The researchers who study the local area network( LAN) eXtension for instrumentation( LXI) instrument are pursuing instrument's high-precision synchronization. In the paper,three synchronization modes were discuss...The researchers who study the local area network( LAN) eXtension for instrumentation( LXI) instrument are pursuing instrument's high-precision synchronization. In the paper,three synchronization modes were discussed which were clock synchronization, trigger synchronization, and response synchronization. Synchronous process between LXI instruments was analyzed and each time factor affecting the synchronization accuracy was discussed. On the basis of the analysis,it can be found that delay trigger plays an important role in the network testing system's synchronization. Delay trigger can produce an additional time interval to correct the difference of each LXI instrument's response time. Then,a method to realize the delay trigger was introduced. Delay time can be adjustable according to the actual demand. Finally,synchronization accuracy of network testing system can reach nanoseconds.展开更多
The network structures of smart substations and the characteristics of industrial Ethernet switches are analyzed.The testing technologies of network systems based on smart substations are specifically elaborated.A vie...The network structures of smart substations and the characteristics of industrial Ethernet switches are analyzed.The testing technologies of network systems based on smart substations are specifically elaborated.A viewpoint is proposed that special testing policy&method of smart substation networks should be followed,so that the results can reveal the real network data exchange performance of the whole station.This view ensures the safety and stability of smart substations and lays a foundation for future upgrades and expansions.展开更多
Objective:To explore the effect differences between moxibustion and donepezil hydrochloride on the attention network function of patients with mild cognitive impairment(MCI).Methods:A total of 64 patients of MCI were ...Objective:To explore the effect differences between moxibustion and donepezil hydrochloride on the attention network function of patients with mild cognitive impairment(MCI).Methods:A total of 64 patients of MCI were randomly divided into the moxibustion group and donepezil hydrochloride group,32 cases in each one.On the basis of conventional treatment,the patients in the moxibustion group were given moxibustion,6 times a week,and the patients in the donepezil hydrochloride group were given donepezil hydrochloride orally,5 mg/day.The course of treatment was 60 days for both of the groups.Cognitive attention network function and activities of daily living(ADL)score were examined before and after treatment.Results:The differences of alerting reaction time(RT),executive control RT,overall mean RT and accuracy of the moxibustion group after treatment were significantly higher than those of the donepezil hydrochloride group[alert:(60.3±3.3)ms vs(48.3±3.7)ms,P<0.05;executive control:(81.2±3.2)ms vs(91.7±4.2)ms,P<0.05;total reaction time:(500.4±17.2)ms vs(536.2±20.1)ms.P<0.05;accuracy:(83.7±4.6)%vs(77.4±4.3)%,P<0.05].After treatment,the ADL scores of the both groups were significantly higher than those before treatment[the moxibustion group:(56.47±4.02)points vs(41.53±4.06)points,P<0.05;the donepezil hydrochloride group:(50.75±4.05)points vs(40.84±3.67)points,P<0.05],and the ADL score of the moxibustion group was significantly higher than that of the donepezil hydrochloride group[(56.47±4.02)points vs(50.75±4.05)points,P<0.05].Conclusion:Compared with donepezil hydrochloride,moxibustion has a better effect on the cognitive function of MCI patients.展开更多
Learning Bayesian network structure is one of the most exciting challenges in machine learning. Discovering a correct skeleton of a directed acyclic graph（DAG） is the foundation for dependency analysis algorithms fo...Learning Bayesian network structure is one of the most exciting challenges in machine learning. Discovering a correct skeleton of a directed acyclic graph（DAG） is the foundation for dependency analysis algorithms for this problem. Considering the unreliability of high order condition independence（CI） tests, and to improve the efficiency of a dependency analysis algorithm, the key steps are to use few numbers of CI tests and reduce the sizes of conditioning sets as much as possible. Based on these reasons and inspired by the algorithm PC, we present an algorithm, named fast and efficient PC（FEPC）, for learning the adjacent neighbourhood of every variable. FEPC implements the CI tests by three kinds of orders, which reduces the high order CI tests significantly. Compared with current algorithm proposals, the experiment results show that FEPC has better accuracy with fewer numbers of condition independence tests and smaller size of conditioning sets. The highest reduction percentage of CI test is 83.3% by EFPC compared with PC algorithm.展开更多
