With the rapid advancement of social economies,intelligent transportation systems are gaining increasing atten-tion.Central to these systems is the detection of abnormal vehicle behavior,which remains a critical chall...With the rapid advancement of social economies,intelligent transportation systems are gaining increasing atten-tion.Central to these systems is the detection of abnormal vehicle behavior,which remains a critical challenge due to the complexity of urban roadways and the variability of external conditions.Current research on detecting abnormal traffic behaviors is still nascent,with significant room for improvement in recognition accuracy.To address this,this research has developed a new model for recognizing abnormal traffic behaviors.This model employs the R3D network as its core architecture,incorporating a dense block to facilitate feature reuse.This approach not only enhances performance with fewer parameters and reduced computational demands but also allows for the acquisition of new features while simplifying the overall network structure.Additionally,this research integrates a self-attentive method that dynamically adjusts to the prevailing traffic conditions,optimizing the relevance of features for the task at hand.For temporal analysis,a Bi-LSTM layer is utilized to extract and learn from time-based data nuances.This research conducted a series of comparative experiments using the UCF-Crime dataset,achieving a notable accuracy of 89.30%on our test set.Our results demonstrate that our model not only operates with fewer parameters but also achieves superior recognition accuracy compared to previous models.展开更多
With the changes in educational models,applying computer algorithms and artificial intelligence technologies to data analysis in universities has become a research hotspot in the field of intelligent education.In resp...With the changes in educational models,applying computer algorithms and artificial intelligence technologies to data analysis in universities has become a research hotspot in the field of intelligent education.In response to the increasing amount of student data in universities,this study proposes to use an optimized isolated forest algorithm for recognizing features to detect abnormal student behavior concealed in big data for educational management.Firstly,it uses a logistic regression algorithm to update the calculation method of isolated forest weights and then uses residual statistics to eliminate redundant forests.Finally,it utilizes discrete particle swarm optimization to optimize the isolated forest algorithm.On this basis,improvements have also been made to the traditional gated loop unit network.It merges the two improved algorithm models and builds an anomaly detection model for collecting college student education data.The experiment shows that the optimized isolated forest algorithm has a recognition accuracy of 0.986 and a training time of 1s.The recognition accuracy of the improved gated loop unit network is 0.965,and the training time is 0.16s.In summary,the constructed model can effectively identify abnormal data of college students,thereby helping educators to detect students’problems in time and helping students to improve their learning status.展开更多
The core technology in an intelligent video surveillance system is that detecting and recognizing abnormal behaviors timely and accurately.The key breakthrough point in recognizing abnormal behaviors is how to obtain ...The core technology in an intelligent video surveillance system is that detecting and recognizing abnormal behaviors timely and accurately.The key breakthrough point in recognizing abnormal behaviors is how to obtain the effective features of the picture,so as to solve the problem of recognizing them.In response to this difficulty,this paper introduces an adjustable jump link coefficients model based on the residual network.The effective coefficients for each layer of the network can be set after using this model to further improving the recognition accuracy of abnormal behavior.A convolution kernel of 1×1 size is added to reduce the number of parameters for the purpose of improving the speed of the model in this paper.In order to reduce the noise of the data edge,and at the same time,improve the accuracy of the data and speed up the training,a BN(Batch Normalization)layer is added before the activation function in this network.This paper trains this network model on the public ImageNet dataset,and then uses the transfer learning method to recognize these abnormal behaviors of human in the UTI behavior dataset processed by the YOLO_v3 target detection network.Under the same experimental conditions,compared with the original ResNet-50 model,the improved model in this paper has a 2.8%higher accuracy in recognition of abnormal behaviors on the public UTI dataset.展开更多
AIM： Biological medicine is hard to fully and scientifically explain the etiological factor and pathogenesis of abnormal behaviors; while, researches on philosophy and psychology （including memetics） are beneficial...AIM： Biological medicine is hard to fully and scientifically explain the etiological factor and pathogenesis of abnormal behaviors; while, researches on philosophy and psychology （including memetics） are beneficial to better understand and explain etiological factor and pathogenesis of abnormal behaviors. At present, the theory of philosophy and psychology is to investigate the entity of abnormal behavior based on the views of memetics. METHODS： Abnormal behavior was researched in this study based on three aspects, including instinctive behavior disorder, poorly social-adapted behavior disorder and mental or body disease associated behavior disorder. Most main viewpoints of memetics were derived from ＂The Meme Machine＂, which was written by Susan Blackmore. When questions about abnormal behaviors induced by mental and psychological diseases and conduct disorder of teenagers were discussed, some researching achievements which were summarized by authors previously were added in this study, such as aggressive behaviors, pathologically aggressive behaviors, etc. RESULTS： The abnormal behaviors mainly referred to a part of people＇s substandard behaviors which were not according with the realistic social environment, culture background and the pathologic behaviors resulted from people＇s various psychological diseases. According to the theory of ＂meme＂, it demonstrated that the relevant behavioral obstacles of various psychological diseases, for example, the unusual behavior of schizophrenia, were caused, because the old meme was destroyed thoroughly but the new meme was unable to establish; psychoneurosis and personality disorder were resulted in hard establishment of meme; the behavioral obstacles which were ill-adapted to society, for example, various additional and homosexual behaviors, were because of the selfish replications and imitations of ＂additional meme＂ and ＂homosexual meme＂; various instinct behavioral and congenital intelligent obstacles were not significance for memetics. CONCLUSION： Generation of abnormal behavior is not only caused by complexly biological factors, also by philosophical and psychological entities.展开更多
