For the past few years,the prevalence of cardiovascular disease has been showing a year-on-year increase,with a death rate of 2/5.Coronary heart disease(CHD)rates have increased 41%since 1990,which is the number one d...For the past few years,the prevalence of cardiovascular disease has been showing a year-on-year increase,with a death rate of 2/5.Coronary heart disease(CHD)rates have increased 41%since 1990,which is the number one disease endangering human health in the world today.The risk indicators of CHD are complicated,so selecting effective methods to screen the risk characteristics can make the risk predictionmore efficient.In this paper,we present a comprehensive analysis ofCHDrisk indicators fromboth data and algorithmic levels,propose a method for CHDrisk indicator identification based on multi-angle integrated measurements and Sequential Backward Selection(SBS),and then build a risk prediction model.In the multi-angle integrated measurements stage,mRMR(Maximum Relevance Minimum Redundancy)is selected from the angle of feature correlation and redundancy of the dataset itself,SHAPRF(SHapley Additive exPlanations-Random Forest)is selected from the angle of interpretation of each feature to the results,and ARFS-RF(Algorithmic Randomness Feature Selection Random Forest)is selected from the angle of statistical interpretation of classification algorithm to measure the degree of feature importance.In the SBS stage,the features with low scores are deleted successively,and the accuracy of LightGBM(Light Gradient Boosting Machine)model is used as the evaluation index to select the final feature subset.This new risk assessment method is used to identify important factors affecting CHD,and the CHD dataset from the Kaggle website is used as the study subject.Finally,11 features are retained to construct a risk assessment indicator system for CHD.Using the LightGBM classifier as the core evaluationmetric,ourmethod achieved an accuracy of 0.8656 on the Kaggle CHD dataset(4238 samples,16 initial features),outperforming individual feature selection methods(mRMR,SHAP-RF,ARFS-RF)in both accuracy and feature reduction.This demonstrates the novelty and effectiveness of our multi-angle integrated measurement approach combined with SBS in building a concise yet highly predictive CHD risk model.展开更多
The electric vertical takeoff and landing(e VTOL)aircraft shows great potential for rapid military personnel deployment on the battlefield.However,its susceptibility to control loss,complex crashes,and extremely limit...The electric vertical takeoff and landing(e VTOL)aircraft shows great potential for rapid military personnel deployment on the battlefield.However,its susceptibility to control loss,complex crashes,and extremely limited bottom energy-absorbing space demands higher comprehensive crashworthiness of its subfloor thin-walled structures.This study investigated the energy absorption capacity of novel concave polygonal carbon fiber reinforced plastics(CFRP)tubes under multi-angle collisions.Quasistatic compression experiments and finite element simulations were conducted to assess the failure mode and energy absorption.The influences of cross-section shapes,loading conditions,and geometry parameters on crashworthiness metrics were further analyzed.The results revealed that,under the similar weight,concave polygonal tubes exhibited superior energy absorption under axial loads compared to regular polygonal and circular tubes,attributed to the increased number of axial splits.However,both regular and concave polygonal tubes,particularly the latter,demonstrated reduced oblique energy absorption compared to traditional square tubes with the increasing ratio of SEA value decreased from 20%-16%.Notably,this reduction in energy absorption can be compensated for by the implementation of inward and outward crusher plugs,and with them,the concave polygonal tubes demonstrated outstanding overall crashworthiness performance under multiple loading conditions.This concave cross-sectional design methods could serve as a guidance for the development of the eVTOL subfloor.展开更多
Plant phenomics has the potential to accelerate progress in understanding gene functions and environmental responses. Progress has been made in automating high-throughput plant phenotyping. However, few studies have i...Plant phenomics has the potential to accelerate progress in understanding gene functions and environmental responses. Progress has been made in automating high-throughput plant phenotyping. However, few studies have investigated automated rice panicle counting. This paper describes a novel method for automatically and nonintrusively determining rice panicle numbers during the full heading stage by analyzing color images of rice plants taken from multiple angles. Pot-grown rice plants were transferred via an industrial conveyer to an imaging chamber. Color images from different angles were automatically acquired as a turntable rotated the plant. The images were then analyzed and the panicle number of each plant was determined. The image analysis pipeline consisted of extracting the i2 plane from the original color image, segmenting the image, discriminating the panicles from the rest of the plant using an artificial neural network, and calculating the panicle number in the current image. The panicle number of the plant was taken as the maximum of the panicle numbers extracted from all 12 multi-angle images. A total of 105 rice plants during the full heading stage were examined to test the performance of the method. The mean absolute error of the manual and automatic count was 0.5, with 95.3% of the plants yielding absolute errors within ± 1. The method will be useful for evaluating rice panicles and will serve as an important supplementary method for high-throughput rice phenotyping.展开更多
