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Article: A novel transformer-based method for predicting air absorbed dose rates in nuclear radiation environmental monitoring.

Cao, Yizhi / Liu, Zhaoran / Niu, Yunlong / Liu, Xinggao

2023 Volume 9, Issue 9, Page(s) e19870

Abstract: Many studies have used various methods to estimate future nuclear radiation levels to control radiation contamination, provide early warnings, and protect public health and the environment. However, due to the high uncertainty and complexity of nuclear ... ...

Abstract	Many studies have used various methods to estimate future nuclear radiation levels to control radiation contamination, provide early warnings, and protect public health and the environment. However, due to the high uncertainty and complexity of nuclear radiation data, existing prediction methods face the challenges of low prediction accuracy and short warning time. Therefore, accurate prediction of nuclear radiation levels is essential to safeguard human health and safety. This study proposes a novel Mixformer model to predict future hourly nuclear radiation data. The seasonality and trend of nuclear radiation data are extracted by data decomposition. To address the slow speed problem common in traditional methods for long-time series prediction tasks, Mixformer simplifies the decoder with convolutional layers to speed up the convergence of the model. The experiments consider the air-absorbed dose rate of nuclear radiation data, spectral data, six climatic conditions, and two other conditions. We use MSE and MAE metrics to verify the effectiveness of Mixformer prediction. The results show that the Mixformer proposed in this paper has better prediction performance compared to the currently popular models. Therefore, the proposed model is a feasible method for industrial nuclear radiation data processing and prediction.
Language	English
Publishing date	2023-09-09
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2023.e19870
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Nonlinear-Model-Based Control of a Heat Integrated Distillation Column Using Model Updating Based on Distributed Wave Velocity

Cong, Lin / Liu, Xinggao

Industrial & engineering chemistry process design and development. 2019 Oct. 22, v. 58, no. 45

2019

Abstract: A nonlinear-model-based control scheme for concentration profile position control of a heat integrated distillation column (HIDiC) is presented. First, a nonlinear wave model of the HIDiC is introduced to describe the concentration profile and its ... ...

Abstract	A nonlinear-model-based control scheme for concentration profile position control of a heat integrated distillation column (HIDiC) is presented. First, a nonlinear wave model of the HIDiC is introduced to describe the concentration profile and its propagation velocity. Second, a distributed concentration wave profile velocity is developed to analyze the degree of the entire concentration profile deformation. Then, on the basis of the distributed wave velocity, a model updating method is proposed to determine a proper model parameter updating frequency so as to reduce the computing redundancy while guaranteeing the model accuracy. Finally, the nonlinear wave model and its updating method are combined to generate a model-based control scheme for the HIDiC, and controllers based on different model updating strategies are compared to evaluate the performance of the proposed model updating method.
Keywords	controllers ; deformation ; distillation ; heat ; nonlinear models ; process design ; velocity
Language	English
Dates of publication	2019-1022
Size	p. 20758-20768.
Publishing place	American Chemical Society
Document type	Article
ZDB-ID	1484436-9
ISSN	1520-5045 ; 0888-5885
ISSN (online)	1520-5045
ISSN	0888-5885
DOI	10.1021/acs.iecr.9b04457
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: TMoE-P

Pan, Licheng / Wang, Hao / Chen, Zhichao / Huang, Yuxing / Liu, Xinggao

Towards the Pareto Optimum for Multivariate Soft Sensors

2023

Abstract: Multi-variate soft sensor seeks accurate estimation of multiple quality variables using measurable process variables, which have emerged as a key factor in improving the quality of industrial manufacturing. The current progress stays in some direct ... ...

