This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery(LiB)time series data.As the energy sector increasingly focuses on integrating distributed energy resources,Virtual Power Plants(...This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery(LiB)time series data.As the energy sector increasingly focuses on integrating distributed energy resources,Virtual Power Plants(VPP)have become a vital new framework for energy management.LiBs are key in this context,owing to their high-efficiency energy storage capabilities essential for VPP operations.However,LiBs are prone to various abnormal states like overcharging,over-discharging,and internal short circuits,which impede power transmission efficiency.Traditional methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and irregular nature of LiB data.In response,we introduce an innovative method:a Long Short-Term Memory(LSTM)autoencoder based on Dynamic Frequency Memory and Correlation Attention(DFMCA-LSTM-AE).This unsupervised,end-to-end approach is specifically designed for dynamically monitoring abnormal states in LiB data.The method starts with a Dynamic Frequency Fourier Transform module,which dynamically captures the frequency characteristics of time series data across three scales,incorporating a memory mechanism to reduce overgeneralization of abnormal frequencies.This is followed by integrating LSTM into both the encoder and decoder,enabling the model to effectively encode and decode the temporal relationships in the time series.Empirical tests on a real-world LiB dataset demonstrate that DFMCA-LSTM-AE outperforms existing models,achieving an average Area Under the Curve(AUC)of 90.73%and an F1 score of 83.83%.These results mark significant improvements over existing models,ranging from 2.4%–45.3%for AUC and 1.6%–28.9%for F1 score,showcasing the model’s enhanced accuracy and reliability in detecting abnormal states in LiB data.展开更多
Cognitive state detection using electroencephalogram(EEG)signals for various tasks has attracted significant research attention.However,it is difficult to further improve the performance of crosssubject cognitive stat...Cognitive state detection using electroencephalogram(EEG)signals for various tasks has attracted significant research attention.However,it is difficult to further improve the performance of crosssubject cognitive state detection.Further,most of the existing deep learning models will degrade significantly when limited training samples are given,and the feature hierarchical relationships are ignored.To address the above challenges,we propose an efficient interpretation model based on multiple capsule networks for cross-subject EEG cognitive state detection,termed as Efficient EEG-based Multi-Capsule Framework(E3GCAPS).Specifically,we use a selfexpression module to capture the potential connections between samples,which is beneficial to alleviate the sensitivity of outliers that are caused by the individual differences of cross-subject EEG.In addition,considering the strong correlation between cognitive states and brain function connection mode,the dynamic subcapsule-based spatial attention mechanism is introduced to explore the spatial relationship of multi-channel 1D EEG data,in which multichannel 1D data greatly improving the training efficiency while preserving the model performance.The effectiveness of the E3GCAPS is validated on the Fatigue-Awake EEG Dataset(FAAD)and the SJTU Emotion EEG Dataset(SEED).Experimental results show E3GCAPS can achieve remarkable results on the EEG-based cross-subject cognitive state detection under different tasks.展开更多
This paper presents a new approach for automatical classification of structural state through deep learning.In this work,a Convolutional Neural Network(CNN)was designed to fuse both the feature extraction and classifi...This paper presents a new approach for automatical classification of structural state through deep learning.In this work,a Convolutional Neural Network(CNN)was designed to fuse both the feature extraction and classification blocks into an intelligent and compact learning system and detect the structural state of a steel frame;the input was a series of vibration signals,and the output was a structural state.The digital image correlation(DIC)technology was utilized to collect vibration information of an actual steel frame,and subsequently,the raw signals,without further pre-processing,were directly utilized as the CNN samples.The results show that CNN can achieve 99%classification accuracy for the research model.Besides,compared with the backpropagation neural network(BPNN),the CNN had an accuracy similar to that of the BPNN,but it only consumes 19%of the training time.The outputs of the convolution and pooling layers were visually displayed and discussed as well.It is demonstrated that:1)the CNN can extract the structural state information from the vibration signals and classify them;2)the detection and computational performance of the CNN for the incomplete data are better than that of the BPNN;3)the CNN has better anti-noise ability.展开更多
