To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the accele...To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.展开更多
This paper introduces the design and implementation of sea-water temperature auto-monitoring system based on General Packet Radio Service （GPRS）. This system integrates modern wireless communication technology, the ...This paper introduces the design and implementation of sea-water temperature auto-monitoring system based on General Packet Radio Service （GPRS）. This system integrates modern wireless communication technology, the signal gathering technology and computer network technology. MSC1210 microcontroller is used in data collection device in order to make system accurate and fast. In addition, wireless and Internet technologies are used for transferring and displaying collected field data. A prototype system has been completed and tested in field trials. The results proved the feasibility and usefulness of this system for monitoring the temperature. By using this system, a lot of resources and money can be saved.展开更多
CSIRO has recently developed a real-time roof monitoring system for under-groundcoal mines and successfully tried the system in gate roads at Ulan Mine.The systemintegrated displacement monitoring,stress monitoring an...CSIRO has recently developed a real-time roof monitoring system for under-groundcoal mines and successfully tried the system in gate roads at Ulan Mine.The systemintegrated displacement monitoring,stress monitoring and seismic monitoring in onepackage.It included GEL multianchor extensometers,vibrating wire uniaxial stress meters,ESG seismic monitoring system with microseismic sensors and high-frequency AE sensors.The monitoring system automated and the data can be automatically collected by acentral computer located in an underground nonhazardous area.The data are then transferredto the surface via an optical fiber cable.The real-time data were accessed at anylocation with an Internet connection.The trials of the system in two tailgates at Ulan Minedemonstrate that the system is effective for monitoring the behavior and stability of roadwaysduring Iongwall mining.The continuous roof displacement/stress data show clearprecursors of roof falls.The seismic data (event count and locations) provide insights intothe roof failure process during roof fall.展开更多
Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for position...Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.展开更多
In this paper, we describe a new batch process monitoring method based on multilevel independent component analysis and principal component analysis (MLICA-PCA). Unlike the conventional multi-way principal component a...In this paper, we describe a new batch process monitoring method based on multilevel independent component analysis and principal component analysis (MLICA-PCA). Unlike the conventional multi-way principal component analysis (MPCA) method, MLICA-PCA provides a separated interpretation for multilevel batch process data. Batch process data are partitioned into two levels: the within-batch level and the between-batch level. In each level, the Gaussian and non-Gaussian components of process information can be separately extracted. I2, T2 and SPE statistics are individually built and monitored. The new method facilitates fault diagnosis. Since the two variation levels are decomposed, the variables responsible for faults in each level can be identified and interpreted more easily. A case study of the Dupont benchmark process showed that the proposed method was more efficient and interpretable in fault detection and diagnosis, compared to the alternative batch process monitoring method.展开更多
AIM:To conduct a bacterial culture study for monitoring decontamination of automated endoscope reprocessors(AERs) after high-level disinfection(HLD).METHODS:From February 2006 to January 2011,authors conducted randomi...AIM:To conduct a bacterial culture study for monitoring decontamination of automated endoscope reprocessors(AERs) after high-level disinfection(HLD).METHODS:From February 2006 to January 2011,authors conducted randomized consecutive sampling each month for 7 AERs.Authors collected a total of 420 swab cultures,including 300 cultures from 5 gastroscope AERs,and 120 cultures from 2 colonoscope AERs.Swab cultures were obtained from the residual water from the AERs after a full reprocessing cycle.Samples were cultured to test for aerobic bacteria,anaerobic bacteria,and mycobacterium tuberculosis.RESULTS:The positive culture rate of the AERs was 2.0%(6/300) for gastroscope AERs and 0.8%(1/120) for colonoscope AERs.All the positive cultures,including 6 from gastroscope and 1 from colonoscope AERs,showed monofloral colonization.Of the gastroscopeAER samples,50%(3/6) were colonized by aerobic bacterial and 50%(3/6) by fungal contaminations.CONCLUSION:A full reprocessing cycle of an AER with HLD is adequate for disinfection of the machine.Swab culture is a useful method for monitoring AER decontamination after each reprocessing cycle.Fungal contamination of AERs after reprocessing should also be kept in mind.展开更多
基金The National Natural Science Foundation of China(No.52361165658,52378318,52078459).
