A key sampling formula for discretising a continuos-time system is proved when the signals space is a subclass of the space of Distributions. The result is applied to the analysis of an open-loop hybrid system.
ISBN:
(纸本)9789728865849
A key sampling formula for discretising a continuos-time system is proved when the signals space is a subclass of the space of Distributions. The result is applied to the analysis of an open-loop hybrid system.
This paper presents model-based methods and algorithms for describing and analyzing complex technological interactions in production process chains for the manufacturing of composite materials and components. The pres...
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This paper presents model-based methods and algorithms for describing and analyzing complex technological interactions in production process chains for the manufacturing of composite materials and components. The presented holistic approach includes the modeling of the process chain, the construction of suitable designs of experiments, the logging and the collection of experimental and production data, the dataanalysis, as well as the visualization of analysis results and the deduction of the room of maneuver for production settings. Supported by these methods, the development and the putting into operation of complex process chains will become much more efficient. (C) 2017 Published by Elsevier B.V.
This paper considers the computational modeling of the class of bilinear control systems for hyperbolic conservation laws with nonstandard boundary conditions. These systems arise from (control) engineering applicatio...
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This paper considers the computational modeling of the class of bilinear control systems for hyperbolic conservation laws with nonstandard boundary conditions. These systems arise from (control) engineering applications of systems displaying propagation phenomena, i.e., integrating steam, water, and gas pipes. The aim of this paper is achieved by means of a systematic computational procedure previously introduced and adapted here for the class of systems under consideration. The procedure, based on a convergent Method of Lines ensures the convergence of the approximate numerical solution and also the preservation of the basic properties of the "true" solution as well as its Lyapunov stability. Thus, the approximate computational model allows numerical quantitative and qualitative analysis relevant to a specific problem. The computational efficiency of the procedure is ensured by its implementation based on some, possibly massively, parallel-structured devices belonging to the Artificial Intelligence field-the cell-based recurrent neural networks. As a case study, we consider a control system occurring in the cogeneration process (combined heat and electricity generation). A comparison between the results of the qualitative analysis and those of the numerical simulations demonstrates the correctness and effectiveness of the computational procedure for the dynamics and transients analysis. The paper ends with some conclusions and a list of open problems.
Biological and ecological time series data are usually limited to their time direction, because the most of crop productions are scheduled in year domain. The nonlinear time series analysis has been developed to disti...
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ISBN:
(纸本)4907764227
Biological and ecological time series data are usually limited to their time direction, because the most of crop productions are scheduled in year domain. The nonlinear time series analysis has been developed to distinguish deterministic chaos from noisy time series for the last two decades. However, the methodology requires thousands of data size for their target time series data set. In this tutorial, the outline of the nonlinear time series analysis scheme consisting of time delayed embedding technique, correlation dimension analysis, Lyapunov exponent analysis, nonlinear deterministic prediction etc. is introduced. As a part of tutorial session, I explain the general scheme of nonlinear time series analysis with a few case studies. Based on the scheme, I try to expand it to very short but ensemble ecological time series data set.
This paper reports on the modeling, control design and implementation as well as experimental results of a partial body weight support (PBWS) system, which facilitates walking and the relearning process for people suf...
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ISBN:
(纸本)9781424409884
This paper reports on the modeling, control design and implementation as well as experimental results of a partial body weight support (PBWS) system, which facilitates walking and the relearning process for people suffering from gait impairment as a result of a neurological injury such as strokes. This apparatus is equipped with a load cell which constantly monitors the provided support force, and a linear motor, capable of taking immediate action to keep this support force very close to a predefined profile. To design a feedback control law that governs the action of the linear motor, a dynamic model of the PBWS system with a patient has been developed along with a procedure to identify the model parameters. Based on this model, the design of the feedback law is formulated and solved as an output regulation problem. The implementation of the resulting feedback law is described in detail and practical limitations of output regulation in this application are discussed. Simulation results as well as preliminary data from a clinical study with a stroke patient demonstrate the ability of this novel PBWS system to provide the desired support force patterns.