1553B avionics data bus network may fail due to vibration,temperature,humidity or human error.Therefore,the research on detection technology of 1553B avionics data bus network is an important subject.The key technolog...1553B avionics data bus network may fail due to vibration,temperature,humidity or human error.Therefore,the research on detection technology of 1553B avionics data bus network is an important subject.The key technologies are studied by analyzing the possible faults of the network,including four-wire DC resistance measurement method for conductors-to-shield short test and stub continuity test,equivalent impedance measurement of coupling transformer for main bus continuity test,polarity reversal test base on duty ratio measurement,attenuation measurement base on coupler model,and data path integrity test base on bit error rate calculation.Finally,the implementation methods of key technologies are researched,a portable integrated automatic test system of 1553B data bus network is constructed based on PC 104 computer,and the hardware configuration and test process are especially designed.展开更多
Simulations are conducted using five new artificial neural networks developed herein to demonstrate and investigate the behavior of rock material under polyaxial loading. The effects of the intermediate principal stre...Simulations are conducted using five new artificial neural networks developed herein to demonstrate and investigate the behavior of rock material under polyaxial loading. The effects of the intermediate principal stress on the intact rock strength are investigated and compared with laboratory results from the literature. To normalize differences in laboratory testing conditions, the stress state is used as the objective parameter in the artificial neural network model predictions. The variations of major principal stress of rock material with intermediate principal stress, minor principal stress and stress state are investigated. The artificial neural network simulations show that for the rock types examined, none were independent of intermediate principal stress effects. In addition, the results of the artificial neural network models, in general agreement with observations made by others, show （a） a general trend of strength increasing and reaching a peak at some intermediate stress state factor, followed by a decline in strength for most rock types; （b） a post-peak strength behavior dependent on the minor principal stress, with respect to rock type; （c） sensitivity to the stress state, and to the interaction between the stress state and uniaxial compressive strength of the test data by the artificial neural networks models （two-way analysis of variance; 95% confidence interval）. Artificial neural network modeling, a self-learning approach to polyaxial stress simulation, can thus complement the commonly observed difficult task of conducting true triaxial laboratory tests, and/or other methods that attempt to improve two-dimensional （2D） failure criteria by incorporating intermediate principal stress effects.展开更多
Objective To report the development,validation,and findings of the Multi-dimensional Attention Rating Scale(MARS),a self-report tool crafted to evaluate six-dimension attention levels.Methods The MARS was developed ba...Objective To report the development,validation,and findings of the Multi-dimensional Attention Rating Scale(MARS),a self-report tool crafted to evaluate six-dimension attention levels.Methods The MARS was developed based on Classical Test Theory(CTT).Totally 202 highly educated healthy adult participants were recruited for reliability and validity tests.Reliability was measured using Cronbach's alpha and test-retest reliability.Structural validity was explored using principal component analysis.Criterion validity was analyzed by correlating MARS scores with the Toronto Hospital Alertness Test(THAT),the Attentional Control Scale(ACS),and the Attention Network Test(ANT).Results The MARS comprises 12 items spanning six distinct dimensions of attention:focused attention,sustained attention,shifting attention,selective attention,divided attention,and response inhibition.As assessed by six experts,the content validation index(CVI)was 0.95,the Cronbach's alpha for the MARS was 0.78,and the test-retest reliability was 0.81.Four factors were identified(cumulative variance contribution rate 68.79%).The total score of MARS was correlated positively with THAT(r=0.60,P<0.01)and ACS(r=0.78,P<0.01)and negatively with ANT's reaction time for alerting(r=−0.31,P=0.049).Conclusion The MARS can reliably and validly assess six-dimension attention levels in real-world settings and is expected to be a new tool for assessing multi-dimensional attention impairments in different mental disorders.展开更多