We investigate the production of ultracold ground state x^1∑7＋（u = 0） RbCs molecules in the lowest vibrational level via short-range photoassociation followed by spontaneous emission. The starting point is the las...We investigate the production of ultracold ground state x^1∑7＋（u = 0） RbCs molecules in the lowest vibrational level via short-range photoassociation followed by spontaneous emission. The starting point is the laser cooled 85Rb and laa cs atoms in a dual species, forced dark magneto-optical trap. The special intermediate level （5）O＋ （u = 10） correlated to the （2）311 electric state is achieved by the photoassociation process. The formed ground state X1∑＋ （u = 0） molecule is resonantly excited to the 2111 intermediate state by a 651 nm pulse laser and is ionized by a 532nm pulse laser and then detected by the time-of-flight mass spectrum. Saturation of the photoionization spectroscopy at large ionization laser energy is observed and the ionization efficiency is obtained from the fitting. The production of ultracold ground state 85Rblaacs molecules is facilitative for the further research about the manipulation of ultracold molecules in the rovibrational ground state.展开更多
To investigate the real-time mean orbital elements(MOEs)estimation problem under the influence of state jumping caused by non-fatal spacecraft collision or protective orbit trans-fer,a modified augmented square-root u...To investigate the real-time mean orbital elements(MOEs)estimation problem under the influence of state jumping caused by non-fatal spacecraft collision or protective orbit trans-fer,a modified augmented square-root unscented Kalman filter(MASUKF)is proposed.The MASUKF is composed of sigma points calculation,time update,modified state jumping detec-tion,and measurement update.Compared with the filters used in the existing literature on MOEs estimation,it has three main characteristics.Firstly,the state vector is augmented from six to nine by the added thrust acceleration terms,which makes the fil-ter additionally give the state-jumping-thrust-acceleration esti-mation.Secondly,the normalized innovation is used for state jumping detection to set detection threshold concisely and make the filter detect various state jumping with low latency.Thirdly,when sate jumping is detected,the covariance matrix inflation will be done,and then an extra time update process will be con-ducted at this time instance before measurement update.In this way,the relatively large estimation error at the detection moment can significantly decrease.Finally,typical simulations are per-formed to illustrated the effectiveness of the method.展开更多
The polarization properties of light are widely applied in imaging,communications,materials analy⁃sis,and life sciences.Various methods have been developed that can measure the polarization information of a target.How...The polarization properties of light are widely applied in imaging,communications,materials analy⁃sis,and life sciences.Various methods have been developed that can measure the polarization information of a target.However,conventional polarization detection systems are often bulky and complex,limiting their poten⁃tial for broader applications.To address the challenges of miniaturization,integrated polarization detectors have been extensively explored in recent years,achieving significant advancements in performance and functionality.In this review,we focus mainly on integrated polarization detectors with innovative features,including infinitely high polarization discrimination,ultrahigh sensitivity to polarization state change,full Stokes parameters measure⁃ment,and simultaneous perception of polarization and other key properties of light.Lastly,we discuss the oppor⁃tunities and challenges for the future development of integrated polarization photodetectors.展开更多
A scheme for teleporting an arbitrary n-bit one-photon and vacuum entangled Greenberger-Horne-Zeilinger （GHZ） state is proposed. In this scheme, the maximum entanglement GHZ state is used as a quantum channel. We fi...A scheme for teleporting an arbitrary n-bit one-photon and vacuum entangled Greenberger-Horne-Zeilinger （GHZ） state is proposed. In this scheme, the maximum entanglement GHZ state is used as a quantum channel. We find a method of distinguishing four Bell states just by detecting the atomic states three times, which is irrelevant to the qubit number of the state to be teleported.展开更多
Due to the particularity of its location algorithm,there are some unique difficulties and features regarding the test of target motion states of multilateration(MLAT)system for airport surface surveillance.This paper ...Due to the particularity of its location algorithm,there are some unique difficulties and features regarding the test of target motion states of multilateration(MLAT)system for airport surface surveillance.This paper proposed a test method applicable for the airport surface surveillance MLAT system,which can effectively determine whether the target is static or moving at a certain speed.Via a normalized test statistic designed in the sliding data window,the proposed method not only eliminates the impact of geometry Dilution of precision(GDOP)effectively,but also transforms the test of different motion states into the test of different probability density functions.Meanwhile,by adjusting the size of the sliding window,it can fulfill different test performance requirements.The method was developed through strict theoretical extrapolation and performance analysis,and simulations results verified its correctness and effectiveness.展开更多
