Predicting the performance of a tunneling boring machine is vitally important to avoid any possible accidents during tunneling boring.The prediction is not straightforward due to the uncertain geological conditions an...Predicting the performance of a tunneling boring machine is vitally important to avoid any possible accidents during tunneling boring.The prediction is not straightforward due to the uncertain geological conditions and the complex rock-machine interactions.Based on the big data obtained from the 72.1 km long tunnel in the Yin-Song Diversion Project in China,this study developed a machine learning model to predict the TBM performance in a real-time manner.The total thrust and the cutterhead torque during a stable period in a boring cycle was predicted in advance by using the machine-returned parameters in the rising period.A long short-term memory model was developed and its accuracy was evaluated.The results show that the variation in the total thrust and cutterhead torque with various geological conditions can be well reflected by the proposed model.This real-time predication shows superior performance than the classical theoretical model in which only a single value can be obtained based on the single measurement of the rock properties.To improve the accuracy of the model a filtering process was proposed.Results indicate that filtering the unnecessary parameters can enhance both the accuracy and the computational efficiency.Finally,the data deficiency was discussed by assuming a parameter was missing.It is found that the missing of a key parameter can significantly reduce the accuracy of the model,while the supplement of a parameter that highly-correlated with the missing one can improve the prediction.展开更多
制造系统能效评估及其机床能效评价研究正在国际上迅速兴起,生产现场的机床服役过程(Machine tools’serviceprocess,MTSP)能量效率的方便获取是其关键问题之一。为此,对MTSP中机电主传动系统(Electro-mechanical main drivingsystem,EM...制造系统能效评估及其机床能效评价研究正在国际上迅速兴起,生产现场的机床服役过程(Machine tools’serviceprocess,MTSP)能量效率的方便获取是其关键问题之一。为此,对MTSP中机电主传动系统(Electro-mechanical main drivingsystem,EMDS)能量效率获取方法进行研究。在分析机床能量效率内涵的基础上,基于MTSP中EMDS的时段能量模型,建立基于输入功率检测的和基于输出功率获取的两类4种EMDS能量效率模型;提出对应的能量效率模型中基础数据和基础系数特别是机床载荷损耗系数的获取方法;形成MTSP中EMDS能量效率获取方法。应用该方法,只需现场检测EMDS输入功率,就可获得MTSP中EMDS的现场能量效率。应用案例表明,应用本方法不仅能实现MTSP能量效率现场获取,而且误差较小;可用于机床及制造系统的能效评价、能效监控和能效优化研究中,具有较广阔的应用前景。展开更多
The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabli...The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems.展开更多
基金supported by the Natural Science Foundation of China(Grant No.51679060)。
文摘Predicting the performance of a tunneling boring machine is vitally important to avoid any possible accidents during tunneling boring.The prediction is not straightforward due to the uncertain geological conditions and the complex rock-machine interactions.Based on the big data obtained from the 72.1 km long tunnel in the Yin-Song Diversion Project in China,this study developed a machine learning model to predict the TBM performance in a real-time manner.The total thrust and the cutterhead torque during a stable period in a boring cycle was predicted in advance by using the machine-returned parameters in the rising period.A long short-term memory model was developed and its accuracy was evaluated.The results show that the variation in the total thrust and cutterhead torque with various geological conditions can be well reflected by the proposed model.This real-time predication shows superior performance than the classical theoretical model in which only a single value can be obtained based on the single measurement of the rock properties.To improve the accuracy of the model a filtering process was proposed.Results indicate that filtering the unnecessary parameters can enhance both the accuracy and the computational efficiency.Finally,the data deficiency was discussed by assuming a parameter was missing.It is found that the missing of a key parameter can significantly reduce the accuracy of the model,while the supplement of a parameter that highly-correlated with the missing one can improve the prediction.
文摘制造系统能效评估及其机床能效评价研究正在国际上迅速兴起,生产现场的机床服役过程(Machine tools’serviceprocess,MTSP)能量效率的方便获取是其关键问题之一。为此,对MTSP中机电主传动系统(Electro-mechanical main drivingsystem,EMDS)能量效率获取方法进行研究。在分析机床能量效率内涵的基础上,基于MTSP中EMDS的时段能量模型,建立基于输入功率检测的和基于输出功率获取的两类4种EMDS能量效率模型;提出对应的能量效率模型中基础数据和基础系数特别是机床载荷损耗系数的获取方法;形成MTSP中EMDS能量效率获取方法。应用该方法,只需现场检测EMDS输入功率,就可获得MTSP中EMDS的现场能量效率。应用案例表明,应用本方法不仅能实现MTSP能量效率现场获取,而且误差较小;可用于机床及制造系统的能效评价、能效监控和能效优化研究中,具有较广阔的应用前景。
基金support from the National Science Foundation under Grants 1443894,1560437,and 1731017Louisiana Board of Regents under Grant LEQSF(2017-20)-RD-A-29a research gift from Intel Corporation
文摘The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems.
