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OAAFormer:Robust and Efficient Point Cloud Registration Through Overlapping-Aware Attention in Transformer

Journal of Computer Science & Technology 2024年第4期39卷 755-770页

作者： Jun-Jie Gao Qiu-Jie Dong Rui-An Wang Shuang-Min Chen shi-qing xin Chang-He Tu Wenping Wang School of Computer Science and Technology Shandong UniversityQingdao 266237China School of Information and Technology Qingdao University of Science and TechnologyQingdao 266061China College of Engineering Texas A&M UniversityTexasTX 77843U.S.A.

In the domain of point cloud registration,the coarse-to-fine feature matching paradigm has received significant attention due to its impressive *** paradigm involves a two-step process:first,the extraction of multilevel features,and subsequently,the propagation of correspondences from coarse to fine ***,this approach faces two notable ***,the use of the Dual Softmax operation may promote one-to-one correspondences between superpoints,inadvertently excluding valuable ***,it is crucial to closely examine the overlapping areas between point clouds,as only correspondences within these regions decisively determine the actual *** these issues,we propose OAAFormer to enhance correspondence *** the one hand,we introduce a soft matching mechanism to facilitate the propagation of potentially valuable correspondences from coarse to fine *** the other hand,we integrate an overlapping region detection module to minimize mismatches to the greatest extent ***,we introduce a region-wise attention module with linear complexity during the fine-level matching phase,designed to enhance the discriminative capabilities of the extracted *** on the challenging 3DLoMatch benchmark demonstrate that our approach leads to a substantial increase of about 7%in the inlier ratio,as well as an enhancement of 2%-4%in registration ***,to accelerate the prediction process,we replace the Conventional Random Sample Consensus(RANSAC)algorithm with the selection of a limited yet representative set of high-confidence correspondences,resulting in a 100 times speedup while still maintaining comparable registration performance.

关键词： point cloud registration coarse-to-fine overlapping region feature matching Transformer

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Constructing self-supporting surfaces with planar quadrilateral elements

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Computational Visual Media 2022年第4期8卷 571-583页

作者： Long Ma Sidan Yao Jianmin Zheng Yang Liu Yuanfeng Zhou shi-qing xin Ying He School of Software Shandong UniversityJinan 250101China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore Internet Graphics Group Microsoft Research AsiaBeijing 100080China School of Computer Science and Technology Shandong UniversityQingdao 266237China

We present a simple yet effective method for constructing 3D self-supporting surfaces with planar quadrilateral(PQ)*** with a triangular discretization of a self-supporting surface,we firstcompute the principal curvatures and directions of each triangular face using a new discrete differential geometryapproach,yielding more accurate results than existing ***,we smooth the principal direction field to reduce the number of ***,we partition all faces into two groups in terms of principalcurvature *** each face with small curvature difference,we compute a stretch matrix that turns the principal directions into a pair of conjugate *** the remaining triangular faces,we simply keep their smoothed principal ***,applying a mixed-integer programming solver to the mixed principal and conjugate direction field,we obtain a planar quadrilateral *** results show that our method is computationally efficient and can yield high-quality PQ meshes that well approximate the geometry of the input surfaces and maintain their self-supporting properties.

关键词： self-supporting surfaces planar quadri-lateral(PQ)meshes conjugate direction fields(CDFs)

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Learning topological defects formation with neural networks in a quantum phase transition

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Communications in Theoretical Physics 2024年第5期76卷 68-76页

作者： Han-qing shi Hai-qing Zhang Center for Gravitational Physics Department of Space ScienceBeihang UniversityBeijing 100191China Peng Huanwu Collaborative Center for Research and Education Beihang UniversityBeijing 100191China

Neural networks possess formidable representational power,rendering them invaluable in solving complex quantum many-body *** they excel at analyzing static solutions,nonequilibrium processes,including critical dynamics during a quantum phase transition,pose a greater challenge for neural *** address this,we utilize neural networks and machine learning algorithms to investigate time evolutions,universal statistics,and correlations of topological defects in a one-dimensional transverse-field quantum Ising ***,our analysis involves computing the energy of the system during a quantum phase transition following a linear quench of the transverse magnetic field *** excitation energies satisfy a power-law relation to the quench rate,indicating a proportional relationship between the excitation energy and the kink ***,we establish a universal power-law relationship between the first three cumulants of the kink numbers and the quench rate,indicating a binomial distribution of the ***,the normalized kink-kink correlations are also investigated and it is found that the numerical values are consistent with the analytic formula.

