Microstructure and texture in 6016 aluminum alloy during hot compression were researched with a uni- axial compression experiment. Through the electron back- scattered diffraction （EBSD） and X-ray diffraction （XRD...Microstructure and texture in 6016 aluminum alloy during hot compression were researched with a uni- axial compression experiment. Through the electron back- scattered diffraction （EBSD） and X-ray diffraction （XRD） analysis technology, it is shown that the subgrain nucle- ation and recrystallization occur in 6016 aluminum alloy during hot compressing, and strong rolling textures such as （110） fiber texture, Brass, S, and Goss form. With the deformation passes increasing, （110） fiber texture, Brass and S are enhanced. In the heat preservation stage after deformation, recrystallization continues until heat preser- vation for 60 s, and a duplex microstructure of deformation and recrystallization grains is built. At the beginning of heat preservation, recrystallization grains with the Goss texture and random orientation are formed in original grains with S or Brass texture, which makes the volume fraction of S and Brass texture decrease. Then, the complex grain growth process makes the volume fraction of Brass, S, and Goss texture increase, while that of random orien- tation decrease.展开更多
With the popularity of deep learning tools in image decomposition and natural language processing,how to support and store a large number of parameters required by deep learning algorithms has become an urgent problem...With the popularity of deep learning tools in image decomposition and natural language processing,how to support and store a large number of parameters required by deep learning algorithms has become an urgent problem to be solved.These parameters are huge and can be as many as millions.At present,a feasible direction is to use the sparse representation technique to compress the parameter matrix to achieve the purpose of reducing parameters and reducing the storage pressure.These methods include matrix decomposition and tensor decomposition.To let vector take advance of the compressing performance of matrix decomposition and tensor decomposition,we use reshaping and unfolding to let vector be the input and output of Tensor-Factorized Neural Networks.We analyze how reshaping can get the best compress ratio.According to the relationship between the shape of tensor and the number of parameters,we get a lower bound of the number of parameters.We take some data sets to verify the lower bound.展开更多
Of the three mutually coupled fundamental processes （shearing, compressing, and thermal） in a general fluid motion, only the general formulation for the compress- ing process and a subprocess of it, the subject of a...Of the three mutually coupled fundamental processes （shearing, compressing, and thermal） in a general fluid motion, only the general formulation for the compress- ing process and a subprocess of it, the subject of aeroacous- tics, as well as their physical coupling with shearing and thermal processes, have so far not reached a consensus. This situation has caused difficulties for various in-depth complex multiprocess flow diagnosis, optimal configuration design, and flow/noise control. As the first step toward the desired formulation in fully nonlinear regime, this paper employs the operator factorization method to revisit the analytic linear theories of the fundamental processes and their decomposi- tion, especially the further splitting of compressing process into acoustic and entropy modes, developed in 1940s-1980s. The flow treated here is small disturbances of a compressible, viscous, and heat-conducting polytropic gas in an unbounded domain with arbitrary source of mass, external body force, and heat addition. Previous results are thereby revised and extended to a complete and unified theory. The theory pro- vides a necessary basis and valuable guidance for developing corresponding nonlinear theory by clarifying certain basic issues, such as the proper choice of characteristic variables of compressing process and the feature of their governing equations.展开更多
Target tracking is a well studied topic in wireless sensor networks. It is a procedure that nodes in the network collaborate in detecting targets and transmitting their information to the base-station continuously, wh...Target tracking is a well studied topic in wireless sensor networks. It is a procedure that nodes in the network collaborate in detecting targets and transmitting their information to the base-station continuously, which leads to data implosion and redundancy. To reduce traffic load of the network, a data compressing based target tracking protocol is proposed in this work. It first incorporates a clustering based data gather method to group sensor nodes into clusters. Then a novel threshold technique with bounded error is proposed to exploit the spatial correlation of sensed data and compress the data in the same cluster. Finally, the compact data presentations are transmitted to the base-station for targets localization. We evaluate our approach with a comprehensive set of simulations. It can be concluded that the proposed method yields excellent performance in energy savings and tracking quality.展开更多
By means of density functional theory calculations, an orthogonal boron-carbon-nitrogen compound called （3,0）- BC2N is predicted, which can be obtained by transversely compressing （3,03 carbon nanotubes （CNTs） an...By means of density functional theory calculations, an orthogonal boron-carbon-nitrogen compound called （3,0）- BC2N is predicted, which can be obtained by transversely compressing （3,03 carbon nanotubes （CNTs） and boron nitride nanotubes （BNNTs）. Its structural stability, elastic properties, mechanical properties and electronic structure are systematically investigated. The results show that （3,0）-BU2N is a superhard material with a direct bandgap. However, its similar structures, （3,0）-C and （3,0）-BN are indirect semiconductors. Strikingly, （3,0）-C is harder than diamond. We also simulate the x-ray diffraction of （3,0）-BC2N to support future experimental investigations. In addition, our study shows that the transition from （3,03 CNTS and BNNTs to （3,0）-BC2N is irreversible.展开更多
Traditional lightning protection measures can not solve the problem of superimposed lightning strikes.This paper presents a compressing arc extinguishing lightning protection device,which can solve the problem of supe...Traditional lightning protection measures can not solve the problem of superimposed lightning strikes.This paper presents a compressing arc extinguishing lightning protection device,which can solve the problem of superimposed lightning strikes.This device can extinguish the power frequency continuous current arc quickly in 1-2 ms.It is far less than the response time of relay protection,which can avoid lightning trips and improve the reliability of power supply.The computer simulation and engineering practice show that the compressing arc extinguishing device has good protection effect on superimposed lightning strikes.展开更多
