U-Model-Based Active Disturbance Rejection Control for the Dissolved Oxygen in a Wastewater Treatment ProcessRead the full article
Mathematical Problems in Engineering is a broad-based journal publishes results of rigorous engineering research across all disciplines, carried out using mathematical tools.
Chief Editor, Professor Guangming Xie, is currently a full professor of dynamics and control with the College of Engineering, Peking University. His research interests include complex system dynamics and control and intelligent and biomimetic robots.
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Impedance with Finite-Time Control Scheme for Robot-Environment Interaction
For the robot system with the uncertain model and unknown environment parameters, a control scheme combining impedance and finite time is proposed. In order to obtain accurate force control performance indirectly by using position tracking, the control scheme is divided into two parts: an outer loop for force impedance control and an inner loop for position tracking control. In the outer loop, in order to eliminate the force tracking error quickly, the impedance control based on force is adopted; when the robot contacts with the environment, the satisfactory force tracking performance can be obtained. In the inner loop, the finite-time control method based on the homogeneous system is used. Through this method, the desired virtual trajectory generated by the outer loop can be tracked, and the contact force tracking performance can be obtained indirectly in the direction of force. This method does not need the dynamics model knowledge of the robot system, thus avoiding the online real-time calculation of the inverse dynamics of the robot. The unknown uncertainty and external interference of the system are obtained online by using the time-delay estimation, and the control process is effectively compensated, so the algorithm is simple, the convergence speed is fast, and the practical application is easy. The theory of finite-time stability is used to prove that the closed-loop system is finite-time stable, and the effectiveness of the algorithm is proved by simulations.
Option Pricing under Double Stochastic Volatility Model with Stochastic Interest Rates and Double Exponential Jumps with Stochastic Intensity
We present option pricing under the double stochastic volatility model with stochastic interest rates and double exponential jumps with stochastic intensity in this article. We make two contributions based on the existing literature. First, we add double stochastic volatility to the option pricing model combining stochastic interest rates and jumps with stochastic intensity, and we are the first to fill this gap. Second, the stochastic interest rate process is presented in the Hull–White model. Some authors have concentrated on hybrid models based on various asset classes in recent years. Therefore, we build a multifactor model with the term structure of stochastic interest rates. We also approximated the pricing formula for European call options by applying the COS method and fast Fourier transform (FFT). Numerical results display that FFT and the COS method are much faster than the numerical integration approach used for obtaining the semi-closed form prices. The COS method shows higher accuracy, efficiency, and stability than FFT. Therefore, we use the COS method to investigate the impact of the parameters in the stochastic jump intensity process and the existence of the process on the call option prices. We also use it to examine the impact of the parameters in the interest rate process on the call option prices.
Mining Negative Comment Data of Microblog Based on Merge-AP
A new depiction method based on the merge-AP algorithm is proposed to effectively improve the mining accuracy of negative comment data on microblog. In this method, we first employ the AP algorithm to analyze negative comment data on microblog and calculate the similarity value and the similarity matrix of data points by Euclidean distance. Then, we introduce the distance-based merge process to solve the problem of poor clustering effect of the AP algorithm for datasets with the complex clustering structure. Finally, we compare and analyze the performance of K-means, AP, and merge-AP algorithms by collecting the actual microblog data for algorithm evaluation. The results show that the merge-AP algorithm has good adaptability.
Three-Dimensional MHD Mixed Convection Flow of Casson Nanofluid with Hall and Ion Slip Effects
The intention of the present study is to scrutinize the three-dimensional MHD mixed convection flow of Casson nanofluid over an exponentially stretching sheet using the impacts of Hall and ion slip currents. Moreover, the impacts of thermal radiation and heat source are considered in this study. The governing partial differential equations are transformed into a system of joined nonlinear ordinary differential equations using similarity transformations, and they are solved numerically employing a spectral relaxation method (SRM). The obtained results are contrasted with existing specific cases, and a reasonable harmony is established. The impacts of noteworthy physical parameters on the velocities, thermal and concentration distributions, skin friction coefficients, local Nusselt number, and local Sherwood number are investigated graphically. It is found that the rise in Casson fluid and magnetic field parameters reduce the velocity profiles along both and directions while the reverse tendency is observed with an increment in Hall, ion slip, and mixed convection parameters. Moreover, the increase in both radiation and heat source parameters enhances the temperature profile. It is also observed that both the skin friction coefficients reduced with an increase in Casson fluid, Hall, and ion slip parameters. Furthermore, the local Nusselt number enhances with an augment in radiation parameter, whereas the opposite trends of local Nusselt and Sherwood numbers are found with an increase in heat source parameter.
Boosting Up Operational Performance of Manufacturing Organizations through Interpretive Structural Modelling of Enabling Practices
For achieving world-class performance and competitiveness in today’s global highly complex environment and competition, there is an immense pressure on manufacturing organizations having limited resources to pursue operational excellence in terms of productivity and quality, reduce cost, and provide high-quality products in shorter lead times. Identification of representative practices affecting operational performance (OP) and understanding of mutual dependence among those practices will improve the performance of manufacturing organizations with available resources. A sample of 10 experts of academicians and senior company managers from different functional areas of large-scale textile, sugar, and cement industries were surveyed to identify critical practices influencing OP and then to develop the relationships among them. It also identifies those practices that support other practices (driving practices) and also those that are most influenced by other practices (dependent practices). Firstly, through in-depth review of relevant literature, 10 most influencing practices were identified. Then expert elicitation was employed, and a hierarchical structure was established by using interpretive structural modelling (ISM) technique. After determining driving and dependence power of practices, classification or clusters of these practices were made through an additionally carried out MICMAC (cross-impact matrix multiplication applied to classification) analysis. The results contribute that lean management, organizational culture, ISO 9001 (quality management system), and human resource management practices indicate strong driving power and are at the bottom of ISM model. This is a novel and useful application of ISM to identify inherent interactions among these practices and construct a structural graph. With the help of this strategic understanding, researchers and practitioners will be able to identify the focal areas and to pay more attention to driver practices to effectively incorporate them at their strategic and operational levels and the effectiveness in addressing these driver practices will influence the OP to a large extent.
Reliability Evaluation of Public Security Face Recognition System Based on Continuous Bayesian Network
For the sake of measuring the reliability of actual face recognition system with continuous variables, after analyzing system structure, common failures, influencing factors of reliability, and maintenance data of a public security face recognition system in use, we propose a reliability evaluation model based on Continuous Bayesian Network. We design a Clique Tree Propagation algorithm to reason and solve the model, which is realized by R programs, and as a result, the reliability coefficient of the actual system is obtained. Subsequently, we verify the Continuous Bayesian Network by comparing its evaluation results with those of traditional Bayesian Network and Ground Truth. According to these evaluation results, we find out some weaknesses of the system and propose some optimization strategies by the way of finding the right remedies and filling in blanks. In this paper, we synthetically apply a variety of methods, such as qualitative analysis, quantitative analysis, theoretical analysis, and empirical analysis, to solve the unascertained causal reasoning problem. The evaluation method is reasonable and valid, the results are consistent with realities and objective, and the proposed strategies are very operable and targeted. This work is of theoretical significance to research on reliability theory. It is also of practical significance to the improvement of the system’s reliability and the ability of public order maintenance.