On-line Identification Research Articles

Traffic Signal Control With Adaptive Online-Learning Scheme Using Multiple-Model Neural Networks.

This article proposes a new traffic signal control algorithm to deal with unknown-traffic-system uncertainties and reduce delays in vehicle travel time. Unknown-traffic-system dynamics are approximated using a recurrent neural network (NN). To accurately identify the traffic system model, an online-learning scheme is developed to switch among a set of candidate NNs (i.e., multiple-model NNs) based on their estimation errors. Then, a bank of optimal signal-timing controllers is designed based on the online identification of the traffic system. Simulation studies have been carried out for the obtained control strategies using multiple-model NNs, and the desired results have been obtained. Moreover, compared with the widely used actuated traffic signal control schemes, it is shown that the proposed method can reduce vehicle travel delays and improve traffic system robustness.

Parametric Modeling and Deep Learning for Enhancing Pain Assessment in Postanesthesia.

The problem of reliable and widely accepted measures of pain is still open. It follows the objective of this work as pain estimation through post-surgical trauma modeling and classification, to increase the needed reliability compared to measurements only. This article proposes (i) a recursive identification method to obtain the frequency response and parameterization using fractional-order impedance models (FOIM), and (ii) deep learning with convolutional neural networks (CNN) classification algorithms using time-frequency data and spectrograms. The skin impedance measurements were conducted on 12 patients throughout the postanesthesia care in a proof-of-concept clinical trial. Recursive least-squares system identification was performed using a genetic algorithm for initializing the parametric model. The online parameter estimates were compared to the self-reported level by the Numeric Rating Scale (NRS) for analysis and validation of the results. Alternatively, the inputs to CNNs were the spectrograms extracted from the time-frequency dataset, being pre-labeled in four intensities classes of pain during offline and online training with the NRS. The tendency of nociception could be predicted by monitoring the changes in the FOIM parameters' values or by retraining online the network. Moreover, the tissue heterogeneity, assumed during nociception, could follow the NRS trends. The online predictions of retrained CNN have more specific trends to NRS than pain predicted by the offline population-trained CNN. We propose tailored online identification and deep learning for artefact corrupted environment. The results indicate estimations with the potential to avoid over-dosing due to the objectivity of the information. Models and artificial intelligence (AI) allow objective and personalized nociception-antinociception prediction in the patient safety era for the design and evaluation of closed-loop analgesia controllers.

DBN-based online identification of porosity regions during laser welding of aluminum alloys using coherent optical diagnosis

As a typical serious defect during laser welding of aluminum alloys, porosity can significantly impair the performance of the weld seam. Real-time monitoring of porosity defect can provide guidance for feedback adjustment of welding quality to prevent the subsequent continued formation of porosity, which has attracted increasing attention. This paper implements the deep learning-based online identification of porosity regions using coherent optical diagnosis. Considering the formation mechanism of porosity, a monitoring platform including a coherent optical sensor is established to measure the keyhole 3D morphology and extract the keyhole depth as the coherent optical signal. The acquired keyhole depth signal is processed by ensemble empirical mode decomposition, and it is discovered that there is a segmentation threshold in the reconstructed signal to separate the porosity regions and the non-porosity regions. A deep belief network is constructed to complete the reception of the reconstructed keyhole depth signal and the response of the prediction state, realizing the online identification of the porosity regions. The results show that the identification model has high accuracy, and the validity verification demonstrates the reliability of the proposed method.