The use-case described in this paper covers data acquisition and real-time analysis of the gathered medical data from wearable sensor system. Accumulated data is essential for monitoring vital signs and tracking the d...
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ISBN:
(纸本)9783319706252;9783319706245
The use-case described in this paper covers data acquisition and real-time analysis of the gathered medical data from wearable sensor system. Accumulated data is essential for monitoring vital signs and tracking the dynamics of the treatment process of disabled patients or patients undergoing the recovery after traumatic knee joint injury (e.g. post-operative rehabilitation). The main goal of employing the wearable sensor system is to conduct rehabilitation process more effectively and increase the rate of successful rehabilitation. The results of dataanalysis of patient's vital signs and feedback allow a physiotherapist to adjust the rehabilitation scenario on the fly. In this paper, we focus on the methodology for data modelling with a purpose to design a computer-aided rehabilitation system that would support agility of changing information requirements by being flexible and augmentable.
Load modeling using data-driven algorithms is a widely used technique in applications like load identification. It is also one of the fundamental concepts which enable Non-Intrusive Appliance Load modeling (NIALM). Th...
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ISBN:
(纸本)9781665420464
Load modeling using data-driven algorithms is a widely used technique in applications like load identification. It is also one of the fundamental concepts which enable Non-Intrusive Appliance Load modeling (NIALM). This paper develops a load modeling framework using Hidden Markov Models (HMMs) to identify a two-state home appliance. Unlike previous studies, the training and testing dataset is derived from different monitored domestic houses to analyze the effect of the training data trends on the model's accuracy. We used the Reference Energy Disaggregation dataset (REDD) in the load modelingprocess. The developed system utilizes adaptive measures to construct HMM models that can identify foreign variants of the same two-state appliance. We measured the accuracy of our proposed methodology by comparing a known state sequence with a Viterbi-generated one. The accuracy results are up to 96%, depending on the nature of the used training dataset.
The concepts of symbolic dynamics and partitioning of time series data have been used for feature extraction and anomaly detection. Although much attention has been paid to modeling of finite state machines from symbo...
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ISBN:
(纸本)9781424438723
The concepts of symbolic dynamics and partitioning of time series data have been used for feature extraction and anomaly detection. Although much attention has been paid to modeling of finite state machines from symbol sequences, similar efforts have not been expended for partitioning of time series data to optimally generate symbol sequences. This paper addresses this issue and proposes a partitioning method based on maximum migration of data points across cell boundaries. Various aspects of the proposed partitioning tool, such as identification of evolution characteristics of dynamical systems and adaptive selection of alphabet size, are discussed. Experimental results on an electronic circuit apparatus implementing the Duffing equation show that maximum-migration partitioning yields significant improvement over existing partitioning methods (e.g., maximum entropy partitioning) for the purpose of anomaly detection.
With the advent of the era of massive data, algorithmic optimization and data mining have been applied in many industries in an unprecedented and innovative way. In the field of enterprise supply chain management, big...
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An intelligent control allocation method for modern reentry vehicles with a strong aerodynamic nonlinearity is presented in this paper. A specially designed deep auto-encoder (DAE) neural network is proposed that shar...
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ISBN:
(纸本)9783952426982
An intelligent control allocation method for modern reentry vehicles with a strong aerodynamic nonlinearity is presented in this paper. A specially designed deep auto-encoder (DAE) neural network is proposed that shares similar information flow with the control allocation process. This similarity is showed by setting the decoder to approximate the control effector function, and by setting the encoder to allocate the expected control moments. The decoder is trained independently based on the aerodynamic coefficients database, and with the help of the well- trained decoder, the encoder is then trained in an unsupervised way without labeled data. This proposed control allocation method could deal with strong nonlinearity of the control effectors at a high accuracy, thanks to the powerful modeling and regression ability of deep neural networks. Numerical examples are provided by the end that explain the training and implementation details, as well as the strong learning and modeling ability of the deep neural network.
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