关键词： neural networks machine learning transverse-field quantum Ising model kibble-zurek mechanism

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Oilfield analogy and productivity prediction based on machine learning: Field cases in PL oilfield, China

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Petroleum Science 2024年第4期21卷 2554-2570页

作者： Wen-Peng Bai shi-qing Cheng xin-Yang Guo Yang Wang Qiao Guo Chao-Dong Tan State Key Laboratory of Petroleum Resources and Engineering China University of Petroleum (Beijing)

In the early time of oilfield development, insufficient production data and unclear understanding of oil production presented a challenge to reservoir engineers in devising effective development plans. To address this challenge, this study proposes a method using data mining technology to search for similar oil fields and predict well productivity. A query system of 135 analogy parameters is established based on geological and reservoir engineering research, and the weight values of these parameters are calculated using a data algorithm to establish an analogy system. The fuzzy matter-element algorithm is then used to calculate the similarity between oil fields, with fields having similarity greater than 70% identified as similar oil fields. Using similar oil fields as sample data, 8 important factors affecting well productivity are identified using the Pearson coefficient and mean decrease impurity（MDI） method. To establish productivity prediction models, linear regression（LR）, random forest regression（RF）, support vector regression（SVR）, backpropagation（BP）, extreme gradient boosting（XGBoost）, and light gradient boosting machine（Light GBM） algorithms are used. Their performance is evaluated using the coefficient of determination（R2）, explained variance score（EV）, mean squared error（MSE）, and mean absolute error（MAE） metrics. The Light GBM model is selected to predict the productivity of 30 wells in the PL field with an average error of only 6.31%, which significantly improves the accuracy of the productivity prediction and meets the application requirements in the field. Finally, a software platform integrating data query,oil field analogy, productivity prediction, and knowledge base is established to identify patterns in massive reservoir development data and provide valuable technical references for new reservoir development.

关键词： Data mining technique Analogy parameters Oilfield analogy Productivity prediction Software platform

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Effective utilization of dolomitic lead-zinc waste rock by replacing dolomite as flux in iron ore sintering process

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Journal of Iron and Steel Research International 2024年第8期31卷 1861-1872页

作者： xin Wang Cong-cong Yang Jian Pan De-qing Zhu Zheng-qi Guo Guang-hui Xia shi-juan Qu School of Minerals Processing and Bioengineering Central South UniversityChangsha 410083HunanChina Low-Carbon and Hydrogen Metallurgy Research Center Central South UniversityChangsha 410083HunanChina

With increasingly stringent environmental protection policies,cost-effective and value-added treatment of massive lead-zinc waste rock(LZWR)generated from the preconcentration process has attracted substantial attention.A type of dolomitic LZWR with 18 wt.%MgO,27 wt.%CaO,0.29 wt.%Zn and 1.43 wt.%S was used as a replacement flux in the iron ore sintering *** sinter pot tests were carried out based on an industrial ore *** results show that replacing 0%-60%of dolomite with LZWR does not adversely affect the sintering productivity,fuel consumption rate,and the quality of the sinter products,while the Zn and S elements contained in LZWR can be effectively removed with the residual Zn and S contents of the resultant sinter products being less than 0.02 and 0.055 wt.%,***,substituting LZWR for dolomite as a flux inevitably increases the SO_(2) concentration in the sintering flue gas due to its high sulfur *** the processing capacity of the sintering flue gas desulfurization system,the replacement ratio of dolomite can reach 40%,equal to LZWR consumption of 24.3 kg per ton of *** SO_(2) in sintering flue gas can be recovered to produce(NH_(4))_(2)SO_(4) or HSO_(4),while the volatilized Zn collected into the zinc-containing dust can be subsequently recycled by the rotary hearth furnace ***,it is technologically and economically feasible to use the LZWR as a replacement flux in the iron ore sintering process,providing a new way for the safe,large-scale and low-cost treatment of LZWR.

