First principle process model of GUNT RT 010 experimental unit is designed for the purpose of various modern control methods laboratory testing and applications. Unknown parameters are estimated from experimental data...
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作者：
Chen YongjunRuan BoYangtze Univ
Hubei Cooperat Innovat Ctr Unconvent Oil & Gas Wuhan 430100 Hubei Peoples R China Yangtze Univ
Coll Elect & Informat Jingzhou 434023 Hubei Peoples R China
This article proposed a novel fracture control system for shale gas fracturing truck used in the construction, blender, equipment vehicles connected by a PLC control network, each equipment is contacted with automatic...
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ISBN:
(纸本)9781467386449
This article proposed a novel fracture control system for shale gas fracturing truck used in the construction, blender, equipment vehicles connected by a PLC control network, each equipment is contacted with automatic remote control unit, the pressure to achieve through the network and treatment plant centralized control cracking operations, network control system uses ring industrial Ethernet connection. Each field blender, fracturing truck station data, parameters can be transmitted through the network in order to achieve data sharing and downloading function parameters pass each other. Meanwhile, An adaptive displacement control with hysteresis modeling for a fracturing truck is proposed in this paper, the ladder program has been used in the AB-PLC for displacement control, the result of experiment also show that the control system have improved the fracturing construction effect.
A new FEA approach utilizing the concept of visual computing is proposed. Conventional FEA is improved by a new visual computing method. This new method is based on rapid data interchange among program modules through...
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A new FEA approach utilizing the concept of visual computing is proposed. Conventional FEA is improved by a new visual computing method. This new method is based on rapid data interchange among program modules through the use of shared computer memory. The concept enables easy control of the analysisprocess through interactive user input, It also provides the possibility for an-line visualization and animation in real time of various one-dimensional scalar quantities and one-dimensional and two-dimensional distributions, using a fast computer-graphics engine, By visualizing the numerical data in progress, the user can easily detect errors in the initial data, achieving rapid and accurate analysis with reduced computation cost, The proposed approach is also very useful for analysis of time-periodical, nonlinear or any other problems having many successive iterations and solutions.
Recording of neural response to specific stimulus in a repeated trial is very common in neuroscience protocol. The perstimulus time histogram (PSTH) is a standard tool for analysis of neural response. However it could...
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ISBN:
(纸本)9781424420728
Recording of neural response to specific stimulus in a repeated trial is very common in neuroscience protocol. The perstimulus time histogram (PSTH) is a standard tool for analysis of neural response. However it could not capture the non-deterministic properties of the neuron especially in higher level cortical area such as inferior temporal cortex. The stochastic state point process filter theory is used for the estimation of the conditional intensity of the point process observation as a time varying firing rate and the particle filter is used to numerically estimate this density in time. The particle filters were applied to the results of the point process observation for compensating the Gaussian assumption. The results of applying point processmodeling on a real data from inferior temporal cortex of macaque monkey indicates that, based on the assessment of goodness-of-fit, the neural spiking activity and biophysical property of neuron could be captured more accurately in compare to conventional methods.
Recently, the consideration of data aspects has seen a surge in interest both from the perspective of designing processes as from a model discovery perspective. However, it seems that both research domains (models for...
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ISBN:
(纸本)9783031342400;9783031342417
Recently, the consideration of data aspects has seen a surge in interest both from the perspective of designing processes as from a model discovery perspective. However, it seems that both research domains (models for design and model discovery) use different conceptualisations of data/object-aware systems. In an ideal situation, when (designed) models are implemented, the resulting information systems are equipped with logging functionalities that allow the rediscovery of the models based on which the information systems were implemented. However, there is a lack of guidelines on how to set up logging. From a logging perspective, logging formats are unclear about the granularity of events: the logging may be done at the level of entire tasks or at the level of the operations on individual objects, or a single log may even contain a mix of events at different granularity levels. The lack of clarity in this matter complicates the correct interpretation of log information. The goal of this paper is therefore to investigate how the concepts of object-centric logging and those for data-aware process modelling may be better aligned. This will facilitate setting up proper logging at system implementation time, and facilitate the connection of discovered models to models-for-design. The investigation resulted in iDOCEM, a metamodel that aligns the DOCEL and the Merode meta-model. Comparing iDOCEM to different other logging meta-models demonstrates that the proposed meta-model is complete enough to capture (more than) existing logging formats.
