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Gaussian-based Interval-Aware Transformer With Interval Embedding for Data Sequence Modeling With irregular sampling Frequency in Industrial Processes

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年

作者： Yang, Ziyi Wang, Kai Yuan, Xiaofeng Wang, Yalin Yang, Chunhua Gui, Weihua Ye, Lingjian Shen, Feifan Cent South Univ Sch Automat Changsha 410083 Peoples R China Huzhou Univ Sch Engn Huzhou 313000 Peoples R China NingboTech Univ Sch Informat Sci & Engn Ningbo 315100 Peoples R China

Temporal feature representation is critical for soft sensor modeling in industrial time sequences. Deep learning networks like long short-term memory are often used to model the temporal dynamics of data sequences. However, the data collected from industrial plants are usually sampled with irregular frequency, making it challenging for traditional methods to handle these temporally changeable relationships. Therefore, a Gaussian-based interval-aware transformer (GIA-Trans) with interval embedding is proposed in this article to model industrial data with irregular sampling frequency. In GIA-Trans, positional and temporal embedding layers are established to take positional distances and time intervals of samples into account. Then, Gaussian-based time-aware attention is proposed to tackle the changeable time intervals with adaptive weights. In this way, the temporal correlations between samples can be adaptively captured. The GIA-Trans is applied to an industrial hydrocracking process to predict the C5 and C6 content of light naphtha.

关键词： Feature extraction Transformers Soft sensors Data models Long short term memory Production Vectors Time-frequency analysis Time series analysis Recurrent neural networks Gaussian function irregular sampling quality prediction self-attention mechanism soft sensor transformer

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A Practical Root Cause Diagnosis Framework for Quality-Related Faults in Manufacturing Processes With irregular sampling Measurements

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷

作者： Ma, Liang Dong, Jie Peng, Kaixiang Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China

In actual manufacturing processes, because of the difference of sampling intervals of different process and quality variables, most of the collected data have characteristics of multiple sampling rates. How to fully use those irregular sampling data to realize nonlinear dynamic causality modeling for root cause location and propagation path identification of quality-related faults remains an open question. For this end, a practical root cause diagnosis framework is developed for manufacturing processes with irregular sampling measurements, in which a k-nearest mutual information (MI)-based optimal variable division method is proposed to obtain quality-related and quality-unrelated variables. Then, a sampling interval attention network is designed for adaptively adjusting the dynamic temporal relationships among consecutive samples. Subsequently, a gated recurrent neural network (RNN) for improved design of conditional Granger causality (GC) analysis models is designed for diagnosing root causes and identifying propagation pathways of quality-related faults. Finally, the hot rolling process (HRP) is taken as an example for verification, and the accuracy rate of root cause diagnosis reaches 90.91% and 92.31%, which indicates its practicability and feasibility.

关键词： Attention-based gated recurrent neural networks (RNNs) irregular sampling propagation path identification quality-related root cause location

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On multivariable Plancherel-Polya inequality and truncation error upper bounds in irregular sampling

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sampling THEORY SIGNAL PROCESSING AND DATA ANALYSIS 2023年第1期21卷 1-13页

作者： Pogany, Tibor K. Obuda Univ Inst Appl Math H-1034 Budapest Hungary Univ Rijeka Fac Maritime Studies Rijeka 51000 Croatia

In the note is shown that for the d-dimensional Bernstein functions class B-sigma,B-d,B- (p) p>0 the Plancherel-Polya inequality holds with the constant which equals to the product of the constants occuring in the one-dimensional cases. Related truncation error upper bounds are precised in the irregular sampling restoration of functions in several variables.

关键词： Bernstein function class Entire exponentially bounded functions of several variables Paley-Wiener function class Placherel-P & oacute lya inequality irregular sampling Truncation error upper bounds

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sampling-Interval-Aware LSTM for Industrial Process Soft Sensing of Dynamic Time Sequences With irregular sampling Measurements

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IEEE SENSORS JOURNAL 2021年第9期21卷 10787-10795页

作者： Yuan, Xiaofeng Li, Lin Wang, Kai Wang, Yalin Cent South Univ Sch Automat Changsha 410083 Peoples R China PengCheng Lab Shenzhen 518066 Peoples R China