Recent advancements in high-energy terahertz(THz)sources,driven by powerful laser systems,now enable the generation of ultrashort THz pulses with energies up to several millijoules,spanning frequencies from 1 to 30 TH...Recent advancements in high-energy terahertz(THz)sources,driven by powerful laser systems,now enable the generation of ultrashort THz pulses with energies up to several millijoules,spanning frequencies from 1 to 30 THz.A key breakthrough is developing a reliable single-shot detection method,essential for measuring the electric field of these broadband,low-repetition-rate pulses,which is vital for exploring the complex dynamics of THz emission and studying extreme nonlinear material responses in this range.Existing detection methods have been limited to lower frequencies.Here,we introduce the first potentially single-shot-capable THz detection technique for capturing ultra-broadband waveforms.Utilizing a 1-μm-thick SiN detection chip,we exploit THz field-induced second harmonic generation to achieve real-time monitoring of THz waveforms with frequency content up to 30 THz.By adjusting the angle between the THz and optical probe beams,we can fine-tune the detection window for enhanced flexibility.Our novel THz detector is ideally suited for high-energy,low-repetition-rate sources,unlocking new frontiers in THz research.展开更多
Time series segmentation has attracted more interests in recent years,which aims to segment time series into different segments,each reflects a state of the monitored objects.Although there have been many surveys on t...Time series segmentation has attracted more interests in recent years,which aims to segment time series into different segments,each reflects a state of the monitored objects.Although there have been many surveys on time series segmentation,most of them focus more on change point detection(CPD)methods and overlook the advances in boundary detection(BD)and state detection(SD)methods.In this paper,we categorize time series segmentation methods into CPD,BD,and SD methods,with a specific focus on recent advances in BD and SD methods.Within the scope of BD and SD,we subdivide the methods based on their underlying models/techniques and focus on the milestones that have shaped the development trajectory of each category.As a conclusion,we found that:(1)Existing methods failed to provide sufficient support for online working,with only a few methods supporting online deployment;(2)Most existing methods require the specification of parameters,which hinders their ability to work adaptively;(3)Existing SD methods do not attach importance to accurate detection of boundary points in evaluation,which may lead to limitations in boundary point detection.We highlight the ability to working online and adaptively as important attributes of segmentation methods,the boundary detection accuracy as a neglected metrics for SD methods.展开更多
In order to diagnose the working status of each module on sensor node and make sure the wireless sensor networks （WSN） work properly, the components of sensor node and their working characteristics are studied. An o...In order to diagnose the working status of each module on sensor node and make sure the wireless sensor networks （WSN） work properly, the components of sensor node and their working characteristics are studied. An on-line fault self-diagnosis method for sensor node is proposed. First, a flexible fault sensing circuit is designed as a state detection module on sensor node. Second, a self- diagnosis algorithm is proposed based on the hardware design and the failure analysis on sensor node. Finally, in order to ensure the WSN reliability, the voltage changes of each module working statuses can be observed using the state detection module and the faulty module will be found out timely. The experimental results show that this self-diagnosis method is suitable to sensor nodes in WSN.展开更多
The process by which a kinesin motor couples its ATPase activity with concerted mechanical hand- over-hand steps is a foremost topic of molecular motor physics. Two major routes toward elucidating kinesin mechanisms a...The process by which a kinesin motor couples its ATPase activity with concerted mechanical hand- over-hand steps is a foremost topic of molecular motor physics. Two major routes toward elucidating kinesin mechanisms are the motility performance characterization of velocity and run length, and single-molecular state detection experiments. However, these two sets of experimental approaches are largely uncoupled to date. Here, we introduce an integrative motility state analysis based on a theorized kinetic graph theory for kinesin, which, on one hand, is validated by a wealth of accumulated motility data, and, on the other hand, allows for rigorous quantification of state occurrences and chemomechanical cycling probabilities. An interesting linear scaling for kincsin motility performance across species is discussed as well. An integrative kinetic graph theory analysis provides a powerful tool to bridge motility and state characterization experiments, so as to forge a unified effort for the elucidation of the working mechanisms of molecular motors.展开更多
Device-free Passive（DfP） detection has received increasing attention for its ability to support various pervasive applications. Instead of relying on variable Received Signal Strength（RSS）, most recent studies rel...Device-free Passive（DfP） detection has received increasing attention for its ability to support various pervasive applications. Instead of relying on variable Received Signal Strength（RSS）, most recent studies rely on finer-grained Channel State Information（CSI）. However, existing methods have some limitations, in that they are effective only in the Line-Of-Sight（LOS） or for more than one moving individual. In this paper, we analyze the human motion effect on CSI and propose a novel scheme for Robust Passive Motion Detection（R-PMD）. Since traditional low-pass filtering has a number of limitations with respect to data denoising, we adopt a novel Principal Component Analysis（PCA）-based filtering technique to capture the representative signals of human motion and extract the variance profile as the sensitive metric for human detection. In addition, existing schemes simply aggregate CSI values over all the antennas in MIMO systems. Instead, we investigate the sensing quality of each antenna and aggregate the best combination of antennas to achieve more accurate and robust detection. The R-PMD prototype uses off-the-shelf WiFi devices and the experimental results demonstrate that R-PMD achieves an average detection rate of 96.33% with a false alarm rate of 3.67%.展开更多