Solving the shortest tool length quickly under a known tool trajectory in multi-axis machining of complex channel parts is an urgent problem in industrial production. To solve this problem, a novel and efficient metho...Solving the shortest tool length quickly under a known tool trajectory in multi-axis machining of complex channel parts is an urgent problem in industrial production. To solve this problem, a novel and efficient method is proposed which is featured by extracting only a few necessary curves from the check surface instead of sampling the entire surface. By rotating and compressing the 3 D check surface relative to all tool postures, the boundaries of the area occupied by the 2 D compressed surfaces are the essential elements for determining the shortest tool length. A tracking-based numerical algorithm is introduced to efficiently solve the silhouette curves which are formed in compressing. To define the multi-taper shaped tool holding system(THS) which is commonly used in production, a characterization model for THS profile is established. A model for solving the shortest tool length is finally constructed based on the critical interference relationship between the THS profile and all compressed boundary curves. For acceleration, the boundary splines are segmented according to their knot vectors. Then a new concept called the axis-aligned tool length box(AATB) is introduced,which can provide a conservative range of tool length for a spline segment. By scanning the AATBs of all spline segments, the very few effective spline segments that may ultimately determine the shortest tool length are filtered out. This acceleration method makes the solution for the shortest tool length more focused and efficient. The results of experimental examples are also reported to validate the efficiency and accuracy of the proposed algorithm.展开更多
The increasing grid data in CFD simulation has brought some new difficulties and challenges,such as high storage cost,low transmission efficiency.In order to overcome these problems,a novel method for compressing and ...The increasing grid data in CFD simulation has brought some new difficulties and challenges,such as high storage cost,low transmission efficiency.In order to overcome these problems,a novel method for compressing and saving the structured grid are proposed.In the present method,the geometric coordinates of the six logical domains of one grid block is saved instead of all grid vertex coordinates to reduce the size of the structured grid file when the grid is compressed.And all grid vertex coordinates are recovered from the compressed data with the use of the transfinite interpolation algorithm when the grid is decompressed.Firstly,single-block grid cases with different edge vertexes are tested to investigate the compression effect.The test results show that a higher compression ratio will be obtained on a larger grid.Secondly,further theoretical analysis is carried out to investigate the effects of parameters on grid compression.The analysis on single-block grid compression shows that the compression ratio is proportionate to the cubic root of the number of total vertexes.The highest compression ratio of single-block grid is obtained when the numbers of vertexes in three logical directions are equal.The analysis on multi-block grid compression shows that a higher compression ratio will be obtained when a larger difference of total vertexes number exists among the grid blocks.Finally,multi-blockgrids of two industrial aircraft configurations are compressed to validate the method.The compression results demonstrate that the present method has an excellent ability on structured grid compression.For a million-vertex structured grid,more than 80 percent disk space can be saved after compression.展开更多
An ultrafast electron diffraction technique with both high temporal and spatial resolution has been shown to be a powerful tool to observe the material transient structural change on an atomic scale.The space charge f...An ultrafast electron diffraction technique with both high temporal and spatial resolution has been shown to be a powerful tool to observe the material transient structural change on an atomic scale.The space charge forces in a multi-electron bunch will greatly broaden the electron pulse width,and therefore limit the temporal resolution of the high brightness electron pulse.Here in this work,we design an ultrafast electron diffraction system,and utilize a radio frequency cavity to realize the ultrafast electron pulse compression.We experimentally demonstrate that the stretched electron pulse width of14.98 ps with an electron energy of 40 keV and the electron number of 1.0 ×10;can be maximally compressed to about0.61 ps for single-pulse measurement and 2.48 ps for multi-pulse measurement by using a 3.2-GHz radiofrequency cavity.We also theoretically and experimentally analyze the parameters influencing the electron pulse compression efficiency for single-and multi-pulse measurements by considering radiofrequency field time jitter,electron pulse time jitter and their relative time jitter.We suggest that increasing the electron energy or shortening the distance between the compression cavity and the streak cavity can further improve the electron pulse compression efficiency.These experimental and theoretical results are very helpful for designing the ultrafast electron diffraction experiment equipment and compressing the ultrafast electron pulse width in a future study.展开更多
BACKGROUND A large ganglionic cyst extending from the hip joint to the intrapelvic cavity through the sciatic notch is a rare space-occupying lesion associated with compressive lower-extremity neuropathy.A cyst in the...BACKGROUND A large ganglionic cyst extending from the hip joint to the intrapelvic cavity through the sciatic notch is a rare space-occupying lesion associated with compressive lower-extremity neuropathy.A cyst in the pelvic cavity compressing the intrapelvic-sciatic nerve is easily missed in the diagnostic process because it usually presents as atypical symptoms of an extraperitoneal-intrapelvic tumor.We present a case of a huge ganglionic cyst that was successfully excised laparoscopically and endoscopically by a gynecologist and an orthopedic surgeon.CASE SUMMARY A 52-year-old woman visited our hospital complaining of pain and numbness in her left buttock while sitting.The pain began 3 years ago and worsened,while the numbness in the left lower extremity lasted 1 mo.She was diagnosed and unsuccessfully treated at several tertiary referral centers many years ago.Magnetic resonance imaging revealed a suspected paralabral cyst(5 cm×5 cm×4.6 cm)in the left hip joint,extending to the pelvic cavity through the greater sciatic notch.The CA-125 and CA19-9 tumor marker levels were within normal limits.However,the cyst was compressing the sciatic nerve.Accordingly,endoscopic and laparoscopic neural decompression and mass excision were performed simultaneously.A laparoscopic examination revealed a tennis-ball-sized cyst filled with gelatinous liquid,stretching deep into the hip joint.An excisional biopsy performed in the pelvic cavity and deep gluteal space confirmed the accumulation of ganglionic cysts from the hip joint into the extrapelvic intraperitoneal cavity.CONCLUSION Intra-or extra-sciatic nerve-compressing lesion should be considered in cases of sitting pain radiating down the ipsilateral lower extremity.This large juxta-articular ganglionic cyst was successfully treated simultaneously using laparoscopy and arthroscopy.展开更多
