Due to the calculation problem of classical methods (such as Lyapunovexponent) for chaotic behavior, a new method of identifying nonlinear dynamics with higher-ordertime-frequency entropy (HOTFE) based on time-frequen...Due to the calculation problem of classical methods (such as Lyapunovexponent) for chaotic behavior, a new method of identifying nonlinear dynamics with higher-ordertime-frequency entropy (HOTFE) based on time-frequency analysis and information theorem is proposed.Firstly, the meaning of HOTFE is defined, and then its validity is testified by numericalsimulation. In the end vibration data from rotors are analyzed by HOTFE. The results demonstratethat it can indeed identify the early rub-impact chaotic behavior in rotors and also is simpler tocalculate than previous methods.展开更多
The use of time-frequency entropy to quantitatively assess the stability of submerged arc welding process considering the distribution features of arc energy is reported in this paper. Time-frequency entropy is employ...The use of time-frequency entropy to quantitatively assess the stability of submerged arc welding process considering the distribution features of arc energy is reported in this paper. Time-frequency entropy is employed to calculate and analyze the stationary current signals, non-stationary current and voltage signals in the submerged arc welding process. It is obtained that time-frequency entropy of arc signal can be used as arc stability judgment criteria of submerged arc welding. Experimental results are provided to confirm the effectiveness of this approach.展开更多
High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an eff...High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.展开更多
Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected in...Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.展开更多
The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sus...The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.展开更多
Entropy production in quasi-isentropic compression (QIC) is critically important for understanding the properties of materials under extremeconditions. However, the origin and accurate quantification of entropy in thi...Entropy production in quasi-isentropic compression (QIC) is critically important for understanding the properties of materials under extremeconditions. However, the origin and accurate quantification of entropy in this situation remain long-standing challenges. In this work, a framework is established for the quantification of entropy production and partition, and their relation to microstructural change in QIC. Cu50Zr50is taken as a model material, and its compression is simulated by molecular dynamics. On the basis of atomistic simulation-informed physicalproperties and free energy, the thermodynamic path is recovered, and the entropy production and its relation to microstructural change aresuccessfully quantified by the proposed framework. Contrary to intuition, entropy production during QIC of metallic glasses is relativelyinsensitive to the strain rate ˙γ when ˙γ ranges from 7.5 × 10^(8) to 2 × 10^(9)/s, which are values reachable in QIC experiments, with a magnitudeof the order of 10^(−2)kB/atom per GPa. However, when ˙γ is extremely high (>2 × 10^(9)/s), a notable increase in entropy production rate with˙γ is observed. The Taylor–Quinney factor is found to vary with strain but not with strain rate in the simulated regime. It is demonstrated thatentropy production is dominated by the configurational part, compared with the vibrational part. In the rate-insensitive regime, the increase inconfigurational entropy exhibits a linear relation to the Shannon-entropic quantification of microstructural change, and a stretched exponential relation to the Taylor–Quinney factor. The quantification of entropy is expected to provide thermodynamic insights into the fundamentalrelation between microstructure evolution and plastic dissipation.展开更多
Due to the energy crisis caused by limited fossil fuel reserves,extensive use of the renewable energy sources such as wind or solar energy is deemed to replace the use of traditional fossil fuels in the future^([1−3])...Due to the energy crisis caused by limited fossil fuel reserves,extensive use of the renewable energy sources such as wind or solar energy is deemed to replace the use of traditional fossil fuels in the future^([1−3]).However,most renewable energy sources face the same problem,which is the intermittency of energy.For example,solar energy cannot be utilized at night.That means the continuous energy demand required for large-scale power grids can’t be satisfied by a single solar panel model.展开更多
