Hierarchical multihead self-attention for time-series-based fault diagnosis

Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.

Modeling urban redevelopment:A novel approach using time-series remote sensing data and machine learning

Accurate mapping and timely monitoring of urban redevelopment are pivotal for urban studies and decisionmakers to foster sustainable urban development.Traditional mapping methods heavily depend on field surveys and subjective questionnaires,yielding less objective,reliable,and timely data.Recent advancements in Geographic Information Systems(GIS)and remote-sensing technologies have improved the identification and mapping of urban redevelopment through quantitative analysis using satellite-based observations.Nonetheless,challenges persist,particularly concerning accuracy and significant temporal delays.This study introduces a novel approach to modeling urban redevelopment,leveraging machine learning algorithms and remote-sensing data.This methodology can facilitate the accurate and timely identification of urban redevelopment activities.The study’s machine learning model can analyze time-series remote-sensing data to identify spatio-temporal and spectral patterns related to urban redevelopment.The model is thoroughly evaluated,and the results indicate that it can accurately capture the time-series patterns of urban redevelopment.This research’s findings are useful for evaluating urban demographic and economic changes,informing policymaking and urban planning,and contributing to sustainable urban development.The model can also serve as a foundation for future research on early-stage urban redevelopment detection and evaluation of the causes and impacts of urban redevelopment.

Missing Value Imputation for Radar-Derived Time-Series Tracks of Aerial Targets Based on Improved Self-Attention-Based Network

The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random missing(RM)that differs significantly from common missing patterns of RTT-AT.The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation.Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss.In this paper,a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.Our model consists of two probabilistic sparse diagonal masking self-attention(PSDMSA)units and a weight fusion unit.It learns missing values by combining the representations outputted by the two units,aiming to minimize the difference between the missing values and their actual values.The PSDMSA units effectively capture temporal dependencies and attribute correlations between time steps,improving imputation quality.The weight fusion unit automatically updates the weights of the output representations from the two units to obtain a more accurate final representation.The experimental results indicate that,despite varying missing rates in the two missing patterns,our model consistently outperforms other methods in imputation performance and exhibits a low frequency of deviations in estimates for specific missing entries.Compared to the state-of-the-art autoregressive deep learning imputation model Bidirectional Recurrent Imputation for Time Series(BRITS),our proposed model reduces mean absolute error(MAE)by 31%~50%.Additionally,the model attains a training speed that is 4 to 8 times faster when compared to both BRITS and a standard Transformer model when trained on the same dataset.Finally,the findings from the ablation experiments demonstrate that the PSDMSA,the weight fusion unit,cascade network design,and imputation loss enhance imputation performance and confirm the efficacy of our design.

An Artificial Intelligence‑Assisted Flexible and Wearable Mechanoluminescent Strain Sensor System

The complex wiring,bulky data collection devices,and difficulty in fast and on-site data interpretation significantly limit the practical application of flexible strain sensors as wearable devices.To tackle these challenges,this work develops an artificial intelligenceassisted,wireless,flexible,and wearable mechanoluminescent strain sensor system(AIFWMLS)by integration of deep learning neural network-based color data processing system(CDPS)with a sandwich-structured flexible mechanoluminescent sensor(SFLC)film.The SFLC film shows remarkable and robust mechanoluminescent performance with a simple structure for easy fabrication.The CDPS system can rapidly and accurately extract and interpret the color of the SFLC film to strain values with auto-correction of errors caused by the varying color temperature,which significantly improves the accuracy of the predicted strain.A smart glove mechanoluminescent sensor system demonstrates the great potential of the AIFWMLS system in human gesture recognition.Moreover,the versatile SFLC film can also serve as a encryption device.The integration of deep learning neural network-based artificial intelligence and SFLC film provides a promising strategy to break the“color to strain value”bottleneck that hinders the practical application of flexible colorimetric strain sensors,which could promote the development of wearable and flexible strain sensors from laboratory research to consumer markets.

Bioinspired Ultrasensitive Flexible Strain Sensors for Real‑Time Wireless Detection of Liquid Leakage

Liquid leakage of pipeline networks not only results in considerableresource wastage but also leads to environmental pollution and ecological imbalance.In response to this global issue, a bioinspired superhydrophobic thermoplastic polyurethane/carbon nanotubes/graphene nanosheets flexible strain sensor (TCGS) hasbeen developed using a combination of micro-extrusion compression molding andsurface modification for real-time wireless detection of liquid leakage. The TCGSutilizes the synergistic effects of Archimedean spiral crack arrays and micropores,which are inspired by the remarkable sensory capabilities of scorpions. This designachieves a sensitivity of 218.13 at a strain of 2%, which is an increase of 4300%. Additionally, it demonstrates exceptional durability bywithstanding over 5000 usage cycles. The robust superhydrophobicity of the TCGS significantly enhances sensitivity and stability indetecting small-scale liquid leakage, enabling precise monitoring of liquid leakage across a wide range of sizes, velocities, and compositionswhile issuing prompt alerts. This provides critical early warnings for both industrial pipelines and potential liquid leakage scenariosin everyday life. The development and utilization of bioinspired ultrasensitive flexible strain sensors offer an innovative and effectivesolution for the early wireless detection of liquid leakage.

