Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficient...Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.展开更多
The lifespan models of commercial 18650-type lithium ion batteries （nominal capacity of 1150 mA-h） were presented. The lifespan was extrapolated based on this model. The results indicate that the relationship of cap...The lifespan models of commercial 18650-type lithium ion batteries （nominal capacity of 1150 mA-h） were presented. The lifespan was extrapolated based on this model. The results indicate that the relationship of capacity retention and cycle number can be expressed by Gaussian function. The selecting function and optimal precision were verified through actual match detection and a range of alternating current impedance testing. The cycle life model with high precision （〉99%） is beneficial to shortening the orediction time and cutting the prediction cost.展开更多
The motivation for this study is that the quality of deep fakes is constantly improving,which leads to the need to develop new methods for their detection.The proposed Customized Convolutional Neural Network method in...The motivation for this study is that the quality of deep fakes is constantly improving,which leads to the need to develop new methods for their detection.The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection,which is then used as input to the CNN.The customized Convolutional Neural Network method is the date augmented-based CNN model to generate‘fake data’or‘fake images’.This study was carried out using Python and its libraries.We used 242 films from the dataset gathered by the Deep Fake Detection Challenge,of which 199 were made up and the remaining 53 were real.Ten seconds were allotted for each video.There were 318 videos used in all,199 of which were fake and 119 of which were real.Our proposedmethod achieved a testing accuracy of 91.47%,loss of 0.342,and AUC score of 0.92,outperforming two alternative approaches,CNN and MLP-CNN.Furthermore,our method succeeded in greater accuracy than contemporary models such as XceptionNet,Meso-4,EfficientNet-BO,MesoInception-4,VGG-16,and DST-Net.The novelty of this investigation is the development of a new Convolutional Neural Network(CNN)learning model that can accurately detect deep fake face photos.展开更多
This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patie...This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patient recovery.Highlightingthe paradox of modern medical advances,it emphasizes the urgent needfor early identification and intervention to mitigate ICU-AW's impact.Innovatively,the study by Wang et al is showcased for employing a multilayer perceptronneural network model,achieving high accuracy in predicting ICU-AWrisk.This advancement underscores the potential of neural network models inenhancing patient care but also calls for continued research to address limitationsand improve model applicability.The editorial advocates for the developmentand validation of sophisticated predictive tools,aiming for personalized carestrategies to reduce ICU-AW incidence and severity,ultimately improving patientoutcomes in critical care settings.展开更多
Recent advancement in low-cost cameras has facilitated surveillance in various developing towns in India.The video obtained from such surveillance are of low quality.Still counting vehicles from such videos are necess...Recent advancement in low-cost cameras has facilitated surveillance in various developing towns in India.The video obtained from such surveillance are of low quality.Still counting vehicles from such videos are necessity to avoid traf-fic congestion and allows drivers to plan their routes more precisely.On the other hand,detecting vehicles from such low quality videos are highly challenging with vision based methodologies.In this research a meticulous attempt is made to access low-quality videos to describe traffic in Salem town in India,which is mostly an un-attempted entity by most available sources.In this work profound Detection Transformer(DETR)model is used for object(vehicle)detection.Here vehicles are anticipated in a rush-hour traffic video using a set of loss functions that carry out bipartite coordinating among estimated and information acquired on real attributes.Every frame in the traffic footage has its date and time which is detected and retrieved using Tesseract Optical Character Recognition.The date and time extricated and perceived from the input image are incorporated with the length of the recognized objects acquired from the DETR model.This furnishes the vehicles report with timestamp.Transformer Timeseries Prediction Model(TTPM)is proposed to predict the density of the vehicle for future prediction,here the regular NLP layers have been removed and the encoding temporal layer has been modified.The proposed TTPM error rate outperforms the existing models with RMSE of 4.313 and MAE of 3.812.展开更多
In order to improve the efficiency of automatic management and self-healing of the self-organizing network(SON),a cell outage problem is investigated and a cooperative prediction-based automatic cell outage detection ...In order to improve the efficiency of automatic management and self-healing of the self-organizing network(SON),a cell outage problem is investigated and a cooperative prediction-based automatic cell outage detection algorithm is proposed.By the improved collaborative filtering prediction algorithm,the location correlation of users in the wireless network is considered.By incorporating the cooperative grey model prediction algorithm,the time correlation of users motion trajectory is also introduced.Data of users in a normal scenario is simulated and collected for model training and threshold calculating and the outage cell can be effectively detected using the proposed approach.The simulation results demonstrate that the proposed scheme has a higher detection rate for different extents of outage while ensuring the lower communication overhead and false alarm rate than traditional outage detection methods.The detection rate of the proposed approach outperforms the traditional method by around 14%,especially when there are sparse users in the network,and it is able to detect the outage cell with no active users with the help of neighbor cells.展开更多
