Detecting a pipeline's abnormal status,which is typically a blockage and leakage accident,is important for the continuity and safety of mine backfill.The pipeline system for gravity-transport high-density backfill...Detecting a pipeline's abnormal status,which is typically a blockage and leakage accident,is important for the continuity and safety of mine backfill.The pipeline system for gravity-transport high-density backfill(GHB)is complex.Specifically designed,efficient,and accurate abnormal pipeline detection methods for GHB are rare.This work presents a long short-term memory-based deep learning(LSTM-DL)model for GHB pipeline blockage and leakage diagnosis.First,an industrial pipeline monitoring system was introduced using pressure and flow sensors.Second,blockage and leakage field experiments were designed to solve the problem of negative sample deficiency.The pipeline's statistical characteristics with different working statuses were analyzed to show their complexity.Third,the architecture of the LSTM-DL model was elaborated on and evaluated.Finally,the LSTM-DL model was compared with state-of-the-art(SOTA)learning algorithms.The results show that the backfilling cycle comprises multiple working phases and is intermittent.Although pressure and flow signals fluctuate stably in a normal cycle,their values are diverse in different cycles.Plugging causes a sudden change in interval signal features;leakage results in long variation duration and a wide fluctuation range.Among the SOTA models,the LSTM-DL model has the highest detection accuracy of98.31%for all states and the lowest misjudgment or false positive rate of 3.21%for blockage and leakage states.The proposed model can accurately recognize various pipeline statuses of complex GHB systems.展开更多
Oil leakage between the slipper and swash plate of an axial piston pump has a significant effect on the efficiency of the pump.Therefore,it is extremely important that any leakage can be predicted.This study investiga...Oil leakage between the slipper and swash plate of an axial piston pump has a significant effect on the efficiency of the pump.Therefore,it is extremely important that any leakage can be predicted.This study investigates the leakage,oil film thickness,and pocket pressure values of a slipper with circular dimples under different working conditions.The results reveal that flat slippers suffer less leakage than those with textured surfaces.Also,a deep learning-based framework is proposed for modeling the slipper behavior.This framework is a long short-term memory-based deep neural network,which has been extremely successful in predicting time series.The model is compared with four conventional machine learning methods.In addition,statistical analyses and comparisons confirm the superiority of the proposed model.展开更多
We generalize the conception of quantum leakage for the atomic collective excitation states. By making use of the atomic coherence state approach, we study the influence of the atomic spatial motion on the symmetric c...We generalize the conception of quantum leakage for the atomic collective excitation states. By making use of the atomic coherence state approach, we study the influence of the atomic spatial motion on the symmetric collective states of 2-level atomic ensemble due to inhomogeneous coupling. In the macroscopic limit, we analyze the quantum decoherence of the collective atomic state by calculating the quantum leakage for a very large ensemble at a finite temperature. Our investigations show that the fidelity of the atomic system will not be good in the case of atom number N →∞. Therefore, quantum leakage is an inevitable problem in using the atomic ensemble as a quantum information memory. The detailed calculations shed theoretical light on quantum processing using atomic ensemble collective qubit.展开更多
In order to minimize leakage current increase under total ionizing dose （TID） radiation in high density memory circuits, a new isolation technique, combining deep trench isolation （DTI） between the wells, local ox...In order to minimize leakage current increase under total ionizing dose （TID） radiation in high density memory circuits, a new isolation technique, combining deep trench isolation （DTI） between the wells, local oxi- dation of silicon （LOCOS） isolation between the devices within the well, and a P-diffused area in order to limit leakage at the isolation edge is implemented in partly-depleted silicon-on-insulator （PD-SOI） technology. This ra- diation hardening technique can minimize the layout area by more than 60%, and allows flexible placement of the body contact. Radiation hardened transistors and 256 Kb flash memory chips are designed and fabricated in a 0.6 μm PD-SOI process. Experiments show that no obvious increase in leakage current is observed for single tran- sistors under 1 Mrad（Si） radiation, and that the 256 Kb memory chip still functions well after a TID of 100 krad（Si）, with only 50% increase of the active power consumption in read mode.展开更多
In order to investigate the physical mechanism of metal magnetic memory testing, both the influences of earth magnetic field and applied stress on magnetic domain structure were discussed. Static tension and fatigue t...In order to investigate the physical mechanism of metal magnetic memory testing, both the influences of earth magnetic field and applied stress on magnetic domain structure were discussed. Static tension and fatigue tests for low carbon steel plate specimens were carried out on hydraulic servo testing machine of MTS810 type and magnetic signals were measured during the processes by the type of EMS-2003 instrument. The results indicate that the initial magnetic signals of specimens are different before loading. The magnetic signals curves are transformed from initial random to regular pattern due to the effect of two types of loads. However, the shape and distribution of magnetic signal curves in the elastic region are different from that of plastic region in tension test. While in fatigue test those magnetic signals curves corresponding to different cycles are similar. The H_p(y) value of magnetic signals on the fracture zone increases dramatically at the breaking transient time and positive-negative magnetic poles occur on the two parts of fracture zone.展开更多