The traditional remote sensing mainly detects the ground vertically to obtain the 2D information but it is hard to get adequate parameters for the quantitative remote sensing to invert land features. The multi-angle o...The traditional remote sensing mainly detects the ground vertically to obtain the 2D information but it is hard to get adequate parameters for the quantitative remote sensing to invert land features. The multi-angle observation can get more detailed and reliable 3D structural parameters of targets, so it makes the quantitative remote sensing applicable. During the process of reflecting, scattering and transmitting the electromagnetic wave, minerals and rocks could reveal the polarized features related to the nature of themselves. Therefore, it has become a new approach of quantitative remote sensing to detect multi-angle polarized information of minerals and rocks. In respect that the polarized reflectance always goes with the bidirectional one, we can obtain the 3D spatial distribution of targets by a polarized means together with detecting its bi-directional reflectance. From the perspective of multi-angle polarized remote sensing mechanism, the quantitative relationship between multi-angle polarized reflectance and the BRDF is studied in this paper. And it is testified that the bi-directional reflectance, polarized reflectance of 45° and the mean value of polarized reflectance are equal to that of the corresponding azimuth angle, zenith angle, detection angle and detection channels in 27t space by experiment.展开更多
Ground-based synthetic aperture radar(GB-SAR) has been successfully applied to the ground deformation monitoring.However, due to the short length of the GB-SAR platform, the scope of observation is largely limited. Th...Ground-based synthetic aperture radar(GB-SAR) has been successfully applied to the ground deformation monitoring.However, due to the short length of the GB-SAR platform, the scope of observation is largely limited. The practical applications drive us to make improvements on the conventional linear rail GB-SAR system in order to achieve larger field imaging. First, a turntable is utilized to support the rotational movement of the radar.Next, a series of high-squint scanning is performed with multiple squint angles. Further, the high squint modulation phase of the echo data is eliminated. Then, a new multi-angle imaging method is performed in the wave number domain to expand the field of view. Simulation and real experiments verify the effectiveness of this method.展开更多
Although important progresses have been already made in face detection,many false faces can be found in detection results and false detection rate is influenced by some factors,such as rotation and tilt of human face,...Although important progresses have been already made in face detection,many false faces can be found in detection results and false detection rate is influenced by some factors,such as rotation and tilt of human face,complicated background,illumination,scale,cloak and hairstyle.This paper proposes a new method called DP-Adaboost algorithm to detect multi-angle human face and improve the correct detection rate.An improved Adaboost algorithm with the fusion of frontal face classifier and a profile face classifier is used to detect the multi-angle face.An improved horizontal differential projection algorithm is put forward to remove those non-face images among the preliminary detection results from the improved Adaboost algorithm.Experiment results show that compared with the classical Adaboost algorithm with a frontal face classifier,the textual DP-Adaboost algorithm can reduce false rate significantly and improve hit rate in multi-angle face detection.展开更多
This study clarifies the seepage characteristics of complex fractured pressure-sensitive reservoirs,and addresses a common technological problem,that is the alteration of the permeability degree of the reservoir bed(k...This study clarifies the seepage characteristics of complex fractured pressure-sensitive reservoirs,and addresses a common technological problem,that is the alteration of the permeability degree of the reservoir bed(known to be responsible for changes in the direction and velocity of fluid flows between wells).On the basis of a new pressuresensitive equation that considers the fracture directional pressure-sensitive effect,an oil-gas-water three-phase seepage mathematical model is introduced,which can be applied to pressure-sensitive,full-tensor permeability,ultralow-permeability reservoirs with fracture-induced anisotropy.Accordingly,numerical simulations are conducted to explore the seepage laws for ultralow-permeability reservoirs.The results show that element patterns have the highest recovery percentage under a fracture angle of 45°.Accounting for the pressure-sensitive effect produces a decrease in the recovery percentage.Several patterns are considered:inverted five-seven-and nine-spot patterns and a cross-row well pattern.Finally,two strategies are introduced to counteract the rotation of the direction of the principal permeability due to the fracture directional pressure-sensitive effect.展开更多
Coherent change detection(CCD) is an effective method to detect subtle scene changes that occur between temporal synthetic aperture radar(SAR) observations. Most coherence estimators are obtained from a Hermitian prod...Coherent change detection(CCD) is an effective method to detect subtle scene changes that occur between temporal synthetic aperture radar(SAR) observations. Most coherence estimators are obtained from a Hermitian product based on local statistics. Increasing the number of samples in the local window can improve the estimation bias, but cause the loss of the estimated images spatial resolution. The limitations of these estimators lead to unclear contour of the disturbed region, and even the omission of fine change targets. In this paper, a CCD approach is proposed to detect fine scene changes from multi-temporal and multi-angle SAR image pairs. Multi-angle CCD estimator can improve the contrast between the change target and the background clutter by jointly accumulating singleangle alternative estimator results without further loss of image resolution. The sensitivity of detection performance to image quantity and angle interval is analyzed. Theoretical analysis and experimental results verify the performance of the proposed algorithm.展开更多
