detect,emit,...CONSPECTUS:Chiral optoelectronics,which utilize the unique interactions between circularly polarized(CP)light and chiral materials,open up exciting possibilities in advanced technologies.These devices can detect,emit,or manipulate light with specific polarization,enabling applications in secure communication,sensing,and data processing.A key aspect of chiral optoelectronics is the ability to generate or detect optical and electrical signals by controlling or distinguishing CP light based on its polarization direction.This capability is rooted in the selective interaction of CP light with the stereogenic(non-superimposable)molecular geometry of chiral substances,wherein the polarization of CP light aligns with the intrinsic asymmetry of the material.Among the diverse chiral materials explored for this purpose,π-conjugated molecules offer special advantages due to their tunable optoelectronic properties,efficient light−matter interactions,and cost-effective processability.Recent advancements inπ-conjugated molecule research have demonstrated their ability to generate strong chiroptical responses,thereby paving the way for compact and multifunctional device designs.Building on these unique advantages,π-conjugated molecules have advanced organic electronics into rapidly evolving technological fields.The combination of chiralπ-conjugated molecules with organic electronics is anticipated to simplify the fabrication of chiroptical devices,thereby lowering technical barriers and accelerating progress in chiral optoelectronics.This Account introduces strategies for incorporating chiroptical activity into organic optoelectronic devices,focusing on two main approaches:direct incorporation of chiroptical activity intoπ-conjugated polymer semiconductors and integration of chiral organic nanoarchitectures with conventional organic optoelectronic devices.In the first approach,we especially highlight simple methods to induce chiroptical activity in various achiralπ-conjugated polymers through the transfer of chirality from small chiral molecules.This hybrid approach effectively combines the excellent electrical properties and various optical transition properties of achiral polymers with the strong chiroptical activity of small molecules.Moreover,we address a fundamental challenge in achieving chiroptical transitions in planarπ-conjugated polymers,demonstrating the development of low-bandgapπ-conjugated polymers that exhibit both strong chiroptical activity and excellent electrical performance.Another approach,incorporating chiroptical activity into existing organic optoelectronic devices,which have already achieved significant performance advances,presents an effective strategy for high-performance chiral optoelectronics.For this purpose,we introduce the use of supramolecular assemblies ofπ-conjugated molecules to impart chiroptical responses into high-performance optoelectronic systems,utilizing efficient charge transfer of photoexcited electrons in chiroptical supramolecular nanoarchitectures.Additionally,we explore the integration of organic chiral photonic structure into organic optoelectronic systems,which act as optical filters tailored for CP light.These architectures offer unique advantages,including easy processability and seamless compatibility with existing organic electronic platforms.By bridging concepts from chiral organic optoelectronic materials and advanced organic electronics,this work outlines actionable approaches for advancing chiral optoelectronic technologies.These strategies underscore the versatility ofπ-conjugated molecules while also expanding the framework for next-generation applications.As the field of chiral optoelectronics evolves,integrating chiroptical functionalities into organic devices will facilitate transformative innovations in quantum computing,biosensing,and photonic encryption.展开更多
基金Supported by the National Key Research and Development Program of China under Grant Nos 2016YFB0400104 and 2016YFB0400301the National Natural Science Foundation of China under Grant No 61334002the National Science and Technology Major Project
文摘As one of the most important tumor-associated antigens of colorectal adenocarcinoma, the carcinoembryonic antigen （CEA） threatens human health seriously ali over the globe. Fast electrical and highly sensitive detection of the CEA with A1GaN/GaN high electron mobility transistor is demonstrated experimentally. To achieve a low detection limit, the Au-gated sensing area of the sensor is functionalized with a CEA aptamer instead of the corresponding antibody. The proposed aptasensor has successfully detected different concentrations （ranging from 50picogram/milliliter （pg/ml） to 50 nanogram/milliliter （ng/ml）） of CEA and achieved a detection limit as low as 50pg/ml at Vas = 0.5 V. The drain-source current shows a c/ear increase of 11.5μA under this bias.
基金supported by the National Key R&D Program of China(Grant No.2022YFA1405100)the NSFC distinguished Young Scholar program(Grant No.12225409)+6 种基金the Basic Science Center Project of National Natural Science Foundation of China(NSFC)(Grant No.52388201)the NSFC general program(Grant Nos.52271181,51831005,and 12421004)the Innovation Program for Quantum Science and Technology(Grant No.2023ZD0300500)Beijing Natural Science Foundation(Grant No.Z240006)supported by the KAUST Office of Sponsored Research(OSR)under Award Nos.ORA-CRG102021-4665 and ORA-CRG11-2022-5031supported by the National Key Research and Development Program of China(No.2024YFA1408503)Sichuan Province Science and Technology Support Program(No.2025YFHZ0147)。
文摘Magnetic skyrmions are recognized as potential information carriers for building the next-generation spintronic memory and logic devices.Towards functional device applications,efficient electrical detection of skyrmions at room temperature is one of the most important prerequisites.