基金financially supported by the China Postdoctoral Science Foundation (No.2021M690362)the National Natural Science Foundation of China (Nos.51974014 and U2034206)。
文摘Detecting a pipeline's abnormal status,which is typically a blockage and leakage accident,is important for the continuity and safety of mine backfill.The pipeline system for gravity-transport high-density backfill(GHB)is complex.Specifically designed,efficient,and accurate abnormal pipeline detection methods for GHB are rare.This work presents a long short-term memory-based deep learning(LSTM-DL)model for GHB pipeline blockage and leakage diagnosis.First,an industrial pipeline monitoring system was introduced using pressure and flow sensors.Second,blockage and leakage field experiments were designed to solve the problem of negative sample deficiency.The pipeline's statistical characteristics with different working statuses were analyzed to show their complexity.Third,the architecture of the LSTM-DL model was elaborated on and evaluated.Finally,the LSTM-DL model was compared with state-of-the-art(SOTA)learning algorithms.The results show that the backfilling cycle comprises multiple working phases and is intermittent.Although pressure and flow signals fluctuate stably in a normal cycle,their values are diverse in different cycles.Plugging causes a sudden change in interval signal features;leakage results in long variation duration and a wide fluctuation range.Among the SOTA models,the LSTM-DL model has the highest detection accuracy of98.31%for all states and the lowest misjudgment or false positive rate of 3.21%for blockage and leakage states.The proposed model can accurately recognize various pipeline statuses of complex GHB systems.
基金Supported by Erciyes University Scientific Research Projects Coordination Unit(Grant No.FDK-2016-6986).
文摘Oil leakage between the slipper and swash plate of an axial piston pump has a significant effect on the efficiency of the pump.Therefore,it is extremely important that any leakage can be predicted.This study investigates the leakage,oil film thickness,and pocket pressure values of a slipper with circular dimples under different working conditions.The results reveal that flat slippers suffer less leakage than those with textured surfaces.Also,a deep learning-based framework is proposed for modeling the slipper behavior.This framework is a long short-term memory-based deep neural network,which has been extremely successful in predicting time series.The model is compared with four conventional machine learning methods.In addition,statistical analyses and comparisons confirm the superiority of the proposed model.
基金the National Natural Science Foundation of Chinathe Knowledge Innovation program (KIP) of the Chinese Academy of Sciencesthe National Fundamental Research Program of China (Grant No. 001GB309310)
文摘We generalize the conception of quantum leakage for the atomic collective excitation states. By making use of the atomic coherence state approach, we study the influence of the atomic spatial motion on the symmetric collective states of 2-level atomic ensemble due to inhomogeneous coupling. In the macroscopic limit, we analyze the quantum decoherence of the collective atomic state by calculating the quantum leakage for a very large ensemble at a finite temperature. Our investigations show that the fidelity of the atomic system will not be good in the case of atom number N →∞. Therefore, quantum leakage is an inevitable problem in using the atomic ensemble as a quantum information memory. The detailed calculations shed theoretical light on quantum processing using atomic ensemble collective qubit.
基金Project supported by the National Key Basic Research Program(No.2011CBA00602)the National Natural Science Foundation of China(Nos.61106102,61176033)
文摘In order to minimize leakage current increase under total ionizing dose （TID） radiation in high density memory circuits, a new isolation technique, combining deep trench isolation （DTI） between the wells, local oxi- dation of silicon （LOCOS） isolation between the devices within the well, and a P-diffused area in order to limit leakage at the isolation edge is implemented in partly-depleted silicon-on-insulator （PD-SOI） technology. This ra- diation hardening technique can minimize the layout area by more than 60%, and allows flexible placement of the body contact. Radiation hardened transistors and 256 Kb flash memory chips are designed and fabricated in a 0.6 μm PD-SOI process. Experiments show that no obvious increase in leakage current is observed for single tran- sistors under 1 Mrad（Si） radiation, and that the 256 Kb memory chip still functions well after a TID of 100 krad（Si）, with only 50% increase of the active power consumption in read mode.
文摘In order to investigate the physical mechanism of metal magnetic memory testing, both the influences of earth magnetic field and applied stress on magnetic domain structure were discussed. Static tension and fatigue tests for low carbon steel plate specimens were carried out on hydraulic servo testing machine of MTS810 type and magnetic signals were measured during the processes by the type of EMS-2003 instrument. The results indicate that the initial magnetic signals of specimens are different before loading. The magnetic signals curves are transformed from initial random to regular pattern due to the effect of two types of loads. However, the shape and distribution of magnetic signal curves in the elastic region are different from that of plastic region in tension test. While in fatigue test those magnetic signals curves corresponding to different cycles are similar. The H_p(y) value of magnetic signals on the fracture zone increases dramatically at the breaking transient time and positive-negative magnetic poles occur on the two parts of fracture zone.
