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FLOW STRESS MODELING FOR AERONAUTICAL ALUMINUM ALLOY 7050-T7451 IN HIGH-SPEED CUTTING 被引量:15
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作者 付秀丽 艾兴 +1 位作者 万熠 张松 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2007年第2期139-144,共6页
The high temperature split Hopkinson pressure bar (SHPB) compression experiment is conducted to obtain the data relationship among strain, strain rate and flow stress from room temperature to 550 C for aeronautical ... The high temperature split Hopkinson pressure bar (SHPB) compression experiment is conducted to obtain the data relationship among strain, strain rate and flow stress from room temperature to 550 C for aeronautical aluminum alloy 7050-T7451. Combined high-speed orthogonal cutting experiments with the cutting process simulations, the data relationship of high temperature, high strain rate and large strain in high-speed cutting is modified. The Johnson-Cook empirical model considering the effects of strain hardening, strain rate hardening and thermal softening is selected to describe the data relationship in high-speed cutting, and the material constants of flow stress constitutive model for aluminum alloy 7050-T7451 are determined. Finally, the constitutive model of aluminum alloy 7050-T7451 is established through experiment and simulation verification in high-speed cutting. The model is proved to be reasonable by matching the measured values of the cutting force with the estimated results from FEM simulations. 展开更多
关键词 high-speed cutting flow stress models SHPB compression experiment FEM simulation
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Failure behavior and strength model of blocky rock mass with and without rockbolts
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作者 Chun Zhu Xiansen Xing +4 位作者 Manchao He Zhicheng Tang Feng Xiong Zuyang Ye Chaoshui Xu 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2024年第6期747-762,共16页
To better understand the failure behaviours and strength of bolt-reinforced blocky rocks,large scale extensive laboratory experiments are carried out on blocky rock-like specimens with and without rockbolt reinforceme... To better understand the failure behaviours and strength of bolt-reinforced blocky rocks,large scale extensive laboratory experiments are carried out on blocky rock-like specimens with and without rockbolt reinforcement.The results show that both shear failure and tensile failure along joint surfaces are observed but the shear failure is a main controlling factor for the peak strength of the rock mass with and without rockbolts.The rockbolts are necked and shear deformation simultaneously happens in bolt reinforced rock specimens.As the joint dip angle increases,the joint shear failure becomes more dominant.The number of rockbolts has a significant impact on the peak strain and uniaxial compressive strength(UCS),but little influence on the deformation modulus of the rock mass.Using the Winkler beam model to represent the rockbolt behaviours,an analytical model for the prediction of the strength of boltreinforced blocky rocks is proposed.Good agreement between the UCS values predicted by proposed model and obtained from experiments suggest an encouraging performance of the proposed model.In addition,the performance of the proposed model is further assessed using published results in the literature,indicating the proposed model can be used effectively in the prediction of UCS of bolt-reinforced blocky rocks. 展开更多
关键词 Blocky rock mass Rockbolt ground support Uniaxial compression test Failure mechanism Uniaxial compressive strength model
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Optimized Binary Neural Networks for Road Anomaly Detection:A TinyML Approach on Edge Devices
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作者 Amna Khatoon Weixing Wang +2 位作者 Asad Ullah Limin Li Mengfei Wang 《Computers, Materials & Continua》 SCIE EI 2024年第7期527-546,共20页
Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural N... Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural Network(BNN)for road feature extraction,utilizing quantization and compression through a pruning strategy.The modifications resulted in a 28-fold decrease in memory usage and a 25%enhancement in inference speed while only experiencing a 2.5%decrease in accuracy.It showcases its superiority over conventional detection algorithms in different road image scenarios.Although constrained by computer resources and training datasets,our results indicate opportunities for future research,demonstrating that quantization and focused optimization can significantly improve machine learning models’accuracy and operational efficiency.ARM Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device:Advanced machine learning in edge computing.The analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing,suggesting ways to improve smart city frameworks in road network assessment,traffic management,and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks. 展开更多
关键词 Edge computing remote sensing TinyML optimization BNNs road anomaly detection QUANTIZATION model compression
