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.

Gyrokinetic simulations of the kinetic electron effects on the electrostatic instabilities on the ITER baseline scenario

The linear and nonlinear simulations are carried out using the gyrokinetic code NLT for the electrostatic instabilities in the core region of a deuterium plasma based on the International Thermonuclear Experimental Reactor(ITER)baseline scenario.The kinetic electron effects on the linear frequency and nonlinear transport are studied by adopting the adiabatic electron model and the fully drift-kinetic electron model in the NLT code,respectively.The linear simulations focus on the dependence of linear frequency on the plasma parameters,such as the ion and electron temperature gradientsκ_(Ti,e)≡R=L_(Ti,e),the density gradientκ_(n)≡R/L_(n)and the ion-electron temperature ratioτ=T_(e)=T_(i).Here,is the major radius,and T_(e)and T_(i)denote the electron and ion temperatures,respectively.L_(A)=-(δ_(r)lnA)^(-1)is the gradient scale length,with denoting the density,the ion and electron temperatures,respectively.In the kinetic electron model,the ion temperature gradient(ITG)instability and the trapped electron mode(TEM)dominate in the small and large k_(θ)region,respectively,wherek_(θ)is the poloidal wavenumber.The TEMdominant region becomes wider by increasing(decreasing)κ_(T_(e))(κ_(T_(i)))or by decreasingκ_(n).For the nominal parameters of the ITER baseline scenario,the maximum growth rate of dominant ITG instability in the kinetic electron model is about three times larger than that in the adiabatic electron model.The normalized linear frequency depends on the value ofτ,rather than the value of T_(e)or T_(i),in both the adiabatic and kinetic electron models.The nonlinear simulation results show that the ion heat diffusivity in the kinetic electron model is quite a lot larger than that in the adiabatic electron model,the radial structure is finer and the time oscillation is more rapid.In addition,the magnitude of the fluctuated potential at the saturated stage peaks in the ITGdominated region,and contributions from the TEM(dominating in the higher k_(θ)region)to the nonlinear transport can be neglected.In the adiabatic electron model,the zonal radial electric field is found to be mainly driven by the turbulent energy flux,and the contribution of turbulent poloidal Reynolds stress is quite small due to the toroidal shielding effect.However,in the kinetic electron model,the turbulent energy flux is not strong enough to drive the zonal radial electric field in the nonlinear saturated stage.The kinetic electron effects on the mechanism of the turbulence-driven zonal radial electric field should be further investigated.

Mean nocturnal baseline impedance in gastro-esophageal reflux disease diagnosis:Should we strictly follow the Lyon 2 Consensus?

Clinical practice guidelines drive clinical practice and clinicians rely to them when trying to answer their most common questions.One of the most important position papers in the field of gastro-esophageal reflux disease(GERD)is the one produced by the Lyon Consensus.Recently an updated second version has been released.Mean nocturnal baseline impedance(MNBI)was proposed by the first Consensus to act as supportive evidence for GERD diagnosis.Originally a cut-off of 2292 Ohms was proposed,a value revised in the second edition.The updated Consensus recommended that an MNBI<1500 Ohms strongly suggests GERD while a value>2500 Ohms can be used to refute GERD.The proposed cut-offs move in the correct direction by diminishing the original cut-off,nevertheless they arise from a study of normal subjects where cut-offs were provided by measuring the mean value±2SD and not in symptomatic patients.However,data exist that even symptomatic patients with inconclusive disease or reflux hypersensitivity(RH)show lower MNBI values in comparison to normal subjects or patients with functional heartburn(FH).Moreover,according to the data,MNBI,even among symptomatic patients,is affected by age and body mass index.Also,various studies have proposed different cut-offs by using receiver operating characteristic curve analysis even lower than the one proposed.Finally,no information is given for patients submitted to on-proton pump inhibitors pH-impedance studies even if new and extremely important data now exist.Therefore,even if MNBI is an extremely important tool when trying to approach patients with reflux symptoms and could distinguish conclusive GERD from RH or FH,its values should be interpreted with caution.

A New Result on Regular Designs under Baseline Parameterization

The study on designs for the baseline parameterization has aroused attention in recent years. This paper focuses on two-level regular designs for the baseline parameterization. A general result on the relationship between K-aberration and word length pattern is developed.

Baseline Sensitivity of Botrytis cinerea from Liaoning Province to Boscalid

[Objective] This study aimed to avoid and delay the generation of resis- tance in Botrytis cinerea to boscalid. [Method] The sensitivity of Botrytis cinerea to boscalid was tested by the mycelial growth rate method. The distribution of sensitivity variation of B. cinerea to boscalid in different regions was cleared, and the sen- sitivity baseline of B. cinerea from Liaoning Province to boscalid was established. [Result] The ECso values of B. cinerea strains from Liaoning Province ranged from 0.080 0 to 7.787 2μg/ml, and the highest ECho value was 97.34 times higher than the minimum. The average EC50 value （1.973μg/ml） of the 158 strains was treated as the baseline sensitivity of B. cinerea from Liaoning Province to boscalid. [Conclu- sion] It is essential to conduct a risk assessment of drug resistance in B. cinerea to boscalid.