Activated Sludge System (ASS) is one of the most commonly used techniques in a Wastewater Treatment Plant (WTP). The artificial neural network (ANN) techniques have been studied for modeling the operation process of A...
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During the course of most bioprocess development programs a large amount of processdata is generated and stored. However, while these data records contain important information about the process, little or no use is ...
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ISBN:
(纸本)0080417108
During the course of most bioprocess development programs a large amount of processdata is generated and stored. However, while these data records contain important information about the process, little or no use is made of this asset. The work described here uses a neural network approach to `learn' to recognize patterns in fermentation data. Neural networks, trained using fermentation data generated from previous runs, are then used to interpret data from a new fermentation. We propose a task decomposition approach to the problem. The approach involves decomposing the problem of bioprocessdata interpretation into specific tasks. Separate neural networks are trained to perform each of these tasks which include fault diagnosis, growth phase determination and metabolic condition evaluation. These trained networks are combined into a multiple neural network hierarchy for the diagnosis of bioprocessdata. The methodology is evaluated using experimental data from fed-batch, Saccharomyces cerevisiae fermentations. We argue that the task decomposition approach taken here allows for each network to develop a task specific representation and that this in turn, can lead to network activations and connection weights that are more clearly interpretable. These expert networks can now be pruned to remove nodes that do not contribute significant additional information.
Fuzzy models can represent highly nonlinear processes and can smoothly integrate a priori knowledge with information obtained from processdata. A nonlinear controller can be designed by incorporating an inverted fuzz...
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ISBN:
(纸本)0780337972
Fuzzy models can represent highly nonlinear processes and can smoothly integrate a priori knowledge with information obtained from processdata. A nonlinear controller can be designed by incorporating an inverted fuzzy model of rite process in an internal model control (IMC) scheme. This paper presents an identification cation procedure for a Takagi-Sugeno fuzzy model, which is based on product-space fuzzy clustering The obtained model can be inverted analytically and hence can be easily included in a nonlinear IMC scheme. The described method is applied to temperature control in an air-conditioning system. The performance is compared with the performance of a well-toned PID controller.
Quantitative evaluation of chemical exchange saturation transfer (CEST) is usually done by solving Bloch-McConnell equations (BME). BMEs are not easily extended and applying them to describe the multi-pool data involv...
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ISBN:
(纸本)9781479984459
Quantitative evaluation of chemical exchange saturation transfer (CEST) is usually done by solving Bloch-McConnell equations (BME). BMEs are not easily extended and applying them to describe the multi-pool data involves a complex process. In this paper, we developed a Gaussian mixture model (GMM) to represent each component involved in the Z-spectrum by a Gaussian distribution. We then tested and evaluated the GMM for the two-pool exchange site and experimental data. The results showed that GMM is able to fit the experimental data and its accuracy is almost similar to that of the BME model. (average percent of Relative Sum Square Error (%RSSE) < 0.6). Accuracy and simplicity were found to be the advantages of the GMM and lack of analytical relationships among the GMM parameters and physical characteristics of the CEST effect turned out to be its main limitations. We quantified contrast agent (CA) concentration (population fraction of CEST pool) and chemical exchange rate applying the GMM to the simulated data of a two-pool exchange site. It was found that the means and variances of the Gaussians can be used for this purpose. In addition, GMM determines the resonance frequency of each pool easily and accurately because these frequencies are equal to the mean values of GMM.
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