Based on the minimum principle of acceleration in the elastic-plastic continua under finite def ormation, the dynamic response of an elastic-perfectly plastic pin-ended beam subjected to rectangular impulse loading is...Based on the minimum principle of acceleration in the elastic-plastic continua under finite def ormation, the dynamic response of an elastic-perfectly plastic pin-ended beam subjected to rectangular impulse loading is studied with the help of a numerical approach. The calculated results once again show the anomalous behavior of the beam during its response process, which was previously found in [1]. By carefully analyzing the instantaneous distribution of the bending moment, the membrane force, the curvature and displacement during the response process, it is concluded that the interactive effect between the geometry and materials nonlinearities of the structure is the key reason for leading to the anomalous behavior. This will be helpful for clarifying some misunderstandings in explaining the problem before.展开更多
There is a long-standing confusion concerning the physical origin of the anomalous resistivity peak in transition metal pentatelluride HfTe5. Several mechanisms, such as the formation of charge density wave or polaron...There is a long-standing confusion concerning the physical origin of the anomalous resistivity peak in transition metal pentatelluride HfTe5. Several mechanisms, such as the formation of charge density wave or polaron, have been proposed, but so far no conclusive evidence has been presented. In this work, we investigate the unusual temperature dependence of magneto-transport properties in HfTe5. It is found that a three-dimensional topological Dirac semimetal state emerges only at around Tp （at which the resistivity shows a pronounced peak）, as manifested by a large negative magnetoresistance. This accidental Dirac semimetal state mediates the topological quantum phase transition between the two distinct weak and strong topological insulator phases in HfTe5. Our work not only provides the first evidence of a temperature-induced critical topological phase transition inHfTe5 but also gives a reasonable explanation on the long-lasting question.展开更多
Miniaturized sound generators are attractive to realize intriguing functions.Thermoacoustic device’s application is seriously limited due to the frequency-doubling phenomenon.To address this issue,photoacoustic sound...Miniaturized sound generators are attractive to realize intriguing functions.Thermoacoustic device’s application is seriously limited due to the frequency-doubling phenomenon.To address this issue,photoacoustic sound generator is considered as a promising alternative.Here,based on vertical single-wall carbon nanotubes(CNTs)array,we introduce a photoacoustic sound generator with internal nano-Helmholtz cavity.Different from traditional device that generates sound by periodically heating up the open space air around material,this sound generator produces an audio signal by forming a forced vibration of the air inside the CNTs.Interestingly,anomalous photoacoustic behavior is observed that the sound pressure level(SPL)curve has a resonance peak,the corresponding frequency of which is inversely proportional to the CNTs array’s height.Furthermore,the energy conversion efficiency of this photoacoustic device is 1.64 times as large as that of a graphene sponge-based photoacoustic device.Most importantly,this device can be employed for music playing,bringing a new clew for the development of musical instruments in the future.展开更多
Anomalous situations in surveillance videos or images that may result in security issues,such as disasters,accidents,crime,violence,or terrorism,can be identified through video anomaly detection.However,differentiat-i...Anomalous situations in surveillance videos or images that may result in security issues,such as disasters,accidents,crime,violence,or terrorism,can be identified through video anomaly detection.However,differentiat-ing anomalous situations from normal can be challenging due to variations in human activity in complex environments such as train stations,busy sporting fields,airports,shopping areas,military bases,care centers,etc.Deep learning models’learning capability is leveraged to identify abnormal situations with improved accuracy.This work proposes a deep learning architecture called Anomalous Situation Recognition Network(ASRNet)for deep feature extraction to improve the detection accuracy of various anomalous image situations.The proposed framework has five steps.In the first step,pretraining of the proposed architecture is performed on the CIFAR-100 dataset.In the second step,the proposed pre-trained model and Inception V3 architecture are used for feature extraction by utilizing the suspicious activity recognition dataset.In the third step,serial feature fusion is performed,and then the Dragonfly algorithm is utilized for feature optimization in the fourth step.Finally,using optimized features,various Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)based classification models are utilized to detect anomalous situations.The proposed framework is validated on the suspicious activity dataset by varying the number of optimized features from 100 to 1000.The results show that the proposed method is effective in detecting anomalous situations and achieves the highest accuracy of 99.24%using cubic SVM.展开更多
基金the National Natural Science Foundation of China.
