This paper aims at effectively predicting the dynamic network traffic flow based on quantum-behaved particle swarm optimization algorithm. Firstly, the dynamic network traffic flow prediction problem is analyzed through formal description. Secondly, the structure of the network traffic flow prediction model is given. In this structure, Users can used a computer to start the traffic flow predict...

In this article, a novel dynamic Bayesian network based networked process monitoring approach is proposed for fault detection, propagation pathway identification and root cause diagnosis. First, process network structure is designed according to the prior process knowledge including process flow sheets and used to characterize the causal relationships among different measurement variables. Then...

a new dynamic friction model for modeling of elliptical vibration assisted turning (evat) was developed in this research. the periodic change of the friction force direction is known to be one of the most important causes of this phenomenon. in modeling of machining processes (including the evat process), static coulomb friction model was employed by most of the researchers. because of the peri...

production of highly viscous tar sand bitumen using steam assisted gravity drainage (sagd) with a pair of horizontal wells has advantages over conventional steam flooding. this paper explores the use of artificial neural networks (anns) as an alternative to the traditional sagd simulation approach. feed forward, multi-layered neural network meta-models are trained through the back-error-propaga...

For any agency dealing with the design of the water distribution network, an economic design will be an objective. In this research, Central Force Optimization (CFO) and Differential Evolution (DE) algorithm were used to optimize Ismail Abad water Distribution network. Optimization of the network has been evaluated by developing an optimization model based on CFO and DE algorithm in MATLAB and ...

Dynamic Bayesian network is the extension of Bayesian network in solving time series problems .It can be well dealt with the time-varying multivariable problem. A state model is given based on Dynamic Bayesian network. The model can more accurately describe the relationship between the system state and the influencing factors. Single-step and multi-step prediction algorithms are given to predic...

Industrial-Scale R&D IT Projects depend on many sub-technologies which need to be understood and have their risks analysed before the project can begin for their success. When planning such an industrial-scale project, the list of technologies and the associations of these technologies with each other is often complex and form a network. Discovery of this network of technologies is time consumi...

The purpose of this paper is twofold. First, we develop a multi-period dynamic multi-process network DEA (data envelopment analysis) model. Second, we apply this methodology to Japanese prefectural time series data. In this framework, we specify that prefectural technology consists of two sectors, called the human capital generating sector and the physical capital formation sector. Each sector ...