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An Efficient Approach to Escalate the Speed of Training Convolution Neural Networks
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作者 P Pabitha Anusha Jayasimhan 《China Communications》 SCIE CSCD 2024年第2期258-269,共12页
Deep neural networks excel at image identification and computer vision applications such as visual product search, facial recognition, medical image analysis, object detection, semantic segmentation,instance segmentat... Deep neural networks excel at image identification and computer vision applications such as visual product search, facial recognition, medical image analysis, object detection, semantic segmentation,instance segmentation, and many others. In image and video recognition applications, convolutional neural networks(CNNs) are widely employed. These networks provide better performance but at a higher cost of computation. With the advent of big data, the growing scale of datasets has made processing and model training a time-consuming operation, resulting in longer training times. Moreover, these large scale datasets contain redundant data points that have minimum impact on the final outcome of the model. To address these issues, an accelerated CNN system is proposed for speeding up training by eliminating the noncritical data points during training alongwith a model compression method. Furthermore, the identification of the critical input data is performed by aggregating the data points at two levels of granularity which are used for evaluating the impact on the model output.Extensive experiments are conducted using the proposed method on CIFAR-10 dataset on ResNet models giving a 40% reduction in number of FLOPs with a degradation of just 0.11% accuracy. 展开更多
关键词 CNN deep learning image classification model compression
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De-biased knowledge distillation framework based on knowledge infusion and label de-biasing techniques
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作者 Yan Li Tai-Kang Tian +1 位作者 Meng-Yu Zhuang Yu-Ting Sun 《Journal of Electronic Science and Technology》 EI CAS CSCD 2024年第3期57-68,共12页
Knowledge distillation,as a pivotal technique in the field of model compression,has been widely applied across various domains.However,the problem of student model performance being limited due to inherent biases in t... Knowledge distillation,as a pivotal technique in the field of model compression,has been widely applied across various domains.However,the problem of student model performance being limited due to inherent biases in the teacher model during the distillation process still persists.To address the inherent biases in knowledge distillation,we propose a de-biased knowledge distillation framework tailored for binary classification tasks.For the pre-trained teacher model,biases in the soft labels are mitigated through knowledge infusion and label de-biasing techniques.Based on this,a de-biased distillation loss is introduced,allowing the de-biased labels to replace the soft labels as the fitting target for the student model.This approach enables the student model to learn from the corrected model information,achieving high-performance deployment on lightweight student models.Experiments conducted on multiple real-world datasets demonstrate that deep learning models compressed under the de-biased knowledge distillation framework significantly outperform traditional response-based and feature-based knowledge distillation models across various evaluation metrics,highlighting the effectiveness and superiority of the de-biased knowledge distillation framework in model compression. 展开更多
关键词 De-biasing Deep learning Knowledge distillation Model compression
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A Novel Quantization and Model Compression Approach for Hardware Accelerators in Edge Computing
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作者 Fangzhou He Ke Ding +3 位作者 DingjiangYan Jie Li Jiajun Wang Mingzhe Chen 《Computers, Materials & Continua》 SCIE EI 2024年第8期3021-3045,共25页
Massive computational complexity and memory requirement of artificial intelligence models impede their deploy-ability on edge computing devices of the Internet of Things(IoT).While Power-of-Two(PoT)quantization is pro... Massive computational complexity and memory requirement of artificial intelligence models impede their deploy-ability on edge computing devices of the Internet of Things(IoT).While Power-of-Two(PoT)quantization is pro-posed to improve the efficiency for edge inference of Deep Neural Networks(DNNs),existing PoT schemes require a huge amount of bit-wise manipulation and have large memory overhead,and their efficiency is bounded by the bottleneck of computation latency and memory footprint.To tackle this challenge,we present an efficient inference approach on the basis of PoT quantization and model compression.An integer-only scalar PoT quantization(IOS-PoT)is designed jointly with a distribution loss regularizer,wherein the regularizer minimizes quantization errors and training disturbances.Additionally,two-stage model compression is developed to effectively reduce memory requirement,and alleviate bandwidth