An integrated metallurgical model was developed to predict microstructure evolution and mechanical properties of low-carbon steel plates produced by TMCP. The metallurgical phenomena occurring during TMCP and mechanic...An integrated metallurgical model was developed to predict microstructure evolution and mechanical properties of low-carbon steel plates produced by TMCP. The metallurgical phenomena occurring during TMCP and mechanical properties were predicted for different process parameters. In the later passes full recrystallization becomes difficult to occur and higher residual strain remains in austenite after rolling. For the reasonable temperature and cooling schedule, yield strength of 30 mm plain carbon steel plate can reach 310 MPa. The first on-line application of prediction and control of microstructure and properties (PCMP) in the medium plate production was achieved. The predictions of the system are in good agreement with measurements.展开更多
Over the course of storm or rainfall event,water thickness builds up on road surface resulting in a loss of contact between vehicle tires and road surface and puts drivers into immediate danger especially at high spee...Over the course of storm or rainfall event,water thickness builds up on road surface resulting in a loss of contact between vehicle tires and road surface and puts drivers into immediate danger especially at high speeds.Therefore this is a considerably dangerous condition of the road and the realistic measurements and prediction model of water film thickness(WFT)on pavement surface is crucial for determining the road friction coefficient and evaluating the impact of rainfall on traffic safety.A review of the principle as well as critical evaluation of current detection methods of pavement WFT were compared for consistency and accuracy in this paper.The method selection guidelines are given for different road surface water film thickness detection requirements.This paper also introduces the latest development of WFT detection and prediction models for asphalt pavement,and gives the calculation elements and conditions of different WFT prediction models from different modeling ideas,which provides a basis for the selection and optimization of WFT models for future researchers.This article also suggests a few insights as further research directions on this topic.(1)The research can consider the influencing factors of WFT to conduct research on the delineation standard of pavement WFT.(2)In order to meet the future traffic safety dynamic early warning needs,road factors of different material types,disease conditions and linear conditions should be studied,as well as a comprehensive and accurate real-time water film thickness detection and evaluation method considering meteorological factors of rainfall timing,scale and intensity.(3)The prediction model of WFT should be further studied by the analytical method to clarify the influence of the pavement WFT on the driving safety.展开更多
Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predic...Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predictive model for normal data.Furthermore,the prediction errors from the predictive models are used to indicate normal or abnormal behavior.An additional advantage of using the LSTM networks is that the earthquake precursor data can be directly fed into the network without any elaborate preprocessing as required by other approaches.Furthermore,no prior information on abnormal data is needed by these networks as they are trained only using normal data.Experiments using three groups of real data were conducted to compare the anomaly detection results of the proposed method with those of manual recognition.The comparison results indicated that the proposed LSTM network achieves promising results and is viable for detecting anomalies in earthquake precursor data.展开更多
This paper presents an automatic system for failure detection in hydro-power generators. The main idea of this system is to detect failure using current and voltage signals acquired without any type of internal interf...This paper presents an automatic system for failure detection in hydro-power generators. The main idea of this system is to detect failure using current and voltage signals acquired without any type of internal interference in the generator operation. The detected failures could be mechanical or electrical origins, such as: problems in bearings, unwanted vibrations, partial discharges, misalignment, unbalancing, among others. It is possible because the generator acts as a transducer for mechanical problems, and they appear in current and voltage signals. This automatic system based on electric signature analysis has been installed in Itapebi Power Plant generators since 2012. Some results are presented in this paper.展开更多
Vision-based road detection is an important research topic in different areas of computer vision such as the autonomous navigation of mobile robots.In outdoor unstructured environments such as villages and deserts,the...Vision-based road detection is an important research topic in different areas of computer vision such as the autonomous navigation of mobile robots.In outdoor unstructured environments such as villages and deserts,the roads are usually not well-paved and have variant colors or texture distributions.Traditional region- or edge-based approaches,however,are effective only in specific environments,and most of them have weak adaptability to varying road types and appearances.In this paper we describe a novel top-down based hybrid algorithm which properly combines both region and edge cues from the images.The main difference between our proposed algorithm and previous ones is that,before road detection,an off-line scene classifier is efficiently learned by both low- and high-level image cues to predict the unstructured road model.This scene classification can be considered a decision process which guides the selection of the optimal solution from region- or edge-based approaches to detect the road.Moreover,a temporal smoothing mechanism is incorporated,which further makes both model prediction and region classification more stable.Experimental results demonstrate that compared with traditional region- and edge-based algorithms,our algorithm is more robust in detecting the road areas with diverse road types and varying appearances in unstructured conditions.展开更多
基金supported by the Research and Development Center of Transport Industry of New Generation of Artificial Intelligence Technology(Grant No.202202H)the National Key R&D Program of China(Grant No.2019YFB1600702)the National Natural Science Foundation of China(Grant Nos.51978600&51808336).
