Abnormal State Detection in Lithium-ion Battery Using Dynamic Frequency Memory and Correlation Attention LSTM Autoencoder

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.

E3GCAPS: Efficient EEG-Based Multi-Capsule Framework with Dynamic Attention for Cross-Subject Cognitive State Detection

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.

Digital image correlation-based structural state detection through deep learning

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.

Super-resolution and super-sensitivity of entangled squeezed vacuum state using optimal detection strategy

Interference metrology is a method for achieving high precision detection by phase estimation. The phase sensitivity of a traditional interferometer is subject to the standard quantum limit, while its resolution is constrained by the Rayleigh diffraction limit. The resolution and sensitivity of phase measurement can be enhanced by using quantum metrology. We propose a quantum interference metrology scheme using the entangled squeezed vacuum state, which is obtained using the magic beam splitter, expressed as ｜ψ〉=（｜ξ〉｜0〉＋｜0〉｜ξ〉）/√2＋2/coshr, such as the N00 N state. We derive the phase sensitivity and the resolution of the system with Z detection, project detection, and parity detection. By simulation and analysis, we determine that parity detection is an optimal detection method, which can break through the Rayleigh diffraction limit and the standard quantum limit.

Production and Detection of Ultracold Ground State 85Rb133Cs Molecules in the Lowest Vibrational Level by Short-Range Photoassociation

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.

A Test Method for the Static/Moving State of Targets Applied to Airport Surface Surveillance MLAT System

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.

Self-diagnosis method for faulty modules on wireless sensor node

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.

Detectable states, cycle fluxes, and motility scaling of molecular motor kinesin： An integrative kinetic graph theory analysis

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.

Robust and Passive Motion Detection with COTS WiFi Devices

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%.

A vector hybrid triboelectric sensor(HTS)for motion identification via machine learning

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.

A new method to determine composition of sphalerite without secondary pollution based on CIELAB color space

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.