usage in communications of networked heterogenous learning systems.The product look-up table(P-LUT)inference scheme is leveraged to replace bit-shifting with only indexing and addition operations for achieving low-latency computation and implementing efficient edge accelerators.Finally,comprehensive experiments on Residual Networks(ResNets)and efficient architectures with Canadian Institute for Advanced Research(CIFAR),ImageNet,and Real-world Affective Faces Database(RAF-DB)datasets,indicate that our approach achieves 2×∼10×improvement in the reduction of both weight size and computation cost in comparison to state-of-the-art methods.A P-LUT accelerator prototype is implemented on the Xilinx KV260 Field Programmable Gate Array(FPGA)platform for accelerating convolution operations,with performance results showing that P-LUT reduces memory footprint by 1.45×,achieves more than 3×power efficiency and 2×resource efficiency,compared to the conventional bit-shifting scheme. 展开更多
关键词 Edge computing model compression hardware accelerator power-of-two quantization
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Low rank optimization for efficient deep learning:making a balance between compact architecture and fast training
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作者 OU Xinwei CHEN Zhangxin +1 位作者 ZHU Ce LIU Yipeng 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第3期509-531,F0002,共24页
Deep neural networks(DNNs)have achieved great success in many data processing applications.However,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices... Deep neural networks(DNNs)have achieved great success in many data processing applications.However,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices,and it is not environmental-friendly with much power cost.In this paper,we focus on low-rank optimization for efficient deep learning techniques.In the space domain,DNNs are compressed by low rank approximation of the network parameters,which directly reduces the storage requirement with a smaller number of network parameters.In the time domain,the network parameters can be trained in a few subspaces,which enables efficient training for fast convergence.The model compression in the spatial domain is summarized into three categories as pre-train,pre-set,and compression-aware methods,respectively.With a series of integrable techniques discussed,such as sparse pruning,quantization,and entropy coding,we can ensemble them in an integration framework with lower computational complexity and storage.In addition to summary of recent technical advances,we have two findings for motivating future works.One is that the effective rank,derived from the Shannon entropy of the normalized singular values,outperforms other conventional sparse measures such as the?_1 norm for network compression.The other is a spatial and temporal balance for tensorized neural networks.For accelerating the training of tensorized neural networks,it is crucial to leverage redundancy for both model compression and subspace training. 展开更多
关键词 model compression subspace training effective rank low rank tensor optimization efficient deep learning
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DPAL-BERT:A Faster and Lighter Question Answering Model
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作者 Lirong Yin Lei Wang +8 位作者 Zhuohang Cai Siyu Lu Ruiyang Wang Ahmed AlSanad Salman A.AlQahtani Xiaobing Chen Zhengtong Yin Xiaolu Li Wenfeng Zheng 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第10期771-786,共16页
Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the ... Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency. 展开更多
关键词 DPAL-BERT question answering systems knowledge distillation model compression BERT Bi-directional long short-term memory(BiLSTM) knowledge information transfer PAL-BERT training efficiency natural language processing
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A Secure and Effective Energy-Aware Fixed-Point Quantization Scheme for Asynchronous Federated Learning
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作者 Zerui Zhen Zihao Wu +3 位作者 Lei Feng Wenjing Li Feng Qi Shixuan Guo 《Computers, Materials & Continua》 SCIE EI 2023年第5期2939-2955,共17页
Asynchronous federated learning(AsynFL)can effectivelymitigate the impact of heterogeneity of edge nodes on joint training while satisfying participant user privacy protection and data security.However,the frequent ex... Asynchronous federated learning(AsynFL)can effectivelymitigate the impact of heterogeneity of edge nodes on joint training while satisfying participant user privacy protection and data security.However,the frequent exchange of massive data can lead to excess communication overhead between edge and central nodes regardless of whether the federated learning(FL)algorithm uses synchronous or asynchronous aggregation.Therefore,there is an urgent need for a method that can simultaneously take into account device heterogeneity and edge node energy consumption reduction.This paper proposes a novel Fixed-point Asynchronous Federated Learning(FixedAsynFL)algorithm,which could mitigate the resource consumption caused by frequent data communication while