With the increasing number of digital devices generating a vast amount of video data,the recognition of abnormal image patterns has become more important.Accordingly,it is necessary to develop a method that achieves t...With the increasing number of digital devices generating a vast amount of video data,the recognition of abnormal image patterns has become more important.Accordingly,it is necessary to develop a method that achieves this task using object and behavior information within video data.Existing methods for detecting abnormal behaviors only focus on simple motions,therefore they cannot determine the overall behavior occurring throughout a video.In this study,an abnormal behavior detection method that uses deep learning(DL)-based video-data structuring is proposed.Objects and motions are first extracted from continuous images by combining existing DL-based image analysis models.The weight of the continuous data pattern is then analyzed through data structuring to classify the overall video.The performance of the proposed method was evaluated using varying parameter settings,such as the size of the action clip and interval between action clips.The model achieved an accuracy of 0.9817,indicating excellent performance.Therefore,we conclude that the proposed data structuring method is useful in detecting and classifying abnormal behaviors.展开更多
Purpose-The abnormal behaviors of staff at petroleum stations pose significant safety hazards.Addressing the challenges of high parameter counts,lengthy training periods and low recognition rates in existing 3D ResNet...Purpose-The abnormal behaviors of staff at petroleum stations pose significant safety hazards.Addressing the challenges of high parameter counts,lengthy training periods and low recognition rates in existing 3D ResNet behavior recognition models,this paper proposes GTB-ResNet,a network designed to detect abnormal behaviors in petroleum station staff.Design/methodology/approach-Firstly,to mitigate the issues of excessive parameters and computational complexity in 3D ResNet,a lightweight residual convolution module called the Ghost residual module(GhostNet)is introduced in the feature extraction network.Ghost convolution replaces standard convolution,reducing model parameters while preserving multi-scale feature extraction capabilities.Secondly,to enhance the model’s focus on salient features amidst wide surveillance ranges and small target objects,the triplet attention mechanism module is integrated to facilitate spatial and channel information interaction.Lastly,to address the challenge of short time-series features leading to misjudgments in similar actions,a bidirectional gated recurrent network is added to the feature extraction backbone network.This ensures the extraction of key long time-series features,thereby improving feature extraction accuracy.Findings-The experimental setup encompasses four behavior types:illegal phone answering,smoking,falling(abnormal)and touching the face(normal),comprising a total of 892 videos.Experimental results showcase GTB-ResNet achieving a recognition accuracy of 96.7%with a model parameter count of 4.46 M and a computational complexity of 3.898 G.This represents a 4.4%improvement over 3D ResNet,with reductions of 90.4%in parameters and 61.5%in computational complexity.Originality/value-Specifically designed for edge devices in oil stations,the 3D ResNet network is tailored for real-time action prediction.To address the challenges posed by the large number of parameters in 3D ResNet networks and the difficulties in deployment on edge devices,a lightweight residual module based on ghost convolution is developed.Additionally,to tackle the issue of low detection accuracy of behaviors amidst the noisy environment of petroleum stations,a triple attention mechanism is introduced during feature extraction to enhance focus on salient features.Moreover,to overcome the potential for misjudgments arising from the similarity of actions,a Bi-GRU model is introduced to enhance the extraction of key long-term features.展开更多
The behavior safety testing ofmore andmore elderly people living alone has become a hot research topic along with the arrival of an aging society.A YOLO-Abnormal Behaviour(YOLO-AB)algorithm for fusion detection of fal...The behavior safety testing ofmore andmore elderly people living alone has become a hot research topic along with the arrival of an aging society.A YOLO-Abnormal Behaviour(YOLO-AB)algorithm for fusion detection of falling and smoking behaviors of elderly people living alone has been proposed in this paper,which can fully utilize the potential of the YOLOv8 algorithm on object detection and deeply explore the characteristics of different types of behaviors among the elderly,to solve the problems of single detection type,low fusion detection accuracy,and high missed detection rate.Firstly,datasets of different types of elderly behavior images such as falling,smoking,and standing are constructed for performance validation of subsequent algorithms.Secondly,the Content-Aware Reassembly of Features Module(CARAFE)is introduced into the YOLOv8 algorithm to enhance content perception,strengthen feature fusion,generate adaptive kernels dynamically,and reduce parameters effectively.Then,the Large Selective Kernel Network(LSKNet)module is added to the backbone network part to strengthen the framing of human targets and improve detection accuracy.Next,the Focal-SCYLLA-IOU(F-SIOU)loss function is used to improve the positioning accuracy of the edge part of the target detection frame.Finally,YOLO-AB and other different algorithms are tested and compared using the falling dataset,the smoking dataset,and the falling and smoking mixed dataset,respectively.The results show that the detection accuracy of the YOLO-AB algorithmis 0.93 on the falling dataset alone,0.864 on the smoking dataset alone,and 0.923 on the falling and smoking mixed dataset,all of which are better than those of the other algorithms.The performance of YOLO-AB is better than that of YOLOv8 on multiple metrics,such as 4.1%improvement in the mAP50 index,4.9%increase in the P index,and 3.5%boost in the R index,which verifies the effectiveness of the algorithm.展开更多
With the wide application and development of blockchain technology in various fields such as finance, government affairs and medical care, security incidents occur frequently on it, which brings great threats to users...With the wide application and development of blockchain technology in various fields such as finance, government affairs and medical care, security incidents occur frequently on it, which brings great threats to users’ assets and information. Many researchers have worked on blockchain abnormal behavior awareness in respond to these threats. We summarize respectively the existing public blockchain and consortium blockchain abnormal behavior awareness methods and ideas in detail as the difference between the two types of blockchain. At the same time, we summarize and analyze the existing data sets related to mainstream blockchain security, and finally discuss possible future research directions. Therefore, this work can provide a reference for blockchain security awareness research.展开更多
Analyzing a vehicle’s abnormal behavior in surveillance videos is a challenging field,mainly due to the wide variety of anomaly cases and the complexity of surveillance videos.In this study,a novel intelligent vehicl...Analyzing a vehicle’s abnormal behavior in surveillance videos is a challenging field,mainly due to the wide variety of anomaly cases and the complexity of surveillance videos.In this study,a novel intelligent vehicle behavior analysis framework based on a digital twin is proposed.First,detecting vehicles based on deep learning is implemented,and Kalman filtering and feature matching are used to track vehicles.Subsequently,the tracked vehicle is mapped to a digital-twin virtual scene developed in the Unity game engine,and each vehicle’s behavior is tested according to the customized detection conditions set up in the scene.The stored behavior data can be used to reconstruct the scene again in Unity for a secondary analysis.The experimental results using real videos from traffic cameras illustrate that the detection rate of the proposed framework is close to that of the state-of-the-art abnormal event detection systems.In addition,the implementation and analysis process show the usability,generalization,and effectiveness of the proposed framework.展开更多