In the present study, energetic and entropic changes are investigated on a comparative basis, as they occur in the volume changes of an ideal gas in the Carnot cycle and in the course of the chemical reaction in a lea...In the present study, energetic and entropic changes are investigated on a comparative basis, as they occur in the volume changes of an ideal gas in the Carnot cycle and in the course of the chemical reaction in a lead-acid battery. Differences between reversible and irreversible processes have been worked out, in particular between reversibly exchanged entropy (∆<sub>e</sub>S) and irreversibly produced entropy (∆<sub>i</sub>S). In the partially irreversible case, ∆<sub>e</sub>S and ∆<sub>i</sub>S add up to the sum ∆S for the volume changes of a gas, and only this function has an exact differential. In a chemical reaction, however, ∆<sub>e</sub>S is independent on reversibility. It arises from the different intramolecular energy contents between products and reactants. Entropy production in a partially irreversible Carnot cycle is brought about through work-free expansions, whereas in the irreversible battery reaction entropy is produced via activated complexes, whereby a certain, variable fraction of the available chemical energy becomes transformed into electrical energy and the remaining fraction dissipated into heat. The irreversible reaction process via activated complexes has been explained phenomenologically. For a sufficiently high power output of coupled reactions, it is essential that the input energy is not completely reversibly transformed, but rather partially dissipated, because this can increase the process velocity and consequently its power output. A reduction of the counter potential is necessary for this purpose. This is not only important for man-made machines, but also for the viability of cells.展开更多
We present a formulation of the single-trajectory entropy using the trajectories ensemble. The single-trajectory entropy is affected by its surrounding trajectories via the distribution function. The single-trajectory...We present a formulation of the single-trajectory entropy using the trajectories ensemble. The single-trajectory entropy is affected by its surrounding trajectories via the distribution function. The single-trajectory entropies are studied in two typical potentials, i.e., harmonic potential and double-well potential, and in viscous environment by interacting trajectory method. The results of the trajectory methods are in agreement well with the numerical methods(Monte Carlo simulation and difference equation). The single-trajectory entropies increasing(decreasing) could be caused by absorption(emission) heat from(to) the thermal environment. Also, some interesting trajectories, which correspond to the rare evens in the processes, are demonstrated.展开更多
As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly vital.We propose an intelligent detection model named IGED by using improved general...As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly vital.We propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data volume.Then the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization capabilities.Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic.Compared with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.展开更多
The development of tellurium(Te)-based semiconductor nanomaterials for efficient light-to-heat conversion may offer an effective means of harvesting sunlight to address global energy concerns.However,the nanosized Te(...The development of tellurium(Te)-based semiconductor nanomaterials for efficient light-to-heat conversion may offer an effective means of harvesting sunlight to address global energy concerns.However,the nanosized Te(nano-Te)materials reported to date suffer from a series of drawbacks,including limited light absorption and a lack of surface structures.Herein,we report the preparation of nano-Te by electrochemical exfoliation using an electrolyzable room-temperature ionic liquid.Anions,cations,and their corresponding electrolytic products acting as chemical scissors can precisely intercalate and functionalize bulk Te.The resulting nano-Te has high morphological entropy,rich surface functional groups,and broad light absorption.We also constructed foam hydrogels based on poly(vinyl alcohol)/nano-Te,which achieved an evaporation rate and energy efficiency of 4.11 kg m^(−2)h^(−1)and 128%,respectively,under 1 sun irradiation.Furthermore,the evaporation rate was maintained in the range 2.5-3.0 kg m^(−2)h^(−1)outdoors under 0.5-1.0 sun,providing highly efficient evaporation under low light conditions.展开更多
It is explicitly shown how the Schwarzschild Black Hole Entropy (in all dimensions) emerges from truly point mass sources at r=0due to a non-vanishing scalar curvature involving the Dirac delta distribution. In order ...It is explicitly shown how the Schwarzschild Black Hole Entropy (in all dimensions) emerges from truly point mass sources at r=0due to a non-vanishing scalar curvature involving the Dirac delta distribution. In order to achieve this, one is required to extend the domain of r to negative values −∞≤r≤+∞. It is the density and anisotropic pressure components associated with the point mass delta function source at the origin r=0which furnish the Schwarzschild black hole entropy in all dimensions D≥4after evaluating the Euclidean Einstein-Hilbert action. Two of the most salient results are i) that the observed spacetime dimension D=4is precisely singled out from all the other dimensions when the strong and weak energy conditions are met, and ii) the point mass source described in this work is not the result of a spherically symmetric gravitational collapse of a star as described by the Oppenheimer-Snyder model because we are not neglecting the pressure. As usual, it is required to take the inverse Hawking temperature βHas the length of the circle Sβ1obtained from a compactification of the Euclidean time in thermal field theory which results after a Wick rotation, it=τ, to imaginary time. This approach can be generalized to the Reissner-Nordstrom and Kerr-Newman metrics. The physical implications of this finding warrant further investigation since it suggests a profound connection between the notion of gravitational entropy and spacetime singularities.展开更多