文摘To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.
文摘This paper introduces the design and implementation of sea-water temperature auto-monitoring system based on General Packet Radio Service （GPRS）. This system integrates modern wireless communication technology, the signal gathering technology and computer network technology. MSC1210 microcontroller is used in data collection device in order to make system accurate and fast. In addition, wireless and Internet technologies are used for transferring and displaying collected field data. A prototype system has been completed and tested in field trials. The results proved the feasibility and usefulness of this system for monitoring the temperature. By using this system, a lot of resources and money can be saved.
文摘CSIRO has recently developed a real-time roof monitoring system for under-groundcoal mines and successfully tried the system in gate roads at Ulan Mine.The systemintegrated displacement monitoring,stress monitoring and seismic monitoring in onepackage.It included GEL multianchor extensometers,vibrating wire uniaxial stress meters,ESG seismic monitoring system with microseismic sensors and high-frequency AE sensors.The monitoring system automated and the data can be automatically collected by acentral computer located in an underground nonhazardous area.The data are then transferredto the surface via an optical fiber cable.The real-time data were accessed at anylocation with an Internet connection.The trials of the system in two tailgates at Ulan Minedemonstrate that the system is effective for monitoring the behavior and stability of roadwaysduring Iongwall mining.The continuous roof displacement/stress data show clearprecursors of roof falls.The seismic data (event count and locations) provide insights intothe roof failure process during roof fall.
基金supported by the National Key R&D Program of China(No.2018AAA0100804)the Talent Project of Revitalization Liaoning(No.XLYC1907022)+5 种基金the Key R&D Projects of Liaoning Province(No.2020JH2/10100045)the Capacity Building of Civil Aviation Safety(No.TMSA1614)the Natural Science Foundation of Liaoning Province(No.2019-MS-251)the Scientific Research Project of Liaoning Provincial Department of Education(Nos.L201705,L201716)the High-Level Innovation Talent Project of Shenyang(No.RC190030)the Second Young and Middle-Aged Talents Support Program of Shenyang Aerospace University.
文摘Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.
基金Project (No. 60774067) supported by the National Natural ScienceFoundation of China
文摘In this paper, we describe a new batch process monitoring method based on multilevel independent component analysis and principal component analysis (MLICA-PCA). Unlike the conventional multi-way principal component analysis (MPCA) method, MLICA-PCA provides a separated interpretation for multilevel batch process data. Batch process data are partitioned into two levels: the within-batch level and the between-batch level. In each level, the Gaussian and non-Gaussian components of process information can be separately extracted. I2, T2 and SPE statistics are individually built and monitored. The new method facilitates fault diagnosis. Since the two variation levels are decomposed, the variables responsible for faults in each level can be identified and interpreted more easily. A case study of the Dupont benchmark process showed that the proposed method was more efficient and interpretable in fault detection and diagnosis, compared to the alternative batch process monitoring method.
基金Supported by The Gastrointestinal Scope Unit of the Chang Gung Memorial Hospital(Kaohsiung)of Taiwan
文摘AIM:To conduct a bacterial culture study for monitoring decontamination of automated endoscope reprocessors(AERs) after high-level disinfection(HLD).METHODS:From February 2006 to January 2011,authors conducted randomized consecutive sampling each month for 7 AERs.Authors collected a total of 420 swab cultures,including 300 cultures from 5 gastroscope AERs,and 120 cultures from 2 colonoscope AERs.Swab cultures were obtained from the residual water from the AERs after a full reprocessing cycle.Samples were cultured to test for aerobic bacteria,anaerobic bacteria,and mycobacterium tuberculosis.RESULTS:The positive culture rate of the AERs was 2.0%(6/300) for gastroscope AERs and 0.8%(1/120) for colonoscope AERs.All the positive cultures,including 6 from gastroscope and 1 from colonoscope AERs,showed monofloral colonization.Of the gastroscopeAER samples,50%(3/6) were colonized by aerobic bacterial and 50%(3/6) by fungal contaminations.CONCLUSION:A full reprocessing cycle of an AER with HLD is adequate for disinfection of the machine.Swab culture is a useful method for monitoring AER decontamination after each reprocessing cycle.Fungal contamination of AERs after reprocessing should also be kept in mind.