Remote sensing methods are commonly used to assess and monitor ecosystem conditions in drylands,but accurate classification and detection of ecological state change are challenging due to sparse vegetation cover,high ...Remote sensing methods are commonly used to assess and monitor ecosystem conditions in drylands,but accurate classification and detection of ecological state change are challenging due to sparse vegetation cover,high spatial heterogeneity,and high interannual variability in production.We evaluated whether phenological metrics are effective for distinguishing dryland ecological states using imagery from near-surface camera(PhenoCam)and satellite(Harmonized Landsat 8 and Sentinel-2,hereafter HLS)sources,and how effectiveness varied across wet and dry rainfall years.We analyzed time series over 92 site-years at a site in southern New Mexico undergoing transitions from grassland to shrubland on different soil types.Rainfall was a driver of phenological response across all ecological states,with wet years correlating with later start of season,later peak,higher peak greenness,and shorter growing season.This rainfall response was strongest in shrub-invaded grasslands on sandy soils.PhenoCam estimated significantly earlier start of season than HLS for shrublands on gravelly soils and earlier end of season than HLS for shrub-invaded grasslands on sandy soils.We propose integrating seasonal metrics from high-frequency PhenoCam time series with satellite assessments to improve monitoring efforts in drylands,use phenological differences across variable rainfall years to measure differences in ecosystem function among states,and use the timing and strength of peak greenness of key plant functional groups(grasses in our study site)as an indicator of ecological state change.展开更多
Rapidly responding and cost-effective sensors played a crucial role in industrial detection.However,the lack of versatile strategies for identifying and classifying operating states on various practical behaviors has ...Rapidly responding and cost-effective sensors played a crucial role in industrial detection.However,the lack of versatile strategies for identifying and classifying operating states on various practical behaviors has limited the rapid development of monitoring technology.This study developed a vector hybrid triboelectric sensor(HTS)with surface nanocrystalline containing triboelectric vibration and rotation units(triboelectric vibration unit(TVU),triboelectric rotation unit(TRU))capable of detecting the vibrational and rotary states of the device.The synchronous detection of two sensing signals can be achieved due to the hierarchical structure as the basic unit of the HTS,which contributed to reducing the volume and spatial distribution of the HTS.Based on the voltage/current/charge(U-I-Q)signal amplitudes and phase features generated by the TVU,the vibration frequency and orientation of the device can be identified by using a double-layer neural network(D-LNN),in which the accuracy reaches 96.5%and 95.5%respectively.Additionally,by combining logistic regression,D-LNN,and linear regression,the accuracy of the TRU for rotary classification exceeds 93.5%in practical application.In this study,the great potential application of the HTS combined with the machine learning methods was successfully explored and exhibited and it might speed up the development of industrial detection in the near future.展开更多
Currently,most of the methods formineral materials analysis generate secondary pollution,which is detrimental to human health.For instance,traditionalmethods for sphalerite analysis in the zinc(Zn)smelting industry in...Currently,most of the methods formineral materials analysis generate secondary pollution,which is detrimental to human health.For instance,traditionalmethods for sphalerite analysis in the zinc(Zn)smelting industry including chemical titration,atomic absorption spectrometry,and inductively coupled atomic emission spectroscopy.Colored indicators and toxic heavy metals are used in the analytical processes,causing severe pollution.For some methods,liquid is transformed into gaseous plasma,which is more dangerous to human health.Due to large quantities of sphalerite being used,secondary pollution cannot be ignored.This study proposes a green analysis method for the detection of sphalerite based on colorimetry,which does not generate secondary pollution.The results show that the strong substitution ability of iron(Fe)for Zn contributes to their inverse correlation in contents.The lattice parameters decrease with the increasing Fe content,resulting in a darker coloration.Here,key colorimetry parameters of L*,a*,and b*show clear linear correlations with the Zn and Fe contents.Compared with traditional approaches,this new method is environmental friendly with high sensitivity and accuracy.The relative error and relative standard deviation were less than 10%and 5%,respectively.This study provides a significant reference for nonpollution determination of other mineral materials.展开更多
基金supported by“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-002)the Technology Development Program(RS-2023-00278623)funded by the Ministry of SMEs and Startups(MSS,Korea).