In modern industrial processes, dynamics and nonlinearities are two main difficulties for soft sensing of key quality variables. Thus, nonlinear dynamic models like long short-term memory (LSTM) network have been applied for data sequence modeling due to its powerful representation ability. Nevertheless, most dynamic methods cannot deal with data series with irregular sampling intervals, which is a common phenomenon in many industrial plants. To handle this problem, a novel sampling-interval-aware LSTM (SIA-LSTM) is proposed in this paper, which takes the sampling intervals between sequential samples into consideration to adjust the influence of the previous sample on the current one. To this end, two non-increasing functions of the sampling interval are designed to weight different sampling intervals in the dynamic data sequence. Then, each sampling-interval weight is multiplied to the corresponding previous hidden state to adjust its impact. Finally, the adjusted hidden state is used as an adaptive input for the three control gates in each LSTM unit to obtain the current hidden state. The proposed SIA-LSTM is applied to an actual hydrocracking process for soft sensor of the C5 content in the light naphtha and the final boiling point of the heavy naphtha.

关键词： Sensors Logic gates Data models Correlation Current measurement Biological system modeling Weight measurement irregular sampling soft sensor long short-term memory sampling-interval-aware LSTM prediction

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irregular sampling of Regular Waveform Based Random Number Generator Exploiting Two Simultaneous Metastable Events of Tetrahedral Oscillators 63

Irregular Sampling of Regular Waveform Based Random Number G...

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63rd IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Gunay, Recep Ergun, Salih TUBITAK Informat & Informat Secur Res Ctr PO 74 TR-41470 Kocaeli Turkey

ISBN: (纸本)9781728180588

An irregular sampling of regular waveform based random number generator(RNG) is proposed. The entropy source is constructed by using modified tetrahedral ring oscillators which exploits two metastable events in the circuit by switching the stable and unstable loops with a clock signal with help of additional inverters which can be turned on and off. The obtained circuit has higher entropy than the classical tetrahedral ring oscillator. The design is implemented using VHDL on Xilinx Zedboard Zynq Development Board as a proof of concept. The regular clock is adjusted to have close 50% duty cycle and a frequency at least 200 times of the irregular clock which has a center frequency of 70MHz and the rings are placed around the XOR gate in a circle to achieve higher entropy. The regular clock is sampled with the irregular clock and the sampled data is collected by using a UART module and TTL cable. The collected data sets are tested with National Institute of Standard and Technology (NIST) test suite to prove the randomness and they passed all the tests with success. The RNG has a bit rate of 273kb/s and it is implemented with 5 oscillators. The proposed architecture has a 2.73 times speed improvement and uses half the ring number to generate random number sequence without any post processing.

关键词： RNG Tetrahedral Ring Oscillator irregular sampling NIST

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Random Number Generator Based on Metastabilities of Ring Oscillators and irregular sampling 27

Random Number Generator Based on Metastabilities of Ring Osc...

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27th IEEE International Conference on Electronics, Circuits and Systems (IEEE ICECS)

作者： Kaysici, Halil Ibrahim Ergun, Salih TUBITAK Informat & Informat Secur Res Ctr Kocaeli Turkey

ISBN: (纸本)9781728160443

This paper presents a new random number generator based on irregular sampling of regular waveform method. Wake-up uncertainty and shut-down metastabilities are used as additional entropy source to the timing jitter of the ring oscillators. To be able to change the stability of the rings, multiplexer and inverter are added to each ring. 34 ring oscillators are combined with an XOR gate to generate an irregular waveform that is used as the sampling clock. The select input of the multiplexers are controlled with a control unit. A signal that causes stability change with 2MHz frequency is routed from controller to the select inputs of the multiplexers and the XORed signal is masked with 2MHz, and 10ns width pulses. To reduce the speed of sampling, the irregular signal is downsampled that results in a final throughput of 13kbps. All of the circuits mentioned in this paper is implemented in Xilinx Zynq7000 FPGA. Furthermore, NIST 800-22 test suite is used to test the designed RNGs. Data sets of one million bits are collected with a UART module. All the NIST 800-22 test suite requirements are satisfied with the new proposed RNG.

关键词： irregular sampling ring oscillators shut-down metastability true random number generators wake-up time uncertainty

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Application of Wavelets for Evaluation of Soft Sensors under irregular sampling of Output Measurements 12

Application of Wavelets for Evaluation of Soft Sensors under...

