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Analysis of problems of algorithm modules structural-parametric integration has identified the need for the synthesis of algorithmic systems of the information flow tasks formation algorithm. It is advisable to use an informational approach which is based on the assumption by V. M. Glushkov that the multitude of the model conditions can be viewed as the data processed by the algorithm in the model...
PID control architectures are widely used in industrial applications. Despite their low number of open parameters, tuning multiple, coupled PID controllers can become tedious in practice. In this paper, we extend PILCO, a model-based policy search framework, to automatically tune multivariate PID controllers purely based on data observed on an otherwise unknown system. The system's state is extended...
Current process industries such as refineries and pharmaceutical industries have been facing increasing challenges with respect to productivity, reducing waste and energy consumption, as well as environmental and safety issues becomingly increasingly comprehensive. One way to address these issues is to utilize online dynamic simulation via process modelling and control software. While there are multiple...
This paper addresses the problem of damage detection technique of structural health monitoring (SHM). Kernel principal components analysis (KPCA)-based generalized likelihood ratio (GLR) technique is developed to enhance the damage detection of SHM processes. The data are collected from the complex three degree of freedom spring-mass-dashpot system in order to calculate the KPCA model. The developed...
With the progressive development of information and communication technologies, we are now forming a new world called hyperworld that is composed by the cyber world and the physical world with various digital explosions including data, connectivity, service and intelligence. Therefore, Cyber-I has been proposed, which is a real individual's counterpart in cyberspace, and is to create a unique, digital,...
Combining the role-based access control (RBAC) model with the attribute-based access control (ABAC) model is a popular direction of current research on access control models. At present, many RABAC (RBAC + ABAC) models have been proposed. On the basis of RBAC model, these models dynamically apply ABAC rules to user-role mapping, role-permission mapping and user-permission mapping, thus realizing the...
Maximum Causal Entropy (MCE) Inverse Optimal Control (IOC) has become an effective tool for modeling human behavior in many control tasks. Its advantage over classic techniques for estimating human policies is the transferability of the inferred objectives: Behavior can be predicted in variations of the control task by policy computation using a relaxed optimality criterion. However, exact policy...
The computational method and model of Module of Modeling of Computational Systemic Mind Under Uncertainty is oriented on use as plug-in in systems of Artificial Intelligence, which are characterized by the ability to be self-organized and to operate computationally, intellectually, autonomously, systemically and continuously real time, under uncertainty, in inhomogeneous subject areas, in unknown...
Nowadays, the current global socio-economic challenges offer new opportunities for engineering. Advances in electrical engineering have been central to human progress ever since the discovery of the electromagnetic field. In the last hundred and fifty years, electrical engineering has transformed the world we live in, contributing to a significantly longer life expectancy and has enhanced life quality...
Fault detection is important for safe operation of various modern engineering systems. Partial least square (PLS) has been widely used in monitoring highly correlated process variables. Conventional PLS-based methods, nevertheless, often fail to detect incipient faults. In this paper, we develop new PLS-based monitoring chart, combining PLS with multivariate memory control chart, the multivariate...
Availability of an accurate and robust dynamic model is essential for implementing the model dependent process control. When first principles based modeling becomes difficult, tedious and/or costly, a dynamic model in the black-box form is obtained (process identification) by using the measured input-output process data. Such a dynamic model frequently contains a number of time delayed inputs and...
Advanced process control techniques use at some point a model of the process that is controlled. In real industrial processes, usually there are present nonlinearities, the time changing parameters of the equipment, noise and uncertainties. These processes are sometimes modeled by NARMAX models. The current paper approaches system modeling with NARMAX polynomials of a distillation process with the...
In this paper, we design and implement a scientific workflow process designer with a conceptual building block depicting its architectural structure. The designer is theoretically designed from the scientific information control net[l], and it is graphically implemented by expanding the standardized BPMN(business process modeling notations)[2]. In particular, the designer is able to automatically...
In the paper some problems of the mathematical modeling of anaerobic (methane) fermentation of animal waste in stirred tank bioreactors are considered. Laboratory experiments are carried out with highly concentrated organic pollutants and transient step responses of the control output for continuous methane fermentation are obtained. The dynamic behavior of this process is described by sets of deterministic...
In order to solve the power system transient simulation problem, which is caused by the contradiction between efficiency and accuracy due to insufficient calculation ability, this paper proposes a Cloud Computing technology application framework for power system transient simulation. Firstly, it expounds the characteristics and current situation of power system transient simulation, and points out...
Extreme Learning Adaptive Neuro Fuzzy Inference System (ELANFIS) is a new learning machine which combines the learning capabilities of neural networks and the explicit knowledge of the fuzzy systems as in the case of conventional adaptive neuro-fuzzy inference system (ANFIS). ELANFIS reduces the computational complexity of ANFIS by eliminating the hybrid learning algorithm and avoids the randomness...
Massive cloud-based data-intensive applications (e.g., iterative MapReduce-based) could involve graph data processing. How to effectively analyze and process large-scale graph data is an unsolved challenging problem. We present a parallel computation framework, named MyBSP, which is inspired by Google's Pregel system. MyBSP supports and implements the Bulk Synchronous Parallel (BSP) programming model,...
This paper presents a modified Hammerstein-Hammerstein nonlinear online identification method for an industrial air pre-heating furnace involves drying and conveying of materials, by relating set point, error signal, the control variable and process variable in a systematic manner using the prediction error framework. The method developed here to address the gray HammersteinHammerstein estimation...
During its life cycle, data has to go through different stages, from generation, storage, query, various processing, to deletion or archiving. Meanwhile, all these evolutions can be recorded by data provenance, which can be used for data deduction and credibility verification. Starting from the status of data application and processing, current problems exist in data management have been raised in...
Big Data will have a profound impact for the future of science and technology and economic development. The direct purpose of doing scientific research on Big Data is rapidly obtaining valuable information from a variety of types of data. Study on effective and simple data representation method is one of the technical problems which must be solved in the network big data processing. This paper proposes...
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