Ionosit-Baseliner在深龋盖髓中的应用

目的观察Ionosit-Baseliner(爱诺赛光固化复合体)用于深龋患牙盖髓术的临床疗效。方法将18～72岁152例患者的175颗深龋患牙,分为两组,治疗组以Ionosit-Baseliner间接盖髓,对照组以氢氧化钙间接盖髓。6个月、1年后分别复查并进行临床评价,观察两组盖髓术后的临床症状、牙髓活力、X线摄片情况。结果 6个月及1年后复查,两组盖髓作用的差别均无统计学意义(P>0.05)。结论 Ionosit-Baseliner可作为间接盖髓剂应用于临床深龋治疗,并且疗效很好,值得推广。

An Analog Equalizer and Baseline-Wander Cancelle rfor 100/1000Base-TX Transceiver

A frequency-domain equalizer with a mixed-signal adaptive control loop and a novel baseline wander （BLW） canceller are proposed. The equalizer is independent of channel-modeling accuracy,and its control loop is intrinsically stable. An AGC function is incorporated into the equalizer without an extra AGC circuit. The proposed BLW canceller uses a peak detector to monitor the BLW and full feedback method to accomplish BLW canceling. High canceling accuracy and robust performance are achieved. The circuits are tested in 0.25μm CMOS technology. Better performance and smaller silicon area are achieved compared with results in the literature.

基于MAX+plusII 10.0 Baseline的数字电路设计

讨论 MAX+plus II10 .0 Baseline的功能和特点 ,提出以 VHDL 设计语言为手段、以 MAX+plus II10 .0Baseline为平台的数字电路的设计方法。

Baseline——一种容忍与检测DoS/DDoS攻击的被动方法(英文)

提出一种检测DoS/DDoS攻击(拒绝服务攻击/分布式拒绝服务攻击)的“Baseline”服务体系结构,并实现了其中关键的“Shepherd”检测算法。该体系结构通过把服务及其质量是否受到影响的判断指标交由具体的通信进程来定义而具有较强的适应性,可以与各种现有入侵检测系统很好地结合且不给宿主系统增加过多的开销。同时相比其他DoS/DDoS检测方式,Baseline服务无须对Internet路由基础设施作出更改来提供支持,是一种十分可行的DoS/DDoS检测方案。在理想情况下,Baseline服务可以做到对DoSDDoS入侵零误报。

基于Baseline SVD主动学习算法的推荐系统

推荐系统是一种解决信息过载的新型技术,为了解决推荐系统中新用户带来的冷启动问题,提出一种基于主动学习的推荐系统。主动学习方法能有效减少需要标记的样本数量,快速建立模型,在此选择将主动学习方法和Baseline SVD推荐算法结合起来,通过记录模型训练得到的预估评价的改变程度,认为改变最大的样例即是最具有信息量的样例,供新用户标记,并重新训练模型。通过与其他选择策略进行实验比较,证实了该方法确实有效解决了新用户带来的冷启动问题。

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.

Baseline JPEG压缩器在Xilinx FPGA上的设计与实现

由于近年来胶囊内窥镜需求的发展,以及FPGA在小型化和低功耗的技术进步,在内窥镜系统中应用FPGA为核心的解决方案已经成为可能。在目前的FPGA数字胶囊系统,以及未来用于胶囊窥镜的专用集成电路中,图像数据压缩都是重要组成部分。本文使用Verilog对普遍应用的Baseline JPEG压缩核进行封装并在FPGA进行实现和验证,并给出了电路设计和测试结果。在Xilinx Spartan LX9 FPGA上,所实现的Baseline JPEG压缩器达到最高166 MHz的时钟频率。

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.

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.

GPS dynamic cycle slip detection and correction with baseline constraint

When the cycle slips take place in the attitude determination of a moving platform, the precision of the attitude will be impaired badly. A method of cycle slip detection and correction is proposed, which is suitable to the dynamic measurement using GPS carrier phase： the cycle slips detection is first achieved by triple difference observables, then the cycle slips correction is performed with baseline length constraint. The simulation results show that the proposed method is effective to the dynamic cycle slips problem.

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.

Baseline characterization of patients aged 70 years and above with hepatocellular carcinoma

AIM: To characterize the baseline profiles of patients aged 70 years and above with hepatocellular carcinoma (HCC).METHODS: A series of 127 consecutive patients with HCC were enrolled between 2000 and 2004, and none of them had been diagnosed as having HCC previously. Baseline profiles, including parameters of hepatic function such as serum transaminase and prothrombin time [PT (% activity)] were compared between patients aged ≥70 and ＜70 years.RESULTS: Patients ≥70 years old showed significantly lower levels of aspartate aminotransferase (P = 0.04)and alanine aminotransferase (P = 0.01), and significantly higher PTs (P= 0.04) and platelet counts (P = 0.02).Concomitantly, among ≥70-year-old patients, HCC was more common in non-cirrhotics, whereas among patients ＜70 years old, HCC was more common in cirrhotics. There was no significant difference between the groups in the number or size of tumors.CONCLUSION: Older HCC patients showed less inflammation and better preservation of hepatic function, indicating that not only cirrhotic patients but also noncirrhotic patients should be considered as a high-risk group among the elderly.