针对飞行器飞行试验中外测级间段数据缺失和精度不高的问题,提出了基于长短期记忆(long-short term memory,LSTM)网络的外测级间段数据预测方法。利用遥测视速度数据和外测融合数据建立LSTM网络回归模型,将外测级间段数据作为缺失数据...针对飞行器飞行试验中外测级间段数据缺失和精度不高的问题,提出了基于长短期记忆(long-short term memory,LSTM)网络的外测级间段数据预测方法。利用遥测视速度数据和外测融合数据建立LSTM网络回归模型,将外测级间段数据作为缺失数据进行预测插值,可将制导工具系统误差以及飞行器初始误差,包括遥外测时间对不准误差,一并利用回归网络表示,从而将遥测视速度数据作为网络输入,得到外测级间段的预测数据。试验数据处理结果证明,基于LSTM网络获得的外测级间段预测数据满足精度要求,所提方法具有实际应用价值。展开更多
基金Sino-German Joint Research Project of the Sino-German Center for Science(No.GZ817)the Fundamental Research Funds for the Central Universities,China(No.ZYGX2012 J090)National Natural Science Foundation of China(No.61271035)
文摘The researchers who study the local area network( LAN) eXtension for instrumentation( LXI) instrument are pursuing instrument's high-precision synchronization. In the paper,three synchronization modes were discussed which were clock synchronization, trigger synchronization, and response synchronization. Synchronous process between LXI instruments was analyzed and each time factor affecting the synchronization accuracy was discussed. On the basis of the analysis,it can be found that delay trigger plays an important role in the network testing system's synchronization. Delay trigger can produce an additional time interval to correct the difference of each LXI instrument's response time. Then,a method to realize the delay trigger was introduced. Delay time can be adjustable according to the actual demand. Finally,synchronization accuracy of network testing system can reach nanoseconds.
文摘The network structures of smart substations and the characteristics of industrial Ethernet switches are analyzed.The testing technologies of network systems based on smart substations are specifically elaborated.A viewpoint is proposed that special testing policy&method of smart substation networks should be followed,so that the results can reveal the real network data exchange performance of the whole station.This view ensures the safety and stability of smart substations and lays a foundation for future upgrades and expansions.
基金Supported by National natural science foundation:81574075Natural Science Foundation of Anhui Province:1608085MH184。
文摘Objective:To explore the effect differences between moxibustion and donepezil hydrochloride on the attention network function of patients with mild cognitive impairment(MCI).Methods:A total of 64 patients of MCI were randomly divided into the moxibustion group and donepezil hydrochloride group,32 cases in each one.On the basis of conventional treatment,the patients in the moxibustion group were given moxibustion,6 times a week,and the patients in the donepezil hydrochloride group were given donepezil hydrochloride orally,5 mg/day.The course of treatment was 60 days for both of the groups.Cognitive attention network function and activities of daily living(ADL)score were examined before and after treatment.Results:The differences of alerting reaction time(RT),executive control RT,overall mean RT and accuracy of the moxibustion group after treatment were significantly higher than those of the donepezil hydrochloride group[alert:(60.3±3.3)ms vs(48.3±3.7)ms,P<0.05;executive control:(81.2±3.2)ms vs(91.7±4.2)ms,P<0.05;total reaction time:(500.4±17.2)ms vs(536.2±20.1)ms.P<0.05;accuracy:(83.7±4.6)%vs(77.4±4.3)%,P<0.05].After treatment,the ADL scores of the both groups were significantly higher than those before treatment[the moxibustion group:(56.47±4.02)points vs(41.53±4.06)points,P<0.05;the donepezil hydrochloride group:(50.75±4.05)points vs(40.84±3.67)points,P<0.05],and the ADL score of the moxibustion group was significantly higher than that of the donepezil hydrochloride group[(56.47±4.02)points vs(50.75±4.05)points,P<0.05].Conclusion:Compared with donepezil hydrochloride,moxibustion has a better effect on the cognitive function of MCI patients.
基金Supported by the National Natural Science Foundation of China(61403290,11301408,11401454)the Foundation for Youths of Shaanxi Province(2014JQ1020)+1 种基金the Foundation of Baoji City(2013R7-3)the Foundation of Baoji University of Arts and Sciences(ZK15081)
文摘Learning Bayesian network structure is one of the most exciting challenges in machine learning. Discovering a correct skeleton of a directed acyclic graph（DAG） is the foundation for dependency analysis algorithms for this problem. Considering the unreliability of high order condition independence（CI） tests, and to improve the efficiency of a dependency analysis algorithm, the key steps are to use few numbers of CI tests and reduce the sizes of conditioning sets as much as possible. Based on these reasons and inspired by the algorithm PC, we present an algorithm, named fast and efficient PC（FEPC）, for learning the adjacent neighbourhood of every variable. FEPC implements the CI tests by three kinds of orders, which reduces the high order CI tests significantly. Compared with current algorithm proposals, the experiment results show that FEPC has better accuracy with fewer numbers of condition independence tests and smaller size of conditioning sets. The highest reduction percentage of CI test is 83.3% by EFPC compared with PC algorithm.