Abnormal driving behavior identification( ADBI) has become a research hotspot because of its significance in driver assistance systems. However,current methods still have some limitations in terms of accuracy and reli...Abnormal driving behavior identification( ADBI) has become a research hotspot because of its significance in driver assistance systems. However,current methods still have some limitations in terms of accuracy and reliability under severe traffic scenes. This paper proposes a new ADBI method based on direction and position offsets,where a two-factor identification strategy is proposed to improve the accuracy and reliability of ADBI. Self-adaptive edge detection based on Sobel operator is used to extract edge information of lanes. In order to enhance the efficiency and reliability of lane detection,an improved lane detection algorithm is proposed,where a Hough transform based on local search scope is employed to quickly detect the lane,and a validation scheme based on priori information is proposed to further verify the detected lane. Experimental results under various complex road conditions demonstrate the validity of the proposed ADBI.展开更多
Understanding the abnormal electricity usage behavior of buildings is essential to enhance the resilience,efficiency,and security of urban/building energy systems while safeguarding occupant comfort.However,data refle...Understanding the abnormal electricity usage behavior of buildings is essential to enhance the resilience,efficiency,and security of urban/building energy systems while safeguarding occupant comfort.However,data reflecting such behavior are often considered as outliers,and removed or smoothed during preprocessing,limiting insights into their potential impacts.This paper proposes an abnormal behavior analysis method that identifies outliers(considering data distribution)and anomalies(considering the physical context)based on the statistical principle and domain knowledge,assessing their effects on energy supply security.A 4-quadrant graph is proposed to quantify and categorize the impacts of buildings on urban energy systems.The method is illustrated by data from 1,451 buildings in a city.Results show that the proposed method can identify abnormal data effectively.Buildings in the primary industry have more outliers,while those in the tertiary industry have more anomalies.Seven buildings affecting both the security and economy of urban energy systems are identified.The outliers rise more frequently from 8:00 to 18:00,on weekdays and in the summer and winter months.However,the anomaly distribution has a weak connection with time.Moreover,the abnormal electricity usage behavior positively correlates with outdoor air temperatures.This method provides a new perspective for identifying potential risks,managing energy usage behavior,and enhancing flexibility of the urban energy systems.展开更多
After the 2011 Tohoku earthquake (EQ), there have been numerous aftershocks in the eastern and Pacific Ocean of Japan, but EQs are still rare in the western part of Japan. In this situation a relatively large (magnitu...After the 2011 Tohoku earthquake (EQ), there have been numerous aftershocks in the eastern and Pacific Ocean of Japan, but EQs are still rare in the western part of Japan. In this situation a relatively large (magnitude (M) ~6) EQ happened on April 12 (UT), 2013 at a place close to the former 1995 Kobe EQ (M~7), so we have tried to find whether there existed any precursors to this EQ, especially abnormal animal behavior (milk yield of cows), observed at Kagawa, Shikoku, near the EQ epicenter. The milk yield of cows has been continuously monitored at Kagawa, and it is found that the milk yield exhibited an abnormal depletion about 10 days before the EQ. This behavior has been extensively compared with the former electromagnetic precursors (ULF radiation, ionos-pheric perturbation). This leads to the discussion on the sensory mechanism of unusual behavior of mild yield of cows, and it may be suggested that ULF radiation among different electromagnetic precursors is a mostly likely driver, at least, for this EQ.展开更多
Abnormal driving behavior includes driving distraction,fatigue,road anger,phone use,and an exceptionally happy mood.Detecting abnormal driving behavior in advance can avoid traffic accidents and reduce the risk of tra...Abnormal driving behavior includes driving distraction,fatigue,road anger,phone use,and an exceptionally happy mood.Detecting abnormal driving behavior in advance can avoid traffic accidents and reduce the risk of traffic conflicts.Traditional methods of detecting abnormal driving behavior include using wearable devices to monitor blood pressure,pulse,heart rate,blood oxygen,and other vital signs,and using eye trackers to monitor eye activity(such as eye closure,blinking frequency,etc.)to estimate whether the driver is excited,anxious,or distracted.Traditional monitoring methods can detect abnormal driving behavior to a certain extent,but they will affect the driver’s normal driving state,thereby introducing additional driving risks.This research uses the combined method of support vector machine and dlib algorithm to extract 68 facial feature points from the human face,and uses an SVM model as a strong classifier to classify different abnormal driving statuses.The combined method reaches high accuracy in detecting road anger and fatigue status and can be used in an intelligent vehicle cabin to improve the driving safety level.展开更多
The temperature coefficient of the resonant frequency(τ_(f))of low-permittivity(ε_(r))microwave dielectric ceramics is required to be near 0 ppm/℃for practical application.However,owing to the polarization mechani...The temperature coefficient of the resonant frequency(τ_(f))of low-permittivity(ε_(r))microwave dielectric ceramics is required to be near 0 ppm/℃for practical application.However,owing to the polarization mechanism,τ_(f)of low-ε_(r)microwave dielectric ceramics is generally negative.Here,a novel microwave dielectric ceramic,Ba_(3−x)Sr_(x)MgSi_(2)O_(8),with an abnormal positiveτ_(f)at the applied temperature is presented.In this study,Sr^(2+)with a relatively small ionic radius was introduced to replace Ba^(2+),and a single-phase solid solution was formed(x>0.5).Ba_(3−x)Sr_(x)MgSi_(2)O_(8)ceramics were discussed in glaserite-type topology with space groups of P_(3)^(-)for x≤0.5,relatively high symmetry P_(3)^(-)m1 for 0.5<x<2.5,and C2 for x≥2.5.Theε_(r)peaks as a function of temperature initially shift to low temperatures and then return to high temperatures through an ion substitution strategy.Notably,remarkable microwave dielectric properties for BaSr_(2)MgSi_(2)O_(8)were observed:ε_(r)≈14.2,Q×f≈38,900 GHz,andτ_(f)≈+117 ppm/℃,which are superior to those of other low-ε_(r)silicate ceramics with positiveτ_(f)values.Density functional theory simulation calculations revealed that the preferential occupation of Sr^(2+)ions could decrease the intrinsic formation energy and improve the microwave dielectric properties by mitigating the ionic size mismatch within the crystal structure.The present research offers a strategy for discovering novel microwave dielectric ceramics with abnormalτ_(f)values,which could serve as τ_(f)regulators in practical applications because of their low cost and excellent microwave dielectric properties.展开更多
It was found that the interface effects in viscous capillary flow influenced the process of viscosity measurement greatly, and the abnormal viscosity behaviors of polyelectrolytes as well as neutral polymers in dilute...It was found that the interface effects in viscous capillary flow influenced the process of viscosity measurement greatly, and the abnormal viscosity behaviors of polyelectrolytes as well as neutral polymers in dilute solution region were ascribed to interface effect. According to this theory, we have reviewed the previous viscosity data of derivatives of poly-2- vinylpyridine reported by Maclay and Fuoss first. Then, the abnormal viscosity behaviors of a series of sodium polystyrene sulfonate samples with various molecular weights in dilute aqueous solutions were studied further. The solute adsorption behaviors and structural information of polymers have been discussed carefully.展开更多
Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions.In the context of the heightene...Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions.In the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication protocols.Smart grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for IEDs.Hence,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for IEDs.Initially,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security assessment.Subsequently,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal behaviors.This index system contains 18 indicators in 3 categories.Additionally,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and 5.The higher the level,the greater the security risk.Finally,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by IEDs.The results show that calculated security risk level based on the proposed assessment method are consistent with actual simulation.Thus,the reasonableness and effectiveness of the proposed index system and assessment method are validated.展开更多
In order to quickly and accurately find the implementer of the network crime,based on the user portrait technology,a rapid detection method for users with abnormal behaviors is proposed.This method needs to construct ...In order to quickly and accurately find the implementer of the network crime,based on the user portrait technology,a rapid detection method for users with abnormal behaviors is proposed.This method needs to construct the abnormal behavior rule base on various kinds of abnormal behaviors in advance,and construct the user portrait including basic attribute tags,behavior attribute tags and abnormal behavior similarity tags for network users who have abnormal behaviors.When a network crime occurs,firstly get the corresponding tag values in all user portraits according to the category of the network crime.Then,use the Naive Bayesian method matching each user portrait,to quickly locate the most likely network criminal suspects.In the case that no suspect is found,all users are audited comprehensively through matching abnormal behavior rule base.The experimental results show that,the accuracy rate of using this method for fast detection of network crimes is 95.9%,and the audit time is shortened to 1/35 of that of the conventional behavior audit method.展开更多
A growing number of studies have demonstrated that repeated exposure to sevoflurane during development results in persistent social abnormalities and cognitive impairment.Davunetide,an active fragment of the activity-...A growing number of studies have demonstrated that repeated exposure to sevoflurane during development results in persistent social abnormalities and cognitive impairment.Davunetide,an active fragment of the activity-dependent neuroprotective protein(ADNP),has been implicated in social and cognitive protection.However,the potential of davunetide to attenuate social deficits following sevoflurane exposure and the underlying developmental mechanisms remain poorly understood.In this study,ribosome and proteome profiles were analyzed to investigate the molecular basis of sevoflurane-induced social deficits in neonatal mice.The neuropathological basis was also explored using Golgi staining,morphological analysis,western blotting,electrophysiological analysis,and behavioral analysis.Results indicated that ADNP was significantly down-regulated following developmental exposure to sevoflurane.In adulthood,anterior cingulate cortex(ACC)neurons exposed to sevoflurane exhibited a decrease in dendrite number,total dendrite length,and spine density.Furthermore,the expression levels of Homer,PSD95,synaptophysin,and vglut2 were significantly reduced in the sevoflurane group.Patch-clamp recordings indicated reductions in both the frequency and amplitude of miniature excitatory postsynaptic currents(mEPSCs).Notably,davunetide significantly ameliorated the synaptic defects,social behavior deficits,and cognitive impairments induced by sevoflurane.Mechanistic analysis revealed that loss of ADNP led to dysregulation of Ca^(2+)activity via the Wnt/β-catenin signaling,resulting in decreased expression of synaptic proteins.Suppression of Wnt signaling was restored in the davunetide-treated group.Thus,ADNP was identified as a promising therapeutic target for the prevention and treatment of neurodevelopmental toxicity caused by general anesthetics.This study provides important insights into the mechanisms underlying social and cognitive disturbances caused by sevoflurane exposure in neonatal mice and elucidates the regulatory pathways involved.展开更多
The frame rate of conventional vision systems is restricted to the video signal formats （e.g., NTSC 30 fps and PAL 25 fps） that are designed on the basis of the characteristics of the human eye, which implies that t...The frame rate of conventional vision systems is restricted to the video signal formats （e.g., NTSC 30 fps and PAL 25 fps） that are designed on the basis of the characteristics of the human eye, which implies that the processing speed of these systems is limited to the recognition speed of the human eye. However, there is a strong demand for real-time high-speed vision sensors in many application fields, such as factory automation, biomedicine, and robotics, where high-speed operations are carried out. These high-speed operations can be tracked and inspected by using high-speed vision systems with intelligent sensors that work at hundreds of Hertz or more, especially when the operation is difficult to observe with the human eye. This paper reviews advances in developing real-time high Speed vision systems and their applications in various fields, such as intelligent logging systems, vibration dynamics sensing, vision-based mechanical control, three-dimensional measurement/automated visual inspection, vision-based human interface, and biomedical applications.展开更多
The hot deformation characteristics of as-forged Ti−3.5Al−5Mo−6V−3Cr−2Sn−0.5Fe−0.1B−0.1C alloy within a temperature range from 750 to 910℃and a strain rate range from 0.001 to 1 s^(-1) were investigated by hot compre...The hot deformation characteristics of as-forged Ti−3.5Al−5Mo−6V−3Cr−2Sn−0.5Fe−0.1B−0.1C alloy within a temperature range from 750 to 910℃and a strain rate range from 0.001 to 1 s^(-1) were investigated by hot compression tests.The stress−strain curves show that the flow stress decreases with the increase of temperature and the decrease of strain rate.The microstructure is sensitive to deformation parameters.The dynamic recrystallization(DRX)grains appear while the temperature reaches 790℃at a constant strain rate of 0.001 s^(-1) and strain rate is not higher than 0.1 s^(-1) at a constant temperature of 910℃.The work-hardening rateθis calculated and it is found that DRX prefers to happen at high temperature and low strain rate.The constitutive equation and processing map were obtained.The average activation energy of the alloy is 242.78 kJ/mol and there are few unstable regions on the processing map,which indicates excellent hot workability.At the strain rate of 0.1 s^(-1),the stress−strain curves show an abnormal shape where there are two stress peaks simultaneously.This can be attributed to the alternation of hardening effect,which results from the continuous dynamic recrystallization(CDRX)and the rotation of DRX grains,and dynamic softening mechanism.展开更多
基金supported by the National Natural Science Foundation of China(61971007&61571013).