Model-free adaptive control of average coolant temperature in a novel marine small lead–bismuth-cooled reactor

Marine lead–bismuth-cooled nuclear reactors are sensitive to the marine environment, facing motion-induced changes like heeling and rolling. These changes induce heavy oscillatory processes, and general controllers cannot totally meet the power output requirements from reactors. To address these challenges, this paper proposes a model-free adaptive control (MFAC) method. Initially, an initial model is established based on mechanism analysis, incorporating neutron kinetics, fuel thermal dynamics, and core thermal dynamics of the reactor while considering a dynamic marine environment. Subsequently, a model-free adaptive controller with online identification and adaptive capability is designed, employing a projection algorithm to continuously identify initial model parameters to match the harsh marine condition and correct the control law based on the input and output data. Finally, through simulation experiments, the effectiveness of the MFAC controller is demonstrated, showcasing superior adaptive capabilities and robustness, enabling rapid and precise tracking of the desired setpoint for average coolant temperature, even under fluctuating marine conditions.

Market mavenism, tourists’ co-creation experience, loyalty, vaxication intention: mediating role of travel incentives in the post-COVID-19 environment

PurposeThe purpose of this study is to examine the relationships between market mavenism, tourists’ cocreation experience, loyalty and the mediating role of travel incentives in the post-COVID-19 tourism environment in the context of vaxication intentions in Turkey.Design/methodology/approachThis study used covariance-based structural equation modeling to empirically test the proposed hypotheses. A total of 348 respondents participated in the survey.FindingsThe findings of this study support the assumption that market mavenism (MM) engage in cocreating and sharing travel experiences (CCTE). Furthermore, the study reveals that market mavens tend to demonstrate loyalty (LTY) toward service providers when engaging in cocreation with travel industry professionals. In addition, the study establishes the significant mediating effect of travel incentives (TI) between MM and vaxication intention (VI).Research limitations/implicationsThe study uses the theory of planned behavior to examine tourists’ intentions for vaxication and the psychological factors influencing their decisions, while also using the macro–micro theory to explore industry-level factors like market mavens and customer engagement, leading to a comprehensive understanding of COVID-19 vaccination and tourism behavior. Further research is needed to address limitations such as country diversity, multiple locations and service providers, online behavior analysis, authenticity perception and identification of market mavens’ personality traits and travel preferences.Originality/valueThis study contributes to the scholarly literature in several ways. First, it explores the influence of market mavens on tourism experience cocreation and customer loyalty during the postpandemic era. Second, it empirically examines the mediating role of travel incentives, adding to the understanding of market mavens and vaccination intentions. Lastly, the study addresses the implications during and after the COVID-19 for managers and service providers in the tourism industry.

A totally coupled multi time-scale framework containing full parameters online identification and SOC real-time estimation of lithium-ion battery based on a fractional order model

Fractional order model (FOM) is one of the most promising choices for lithium-ion battery simulation and internal states estimation due to its high modeling accuracy. However, online identifying the order and parameters of FOM is extremely tough, which also makes it difficult to be applied in complex and variable operating conditions. To address this issue, this paper proposes a totally coupled multi time-scale framework that contains full parameters online identification of FOM and state-of-charge (SOC) real-time estimation of lithium-ion battery. Firstly, a novel global optimization algorithm named improved particle swarm optimization is designed to update the order of FOM on macro-time scale with low computational burden. On the basis of the determined order, the rest parameters of FOM are further identified by forgetting factor recursive least square algorithm on micro-time scale. With those online identified parameters, adaptive extended Kalman filter is finally adopted to track battery SOC in every instant. The estimation accuracy, adaptability to different operating temperatures and battery aging, and robustness ability against uncertain initialization of the developed method is verified under Federal Urban Driving Schedule tests. The corresponding results demonstrate that the proposed method can realize accurate full parameters online identification of FOM and precise SOC real-time estimation against different temperatures, battery aging states and initialization. Using the developed method, the mean absolute error (MAE) and root mean square error (RMSE) of SOC estimation of the fresh battery under temperature ranging from 0 °C to 50 °C can be limited below 0.9 % and 1.1 %, respectively. Even if the SOC-OCV relationship is not timely updated, those two indexes of the aged battery achieved by the proposed method can still be controlled within 3 % under temperature ranging from 0 °C to 50 °C. Moreover, the comparison with other typical FOM-based estimation methods is also conducted, whose results indicate that the developed method has apparently superior comprehensive performance on estimation accuracy and adaptation.