Abstract	Multi-variate soft sensor seeks accurate estimation of multiple quality variables using measurable process variables, which have emerged as a key factor in improving the quality of industrial manufacturing. The current progress stays in some direct applications of multitask network architectures; however, there are two fundamental issues remain yet to be investigated with these approaches: (1) negative transfer, where sharing representations despite the difference of discriminate representations for different objectives degrades performance; (2) seesaw phenomenon, where the optimizer focuses on one dominant yet simple objective at the expense of others. In this study, we reformulate the multi-variate soft sensor to a multi-objective problem, to address both issues and advance state-of-the-art performance. To handle the negative transfer issue, we first propose an Objective-aware Mixture-of-Experts (OMoE) module, utilizing objective-specific and objective-shared experts for parameter sharing while maintaining the distinction between objectives. To address the seesaw phenomenon, we then propose a Pareto Objective Routing (POR) module, adjusting the weights of learning objectives dynamically to achieve the Pareto optimum, with solid theoretical supports. We further present a Task-aware Mixture-of-Experts framework for achieving the Pareto optimum (TMoE-P) in multi-variate soft sensor, which consists of a stacked OMoE module and a POR module. We illustrate the efficacy of TMoE-P with an open soft sensor benchmark, where TMoE-P effectively alleviates the negative transfer and seesaw issues and outperforms the baseline models. Comment: 13 pages,14 figures
Keywords	Computer Science - Artificial Intelligence ; Statistics - Applications
Subject code	670
Publishing date	2023-02-21
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Modeling Task Relationships in Multi-variate Soft Sensor with Balanced Mixture-of-Experts

Huang, Yuxin / Wang, Hao / Liu, Zhaoran / Pan, Licheng / Li, Haozhe / Liu, Xinggao

2023

Abstract: Accurate estimation of multiple quality variables is critical for building industrial soft sensor models, which have long been confronted with data efficiency and negative transfer issues. Methods sharing backbone parameters among tasks address the data ... ...

Abstract	Accurate estimation of multiple quality variables is critical for building industrial soft sensor models, which have long been confronted with data efficiency and negative transfer issues. Methods sharing backbone parameters among tasks address the data efficiency issue; however, they still fail to mitigate the negative transfer problem. To address this issue, a balanced Mixture-of-Experts (BMoE) is proposed in this work, which consists of a multi-gate mixture of experts (MMoE) module and a task gradient balancing (TGB) module. The MoE module aims to portray task relationships, while the TGB module balances the gradients among tasks dynamically. Both of them cooperate to mitigate the negative transfer problem. Experiments on the typical sulfur recovery unit demonstrate that BMoE models task relationship and balances the training process effectively, and achieves better performance than baseline models significantly.
Keywords	Computer Science - Machine Learning ; Computer Science - Computational Engineering ; Finance ; and Science ; Statistics - Applications
Publishing date	2023-05-25
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Fast control parameterization optimal control with improved Polak-Ribière-Polyak conjugate gradient implementation for industrial dynamic processes.

Liu, Ping / Hu, Qingquan / Li, Lei / Liu, Mingjie / Chen, Xiaolei / Piao, Changhao / Liu, Xinggao

ISA transactions

2021 Volume 123, Page(s) 188–199

Abstract: This paper proposes a fast control parameterization optimal control algorithm for industrial dynamic process with constraints. Derived from the frame of control variable parameterization (CVP) technique, the proposed method combines an efficient gradient ...

Abstract	This paper proposes a fast control parameterization optimal control algorithm for industrial dynamic process with constraints. Derived from the frame of control variable parameterization (CVP) technique, the proposed method combines an efficient gradient computation strategy with an improved nonlinear optimization computation approach to overcome the challenge of computation efficiency caused by gradients and bounds in optimal control problems. Firstly, a fast gradient computation method based on the costate system of Hamiltonian function is developed to decrease the computational expense of gradients by employing approximate treatments and numerical integration strategy. Then, a trigonometric function transformation scheme is presented to tackle the boundary constraints so that the original optimal control problem is further converted into an unconstrained one. On this basis, an improved restricted Polak-Ribière-Polyak (PRP) conjugate gradient approach is introduced to solve the nonlinear optimization problem by using conjugate gradient iterations and strong Wolfe line search. Meanwhile, to enhance the convergence, a restricting condition is imposed in strong Wolfe line search to create iteration step-length. Finally, the proposed algorithm is implemented on three dynamic processes. The detailed comparison among the classical CVP method, literature results and the proposed method are carried out. Simulation studies show that the proposed fast approach averagely saves more than 90% computation time in contrast to the classical CVP method, demonstrating the effectiveness of the proposed fast optimal control approach.
Language	English
Publishing date	2021-05-14
Publishing country	United States
Document type	Journal Article
ZDB-ID	2012746-7
ISSN	1879-2022 ; 0019-0578
ISSN (online)	1879-2022
ISSN	0019-0578
DOI	10.1016/j.isatra.2021.05.020
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Article ; Online: Causal augmented ConvNet: A temporal memory dilated convolution model for long-sequence time series prediction.