基金supported by the National Natural Science Foundation of China(U1766210).
文摘With the application of the advanced measurement infrastructure in power grids,data driven electricity theft detection methods become the primary stream for pinpointing electricity thieves.However,owing to anomaly submergence,which shows that the usage patterns of electricity thieves may not always deviate from those of normal users,the performance of the existing usage-pattern-based method could be affected.In addition,the detection results of some unsupervised learning algorithm models are abnormal degrees rather than“0-1”to ascertain whether electricity theft has occurred.The detection with fixed threshold value may lead to deviation and would not be sufficiently flexible to handle the detection for different scenes and users.To address these issues,this study proposes a new electricity theft detection method based on load shape dictionary of users.A corresponding strategy for tunable threshold is proposed to optimize the detection effect of electricity theft,and the efficacy and applicability of the proposed adaptive electricity theft detection method were verified from numerical experiments.
基金supported by the Ningxia Key Research and Development Program(Talent Introduction Special Project)Project(2022YCZX0013)North Minzu University 2022 School-Level Scientific Research Platform“Digital Agriculture Enabling Ningxia Rural Revitalization Innovation Team”(2022PT_S10)+1 种基金Yinchuan City University-Enterprise Joint Innovation Project(2022XQZD009)Ningxia Key Research and Development Program(Key Project)Project(2023BDE02001).
文摘Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic accidents.To effectively monitor compliance,the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles.However,manual enforcement by traffic police is time-consuming and labor-intensive.Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques.This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles,addressing these challenges.The proposedmodel improves uponYOLOv8n by deepening the network structure,incorporating weighted connections,and introducing lightweight convolutional modules.These modifications aim to enhance the precision of small target recognition while reducing the model’s parameters,making it suitable for deployment on low-performance devices in real traffic scenarios.Experimental results demonstrate that the model achieves an mAP@0.5 of 91.8%,showing an 11.5%improvement over the baselinemodel,with a 16.2%reduction in parameters.Additionally,themodel achieves a frames per second(FPS)rate of 58,meeting the accuracy and speed requirements for detection in actual traffic scenarios.
基金supported by the State Key Project of Fundamental Research of China No.2007CB924903 and NSFC No.50572053
文摘[FeNi（3 nm）/Zn1-xCoxO（3 nm）]2/ZnO（d nm）/[Zn1-xCoxO（3 nm）/Co（3 nm）]2 （d=3 and 10） semiconductor junctions were prepared by magnetron sputtering system and photolithography. The spin valve effect was observed in these junctions because the utility of the ferromagnetic composite layers acted as soft and hard magnetic layers. The electrical detection was performed by measuring the magnetoresistance of these junctions to investigate the current spin polarization asc in the ZnO layer and the spin injection efficiency η of spin-polarized electrons. asc was reduced from 11.7% （and 10.5%） at 90 K to 7.31% （and 5.93%） at room temperature for d=3 （and d=10）. And η was reduced from 39.5% （and 35.5%） at 90 K to 24.7% （and 20.0%） at room temperature for d=3 （and d=10）.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 11904017, 11974145, 51901008, and 12004024)Shandong Provincial Natural Science Foundation, China (Grant No. ZR2020ZD28)+1 种基金platform from Qingdao Science and Technology Commissionthe Fundamental Research Funds for the Central Universities of China
文摘Effective probing current-induced magnetization switching is highly required in the study of emerging spin-orbit torque(SOT)effect.However,the measurement of in-plane magnetization switching typically relies on the giant/tunneling magnetoresistance measurement in a spin valve structure calling for complicated fabrication process,or the non-electric approach of Kerr imaging technique.Here,we present a reliable and convenient method to electrically probe the SOT-induced in-plane magnetization switching in a simple Hall bar device through analyzing the MR signal modified by a magnetic field.In this case,the symmetry of MR is broken,resulting in a resistance difference for opposite magnetization orientations.Moreover,the feasibility of our method is widely evidenced in heavy metal/ferromagnet(Pt/Ni_(20)Fe_(80) and W/Co_(20)Fe_(60)B_(20))and the topological insulator/ferromagnet(Bi_(2)Se_(3)/Ni_(20)Fe_(80)).Our work simplifies the characterization process of the in-plane magnetization switching,which can promote the development of SOT-based devices.