The establishment of a sound science and technology ethics governance system is an inevitable requirement for national modernization.Faced with the development of human gene technology and the chaos in research activi...The establishment of a sound science and technology ethics governance system is an inevitable requirement for national modernization.Faced with the development of human gene technology and the chaos in research activities,the ethical standards and legal positioning of human gene research activities urgently need to be clarified.The human rights ethics view has value inclusiveness and value fundamentality,and includes three levels of connotations:content dimension,relationship dimension,and obligation dimension.It should serve as the ethical standard for human gene research activities.Based on the provisions of China’s Constitution,the human rights ethics view on human gene research,as a constitutional ethics view,can elucidate different levels of rights content,such as human dignity,life and health,and research freedom.It also addresses the weighing of basic rights conflicts and the dual obligation subjects of public and private nature.Relying on the constitutional value embedding of the research ethics view to form ethical consensus,improving ethical review through framework legislation for human rights interests,and implementing ethical responsibility through the human rights-oriented interpretation of ethical legal norms are the three pathways to realizing the human rights ethics view on human gene research.展开更多
In recent years,with the rapid development of software systems,the continuous expansion of software scale and the increasing complexity of systems have led to the emergence of a growing number of software metrics.Defe...In recent years,with the rapid development of software systems,the continuous expansion of software scale and the increasing complexity of systems have led to the emergence of a growing number of software metrics.Defect prediction methods based on software metric elements highly rely on software metric data.However,redundant software metric data is not conducive to efficient defect prediction,posing severe challenges to current software defect prediction tasks.To address these issues,this paper focuses on the rational clustering of software metric data.Firstly,multiple software projects are evaluated to determine the preset number of clusters for software metrics,and various clustering methods are employed to cluster the metric elements.Subsequently,a co-occurrence matrix is designed to comprehensively quantify the number of times that metrics appear in the same category.Based on the comprehensive results,the software metric data are divided into two semantic views containing different metrics,thereby analyzing the semantic information behind the software metrics.On this basis,this paper also conducts an in-depth analysis of the impact of different semantic view of metrics on defect prediction results,as well as the performance of various classification models under these semantic views.Experiments show that the joint use of the two semantic views can significantly improve the performance of models in software defect prediction,providing a new understanding and approach at the semantic view level for defect prediction research based on software metrics.展开更多
The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations.However,traditional fuzzy clustering algorithms show...The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations.However,traditional fuzzy clustering algorithms show limitations with the inherent uncertainty and imprecision of such data,as they rely on a single-dimensional membership value.To overcome these limitations,we propose an auto-weighted multi-view neutrosophic fuzzy clustering(AW-MVNFC)algorithm.Our method leverages the neutrosophic framework,an extension of fuzzy sets,to explicitly model imprecision and ambiguity through three membership degrees.The core novelty of AWMVNFC lies in a hierarchical weighting strategy that adaptively learns the contributions of both individual data views and the importance of each feature within a view.Through a unified objective function,AW-MVNFC jointly optimizes the neutrosophic membership assignments,cluster centers,and the distributions of view and feature weights.Comprehensive experiments conducted on synthetic and real-world datasets demonstrate that our algorithm achieves more accurate and stable clustering than existing methods,demonstrating its effectiveness in handling the complexities of multi-view data.展开更多
Volumetric additive manufacturing(VAM) transforms traditional 2D light pattern projection into spatial light field energy superposition,maximizing the utilization of radiated light and allowing for ultra-fast,support-...Volumetric additive manufacturing(VAM) transforms traditional 2D light pattern projection into spatial light field energy superposition,maximizing the utilization of radiated light and allowing for ultra-fast,support-free printing,which has specific applications in fields such as life sciences and optics.However,traditional VAM processes require numerous projections and extensive computational preparation,limiting practical applications due to low projection efficiency and prolonged calculation times.In this study,we developed sparse-view irradiation processing VAM(SVIP-VAM),employing an optimized odd-even(OE) irradiation strategy inspired by sparse-view computed tomography.Theoretically,we demonstrated structural contour reconstruction feasibility with as few as 8 projections.Using this sparse-view approach,we achieved high-quality fabrication with only 15 projections,enhancing each projection efficiency by over 60 times and reducing projection set computational time by nearly 10-fold.Ultimately,this efficient sparse-view method significantly expands VAM applications into fields requiring rapid manufacturing,such as tissue engineering,medical implants,and aerospace manufacturing.展开更多
