In this paper, a tension data-driven control method for compressor motor production equipment of new energy vehicles is proposed, which is mainly used to solve the problems of loose winding and uneven distribution of ...
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ISBN:
(纸本)9798350321050
In this paper, a tension data-driven control method for compressor motor production equipment of new energy vehicles is proposed, which is mainly used to solve the problems of loose winding and uneven distribution of enameled wire caused by difficult modeling and low precision of tension control in the process of tension control of compressor motor production equipment of new energy *** proposed model-free adaptive control method avoids the difficulty of modeling the traditional winding machine. By comparing with the existing methods, the results show that the dynamic performance and steady-state performance of the winding machine control system are effectively improved, and the tension control accuracy is improved.
In recent years, deep learning techniques have been widely applied in soft sensor modeling. Stacked autoencoder (SAE) networks are particularly effective at discovering complex data patterns due to their hierarchical ...
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ISBN:
(纸本)9798350321050
In recent years, deep learning techniques have been widely applied in soft sensor modeling. Stacked autoencoder (SAE) networks are particularly effective at discovering complex data patterns due to their hierarchical structures. However, processdata are typically generated as data streams, which poses a great challenge to capture the time-varying characteristics of the process for traditional soft sensor models based on SAE. Furthermore, the insufficiency of offline pre-training data further limits the feature representation capability of SAE. To address these problems, an online deep evolving fuzzy system (ODEFS) based adaptive soft sensor method for processdata streams is proposed. In the offline modeling phase, quality-related stacked autoencoder (QSAE) is pre-trained as representation layer to mine quality-related feature representations, while an evolving fuzzy system with self-organization capability is built as the prediction layer. In the online implementation phase, the topology-preserving loss is added to the learning process of QSAE feature network to enable continuous learning of feature representations and alleviate the catastrophic forgetting problem. Meanwhile, the shallow EFS network handles concept drift in data patterns by self-adjusting the structure and parameters. The proposed ODEFS method can improve the feature representation capability of SAE in a data streaming environment and the ability to handle time-varying characteristics, thus ensuring better prediction accuracy. The effectiveness and superiority of the proposed method are verified on TE process.
Event logs are the main source for business process mining techniques. However, not all information systems produce a standard event log. Furthermore, logs may reflect only parts of the process which may span multiple...
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ISBN:
(纸本)9783031342400;9783031342417
Event logs are the main source for business process mining techniques. However, not all information systems produce a standard event log. Furthermore, logs may reflect only parts of the process which may span multiple systems. We suggest using network traffic data to fill these gaps. However, traffic data is interleaved and noisy, and there is a conceptual gap between this data and event logs at the business level. This paper proposes a method for producing event logs from network traffic data. The specific challenges addressed are (a) abstracting the low-level data to business-meaningful activities, (b) overcoming the interleaving of low-level events due to concurrency of activities and processes, and (c) associating the abstracted events to cases. The method uses two trained sequence models based on Conditional random fields (CRF), applied to data reflecting interleaved activities. We use simulated traffic data generated by a predefined business process. The data is annotated for sequence learning to produce models which are used for identifying concurrently performed activities and cases to produce an event log. The event log is conformed against the process models with high fitness and precision scores.
This paper presents (DNN)-N-3, a neural network learning tool for dual-level dynamic system modeling. This tool aims to model the dynamic system under a dual-level modeling framework, i.e., modeling the system dynamic...
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This paper presents (DNN)-N-3, a neural network learning tool for dual-level dynamic system modeling. This tool aims to model the dynamic system under a dual-level modeling framework, i.e., modeling the system dynamics through a computationally efficient and precise low-level neural hybrid system and an interpretable high-level transition system abstraction. Given samples of the dynamic system and user specifications, the proposed tool can generate a low-level distributive learning framework consisting of shallow neural network approximations for local dynamics and its high-level transition system abstraction that allows Computational Tree Logic (CTL) formulae verification. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)
The position tracking accuracy of a two-dimensional linear motor is the most important accuracy index in the servo motion process of a two-dimensional linear motor, and it is of great significance to the servo motion ...
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ISBN:
(纸本)9798350321050
The position tracking accuracy of a two-dimensional linear motor is the most important accuracy index in the servo motion process of a two-dimensional linear motor, and it is of great significance to the servo motion process of two-dimensional linear motor modeling and control. Aiming at the problem that the complex dynamic characteristics of the two-dimensional linear motor are difficult to carry out conventional mechanism modeling and other disturbances such as friction impedance during its movement a compensation scheme founded on the combination of tight format dynamic linearization model-free adaptive control and active disturbance rejection control technology is proposed, according to the data-driven control idea. The scheme provides an idea for solving the problem of friction disturbance of two-dimensional linear motors. After establishing the mathematical model of a two-dimensional linear motor, the scheme uses Matlab to simulate the algorithm. Then, owing to the influence of many adjustable parameters on the performance of the controller, and the problems of time-consuming and unsatisfactory optimization of many parameters, the controller parameters are optimized based on a genetic algorithm to improve the efficiency of parameter tuning.
Impurity removal is a momentous part of zinc hydrometallurgy process, and the quality of products and the stability of the whole process are affected directly by its control effect. The application of dynamic model is...