关键词： Lead Zinc waste rock Dolomite Sintering Flue gas Sinter product

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Biogenic volatile organic compounds dominated the near-surface ozone generation in Sichuan Basin,China,during fall and wintertime

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Journal of Environmental Sciences 2024年第7期141卷 215-224页

作者： Dasheng Huang qing Li Yan Han shi-Yong Xia Jiawei Zhou Hanxiong Che Keding Lu Fumo Yang xin Long Yang Chen Research Center for Atmospheric Environment Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714China College of Resources and Environment Chongqing SchoolUniversity of the Chinese Academy of Sciences(UCAS Chongqing)Chongqing 400714China College of Environmental and Chemical Engineering Chongqing Three Gorges UniversityChongqing 404199China Key Laboratory for Urban Habitat Environmental Science and Technology School of Environment and EnergyPeking University Shenzhen Graduate SchoolLishui RoadNanshan DistrictShenzhen 518055China SKL-ESPC and BIC-ESAT College of Environmental Sciences and EngineeringPeking UniversityBeijing 100871China College of Architecture&Environment Sichuan UniversityChengdu 610065China

The complex air pollution driven by both Ozone(O_(3))and fine particulate matter(PM2.5)sig nificantly influences the air quality in the Sichuan Basin(SCB).Understanding the O_(3)for mation during autumn and winter is necessary to understand the atmospheric oxidative ***,continuous in-site field observations were carried out during the late summer,early autumn and winter of 2020 in a rural area of *** total volatile organic compounds(VOCs)concentration reported by a Proton-Transfer-Reaction Time-of Flight Mass Spectrometry(PTR-ToF-MS)were 13.66±9.75 ppb,5.50±2.64 ppb,and 9.41±5.11 ppb in late summer,early autumn and winter,*** anthropogenic VOCs(AVOCs)and biogenic VOCs(BVOCs)were 8.48±7.92 ppb and 5.18±2.99 ppb in late sum mer,3.31±1.89 ppb and 2.19±0.93 ppb in autumn,and 6.22±3.99 ppb and 3.20±1.27ppb in winter.A zero-dimensional atmospheric box model was employed to investigate the sensitivity of O_(3)-precursors by relative incremental reactivity(RIR).The RIR values of AV OCs,BVOCs,carbon monoxide(CO),and nitrogen oxides(NOx)were 0.31,0.71,0.09,and-0.36 for late summer,0.24,0.59,0.22,and-0.38 for early autumn,and 0.30,0.64,0.33 and-0.70 for winter,and the results showed that the O_(3)formation of sampling area was in the VOC-limited region,and O_(3)was most sensitive to BVOCs(with highest RIR values,>0.6)This study can be helpful in understanding O_(3)formation and interpreting the secondary formation of aerosols in the winter.

关键词： VOCs PTR-MS Model Ozone

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Microstructure and magnetocaloric properties of melt-extracted SmGdDyCoAl high-entropy amorphous microwires

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Rare Metals 2024年第3期43卷 1234-1242页

作者： shi-Jie Wei Hong-Xian Shen Lun-Yong Zhang Lin Luo xin-xing Tang Jian-Fei Sun Xiao-qing Li School of Materials Science and Engineering Harbin Institute of TechnologyHarbin150001China Applied Materials Physics Department of Materials Science and EngineeringKTH-Royal Institute of Technology10044StockholmSweden

This paper presents a systematic investigation of the microstructure and magnetocaloric properties of melt-extracted Sm_(20)Gd_(20)Dy_(20)Co_(20)Al_(20)high-entropy *** fabricated wires exhibited an amorphous structure,and the temperature interval of the undercooled liquid AT was 45 *** microwires underwent a second-order magnetic transition from a ferromagnetic to a paramagnetic state near the Curie temperature(T_(C)=52 K),The maximum magnetic entropy change(-ΔS_M^(max)),the relative cooling power and the refrigeration capacity reached 6.34 J·kg^(-1)·K^(-1).422.09 J·kg^(-1)and 332.94 J·kg^(-1),respectively,under a magnetic field change of 5 *** addition,the temperature-averaged entropy changes with two temperature lifts(3 and 10 K)were 6.32 and 6.27 J·kg^(-1)·K^(-1),*** good magnetocalorie performance highlights the significant potential for the Sm_(20)Gd_(20)Dy_(20)Co_(20)Al_(20)microwires to be used as magnetic refrigerant materials in low-temperature region *** work will serve as a valuable reference for future investigations on low-temperature high-entropy magnetocaloric materials.