文摘Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.
基金Projects(51204209,51274240)supported by the National Natural Science Foundation of ChinaProject(HNDLKJ[2012]001-1)supported by Henan Electric Power Science&Technology Supporting Program,China
文摘The lifespan models of commercial 18650-type lithium ion batteries （nominal capacity of 1150 mA-h） were presented. The lifespan was extrapolated based on this model. The results indicate that the relationship of capacity retention and cycle number can be expressed by Gaussian function. The selecting function and optimal precision were verified through actual match detection and a range of alternating current impedance testing. The cycle life model with high precision （〉99%） is beneficial to shortening the orediction time and cutting the prediction cost.
基金Science and Technology Funds from the Liaoning Education Department(Serial Number:LJKZ0104).
文摘The motivation for this study is that the quality of deep fakes is constantly improving,which leads to the need to develop new methods for their detection.The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection,which is then used as input to the CNN.The customized Convolutional Neural Network method is the date augmented-based CNN model to generate‘fake data’or‘fake images’.This study was carried out using Python and its libraries.We used 242 films from the dataset gathered by the Deep Fake Detection Challenge,of which 199 were made up and the remaining 53 were real.Ten seconds were allotted for each video.There were 318 videos used in all,199 of which were fake and 119 of which were real.Our proposedmethod achieved a testing accuracy of 91.47%,loss of 0.342,and AUC score of 0.92,outperforming two alternative approaches,CNN and MLP-CNN.Furthermore,our method succeeded in greater accuracy than contemporary models such as XceptionNet,Meso-4,EfficientNet-BO,MesoInception-4,VGG-16,and DST-Net.The novelty of this investigation is the development of a new Convolutional Neural Network(CNN)learning model that can accurately detect deep fake face photos.
文摘This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patient recovery.Highlightingthe paradox of modern medical advances,it emphasizes the urgent needfor early identification and intervention to mitigate ICU-AW's impact.Innovatively,the study by Wang et al is showcased for employing a multilayer perceptronneural network model,achieving high accuracy in predicting ICU-AWrisk.This advancement underscores the potential of neural network models inenhancing patient care but also calls for continued research to address limitationsand improve model applicability.The editorial advocates for the developmentand validation of sophisticated predictive tools,aiming for personalized carestrategies to reduce ICU-AW incidence and severity,ultimately improving patientoutcomes in critical care settings.
文摘Recent advancement in low-cost cameras has facilitated surveillance in various developing towns in India.The video obtained from such surveillance are of low quality.Still counting vehicles from such videos are necessity to avoid traf-fic congestion and allows drivers to plan their routes more precisely.On the other hand,detecting vehicles from such low quality videos are highly challenging with vision based methodologies.In this research a meticulous attempt is made to access low-quality videos to describe traffic in Salem town in India,which is mostly an un-attempted entity by most available sources.In this work profound Detection Transformer(DETR)model is used for object(vehicle)detection.Here vehicles are anticipated in a rush-hour traffic video using a set of loss functions that carry out bipartite coordinating among estimated and information acquired on real attributes.Every frame in the traffic footage has its date and time which is detected and retrieved using Tesseract Optical Character Recognition.The date and time extricated and perceived from the input image are incorporated with the length of the recognized objects acquired from the DETR model.This furnishes the vehicles report with timestamp.Transformer Timeseries Prediction Model(TTPM)is proposed to predict the density of the vehicle for future prediction,here the regular NLP layers have been removed and the encoding temporal layer has been modified.The proposed TTPM error rate outperforms the existing models with RMSE of 4.313 and MAE of 3.812.
基金The National Natural Science Foundation of China(No.61571123,61521061)the Research Fund of National Mobile Communications Research Laboratory of Southeast University(No.2018A03,2019A03)+1 种基金the National Major Science and Technology Project(No.2017ZX03001002-004)the 333 Program of Jiangsu Province(No.BRA2017366)
文摘In order to improve the efficiency of automatic management and self-healing of the self-organizing network(SON),a cell outage problem is investigated and a cooperative prediction-based automatic cell outage detection algorithm is proposed.By the improved collaborative filtering prediction algorithm,the location correlation of users in the wireless network is considered.By incorporating the cooperative grey model prediction algorithm,the time correlation of users motion trajectory is also introduced.Data of users in a normal scenario is simulated and collected for model training and threshold calculating and the outage cell can be effectively detected using the proposed approach.The simulation results demonstrate that the proposed scheme has a higher detection rate for different extents of outage while ensuring the lower communication overhead and false alarm rate than traditional outage detection methods.The detection rate of the proposed approach outperforms the traditional method by around 14%,especially when there are sparse users in the network,and it is able to detect the outage cell with no active users with the help of neighbor cells.