This paper is dedicated to constructing a theoretical framework for the identification and treatment of affective disorders in traditional Chinese medicine based on the“five-organ view”.Through in-depth analysis of ...This paper is dedicated to constructing a theoretical framework for the identification and treatment of affective disorders in traditional Chinese medicine based on the“five-organ view”.Through in-depth analysis of the theoretical connotation of the“five-organ concept”,we discussed the characteristics of the five-organ mechanism of affective-philosophical disorders in detail,systematically constructed a system of identification based on the association of the five organs,and proposed a comprehensive and holistic treatment strategy.The results of the study clearly show that the theoretical framework can provide systematic theoretical guidance for the clinical diagnosis and treatment of affective-philosophical disorders in Chinese medicine,help to improve the diagnostic and therapeutic effects of affective-philosophical disorders,and provide new ideas and methods for the theoretical development and clinical practice of affective-philosophical disorders in Chinese medicine,which is of important theoretical and practical significance,and can further promote the modernization of the development of affective-philosophical disorders in Chinese medicine.展开更多
Trends Traveler Issue6,2025 The Young Traveler The"Grand Tour,"a form of long distance travel that allows young adults to gain insights and broeden their view of the world,began to emerge around the world du...Trends Traveler Issue6,2025 The Young Traveler The"Grand Tour,"a form of long distance travel that allows young adults to gain insights and broeden their view of the world,began to emerge around the world during the Renaissance in Burope and the Tang Dynasty in China.展开更多
Urban environments offer a wealth of opportunities for residents to respite from their hectic life.Outdoor running or jogging becomes increasingly popular of an option.Impacts of urban environments on outdoor running,...Urban environments offer a wealth of opportunities for residents to respite from their hectic life.Outdoor running or jogging becomes increasingly popular of an option.Impacts of urban environments on outdoor running,despite some initial studies,remain underexplored.This study aims to establish an analytical framework that can holistically assess the urban environment on the healthy vitality of running.The proposed framework is applied to two modern Chinese cities,i.e.,Guangzhou and Shenzhen.We construct three interpretable random forest models to explore the non-linear relationship between environmental variables and running intensity(RI)through analyzing the runners'trajectories and integrating with multi-source urban big data(e.g.,street view imagery,remote sensing,and socio-economic data)across the built,natural,and social dimensions,The findings uncover that road density has the greatest impact on RI,and social variables(e.g.,population density and housing price)and natural variables(e.g.,slope and humidity)all make notable impact on outdoor running.Despite these findings,the impact of environmental variables likely change across different regions due to disparate regional construction and micro-environments,and those specific impacts as well as optimal thresholds also alter.Therefore,construction of healthy cities should take the whole urban environment into account and adapt to local conditions.This study provides a comprehensive evaluation on the influencing variables of healthy vitality and guides sustainable urban planning for creating running-friendly cities.展开更多
In order to enhance the efficiency of visual inspection and effectively carry out 3D visual coverage tasks,this paper focuses on the 3D view planning problem concerning the visual coverage of an airplane's surface...In order to enhance the efficiency of visual inspection and effectively carry out 3D visual coverage tasks,this paper focuses on the 3D view planning problem concerning the visual coverage of an airplane's surface using unmanned aerial vehicles(UAv).Our objective is to attain a sufficiently high coverage rate with the least number of viewpoints.The contributions of this work are enumerated as follows.Firstly,the 3D model of the target aircraft is spatially extended in accordance with the depth range of the camera mounted on the drone,thereby confining the sampling range of 3D viewpoints.Next,a candidate set of viewpoints is generated through random sampling and the probabilistic potential field technique.Subsequently,we propose a novel hyper-heuristic algorithm.In this algorithm,a genetic algorithm serves as a high-level heuristic strategy,in tandem with multiple low-level heuristic operators devised for combinatorial optimization.This not only augments the capacity to seek the global optimal solution but also expedites the convergence rate,aiming to ascertain the optimal subset of viewpoints.Moreover,we devise a new fitness function for appraising candidate solution vectors in the set covering problem(ScP),strengthening the evolutionary guidance for genetic algorithms.Eventually,experimental findings on the simulated and real airplanes corroborate the efficacy of the proposed method,i.e.,it markedly diminishes the requisite number of viewpoints and augments inspection efficiency.展开更多
基金supported by the National Natural Science Foundation of China(No.72071150)the Fujian Provincial Natural Science Foundation of China(Nos.2024J01903,2025J01393).