关键词： Melt-extraction High-entropy amorphous microwires Magnetocaloric effect Magnetic refrigeration

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Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

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International Journal of Minerals,Metallurgy and Materials 2024年第1期31卷 106-117页

作者： xin Shao qing Liu Zicheng xin Jiangshan Zhang Tao Zhou Shaoshuai Li State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing 100083China Engineering Research Center of MES Technology for Iron&Steel Production Ministry of EducationBeijing 100083China Laiwu Iron and Steel Group Yinshan Section Steel Co. Ltd.Jinan 271104China

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.

关键词： basic oxygen furnace oxygen consumption oxygen blowing time oxygen balance mechanism deep neural network hybrid model

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A Coupled Thermomechanical Crack Propagation Behavior of Brittle Materials by Peridynamic Differential Operator

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 339-361页

作者： Tianyi Li xin Gu qing Zhang College of Architectural Engineering Jiangsu Open UniversityNanjing210036China Department of Engineering Mechanics Hohai UniversityNanjing211100China

This study proposes a comprehensive,coupled thermomechanical model that replaces local spatial derivatives in classical differential thermomechanical equations with nonlocal integral forms derived from the peridynamic differential operator(PDDO),eliminating the need for calibration *** model employs a multi-rate explicit time integration scheme to handle varying time scales in multi-physics *** simulations conducted on granite and ceramic materials,this model demonstrates its *** successfully simulates thermal damage behavior in granite arising from incompatible mineral expansion and accurately calculates thermal crack propagation in ceramic slabs during *** account for material heterogeneity,the model utilizes the Shuffle algorithm andWeibull distribution,yielding results that align with numerical simulations and experimental *** coupled thermomechanical model shows great promise for analyzing intricate thermomechanical phenomena in brittle materials.

关键词： Peridynamic differential operator thermomechanical coupling heterogeneity numerical simulation

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Pressure transient characteristics of non-uniform conductivity fractured wells in viscoelasticity polymer flooding based on oil-water two-phase flow

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Petroleum Science 2024年第1期21卷 343-351页

作者： Yang Wang Jia Zhang shi-Long Yang Ze-Xuan Xu shi-qing Cheng National Key Laboratory of Petroleum Resources and Engineering China University of Petroleum(Beijing)Beijing102249China

Polymer flooding in fractured wells has been extensively applied in oilfields to enhance oil *** contrast to water,polymer solution exhibits non-Newtonian and nonlinear behavior such as effects of shear thinning and shear thickening,polymer convection,diffusion,adsorption retention,inaccessible pore volume and reduced effective ***,the flux density and fracture conductivity along the hydraulic fracture are generally non-uniform due to the effects of pressure distribution,formation damage,and proppant *** this paper,we present an oil-water two-phase flow model that captures these complex non-Newtonian and nonlinear behavior,and non-uniform fracture characteristics in fractured polymer *** hydraulic fracture is firstly divided into two parts:high-conductivity fracture near the wellbore and low-conductivity fracture in the far-wellbore section.A hybrid grid system,including perpendicular bisection(PEBI)and Cartesian grid,is applied to discrete the partial differential flow equations,and the local grid refinement method is applied in the near-wellbore region to accurately calculate the pressure distribution and shear rate of polymer *** combination of polymer behavior characterizations and numerical flow simulations are applied,resulting in the calculation for the distribution of water saturation,polymer concentration and reservoir *** with the polymer flooding well with uniform fracture conductivity,this non-uniform fracture conductivity model exhibits the larger pressure difference,and the shorter bilinear flow period due to the decrease of fracture flow ability in the far-wellbore *** field case of the fall-off test demonstrates that the proposed method characterizes fracture characteristics more accurately,and yields fracture half-lengths that better match engineering reality,enabling a quantitative segmented characterization of the near-wellbore section with high fracture conductivity and th

关键词： Polymer flooding Non-Newtonian fluid Non-uniform fracture conductivity Two-phase flow Pressure transient analysis

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