基金This work was financially supported by the High Technology Development Program(No.2001AA339030)the National Natural Science Foundation of China(No.50334010).
文摘An integrated metallurgical model was developed to predict microstructure evolution and mechanical properties of low-carbon steel plates produced by TMCP. The metallurgical phenomena occurring during TMCP and mechanical properties were predicted for different process parameters. In the later passes full recrystallization becomes difficult to occur and higher residual strain remains in austenite after rolling. For the reasonable temperature and cooling schedule, yield strength of 30 mm plain carbon steel plate can reach 310 MPa. The first on-line application of prediction and control of microstructure and properties (PCMP) in the medium plate production was achieved. The predictions of the system are in good agreement with measurements.
基金This research is supported by the National Key Research and Development Program of China(No.2021YFB2601000)the National Natural Science Foundation of China(NSFC)(No.51878063 and No.52008029)the Fundamental Research Funds for the Central Universities,CHD(300102213504).
文摘Over the course of storm or rainfall event,water thickness builds up on road surface resulting in a loss of contact between vehicle tires and road surface and puts drivers into immediate danger especially at high speeds.Therefore this is a considerably dangerous condition of the road and the realistic measurements and prediction model of water film thickness(WFT)on pavement surface is crucial for determining the road friction coefficient and evaluating the impact of rainfall on traffic safety.A review of the principle as well as critical evaluation of current detection methods of pavement WFT were compared for consistency and accuracy in this paper.The method selection guidelines are given for different road surface water film thickness detection requirements.This paper also introduces the latest development of WFT detection and prediction models for asphalt pavement,and gives the calculation elements and conditions of different WFT prediction models from different modeling ideas,which provides a basis for the selection and optimization of WFT models for future researchers.This article also suggests a few insights as further research directions on this topic.(1)The research can consider the influencing factors of WFT to conduct research on the delineation standard of pavement WFT.(2)In order to meet the future traffic safety dynamic early warning needs,road factors of different material types,disease conditions and linear conditions should be studied,as well as a comprehensive and accurate real-time water film thickness detection and evaluation method considering meteorological factors of rainfall timing,scale and intensity.(3)The prediction model of WFT should be further studied by the analytical method to clarify the influence of the pavement WFT on the driving safety.
基金supported by the Science for Earthquake Resilience of China(No.XH18027)Research and Development of Comprehensive Geophysical Field Observing Instrument in China's Mainland(No.Y201703)Research Fund Project of Shandong Earthquake Agency(Nos.JJ1505Y and JJ1602)
文摘Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predictive model for normal data.Furthermore,the prediction errors from the predictive models are used to indicate normal or abnormal behavior.An additional advantage of using the LSTM networks is that the earthquake precursor data can be directly fed into the network without any elaborate preprocessing as required by other approaches.Furthermore,no prior information on abnormal data is needed by these networks as they are trained only using normal data.Experiments using three groups of real data were conducted to compare the anomaly detection results of the proposed method with those of manual recognition.The comparison results indicated that the proposed LSTM network achieves promising results and is viable for detecting anomalies in earthquake precursor data.
文摘This paper presents an automatic system for failure detection in hydro-power generators. The main idea of this system is to detect failure using current and voltage signals acquired without any type of internal interference in the generator operation. The detected failures could be mechanical or electrical origins, such as: problems in bearings, unwanted vibrations, partial discharges, misalignment, unbalancing, among others. It is possible because the generator acts as a transducer for mechanical problems, and they appear in current and voltage signals. This automatic system based on electric signature analysis has been installed in Itapebi Power Plant generators since 2012. Some results are presented in this paper.
文摘Vision-based road detection is an important research topic in different areas of computer vision such as the autonomous navigation of mobile robots.In outdoor unstructured environments such as villages and deserts,the roads are usually not well-paved and have variant colors or texture distributions.Traditional region- or edge-based approaches,however,are effective only in specific environments,and most of them have weak adaptability to varying road types and appearances.In this paper we describe a novel top-down based hybrid algorithm which properly combines both region and edge cues from the images.The main difference between our proposed algorithm and previous ones is that,before road detection,an off-line scene classifier is efficiently learned by both low- and high-level image cues to predict the unstructured road model.This scene classification can be considered a decision process which guides the selection of the optimal solution from region- or edge-based approaches to detect the road.Moreover,a temporal smoothing mechanism is incorporated,which further makes both model prediction and region classification more stable.Experimental results demonstrate that compared with traditional region- and edge-based algorithms,our algorithm is more robust in detecting the road areas with diverse road types and varying appearances in unstructured conditions.