alleviating the effect of device heterogeneity.FixedAsynFL uses fixed-point quantization to compress the local and global models in AsynFL.In order to balance energy consumption and learning accuracy,this paper proposed a quantization scale selection mechanism.This paper examines the mathematical relationship between the quantization scale and energy consumption of the computation/communication process in the FixedAsynFL.Based on considering the upper bound of quantization noise,this paper optimizes the quantization scale by minimizing communication and computation consumption.This paper performs pertinent experiments on the MNIST dataset with several edge nodes of different computing efficiency.The results show that the FixedAsynFL algorithm with an 8-bit quantization can significantly reduce the communication data size by 81.3%and save the computation energy in the training phase by 74.9%without significant loss of accuracy.According to the experimental results,we can see that the proposed AsynFixedFL algorithm can effectively solve the problem of device heterogeneity and energy consumption limitation of edge nodes. 展开更多
关键词 Asynchronous federated learning artificial intelligence model compression energy consumption fixed-point quantization learning accuracy
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Experimental Study on the Repair Effect of Xianlinggubao Capsule on Osteoporotic Vertebral Compression Fracture in Rabbits
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作者 Lihong GUO Lizhu LIU +2 位作者 Xi WANG Heng LIAO Jingping MU 《Medicinal Plant》 2023年第6期64-66,70,共4页
[Objectives]To observe the effect of Xianlinggubao Capsule on osteoporotic vertebral compression fracture(OVCF)in rabbits and the influence mechanism of the repair of fractures.[Methods]Female June age 30 rabbits were... [Objectives]To observe the effect of Xianlinggubao Capsule on osteoporotic vertebral compression fracture(OVCF)in rabbits and the influence mechanism of the repair of fractures.[Methods]Female June age 30 rabbits were randomly divided into control group,model control group and Xianlinggubao group.After bilateral ovariectomy,the model control group and Xianlinggubao group were injected with dexamethasone continuously for 4 weeks,and then the OVCF compound model was established by surgery.The Xianlinggubao group was treated with Xianlinggubao at a dose of 300 mg/(kg·d)for 60 d,while the blank control group and the model control group were treated with the same amount of normal saline for 60 d.The number of blood vessels and the expression of bone morphogenetic protein-2(BMP-2)were detected by immunohistochemical staining and the bone mineral density(BMD)in the callus of the third lumbar fracture area of rabbits was measured.The content of serum phosphorus(P),alkaline phosphatase(ALP)and total calcium(TCa)in rabbit venous blood were measured by automatic biochemical analyzer.The content of vascular endothelial growth factor(VEGF)and platelet-derived growth factor(PDGF)in rabbit venous blood were measured by ELISA kit.[Results]The number of blood vessels and the expression of BMP-2 in the callus of the third lumbar fracture area of rabbits was high in Xianlinggubao group,the content of serum P,ALP,TCa,VEGF and PDGF was obviously increased,BMD was obviously increased,the bone microstructure of the third lumbar vertebrae fracture area of rabbits was basically restored.Compared with the model control group(P<0.05),the difference was statistically significant.[Conclusions]Xianlinggubao Capsule can increase calcium and phosphorus deposition,promote the formation of blood vessels in the fracture area of OVCF in rabbits,and have a strong repair effect on OVCF in rabbits. 展开更多
关键词 RABBIT Xianlinggubao Capsule OSTEOPOROSIS Compound model of vertebral compression fracture REPAIR
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Effect of warm acupuncture on nitric oxide synthase and calcitonin gene-related peptide in a rat model of lumbar nerve root compression 被引量:5
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作者 Yaochi Wu Yiqun Mi Peng Zhang Junfeng Zhang Wei Chen 《Neural Regeneration Research》 SCIE CAS CSCD 2009年第6期449-454,共6页
BACKGROUND: Varying degrees of inflammatory responses occur during lumbar nerve root compression. Studies have shown that nitric oxide synthase (NOS) and calcitonin gene-related peptide (CGRP) are involved in sec... BACKGROUND: Varying degrees of inflammatory responses occur during lumbar nerve root compression. Studies have shown that nitric oxide synthase (NOS) and calcitonin gene-related peptide (CGRP) are involved in secondary disc inflammation. OBJECTIVE: To observe the effects of warm acupuncture on the ultrastructure of inflammatory mediators in a rat model of lumbar nerve root compression, including NOS and CGRP contents. DESIGN, TIME AND SETTING: Randomized, controlled study, with molecular biological analysis, was performed at the Experimental Center, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University, between September 2006 and April 2007. MATERIALS: Acupuncture needles and refined Moxa grains were purchased from Shanghai Taicheng Technology Development Co., Ltd., China; Mobic