文摘With the rapid advancement of social economies,intelligent transportation systems are gaining increasing atten-tion.Central to these systems is the detection of abnormal vehicle behavior,which remains a critical challenge due to the complexity of urban roadways and the variability of external conditions.Current research on detecting abnormal traffic behaviors is still nascent,with significant room for improvement in recognition accuracy.To address this,this research has developed a new model for recognizing abnormal traffic behaviors.This model employs the R3D network as its core architecture,incorporating a dense block to facilitate feature reuse.This approach not only enhances performance with fewer parameters and reduced computational demands but also allows for the acquisition of new features while simplifying the overall network structure.Additionally,this research integrates a self-attentive method that dynamically adjusts to the prevailing traffic conditions,optimizing the relevance of features for the task at hand.For temporal analysis,a Bi-LSTM layer is utilized to extract and learn from time-based data nuances.This research conducted a series of comparative experiments using the UCF-Crime dataset,achieving a notable accuracy of 89.30%on our test set.Our results demonstrate that our model not only operates with fewer parameters but also achieves superior recognition accuracy compared to previous models.
文摘With the changes in educational models,applying computer algorithms and artificial intelligence technologies to data analysis in universities has become a research hotspot in the field of intelligent education.In response to the increasing amount of student data in universities,this study proposes to use an optimized isolated forest algorithm for recognizing features to detect abnormal student behavior concealed in big data for educational management.Firstly,it uses a logistic regression algorithm to update the calculation method of isolated forest weights and then uses residual statistics to eliminate redundant forests.Finally,it utilizes discrete particle swarm optimization to optimize the isolated forest algorithm.On this basis,improvements have also been made to the traditional gated loop unit network.It merges the two improved algorithm models and builds an anomaly detection model for collecting college student education data.The experiment shows that the optimized isolated forest algorithm has a recognition accuracy of 0.986 and a training time of 1s.The recognition accuracy of the improved gated loop unit network is 0.965,and the training time is 0.16s.In summary,the constructed model can effectively identify abnormal data of college students,thereby helping educators to detect students’problems in time and helping students to improve their learning status.
基金This research was funded by the Science and Technology Department of Shaanxi Province,China,Grant Number 2019GY-036.
文摘The core technology in an intelligent video surveillance system is that detecting and recognizing abnormal behaviors timely and accurately.The key breakthrough point in recognizing abnormal behaviors is how to obtain the effective features of the picture,so as to solve the problem of recognizing them.In response to this difficulty,this paper introduces an adjustable jump link coefficients model based on the residual network.The effective coefficients for each layer of the network can be set after using this model to further improving the recognition accuracy of abnormal behavior.A convolution kernel of 1×1 size is added to reduce the number of parameters for the purpose of improving the speed of the model in this paper.In order to reduce the noise of the data edge,and at the same time,improve the accuracy of the data and speed up the training,a BN(Batch Normalization)layer is added before the activation function in this network.This paper trains this network model on the public ImageNet dataset,and then uses the transfer learning method to recognize these abnormal behaviors of human in the UTI behavior dataset processed by the YOLO_v3 target detection network.Under the same experimental conditions,compared with the original ResNet-50 model,the improved model in this paper has a 2.8%higher accuracy in recognition of abnormal behaviors on the public UTI dataset.
基金the Scientific and Technological Programof Sichuan Public Health Bureau, No. 050104TenthFive-Year Key Research Project in 2005 of Philosophy and Social Science of Sichuan Province (MunicipalProgram) Social Science Key Project of Luzhou in2005
文摘AIM： Biological medicine is hard to fully and scientifically explain the etiological factor and pathogenesis of abnormal behaviors; while, researches on philosophy and psychology （including memetics） are beneficial to better understand and explain etiological factor and pathogenesis of abnormal behaviors. At present, the theory of philosophy and psychology is to investigate the entity of abnormal behavior based on the views of memetics. METHODS： Abnormal behavior was researched in this study based on three aspects, including instinctive behavior disorder, poorly social-adapted behavior disorder and mental or body disease associated behavior disorder. Most main viewpoints of memetics were derived from ＂The Meme Machine＂, which was written by Susan Blackmore. When questions about abnormal behaviors induced by mental and psychological diseases and conduct disorder of teenagers were discussed, some researching achievements which were summarized by authors previously were added in this study, such as aggressive behaviors, pathologically aggressive behaviors, etc. RESULTS： The abnormal behaviors mainly referred to a part of people＇s substandard behaviors which were not according with the realistic social environment, culture background and the pathologic behaviors resulted from people＇s various psychological diseases. According to the theory of ＂meme＂, it demonstrated that the relevant behavioral obstacles of various psychological diseases, for example, the unusual behavior of schizophrenia, were caused, because the old meme was destroyed thoroughly but the new meme was unable to establish; psychoneurosis and personality disorder were resulted in hard establishment of meme; the behavioral obstacles which were ill-adapted to society, for example, various additional and homosexual behaviors, were because of the selfish replications and imitations of ＂additional meme＂ and ＂homosexual meme＂; various instinct behavioral and congenital intelligent obstacles were not significance for memetics. CONCLUSION： Generation of abnormal behavior is not only caused by complexly biological factors, also by philosophical and psychological entities.
基金supported by Basic Science Research Program through the NationalResearch Foundation of Korea (NRF)funded by the Ministry of Education (2020R1A6A1A03040583).
文摘With the increasing number of digital devices generating a vast amount of video data,the recognition of abnormal image patterns has become more important.Accordingly,it is necessary to develop a method that achieves this task using object and behavior information within video data.Existing methods for detecting abnormal behaviors only focus on simple motions,therefore they cannot determine the overall behavior occurring throughout a video.In this study,an abnormal behavior detection method that uses deep learning(DL)-based video-data structuring is proposed.Objects and motions are first extracted from continuous images by combining existing DL-based image analysis models.The weight of the continuous data pattern is then analyzed through data structuring to classify the overall video.The performance of the proposed method was evaluated using varying parameter settings,such as the size of the action clip and interval between action clips.The model achieved an accuracy of 0.9817,indicating excellent performance.Therefore,we conclude that the proposed data structuring method is useful in detecting and classifying abnormal behaviors.