文摘One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.
基金National Natural Science Foundation of China(Nos.4180130642171466)The Scientific Research Program of the Department of Natural Resources of Hubei Province(No.ZRZY2021KJ02)。
文摘Effective detection of abnormal electricity users and analysis of the spatial distribution and influencing factors of abnormal electricity consumption in urban areas have positive effects on the quality of electricity consumption by customers,safe operation of power grids,and sustainable development of cities.However,current abnormal electricity consumption detection models do not consider the time dependence of time-series data and rely on a large number of training samples,and no study has analyzed the spatial distribution and influencing factors of abnormal electricity consumption in urban areas.In this study,we use the Seasonal-Trend decomposition procedure based on Loess(STL)based time series decomposition and outlier detection to detect abnormal electricity consumption in the central city of Pingxiang,and analyze the relationship between spatial variation and urban functions through Geodetector.The results show that the degree of abnormal electricity consumption in urban areas is related to geographic location and has spatial heterogeneity,and the abnormal electricity users are mainly located in areas with highly mixed residential,commercial and entertainment functions in the city.The results obtained from this study can provide a reference basis and a theoretical foundation for the detection of abnormal electricity consumption by users and the arming of electricity theft devices in the power grid.
基金supported by National Natural Science Foundation of China(No.52277083).
文摘With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft detection.However,the imbalance of electricity consumption data is violent,which makes the training of detection model challenging.In this case,this paper proposes an electricity theft detection method based on ensemble learning and prototype learning,which has great performance on imbalanced dataset and abnormal data with different abnormal level.In this paper,convolutional neural network(CNN)and long short-term memory(LSTM)are employed to obtain abstract feature from electricity consumption data.After calculating the means of the abstract feature,the prototype per class is obtained,which is used to predict the labels of unknown samples.In the meanwhile,through training the network by different balanced subsets of training set,the prototype is representative.Compared with some mainstream methods including CNN,random forest(RF)and so on,the proposed method has been proved to effectively deal with the electricity theft detection when abnormal data only account for 2.5%and 1.25%of normal data.The results show that the proposed method outperforms other state-of-the-art methods.
文摘the power network fault detection system can only analyze all kinds of fault signal in stationary sequence： the internal grid and external disturbance presents degeneration, tiny, signal intensity changes randomly fluctuate, that will cause the system to detect the fault isolation ability of confusion and small fault signal is not strong; The article propose a method of power network fault detection for based on GSM, Through the underlying sensing equipment acquisition abnormal information of current, voltage power in the network and GSM networking scheme can filter the interference factors in extraction of fault information from and attribute value. The embedded gateway take STM32 chip as the core to monitoring data processing, to achieve a unified data management and user remote access, realizing method of system software are given, to construct the monitor management information platform. The actual test system show that, identification diagnosis ability of fault signal separation ability and small signal increases 17%, also meet the requirements.
基金supported by the National Natural Science Foundation of China(Nos.62076150,62133008,61903226,and 62173216)the Taishan Scholar Project of Shandong Province(No.TSQN201812092)+1 种基金the Key Research and Development Program of Shandong Province(Nos.2021CXGC011205 and 2021TSGC1053)the Youth Innovation Technology Project of Higher School in Shandong Province(No.2019KJN005).
文摘Timely detection of abnormal electricity consumption behaviors plays a key role in saving energy.However,the detection of abnormal electricity consumption faces many problems.Imbalanced data are important challenges in this field.When the normal data are much more than the abnormal data,the network can hardly recognize the features of the minority class data,which generates low detection efficiency.Therefore,in this paper,we employ adaptive synthetic sampling(ADASYN)to achieve effective expansion of the minority class data.In addition,we adopt gated recurrent units to complete the classification of electricity consumption data.We conduct detailed experiments to verify this proposed method.Experimental results show that this method is more effective than other methods.
基金supported by a grant of the Ministry of Research,Innovation and Digitization,CNCS–UEFISCDI,project number PN-III-P4-PCE-2021-0906within PNCDI III and the Institute desÉtudes Avancées(IEA)of Cergy-Pontoise University(Project INEX“Pi-ROT”#73).