Learning to see, seeing to learn: Impacts of an online tool on volunteers' observational practices during aquatic macroinvertebrate identification

AbstractScientific observation is a disciplinary‐informed way of looking at the world that requires the coordination of domain knowledge and perceptual skills with specialized tools and techniques to systematically identify objects, organisms, specimens, or phenomena of interest. Identification is a particular form of skilled observational practice where the observer recognizes key features for classification. Learning to see like a scientist is crucial for many citizen science activities, yet participants often have limited access to training and resources needed to practice. We posit that observational fluency development in taxonomic identification activities for citizen science has two components: (1) increased accuracy in noticing relevant features, and (2) use of more disciplinary‐based explanatory language; and self‐efficacy is crucial while developing these competencies. This study explores the extent to which an interactive visual identification platform supports learners' observational fluency development in the context of aquatic macroinvertebrate identification in environmental education. We examined changes in water quality monitoring volunteers' accuracy, self‐efficacy, and explanatory language use after participation in a training that integrated the online identification (ID) tool. Methods included surveys, focus groups, and an ID accuracy task before and after training. Our findings indicate that learners, especially novices, demonstrated increased self‐efficacy and accuracy in specimen identifications after participating in a training utilizing the online tool. They also shifted their ID justifications, incorporating more scientific terminology and salient diagnostic characters, and fewer generic descriptive adjectives. Results suggest that trainings using dynamic visual tools can positively impact observational fluency development and the quality of data contributed by volunteers.

Online identification of non-homogeneous fractional order Hammerstein continuous systems based on the principle of multi-innovation

Online identification of non-homogeneous fractional order Hammerstein continuous systems based on the principle of multi-innovation

Stiffness Compensation Control for Centrifugal Compressors Based on Online Parameter Identification of Magnetic Bearings

Significant temperature rise will degrade the stability of the active magnetic bearing (AMB) rotor system, which is an obstacle to the industrial implementation of magnetically suspended centrifugal compressors (MSCCs) with high power density. Aiming to ensure stable operation of the suspended rotor at high temperatures, this paper presents a stiffness compensation method based on online parameter identification. First, the linearized model at the operating point is derived through the establishment of a magnetic circuit model. The analysis of the temperature factor of bearing stiffness parameters is conducted. Then, the forgetting factor recursive least squares online identification algorithm is employed to obtain the coil resistance affected by bearing temperature in real time. Finally, a stiffness compensation control scheme is designed based on the identification results and current rotor displacement, in combination with series and feedback compensation to apply the same electromagnetic force to the rotor during temperature rise. Simulation and experimental results validate that the proposed method can effectively suppress the low-frequency fluctuation and guarantee the stability of the AMB rotor system.

Predictive temperature control design for reaction calorimeter based on mechanism parameter model.

Isothermal control is the most basic and crucial function in the principle of a reaction calorimeter system and affects the speed and validity of the calorimetric experiment. However, the complex and uncertain working conditions in different reaction processes pose a challenge to the adaptability of temperature control algorithms. Aiming at the problem, a heat transfer model of the system is first established for temperature control design. From the simulation results, a prediction model based on equivalent mechanism parameters is determined for the control. Then, an integrated model predictive control (MPC) strategy is presented. To reduce the influence on the temperature control caused by the mismatch of the prediction model, a set of online parameter identification and adjustment methods is proposed. Simulations of the MPC control were implemented to analyze the control's performance. Experiments were also carried out to verify the advantages of the proposed strategy over the proportional-integral-derivative algorithm and demonstrate the role and efficiency of online identification. This control strategy can be applied to other laboratory-scale instruments with tank reactors.

An adsorption isotherm identification method based on CNN-LSTM neural network.

