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Modern scientific collaborations require large-scale data mining and integration processes. Their investigations involve multi-disciplinary expertise and large-scale computational experiments on top of large amounts of data that are located in distributed data repositories running various software systems, and managed by different organizations. Higher-level dataflow languages are used on top of parallel...
This paper analyzes the predictivity and return of the Barmish-Iwarere trading algorithm described in. In the first part of the paper, we study the trade triggering algorithm using either an Ito process model, or real data from indexes and ETFs. It is shown through hypothesis testing that the trigger provides mixed results in predicting the sign of the single trade, for both the Ito process and real...
The application of self-optimization allows future manufacturing processes to adapt to changing process boundary conditions automatically. Integrated intelligence in the form of expert knowledge allows the extension of application fields of the manufacturing processes, the guarantee of the product quality and the reduction of machine down time. The subject of this paper is research work about the...
The aim of the present study is to optimize PID controllers already working in cement mill installations (CM) by investigating the performance. The M-constrained Integral Gain Optimization (MIGO) method is implemented to compute the basic parameters set. The analysis is based on simulation of an actual cement mill using long term operational data. As optimization criterion the Integral of Absolute...
In this paper, an intelligent model constructed with fuzzy TS dynamic nonlinear autoregressive with exogenous input (NARX) is introduced for process state identification and behavior prediction for complex processes. In the model, fuzzy neural networks (FNNs) are applied as process state classifiers for process state (fault) detection. An optimization schemes are also investigated for model adaptability...
Aiming at structure optimization and manufacturing process planning problem of modern manufacturing equipment in designing process, we use Open Inventor and level of detail (LOD) technology to construct and optimize the simulation scene of equipment, besides we put forward a kind of virtual language (V-Code) based on virtual reality, which can plan the manufacturing process of equipment, and virtual...
Analyzing the characteristics of wood biomass gasification process comprehensively and considering the impact of gasification temperature as well as the amount of catalyst on gasification gas components, gas yield, gas production rate, the calorific value and gasification efficiency, sawdust gasification process model based on LSSVM (least squares support vector machine) was set up. Based on this...
Currently considerable attention has been given to the effect of data correlation on statistical process control (SPC). Use of traditional SPC methods when observations are correlated often leads to misleading conclusions as to whether or not the process is under control. The objective of this paper is to develop an algorithm to adjust a Dynamic Linear Model, to calculate the run length distribution...
A distributed and integrated framework for missile design and manufacturing is developed in this study, using multidisciplinary optimization and workflow technology. The framework contains technologies of application integration, information integration and process integration. It can reduce the complexity of data exchange among disciplines, shorten the product development cycle and cut down the cost...
Three applications in wireless networks where model-free stochastic learning is applicable, are discussed. The learning based optimization problems are formulated and simulation results are presented. Some open issues are also discussed.
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