Water-based aerosol is widely used as an effective strategy in electro-optical countermeasure on the battlefield used to the preponderance of high efficiency,low cost and eco-friendly.Unfortunately,the stability of th...Water-based aerosol is widely used as an effective strategy in electro-optical countermeasure on the battlefield used to the preponderance of high efficiency,low cost and eco-friendly.Unfortunately,the stability of the water-based aerosol is always unsatisfactory due to the rapid evaporation and sedimentation of the aerosol droplets.Great efforts have been devoted to improve the stability of water-based aerosol by using additives with different composition and proportion.However,the lack of the criterion and principle for screening the effective additives results in excessive experimental time consumption and cost.And the stabilization time of the aerosol is still only 30 min,which could not meet the requirements of the perdurable interference.Herein,to improve the stability of water-based aerosol and optimize the complex formulation efficiently,a theoretical calculation method based on thermodynamic entropy theory is proposed.All the factors that influence the shielding effect,including polyol,stabilizer,propellant,water and cosolvent,are considered within calculation.An ultra-stable water-based aerosol with long duration over 120 min is obtained with the optimal fogging agent composition,providing enough time for fighting the electro-optic weapon.Theoretical design guideline for choosing the additives with high phase transition temperature and low phase transition enthalpy is also proposed,which greatly improves the total entropy change and reduce the absolute entropy change of the aerosol cooling process,and gives rise to an enhanced stability of the water-based aerosol.The theoretical calculation methodology contributes to an abstemious time and space for sieving the water-based aerosol with desirable performance and stability,and provides the powerful guarantee to the homeland security.展开更多
Laser-induced fluorescence(LIF)spectroscopy is employed for plasma diagnosis,necessitating the utilization of deconvolution algorithms to isolate the Doppler effect from the raw spectral signal.However,direct deconvol...Laser-induced fluorescence(LIF)spectroscopy is employed for plasma diagnosis,necessitating the utilization of deconvolution algorithms to isolate the Doppler effect from the raw spectral signal.However,direct deconvolution becomes invalid in the presence of noise as it leads to infinite amplification of high-frequency noise components.To address this issue,we propose a deconvolution algorithm based on the maximum entropy principle.We validate the effectiveness of the proposed algorithm by utilizing simulated LIF spectra at various noise levels(signal-to-noise ratio,SNR=20–80 d B)and measured LIF spectra with Xe as the working fluid.In the typical measured spectrum(SNR=26.23 d B)experiment,compared with the Gaussian filter and the Richardson–Lucy(R-L)algorithm,the proposed algorithm demonstrates an increase in SNR of 1.39 d B and 4.66 d B,respectively,along with a reduction in the root-meansquare error(RMSE)of 35%and 64%,respectively.Additionally,there is a decrease in the spectral angle(SA)of 0.05 and 0.11,respectively.In the high-quality spectrum(SNR=43.96 d B)experiment,the results show that the running time of the proposed algorithm is reduced by about98%compared with the R-L iterative algorithm.Moreover,the maximum entropy algorithm avoids parameter optimization settings and is more suitable for automatic implementation.In conclusion,the proposed algorithm can accurately resolve Doppler spectrum details while effectively suppressing noise,thus highlighting its advantage in LIF spectral deconvolution applications.展开更多