The morphology of adsorption isotherms embodies a wealth of information regarding various adsorption mechanisms, rendering the classification and identification methodologies predicated on the shape of adsorption isotherms indispensably crucial. While research on classification techniques has been extensively developed, traditional methods of adsorption isotherm identification grapple with inefficiencies and a high margin of error. Neural network-based methodologies for adsorption isotherm identification serve as a countermeasure to these shortcomings, as they facilitate swift online identification while delivering precise results. In this paper, we deploy a hybrid of convolutional neural networks (CNN) and long short-term memory (LSTM) networks for the identification of adsorption isotherms. Extensive theoretical adsorption isotherms are generated via adsorption equations, forming a comprehensive training database, thereby circumventing the need for time-consuming and costly repetitive experiments. The F1-score, receiver operating characteristic (ROC) curves, and area under the ROC curve (AUC) are introduced as criteria to evaluate the identification performance and generalization ability of the model during the testing phase. The results highlight the model's superlative performance in the task of adsorption isotherm identification, with accuracy rates of 100% in both the training and validation sets. The mean F1-score obtained from the testing set reached 0.8885, with both macro-average and micro-average AUC exceeding 0.95. PyCharm was employed as an experimental and testing platform, with Python 3.9 serving as the programming language. TensorFlow 2.11.0 and Keras 2.10.0 were harnessed for the training and testing of CNN-LSTM, while numpy 1.21.5 and scipy 1.81 were utilized for the creation of training and validation datasets.

Online Coal Identification Based on One-Dimensional Convolution and Its Industrial Applications

In order to improve the utilization rate of coal generation and reduce carbon emissions from coal-fired boilers, the operation parameters of power plant boilers should be matched with the actual burning coal. Due to the complex and high-risk blending process of multiple coal types, the actual application of coal types inconsistent with expectations may lead to low combustion efficiency of boilers, cause disturbances to the normal operation of thermal power units, increased energy waste and carbon emissions, and even lead to serious explosion accidents. Therefore, the online identification of coal types for thermal power units is of great significance. To obtain the precise type of coal online, in the present work, a data-driven coal identification method is proposed based on convolutional networks that do not necessitate additional hardware detection equipment and are easy to implement. Experimental results indicate that the proposed method exhibits superior performance in comparison to traditional methods, thus ultimately improving the performance of thermal power plant.

Integrated separation-electrochemical detection device based on wood column for online identification of enantiomer

Integrated separation-electrochemical detection device based on wood column for online identification of enantiomer

Adaptive predictive control of a fan-ventilated tunnel greenhouse with evaporative cooling

In the present work, micro-climate control of a fan-ventilated tunnel greenhouse with evaporative cooling is investigated via simulation study. For this purpose, the adaptive generalized predictive control (AGPC) strategy is adopted to control temperature, relative humidity, and CO2 concentration in the greenhouse on extremely hot summer days. A dynamic model which integrates a distributed model of greenhouse micro-climate with a crop growth model is used for numerical simulation of the tunnel greenhouse. The controller performance is analyzed via numerical simulation. The relative gain array analysis confirms that greenhouse micro-climatic parameters are highly interactive. However, simulation results showed that AGPC can efficiently handle variables interaction and follow set-points despite actuator limitations and high outside temperature. Besides, by considering output constraints in applying AGPC, the control system can keep the humidity of the greenhouse in a specific range instead of in a particular set point. The calculation burden for the AGPC strategy is kept low without increasing prediction error by employing a linear model and on-line adaptive identification. The applied strategy can handle the uncertainty of the design model due to the model mismatch by on-line identification of model parameters using the recursive least square method.