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Impurity removal is a momentous part of zinc hydrometallurgy process, and the quality of products and the stability of the whole process are affected directly by its control effect. The application of dynamic model is of great significance to the prediction of key indexes and the optimization of processcontrol. In this paper, considering the complex coupling relationship of stage II purification process, a hybrid modeling method of mechanism modeling and parameter identification modeling was proposed on the basis of not changing the actual production process of lead-zinc smeltery. Firstly, the overall nonlinear dynamic mechanism model was established, and then the deviation between the theoretical value and the actual detected outlet ion concentration was taken as the objective function to establish the parameter identification optimization model. Since the built model is nonlinear, it may pose implementation problems. On the premise of deriving the gradient vector and Hessian matrix of the objective function with respect to the parameter vector, an optimization algorithm based on the steepest descent method and Newton method is proposed. Finally, using the historical production data of a lead-zinc smeltery in China, the model parameters were accurately inversed. An intensive simulation validation and analysis of the dynamic characteristics about the whole model shows the accuracy and the potential of the model, also in the perspective of practical implementation, which provides the basis for the optimal control of system output and the guidance for the optimal control of zinc powder addition.
High spatial heterogeneity of urban floods poses challenges in its modeling and assessment, and a flood database is a basic requirement but it is lacking for many cities. The proposed Twitter-based framework addresses...
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High spatial heterogeneity of urban floods poses challenges in its modeling and assessment, and a flood database is a basic requirement but it is lacking for many cities. The proposed Twitter-based framework addresses the issues via developing a finer resolution flood database and a product. The framework has multiple components including data quality control, validation of flood database via newspapers and flood impact assessment. Three flood events differing in rainfall characteristics are selected to showcase the utility of the proposed framework for the city of Hyderabad, India. analysis of tweets highlighted the resourcefulness of video tweets and wide coverage of the study area in terms of flood reporting. Tweets exhibited an association with tweet time and rainfall aspects. Further, tweets based flood locations are found to be in agreement with newspaper based flooding instances. A novel flood impact score (FIS) is developed for each flood location using analytical hierarchy process based weights for five variables (Twitter based attributes, rainfall, elevation), and the use of FIS is demonstrated in identifying flood impact areas. These kinds of databases and products, with a scope to improve further, serve as a potential tool to cater flood preparedness and management, thereby making cities flood resilient.
Multifidelity surrogate modeling offers a cost-effective approach to reducing extensive evaluations of expensive physics-based simulations for reliability prediction. However, considering spatial uncertainties in mult...
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Multifidelity surrogate modeling offers a cost-effective approach to reducing extensive evaluations of expensive physics-based simulations for reliability prediction. However, considering spatial uncertainties in multifidelity surrogate modeling remains extremely challenging due to the curse of dimensionality. To address this challenge, this paper introduces a deep learning-based multifidelity surrogate modeling approach that fuses multifidelity datasets for high-dimensional reliability analysis of complex structures. It first involves a heterogeneous dimension transformation approach to bridge the gap in terms of input format between the low-fidelity and high-fidelity domains. Then, an explainable deep convolutional dimension-reduction network (ConvDR) is proposed to effectively reduce the dimensionality of the structural reliability problems. To obtain a meaningful low-dimensional space, a new knowledge reasoning-based loss regularization mechanism is integrated with the covariance matrix adaptation evolution strategy (CMA-ES) to encourage an unbiased linear pattern in the latent space for reliability prediction. Then, the high-fidelity data can be utilized for bias modeling using Gaussian process (GP) regression. Finally, Monte Carlo simulation (MCS) is employed for the propagation of high-dimensional spatial uncertainties. Two structural examples are utilized to validate the effectiveness of the proposed method.
The hierarchical modeling framework is widely used in designing the longitudinal control of connected autonomous vehicles (CAVs). To describe the throttle/brake maneuvers, the lower-level controller is constructed and...
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The hierarchical modeling framework is widely used in designing the longitudinal control of connected autonomous vehicles (CAVs). To describe the throttle/brake maneuvers, the lower-level controller is constructed and modeled. Meanwhile, it leads to a problem that how CAV traffic flow evolve with different lower-level controller. In this paper, two typical lower-level controller models are introduced and compared. One is the first-order lag model which is widely used in theoretical analysis and the other one is a second-order response model with delay and feedback control, which is identified from field test data in our previous work. The result of stability analysis exhibits a smaller string stable region under second-order response model with delay. Moreover, the throughput at a lane drop on two lane highways is also lower than first-order lag model. In spatiotemporal diagram, phase transition markedly differs among two CAV systems. Under second-order response model with delay and feedback control, it shows a characteristic of 'Three-stage' phase transition. First-order lag model, widely used in theoretical analysis, does not accurately describe the acceleration and deceleration of actual CAVs. It usually overestimates the executive process of the vehicle's mechanical structure. Some conclusion based on the first-order lag model may not match the actual traffic. This comparative paper is hoped to draw more attention about the differences caused by lower-level controller model, especially in some typical traffic scenarios. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)
Force perception is important for the manipulation of soft robotic hands. Multiple-direction interactions between fingers and objects occur predominantly at the fingertips during manipulation. Integrating physical mul...
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Force perception is important for the manipulation of soft robotic hands. Multiple-direction interactions between fingers and objects occur predominantly at the fingertips during manipulation. Integrating physical multi-dimensional force sensors for soft fingertips poses stringent demands on the manufacturing process, material selection, and structural design. Therefore, we proposed a model-based and data-driven combined method to estimate the forces on the fingertip using the embedded liquid metal position and pressure sensors. This approach reduces the complexity of sensor system design and integration. In this letter, we established the theoretical model of the rigid-soft finger, to generate a pre-training dataset after a pre-identification for theoretical model parameters. This dataset is used to pre-train a source network, and then transfer the source network with the real-world dataset to obtain the final force estimator with great generalization ability. The proposed method contributes to obtaining a better force estimator in fewer samples and biased datasets, effectively reducing the difficulty of data acquisition.
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