The high entropy alloys(HEAs)are the newly developed high-performance materials that have gained significant importance in defence,nuclear and aerospace sector due to their superior mechanical properties,heat resistan...The high entropy alloys(HEAs)are the newly developed high-performance materials that have gained significant importance in defence,nuclear and aerospace sector due to their superior mechanical properties,heat resistance,high temperature strength and corrosion resistance.These alloys are manufactured by the equal mixing or larger proportions of five or more alloying elements.HEAs exhibit superior mechanical performance compared to traditional engineering alloys because of the extensive alloying composition and higher entropy of mixing.Solid state welding(SSW)techniques such as friction stir welding(FSW),rotary friction welding(RFW),diffusion bonding(DB)and explosive welding(EW)have been efficiently deployed for improving the microstructural integrity and mechanical properties of welded HEA joints.The HEA interlayers revealed greater potential in supressing the formation of deleterious intermetallic phases and maximizing the mechanical properties of HEAs joints.The similar and dissimilar joining of HEAs has been manifested to be viable for HEA systems which further expands their industrial applications.Thus,the main objective of this review paper is to present a critical review of current state of research,challenges and opportunities and main directions in SSW of HEAs mainly CoCrFeNiMn and Al_xCoCrFeNi alloys.The state of the art of problems,progress and future outlook in SSW of HEAs are critically reviewed by considering the formation of phases,microstructural evolution and mechanical properties of HEAs joints.展开更多
Short-range ordering(SRO)is one of the most important structural features of high entropy alloys(HEAs).However,the chemical and structural analyses of SROs are very difficult due to their small size,complexed composit...Short-range ordering(SRO)is one of the most important structural features of high entropy alloys(HEAs).However,the chemical and structural analyses of SROs are very difficult due to their small size,complexed compositions,and varied locations.Transmission electron microscopy(TEM)as well as its aberration correction techniques are powerful for characterizing SROs in these compositionally complex alloys.In this short communication,we summarized recent progresses regarding characterization of SROs using TEM in the field of HEAs.By using advanced TEM techniques,not only the existence of SROs was confirmed,but also the effect of SROs on the deformation mechanism was clarified.Moreover,the perspective related to application of TEM techniques in HEAs are also discussed.展开更多
Cross entropy is a measure in machine learning and deep learning that assesses the difference between predicted and actual probability distributions. In this study, we propose cross entropy as a performance evaluation...Cross entropy is a measure in machine learning and deep learning that assesses the difference between predicted and actual probability distributions. In this study, we propose cross entropy as a performance evaluation metric for image classifier models and apply it to the CT image classification of lung cancer. A convolutional neural network is employed as the deep neural network (DNN) image classifier, with the residual network (ResNet) 50 chosen as the DNN archi-tecture. The image data used comprise a lung CT image set. Two classification models are built from datasets with varying amounts of data, and lung cancer is categorized into four classes using 10-fold cross-validation. Furthermore, we employ t-distributed stochastic neighbor embedding to visually explain the data distribution after classification. Experimental results demonstrate that cross en-tropy is a highly useful metric for evaluating the reliability of image classifier models. It is noted that for a more comprehensive evaluation of model perfor-mance, combining with other evaluation metrics is considered essential. .展开更多
W-based WTaVCr refractory high entropy alloys (RHEA) may be novel and promising candidate materials for plasma facing components in the first wall and diverter in fusion reactors. This alloy has been developed by a po...W-based WTaVCr refractory high entropy alloys (RHEA) may be novel and promising candidate materials for plasma facing components in the first wall and diverter in fusion reactors. This alloy has been developed by a powder metallurgy process combining mechanical alloying and spark plasma sintering (SPS). The SPSed samples contained two phases, in which the matrix is RHEA with a body-centered cubic structure, while the oxide phase was most likely Ta2VO6through a combined analysis of X-ray diffraction (XRD),energy-dispersive spectroscopy (EDS), and selected area electron diffraction (SAED). The higher oxygen affinity of Ta and V may explain the preferential formation of their oxide phases based on thermodynamic calculations. Electron backscatter diffraction (EBSD) revealed an average grain size of 6.2μm. WTaVCr RHEA showed a peak compressive strength of 2997 MPa at room temperature and much higher micro-and nano-hardness than W and other W-based RHEAs in the literature. Their high Rockwell hardness can be retained to at least 1000°C.展开更多
文摘Due to the calculation problem of classical methods (such as Lyapunovexponent) for chaotic behavior, a new method of identifying nonlinear dynamics with higher-ordertime-frequency entropy (HOTFE) based on time-frequency analysis and information theorem is proposed.Firstly, the meaning of HOTFE is defined, and then its validity is testified by numericalsimulation. In the end vibration data from rotors are analyzed by HOTFE. The results demonstratethat it can indeed identify the early rub-impact chaotic behavior in rotors and also is simpler tocalculate than previous methods.