文摘This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery(LiB)time series data.As the energy sector increasingly focuses on integrating distributed energy resources,Virtual Power Plants(VPP)have become a vital new framework for energy management.LiBs are key in this context,owing to their high-efficiency energy storage capabilities essential for VPP operations.However,LiBs are prone to various abnormal states like overcharging,over-discharging,and internal short circuits,which impede power transmission efficiency.Traditional methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and irregular nature of LiB data.In response,we introduce an innovative method:a Long Short-Term Memory(LSTM)autoencoder based on Dynamic Frequency Memory and Correlation Attention(DFMCA-LSTM-AE).This unsupervised,end-to-end approach is specifically designed for dynamically monitoring abnormal states in LiB data.The method starts with a Dynamic Frequency Fourier Transform module,which dynamically captures the frequency characteristics of time series data across three scales,incorporating a memory mechanism to reduce overgeneralization of abnormal frequencies.This is followed by integrating LSTM into both the encoder and decoder,enabling the model to effectively encode and decode the temporal relationships in the time series.Empirical tests on a real-world LiB dataset demonstrate that DFMCA-LSTM-AE outperforms existing models,achieving an average Area Under the Curve(AUC)of 90.73%and an F1 score of 83.83%.These results mark significant improvements over existing models,ranging from 2.4%–45.3%for AUC and 1.6%–28.9%for F1 score,showcasing the model’s enhanced accuracy and reliability in detecting abnormal states in LiB data.
基金supported by NSFC with grant No.62076083Firstly,the authors would like to express thanks to the Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province with grant No.2020E10010Industrial Neuroscience Laboratory of Sapienza University of Rome.
文摘Cognitive state detection using electroencephalogram(EEG)signals for various tasks has attracted significant research attention.However,it is difficult to further improve the performance of crosssubject cognitive state detection.Further,most of the existing deep learning models will degrade significantly when limited training samples are given,and the feature hierarchical relationships are ignored.To address the above challenges,we propose an efficient interpretation model based on multiple capsule networks for cross-subject EEG cognitive state detection,termed as Efficient EEG-based Multi-Capsule Framework(E3GCAPS).Specifically,we use a selfexpression module to capture the potential connections between samples,which is beneficial to alleviate the sensitivity of outliers that are caused by the individual differences of cross-subject EEG.In addition,considering the strong correlation between cognitive states and brain function connection mode,the dynamic subcapsule-based spatial attention mechanism is introduced to explore the spatial relationship of multi-channel 1D EEG data,in which multichannel 1D data greatly improving the training efficiency while preserving the model performance.The effectiveness of the E3GCAPS is validated on the Fatigue-Awake EEG Dataset(FAAD)and the SJTU Emotion EEG Dataset(SEED).Experimental results show E3GCAPS can achieve remarkable results on the EEG-based cross-subject cognitive state detection under different tasks.
文摘This paper presents a new approach for automatical classification of structural state through deep learning.In this work,a Convolutional Neural Network(CNN)was designed to fuse both the feature extraction and classification blocks into an intelligent and compact learning system and detect the structural state of a steel frame;the input was a series of vibration signals,and the output was a structural state.The digital image correlation(DIC)technology was utilized to collect vibration information of an actual steel frame,and subsequently,the raw signals,without further pre-processing,were directly utilized as the CNN samples.The results show that CNN can achieve 99%classification accuracy for the research model.Besides,compared with the backpropagation neural network(BPNN),the CNN had an accuracy similar to that of the BPNN,but it only consumes 19%of the training time.The outputs of the convolution and pooling layers were visually displayed and discussed as well.It is demonstrated that:1)the CNN can extract the structural state information from the vibration signals and classify them;2)the detection and computational performance of the CNN for the incomplete data are better than that of the BPNN;3)the CNN has better anti-noise ability.
基金Supported by the National Basic Research Program of China under Grant No 2012CB921603the National Natural Science Foundation of China under Grant Nos 61275209,11304189,61378015 and 11434007+1 种基金the National Natural Science Foundation for Excellent Research Team under Grant No 61121064the Program for Changjiang Scholars and Innovative Research Team in University under Grant No IRT13076
文摘We investigate the production of ultracold ground state x^1∑7＋（u = 0） RbCs molecules in the lowest vibrational level via short-range photoassociation followed by spontaneous emission. The starting point is the laser cooled 85Rb and laa cs atoms in a dual species, forced dark magneto-optical trap. The special intermediate level （5）O＋ （u = 10） correlated to the （2）311 electric state is achieved by the photoassociation process. The formed ground state X1∑＋ （u = 0） molecule is resonantly excited to the 2111 intermediate state by a 651 nm pulse laser and is ionized by a 532nm pulse laser and then detected by the time-of-flight mass spectrum. Saturation of the photoionization spectroscopy at large ionization laser energy is observed and the ionization efficiency is obtained from the fitting. The production of ultracold ground state 85Rblaacs molecules is facilitative for the further research about the manipulation of ultracold molecules in the rovibrational ground state.