tablets were purchased from Shanghai Boehringer Ingelheim Pharmaceuticals Co., Ltd., China; enzyme linked immunosorbent assay (ELISA) kits for NOS and CGRP were purchased from ADL Biotechnology, Inc., USA. METHODS: A total of 50, healthy, adult Sprague-Dawley rats, were randomly divided into five groups normal, model, warm acupuncture, acupuncture, and drug, with 10 rats in each group. Rats in the four groups, excluding the normal group, were used to establish models of lumbar nerve root compression. After 3 days, Jiaji points were set using reinforcing-reducing manipulation in the warm acupuncture group. Moxa grains were burned on each needle, with 2 grains each daily. The acupuncture group was the same as the warm acupuncture group, with the exception of non-moxibustion. Mobic suspension (3.75 mg/kg) was used in the oral drug group, once a day. Treatment of each group lasted for 14 consecutive days. Modeling and medication were not performed in the normal group. MAIN OUTCOME MEASURES: The ultrastructure of damaged nerve roots was observed with transmission electron microscopy; NOS and CGRP contents were measured using ELISA. RESULTS: The changes of the radicular ultramicrostructure were characterized by Wallerian degeneration; nerve fibers were clearly demyelinated; axons collapsed or degenerated; outer Schwann cell cytoplasm was swollen and its nucleus was compacted. Compared with the normal group, NOS and CGRP contents in the nerve root compression zone in the model group were significantly increased (P 〈 0.01). Nerve root edema was improved in the drug, acupuncture and the warm acupuncture groups over the model group. NOS and CGRP expressions were also decreased with the warm acupuncture group having the lowest concentration (P 〈 0.01). CONCLUSION: In comparison to the known effects of Mobic drug and acupuncture treatments, the warm acupuncture significantly decreased NOS and CGRP expression which helped improve the ultrastructure of the compressed nerve root. 展开更多
关键词 warm acupuncture nerve root compression model ULTRASTRUCTURE nitric oxide synthase calcitonin gene-related peptide
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A Novel Deep Neural Network Compression Model for Airport Object Detection 被引量:3
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作者 LYU Zonglei PAN Fuxi XU Xianhong 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2020年第4期562-573,共12页
A novel deep neural network compression model for airport object detection has been presented.This novel model aims at disadvantages of deep neural network,i.e.the complexity of the model and the great cost of calcula... A novel deep neural network compression model for airport object detection has been presented.This novel model aims at disadvantages of deep neural network,i.e.the complexity of the model and the great cost of calculation.According to the requirement of airport object detection,the model obtains temporal and spatial semantic rules from the uncompressed model.These spatial semantic rules are added to the model after parameter compression to assist the detection.The rules can improve the accuracy of the detection model in order to make up for the loss caused by parameter compression.The experiments show that the effect of the novel compression detection model is no worse than that of the uncompressed original model.Even some of the original model false detection can be eliminated through the prior knowledge. 展开更多
关键词 compression model semantic rules PRUNING prior probability lightweight detection
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BLOWUP CRITERION FOR THE COMPRESSIBLE FLUID-PARTICLE INTERACTION MODEL IN 3D WITH VACUUM 被引量:3
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作者 丁时进 黄炳远 卢友波 《Acta Mathematica Scientia》 SCIE CSCD 2016年第4期1030-1048,共19页
In this article, we consider the blowup criterion for the local strong solution to the compressible fluid-particle interaction model in dimension three with vacuum. We establish a BKM type criterion for possible break... In this article, we consider the blowup criterion for the local strong solution to the compressible fluid-particle interaction model in dimension three with vacuum. We establish a BKM type criterion for possible breakdown of such solutions at critical time in terms of both the L^∞ (0, T; L^6)-norm of the density of particles and the ^L1(0, T; L^∞)-norm of the deformation tensor of velocity gradient. 展开更多
关键词 Blowup criterion compressible fluid-particle interaction model VACUUM
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REVIEW ON MATHEMATICAL ANALYSIS OF SOME TWO-PHASE FLOW MODELS 被引量:3
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作者 Huanyao WEN Lei YAO Changjiang ZHU 《Acta Mathematica Scientia》 SCIE CSCD 2018年第5期1617-1636,共20页
The two-phase flow models are commonly used in industrial applications, such as nuclear, power, chemical-process, oil-and-gas, cryogenics, bio-medical, micro-technology and so on. This is a survey paper on the study o... The two-phase flow models are commonly used in industrial applications, such as nuclear, power, chemical-process, oil-and-gas, cryogenics, bio-medical, micro-technology and so on. This is a survey paper on the study of compressible nonconservative two-fluid model, drift-flux model and viscous liquid-gas two-phase flow model. We give the research developments of these three two-phase flow models, respectively. In the last part, we give some open problems about the above models. 展开更多