文摘For the past few years,the prevalence of cardiovascular disease has been showing a year-on-year increase,with a death rate of 2/5.Coronary heart disease(CHD)rates have increased 41%since 1990,which is the number one disease endangering human health in the world today.The risk indicators of CHD are complicated,so selecting effective methods to screen the risk characteristics can make the risk predictionmore efficient.In this paper,we present a comprehensive analysis ofCHDrisk indicators fromboth data and algorithmic levels,propose a method for CHDrisk indicator identification based on multi-angle integrated measurements and Sequential Backward Selection(SBS),and then build a risk prediction model.In the multi-angle integrated measurements stage,mRMR(Maximum Relevance Minimum Redundancy)is selected from the angle of feature correlation and redundancy of the dataset itself,SHAPRF(SHapley Additive exPlanations-Random Forest)is selected from the angle of interpretation of each feature to the results,and ARFS-RF(Algorithmic Randomness Feature Selection Random Forest)is selected from the angle of statistical interpretation of classification algorithm to measure the degree of feature importance.In the SBS stage,the features with low scores are deleted successively,and the accuracy of LightGBM(Light Gradient Boosting Machine)model is used as the evaluation index to select the final feature subset.This new risk assessment method is used to identify important factors affecting CHD,and the CHD dataset from the Kaggle website is used as the study subject.Finally,11 features are retained to construct a risk assessment indicator system for CHD.Using the LightGBM classifier as the core evaluationmetric,ourmethod achieved an accuracy of 0.8656 on the Kaggle CHD dataset(4238 samples,16 initial features),outperforming individual feature selection methods(mRMR,SHAP-RF,ARFS-RF)in both accuracy and feature reduction.This demonstrates the novelty and effectiveness of our multi-angle integrated measurement approach combined with SBS in building a concise yet highly predictive CHD risk model.
基金financially supported by the Fundamental Research Funds for the Central Universities,Sun Yat-sen University(Grant No.24qnpy041)the Science and Technology Innovation Key R&D Program of Chongqing(Grant No.CSTB2023TIAD-STX0030)。
文摘The electric vertical takeoff and landing(e VTOL)aircraft shows great potential for rapid military personnel deployment on the battlefield.However,its susceptibility to control loss,complex crashes,and extremely limited bottom energy-absorbing space demands higher comprehensive crashworthiness of its subfloor thin-walled structures.This study investigated the energy absorption capacity of novel concave polygonal carbon fiber reinforced plastics(CFRP)tubes under multi-angle collisions.Quasistatic compression experiments and finite element simulations were conducted to assess the failure mode and energy absorption.The influences of cross-section shapes,loading conditions,and geometry parameters on crashworthiness metrics were further analyzed.The results revealed that,under the similar weight,concave polygonal tubes exhibited superior energy absorption under axial loads compared to regular polygonal and circular tubes,attributed to the increased number of axial splits.However,both regular and concave polygonal tubes,particularly the latter,demonstrated reduced oblique energy absorption compared to traditional square tubes with the increasing ratio of SEA value decreased from 20%-16%.Notably,this reduction in energy absorption can be compensated for by the implementation of inward and outward crusher plugs,and with them,the concave polygonal tubes demonstrated outstanding overall crashworthiness performance under multiple loading conditions.This concave cross-sectional design methods could serve as a guidance for the development of the eVTOL subfloor.
基金supported by grants from the National High Technology Research and Development Program of China(2013AA102403)the National Natural Science Foundation of China (30921091, 31200274)+1 种基金the Program for New Century Excellent Talents in University (NCET-10-0386)the Fundamental Research Funds for the Central Universities (2013PY034, 2014BQ010)
文摘Plant phenomics has the potential to accelerate progress in understanding gene functions and environmental responses. Progress has been made in automating high-throughput plant phenotyping. However, few studies have investigated automated rice panicle counting. This paper describes a novel method for automatically and nonintrusively determining rice panicle numbers during the full heading stage by analyzing color images of rice plants taken from multiple angles. Pot-grown rice plants were transferred via an industrial conveyer to an imaging chamber. Color images from different angles were automatically acquired as a turntable rotated the plant. The images were then analyzed and the panicle number of each plant was determined. The image analysis pipeline consisted of extracting the i2 plane from the original color image, segmenting the image, discriminating the panicles from the rest of the plant using an artificial neural network, and calculating the panicle number in the current image. The panicle number of the plant was taken as the maximum of the panicle numbers extracted from all 12 multi-angle images. A total of 105 rice plants during the full heading stage were examined to test the performance of the method. The mean absolute error of the manual and automatic count was 0.5, with 95.3% of the plants yielding absolute errors within ± 1. The method will be useful for evaluating rice panicles and will serve as an important supplementary method for high-throughput rice phenotyping.