Ayodeji, Abiodun / Wang, Zhiyu / Wang, Wenhai / Qin, Weizhong / Yang, Chunhua / Xu, Shenghu / Liu, Xinggao

ISA transactions

2021 Volume 123, Page(s) 200–217

Abstract: A number of deep learning models have been proposed to capture the inherent information in multivariate time series signals. However, most of the existing models are suboptimal, especially for long-sequence time series prediction tasks. This work ... ...

Abstract	A number of deep learning models have been proposed to capture the inherent information in multivariate time series signals. However, most of the existing models are suboptimal, especially for long-sequence time series prediction tasks. This work presents a causal augmented convolution network (CaConvNet) and its application for long-sequence time series prediction. First, the model utilizes dilated convolution with enlarged receptive fields to enhance global feature extraction in time series. Secondly, to effectively capture the long-term dependency and to further extract multiscale features that represent different operating conditions, the model is augmented with a long-short term memory network. Thirdly, the CaConvNet is further optimized with a dynamic hyperparameter search algorithm to reduce uncertainties and the cost of manual hyperparameter selection. Finally, the model is extensively evaluated on a predictive maintenance task using the turbofan aircraft engine run-to-failure prognostic benchmark dataset (C-MAPSS). The performance of the proposed CaConvNet is also compared with four conventional deep learning models and seven different state-of-the-art predictive models. The evaluation metrics show that the proposed CaConvNet outperforms other models in most of the prognostic tasks. Moreover, a comprehensive ablation study is performed to provide insights into the contribution of each sub-structure of the CaConvNet model to the observed performance. The results of the ablation study as well as the performance improvement of CaConvNet are discussed in this paper.
Language	English
Publishing date	2021-05-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	2012746-7
ISSN	1879-2022 ; 0019-0578
ISSN (online)	1879-2022
ISSN	0019-0578
DOI	10.1016/j.isatra.2021.05.026
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Nonlinear dynamic behaviors and control based on simulation of high-purity heat integrated air separation column.

Fu, Yao / Liu, Xinggao

ISA transactions

2015 Volume 55, Page(s) 145–153

Abstract: In this paper, the dynamic behaviors on the basis of simulation for high-purity heat integrated air separation column (HIASC) are studied. A nonlinear generic model control (GMC) scheme is proposed based on the nonlinear behavior analyses of a HIASC ... ...

Abstract	In this paper, the dynamic behaviors on the basis of simulation for high-purity heat integrated air separation column (HIASC) are studied. A nonlinear generic model control (GMC) scheme is proposed based on the nonlinear behavior analyses of a HIASC process, and an adaptive generic model control (AGMC) scheme is further presented to correct the model parameters online. Related internal model control (IMC) scheme and multi-loop PID (M-PID) scheme are also developed as the comparative base. The comparative researches are carried out among these linear and nonlinear control schemes in detail. The simulation research results show that the proposed AGMC schemes present advantages in both servo control and regulatory control for the high-purity HIASC.
Language	English
Publishing date	2015-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2012746-7
ISSN	1879-2022 ; 0019-0578
ISSN (online)	1879-2022
ISSN	0019-0578
DOI	10.1016/j.isatra.2014.11.006
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Book ; Online: An empirical evaluation of attention-based multi-head models for improved turbofan engine remaining useful life prediction

Ayodeji, Abiodun / Wang, Wenhai / Su, Jianzhong / Yuan, Jianquan / Liu, Xinggao

2021

Abstract: A single unit (head) is the conventional input feature extractor in deep learning architectures trained on multivariate time series signals. The importance of the fixed-dimensional vector representation generated by the single-head network has been ... ...