文摘Purpose-The abnormal behaviors of staff at petroleum stations pose significant safety hazards.Addressing the challenges of high parameter counts,lengthy training periods and low recognition rates in existing 3D ResNet behavior recognition models,this paper proposes GTB-ResNet,a network designed to detect abnormal behaviors in petroleum station staff.Design/methodology/approach-Firstly,to mitigate the issues of excessive parameters and computational complexity in 3D ResNet,a lightweight residual convolution module called the Ghost residual module(GhostNet)is introduced in the feature extraction network.Ghost convolution replaces standard convolution,reducing model parameters while preserving multi-scale feature extraction capabilities.Secondly,to enhance the model’s focus on salient features amidst wide surveillance ranges and small target objects,the triplet attention mechanism module is integrated to facilitate spatial and channel information interaction.Lastly,to address the challenge of short time-series features leading to misjudgments in similar actions,a bidirectional gated recurrent network is added to the feature extraction backbone network.This ensures the extraction of key long time-series features,thereby improving feature extraction accuracy.Findings-The experimental setup encompasses four behavior types:illegal phone answering,smoking,falling(abnormal)and touching the face(normal),comprising a total of 892 videos.Experimental results showcase GTB-ResNet achieving a recognition accuracy of 96.7%with a model parameter count of 4.46 M and a computational complexity of 3.898 G.This represents a 4.4%improvement over 3D ResNet,with reductions of 90.4%in parameters and 61.5%in computational complexity.Originality/value-Specifically designed for edge devices in oil stations,the 3D ResNet network is tailored for real-time action prediction.To address the challenges posed by the large number of parameters in 3D ResNet networks and the difficulties in deployment on edge devices,a lightweight residual module based on ghost convolution is developed.Additionally,to tackle the issue of low detection accuracy of behaviors amidst the noisy environment of petroleum stations,a triple attention mechanism is introduced during feature extraction to enhance focus on salient features.Moreover,to overcome the potential for misjudgments arising from the similarity of actions,a Bi-GRU model is introduced to enhance the extraction of key long-term features.
基金supported by the Henan Provincial Science and Technology Research Project(242102211022)the Starry Sky Creative Space Innovation Space Innovation Incubation Project of ZhengzhouUniversity of Light Industry(2023ZCKJ211)Research and Practice Project of Higher Education Teaching Reform in Henan Province for Graduate Education(2023SJGLX160Y).
文摘The behavior safety testing ofmore andmore elderly people living alone has become a hot research topic along with the arrival of an aging society.A YOLO-Abnormal Behaviour(YOLO-AB)algorithm for fusion detection of falling and smoking behaviors of elderly people living alone has been proposed in this paper,which can fully utilize the potential of the YOLOv8 algorithm on object detection and deeply explore the characteristics of different types of behaviors among the elderly,to solve the problems of single detection type,low fusion detection accuracy,and high missed detection rate.Firstly,datasets of different types of elderly behavior images such as falling,smoking,and standing are constructed for performance validation of subsequent algorithms.Secondly,the Content-Aware Reassembly of Features Module(CARAFE)is introduced into the YOLOv8 algorithm to enhance content perception,strengthen feature fusion,generate adaptive kernels dynamically,and reduce parameters effectively.Then,the Large Selective Kernel Network(LSKNet)module is added to the backbone network part to strengthen the framing of human targets and improve detection accuracy.Next,the Focal-SCYLLA-IOU(F-SIOU)loss function is used to improve the positioning accuracy of the edge part of the target detection frame.Finally,YOLO-AB and other different algorithms are tested and compared using the falling dataset,the smoking dataset,and the falling and smoking mixed dataset,respectively.The results show that the detection accuracy of the YOLO-AB algorithmis 0.93 on the falling dataset alone,0.864 on the smoking dataset alone,and 0.923 on the falling and smoking mixed dataset,all of which are better than those of the other algorithms.The performance of YOLO-AB is better than that of YOLOv8 on multiple metrics,such as 4.1%improvement in the mAP50 index,4.9%increase in the P index,and 3.5%boost in the R index,which verifies the effectiveness of the algorithm.
基金This research is supported by National Key Research and Development Program of China (Nos. 2021YFF0307203 and 2019QY1300)Youth Innovation Promotion Association CAS (No. 2021156)+2 种基金the Strategic Priority Research Program of Chinese Academy of Sciences (No. XDC02040100)National Natural Science Foundation of China (No. 61802404)This work is also supported by the Program of Key Laboratory of Network Assessment Technology, the Chinese Academy of Sciences, Program of Beijing Key Laboratory of Network Security and Protection Technology.
文摘With the wide application and development of blockchain technology in various fields such as finance, government affairs and medical care, security incidents occur frequently on it, which brings great threats to users’ assets and information. Many researchers have worked on blockchain abnormal behavior awareness in respond to these threats. We summarize respectively the existing public blockchain and consortium blockchain abnormal behavior awareness methods and ideas in detail as the difference between the two types of blockchain. At the same time, we summarize and analyze the existing data sets related to mainstream blockchain security, and finally discuss possible future research directions. Therefore, this work can provide a reference for blockchain security awareness research.
文摘Analyzing a vehicle’s abnormal behavior in surveillance videos is a challenging field,mainly due to the wide variety of anomaly cases and the complexity of surveillance videos.In this study,a novel intelligent vehicle behavior analysis framework based on a digital twin is proposed.First,detecting vehicles based on deep learning is implemented,and Kalman filtering and feature matching are used to track vehicles.Subsequently,the tracked vehicle is mapped to a digital-twin virtual scene developed in the Unity game engine,and each vehicle’s behavior is tested according to the customized detection conditions set up in the scene.The stored behavior data can be used to reconstruct the scene again in Unity for a secondary analysis.The experimental results using real videos from traffic cameras illustrate that the detection rate of the proposed framework is close to that of the state-of-the-art abnormal event detection systems.In addition,the implementation and analysis process show the usability,generalization,and effectiveness of the proposed framework.