基金Project KZCX3-S W-338-1 supported by Science and Technology Innovation Foundation of Chinese Academy of Science and 49771057 supported by theNational Natural Science Foundation of China
文摘The traditional remote sensing mainly detects the ground vertically to obtain the 2D information but it is hard to get adequate parameters for the quantitative remote sensing to invert land features. The multi-angle observation can get more detailed and reliable 3D structural parameters of targets, so it makes the quantitative remote sensing applicable. During the process of reflecting, scattering and transmitting the electromagnetic wave, minerals and rocks could reveal the polarized features related to the nature of themselves. Therefore, it has become a new approach of quantitative remote sensing to detect multi-angle polarized information of minerals and rocks. In respect that the polarized reflectance always goes with the bidirectional one, we can obtain the 3D spatial distribution of targets by a polarized means together with detecting its bi-directional reflectance. From the perspective of multi-angle polarized remote sensing mechanism, the quantitative relationship between multi-angle polarized reflectance and the BRDF is studied in this paper. And it is testified that the bi-directional reflectance, polarized reflectance of 45° and the mean value of polarized reflectance are equal to that of the corresponding azimuth angle, zenith angle, detection angle and detection channels in 27t space by experiment.
基金supported by the National Natural Science Foundation of China(61801007)the Beijing Natural Science Foundation(4194075)。
文摘Ground-based synthetic aperture radar(GB-SAR) has been successfully applied to the ground deformation monitoring.However, due to the short length of the GB-SAR platform, the scope of observation is largely limited. The practical applications drive us to make improvements on the conventional linear rail GB-SAR system in order to achieve larger field imaging. First, a turntable is utilized to support the rotational movement of the radar.Next, a series of high-squint scanning is performed with multiple squint angles. Further, the high squint modulation phase of the echo data is eliminated. Then, a new multi-angle imaging method is performed in the wave number domain to expand the field of view. Simulation and real experiments verify the effectiveness of this method.
文摘Although important progresses have been already made in face detection,many false faces can be found in detection results and false detection rate is influenced by some factors,such as rotation and tilt of human face,complicated background,illumination,scale,cloak and hairstyle.This paper proposes a new method called DP-Adaboost algorithm to detect multi-angle human face and improve the correct detection rate.An improved Adaboost algorithm with the fusion of frontal face classifier and a profile face classifier is used to detect the multi-angle face.An improved horizontal differential projection algorithm is put forward to remove those non-face images among the preliminary detection results from the improved Adaboost algorithm.Experiment results show that compared with the classical Adaboost algorithm with a frontal face classifier,the textual DP-Adaboost algorithm can reduce false rate significantly and improve hit rate in multi-angle face detection.
基金This work is financially supported by the National Natural Science Foundation Project(No.51374222)National Major Project(No.2017ZX05032004-002)+2 种基金the National Key Basic Research&Development Program(No.2015CB250905)CNPC’s Major Scientific and Technological Project(No.2017E-0405)SINOPEC Major Scientific Research Project(No.P18049-1).
文摘This study clarifies the seepage characteristics of complex fractured pressure-sensitive reservoirs,and addresses a common technological problem,that is the alteration of the permeability degree of the reservoir bed(known to be responsible for changes in the direction and velocity of fluid flows between wells).On the basis of a new pressuresensitive equation that considers the fracture directional pressure-sensitive effect,an oil-gas-water three-phase seepage mathematical model is introduced,which can be applied to pressure-sensitive,full-tensor permeability,ultralow-permeability reservoirs with fracture-induced anisotropy.Accordingly,numerical simulations are conducted to explore the seepage laws for ultralow-permeability reservoirs.The results show that element patterns have the highest recovery percentage under a fracture angle of 45°.Accounting for the pressure-sensitive effect produces a decrease in the recovery percentage.Several patterns are considered:inverted five-seven-and nine-spot patterns and a cross-row well pattern.Finally,two strategies are introduced to counteract the rotation of the direction of the principal permeability due to the fracture directional pressure-sensitive effect.