Aiming at the characteristics of the seismic exploration signals, the paper studies the image coding technology, the coding standard and algorithm, brings forward a new scheme of admixing coding for seismic data compr...Aiming at the characteristics of the seismic exploration signals, the paper studies the image coding technology, the coding standard and algorithm, brings forward a new scheme of admixing coding for seismic data compression. Based on it, a set of seismic data compression software has been developed.展开更多
As a continuation of a recent linear analysis by Mao et al.(Acta Mech Sin,2010,26:355),in this paper we propose a general theoretical formulation for the compressing process in complex Newtonian fluid flows,which cove...As a continuation of a recent linear analysis by Mao et al.(Acta Mech Sin,2010,26:355),in this paper we propose a general theoretical formulation for the compressing process in complex Newtonian fluid flows,which covers gas dynamics,aeroacoustics,nonlinear thermoviscous acoustics,viscous shock layer,etc.,as its special branches.The principle on which our formulation is based is the maximally natural and dynamic Helmholtz decomposition of the Navier-Stokes equation,along with the kinematic Helmholtz decomposition of the velocity field.The central results are the new dilatation equation and velocity-potential equation,which are the counterparts of vorticity transport equation and vector stream-function equation for the shearing process,respectively.Various couplings of the compressing process with shearing and thermal processes,including its physical sources,are carefully identified.While the possible applications and influences of the new formulation are yet to be explored,our preliminary discussion on the pros and cons of previous formulations pertain to acoustic analogy and that on the process splitting and coupling in highly compressible turbulence indicates that at least the formulation can serve as a new frame of reference by which one may gain some additional insight and thereby develop new approaches to the multi-process complex flow problems.展开更多
SiC/Al-based composite foams were prepared by a two-step foaming method.The influence of the SiC content and its distribution uniformity on the foaming stability,cell structure,and mechanical properties of the aluminu...SiC/Al-based composite foams were prepared by a two-step foaming method.The influence of the SiC content and its distribution uniformity on the foaming stability,cell structure,and mechanical properties of the aluminum foams was investigated.The macro/micro-features of the aluminum foams were characterized and analyzed.Results demonstrate that an appropriate increase in SiC content and the uniform distribution of SiC can improve the foaming stability,optimize the cell diameter and cell wall thickness,ameliorate the cell distribution,and enhance the hardness and compressive strength of the aluminum foams.However,either insufficient or excessive SiC leads to uneven distribution of SiC particles,which is unfavorable to foaming stability and good cell structure formation.With 6wt%SiC,both the foaming stability and cell structure of the aluminum foam reach the optimal state,resulting in the highest compressive strength and optimal energy absorption capacity.展开更多
The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for e...The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for evaluating the efficacy of WGPC.Unlike conventional testing methods,machine learning techniques offer precise and reliable predictions of concrete’s compressive strength,especially in its long-term mechanical properties.In this work,four models,namely Multiple Linear Regression(MLR),Back Propagation Neural Network(BPNN),Support Vector Regression(SVR),and Random Forest Regression(RFR)were employed.Furthermore,particle swarm optimization(PSO)algorithm and cross-validation techniques were applied to fine-tune the model parameters,striving for peak prediction performance.The results indicated that optimized models generally exhibit enhanced predictive accuracy compared to their basic counterparts.Notably,the PSO-RFR model excels among all evaluated models,showcasing superior performance on the testing dataset.It achieves a coefficient of determination(R^(2))of 0.9231,a mean absolute error(MAE)of 2.1073,and a root mean square error(RMSE)of 3.6903.When compared to experimental results,the PSO-RFR and PSO-BPNN models demonstrate exceptional predictive accuracy.Notably,the PSO-BPNN model exhibits the closest R^(2)values between its training and test sets.This close alignment of R^(2)values between the training and testing sets reflects the PSO-BPNN model’s superior generalization ability for unseen data.The findings present an efficient method for predicting concrete’s compressive strength,contributing to the sustainable development of concrete materials,and providing theoretical support for their research and application.展开更多
Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression...Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression is crucial for deploying deep neural network(DNN)models on resource-constrained embedded devices.展开更多
In GNSS-denied environments,signals of opportunity(SOP)offer an efficient and passive solution for navigation and positioning by utilizing ambient signals.Nevertheless,conventional SOP techniques face significant chal...In GNSS-denied environments,signals of opportunity(SOP)offer an efficient and passive solution for navigation and positioning by utilizing ambient signals.Nevertheless,conventional SOP techniques face significant challenges in real-time processing,especially under sub-Nyquist sampling conditions,due to high data acquisition rates and offgrid errors.To address this,this paper proposes the signal reconstruction and kernel sparse encoding(SRKSE)model,a novel general framework for high-precision parameter estimation.By combining compressed sensing with a deep unfolding network,the SRKSE model not only achieves robust signal reconstruction but also effectively reduces quantization errors.Key innovations of SRKSE include dual crossattention mechanisms for enhanced feature extraction,sinc sparse kernel encoding to minimize quantization errors,and a custom loss function for balanced optimization.With these advancements,SRKSE achieves up to a 650-fold improvement in time of arrival(TOA)estimation accuracy while operating at just 1%of the Nyquist sampling rate.The SRKSE surpasses both conventional and deep learning-based techniques in accuracy and efficiency,especially when operating under sub-Nyquist sampling conditions.Simulations and real-world experiments confirm the reliability and potential of SRKSE for real-time applications in IoT and wireless communication.展开更多