基金supported by the Key Equipment Research Project of Air Force of China(KJ2011215)
文摘1553B avionics data bus network may fail due to vibration,temperature,humidity or human error.Therefore,the research on detection technology of 1553B avionics data bus network is an important subject.The key technologies are studied by analyzing the possible faults of the network,including four-wire DC resistance measurement method for conductors-to-shield short test and stub continuity test,equivalent impedance measurement of coupling transformer for main bus continuity test,polarity reversal test base on duty ratio measurement,attenuation measurement base on coupler model,and data path integrity test base on bit error rate calculation.Finally,the implementation methods of key technologies are researched,a portable integrated automatic test system of 1553B data bus network is constructed based on PC 104 computer,and the hardware configuration and test process are especially designed.
文摘Simulations are conducted using five new artificial neural networks developed herein to demonstrate and investigate the behavior of rock material under polyaxial loading. The effects of the intermediate principal stress on the intact rock strength are investigated and compared with laboratory results from the literature. To normalize differences in laboratory testing conditions, the stress state is used as the objective parameter in the artificial neural network model predictions. The variations of major principal stress of rock material with intermediate principal stress, minor principal stress and stress state are investigated. The artificial neural network simulations show that for the rock types examined, none were independent of intermediate principal stress effects. In addition, the results of the artificial neural network models, in general agreement with observations made by others, show （a） a general trend of strength increasing and reaching a peak at some intermediate stress state factor, followed by a decline in strength for most rock types; （b） a post-peak strength behavior dependent on the minor principal stress, with respect to rock type; （c） sensitivity to the stress state, and to the interaction between the stress state and uniaxial compressive strength of the test data by the artificial neural networks models （two-way analysis of variance; 95% confidence interval）. Artificial neural network modeling, a self-learning approach to polyaxial stress simulation, can thus complement the commonly observed difficult task of conducting true triaxial laboratory tests, and/or other methods that attempt to improve two-dimensional （2D） failure criteria by incorporating intermediate principal stress effects.
文摘Objective To report the development,validation,and findings of the Multi-dimensional Attention Rating Scale(MARS),a self-report tool crafted to evaluate six-dimension attention levels.Methods The MARS was developed based on Classical Test Theory(CTT).Totally 202 highly educated healthy adult participants were recruited for reliability and validity tests.Reliability was measured using Cronbach's alpha and test-retest reliability.Structural validity was explored using principal component analysis.Criterion validity was analyzed by correlating MARS scores with the Toronto Hospital Alertness Test(THAT),the Attentional Control Scale(ACS),and the Attention Network Test(ANT).Results The MARS comprises 12 items spanning six distinct dimensions of attention:focused attention,sustained attention,shifting attention,selective attention,divided attention,and response inhibition.As assessed by six experts,the content validation index(CVI)was 0.95,the Cronbach's alpha for the MARS was 0.78,and the test-retest reliability was 0.81.Four factors were identified(cumulative variance contribution rate 68.79%).The total score of MARS was correlated positively with THAT(r=0.60,P<0.01)and ACS(r=0.78,P<0.01)and negatively with ANT's reaction time for alerting(r=−0.31,P=0.049).Conclusion The MARS can reliably and validly assess six-dimension attention levels in real-world settings and is expected to be a new tool for assessing multi-dimensional attention impairments in different mental disorders.
文摘针对飞行器飞行试验中外测级间段数据缺失和精度不高的问题,提出了基于长短期记忆(long-short term memory,LSTM)网络的外测级间段数据预测方法。利用遥测视速度数据和外测融合数据建立LSTM网络回归模型,将外测级间段数据作为缺失数据进行预测插值,可将制导工具系统误差以及飞行器初始误差,包括遥外测时间对不准误差,一并利用回归网络表示,从而将遥测视速度数据作为网络输入,得到外测级间段的预测数据。试验数据处理结果证明,基于LSTM网络获得的外测级间段预测数据满足精度要求,所提方法具有实际应用价值。