文摘Coherent change detection(CCD) is an effective method to detect subtle scene changes that occur between temporal synthetic aperture radar(SAR) observations. Most coherence estimators are obtained from a Hermitian product based on local statistics. Increasing the number of samples in the local window can improve the estimation bias, but cause the loss of the estimated images spatial resolution. The limitations of these estimators lead to unclear contour of the disturbed region, and even the omission of fine change targets. In this paper, a CCD approach is proposed to detect fine scene changes from multi-temporal and multi-angle SAR image pairs. Multi-angle CCD estimator can improve the contrast between the change target and the background clutter by jointly accumulating singleangle alternative estimator results without further loss of image resolution. The sensitivity of detection performance to image quantity and angle interval is analyzed. Theoretical analysis and experimental results verify the performance of the proposed algorithm.
基金This paper is an interim result of“Constitutional Boundaries of the Application of Human Gene Editing Technology,”a Youth Project of the National Social Science Fund of China(Project Approval Number 23CFX040)supported by the“National Funded Programs for Postdoctoral Researchers”(GZC20230937).
文摘The establishment of a sound science and technology ethics governance system is an inevitable requirement for national modernization.Faced with the development of human gene technology and the chaos in research activities,the ethical standards and legal positioning of human gene research activities urgently need to be clarified.The human rights ethics view has value inclusiveness and value fundamentality,and includes three levels of connotations:content dimension,relationship dimension,and obligation dimension.It should serve as the ethical standard for human gene research activities.Based on the provisions of China’s Constitution,the human rights ethics view on human gene research,as a constitutional ethics view,can elucidate different levels of rights content,such as human dignity,life and health,and research freedom.It also addresses the weighing of basic rights conflicts and the dual obligation subjects of public and private nature.Relying on the constitutional value embedding of the research ethics view to form ethical consensus,improving ethical review through framework legislation for human rights interests,and implementing ethical responsibility through the human rights-oriented interpretation of ethical legal norms are the three pathways to realizing the human rights ethics view on human gene research.
基金supported by the CCF-NSFOCUS‘Kunpeng’Research Fund(CCF-NSFOCUS2024012).
文摘In recent years,with the rapid development of software systems,the continuous expansion of software scale and the increasing complexity of systems have led to the emergence of a growing number of software metrics.Defect prediction methods based on software metric elements highly rely on software metric data.However,redundant software metric data is not conducive to efficient defect prediction,posing severe challenges to current software defect prediction tasks.To address these issues,this paper focuses on the rational clustering of software metric data.Firstly,multiple software projects are evaluated to determine the preset number of clusters for software metrics,and various clustering methods are employed to cluster the metric elements.Subsequently,a co-occurrence matrix is designed to comprehensively quantify the number of times that metrics appear in the same category.Based on the comprehensive results,the software metric data are divided into two semantic views containing different metrics,thereby analyzing the semantic information behind the software metrics.On this basis,this paper also conducts an in-depth analysis of the impact of different semantic view of metrics on defect prediction results,as well as the performance of various classification models under these semantic views.Experiments show that the joint use of the two semantic views can significantly improve the performance of models in software defect prediction,providing a new understanding and approach at the semantic view level for defect prediction research based on software metrics.
文摘The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations.However,traditional fuzzy clustering algorithms show limitations with the inherent uncertainty and imprecision of such data,as they rely on a single-dimensional membership value.To overcome these limitations,we propose an auto-weighted multi-view neutrosophic fuzzy clustering(AW-MVNFC)algorithm.Our method leverages the neutrosophic framework,an extension of fuzzy sets,to explicitly model imprecision and ambiguity through three membership degrees.The core novelty of AWMVNFC lies in a hierarchical weighting strategy that adaptively learns the contributions of both individual data views and the importance of each feature within a view.Through a unified objective function,AW-MVNFC jointly optimizes the neutrosophic membership assignments,cluster centers,and the distributions of view and feature weights.Comprehensive experiments conducted on synthetic and real-world datasets demonstrate that our algorithm achieves more accurate and stable clustering than existing methods,demonstrating its effectiveness in handling the complexities of multi-view data.