文摘The use of time-frequency entropy to quantitatively assess the stability of submerged arc welding process considering the distribution features of arc energy is reported in this paper. Time-frequency entropy is employed to calculate and analyze the stationary current signals, non-stationary current and voltage signals in the submerged arc welding process. It is obtained that time-frequency entropy of arc signal can be used as arc stability judgment criteria of submerged arc welding. Experimental results are provided to confirm the effectiveness of this approach.
基金We would like to thank the associate editor and the reviewers for their constructive comments.This work was supported in part by the National Natural Science Foundation of China under Grant 62203234in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03+1 种基金in part by the Natural Science Foundation of Liaoning Province under Grant 2023-BS-025in part by the Research Program of Liaoning Liaohe Laboratory under Grant LLL23ZZ-02-02.
文摘High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.
基金The National Key Research and Development Program of China:Design and Key Technology Research of Non-metallic Flexible Risers for Deep Sea Mining(2022YFC2803701)The General Program of National Natural Science Foundation of China(52071336,52374022).
文摘Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.
基金This work was funded by Beijing Key Laboratory of Distribution Transformer Energy-Saving Technology(China Electric Power Research Institute).
文摘The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.
基金supported by the NSAF under Grant No.U1830206,the National Key R&D Program of China under Grant No.2017YFA0403200the National Natural Science Foundation of China under Grant Nos.11874424 and 12104507the Science and Technology Innovation Program of Hunan Province under Grant No.2021RC4026.
文摘Entropy production in quasi-isentropic compression (QIC) is critically important for understanding the properties of materials under extremeconditions. However, the origin and accurate quantification of entropy in this situation remain long-standing challenges. In this work, a framework is established for the quantification of entropy production and partition, and their relation to microstructural change in QIC. Cu50Zr50is taken as a model material, and its compression is simulated by molecular dynamics. On the basis of atomistic simulation-informed physicalproperties and free energy, the thermodynamic path is recovered, and the entropy production and its relation to microstructural change aresuccessfully quantified by the proposed framework. Contrary to intuition, entropy production during QIC of metallic glasses is relativelyinsensitive to the strain rate ˙γ when ˙γ ranges from 7.5 × 10^(8) to 2 × 10^(9)/s, which are values reachable in QIC experiments, with a magnitudeof the order of 10^(−2)kB/atom per GPa. However, when ˙γ is extremely high (>2 × 10^(9)/s), a notable increase in entropy production rate with˙γ is observed. The Taylor–Quinney factor is found to vary with strain but not with strain rate in the simulated regime. It is demonstrated thatentropy production is dominated by the configurational part, compared with the vibrational part. In the rate-insensitive regime, the increase inconfigurational entropy exhibits a linear relation to the Shannon-entropic quantification of microstructural change, and a stretched exponential relation to the Taylor–Quinney factor. The quantification of entropy is expected to provide thermodynamic insights into the fundamentalrelation between microstructure evolution and plastic dissipation.
基金support from the National Natural Science Foundation of China(22076116)the Sino-German Center for Research Promotion(GZ1579)the China Scholarship Council(202007030003)for the financial support.
文摘Due to the energy crisis caused by limited fossil fuel reserves,extensive use of the renewable energy sources such as wind or solar energy is deemed to replace the use of traditional fossil fuels in the future^([1−3]).However,most renewable energy sources face the same problem,which is the intermittency of energy.For example,solar energy cannot be utilized at night.That means the continuous energy demand required for large-scale power grids can’t be satisfied by a single solar panel model.