Activation pruning reduces neural network complexity by eliminating low-importance neuron activations,yet identifying the critical pruning threshold—beyond which accuracy rapidly deteriorates—remains computationally...Activation pruning reduces neural network complexity by eliminating low-importance neuron activations,yet identifying the critical pruning threshold—beyond which accuracy rapidly deteriorates—remains computationally expensive and typically requires exhaustive search.We introduce a thermodynamics-inspired framework that treats activation distributions as energy-filtered physical systems and employs the free energy of activations as a principled evaluation metric.Phase-transition-like phenomena in the free-energy profile—such as extrema,inflection points,and curvature changes—yield reliable estimates of the critical pruning threshold,providing a theoretically grounded means of predicting sharp accuracy degradation.To further enhance efficiency,we propose a renormalized free energy technique that approximates full-evaluation free energy using only the activation distribution of the unpruned network.This eliminates repeated forward passes,dramatically reducing computational overhead and achieving speedups of up to 550×for MLPs.Extensive experiments across diverse vision architectures(MLP,CNN,ResNet,MobileNet,Vision Transformer)and text models(LSTM,BERT,ELECTRA,T5,GPT-2)on multiple datasets validate the generality,robustness,and computational efficiency of our approach.Overall,this work establishes a theoretically grounded and practically effective framework for activation pruning,bridging the gap between analytical understanding and efficient deployment of sparse neural networks.展开更多
This study investigates the impact of Type D additive,Plastiment 83 AM,on the compressive strength and microstructure of Portland Composite Cement(PCC)concrete with a target compressive strength of 18.7 MPa,utilizing ...This study investigates the impact of Type D additive,Plastiment 83 AM,on the compressive strength and microstructure of Portland Composite Cement(PCC)concrete with a target compressive strength of 18.7 MPa,utilizing a mixing,stirring,and treatment model that simulates batching plant conditions.The study investigated additive dosages of 0%,0.15%,0.25%,0.35%,and 0.40%,with stirring durations of 15 min,2,4,6,and 6.5 h.Compressive strength tests were conducted at the ages of 7,14,28,56,and 90 days on cylindrical specimens,and at 24 h for setting time tests.Microstructural analysis using Energy Dispersive X-ray Spectroscopy(EDX)was performed at 56 days of age.The results showed that the optimal dosage was 0.15%,combined with the addition of Plastiment 83 AM 0.10%at 2 h of stirring,which achieved the highest compressive strength of 20.5 MPa at 90 days.A reduction in compressive strength of the setting time samples from the initial value to 24 h was observed in mixtures stirred for 6 and 6.5 h.A decrease in compressive strength was also observed in both mixtures between 56 and 90 days.EDX analysis revealed different chemical compositions in each mix.At a stirring duration of 6 and 6.5 h,Plastiment 83 AM dosages of 0.35%and 0.40%showed the presence of Magnesium(Mg)and Aluminium(Al)(at 6 h)and Al and phosphorus(at 6.5 h).The presence of inhibited the hydration process,resulting in a very small increase in compressive strength from 14 to 28 days.Magnesium reduced the compressive strength to 75%,and phosphorus to 63%of the target compressive strength.展开更多
To achieve the potential performance gain of massive multiple-input multiple-output(MIMO)systems,base stations(BS)require downlink channel state information(CSI)fed back by users to execute beamforming design,especial...To achieve the potential performance gain of massive multiple-input multiple-output(MIMO)systems,base stations(BS)require downlink channel state information(CSI)fed back by users to execute beamforming design,especially in the frequency division duplex(FDD)systems.However,due to the enormous number of antennas in massive MIMO systems,the feedback overhead of downlink CSI acquisition is extremely large.To address this issue,deep learning(DL)techniques have been introduced to de velop high-accuracy feedback strategies under limited backhaul constraints.In this paper,we provide an overview of DL-based CSI compression and feedback approaches in massive MIMO systems.Specifically,we introduce the conventional CSI compression and feedback schemes and the existing problems.Besides,we elaborate on various DL techniques employed in CSI compression from the perspective of network architecture and analyze the advantages of different techniques.We also enumerate the applications of DL-based methods for solving practical challenges in CSI compression and feedback.In addition,we brief the remaining issues in deep CSI compression and indicate potential directions in future wireless networks.展开更多
Rock brittleness is a critical property in geotechnical and energy engineering,as it directly influences the prediction of rock failure and stability assessment.Although numerous methods have been developed to evaluat...Rock brittleness is a critical property in geotechnical and energy engineering,as it directly influences the prediction of rock failure and stability assessment.Although numerous methods have been developed to evaluate brittleness,many fail to comprehensively account for the impacts of microstructural changes,mineralogical characteristics,and stress conditions on energy evolution during failure.This study proposes a novel approach for brittleness evaluation based on the energy evolution throughout the post-peak failure process,integrating two micromechanical mechanisms:crack propagation and frictional sliding.A new brittleness index is defined as the ratio of generated surface energy to released elastic energy,providing a unified framework for assessing both Class I and Class II mechanical behaviors.The brittleness of cyan,white,and gray sandstones was investigated under various confining pressures and moisture conditions using X-ray diffraction(XRD),scanning electron microscopy(SEM),and conventional triaxial compression(CTC)tests.The results demonstrate that brittleness decreases with increasing confining pressure,due to suppressed crack propagation,and increases under saturated conditions,as moisture enhances crack propagation.By establishing connections between mineral composition,microstructural features,and stress-induced responses,the proposed method overcame limitations of previous approaches and offered a more precise tool for evaluating rock brittleness under diverse environmental scenarios.展开更多
基金financially supported by the Original Program of Chongqing Foundational and Frontier Research Plan(No.cstc2013jcyjA70015)the Science and Technology Research Program of Education Council of Chongqing(No.KJ080407)
文摘Microstructure and texture in 6016 aluminum alloy during hot compression were researched with a uni- axial compression experiment. Through the electron back- scattered diffraction （EBSD） and X-ray diffraction （XRD） analysis technology, it is shown that the subgrain nucle- ation and recrystallization occur in 6016 aluminum alloy during hot compressing, and strong rolling textures such as （110） fiber texture, Brass, S, and Goss form. With the deformation passes increasing, （110） fiber texture, Brass and S are enhanced. In the heat preservation stage after deformation, recrystallization continues until heat preser- vation for 60 s, and a duplex microstructure of deformation and recrystallization grains is built. At the beginning of heat preservation, recrystallization grains with the Goss texture and random orientation are formed in original grains with S or Brass texture, which makes the volume fraction of S and Brass texture decrease. Then, the complex grain growth process makes the volume fraction of Brass, S, and Goss texture increase, while that of random orien- tation decrease.