基金Supported by the National Natural Science Foundation of China(No.61304205,61502240)Natural Science Foundation of Jiangsu Province(BK20141002)+1 种基金Innovation and Entrepreneurship Training Project of College Students(No.201710300051,201710300050)Foundation for Excellent Undergraduate Dissertation(Design) of Naning University of Information Science & Technology
文摘Abnormal driving behavior identification( ADBI) has become a research hotspot because of its significance in driver assistance systems. However,current methods still have some limitations in terms of accuracy and reliability under severe traffic scenes. This paper proposes a new ADBI method based on direction and position offsets,where a two-factor identification strategy is proposed to improve the accuracy and reliability of ADBI. Self-adaptive edge detection based on Sobel operator is used to extract edge information of lanes. In order to enhance the efficiency and reliability of lane detection,an improved lane detection algorithm is proposed,where a Hough transform based on local search scope is employed to quickly detect the lane,and a validation scheme based on priori information is proposed to further verify the detected lane. Experimental results under various complex road conditions demonstrate the validity of the proposed ADBI.
基金funded by the program Research and Application of Demand Response Potential Evaluation Technologies Based on Massive Electricity Data(No.B31532238944)supported by the State Grid Hubei Electric Power Research Institute.
文摘Understanding the abnormal electricity usage behavior of buildings is essential to enhance the resilience,efficiency,and security of urban/building energy systems while safeguarding occupant comfort.However,data reflecting such behavior are often considered as outliers,and removed or smoothed during preprocessing,limiting insights into their potential impacts.This paper proposes an abnormal behavior analysis method that identifies outliers(considering data distribution)and anomalies(considering the physical context)based on the statistical principle and domain knowledge,assessing their effects on energy supply security.A 4-quadrant graph is proposed to quantify and categorize the impacts of buildings on urban energy systems.The method is illustrated by data from 1,451 buildings in a city.Results show that the proposed method can identify abnormal data effectively.Buildings in the primary industry have more outliers,while those in the tertiary industry have more anomalies.Seven buildings affecting both the security and economy of urban energy systems are identified.The outliers rise more frequently from 8:00 to 18:00,on weekdays and in the summer and winter months.However,the anomaly distribution has a weak connection with time.Moreover,the abnormal electricity usage behavior positively correlates with outdoor air temperatures.This method provides a new perspective for identifying potential risks,managing energy usage behavior,and enhancing flexibility of the urban energy systems.
文摘After the 2011 Tohoku earthquake (EQ), there have been numerous aftershocks in the eastern and Pacific Ocean of Japan, but EQs are still rare in the western part of Japan. In this situation a relatively large (magnitude (M) ~6) EQ happened on April 12 (UT), 2013 at a place close to the former 1995 Kobe EQ (M~7), so we have tried to find whether there existed any precursors to this EQ, especially abnormal animal behavior (milk yield of cows), observed at Kagawa, Shikoku, near the EQ epicenter. The milk yield of cows has been continuously monitored at Kagawa, and it is found that the milk yield exhibited an abnormal depletion about 10 days before the EQ. This behavior has been extensively compared with the former electromagnetic precursors (ULF radiation, ionos-pheric perturbation). This leads to the discussion on the sensory mechanism of unusual behavior of mild yield of cows, and it may be suggested that ULF radiation among different electromagnetic precursors is a mostly likely driver, at least, for this EQ.
文摘Abnormal driving behavior includes driving distraction,fatigue,road anger,phone use,and an exceptionally happy mood.Detecting abnormal driving behavior in advance can avoid traffic accidents and reduce the risk of traffic conflicts.Traditional methods of detecting abnormal driving behavior include using wearable devices to monitor blood pressure,pulse,heart rate,blood oxygen,and other vital signs,and using eye trackers to monitor eye activity(such as eye closure,blinking frequency,etc.)to estimate whether the driver is excited,anxious,or distracted.Traditional monitoring methods can detect abnormal driving behavior to a certain extent,but they will affect the driver’s normal driving state,thereby introducing additional driving risks.This research uses the combined method of support vector machine and dlib algorithm to extract 68 facial feature points from the human face,and uses an SVM model as a strong classifier to classify different abnormal driving statuses.The combined method reaches high accuracy in detecting road anger and fatigue status and can be used in an intelligent vehicle cabin to improve the driving safety level.
基金supported by the National Natural Science Foundation of China(Nos.NSFC-52072133 and 52302140)Joint Funds of the National Natural Science Foundation of China-Key Program(No.U21B2068)Major Scientific and Technological Innovation Project of Wenzhou(No.ZG2023040).
文摘The temperature coefficient of the resonant frequency(τ_(f))of low-permittivity(ε_(r))microwave dielectric ceramics is required to be near 0 ppm/℃for practical application.However,owing to the polarization mechanism,τ_(f)of low-ε_(r)microwave dielectric ceramics is generally negative.Here,a novel microwave dielectric ceramic,Ba_(3−x)Sr_(x)MgSi_(2)O_(8),with an abnormal positiveτ_(f)at the applied temperature is presented.In this study,Sr^(2+)with a relatively small ionic radius was introduced to replace Ba^(2+),and a single-phase solid solution was formed(x>0.5).Ba_(3−x)Sr_(x)MgSi_(2)O_(8)ceramics were discussed in glaserite-type topology with space groups of P_(3)^(-)for x≤0.5,relatively high symmetry P_(3)^(-)m1 for 0.5<x<2.5,and C2 for x≥2.5.Theε_(r)peaks as a function of temperature initially shift to low temperatures and then return to high temperatures through an ion substitution strategy.Notably,remarkable microwave dielectric properties for BaSr_(2)MgSi_(2)O_(8)were observed:ε_(r)≈14.2,Q×f≈38,900 GHz,andτ_(f)≈+117 ppm/℃,which are superior to those of other low-ε_(r)silicate ceramics with positiveτ_(f)values.Density functional theory simulation calculations revealed that the preferential occupation of Sr^(2+)ions could decrease the intrinsic formation energy and improve the microwave dielectric properties by mitigating the ionic size mismatch within the crystal structure.The present research offers a strategy for discovering novel microwave dielectric ceramics with abnormalτ_(f)values,which could serve as τ_(f)regulators in practical applications because of their low cost and excellent microwave dielectric properties.