文摘In the present study, energetic and entropic changes are investigated on a comparative basis, as they occur in the volume changes of an ideal gas in the Carnot cycle and in the course of the chemical reaction in a lead-acid battery. Differences between reversible and irreversible processes have been worked out, in particular between reversibly exchanged entropy (∆<sub>e</sub>S) and irreversibly produced entropy (∆<sub>i</sub>S). In the partially irreversible case, ∆<sub>e</sub>S and ∆<sub>i</sub>S add up to the sum ∆S for the volume changes of a gas, and only this function has an exact differential. In a chemical reaction, however, ∆<sub>e</sub>S is independent on reversibility. It arises from the different intramolecular energy contents between products and reactants. Entropy production in a partially irreversible Carnot cycle is brought about through work-free expansions, whereas in the irreversible battery reaction entropy is produced via activated complexes, whereby a certain, variable fraction of the available chemical energy becomes transformed into electrical energy and the remaining fraction dissipated into heat. The irreversible reaction process via activated complexes has been explained phenomenologically. For a sufficiently high power output of coupled reactions, it is essential that the input energy is not completely reversibly transformed, but rather partially dissipated, because this can increase the process velocity and consequently its power output. A reduction of the counter potential is necessary for this purpose. This is not only important for man-made machines, but also for the viability of cells.
基金supported by the National Natural Science Foundation of China (Grant No. 12234013)the Natural Science Foundation of Shandong Province (Grant No. ZR2021LLZ009)。
文摘We present a formulation of the single-trajectory entropy using the trajectories ensemble. The single-trajectory entropy is affected by its surrounding trajectories via the distribution function. The single-trajectory entropies are studied in two typical potentials, i.e., harmonic potential and double-well potential, and in viscous environment by interacting trajectory method. The results of the trajectory methods are in agreement well with the numerical methods(Monte Carlo simulation and difference equation). The single-trajectory entropies increasing(decreasing) could be caused by absorption(emission) heat from(to) the thermal environment. Also, some interesting trajectories, which correspond to the rare evens in the processes, are demonstrated.
基金This work is supported by the National Natural Science Foundation of China(Grant Nos.U22B2005,62072109)the Natural Science Foundation of Fujian Province(Grant No.2021J01625)the Major Science and Technology Project of Fuzhou(Grant No.2023-ZD-003).
文摘As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly vital.We propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data volume.Then the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization capabilities.Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic.Compared with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.
基金the Science and Technology Innovation Council of Shenzhen(Grant Nos.JCYJ20200109105212568,KQTD20170810105439418,JCYJ20200109114237902,20200812203318002,and 20200810103814002)the National Natural Science Foundation of China(Grant No.12274197)the Guangdong Basic and Applied Basic Research Foundation(Grant Nos.2023A1515030240,2019A1515010790,2021A0505110015).
文摘The development of tellurium(Te)-based semiconductor nanomaterials for efficient light-to-heat conversion may offer an effective means of harvesting sunlight to address global energy concerns.However,the nanosized Te(nano-Te)materials reported to date suffer from a series of drawbacks,including limited light absorption and a lack of surface structures.Herein,we report the preparation of nano-Te by electrochemical exfoliation using an electrolyzable room-temperature ionic liquid.Anions,cations,and their corresponding electrolytic products acting as chemical scissors can precisely intercalate and functionalize bulk Te.The resulting nano-Te has high morphological entropy,rich surface functional groups,and broad light absorption.We also constructed foam hydrogels based on poly(vinyl alcohol)/nano-Te,which achieved an evaporation rate and energy efficiency of 4.11 kg m^(−2)h^(−1)and 128%,respectively,under 1 sun irradiation.Furthermore,the evaporation rate was maintained in the range 2.5-3.0 kg m^(−2)h^(−1)outdoors under 0.5-1.0 sun,providing highly efficient evaporation under low light conditions.
文摘It is explicitly shown how the Schwarzschild Black Hole Entropy (in all dimensions) emerges from truly point mass sources at r=0due to a non-vanishing scalar curvature involving the Dirac delta distribution. In order to achieve this, one is required to extend the domain of r to negative values −∞≤r≤+∞. It is the density and anisotropic pressure components associated with the point mass delta function source at the origin r=0which furnish the Schwarzschild black hole entropy in all dimensions D≥4after evaluating the Euclidean Einstein-Hilbert action. Two of the most salient results are i) that the observed spacetime dimension D=4is precisely singled out from all the other dimensions when the strong and weak energy conditions are met, and ii) the point mass source described in this work is not the result of a spherically symmetric gravitational collapse of a star as described by the Oppenheimer-Snyder model because we are not neglecting the pressure. As usual, it is required to take the inverse Hawking temperature βHas the length of the circle Sβ1obtained from a compactification of the Euclidean time in thermal field theory which results after a Wick rotation, it=τ, to imaginary time. This approach can be generalized to the Reissner-Nordstrom and Kerr-Newman metrics. The physical implications of this finding warrant further investigation since it suggests a profound connection between the notion of gravitational entropy and spacetime singularities.