基金This work was supported by National Natural Science Foundation of China(Nos.61802030,61572184)the Science and Technology Projects of Hunan Province(No.2016JC2075)the International Cooperative Project for“Double First-Class”,CSUST(No.2018IC24).
文摘With the popularity of deep learning tools in image decomposition and natural language processing,how to support and store a large number of parameters required by deep learning algorithms has become an urgent problem to be solved.These parameters are huge and can be as many as millions.At present,a feasible direction is to use the sparse representation technique to compress the parameter matrix to achieve the purpose of reducing parameters and reducing the storage pressure.These methods include matrix decomposition and tensor decomposition.To let vector take advance of the compressing performance of matrix decomposition and tensor decomposition,we use reshaping and unfolding to let vector be the input and output of Tensor-Factorized Neural Networks.We analyze how reshaping can get the best compress ratio.According to the relationship between the shape of tensor and the number of parameters,we get a lower bound of the number of parameters.We take some data sets to verify the lower bound.
基金supported by the National Basic Research Program of China(2009CB724100)
文摘Of the three mutually coupled fundamental processes （shearing, compressing, and thermal） in a general fluid motion, only the general formulation for the compress- ing process and a subprocess of it, the subject of aeroacous- tics, as well as their physical coupling with shearing and thermal processes, have so far not reached a consensus. This situation has caused difficulties for various in-depth complex multiprocess flow diagnosis, optimal configuration design, and flow/noise control. As the first step toward the desired formulation in fully nonlinear regime, this paper employs the operator factorization method to revisit the analytic linear theories of the fundamental processes and their decomposi- tion, especially the further splitting of compressing process into acoustic and entropy modes, developed in 1940s-1980s. The flow treated here is small disturbances of a compressible, viscous, and heat-conducting polytropic gas in an unbounded domain with arbitrary source of mass, external body force, and heat addition. Previous results are thereby revised and extended to a complete and unified theory. The theory pro- vides a necessary basis and valuable guidance for developing corresponding nonlinear theory by clarifying certain basic issues, such as the proper choice of characteristic variables of compressing process and the feature of their governing equations.
文摘Target tracking is a well studied topic in wireless sensor networks. It is a procedure that nodes in the network collaborate in detecting targets and transmitting their information to the base-station continuously, which leads to data implosion and redundancy. To reduce traffic load of the network, a data compressing based target tracking protocol is proposed in this work. It first incorporates a clustering based data gather method to group sensor nodes into clusters. Then a novel threshold technique with bounded error is proposed to exploit the spatial correlation of sensed data and compress the data in the same cluster. Finally, the compact data presentations are transmitted to the base-station for targets localization. We evaluate our approach with a comprehensive set of simulations. It can be concluded that the proposed method yields excellent performance in energy savings and tracking quality.
基金Supported by the National Natural Science Foundation of China under Grant No 11464028the Science Foundation of Department of Education of Jiangxi Province under Grant No GJJ150025
文摘By means of density functional theory calculations, an orthogonal boron-carbon-nitrogen compound called （3,0）- BC2N is predicted, which can be obtained by transversely compressing （3,03 carbon nanotubes （CNTs） and boron nitride nanotubes （BNNTs）. Its structural stability, elastic properties, mechanical properties and electronic structure are systematically investigated. The results show that （3,0）-BU2N is a superhard material with a direct bandgap. However, its similar structures, （3,0）-C and （3,0）-BN are indirect semiconductors. Strikingly, （3,0）-C is harder than diamond. We also simulate the x-ray diffraction of （3,0）-BC2N to support future experimental investigations. In addition, our study shows that the transition from （3,03 CNTS and BNNTs to （3,0）-BC2N is irreversible.
基金the National Natural Science Foundation of China(No.51467002)Special Projects for Innovation-driven Development(No.2018AA03001Y).
文摘Traditional lightning protection measures can not solve the problem of superimposed lightning strikes.This paper presents a compressing arc extinguishing lightning protection device,which can solve the problem of superimposed lightning strikes.This device can extinguish the power frequency continuous current arc quickly in 1-2 ms.It is far less than the response time of relay protection,which can avoid lightning trips and improve the reliability of power supply.The computer simulation and engineering practice show that the compressing arc extinguishing device has good protection effect on superimposed lightning strikes.