文摘Based on the minimum principle of acceleration in the elastic-plastic continua under finite def ormation, the dynamic response of an elastic-perfectly plastic pin-ended beam subjected to rectangular impulse loading is studied with the help of a numerical approach. The calculated results once again show the anomalous behavior of the beam during its response process, which was previously found in [1]. By carefully analyzing the instantaneous distribution of the bending moment, the membrane force, the curvature and displacement during the response process, it is concluded that the interactive effect between the geometry and materials nonlinearities of the structure is the key reason for leading to the anomalous behavior. This will be helpful for clarifying some misunderstandings in explaining the problem before.
基金Supported by the National Basic Research Program of China under Grant Nos 2015CB921303 and 2013CB921700the National Key Research Program of China under Grant No 2016YFA0300604the'Strategic Priority Research Program(B)'of the Chinese Academy of Sciences under Grant No XDB07020100
文摘There is a long-standing confusion concerning the physical origin of the anomalous resistivity peak in transition metal pentatelluride HfTe5. Several mechanisms, such as the formation of charge density wave or polaron, have been proposed, but so far no conclusive evidence has been presented. In this work, we investigate the unusual temperature dependence of magneto-transport properties in HfTe5. It is found that a three-dimensional topological Dirac semimetal state emerges only at around Tp （at which the resistivity shows a pronounced peak）, as manifested by a large negative magnetoresistance. This accidental Dirac semimetal state mediates the topological quantum phase transition between the two distinct weak and strong topological insulator phases in HfTe5. Our work not only provides the first evidence of a temperature-induced critical topological phase transition inHfTe5 but also gives a reasonable explanation on the long-lasting question.
文摘Miniaturized sound generators are attractive to realize intriguing functions.Thermoacoustic device’s application is seriously limited due to the frequency-doubling phenomenon.To address this issue,photoacoustic sound generator is considered as a promising alternative.Here,based on vertical single-wall carbon nanotubes(CNTs)array,we introduce a photoacoustic sound generator with internal nano-Helmholtz cavity.Different from traditional device that generates sound by periodically heating up the open space air around material,this sound generator produces an audio signal by forming a forced vibration of the air inside the CNTs.Interestingly,anomalous photoacoustic behavior is observed that the sound pressure level(SPL)curve has a resonance peak,the corresponding frequency of which is inversely proportional to the CNTs array’s height.Furthermore,the energy conversion efficiency of this photoacoustic device is 1.64 times as large as that of a graphene sponge-based photoacoustic device.Most importantly,this device can be employed for music playing,bringing a new clew for the development of musical instruments in the future.
基金supported by the“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)granted financial resources from the Ministry of Trade,Industry Energy,Republic ofKorea.(No.20204010600090).
文摘Anomalous situations in surveillance videos or images that may result in security issues,such as disasters,accidents,crime,violence,or terrorism,can be identified through video anomaly detection.However,differentiat-ing anomalous situations from normal can be challenging due to variations in human activity in complex environments such as train stations,busy sporting fields,airports,shopping areas,military bases,care centers,etc.Deep learning models’learning capability is leveraged to identify abnormal situations with improved accuracy.This work proposes a deep learning architecture called Anomalous Situation Recognition Network(ASRNet)for deep feature extraction to improve the detection accuracy of various anomalous image situations.The proposed framework has five steps.In the first step,pretraining of the proposed architecture is performed on the CIFAR-100 dataset.In the second step,the proposed pre-trained model and Inception V3 architecture are used for feature extraction by utilizing the suspicious activity recognition dataset.In the third step,serial feature fusion is performed,and then the Dragonfly algorithm is utilized for feature optimization in the fourth step.Finally,using optimized features,various Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)based classification models are utilized to detect anomalous situations.The proposed framework is validated on the suspicious activity dataset by varying the number of optimized features from 100 to 1000.The results show that the proposed method is effective in detecting anomalous situations and achieves the highest accuracy of 99.24%using cubic SVM.
