In the electric discharge machining system, the determination of the gap between the anode and the cathode is a difficult point of this kind of machining approach. An accurate mathematical model of interelectrode gap is obtained, and the precise control of the gap is achieved on this basis. In this paper, based on the example of discharge machining of P-type single crystal Si, the theoretical analysis proved that the discharge channel can be equivalent to pure resistance, and the physical model of the interelectrode gap and voltage and current was established. The order and parameters of the EDM system model were determined by adopting the system identification theory. We designed the minimum variance self-correcting controller to accurately control the interelectrode gap in combination with the actual machining process. Experimental results show that the interelectrode gap model can correctly reflect the interelectrode gap in the actual machining process; the minimum variance self-correcting controller eliminates the short circuit phenomenon during processing and can stably track different desired gaps; the material removal rate and the surface roughness decrease with the increase of the interelectrode gap.
Traffic congestion has become a major problem restricting the development of major cities. ITS (Intelligent Transportation System) can record the state of traffic and predict the future traffic state, then reasonably optimize the travel scheme, so as to achieve the purpose of alleviating traffic congestion. Meanwhile, traffic flow prediction can provide data support for ITS, so many researchers have done a lot of research on traffic flow prediction. Many researchers take the traffic network as an undirected graph, and use the GCN (Graph Convolution Network) model to study the traffic flow prediction, and have achieved good prediction results. However, the traffic network is directed, and the traffic network is regarded as an undirected graph, which loses the direction information of the road network. Therefore, this inspires us to propose a graph convolution operator DGCN (Directed GCN), which can make full use of the in degree and out degree information of each station in the traffic network. The experimental results show that the graph convolution neural network based on this operator has better prediction accuracy than the state-of-the-art models.
Short-term traffic flow forecasting has always been an interesting research at the fields of Intelligent Transportation Systems. This paper presents a time-based combined traffic flow prediction model based on field data collected by loop detectors at signalized intersections, which are used to signal optimization, route choice, traffic monitoring, etc. Firstly, the traffic flow and corresponding travel speed by hour is processed for error elimination and correlation analysis. Secondly, time of day is divided into three groups (peak, flat-peak and low-peak period) in terms of hourly travel speed clustering such as to separately develop prediction formula for each period with avoiding the overfitting of a single 24-hour model. And then, a combined prediction model based on time partition is proposed for 24-hour traffic flow forecasting, which adopts grey theory model for flat-peak and low-peak periods and back-propagation artificial neural network for peak hours, respectively. Finally, in tests that used field data from Xingzhong Rd, Zhongshan, China, the developed combined method based on speed clustering shows promise in reducing mean absolute error, mean absolute percentage error and mean squared error. Further exploration with excessive experiments for comparison analysis exhibits that the period-specific combined model conducts a more accurate and reliable prediction than the individual model and existing combined ones with the same structure for 24-hour. INDEX TERMS Traffic flow prediction, cluster analysis, gray theory model, backpropagation artificial neural network.
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