基金support of National Science and Technology Major Project of China (No. JPPTKF2016)。
文摘Solving the shortest tool length quickly under a known tool trajectory in multi-axis machining of complex channel parts is an urgent problem in industrial production. To solve this problem, a novel and efficient method is proposed which is featured by extracting only a few necessary curves from the check surface instead of sampling the entire surface. By rotating and compressing the 3 D check surface relative to all tool postures, the boundaries of the area occupied by the 2 D compressed surfaces are the essential elements for determining the shortest tool length. A tracking-based numerical algorithm is introduced to efficiently solve the silhouette curves which are formed in compressing. To define the multi-taper shaped tool holding system(THS) which is commonly used in production, a characterization model for THS profile is established. A model for solving the shortest tool length is finally constructed based on the critical interference relationship between the THS profile and all compressed boundary curves. For acceleration, the boundary splines are segmented according to their knot vectors. Then a new concept called the axis-aligned tool length box(AATB) is introduced,which can provide a conservative range of tool length for a spline segment. By scanning the AATBs of all spline segments, the very few effective spline segments that may ultimately determine the shortest tool length are filtered out. This acceleration method makes the solution for the shortest tool length more focused and efficient. The results of experimental examples are also reported to validate the efficiency and accuracy of the proposed algorithm.
基金supported by the National Numerical Windtunnel Project, China
文摘The increasing grid data in CFD simulation has brought some new difficulties and challenges,such as high storage cost,low transmission efficiency.In order to overcome these problems,a novel method for compressing and saving the structured grid are proposed.In the present method,the geometric coordinates of the six logical domains of one grid block is saved instead of all grid vertex coordinates to reduce the size of the structured grid file when the grid is compressed.And all grid vertex coordinates are recovered from the compressed data with the use of the transfinite interpolation algorithm when the grid is decompressed.Firstly,single-block grid cases with different edge vertexes are tested to investigate the compression effect.The test results show that a higher compression ratio will be obtained on a larger grid.Secondly,further theoretical analysis is carried out to investigate the effects of parameters on grid compression.The analysis on single-block grid compression shows that the compression ratio is proportionate to the cubic root of the number of total vertexes.The highest compression ratio of single-block grid is obtained when the numbers of vertexes in three logical directions are equal.The analysis on multi-block grid compression shows that a higher compression ratio will be obtained when a larger difference of total vertexes number exists among the grid blocks.Finally,multi-blockgrids of two industrial aircraft configurations are compressed to validate the method.The compression results demonstrate that the present method has an excellent ability on structured grid compression.For a million-vertex structured grid,more than 80 percent disk space can be saved after compression.
基金Project partially supported by the National Natural Science Foundation of China(Grant Nos.51132004 and 11474096)the Fund from the Science and Technology Commission of Shanghai Municipality,China(Gant No.14JC1401500)the NYU-ECNU Institute of Physics at NYU Shanghai,China
文摘An ultrafast electron diffraction technique with both high temporal and spatial resolution has been shown to be a powerful tool to observe the material transient structural change on an atomic scale.The space charge forces in a multi-electron bunch will greatly broaden the electron pulse width,and therefore limit the temporal resolution of the high brightness electron pulse.Here in this work,we design an ultrafast electron diffraction system,and utilize a radio frequency cavity to realize the ultrafast electron pulse compression.We experimentally demonstrate that the stretched electron pulse width of14.98 ps with an electron energy of 40 keV and the electron number of 1.0 ×10;can be maximally compressed to about0.61 ps for single-pulse measurement and 2.48 ps for multi-pulse measurement by using a 3.2-GHz radiofrequency cavity.We also theoretically and experimentally analyze the parameters influencing the electron pulse compression efficiency for single-and multi-pulse measurements by considering radiofrequency field time jitter,electron pulse time jitter and their relative time jitter.We suggest that increasing the electron energy or shortening the distance between the compression cavity and the streak cavity can further improve the electron pulse compression efficiency.These experimental and theoretical results are very helpful for designing the ultrafast electron diffraction experiment equipment and compressing the ultrafast electron pulse width in a future study.
文摘BACKGROUND A large ganglionic cyst extending from the hip joint to the intrapelvic cavity through the sciatic notch is a rare space-occupying lesion associated with compressive lower-extremity neuropathy.A cyst in the pelvic cavity compressing the intrapelvic-sciatic nerve is easily missed in the diagnostic process because it usually presents as atypical symptoms of an extraperitoneal-intrapelvic tumor.We present a case of a huge ganglionic cyst that was successfully excised laparoscopically and endoscopically by a gynecologist and an orthopedic surgeon.CASE SUMMARY A 52-year-old woman visited our hospital complaining of pain and numbness in her left buttock while sitting.The pain began 3 years ago and worsened,while the numbness in the left lower extremity lasted 1 mo.She was diagnosed and unsuccessfully treated at several tertiary referral centers many years ago.Magnetic resonance imaging revealed a suspected paralabral cyst(5 cm×5 cm×4.6 cm)in the left hip joint,extending to the pelvic cavity through the greater sciatic notch.The CA-125 and CA19-9 tumor marker levels were within normal limits.However,the cyst was compressing the sciatic nerve.Accordingly,endoscopic and laparoscopic neural decompression and mass excision were performed simultaneously.A laparoscopic examination revealed a tennis-ball-sized cyst filled with gelatinous liquid,stretching deep into the hip joint.An excisional biopsy performed in the pelvic cavity and deep gluteal space confirmed the accumulation of ganglionic cysts from the hip joint into the extrapelvic intraperitoneal cavity.CONCLUSION Intra-or extra-sciatic nerve-compressing lesion should be considered in cases of sitting pain radiating down the ipsilateral lower extremity.This large juxta-articular ganglionic cyst was successfully treated simultaneously using laparoscopy and arthroscopy.