基金supported by the Preparation and Characterization of Fogging Agents,Cooperative Project of China(Grant No.1900030040)Preparation and Test of Fogging Agents,Cooperative Project of China(Grant No.2200030085)。
文摘Water-based aerosol is widely used as an effective strategy in electro-optical countermeasure on the battlefield used to the preponderance of high efficiency,low cost and eco-friendly.Unfortunately,the stability of the water-based aerosol is always unsatisfactory due to the rapid evaporation and sedimentation of the aerosol droplets.Great efforts have been devoted to improve the stability of water-based aerosol by using additives with different composition and proportion.However,the lack of the criterion and principle for screening the effective additives results in excessive experimental time consumption and cost.And the stabilization time of the aerosol is still only 30 min,which could not meet the requirements of the perdurable interference.Herein,to improve the stability of water-based aerosol and optimize the complex formulation efficiently,a theoretical calculation method based on thermodynamic entropy theory is proposed.All the factors that influence the shielding effect,including polyol,stabilizer,propellant,water and cosolvent,are considered within calculation.An ultra-stable water-based aerosol with long duration over 120 min is obtained with the optimal fogging agent composition,providing enough time for fighting the electro-optic weapon.Theoretical design guideline for choosing the additives with high phase transition temperature and low phase transition enthalpy is also proposed,which greatly improves the total entropy change and reduce the absolute entropy change of the aerosol cooling process,and gives rise to an enhanced stability of the water-based aerosol.The theoretical calculation methodology contributes to an abstemious time and space for sieving the water-based aerosol with desirable performance and stability,and provides the powerful guarantee to the homeland security.
文摘Laser-induced fluorescence(LIF)spectroscopy is employed for plasma diagnosis,necessitating the utilization of deconvolution algorithms to isolate the Doppler effect from the raw spectral signal.However,direct deconvolution becomes invalid in the presence of noise as it leads to infinite amplification of high-frequency noise components.To address this issue,we propose a deconvolution algorithm based on the maximum entropy principle.We validate the effectiveness of the proposed algorithm by utilizing simulated LIF spectra at various noise levels(signal-to-noise ratio,SNR=20–80 d B)and measured LIF spectra with Xe as the working fluid.In the typical measured spectrum(SNR=26.23 d B)experiment,compared with the Gaussian filter and the Richardson–Lucy(R-L)algorithm,the proposed algorithm demonstrates an increase in SNR of 1.39 d B and 4.66 d B,respectively,along with a reduction in the root-meansquare error(RMSE)of 35%and 64%,respectively.Additionally,there is a decrease in the spectral angle(SA)of 0.05 and 0.11,respectively.In the high-quality spectrum(SNR=43.96 d B)experiment,the results show that the running time of the proposed algorithm is reduced by about98%compared with the R-L iterative algorithm.Moreover,the maximum entropy algorithm avoids parameter optimization settings and is more suitable for automatic implementation.In conclusion,the proposed algorithm can accurately resolve Doppler spectrum details while effectively suppressing noise,thus highlighting its advantage in LIF spectral deconvolution applications.