基金This work was supported by National Natural Science Foundation of China(12372045)Shanghai Aerospace Science and Technology Program(SAST2021-030).
文摘To investigate the real-time mean orbital elements(MOEs)estimation problem under the influence of state jumping caused by non-fatal spacecraft collision or protective orbit trans-fer,a modified augmented square-root unscented Kalman filter(MASUKF)is proposed.The MASUKF is composed of sigma points calculation,time update,modified state jumping detec-tion,and measurement update.Compared with the filters used in the existing literature on MOEs estimation,it has three main characteristics.Firstly,the state vector is augmented from six to nine by the added thrust acceleration terms,which makes the fil-ter additionally give the state-jumping-thrust-acceleration esti-mation.Secondly,the normalized innovation is used for state jumping detection to set detection threshold concisely and make the filter detect various state jumping with low latency.Thirdly,when sate jumping is detected,the covariance matrix inflation will be done,and then an extra time update process will be con-ducted at this time instance before measurement update.In this way,the relatively large estimation error at the detection moment can significantly decrease.Finally,typical simulations are per-formed to illustrated the effectiveness of the method.
基金Supported by the National Key Research and Development Program of China(2022YFA1404602)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0580000)+3 种基金the National Natural Science Foundation of China(U23B2045,62305362)the Program of Shanghai Academic/Technology Research Leader(22XD1424400)the Fund of SITP Innovation Foundation(CX-461 and CX-522)Special Project to Seize the Commanding Heights of Science and Technology of Chinese Academy of Sciences,subtopic(GJ0090406-6).
文摘The polarization properties of light are widely applied in imaging,communications,materials analy⁃sis,and life sciences.Various methods have been developed that can measure the polarization information of a target.However,conventional polarization detection systems are often bulky and complex,limiting their poten⁃tial for broader applications.To address the challenges of miniaturization,integrated polarization detectors have been extensively explored in recent years,achieving significant advancements in performance and functionality.In this review,we focus mainly on integrated polarization detectors with innovative features,including infinitely high polarization discrimination,ultrahigh sensitivity to polarization state change,full Stokes parameters measure⁃ment,and simultaneous perception of polarization and other key properties of light.Lastly,we discuss the oppor⁃tunities and challenges for the future development of integrated polarization photodetectors.
基金Project supported by the Natural Science Foundation of Henan Province, China （Grant No 0511010600） and the Education Department of Henan Province, China （Grant No 2006140005）.
文摘A scheme for teleporting an arbitrary n-bit one-photon and vacuum entangled Greenberger-Horne-Zeilinger （GHZ） state is proposed. In this scheme, the maximum entanglement GHZ state is used as a quantum channel. We find a method of distinguishing four Bell states just by detecting the atomic states three times, which is irrelevant to the qubit number of the state to be teleported.
基金supported by the National Science and Technology Pillar Program of China (No.2011BAH24B06)the National Nature Science Foundation of China+1 种基金Chinese Civil Aviation Jointly Funded Foundation Project (No.U1433129)the Sichuan Provincial Department of Education Foundation(No.13ZB0287)
文摘Due to the particularity of its location algorithm,there are some unique difficulties and features regarding the test of target motion states of multilateration(MLAT)system for airport surface surveillance.This paper proposed a test method applicable for the airport surface surveillance MLAT system,which can effectively determine whether the target is static or moving at a certain speed.Via a normalized test statistic designed in the sliding data window,the proposed method not only eliminates the impact of geometry Dilution of precision(GDOP)effectively,but also transforms the test of different motion states into the test of different probability density functions.Meanwhile,by adjusting the size of the sliding window,it can fulfill different test performance requirements.The method was developed through strict theoretical extrapolation and performance analysis,and simulations results verified its correctness and effectiveness.
基金supported by the Independent Research Fund Denmark(project THz-GRIP:2035-00365B).
文摘Recent advancements in high-energy terahertz(THz)sources,driven by powerful laser systems,now enable the generation of ultrashort THz pulses with energies up to several millijoules,spanning frequencies from 1 to 30 THz.A key breakthrough is developing a reliable single-shot detection method,essential for measuring the electric field of these broadband,low-repetition-rate pulses,which is vital for exploring the complex dynamics of THz emission and studying extreme nonlinear material responses in this range.Existing detection methods have been limited to lower frequencies.Here,we introduce the first potentially single-shot-capable THz detection technique for capturing ultra-broadband waveforms.Utilizing a 1-μm-thick SiN detection chip,we exploit THz field-induced second harmonic generation to achieve real-time monitoring of THz waveforms with frequency content up to 30 THz.By adjusting the angle between the THz and optical probe beams,we can fine-tune the detection window for enhanced flexibility.Our novel THz detector is ideally suited for high-energy,low-repetition-rate sources,unlocking new frontiers in THz research.