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12th International-Federation-of-Automatic-Control (IFAC) Symposium on Dynamics and Control of Process Systems including Biosystems (DYCOPS)

作者： Torgashov, Andrei Goncharov, Anton Yang, Fan FEB RAS Inst OfAutomat & Control Proc 5 Radio St Vladivostok Russia Far Eastern Fed Univ Bld 12 ECampus FEFU Vladivostok Russia Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

The problem of evaluating soft sensors for plants with irregular output measurement time is considered. In order to find the relationship between inputs and output, the wavelet-transformed inputs are integrated into the regression model framework. A suitable procedure for wavelet type selection is discussed. An example is provided to illustrate successful application of the proposed approach and an industrial case study is given for soft sensor evaluation of a reactive distillation process. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： soft sensing wavelet regression irregular sampling time delay distillation columns

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Quality prediction model for process sequential data of irregular measurements with sampling-interval-attention LSTM

Quality prediction model for process sequential data of irre...

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Chinese Automation Congress (CAC)

作者： Li, Lin Wang, Yalin Yuan, Xiaofeng Yang, Chunhua Gui, Weihua Cent South Univ Sch Automat Changsha 410083 Peoples R China

ISBN: (纸本)9781728176871

Modem industrial processes are often characterized with nonlinearities and dynamics. The long short-term memory (LSTM) based soft sensor models have been used for process quality variable prediction. However, most traditional dynamic models like LSTM cannot handle dynamic data sequences with irregular sampling measurements, which is very common in many industrial processes. In this paper, a novel sampling-interval-attention LSTM (SIA-LSTM) is proposed to solve the irregular sampling problem in industrial process. In SIA-LSTM, an additional attention network is designed to learn sampling-interval relevant weight to adjust the dynamic relationship between two adjacent sampled data. The effectiveness and performance of the proposed method is validated on an industrial hydrocracking process to predict the 90% boiling point of aviation kerosene and heavy naphtha.

关键词： Soft sensor irregular sampling Quality prediction sampling-interval-attention LSTM

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Application of Moran Eigenvector Maps (MEM) to irregular sampling designs

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SPATIAL STATISTICS 2018年 26卷 56-68页

作者： Brind'Amour, Anik Mahevas, Stephanie Legendre, Pierre Bellanger, Lise IFREMER Unite Ecol & Modeles Halieut Rue Ile dYeuBP 21105 F-44311 Nantes 03 France Univ Montreal Dept Sci Biol CP 6128Succursale Ctr Ville Montreal PQ H3C 3J7 Canada Univ Nantes UMR CNRS 6629 Lab Math Jean Leray Nantes France

Moran's eigenvectors maps (MEM) are attractive mathematical objects as they are fairly simple to calculate and can be used in most studies of spatially-explicit data. There is, however, an aspect of MEM analysis that still requires some investigation: the effect of irregular sampling on their modeling performance. This study investigates empirically the behavior of MEMs under different irregularity schemes. It is focusing on simulated scenarios representing sampling designs frequently encountered in ecology. We advocate that MEMs can be computed and correctly used with data coming from irregularly designed sampling surveys, given some precautions. We suggest that when the sampling sites are equally spaced but do not cover the entire study area, the MEMs can be computed directly on the coordinates of the sampling sites without any important loss of information. Whereas, when the phenomenon of interest is tackled using randomly stratified sampling designs, the MEMs should be computed on a reconstructed space of regular sampling sites followed by removal of the missing sites, before analysis. This solution of rebuilding a (regular) sampling space guarantees to capture the underlying process under study, improves the modeling results and relaxes the impact of the choice of the weighting matrix on the computation of MEMs. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Autocorrelation irregular sampling sampling schemes Spatial analyses Statistical methods

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Application of Wavelets for Evaluation of Soft Sensors under irregular sampling of Output Measurements

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IFAC-PapersOnLine 2019年第1期52卷 106-111页

作者： Andrei Torgashov Anton Goncharov Fan Yang Institute of Automation and Control Process FEB RAS 5 Radio St. and Far-Eastern Federal University Bld. 12(E) Campus FEFU Russky Island Vladivostok Russia Beijing National Research Center for Information Science and Technology and Department of Automation Tsinghua University Beijing 100084 P.R. China

关键词： soft sensing wavelet regression irregular sampling time delay distillation columns

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