A Review on Soft Sensors for Monitoring, Control, and Optimization of Industrial Processes

Jiang, Yuchen; Yin, Shen; Dong, Jingwei; Kaynak, Okyay

doi:10.1109/jsen.2020.3033153

Cited by 384 publications

(139 citation statements)

References 103 publications

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“…Concretely, factors contributing to the rise of event-based vision are presented in the first place, followed by the superiority of data-driven approaches over other kinds of event-based algorithms. Firstly, as for event-based vision, new areas in commercial markets and industrial fields [27,28], such as robotics and consumer electronics, give rise to an urgent demand for efficiency and robustness [29] in visual systems, which event cameras can just offer. Despite the lack of experience in event-based vision, mature techniques in frame-based vision and biological visual systems provide valuable guidance in the emerging event-based vision and promote its rapid progress.…”

Section: Reasons For the Prevalence Of Data-driven Technologymentioning

confidence: 99%

Data‐Driven Technology in Event‐Based Vision

Sun

Shi²,

Zhang³

et al. 2021

Complexity

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Event cameras which transmit per-pixel intensity changes have emerged as a promising candidate in applications such as consumer electronics, industrial automation, and autonomous vehicles, owing to their efficiency and robustness. To maintain these inherent advantages, the trade-off between efficiency and accuracy stands as a priority in event-based algorithms. Thanks to the preponderance of deep learning techniques and the compatibility between bio-inspired spiking neural networks and event-based sensors, data-driven approaches have become a hot spot, which along with the dedicated hardware and datasets constitute an emerging field named event-based data-driven technology. Focusing on data-driven technology in event-based vision, this paper first explicates the operating principle, advantages, and intrinsic nature of event cameras, as well as background knowledge in event-based vision, presenting an overview of this research field. Then, we explain why event-based data-driven technology becomes a research focus, including reasons for the rise of event-based vision and the superiority of data-driven approaches over other event-based algorithms. Current status and future trends of event-based data-driven technology are presented successively in terms of hardware, datasets, and algorithms, providing guidance for future research. Generally, this paper reveals the great prospects of event-based data-driven technology and presents a comprehensive overview of this field, aiming at a more efficient and bio-inspired visual system to extract visual features from the external environment.

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Section: Reasons For the Prevalence Of Data-driven Technologymentioning

confidence: 99%

Data‐Driven Technology in Event‐Based Vision

Sun

Shi²,

Zhang³

et al. 2021

Complexity

View full text Add to dashboard Cite

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“…Performance of the proposed approach on lake water level prediction for the training data set observation science which provides a huge amount of information using satellite. A huge amount of data gathered from the environment require appropriate analysis to be useful for further analysis and decision making [61]. Machine learning approaches [62] as well as time series analysis [21] may be used to analyze collected data.…”

Section: Application To Prediction Interval Identification Associated With Urmia Lake Water Level Using Satellite Remote Sensingmentioning

confidence: 99%

Prediction Interval Identification Using Interval Type-2 Fuzzy Logic Systems: Lake Water Level Prediction Using Remote Sensing Data

Khanesar

Branson

2021

IEEE Sensors J.

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This paper presents a novel approach to identify the prediction interval associated with data using interval type-2 fuzzy logic systems with support vector regression. For such a purpose, a constrained quadratic objective function is defined which is then solved using well-established quadratic programming approaches. Not only does the output of interval type-2 fuzzy logic system replicates the measured value, but also it provides the lower bound and the upper bound for measured data values. In the proposed approach, to have more valuable information, a penalty term is added in the cost functions to have full control over the width of prediction interval. This method has been successfully applied to two benchmark identification problems. It is observed that by using the control parameter in the cost function, it is possible to obtain a narrower, yet inclusive prediction interval. Furthermore, superior prediction accuracy is obtained compared to existing methods in literature. Motivated by these results, the proposed approach is used to predict time series collected using a satellite from Urmia lake water level which resulted in high accuracy and an inclusive prediction interval. The graphical abstract presented for the paper illustrates the overall data gathering as well as data analysis made to estimate the prediction interval associated with Urmia lake water level data.

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“…Consider an -dimensional, nonlinear and continuoustime single-input single-output (SISO) nonlinear system in companion form, (1)…”

Section: Sliding-mode Observersmentioning

confidence: 99%

“…Robotic manipulators in industry, railway vehicles, medical devices and many other equipment require sensory devices [1]. However, measurement device cannot be available or expensive for an application.…”

Section: Introductionmentioning

confidence: 99%

State and Parameter Estimation of a Nonlinear Servo System Handling Noises and Varying Payloads

Çetin

Beyhan

2021

International Journal of Applied Mathematics Electronics and Computers

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Payload estimators have many parameters, which are trained using the recorded position, velocity and known payload information. To use these payload estimators in the real-time applications, accurate position and velocity information of the system are required. In this paper, first recently proposed sliding-mode observers (SMOs) are designed and compared for velocity estimation of a nonlinear servo system. Second, a parameter estimation based on sliding-mode super-twisting approach is designed to estimate unknown and varying parameter for a class of nonlinear systems. The convergence property of observers is considered using Lyapunov stability method. In the applications, the constant and varying payloads of the servo system have been estimated using the designed method and compared with Extended-Kalman Filter (EKF). In the final section, artificial noises with different SNR are applied to the measurement signal. When the less amplitude of noise signal is applied, second order SMO estimated the states and the payload better than EKF. However, EKF provides much better estimation results than second order SMO for large amplitude of noise signals. For the sake of generalization, second order SMO is a fast and robust observer for small noise cases. In addition, the filtering property of EKF has still importance for large noise cases.

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A Review on Soft Sensors for Monitoring, Control, and Optimization of Industrial Processes

Cited by 384 publications

References 103 publications

Data‐Driven Technology in Event‐Based Vision

Data‐Driven Technology in Event‐Based Vision

Prediction Interval Identification Using Interval Type-2 Fuzzy Logic Systems: Lake Water Level Prediction Using Remote Sensing Data

State and Parameter Estimation of a Nonlinear Servo System Handling Noises and Varying Payloads

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