基金financially supported by Ministry of Science and Higher Education of the Russian Federation(Grant No.FENU-2023-0013)。
文摘The high entropy alloys(HEAs)are the newly developed high-performance materials that have gained significant importance in defence,nuclear and aerospace sector due to their superior mechanical properties,heat resistance,high temperature strength and corrosion resistance.These alloys are manufactured by the equal mixing or larger proportions of five or more alloying elements.HEAs exhibit superior mechanical performance compared to traditional engineering alloys because of the extensive alloying composition and higher entropy of mixing.Solid state welding(SSW)techniques such as friction stir welding(FSW),rotary friction welding(RFW),diffusion bonding(DB)and explosive welding(EW)have been efficiently deployed for improving the microstructural integrity and mechanical properties of welded HEA joints.The HEA interlayers revealed greater potential in supressing the formation of deleterious intermetallic phases and maximizing the mechanical properties of HEAs joints.The similar and dissimilar joining of HEAs has been manifested to be viable for HEA systems which further expands their industrial applications.Thus,the main objective of this review paper is to present a critical review of current state of research,challenges and opportunities and main directions in SSW of HEAs mainly CoCrFeNiMn and Al_xCoCrFeNi alloys.The state of the art of problems,progress and future outlook in SSW of HEAs are critically reviewed by considering the formation of phases,microstructural evolution and mechanical properties of HEAs joints.
基金financially supported by the National Natural Science Foundation of China(Nos.51971017,52271003,52071024,52001184,and 52101188)the National Science Fund for distinguished Young Scholars,China(No.52225103)+3 种基金the Funds for Creative Research Groups of China(No.51921001)the National Key Research and Development Program of China(No.2022YFB4602101)the Projects of International Cooperation and Exchanges NSFC(No.52061135207)the Fundamental Research Funds for the Central Universities,China(No.FRF-TP-22-130A1)。
文摘Short-range ordering(SRO)is one of the most important structural features of high entropy alloys(HEAs).However,the chemical and structural analyses of SROs are very difficult due to their small size,complexed compositions,and varied locations.Transmission electron microscopy(TEM)as well as its aberration correction techniques are powerful for characterizing SROs in these compositionally complex alloys.In this short communication,we summarized recent progresses regarding characterization of SROs using TEM in the field of HEAs.By using advanced TEM techniques,not only the existence of SROs was confirmed,but also the effect of SROs on the deformation mechanism was clarified.Moreover,the perspective related to application of TEM techniques in HEAs are also discussed.
文摘Cross entropy is a measure in machine learning and deep learning that assesses the difference between predicted and actual probability distributions. In this study, we propose cross entropy as a performance evaluation metric for image classifier models and apply it to the CT image classification of lung cancer. A convolutional neural network is employed as the deep neural network (DNN) image classifier, with the residual network (ResNet) 50 chosen as the DNN archi-tecture. The image data used comprise a lung CT image set. Two classification models are built from datasets with varying amounts of data, and lung cancer is categorized into four classes using 10-fold cross-validation. Furthermore, we employ t-distributed stochastic neighbor embedding to visually explain the data distribution after classification. Experimental results demonstrate that cross en-tropy is a highly useful metric for evaluating the reliability of image classifier models. It is noted that for a more comprehensive evaluation of model perfor-mance, combining with other evaluation metrics is considered essential. .
基金supported by the National Science Foundation under Grant No.CMMI-1762190The research was performed in part in the Nebraska Nanoscale Facility:National Nanotechnology Coordinated Infrastructure and the Nebraska Center for Materials and Nanoscience (and/or NERCF),which are supported by the National Science Foundation under Award ECCS:2025298+1 种基金the Nebraska Research Initiativesupported by the U.S.Department of Energy,Office of Nuclear Energy under DOE Idaho Operations Office Contract DE-AC07-051D14517 as part of a Nuclear Science User Facilities experiment。
文摘W-based WTaVCr refractory high entropy alloys (RHEA) may be novel and promising candidate materials for plasma facing components in the first wall and diverter in fusion reactors. This alloy has been developed by a powder metallurgy process combining mechanical alloying and spark plasma sintering (SPS). The SPSed samples contained two phases, in which the matrix is RHEA with a body-centered cubic structure, while the oxide phase was most likely Ta2VO6through a combined analysis of X-ray diffraction (XRD),energy-dispersive spectroscopy (EDS), and selected area electron diffraction (SAED). The higher oxygen affinity of Ta and V may explain the preferential formation of their oxide phases based on thermodynamic calculations. Electron backscatter diffraction (EBSD) revealed an average grain size of 6.2μm. WTaVCr RHEA showed a peak compressive strength of 2997 MPa at room temperature and much higher micro-and nano-hardness than W and other W-based RHEAs in the literature. Their high Rockwell hardness can be retained to at least 1000°C.