文摘A multi-technique approach to prove the preparation of poly(3,4-ethylenedioxythiophene/cucurbit[7]uril)pseudorotaxanes(PEDOT∙CB7-PPs)is reported.Molecular docking simulation and matrix-assisted laser desorption/ionization mass spectrometry(MALDI MS)validate the complexation ability of the CB7 molecule towards 3,4-ethylenedioxythiophene(EDOT),which leads to the EDOT∙CB7 inclusion complex.Oxidative polymerization of EDOT∙CB7 enabled the synthesis of PEDOT∙CB7-PPs.The water-soluble part of PEDOT∙CB7-PPs was selected,freeze-dried,and chemically characterized.Furthermore,dynamic light scattering(DLS)has been used to study the particle size and z-potential(ZP-ζ)of PEDOT∙CB7-PPs.The ZP-ζvalue(35 mV)evidenced that the PEDOT∙CB7-PPs formed stable water dispersion.By combining the emerging nanopore resistive pulse sensing technique(Np-RPS)and computational modeling,we identified strong interactions of PEDOT∙CB7-PPs with the aerolysin(Ael)nanopore.PEDOT∙CB7-PPs behave as positive charged species,and thus trans negative bias promotes its interactions with the Ael nanopore.The computational modeling results are fully consistent with the Np-RPS detection,which also reveals strong interactions between PEDOT∙CB7-PPs and the Ael nanopore.With this study,we hope to provide new insights and a better understanding of the interactions between supramolecular complexes based on CB7 and biological entities,which is instrumental for future applications in the field of nanobiotechnology.
基金supported by the Air Force Office of Scientific Research under award number FA2386-24-1-4030This study was also part of the Future Resource Research Program of the Korea Institute of Science and Technology(KIST)(2E33853)+1 种基金Ministry of CultureSports and Tourism(MCST)and Korea Creative Content Agency(KOCCA)in the Culture Technology(CT)Research&Development Program 2024(RS-2024-00439361)。
文摘CONSPECTUS:Chiral optoelectronics,which utilize the unique interactions between circularly polarized(CP)light and chiral materials,open up exciting possibilities in advanced technologies.These devices can detect,emit,or manipulate light with specific polarization,enabling applications in secure communication,sensing,and data processing.A key aspect of chiral optoelectronics is the ability to generate or detect optical and electrical signals by controlling or distinguishing CP light based on its polarization direction.This capability is rooted in the selective interaction of CP light with the stereogenic(non-superimposable)molecular geometry of chiral substances,wherein the polarization of CP light aligns with the intrinsic asymmetry of the material.Among the diverse chiral materials explored for this purpose,π-conjugated molecules offer special advantages due to their tunable optoelectronic properties,efficient light−matter interactions,and cost-effective processability.Recent advancements inπ-conjugated molecule research have demonstrated their ability to generate strong chiroptical responses,thereby paving the way for compact and multifunctional device designs.Building on these unique advantages,π-conjugated molecules have advanced organic electronics into rapidly evolving technological fields.The combination of chiralπ-conjugated molecules with organic electronics is anticipated to simplify the fabrication of chiroptical devices,thereby lowering technical barriers and accelerating progress in chiral optoelectronics.This Account introduces strategies for incorporating chiroptical activity into organic optoelectronic devices,focusing on two main approaches:direct incorporation of chiroptical activity intoπ-conjugated polymer semiconductors and integration of chiral organic nanoarchitectures with conventional organic optoelectronic devices.In the first approach,we especially highlight simple methods to induce chiroptical activity in various achiralπ-conjugated polymers through the transfer of chirality from small chiral molecules.This hybrid approach effectively combines the excellent electrical properties and various optical transition properties of achiral polymers with the strong chiroptical activity of small molecules.Moreover,we address a fundamental challenge in achieving chiroptical transitions in planarπ-conjugated polymers,demonstrating the development of low-bandgapπ-conjugated polymers that exhibit both strong chiroptical activity and excellent electrical performance.Another approach,incorporating chiroptical activity into existing organic optoelectronic devices,which have already achieved significant performance advances,presents an effective strategy for high-performance chiral optoelectronics.For this purpose,we introduce the use of supramolecular assemblies ofπ-conjugated molecules to impart chiroptical responses into high-performance optoelectronic systems,utilizing efficient charge transfer of photoexcited electrons in chiroptical supramolecular nanoarchitectures.Additionally,we explore the integration of organic chiral photonic structure into organic optoelectronic systems,which act as optical filters tailored for CP light.These architectures offer unique advantages,including easy processability and seamless compatibility with existing organic electronic platforms.By bridging concepts from chiral organic optoelectronic materials and advanced organic electronics,this work outlines actionable approaches for advancing chiral optoelectronic technologies.These strategies underscore the versatility ofπ-conjugated molecules while also expanding the framework for next-generation applications.As the field of chiral optoelectronics evolves,integrating chiroptical functionalities into organic devices will facilitate transformative innovations in quantum computing,biosensing,and photonic encryption.