基金This work is supported by the National Key Research and Development Program of China(2022YFF1203001)National Natural Science Foundation of China(Nos.62072465,62102425)the Science and Technology Innovation Program of Hunan Province(Nos.2022RC3061,2023RC3027).
文摘Time series segmentation has attracted more interests in recent years,which aims to segment time series into different segments,each reflects a state of the monitored objects.Although there have been many surveys on time series segmentation,most of them focus more on change point detection(CPD)methods and overlook the advances in boundary detection(BD)and state detection(SD)methods.In this paper,we categorize time series segmentation methods into CPD,BD,and SD methods,with a specific focus on recent advances in BD and SD methods.Within the scope of BD and SD,we subdivide the methods based on their underlying models/techniques and focus on the milestones that have shaped the development trajectory of each category.As a conclusion,we found that:(1)Existing methods failed to provide sufficient support for online working,with only a few methods supporting online deployment;(2)Most existing methods require the specification of parameters,which hinders their ability to work adaptively;(3)Existing SD methods do not attach importance to accurate detection of boundary points in evaluation,which may lead to limitations in boundary point detection.We highlight the ability to working online and adaptively as important attributes of segmentation methods,the boundary detection accuracy as a neglected metrics for SD methods.
基金Supported by the Basic Research Foundation of Beijing Institute of Technology(200705422009)
文摘In order to diagnose the working status of each module on sensor node and make sure the wireless sensor networks （WSN） work properly, the components of sensor node and their working characteristics are studied. An on-line fault self-diagnosis method for sensor node is proposed. First, a flexible fault sensing circuit is designed as a state detection module on sensor node. Second, a self- diagnosis algorithm is proposed based on the hardware design and the failure analysis on sensor node. Finally, in order to ensure the WSN reliability, the voltage changes of each module working statuses can be observed using the state detection module and the faulty module will be found out timely. The experimental results show that this self-diagnosis method is suitable to sensor nodes in WSN.
文摘The process by which a kinesin motor couples its ATPase activity with concerted mechanical hand- over-hand steps is a foremost topic of molecular motor physics. Two major routes toward elucidating kinesin mechanisms are the motility performance characterization of velocity and run length, and single-molecular state detection experiments. However, these two sets of experimental approaches are largely uncoupled to date. Here, we introduce an integrative motility state analysis based on a theorized kinetic graph theory for kinesin, which, on one hand, is validated by a wealth of accumulated motility data, and, on the other hand, allows for rigorous quantification of state occurrences and chemomechanical cycling probabilities. An interesting linear scaling for kincsin motility performance across species is discussed as well. An integrative kinetic graph theory analysis provides a powerful tool to bridge motility and state characterization experiments, so as to forge a unified effort for the elucidation of the working mechanisms of molecular motors.
基金supported by the National Natural Science Foundation of China (Nos. 61373137, 61572261, 61572260, and 61373017)Major Program of Jiangsu Higher Education Institutions (No. 14KJA520002)Graduate Student Research Innovation Project (Nos. KYLX16_0666 and KYLX16_0670)
文摘Device-free Passive（DfP） detection has received increasing attention for its ability to support various pervasive applications. Instead of relying on variable Received Signal Strength（RSS）, most recent studies rely on finer-grained Channel State Information（CSI）. However, existing methods have some limitations, in that they are effective only in the Line-Of-Sight（LOS） or for more than one moving individual. In this paper, we analyze the human motion effect on CSI and propose a novel scheme for Robust Passive Motion Detection（R-PMD）. Since traditional low-pass filtering has a number of limitations with respect to data denoising, we adopt a novel Principal Component Analysis（PCA）-based filtering technique to capture the representative signals of human motion and extract the variance profile as the sensitive metric for human detection. In addition, existing schemes simply aggregate CSI values over all the antennas in MIMO systems. Instead, we investigate the sensing quality of each antenna and aggregate the best combination of antennas to achieve more accurate and robust detection. The R-PMD prototype uses off-the-shelf WiFi devices and the experimental results demonstrate that R-PMD achieves an average detection rate of 96.33% with a false alarm rate of 3.67%.
