The general trend of improving the fuel consumption efficiency of agricultural tractors goes hand in hand with the progressive increase in drivetrain design complexity. In this regard, the need to ensure low consumpti...
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The linear models of complex unstable flow systems serve as essential foundations for conducting model-based flow characteristic analysis and control design. The primary obstacle faced by linear modeling these systems...
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The linear models of complex unstable flow systems serve as essential foundations for conducting model-based flow characteristic analysis and control design. The primary obstacle faced by linear modeling these systems lies in balancing the inherent flow disturbances with the necessary excitation signals. This article develops a linear modeling approach based on the idea of closed-loop identification. To extend the applicability of traditional linear modeling techniques, this method employs time-variant or strongly nonlinear data-driven controllers, such as model-free adaptive controllers and deep reinforcement learning controllers, to automatically adjust the amplitude of the predesigned training signal in real time during the training process. Consequently, the strict requirements for obtaining the unstable steady base flow of unstable systems and for designing training signals are weakened. These data-driven controllers have been validated to fulfill the identifiability conditions of closed-loop systems derived from the uniqueness and convergence consistency of identification parameters. Then, the feasibility of the proposed linear modeling method is validated through two typical examples: low Reynolds number laminar flow around a cylinder and transonic buffet flow around a NACA0012 airfoil. The results confirm that this method successfully identifies the intrinsic linear characteristics of different flow systems, thus facilitating the advancement and application of model-based control design and simplification methods.
By applying cloud storage, big data, distributed computing and other technologies, integrating business data and real-time data from departments such as marketing, scheduling, and production in power operation, a powe...
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ISBN:
(纸本)9798400709753
By applying cloud storage, big data, distributed computing and other technologies, integrating business data and real-time data from departments such as marketing, scheduling, and production in power operation, a power production big data platform has been constructed, achieving functions such as metadatamodeling, data cleaning, data extraction, DM (data Mining), dataanalysis, data presentation, and data services. By utilizing the power production big data platform, the problems in power production operation and inspection management have been addressed, An Corresponding ***-time power production control system that monitors, controls, and analyzes various abnormal power situations has been implemented, achieving functions such as fault tripping control, shutdown control, load control, low voltage control, three-phase imbalance control, risk assessment and warning, reliability and benefit analysis, providing favorable technical support for power operation, maintenance, and maintenance management.
One of the primary reasons for proper temperature regulation in constructions is to achieve thermal comfort while maximizing energy economy. The purpose of this article is to develop and simulate an environmental moni...
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This study investigates the predictive utility of Google search queries for forecasting influenza-like illness (ILI) in compulsory education schools in Macau. The increasing availability of online data offers a novel ...
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The paper proposes an approach to modelling the processes of smart industrial additive manufacturing and related types of attacks in laboratory conditions. The objective of such modelling is to analyze potentially rel...
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The proceedings contain 24 papers. The topics discussed include: ultra-thick dry-process cathode design through formula optimization;through the anodic oxidation of sodium sulfite aqueous solution to achieve energy sa...
The proceedings contain 24 papers. The topics discussed include: ultra-thick dry-process cathode design through formula optimization;through the anodic oxidation of sodium sulfite aqueous solution to achieve energy saving cathodic hydrogen production;an improved control method of district heating system based on waste heat utilization in data center;a new slope gravity energy storage system with multi parallel and continuous circulation tracks;research and application of the cooling system for water-based drilling fluid in hot dry rock drilling;small signal modeling and frequency oscillation analysis of multi-VSGS in microgrids;extraction of key nuclides in carbonate environment using methyltrioctylammonium carbonate;and production prediction of pumping wells based on multi-mode transfer learning.
In this paper, a finite element modeling method based on biological observation is proposed, which can construct a finite element model that conforms to the structural strength characteristics of insect wings. A fast ...
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In the complex world of software systems, understanding and maintaining system stability and performance is of utmost significance. Finding anomalies in log data has become increasingly difficult due to these systems...
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ISBN:
(纸本)9783031781650
In the complex world of software systems, understanding and maintaining system stability and performance is of utmost significance. Finding anomalies in log data has become increasingly difficult due to these systems’ growing complexity. Motivated by the need to improve software release management and ensure system reliability, this study exploits Generative Pretrained Transformer (GPT)-3’s advanced word embedding and tokenizer functionalities to convert log data to adept at identifying atypical patterns and anomalies, delineated in a two-layered structure: offline and online layers. In the offline layer, historical log data undergoes processing through the GPT model, where it is divided into sentence and word embeddings. Sentence embeddings are clustered to generate labels and taggers for subsequent stages, while word embeddings directly create taggers for the online layer’s sequence labeling. The online layer involves collecting real-time data, processing it through GPT to generate embeddings, and subjecting these embeddings to a sequence labeling process. This process yields templates and variables expediting the formation of train-test data splits for a classifier that detects anomalies. Different classifiers, namely Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost), are evaluated. Experimental analysis on four distinct real-world datasets, namely Apache, BlueGene/L (BGL), Hadoop Distributed File System (HDFS), and Thunderbird, where CatBoost achieved remarkable accuracy rates of 99.75%, 99.00%, 98.75%, and 99.33%, respectively. The study also demonstrates that GPT-based embeddings provide a more effective anomaly detection solution than Bidirectional Encoder Representations from Transformers (BERT)-based embeddings. The proposed methodology is particularly designed to be integrated into software release management processes which enables automatic anomaly detection to augment quality control measures, thereby, e
Business processes modeling typically faces challenges in the integrated representation of processes’ lifecycle, informational, and organizational models. Despite the plethora of processmodeling languages (workflow ...
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