Abstract	A single unit (head) is the conventional input feature extractor in deep learning architectures trained on multivariate time series signals. The importance of the fixed-dimensional vector representation generated by the single-head network has been demonstrated for industrial machinery condition monitoring and predictive maintenance. However, processing heterogeneous sensor signals with a single-head may result in a model that cannot explicitly account for the diversity in time-varying multivariate inputs. This work extends the conventional single-head deep learning models to a more robust form by developing context-specific heads to independently capture the inherent pattern in each sensor reading. Using the turbofan aircraft engine benchmark dataset (CMAPSS), an extensive experiment is performed to verify the effectiveness and benefits of multi-head multilayer perceptron, recurrent networks, convolution network, the transformer-style stand-alone attention network, and their variants for remaining useful life estimation. Moreover, the effect of different attention mechanisms on the multi-head models is also evaluated. In addition, each architecture's relative advantage and computational overhead are analyzed. Results show that utilizing the attention layer is task-sensitive and model dependent, as it does not provide consistent improvement across the models investigated. The best model is further compared with five state-of-the-art models, and the comparison shows that a relatively simple multi-head architecture performs better than the state-of-the-art models. The results presented in this study demonstrate the importance of multi-head models and attention mechanisms to an improved understanding of the remaining useful life of industrial assets. Comment: 32 pages, 13 figures, 8 tables, typos fixed
Keywords	Computer Science - Machine Learning ; J.2 ; D.2.11 ; E.1
Publishing date	2021-09-03
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Analyze and Design Network Architectures by Recursion Formulas

Liao, Yilin / Wang, Hao / Liu, Zhaoran / Li, Haozhe / Liu, Xinggao

2021

Abstract: The effectiveness of shortcut/skip-connection has been widely verified, which inspires massive explorations on neural architecture design. This work attempts to find an effective way to design new network architectures. It is discovered that the main ... ...

Abstract	The effectiveness of shortcut/skip-connection has been widely verified, which inspires massive explorations on neural architecture design. This work attempts to find an effective way to design new network architectures. It is discovered that the main difference between network architectures can be reflected in their recursion formulas. Based on this, a methodology is proposed to design novel network architectures from the perspective of mathematical formulas. Afterwards, a case study is provided to generate an improved architecture based on ResNet. Furthermore, the new architecture is compared with ResNet and then tested on ResNet-based networks. Massive experiments are conducted on CIFAR and ImageNet, which witnesses the significant performance improvements provided by the architecture. Comment: It is hoped that the new network architecture is derived according to a specific purpose
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Subject code	720
Publishing date	2021-08-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Entire Space Counterfactual Learning

Wang, Hao / Chen, Zhichao / Fan, Jiajun / Huang, Yuxin / Liu, Weiming / Liu, Xinggao

Tuning, Analytical Properties and Industrial Applications

2022

Abstract: As a basic research problem for building effective recommender systems, post-click conversion rate (CVR) estimation has long been plagued by sample selection bias and data sparsity issues. To address the data sparsity issue, prevalent methods based on ... ...

Abstract	As a basic research problem for building effective recommender systems, post-click conversion rate (CVR) estimation has long been plagued by sample selection bias and data sparsity issues. To address the data sparsity issue, prevalent methods based on entire space multi-task model leverage the sequential pattern of user actions, i.e. exposure $\rightarrow$ click $\rightarrow$ conversion to construct auxiliary learning tasks. However, they still fall short of guaranteeing the unbiasedness of CVR estimates. This paper theoretically demonstrates two defects of these entire space multi-task models: (1) inherent estimation bias (IEB) for CVR estimation, where the CVR estimate is inherently higher than the ground truth; (2) potential independence priority (PIP) for CTCVR estimation, where the causality from click to conversion might be overlooked. This paper further proposes a principled method named entire space counterfactual multi-task model (ESCM$^2$), which employs a counterfactual risk minimizer to handle both IEB and PIP issues at once. To demonstrate the effectiveness of the proposed method, this paper explores its parameter tuning in practice, derives its analytic properties, and showcases its effectiveness in industrial CVR estimation, where ESCM$^2$ can effectively alleviate the intrinsic IEB and PIP issues and outperform baseline models. Comment: This submission is an extension of arXiv:2204.05125
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Statistics - Machine Learning
Subject code	006
Publishing date	2022-10-20
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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