In this paper, we study an M/M/1 queue with multiple working vacations under following Bernoulli control policy: at the instants of the completion of a service in vacation, the server will interrupt the vacation and e...In this paper, we study an M/M/1 queue with multiple working vacations under following Bernoulli control policy: at the instants of the completion of a service in vacation, the server will interrupt the vacation and enter regular busy period with probability 1 p (if there are customers in the queue) or continue the vacation with probability p. For this model, we drive the analytic expression of the stationary queue length and demonstrate stochastic decomposition structures of the stationary queue length and waiting time, also we obtain the additional queue length and the additional delay of this model. The results we got agree with the corresponding results for working vacation model with or without vacation interruption if we set p = 0 or p = 1, respectively.展开更多
We study a batch arrival MX/M/1 queue with multiple working vacation. The server serves customers at a lower rate rather than completely stopping service during the service period. Using a quasi upper triangular trans...We study a batch arrival MX/M/1 queue with multiple working vacation. The server serves customers at a lower rate rather than completely stopping service during the service period. Using a quasi upper triangular transition probability matrix of two-dimensional Markov chain and matrix analytic method, the probability generating function (PGF) of the stationary system length distribution is obtained, from which we obtain the stochastic decomposition structure of system length which indicates the relationship with that of the MX/M/1 queue without vacation. Some performance indices are derived by using the PGF of the stationary system length distribution. It is important that we obtain the Laplace Stieltjes transform (LST) of the stationary waiting time distribution. Further, we obtain the mean system length and the mean waiting time. Finally, numerical results for some special cases are presented to show the effects of system parameters.展开更多
This paper presents a back-propagation neural network model for sound quality prediction (BPNN-SQP) of multiple working conditions’ vehicle interior noise. According to the standards and regulations, four kinds of ve...This paper presents a back-propagation neural network model for sound quality prediction (BPNN-SQP) of multiple working conditions’ vehicle interior noise. According to the standards and regulations, four kinds of vehicle interior noises under operating conditions, including idle, constant speed, accelerating and braking, are acquired. The objective psychoacoustic parameters and subjective annoyance results are respectively used as the input and output of the BPNN-SQP model. With correlation analysis and significance test, some psychoacoustic parameters, such as loudness, A-weighted sound pressure level, roughness, articulation index and sharpness, are selected for modeling. The annoyance values of unknown noise samples estimated by the BPNN-SQP model are highly correlated with the subjective annoyances. Conclusion can be drawn that the proposed BPNN-SQP model has good generalization ability and can be applied in sound quality prediction of vehicle interior noise under multiple working conditions.展开更多
In the present research,we proposed a scheme to address the issues of severe heat damage,high energy consumption,low cooling system efficiency,and wastage of cold capacity in mines.To elucidate the seasonal variations...In the present research,we proposed a scheme to address the issues of severe heat damage,high energy consumption,low cooling system efficiency,and wastage of cold capacity in mines.To elucidate the seasonal variations of environmental temperature through field measurements,we selected a high-temperature working face in a deep mine as our engineering background.To enhance the heat damage control cability of the working face and minimize unnecessary cooling capac-ity loss,we introduced the multi-dimensional heat hazard prevention and control method called"Heat source barrier and cooling equipment".First,we utilize shotcrete and liquid nitrogen injection to eliminate the heat source and implemented pressure equalization ventilation to disrupt the heat transfer path,thereby creating a heat barrier.Second,we establish divi-sional prediction models for airflow temperature based on the variation patterns obtained through numerical simulation.Third,we devise the location and dynamic control strategy for the cooling equipment based on the prediction models.The results of field application show that the heat resistance and cooling linkage method comply with the safety requirement throughout the entire mining cycle while effectively reducing energy consumption.The ambient temperature is maintained below 30℃,resulting in the energy saving of 10%during the high-temperature period and over 50%during the low-temperature period.These findings serve as a valuable reference for managing heat damage in high-temperature working faces.展开更多
基金Foundation item： Supported by the National Science Foundation of China（60874083） Supported by the 2011 National Statistical Science Development Funds（2011LY014） Supported by the 2012 Soft Science Devel- opment Funds of Science and Technology Committee of Henan Province（122400450090）
文摘In this paper, we study an M/M/1 queue with multiple working vacations under following Bernoulli control policy: at the instants of the completion of a service in vacation, the server will interrupt the vacation and enter regular busy period with probability 1 p (if there are customers in the queue) or continue the vacation with probability p. For this model, we drive the analytic expression of the stationary queue length and demonstrate stochastic decomposition structures of the stationary queue length and waiting time, also we obtain the additional queue length and the additional delay of this model. The results we got agree with the corresponding results for working vacation model with or without vacation interruption if we set p = 0 or p = 1, respectively.
