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旋风分离器内部气体的下行流量与旋风分离器的性能密切相关。通过采用STAR-CCM+对旋风分离器内部流动情况进行数值分析,发现传统下行流量计算方法存在一定缺陷。在模拟结果基础上提出了一种改进的计算旋风分离器计算下行流量的方法,并采用自定义的量纲一化平均行程的概念分析各参数对下行流量的影响。研究表明:随着进气流速减小、升气管插入深度增加、进气口宽度减小,量纲一化平均行程增加;随着升气管直径减小,量纲一化平均行程先减小,后逐渐增加。 相似文献
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针对目前Internet上常用的WWW技术不能解决所有的远程故障诊断事务,本文介绍了高线精轧机组的远程监测与诊断系统的具体解决方案。该系统采用B/S模式并结合CORBA及SOAP/Web Service技术,实现了对大量实时数据的动态显示、传输以及防火墙穿越等功能。经过长期运行实践证明,该系统具有较好的安全性,良好的可移植性、可扩充性和稳定性,能对现场设备的运行状态及时做出判断,具有较高的可靠性和稳定性。 相似文献
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Traffic jams and suboptimal traffic flows are ubiquitous in modern societies, and they create enormous economic losses each year. Delays at traffic lights alone account for roughly 10% of all delays in US traffic. As most traffic light scheduling systems currently in use are static, set up by human experts rather than being adaptive, the interest in machine learning approaches to this problem has increased in recent years. Reinforcement learning (RL) approaches are often used in these studies, as they require little pre-existing knowledge about traffic flows. Distributed constraint optimisation approaches (DCOP) have also been shown to be successful, but are limited to cases where the traffic flows are known. The distributed coordination of exploration and exploitation (DCEE) framework was recently proposed to introduce learning in the DCOP framework. In this paper, we present a study of DCEE and RL techniques in a complex simulator, illustrating the particular advantages of each, comparing them against standard isolated traffic actuated signals. We analyse how learning and coordination behave under different traffic conditions, and discuss the multi-objective nature of the problem. Finally we evaluate several alternative reward signals in the best performing approach, some of these taking advantage of the correlation between the problem-inherent objectives to improve performance. 相似文献
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ABSTRACTMachine learning based mobile traffic classification has become a popular topic in recent years. As mobile traffic data is dynamic in nature, the static model has become ineffective for the task of classifying future traffic. This is known as the concept drift problem in data streams. To this end, this paper presents an adaptive mobile traffic classification method. Specifically, a method based on the fuzzy competence model is devised to detect concept drift, and a dynamic learning method is presented to update the classification model, so as to adapt to an ever-changing environment at an appropriate time. The concept drift detection method relies on the data distribution instead of the classification error rate. Furthermore, the weights of flow samples are dynamically updated and flow samples are resampled for training a new model when a concept drift is detected. Moreover, recently trained models are saved and used for classification in weighted voting. The weight of each model is updated according to the performance it obtains on the most recent flow samples. On mobile traffic data, experimental results show that our proposed method obtains lower classification error rate with less time consumption on updating models as compared to related methods designed for handling concept drift problems. 相似文献
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1.Introduction〔1〕Trafficandtransportdevelopatahighspeedalongwiththedevelopmentofmodemeconomy.Inordertokeepthetrafficinagoodorderandavoidthetrafficaccidence,theroadmarking,trafficinstallation,signal,dividelineetc.onthehighwayshowtheirimportancegradual… 相似文献
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