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

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.

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

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.

Causal augmented ConvNet: A temporal memory dilated convolution model for long-sequence time series prediction.

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.

A robust cutting pattern recognition method for shearer based on Least Square Support Vector Machine equipped with Chaos Modified Particle Swarm Optimization and Online Correcting Strategy.

Accurate cutting pattern recognition method for shearer in coal mining process has drawn more and more attention over the past decades due to its important role in guaranteeing the steady operation of the equipment, which, however, remains challenging caused by the mismatch of cutting pattern recognition especially for dynamic uncertainty of future sampled data. Therefore, a novel approach for cutting pattern recognition with an optimal Online Correcting Strategy (OCS) combined with Least Square Support Vector Machine (LSSVM) and Chaos Modified Particle Swarm Optimization (CMPSO) algorithm, named OCS-CMPSO-LSSVM, is proposed, where LSSVM models the functional relationship between input and output of the system, CMPSO optimizes the parameters of LSSVM, and OCS modifies the model to reduce its mismatch as the system runs, respectively. The performance of the proposed model is demonstrated with a simulation experiment and compared with the existing methods reported in the literature in detail. The experimental results reveal that the proposed models can achieve better cutting pattern recognition performance and higher robustness.

A novel non-uniform control vector parameterization approach with time grid refinement for flight level tracking optimal control problems.

High quality control method is essential for the implementation of aircraft autopilot system. An optimal control problem model considering the safe aerodynamic envelop is therefore established to improve the control quality of aircraft flight level tracking. A novel non-uniform control vector parameterization (CVP) method with time grid refinement is then proposed for solving the optimal control problem. By introducing the Hilbert-Huang transform (HHT) analysis, an efficient time grid refinement approach is presented and an adaptive time grid is automatically obtained. With this refinement, the proposed method needs fewer optimization parameters to achieve better control quality when compared with uniform refinement CVP method, whereas the computational cost is lower. Two well-known flight level altitude tracking problems and one minimum time cost problem are tested as illustrations and the uniform refinement control vector parameterization method is adopted as the comparative base. Numerical results show that the proposed method achieves better performances in terms of optimization accuracy and computation cost; meanwhile, the control quality is efficiently improved.

Production optimization for concentration and volume-limited fed-batch reactors in biochemical processes.

Since a very slight violation of constraint could cause process safety and product quality problems in biochemical processes, an adaptive approach of fed-batch reactor production optimization that can strictly satisfy constraints over the entire operating time is presented. In this approach, an improved smooth function is proposed such that the inequality constraints can be transformed into smooth constraints. Based on this, only an auxiliary state is needed to monitor violations in the augmented performance index. Combined with control variable parameterization (CVP), the dynamic optimization is executed and constraint violations are examined by calculating the sensitivities of states to ensure that the inequality constraints are satisfied everywhere inside the time interval. Three biochemical production optimization problems, including the manufacturing of ethanol, penicillin and protein, are tested as illustrations. Meanwhile, comparisons with pure penalty CVP method, famous dynamic optimization toolbox DOTcvp and literature results are carried out. Research results show that the proposed method achieves better performances in terms of optimization accuracy and computation cost.

Sensitivity-based adaptive mesh refinement collocation method for dynamic optimization of chemical and biochemical processes.

Collocation on finite element (CFE) is an effective simultaneous method of dynamic optimization to increase the profitability or productivity of industrial process. The approach needs to select an optimal mesh of time interval to balance the computational cost with desired solution. A new CFE approach with non-uniform refinement procedure based on the sensitivity analysis for dynamic optimization problems is, therefore, proposed, where a subinterval is further refined if the obtained control parameters have significant effect on the performance index. To improve the efficiency, the sensitivities of state parameters with respect to control parameters are derived from the solution of the discretized dynamic system. The proposed method is illustrated by testing two classic dynamic optimization problems from chemical and biochemical engineering. The detailed comparisons among the proposed method, the CFE with uniform mesh, and other reported methods are also carried out. The research results reveal the effectiveness of the proposed approach.

A new sensitivity-based adaptive control vector parameterization approach for dynamic optimization of bioprocesses.

Dynamic optimization is a very effective way to increase the profitability or productivity of bioprocesses. As an important method of dynamic optimization, the control vector parameterization (CVP) approach needs to select an optimal discretization level to balance the computational cost with the desired solution quality. A new sensitivity-based adaptive refinement method is therefore proposed, by which new time grid points are only inserted where necessary and unnecessary points are eliminated so as to obtain economic and effective discretization grids. Moreover, considering that traditional refinement methods may cost a lot to get the high-quality solutions of some bioprocess problems, whose performance indices are sensitive to some significant time points, an optimization technique is further proposed and embedded into the new sensitivity-based CVP approach to efficiently solve these problems. The proposed methods are applied to two well-known bioprocess optimization problems and the results illustrate their effectiveness.

Fast engineering optimization: A novel highly effective control parameterization approach for industrial dynamic processes.

Control vector parameterization (CVP) is an important approach of the engineering optimization for the industrial dynamic processes. However, its major defect, the low optimization efficiency caused by calculating the relevant differential equations in the generated nonlinear programming (NLP) problem repeatedly, limits its wide application in the engineering optimization for the industrial dynamic processes. A novel highly effective control parameterization approach, fast-CVP, is first proposed to improve the optimization efficiency for industrial dynamic processes, where the costate gradient formulae is employed and a fast approximate scheme is presented to solve the differential equations in dynamic process simulation. Three well-known engineering optimization benchmark problems of the industrial dynamic processes are demonstrated as illustration. The research results show that the proposed fast approach achieves a fine performance that at least 90% of the computation time can be saved in contrast to the traditional CVP method, which reveals the effectiveness of the proposed fast engineering optimization approach for the industrial dynamic processes.

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

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.