文摘Aiming at the characteristics of the seismic exploration signals, the paper studies the image coding technology, the coding standard and algorithm, brings forward a new scheme of admixing coding for seismic data compression. Based on it, a set of seismic data compression software has been developed.
基金supported by the Ministry of Science and Technology of China's Turbulence Program (Grant No.2009CB724101)the National Basic Research Program of China (Grant No.2007CB714600)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No.10921202)
文摘As a continuation of a recent linear analysis by Mao et al.(Acta Mech Sin,2010,26:355),in this paper we propose a general theoretical formulation for the compressing process in complex Newtonian fluid flows,which covers gas dynamics,aeroacoustics,nonlinear thermoviscous acoustics,viscous shock layer,etc.,as its special branches.The principle on which our formulation is based is the maximally natural and dynamic Helmholtz decomposition of the Navier-Stokes equation,along with the kinematic Helmholtz decomposition of the velocity field.The central results are the new dilatation equation and velocity-potential equation,which are the counterparts of vorticity transport equation and vector stream-function equation for the shearing process,respectively.Various couplings of the compressing process with shearing and thermal processes,including its physical sources,are carefully identified.While the possible applications and influences of the new formulation are yet to be explored,our preliminary discussion on the pros and cons of previous formulations pertain to acoustic analogy and that on the process splitting and coupling in highly compressible turbulence indicates that at least the formulation can serve as a new frame of reference by which one may gain some additional insight and thereby develop new approaches to the multi-process complex flow problems.
基金Doctoral Startup Fund(20192066,20212028)Laijin Excellent Doctoral Fund(20202021)+1 种基金Scientific and Technological Innovation of Colleges and Universities in Shanxi Province(2020L0342)Fundamental Research Program of Shanxi Province(202303021222178)。
文摘SiC/Al-based composite foams were prepared by a two-step foaming method.The influence of the SiC content and its distribution uniformity on the foaming stability,cell structure,and mechanical properties of the aluminum foams was investigated.The macro/micro-features of the aluminum foams were characterized and analyzed.Results demonstrate that an appropriate increase in SiC content and the uniform distribution of SiC can improve the foaming stability,optimize the cell diameter and cell wall thickness,ameliorate the cell distribution,and enhance the hardness and compressive strength of the aluminum foams.However,either insufficient or excessive SiC leads to uneven distribution of SiC particles,which is unfavorable to foaming stability and good cell structure formation.With 6wt%SiC,both the foaming stability and cell structure of the aluminum foam reach the optimal state,resulting in the highest compressive strength and optimal energy absorption capacity.
文摘The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for evaluating the efficacy of WGPC.Unlike conventional testing methods,machine learning techniques offer precise and reliable predictions of concrete’s compressive strength,especially in its long-term mechanical properties.In this work,four models,namely Multiple Linear Regression(MLR),Back Propagation Neural Network(BPNN),Support Vector Regression(SVR),and Random Forest Regression(RFR)were employed.Furthermore,particle swarm optimization(PSO)algorithm and cross-validation techniques were applied to fine-tune the model parameters,striving for peak prediction performance.The results indicated that optimized models generally exhibit enhanced predictive accuracy compared to their basic counterparts.Notably,the PSO-RFR model excels among all evaluated models,showcasing superior performance on the testing dataset.It achieves a coefficient of determination(R^(2))of 0.9231,a mean absolute error(MAE)of 2.1073,and a root mean square error(RMSE)of 3.6903.When compared to experimental results,the PSO-RFR and PSO-BPNN models demonstrate exceptional predictive accuracy.Notably,the PSO-BPNN model exhibits the closest R^(2)values between its training and test sets.This close alignment of R^(2)values between the training and testing sets reflects the PSO-BPNN model’s superior generalization ability for unseen data.The findings present an efficient method for predicting concrete’s compressive strength,contributing to the sustainable development of concrete materials,and providing theoretical support for their research and application.
基金supported by the Science and Technology Innovation Key R&D Program of Chongqing(CSTB2025TIAD-STX0032)National Key Research and Development Program of China(2024YFF0908200)+1 种基金the Chongqing Technology Innovation and Application Development Special Key Project(CSTB2024TIAD-KPX0018)the Southwest University Graduate Student Research Innovation(SWUB24051)。
文摘Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression is crucial for deploying deep neural network(DNN)models on resource-constrained embedded devices.
基金National Key Laboratory of Unmanned Aerial Vehicle Technology(No.202408)Key Laboratory of Smart Earth(No.KF2023ZD01-05)。
文摘In GNSS-denied environments,signals of opportunity(SOP)offer an efficient and passive solution for navigation and positioning by utilizing ambient signals.Nevertheless,conventional SOP techniques face significant challenges in real-time processing,especially under sub-Nyquist sampling conditions,due to high data acquisition rates and offgrid errors.To address this,this paper proposes the signal reconstruction and kernel sparse encoding(SRKSE)model,a novel general framework for high-precision parameter estimation.By combining compressed sensing with a deep unfolding network,the SRKSE model not only achieves robust signal reconstruction but also effectively reduces quantization errors.Key innovations of SRKSE include dual crossattention mechanisms for enhanced feature extraction,sinc sparse kernel encoding to minimize quantization errors,and a custom loss function for balanced optimization.With these advancements,SRKSE achieves up to a 650-fold improvement in time of arrival(TOA)estimation accuracy while operating at just 1%of the Nyquist sampling rate.The SRKSE surpasses both conventional and deep learning-based techniques in accuracy and efficiency,especially when operating under sub-Nyquist sampling conditions.Simulations and real-world experiments confirm the reliability and potential of SRKSE for real-time applications in IoT and wireless communication.