Flexible Strain Sensors with Ultra‑High Sensitivity and Wide Range Enabled by Crack‑Modulated Electrical Pathways

This study presents a breakthrough in flexible strain sensor technology with the development of an ultrahigh sensitivity and wide-range sensor,addressing the critical challenge of reconciling sensitivity with measurement range.Inspired by the structure of bamboo slips,we introduce a novel approach that utilises liquid metal to modulate the electrical pathways within a cracked platinum fabric electrode.The resulting sensor demonstrates a gauge factor greater than 108 and a strain measurement capability exceeding 100%.The integration of patterned liquid metal enables customisable tuning of the sensor’s response,while the porous fabric structure ensures superior comfort and air permeability for the wearer.Our design not only optimises the sensor’s performance but also enhances the electrical stability that is essential for practical applications.Through systematic investigation,we reveal the intrinsic mechanisms governing the sensor’s response,offering valuable insights for the design of wearable strain sensors.The sensor’s exceptional performance across a spectrum of applications,from micro-strain to large-strain detection,highlights its potential for a wide range of real-world uses,demonstrating a significant advancement in the field of flexible electronics.

Polyethylene glycol fusion repair of severed sciatic nerves accelerates recovery of nociceptive sensory perceptions in male and female rats of different strains

Successful polyethylene glycol fusion(PEG-fusion)of severed axons following peripheral nerve injuries for PEG-fused axons has been reported to:(1)rapidly restore electrophysiological continuity;(2)prevent distal Wallerian Degeneration and maintain their myelin sheaths;(3)promote primarily motor,voluntary behavioral recoveries as assessed by the Sciatic Functional Index;and,(4)rapidly produce correct and incorrect connections in many possible combinations that produce rapid and extensive recovery of functional peripheral nervous system/central nervous system connections and reflex(e.g.,toe twitch)or voluntary behaviors.The preceding companion paper describes sensory terminal field reo rganization following PEG-fusion repair of sciatic nerve transections or ablations;howeve r,sensory behavioral recovery has not been explicitly explored following PEG-fusion repair.In the current study,we confirmed the success of PEG-fusion surgeries according to criteria(1-3)above and more extensively investigated whether PEG-fusion enhanced mechanical nociceptive recovery following sciatic transection in male and female outbred Sprague-Dawley and inbred Lewis rats.Mechanical nociceptive responses were assessed by measuring withdrawal thresholds using von Frey filaments on the dorsal and midplantar regions of the hindpaws.Dorsal von Frey filament tests were a more reliable method than plantar von Frey filament tests to assess mechanical nociceptive sensitivity following sciatic nerve transections.Baseline withdrawal thresholds of the sciatic-mediated lateral dorsal region differed significantly across strain but not sex.Withdrawal thresholds did not change significantly from baseline in chronic Unoperated and Sham-operated rats.Following sciatic transection,all rats exhibited severe hyposensitivity to stimuli at the lateral dorsal region of the hindpaw ipsilateral to the injury.However,PEG-fused rats exhibited significantly earlier return to baseline withdrawal thresholds than Negative Control rats.Furthermore,PEG-fused rats with significantly improved Sciatic Functional Index scores at or after 4 weeks postoperatively exhibited yet-earlier von Frey filament recove ry compared with those without Sciatic Functional Index recovery,suggesting a correlation between successful PEG-fusion and both motor-dominant and sensory-dominant behavioral recoveries.This correlation was independent of the sex or strain of the rat.Furthermore,our data showed that the acceleration of von Frey filament sensory recovery to baseline was solely due to the PEG-fused sciatic nerve and not saphenous nerve collateral outgrowths.No chronic hypersensitivity developed in any rat up to 12 weeks.All these data suggest that PEG-fusion repair of transection peripheral nerve injuries co uld have important clinical benefits.