基金financially supported by the National Natural Science Foundation of China(Nos.50633030 and 51073077)
文摘It was found that the interface effects in viscous capillary flow influenced the process of viscosity measurement greatly, and the abnormal viscosity behaviors of polyelectrolytes as well as neutral polymers in dilute solution region were ascribed to interface effect. According to this theory, we have reviewed the previous viscosity data of derivatives of poly-2- vinylpyridine reported by Maclay and Fuoss first. Then, the abnormal viscosity behaviors of a series of sodium polystyrene sulfonate samples with various molecular weights in dilute aqueous solutions were studied further. The solute adsorption behaviors and structural information of polymers have been discussed carefully.
基金The financial support from the Program for Science and Technology of Henan Province of China(Grant No.242102210148)Henan Center for Outstanding Overseas Scientists(Grant No.GZS2022011)Songshan Laboratory Pre-Research Project(Grant No.YYJC032022022).
文摘Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions.In the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication protocols.Smart grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for IEDs.Hence,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for IEDs.Initially,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security assessment.Subsequently,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal behaviors.This index system contains 18 indicators in 3 categories.Additionally,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and 5.The higher the level,the greater the security risk.Finally,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by IEDs.The results show that calculated security risk level based on the proposed assessment method are consistent with actual simulation.Thus,the reasonableness and effectiveness of the proposed index system and assessment method are validated.
基金This research is supported by The National Natural Science Foundation of China under Grant(No.61672101)Beijing Key Laboratory of Internet Culture and Digital Dissemination Research(No.ICDDXN004)Key Lab of Information Network Security of Ministry of Public Security(No.C18601).
文摘In order to quickly and accurately find the implementer of the network crime,based on the user portrait technology,a rapid detection method for users with abnormal behaviors is proposed.This method needs to construct the abnormal behavior rule base on various kinds of abnormal behaviors in advance,and construct the user portrait including basic attribute tags,behavior attribute tags and abnormal behavior similarity tags for network users who have abnormal behaviors.When a network crime occurs,firstly get the corresponding tag values in all user portraits according to the category of the network crime.Then,use the Naive Bayesian method matching each user portrait,to quickly locate the most likely network criminal suspects.In the case that no suspect is found,all users are audited comprehensively through matching abnormal behavior rule base.The experimental results show that,the accuracy rate of using this method for fast detection of network crimes is 95.9%,and the audit time is shortened to 1/35 of that of the conventional behavior audit method.
基金supported by the National Natural Science Foundation of China(82171170,81971076,82371277 to H.Z.,82101345 to L.R.L.)。
文摘A growing number of studies have demonstrated that repeated exposure to sevoflurane during development results in persistent social abnormalities and cognitive impairment.Davunetide,an active fragment of the activity-dependent neuroprotective protein(ADNP),has been implicated in social and cognitive protection.However,the potential of davunetide to attenuate social deficits following sevoflurane exposure and the underlying developmental mechanisms remain poorly understood.In this study,ribosome and proteome profiles were analyzed to investigate the molecular basis of sevoflurane-induced social deficits in neonatal mice.The neuropathological basis was also explored using Golgi staining,morphological analysis,western blotting,electrophysiological analysis,and behavioral analysis.Results indicated that ADNP was significantly down-regulated following developmental exposure to sevoflurane.In adulthood,anterior cingulate cortex(ACC)neurons exposed to sevoflurane exhibited a decrease in dendrite number,total dendrite length,and spine density.Furthermore,the expression levels of Homer,PSD95,synaptophysin,and vglut2 were significantly reduced in the sevoflurane group.Patch-clamp recordings indicated reductions in both the frequency and amplitude of miniature excitatory postsynaptic currents(mEPSCs).Notably,davunetide significantly ameliorated the synaptic defects,social behavior deficits,and cognitive impairments induced by sevoflurane.Mechanistic analysis revealed that loss of ADNP led to dysregulation of Ca^(2+)activity via the Wnt/β-catenin signaling,resulting in decreased expression of synaptic proteins.Suppression of Wnt signaling was restored in the davunetide-treated group.Thus,ADNP was identified as a promising therapeutic target for the prevention and treatment of neurodevelopmental toxicity caused by general anesthetics.This study provides important insights into the mechanisms underlying social and cognitive disturbances caused by sevoflurane exposure in neonatal mice and elucidates the regulatory pathways involved.
文摘The frame rate of conventional vision systems is restricted to the video signal formats （e.g., NTSC 30 fps and PAL 25 fps） that are designed on the basis of the characteristics of the human eye, which implies that the processing speed of these systems is limited to the recognition speed of the human eye. However, there is a strong demand for real-time high-speed vision sensors in many application fields, such as factory automation, biomedicine, and robotics, where high-speed operations are carried out. These high-speed operations can be tracked and inspected by using high-speed vision systems with intelligent sensors that work at hundreds of Hertz or more, especially when the operation is difficult to observe with the human eye. This paper reviews advances in developing real-time high Speed vision systems and their applications in various fields, such as intelligent logging systems, vibration dynamics sensing, vision-based mechanical control, three-dimensional measurement/automated visual inspection, vision-based human interface, and biomedical applications.
文摘The hot deformation characteristics of as-forged Ti−3.5Al−5Mo−6V−3Cr−2Sn−0.5Fe−0.1B−0.1C alloy within a temperature range from 750 to 910℃and a strain rate range from 0.001 to 1 s^(-1) were investigated by hot compression tests.The stress−strain curves show that the flow stress decreases with the increase of temperature and the decrease of strain rate.The microstructure is sensitive to deformation parameters.The dynamic recrystallization(DRX)grains appear while the temperature reaches 790℃at a constant strain rate of 0.001 s^(-1) and strain rate is not higher than 0.1 s^(-1) at a constant temperature of 910℃.The work-hardening rateθis calculated and it is found that DRX prefers to happen at high temperature and low strain rate.The constitutive equation and processing map were obtained.The average activation energy of the alloy is 242.78 kJ/mol and there are few unstable regions on the processing map,which indicates excellent hot workability.At the strain rate of 0.1 s^(-1),the stress−strain curves show an abnormal shape where there are two stress peaks simultaneously.This can be attributed to the alternation of hardening effect,which results from the continuous dynamic recrystallization(CDRX)and the rotation of DRX grains,and dynamic softening mechanism.