Distributed online multi‐task sparse identification for multiple systems with asynchronous updates

AbstractSimultaneous online identification of the structure and parameters for multiple systems can use the multi‐task learning to share parameter sparse patterns across different identification tasks to improve the estimation accuracy. To reduce the communication and computational burden, improve the convergence rate and increase the reliability of the identification task, a distributed asynchronous multi‐task sparse identification algorithm is proposed. The alternating minimization algorithm is applied to design a distributed multi‐task loss function. Then by constructing the auxiliary matrix and transforming the sparse constraint on the parameter matrix to the constructed auxiliary matrix, the asynchronous parameter update and sparse model update can be realized. The algorithm uses a recursive form to complete online identification which is feasible to analyze. After proving the convergence of the identification algorithm, we present an example to support the theoretical analysis and show the advantages of the proposed algorithm.

The influence of corporate social responsibility information transparency on the consumption of green agricultural products on digital platforms

ABSTRACT Companies’ information transparency on online platforms is important for online customers. However, determining what key social responsibility information to make available is challenging for firms. In terms of green product transactions online, whether corporate social responsibility (CSR) transparency affects online green purchase willingness remains unclear. Therefore, this study investigates the influence of CSR information transparency on the online consumption of green agricultural products. Data were collected from a sample of 562 Chinese online consumers and the structural equation modelling method was used. The results show that both dimensions of CSR transparency had a positive effect on consumers’ online identification and online green purchase willingness. In addition, consumers’ online identification positively affected their online green purchase willingness. These findings can guide agricultural enterprises in determining what social responsibility information to reveal and be of practical importance in encouraging enterprises to pursue social sustainability on digital platforms.

A Robust DPCC for IPMSM Based on a Full Parameter Identification Method

This paper proposes a robust deadbeat predictive current control (DPCC) method for the interior permanent magnet synchronous motor (IPMSM) based on a full parameter identification method. The DPCC method is innovatively combined with the high frequency (HF) currents injection method, which can observe inductances, flux linkage, and resistance values of the IPMSM online. After the parameter identification results are obtained, the initial crude parameters are replaced by the identification values in the DPCC method to enhance the robustness of the IPMSM control system. Moreover, a novel online accuracy verification method for parameter identification results is proposed to judge the error estimation level. The proposed method extracts the high bandwidth and the high parameter sensitivity characteristic of the DPCC method. The online identification and verification of IPMSM parameters are proceeded subsequently while achieving robust control of the IPMSM with the DPCC method. Finally, the proposed method is experimentally demonstrated with a 1.5kW motor. The experimental results show that the IPMSM with the proposed method can still work well even in the situation of severe initial parameters mismatch.

On-line evolutionary identification technology for milling chatter of thin walled parts based on the incremental-sparse K-means and the online sequential extreme learning machine

On-line evolutionary identification technology for milling chatter of thin walled parts based on the incremental-sparse K-means and the online sequential extreme learning machine

Laplace Distribution Based Online Identification of Linear Systems With Robust Recursive Expectation–Maximization Algorithm

The robust online identification problem of linear systems is considered in this paper using a faster robust recursive expectation-maximization (RREM) framework. To improve the convergence rate, the outliers, which would deteriorate the identified models, are accommodated with a Laplace distribution instead of Student's t-distribution. Then, the recursive transformation of the maximum likelihood function is realized with a recursive Q-function. The extensively recognized autoregressive exogenous (ARX) models are used for the description of general linear systems. As a result, the unknown parameters, including the regression coefficient vector of the ARX models, the variance of the noise without outliers, and the scale parameter of the Laplace distribution, are determined in a recursive manner. The performance of the proposed approach is tested with a simulated continuous fermentation reactor (CFR) system example and a coupled-tank experiment.

Online parameter identification of permanent magnet synchronous motor based on recursive least squares algorithm

In order to solve the problem of variable parameters in the system when the PMSM works, this project intends to use the recursive algorithm in the steady state of alpha-beta. This project identifies the two parameters as the fixed values of two parameters at the same time, so as to reduce the number of parameters to be estimated and avoid the problem of under-classification. The proposed method is studied experimentally on the 2 kW PMSM experimental platform. Simulation results show that this method can quickly and accurately realize the online identification of parameters in the PMSM system.