Temperature and Daily Mortality in Shanghai:A Time-series Study

To investigate the association between temperature and daily mortality in Shanghai from June 1, 2000 to December 31, 2001. Methods Time-series approach was used to estimate the effect of temperature on daily total and cause-specific mortality. We fitted generalized additive Poisson regression using non-parametric smooth functions to control for long-term time trend, season and other variables. We also controlled for day of the week. Results A gently sloping V-like relationship between total mortality and temperature was found, with an optimum temperature (e.g. temperature with lowest mortality risk) value of 26.7癈 in Shanghai. For temperatures above the optimum value, total mortality increased by 0.73% for each degree Celsius increase; while for temperature below the optimum value, total mortality decreased by 1.21% for each degree Celsius increase. Conclusions Our findings indicate that temperature has an effect on daily mortality in Shanghai, and the time-series approach is a useful tool for studying the temperature-mortality association.

Review of the SBAS InSAR Time-series algorithms, applications, and challenges

In the past 30 years,the small baseline subset(SBAS)InSAR time-series technique has emerged as an essential tool for measuring slow surface displacement and estimating geophysical parameters.Because of its ability to monitor large-scale deformation with millimeter accuracy,the SBAS method has been widely used in various geodetic fields,such as ground subsidence,landslides,and seismic activity.The obtained long-term time-series cumulative deformation is vital for studying the deformation mecha-nism.This article reviews the algorithms,applications,and challenges of the SBAS method.First,we recall the fundamental principle and analyze the shortcomings of the traditional SBAS algorithm,which provides a basic framework for the following improved time series methods.Second,we classify the current improved SBAS techniques from different perspectives:solving the ill-posed equation,increasing the density of high-coherence points,improving the accuracy of monitoring deformation and measuring the multi-dimensional deformation.Third,we summarize the application of the SBAS method in monitoring ground subsidence,permafrost degradation,glacier movement,volcanic activity,landslides,and seismic activity.Finally,we discuss the difficulties faced by the SBAS method and explore its future development direction.

Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data

By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.

Clustering Structure Analysis in Time-Series Data With Density-Based Clusterability Measure

Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.

Wavelet matrix transform for time-series similarity measurement

A time-series similarity measurement method based on wavelet and matrix transform was proposed,and its anti-noise ability,sensitivity and accuracy were discussed. The time-series sequences were compressed into wavelet subspace,and sample feature vector and orthogonal basics of sample time-series sequences were obtained by K-L transform. Then the inner product transform was carried out to project analyzed time-series sequence into orthogonal basics to gain analyzed feature vectors. The similarity was calculated between sample feature vector and analyzed feature vector by the Euclid distance. Taking fault wave of power electronic devices for example,the experimental results show that the proposed method has low dimension of feature vector,the anti-noise ability of proposed method is 30 times as large as that of plain wavelet method,the sensitivity of proposed method is 1/3 as large as that of plain wavelet method,and the accuracy of proposed method is higher than that of the wavelet singular value decomposition method. The proposed method can be applied in similarity matching and indexing for lager time series databases.

Time-series gas prediction model using LS-SVR within a Bayesian framework

The traditional least squares support vector regression（LS-SVR）model,using cross validation to determine the regularization parameter and kernel parameter,is time-consuming.We propose a Bayesian evidence framework to infer the LS-SVR model parameters.Three levels Bayesian inferences are used to determine the model parameters,regularization hyper-parameters and tune the nuclear parameters by model comparison.On this basis,we established Bayesian LS-SVR time-series gas forecasting models and provide steps for the algorithm.The gas outburst data of a Hebi 10th mine working face is used to validate the model.The optimal embedding dimension and delay time of the time series were obtained by the smallest differential entropy method.Finally,within a MATLAB7.1 environment,we used actual coal gas data to compare the traditional LS-SVR and the Bayesian LS-SVR with LS-SVMlab1.5 Toolbox simulation.The results show that the Bayesian framework of an LS-SVR significantly improves the speed and accuracy of the forecast.

Spatio-temporal changes of underground coal fires during 2008-2016 in Khanh Hoa coal field(North-east of Viet Nam) using Landsat time-series data

Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing the effects of coal fires, and their environmental impact. In this study, the spatio-temporal changes of underground coal fires in Khanh Hoa coal field(North-East of Viet Nam) were analyzed using Landsat time-series data during the 2008-2016 period. Based on land surface temperatures retrieved from Landsat thermal data, underground coal fires related to thermal anomalies were identified using the MEDIAN+1.5×IQR(IQR: Interquartile range) threshold technique. The locations of underground coal fires were validated using a coal fire map produced by the field survey data and cross-validated using the daytime ASTER thermal infrared imagery. Based on the fires extracted from seven Landsat thermal imageries, the spatiotemporal changes of underground coal fire areas were analyzed. The results showed that the thermalanomalous zones have been correlated with known coal fires. Cross-validation of coal fires using ASTER TIR data showed a high consistency of 79.3%. The largest coal fire area of 184.6 hectares was detected in 2010, followed by 2014(181.1 hectares) and 2016(178.5 hectares). The smaller coal fire areas were extracted with areas of 133.6 and 152.5 hectares in 2011 and 2009 respectively. Underground coal fires were mainly detected in the northern and southern part, and tend to spread to north-west of the coal field.

