A general data inspection software based on the basic data of multi-source radar was designed, which was the central part of the data pre-processing. In this paper, software requirements analysis, overall architecture...
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This paper focuses on the modeling and identification of a Twin Rotor Multi-Input-Multi-Output Systems (TRMS). It begins with an overview of TRMS, followed by the formulation and analysis of a comprehensive nonlinear ...
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ISBN:
(纸本)9783031702846;9783031702853
This paper focuses on the modeling and identification of a Twin Rotor Multi-Input-Multi-Output Systems (TRMS). It begins with an overview of TRMS, followed by the formulation and analysis of a comprehensive nonlinear model based on first principles. The static characteristics of main-elevation and tail-azimuth are explored, considering the influence of rotors on each other. Factors affecting real-time model measurements are investigated. The paper then proceeds to identify TRMS parameters using the "fminsearch" algorithm, the Autoregressive Exogenous Input (ARX) model and Auto-Regressive Moving Average with Exogenous Input (ARMAX) models. Comparative analyses of results from each method are presented. Finally, a comprehensive comparison is made between the nonlinear model, linear models, and real-time experimental data for elevation /pitch and azimuth /yaw angles. This work significantly contributes to understanding TRMS systems, providing insights into nonlinear modeling, identification, and analysis. The findings establish a foundation for advancing comprehension of plant behavior and designing requisite control systems.
The development of a smart irrigation system is critical in addressing water conservation and enhancing agri-cultural practices. The design and implementation of a control system aimed at regulating water delivery to ...
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In order to satisfy different production needs,working modes are often adjusted in real industrial processes,which may lead to the emergence of new working modes with a small amount of modeling ***,most of the traditi...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
In order to satisfy different production needs,working modes are often adjusted in real industrial processes,which may lead to the emergence of new working modes with a small amount of modeling ***,most of the traditional process monitoring algorithm requires that there are sufficient data to establish the reliable *** address the above issue,a process monitoring algorithm is proposed in this work to transfer the common information from the known mode with sufficient modelingdata to the new mode with limited data in multimode ***,a reference mode is selected by evaluating the similarity between new mode and the known ***,the common information is extracted and projected into a manifold subspace by using transfer component *** on the transfer features,two statistics,i.e.,T and SPE,are defined to analyze the process status and identify the *** effectiveness of the proposed method is verified by Tennessee Eastman（TE） process.
Despite the rapid development of edge and fog computing technologies, including significant improvements in the characteristics of communication channels and computing devices themselves, the problems associated with ...
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Industry 4.0 has significantly improved data efficiency by leveraging key technologies such as the Internet of Things and Machine Learning. Among these key technologies, digital twins stand out by offering a promising...
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Chemical plants require reliable systems for pollutant abatement. These processes often operate under cyclic abatement and regeneration cycles over extended periods of time. Throughout this period, the abatement syste...
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Chemical plants require reliable systems for pollutant abatement. These processes often operate under cyclic abatement and regeneration cycles over extended periods of time. Throughout this period, the abatement systems experience a multitude of phenomena that may degrade performance in a fashion that is challenging to predict by first-principle models. These complex phenomena offer an opportunity to leverage data-driven models. To improve their predictive ability, data driven models can be complemented with physics -based information that constrains modeling results. In this contribution, we describe a hybrid modeling approach where physics -derived features are developed to enable data -driven models to effectively predict the performance of real pollutant abatement systems in the Dow Chemical Company.
To accurately predict the oxygen content in flue gas during the municipal solid waste incineration process, this paper uses mutual information, differential evolution algorithm and stochastic configuration network to ...
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ISBN:
(纸本)9798350334722
To accurately predict the oxygen content in flue gas during the municipal solid waste incineration process, this paper uses mutual information, differential evolution algorithm and stochastic configuration network to select the features of the oxygen content in flue gas and predict the modeling. Firstly, the mutual information method is used to eliminate some irrelevant variables. Secondly, the differential evolution algorithm is combined with the stochastic configuration network to further eliminate redundant variables from the above selected feature variables, so as to determine the input variables for predicting the oxygen content in flue gas, and train the stochastic configuration network prediction model for the oxygen content in flue gas. Finally, the actual data sampled from a solid waste incineration plant in Beijing is used to test and verify. The results show that the hybrid feature selection method and prediction modeling method in this paper are effective and can accurately predict the oxygen content in flue gas during the solid waste incineration process.
With a large number of adjustable loads and new energy sources connected. Distribution networks are increasingly in the form of active distribution networks. This situation introduces more stochasticity and uncertaint...
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ISBN:
(纸本)9798350377477;9798350377460
With a large number of adjustable loads and new energy sources connected. Distribution networks are increasingly in the form of active distribution networks. This situation introduces more stochasticity and uncertainty into the active distribution network load equivalence modeling problem. At the same time, in the case of high stochasticity and time- varying, the method of constructing the load equivalence model needs to consider multiple influencing factors. Therefore, this paper proposes a multi-objective reinforcement learning active distribution grid load equivalent modeling method based on evidential reasoning. This method is divided into five steps. First, a multi-attribute analysis is performed to identify the set of evaluation indicators, the set of candidate solutions, the set of evaluation ratings, and the set of evaluation criteria set, candidate solution set, and evaluation level set. The confidence evaluation vector is subsequently obtained. In the second step, multi-evidence fusion is performed after obtaining confidence evaluation vectors of candidate solutions. The approximate degree of superiority of the candidate solutions can subsequently be obtained. In the third step, utility analysis is used to compare the average utility values of different candidate solutions and select the final candidate. In the fourth step, the active distribution network load equivalence modelingprocess is modeled as a Markov decision process. In the fifth step, the scenarios determined in the third step are brought into the fourth step to solve for the key parameters of the isotropic model. Simulation results show that the method proposed in this paper can obtain the required load equivalence model for active distribution networks.
The importance of flavor in foods cannot be overstated, as it plays a crucial role in consumer preferences and choices. Gas Chromatography-Mass Spectrometry (GC-MS) has emerged as a vital tool in food flavor character...
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