关键词 compressible nonconservative two-fluid model drift-flux model viscous liquid-gas two-phase flow model WELL-POSEDNESS
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Two-dimensional simulation on the rolling process of semi-solid 60Si2Mn by finite element method 被引量:4
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作者 Renbo Song, Yonglin Kang, Xueping Ren, Hongbo Dong, and Jiwen WangMaterials Science and Engineering School, University of Science and Technology Beijing, Beijing 100083, China 《Journal of University of Science and Technology Beijing》 CSCD 2002年第4期273-276,共4页
The effect of various process variables on the law of metal flow for semi-solid rolling 60Si2Mn was studied by finite element method. Semi-solid 60Si2Mn can be described as compressible rigid visco-plastic porous mate... The effect of various process variables on the law of metal flow for semi-solid rolling 60Si2Mn was studied by finite element method. Semi-solid 60Si2Mn can be described as compressible rigid visco-plastic porous material saturated with liquid. In terms of ther-mo-mechanical coupling condition, the distributions of stress, velocity and temperature were studied using software MARC. The simulation results show that the rigid visco-plastic model can accurately describe the semi-solid 60Si2Mn rolling process. The great deformation can achieve completely in view of low flow stress of semi-solid slurry. 展开更多
关键词 finite-element analysis SEMI-SOLID thermo-mechanical coupling compressible rigid visco-plastic model ROLLING
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Enhancing the robustness of object detection via 6G vehicular edge computing 被引量:1
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作者 Chen Chen Guorun Yao +2 位作者 Chenyu Wang Sotirios Goudos Shaohua Wan 《Digital Communications and Networks》 SCIE CSCD 2022年第6期923-931,共9页
Academic and industrial communities have been paying significant attention to the 6th Generation (6G) wireless communication systems after the commercial deployment of 5G cellular communications. Among the emerging te... Academic and industrial communities have been paying significant attention to the 6th Generation (6G) wireless communication systems after the commercial deployment of 5G cellular communications. Among the emerging technologies, Vehicular Edge Computing (VEC) can provide essential assurance for the robustness of Artificial Intelligence (AI) algorithms to be used in the 6G systems. Therefore, in this paper, a strategy for enhancing the robustness of AI model deployment using 6G-VEC is proposed, taking the object detection task as an example. This strategy includes two stages: model stabilization and model adaptation. In the former, the state-of-the-art methods are appended to the model to improve its robustness. In the latter, two targeted compression methods are implemented, namely model parameter pruning and knowledge distillation, which result in a trade-off between model performance and runtime resources. Numerical results indicate that the proposed strategy can be smoothly deployed in the onboard edge terminals, where the introduced trade-off outperforms the other strategies available. 展开更多
关键词 6G Vehicular edge computing Object detection Feature fusion Model compression Model deployment
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A BLOW-UP CRITERION OF STRONG SOLUTIONS TO THE QUANTUM HYDRODYNAMIC MODEL 被引量:1
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作者 Guangwu WANG Boling GUO 《Acta Mathematica Scientia》 SCIE CSCD 2020年第3期795-804,共10页
In this article,we focus on the short time strong solution to a compressible quantum hydrodynamic model.We establish a blow-up criterion about the solutions of the compressible quantum hydrodynamic model in terms of t... In this article,we focus on the short time strong solution to a compressible quantum hydrodynamic model.We establish a blow-up criterion about the solutions of the compressible quantum hydrodynamic model in terms of the gradient of the velocity,the second spacial derivative of the square root of the density,and the first order time derivative and first order spacial derivative of the square root of the density. 展开更多
关键词 Compressible quantum hydrodynamic model blow-up criterion strong solution
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An analytical p-y curve method based on compressive soil pressure model in sand soil 被引量:1
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作者 JIANG Jie FU Chen-zhi +2 位作者 WANG Shun-wei CHEN Chao-qi OU Xiao-duo 《Journal of Central South University》 SCIE EI CAS CSCD 2022年第6期1987-2004,共18页