基金supported by appropriated funds to the United States Department of Agriculture(USDA)Agricultural Research Service(ARS)JER project 3050-11210-009-00D and the Jornada Basin Long-Term Ecological Research Program DEB-1832194 and is a contribution from the Long-Term Agroecosystem Research(LTAR)networksupport of PhenoCam Network.The development of PhenoCam has been funded by the Northeastern States Research Cooperative,NSF’s Macrosystems Biology program(awards EF-1065029 and EF-1702697)+3 种基金U.S.Department of Energy’s(DOE’s)Regional and Global Climate Modeling program(award DE-SC0016011)the LTAR network,which is supported by the USDA-ARS(Cooperative agreement 59-3050-2-002)supported by a postdoctoral fellowship funded by the USDA Agricultural Research Service’s SCINet Program and AI Center of Excellence(ARS project numbers 0201-88888-003-000D and 0201-88888-002-000D)administered by the Oak Ridge Institute for Science and Education(ORISE)through an interagency agreement between the DOE and the USDA.ORISE is managed by ORAU under DOE contract number DE-SC0014664.
文摘Remote sensing methods are commonly used to assess and monitor ecosystem conditions in drylands,but accurate classification and detection of ecological state change are challenging due to sparse vegetation cover,high spatial heterogeneity,and high interannual variability in production.We evaluated whether phenological metrics are effective for distinguishing dryland ecological states using imagery from near-surface camera(PhenoCam)and satellite(Harmonized Landsat 8 and Sentinel-2,hereafter HLS)sources,and how effectiveness varied across wet and dry rainfall years.We analyzed time series over 92 site-years at a site in southern New Mexico undergoing transitions from grassland to shrubland on different soil types.Rainfall was a driver of phenological response across all ecological states,with wet years correlating with later start of season,later peak,higher peak greenness,and shorter growing season.This rainfall response was strongest in shrub-invaded grasslands on sandy soils.PhenoCam estimated significantly earlier start of season than HLS for shrublands on gravelly soils and earlier end of season than HLS for shrub-invaded grasslands on sandy soils.We propose integrating seasonal metrics from high-frequency PhenoCam time series with satellite assessments to improve monitoring efforts in drylands,use phenological differences across variable rainfall years to measure differences in ecosystem function among states,and use the timing and strength of peak greenness of key plant functional groups(grasses in our study site)as an indicator of ecological state change.
文摘Rapidly responding and cost-effective sensors played a crucial role in industrial detection.However,the lack of versatile strategies for identifying and classifying operating states on various practical behaviors has limited the rapid development of monitoring technology.This study developed a vector hybrid triboelectric sensor(HTS)with surface nanocrystalline containing triboelectric vibration and rotation units(triboelectric vibration unit(TVU),triboelectric rotation unit(TRU))capable of detecting the vibrational and rotary states of the device.The synchronous detection of two sensing signals can be achieved due to the hierarchical structure as the basic unit of the HTS,which contributed to reducing the volume and spatial distribution of the HTS.Based on the voltage/current/charge(U-I-Q)signal amplitudes and phase features generated by the TVU,the vibration frequency and orientation of the device can be identified by using a double-layer neural network(D-LNN),in which the accuracy reaches 96.5%and 95.5%respectively.Additionally,by combining logistic regression,D-LNN,and linear regression,the accuracy of the TRU for rotary classification exceeds 93.5%in practical application.In this study,the great potential application of the HTS combined with the machine learning methods was successfully explored and exhibited and it might speed up the development of industrial detection in the near future.
基金National Natural Science Foundation of China,Grant/Award Numbers:52174385,41877392Fundamental Research Funds for the Central Universities,Tongji University,Grant/Award Number:22120220166。
文摘Currently,most of the methods formineral materials analysis generate secondary pollution,which is detrimental to human health.For instance,traditionalmethods for sphalerite analysis in the zinc(Zn)smelting industry including chemical titration,atomic absorption spectrometry,and inductively coupled atomic emission spectroscopy.Colored indicators and toxic heavy metals are used in the analytical processes,causing severe pollution.For some methods,liquid is transformed into gaseous plasma,which is more dangerous to human health.Due to large quantities of sphalerite being used,secondary pollution cannot be ignored.This study proposes a green analysis method for the detection of sphalerite based on colorimetry,which does not generate secondary pollution.The results show that the strong substitution ability of iron(Fe)for Zn contributes to their inverse correlation in contents.The lattice parameters decrease with the increasing Fe content,resulting in a darker coloration.Here,key colorimetry parameters of L*,a*,and b*show clear linear correlations with the Zn and Fe contents.Compared with traditional approaches,this new method is environmental friendly with high sensitivity and accuracy.The relative error and relative standard deviation were less than 10%and 5%,respectively.This study provides a significant reference for nonpollution determination of other mineral materials.