基金supported financially by the Beijing Municipal Natural Science Foundation (L232109)National Natural Science Foundation of China (22073003)Fundamental Research Funds for the Central Universities (YWF-22-K-101)。
文摘Volumetric additive manufacturing(VAM) transforms traditional 2D light pattern projection into spatial light field energy superposition,maximizing the utilization of radiated light and allowing for ultra-fast,support-free printing,which has specific applications in fields such as life sciences and optics.However,traditional VAM processes require numerous projections and extensive computational preparation,limiting practical applications due to low projection efficiency and prolonged calculation times.In this study,we developed sparse-view irradiation processing VAM(SVIP-VAM),employing an optimized odd-even(OE) irradiation strategy inspired by sparse-view computed tomography.Theoretically,we demonstrated structural contour reconstruction feasibility with as few as 8 projections.Using this sparse-view approach,we achieved high-quality fabrication with only 15 projections,enhancing each projection efficiency by over 60 times and reducing projection set computational time by nearly 10-fold.Ultimately,this efficient sparse-view method significantly expands VAM applications into fields requiring rapid manufacturing,such as tissue engineering,medical implants,and aerospace manufacturing.
文摘This paper is dedicated to constructing a theoretical framework for the identification and treatment of affective disorders in traditional Chinese medicine based on the“five-organ view”.Through in-depth analysis of the theoretical connotation of the“five-organ concept”,we discussed the characteristics of the five-organ mechanism of affective-philosophical disorders in detail,systematically constructed a system of identification based on the association of the five organs,and proposed a comprehensive and holistic treatment strategy.The results of the study clearly show that the theoretical framework can provide systematic theoretical guidance for the clinical diagnosis and treatment of affective-philosophical disorders in Chinese medicine,help to improve the diagnostic and therapeutic effects of affective-philosophical disorders,and provide new ideas and methods for the theoretical development and clinical practice of affective-philosophical disorders in Chinese medicine,which is of important theoretical and practical significance,and can further promote the modernization of the development of affective-philosophical disorders in Chinese medicine.
文摘Trends Traveler Issue6,2025 The Young Traveler The"Grand Tour,"a form of long distance travel that allows young adults to gain insights and broeden their view of the world,began to emerge around the world during the Renaissance in Burope and the Tang Dynasty in China.
基金National Natural Science Foundation of China,No.42171455The Hong Kong RGC Research Impact Fund,No.R5011-23The Hong Kong General Research Fund,No.15204121。
文摘Urban environments offer a wealth of opportunities for residents to respite from their hectic life.Outdoor running or jogging becomes increasingly popular of an option.Impacts of urban environments on outdoor running,despite some initial studies,remain underexplored.This study aims to establish an analytical framework that can holistically assess the urban environment on the healthy vitality of running.The proposed framework is applied to two modern Chinese cities,i.e.,Guangzhou and Shenzhen.We construct three interpretable random forest models to explore the non-linear relationship between environmental variables and running intensity(RI)through analyzing the runners'trajectories and integrating with multi-source urban big data(e.g.,street view imagery,remote sensing,and socio-economic data)across the built,natural,and social dimensions,The findings uncover that road density has the greatest impact on RI,and social variables(e.g.,population density and housing price)and natural variables(e.g.,slope and humidity)all make notable impact on outdoor running.Despite these findings,the impact of environmental variables likely change across different regions due to disparate regional construction and micro-environments,and those specific impacts as well as optimal thresholds also alter.Therefore,construction of healthy cities should take the whole urban environment into account and adapt to local conditions.This study provides a comprehensive evaluation on the influencing variables of healthy vitality and guides sustainable urban planning for creating running-friendly cities.
基金supported by the Trade Union Employee Innovation Foundation of China(2022270024).
文摘In order to enhance the efficiency of visual inspection and effectively carry out 3D visual coverage tasks,this paper focuses on the 3D view planning problem concerning the visual coverage of an airplane's surface using unmanned aerial vehicles(UAv).Our objective is to attain a sufficiently high coverage rate with the least number of viewpoints.The contributions of this work are enumerated as follows.Firstly,the 3D model of the target aircraft is spatially extended in accordance with the depth range of the camera mounted on the drone,thereby confining the sampling range of 3D viewpoints.Next,a candidate set of viewpoints is generated through random sampling and the probabilistic potential field technique.Subsequently,we propose a novel hyper-heuristic algorithm.In this algorithm,a genetic algorithm serves as a high-level heuristic strategy,in tandem with multiple low-level heuristic operators devised for combinatorial optimization.This not only augments the capacity to seek the global optimal solution but also expedites the convergence rate,aiming to ascertain the optimal subset of viewpoints.Moreover,we devise a new fitness function for appraising candidate solution vectors in the set covering problem(ScP),strengthening the evolutionary guidance for genetic algorithms.Eventually,experimental findings on the simulated and real airplanes corroborate the efficacy of the proposed method,i.e.,it markedly diminishes the requisite number of viewpoints and augments inspection efficiency.