With the high-quality development of urban buildings,higher requirements are come up with for lateral bearing capacity of laterally loaded piles.Consequently,a more accurate analysis to predict the lateral response of... With the high-quality development of urban buildings,higher requirements are come up with for lateral bearing capacity of laterally loaded piles.Consequently,a more accurate analysis to predict the lateral response of the pile within an allowable displacement is an important issue.However,the current p-y curve methods cannot fully take into account the pile-soil interaction,which will lead to a large calculation difference.In this paper,a new analytical p-y curve is established and a finite difference method for determining the lateral response of pile is proposed,which can consider the separation effect of pile-soil interface and the coefficient of circumferential friction resistance.In particular,an analytical expression is developed to determine the compressive soil pressure by dividing the compressive soil pressure into two parts:initial compressive soil pressure and increment of compressive soil pressure.In addition,the relationship between compressive soil pressure and horizontal displacement of the pile is established based on the reasonable assumption.The correctness of the proposed method is verified through four examples.Based on the verified method,a parametric analysis is also conducted to investigate the influences of factors on lateral response of the pile,including internal friction angle,pile length and elastic modulus of pile. 展开更多
关键词 laterally loaded piles compressive soil pressure model separation effect of pile-soil interface coefficient of circumferential friction resistance analytical p-y curve finite difference method
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Selection of regression models for predicting strength and deformability properties of rocks using GA 被引量:9
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作者 Manouchehrian Amin Sharifzadeh Mostafa +1 位作者 Hamidzadeh Moghadam Rasoul Nouri Tohid 《International Journal of Mining Science and Technology》 SCIE EI 2013年第4期492-498,共7页
Recently,many regression models have been presented for prediction of mechanical parameters of rocks regarding to rock index properties.Although statistical analysis is a common method for developing regression models... Recently,many regression models have been presented for prediction of mechanical parameters of rocks regarding to rock index properties.Although statistical analysis is a common method for developing regression models,but still selection of suitable transformation of the independent variables in a regression model is diffcult.In this paper,a genetic algorithm(GA)has been employed as a heuristic search method for selection of best transformation of the independent variables(some index properties of rocks)in regression models for prediction of uniaxial compressive strength(UCS)and modulus of elasticity(E).Firstly,multiple linear regression(MLR)analysis was performed on a data set to establish predictive models.Then,two GA models were developed in which root mean squared error(RMSE)was defned as ftness function.Results have shown that GA models are more precise than MLR models and are able to explain the relation between the intrinsic strength/elasticity properties and index properties of rocks by simple formulation and accepted accuracy. 展开更多
关键词 Regression models Genetic algorithms Heuristics Uniaxial compressive strength Modulus of elasticity Rock index property
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MoTransFrame:Model Transfer Framework for CNNs on Low-Resource Edge Computing Node
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作者 Panyu Liu Huilin Ren +4 位作者 Xiaojun Shi Yangyang Li Zhiping Cai Fang Liu Huacheng Zeng 《Computers, Materials & Continua》 SCIE EI 2020年第12期2321-2334,共14页
Deep learning technology has been widely used in computer vision,speech recognition,natural language processing,and other related fields.The deep learning algorithm has high precision and high reliability.However,the ... Deep learning technology has been widely used in computer vision,speech recognition,natural language processing,and other related fields.The deep learning algorithm has high precision and high reliability.However,the lack of resources in the edge terminal equipment makes it difficult to run deep learning algorithms that require more memory and computing power.In this paper,we propose MoTransFrame,a general model processing framework for deep learning models.Instead of designing a model compression algorithm with a high compression ratio,MoTransFrame can transplant popular convolutional neural networks models to resources-starved edge devices promptly and accurately.By the integration method,Deep learning models can be converted into portable projects for Arduino,a typical edge device with limited resources.Our experiments show that MoTransFrame has good adaptability in edge devices with limited memories.It is more flexible than other model transplantation methods.It can keep a small loss of model accuracy when the number of parameters is compressed by tens of times.At the same time,the computational resources needed in the reasoning process are less than what the edge node could handle. 展开更多
关键词 Edge computing convolutional neural network model transformation model compression
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