基金output of a research project implemented as part of the Basic Research Program at HSE University。
文摘Activation pruning reduces neural network complexity by eliminating low-importance neuron activations,yet identifying the critical pruning threshold—beyond which accuracy rapidly deteriorates—remains computationally expensive and typically requires exhaustive search.We introduce a thermodynamics-inspired framework that treats activation distributions as energy-filtered physical systems and employs the free energy of activations as a principled evaluation metric.Phase-transition-like phenomena in the free-energy profile—such as extrema,inflection points,and curvature changes—yield reliable estimates of the critical pruning threshold,providing a theoretically grounded means of predicting sharp accuracy degradation.To further enhance efficiency,we propose a renormalized free energy technique that approximates full-evaluation free energy using only the activation distribution of the unpruned network.This eliminates repeated forward passes,dramatically reducing computational overhead and achieving speedups of up to 550×for MLPs.Extensive experiments across diverse vision architectures(MLP,CNN,ResNet,MobileNet,Vision Transformer)and text models(LSTM,BERT,ELECTRA,T5,GPT-2)on multiple datasets validate the generality,robustness,and computational efficiency of our approach.Overall,this work establishes a theoretically grounded and practically effective framework for activation pruning,bridging the gap between analytical understanding and efficient deployment of sparse neural networks.
文摘This study investigates the impact of Type D additive,Plastiment 83 AM,on the compressive strength and microstructure of Portland Composite Cement(PCC)concrete with a target compressive strength of 18.7 MPa,utilizing a mixing,stirring,and treatment model that simulates batching plant conditions.The study investigated additive dosages of 0%,0.15%,0.25%,0.35%,and 0.40%,with stirring durations of 15 min,2,4,6,and 6.5 h.Compressive strength tests were conducted at the ages of 7,14,28,56,and 90 days on cylindrical specimens,and at 24 h for setting time tests.Microstructural analysis using Energy Dispersive X-ray Spectroscopy(EDX)was performed at 56 days of age.The results showed that the optimal dosage was 0.15%,combined with the addition of Plastiment 83 AM 0.10%at 2 h of stirring,which achieved the highest compressive strength of 20.5 MPa at 90 days.A reduction in compressive strength of the setting time samples from the initial value to 24 h was observed in mixtures stirred for 6 and 6.5 h.A decrease in compressive strength was also observed in both mixtures between 56 and 90 days.EDX analysis revealed different chemical compositions in each mix.At a stirring duration of 6 and 6.5 h,Plastiment 83 AM dosages of 0.35%and 0.40%showed the presence of Magnesium(Mg)and Aluminium(Al)(at 6 h)and Al and phosphorus(at 6.5 h).The presence of inhibited the hydration process,resulting in a very small increase in compressive strength from 14 to 28 days.Magnesium reduced the compressive strength to 75%,and phosphorus to 63%of the target compressive strength.
基金supported by ZTE Industry-University-Institute Cooperation Funds under Grant No.IA20240319003the NSFC under Grant No.62571112。
文摘To achieve the potential performance gain of massive multiple-input multiple-output(MIMO)systems,base stations(BS)require downlink channel state information(CSI)fed back by users to execute beamforming design,especially in the frequency division duplex(FDD)systems.However,due to the enormous number of antennas in massive MIMO systems,the feedback overhead of downlink CSI acquisition is extremely large.To address this issue,deep learning(DL)techniques have been introduced to de velop high-accuracy feedback strategies under limited backhaul constraints.In this paper,we provide an overview of DL-based CSI compression and feedback approaches in massive MIMO systems.Specifically,we introduce the conventional CSI compression and feedback schemes and the existing problems.Besides,we elaborate on various DL techniques employed in CSI compression from the perspective of network architecture and analyze the advantages of different techniques.We also enumerate the applications of DL-based methods for solving practical challenges in CSI compression and feedback.In addition,we brief the remaining issues in deep CSI compression and indicate potential directions in future wireless networks.
基金supported by the National Natural Science Foundation of China(Grant No.42277147)Ningbo Public Welfare Research Program(Grant No.2024S081)Ningbo Natural Science Foundation(Grant No.2024J186).
文摘Rock brittleness is a critical property in geotechnical and energy engineering,as it directly influences the prediction of rock failure and stability assessment.Although numerous methods have been developed to evaluate brittleness,many fail to comprehensively account for the impacts of microstructural changes,mineralogical characteristics,and stress conditions on energy evolution during failure.This study proposes a novel approach for brittleness evaluation based on the energy evolution throughout the post-peak failure process,integrating two micromechanical mechanisms:crack propagation and frictional sliding.A new brittleness index is defined as the ratio of generated surface energy to released elastic energy,providing a unified framework for assessing both Class I and Class II mechanical behaviors.The brittleness of cyan,white,and gray sandstones was investigated under various confining pressures and moisture conditions using X-ray diffraction(XRD),scanning electron microscopy(SEM),and conventional triaxial compression(CTC)tests.The results demonstrate that brittleness decreases with increasing confining pressure,due to suppressed crack propagation,and increases under saturated conditions,as moisture enhances crack propagation.By establishing connections between mineral composition,microstructural features,and stress-induced responses,the proposed method overcame limitations of previous approaches and offered a more precise tool for evaluating rock brittleness under diverse environmental scenarios.