Time-series gene expression prof iles in AGS cells stimulated with Helicobacter pylori

AIM: To extend the knowledge of the dynamic interaction between Helicobacter pylori (H. pylori) and host mucosa. METHODS: A time-series cDNA microarray was performed in order to detect the temporal gene expression prof iles of human gastric epithelial adenocarcinoma cells infected with H. pylori. Six time points were selected to observe the changes in the model. A differential expression prof ile at each time point was obtained by comparing the microarray signal value with that of 0 h. Real-time polymerase chain reaction was subsequently performed to evaluate the data quality. RESULTS: We found a diversity of gene expression patterns at different time points and identifi ed a group of genes whose expression levels were significantly correlated with several important immune response and tumor related pathways. CONCLUSION: Early infection may trigger some important pathways and may impact the outcome of the infection.

Monitoring of winter wheat distribution and phenological phases based on MODIS time-series： A case study in the Yellow River Delta, China

Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta（YRD） region using moderate resolution imaging spectroradiometer（MODIS） time-series data. The normalized difference vegetation index（NDVI） was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection.

TIME-SERIES MODELI NG AND FAULT FORECAST STUDY ON SPECTRAL ANALYSIS OF LUBRICATING OIL

The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasting analysis for the spectral analysis data co llected from aero-engines. In the oil condition monitoring field of mechanical equipment, the use of the method of time-series analysis has rarely been report ed. As indicated in the satisfactory example, a practical method for condition m onitoring and fault forecasting of mechanical equipment has been achieved.

Time-Series Characteristics of Wind Power and Its Impact on Jilin Power Grid

With the rapid development of wind power, the large-scale wind power integration brings a new range of issues in dispatching operation. In order to gain a better grasp of the influence caused by wind power combined to the grid, the paper first establishes the impact characteristic indexes, and then analyzes the regularity of wind power time series in different spatial and temporal scales. At last, according to the analysis results, this paper assesses the impact of time-series characteristics of wind power on power grid, such as the frequency regulation, peak load regulation, which can provide the reference for wind power optimal dispatching of Jilin Power Grid.

A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based on Multi-Factor Analysis and a Multi-Model Ensemble

Dissolved oxygen(DO)is an important indicator of aquaculture,and its accurate forecasting can effectively improve the quality of aquatic products.In this paper,a new DO hybrid forecasting model is proposed that includes three stages:multi-factor analysis,adaptive decomposition,and an optimizationbased ensemble.First,considering the complex factors affecting DO,the grey relational(GR)degree method is used to screen out the environmental factors most closely related to DO.The consideration of multiple factors makes model fusion more effective.Second,the series of DO,water temperature,salinity,and oxygen saturation are decomposed adaptively into sub-series by means of the empirical wavelet transform(EWT)method.Then,five benchmark models are utilized to forecast the sub-series of EWT decomposition.The ensemble weights of these five sub-forecasting models are calculated by particle swarm optimization and gravitational search algorithm(PSOGSA).Finally,a multi-factor ensemble model for DO is obtained by weighted allocation.The performance of the proposed model is verified by timeseries data collected by the pacific islands ocean observing system(PacIOOS)from the WQB04 station at Hilo.The evaluation indicators involved in the experiment include the Nash–Sutcliffe efficiency(NSE),Kling–Gupta efficiency(KGE),mean absolute percent error(MAPE),standard deviation of error(SDE),and coefficient of determination(R^(2)).Example analysis demonstrates that:①The proposed model can obtain excellent DO forecasting results;②the proposed model is superior to other comparison models;and③the forecasting model can be used to analyze the trend of DO and enable managers to make better management decisions.

Research on Reduction of Solar Power Curtailment with Grid Connected Energy Storage System Based on Time-Series Production Simulation

Due to the variable output of renewable energy (RE) generation, difficulties of dispatching RE for power system operators could not be avoided. One of possible solutions is the energy storage technology, especially the battery storage system. The large-scale energy storage system is available to support power system reliable flexibility for load following and system frequency regulation. In this paper, the bottlenecks of large-scale solar power generation dispatching and operation in Qinghai grid are discussed, and a new PV-energy storage coordinated dispatching method is proposed for reduction of PV curtailment in Qinghai. Moreover, the validation based on the time-series production simulation is provided using real data from Qinghai. The results indicate that the proposed method can effectively decrease the curtailment of solar power and future vision of large-scale solar power coordinated operation with energy storage system is also presented.