The present research involves the selection of suitable edible and non-edible feedstocks for the production of biodiesel in Pakistan. After studying and analyzing different feedstocks: Jatropha, Neem and waste cooking...
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Continuous development of modern technologies and equipment models inevitably needs to improve the process of training highly qualified specialists who carry out their direct operation. As one of the most promising ar...
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Guided by the goals of carbon peaking and carbon neutrality, the power distribution system is gradually evolving into a new type of regional power system that integrates functions such as power collection, transmissio...
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Focusing on the strategic goal of "Safe China", we will build a technical system for modernizing the theory and governance capacity of a smart society, and solve the problems of lagging theoretical innovatio...
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A new method for polarization dependence analysis of periodic nanostructured SERS substrates was proposed. The method combines the Fourier transform and the wave vector matching model of surface plasmon polariton (SPP...
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To the actual date, computer vision and neural network dataanalysis are actively developing and finding more and more applications in the industrial control engineering. These technologies are now used to deal with i...
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The environmental temperature adaptability of electric vehicles is a key pain point issue in their development. Both high and low temperature environments have significant impact on vehicle endurance. Also, range degr...
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Fault tracing technology, including root-cause diagnosis and propagation analysis, has become a growing hot spot in the field of industrial process monitoring. However, it is currently limited by the use of restricted...
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Fault tracing technology, including root-cause diagnosis and propagation analysis, has become a growing hot spot in the field of industrial process monitoring. However, it is currently limited by the use of restricted alarm sequence data and the analysis without fault propagation analysis. To solve these problems, this article proposes a novel fault tracing method, namely causal topology-based variable-wise generative model (CTVGM). The CTVGM is first established according to the topological order of the variable causal graph. It contains a series of causal functions that are trained with normal data. Then, fault samples can be restored by the CTVGM to build up a diagnosis index called the recovery ratio (RR), which is used to determine the root causes. Meanwhile, the fault propagation paths are inferred by the recovery routes. In addition, a hierarchical CTVGM-based fault tracing strategy is designed to reduce the computation burden and enhance the modeling efficiency for large-scale complicated processes. The effectiveness of the proposed fault tracing method is verified on a numerical example and the Tennessee Eastman process (TEP) case. Compared with existing methods, the results show that the proposed method not only achieves more accurate root-cause diagnosis performance but also obtains fault tracing results that are highly consistent with the process mechanisms.
The development of next-generation battery management systems needs models with enhanced performance to enable advanced control, diagnostic, and prognostic techniques for improving the safety and performance of lithiu...
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ISBN:
(数字)9798350382655
ISBN:
(纸本)9798350382662
The development of next-generation battery management systems needs models with enhanced performance to enable advanced control, diagnostic, and prognostic techniques for improving the safety and performance of lithium-ion battery systems. Specifically, battery models must deliver efficient and accurate predictions of physical internal states and output voltage, despite the inevitable presence of various system uncertainties. To facilitate this, we propose a lightweight hybrid modeling framework that couples a high-fidelity physics-based electrochemical battery model with a computationally-efficient Gaussian process regression (GPR) machine learning model to predict and compensate for errors in the electrochemical model output. This is the first time that GPR has been implemented to predict the output residual of an electrochemical battery model, which is significant for the following reasons. First, we demonstrate that GPR is capable of considerably improving output prediction accuracy, as evidenced by an observed average root-mean-square prediction error of 7.3 mV across six testing profiles, versus 119 mV for the standalone electrochemical model. Second, we employ a data sampling procedure to exhibit how GPR can use sparse training data to deliver accurate predictions at minimal computational expense. Our framework yielded a ratio of computation time to modeled time of 0.003, indicating ample suitability for online applications.
The development process of the diesel electric control system includes multiple steps of analysis and design, modeling and simulation, code generation and real-time testing. With the development of modern technology, ...
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