文摘We study a batch arrival MX/M/1 queue with multiple working vacation. The server serves customers at a lower rate rather than completely stopping service during the service period. Using a quasi upper triangular transition probability matrix of two-dimensional Markov chain and matrix analytic method, the probability generating function (PGF) of the stationary system length distribution is obtained, from which we obtain the stochastic decomposition structure of system length which indicates the relationship with that of the MX/M/1 queue without vacation. Some performance indices are derived by using the PGF of the stationary system length distribution. It is important that we obtain the Laplace Stieltjes transform (LST) of the stationary waiting time distribution. Further, we obtain the mean system length and the mean waiting time. Finally, numerical results for some special cases are presented to show the effects of system parameters.
文摘This paper presents a back-propagation neural network model for sound quality prediction (BPNN-SQP) of multiple working conditions’ vehicle interior noise. According to the standards and regulations, four kinds of vehicle interior noises under operating conditions, including idle, constant speed, accelerating and braking, are acquired. The objective psychoacoustic parameters and subjective annoyance results are respectively used as the input and output of the BPNN-SQP model. With correlation analysis and significance test, some psychoacoustic parameters, such as loudness, A-weighted sound pressure level, roughness, articulation index and sharpness, are selected for modeling. The annoyance values of unknown noise samples estimated by the BPNN-SQP model are highly correlated with the subjective annoyances. Conclusion can be drawn that the proposed BPNN-SQP model has good generalization ability and can be applied in sound quality prediction of vehicle interior noise under multiple working conditions.
基金supported by the National Natural Science Foundation of China (51874281)the Graduate Innovation Program of China University of Mining and Technology (2022WLKXJ006)the Postgraduate Research&Practice Innovation Program of Jiangsu Province (KYCX22_2612).
文摘In the present research,we proposed a scheme to address the issues of severe heat damage,high energy consumption,low cooling system efficiency,and wastage of cold capacity in mines.To elucidate the seasonal variations of environmental temperature through field measurements,we selected a high-temperature working face in a deep mine as our engineering background.To enhance the heat damage control cability of the working face and minimize unnecessary cooling capac-ity loss,we introduced the multi-dimensional heat hazard prevention and control method called"Heat source barrier and cooling equipment".First,we utilize shotcrete and liquid nitrogen injection to eliminate the heat source and implemented pressure equalization ventilation to disrupt the heat transfer path,thereby creating a heat barrier.Second,we establish divi-sional prediction models for airflow temperature based on the variation patterns obtained through numerical simulation.Third,we devise the location and dynamic control strategy for the cooling equipment based on the prediction models.The results of field application show that the heat resistance and cooling linkage method comply with the safety requirement throughout the entire mining cycle while effectively reducing energy consumption.The ambient temperature is maintained below 30℃,resulting in the energy saving of 10%during the high-temperature period and over 50%during the low-temperature period.These findings serve as a valuable reference for managing heat damage in high-temperature working faces.
文摘针对工业环境中广泛在多工况下多滚动轴承实时状态监测的需求和部署环境受限的挑战,提出一种基于卷积神经网络(Convolutional Neural Network,CNN)的面向多传感器滚动轴承运行状态监控方法。该方法将两个不同工况下的一维时间序列数据集以均方根(Root Mean Square,RMS)指标标注,并通过将一维时间序列多传感器数据重构为二维空间张量的形式输入卷积神经网络训练。最后利用层融合和16比特量化优化,将网络部署到FPGA上,用以解决CNN的计算开销。实验结果表明,在结合了两种不同工况的数据集下,网络测试推理准确度依然高达99.24%,比多层感知机实现高10.48%,比多层感知机结合支持向量机的实现高2.91%,该算法对于新加入的数据集也有较强的鲁棒性,经过重训练,新加入的数据集准确率可以达到99.17%。基于FPGA部署优化的网络的峰值能效为76.217GPOS/W,为CPU实现的33.09倍,GPU实现的5.39倍。其中,16比特精度部署的网络测试精度相较32比特精度实现仅降低0.001%。
