In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The e...In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.展开更多
Neurons are highly polarized cells with axons reaching over a meter long in adult humans.To survive and maintain their proper function,neurons depend on specific mechanisms that regulate spatiotemporal signaling and m...Neurons are highly polarized cells with axons reaching over a meter long in adult humans.To survive and maintain their proper function,neurons depend on specific mechanisms that regulate spatiotemporal signaling and metabolic events,which need to be carried out at the right place,time,and intensity.Such mechanisms include axonal transport,local synthesis,and liquid-liquid phase separations.Alterations and malfunctions in these processes are correlated to neurodegenerative diseases such as amyotrophic lateral sclerosis(ALS).展开更多
Vehicle re-identification(ReID)aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera scenario.It has gradually become a core technology of int...Vehicle re-identification(ReID)aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera scenario.It has gradually become a core technology of intelligent transportation system.Most existing vehicle re-identification models adopt the joint learning of global and local features.However,they directly use the extracted global features,resulting in insufficient feature expression.Moreover,local features are primarily obtained through advanced annotation and complex attention mechanisms,which require additional costs.To solve this issue,a multi-feature learning model with enhanced local attention for vehicle re-identification(MFELA)is proposed in this paper.The model consists of global and local branches.The global branch utilizes both middle and highlevel semantic features of ResNet50 to enhance the global representation capability.In addition,multi-scale pooling operations are used to obtain multiscale information.While the local branch utilizes the proposed Region Batch Dropblock(RBD),which encourages the model to learn discriminative features for different local regions and simultaneously drops corresponding same areas randomly in a batch during training to enhance the attention to local regions.Then features from both branches are combined to provide a more comprehensive and distinctive feature representation.Extensive experiments on VeRi-776 and VehicleID datasets prove that our method has excellent performance.展开更多
By integrating the traditional power grid with information and communication technology, smart grid achieves dependable, efficient, and flexible grid data processing. The smart meters deployed on the user side of the ...By integrating the traditional power grid with information and communication technology, smart grid achieves dependable, efficient, and flexible grid data processing. The smart meters deployed on the user side of the smart grid collect the users' power usage data on a regular basis and upload it to the control center to complete the smart grid data acquisition. The control center can evaluate the supply and demand of the power grid through aggregated data from users and then dynamically adjust the power supply and price, etc. However, since the grid data collected from users may disclose the user's electricity usage habits and daily activities, privacy concern has become a critical issue in smart grid data aggregation. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring a trusted third party.展开更多
For more than a decade numerous evidence has been reported on the mechanisms of toxicity of α-synuclein（αS） oligomers and aggregates in α-synucleinopathies.These species were thought to form freely in the cytopla...For more than a decade numerous evidence has been reported on the mechanisms of toxicity of α-synuclein（αS） oligomers and aggregates in α-synucleinopathies.These species were thought to form freely in the cytoplasm but recent reports of αS multimer conformations when bound to synaptic vesicles in physiological conditions,have raised the question about where αS aggregation initiates.In this review we focus on recent literature regarding the impact on membrane binding and subcellular localization of αS toxic species to understand how regular cellular function of αS contributes to pathology.Notably αS has been reported to mainly associate with specific membranes in neurons such as those of synaptic vesicles,ER/Golgi and the mitochondria,while toxic species of αS have been shown to inhibit,among others,neurotransmission,protein trafficking and mitochondrial function.Strategies interfering with αS membrane binding have shown to improve αS-driven toxicity in worms and in mice.Thus,a selective membrane binding that would result in a specific subcellular localization could be the key to understand how aggregation and pathology evolves,pointing out to αS functions that are primarily affected before onset of irreversible damage.展开更多
To tackle the problem of simultaneous localization and mapping(SLAM) in dynamic environments, a novel algorithm using landscape theory of aggregation is presented. By exploiting the coherent explanation how actors for...To tackle the problem of simultaneous localization and mapping(SLAM) in dynamic environments, a novel algorithm using landscape theory of aggregation is presented. By exploiting the coherent explanation how actors form alignments in a game provided by the landscape theory of aggregation, the algorithm is able to explicitly deal with the ever-changing relationship between the static objects and the moving objects without any prior models of the moving objects. The effectiveness of the method has been validated by experiments in two representative dynamic environments: the campus road and the urban road.展开更多
A hierarchical particle filter（HPF） framework based on multi-feature fusion is proposed.The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a ...A hierarchical particle filter（HPF） framework based on multi-feature fusion is proposed.The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a complicated environment.In this approach,the Harris algorithm is introduced to detect the corner points of the object,and the corner matching algorithm based on singular value decomposition is used to compute the firstorder weights and make particles centralize in the high likelihood area.Then the local binary pattern（LBP） operator is used to build the observation model of the target based on the color and texture features,by which the second-order weights of particles and the accurate location of the target can be obtained.Moreover,a backstepping controller is proposed to complete the whole tracking system.Simulations and experiments are carried out,and the results show that the HPF algorithm with the backstepping controller achieves stable and accurate tracking with good robustness in complex environments.展开更多
Medical image fusion plays an important role in clinical applications such as image-guided surgery, image-guided radiotherapy, noninvasive diagnosis, and treatment planning. In order to retain useful information and g...Medical image fusion plays an important role in clinical applications such as image-guided surgery, image-guided radiotherapy, noninvasive diagnosis, and treatment planning. In order to retain useful information and get more reliable results, a novel medical image fusion algorithm based on pulse coupled neural networks (PCNN) and multi-feature fuzzy clustering is proposed, which makes use of the multi-feature of image and combines the advantages of the local entropy and variance of local entropy based PCNN. The results of experiments indicate that the proposed image fusion method can better preserve the image details and robustness and significantly improve the image visual effect than the other fusion methods with less information distortion.展开更多
With time concrete / reinforced concrete has become the popular material for construction. Modern industry utilizes this material a lot and has produced various beautiful, eye catching and amazing structures. Due to m...With time concrete / reinforced concrete has become the popular material for construction. Modern industry utilizes this material a lot and has produced various beautiful, eye catching and amazing structures. Due to modern requirements for living and developed construction industries, the old buildings (usually constructed with brick masonry) are demolished and are replaced with new modern buildings. Demolition of buildings results in waste materials which can create waste related problems and environmental issues. By using recycled aggregates weight of concrete can also be reduced, which can also solve problems related to self-weight of concrete. In this paper attempt has been made to use local used bricks from vicinity of Nawabshah, Pakistan, as coarse aggregate. Concrete cubes made with local recycled bricks are cast and tested for overall weight of concrete, moisture content, dynamic modulus of elasticity and compressive strength (nondestructive and destructive methods). The results showed that concrete derived from recycled aggregates attained lower strength than regular concrete. More detailed elaborated work is recommended with different mix ratios and different proportions recycled aggregates for better conclusions.展开更多
Diffusion-limited aggregation （DLA） assumes that particles perform pure random walk at a finite tem- perature and aggregate when they come close enough and stick together. Although it is well known that DLA in two d...Diffusion-limited aggregation （DLA） assumes that particles perform pure random walk at a finite tem- perature and aggregate when they come close enough and stick together. Although it is well known that DLA in two dimensions results in a ramified fractal structure, how the particle shape influences the formed morphology is still un- clear. In this work, we perform the off-lattice two-dimensional DLA simulations with different particle shapes of triangle, quadrangle, pentagon, hexagon, and octagon, respectively, and compare with the results for circular particles. Our results indicate that different particle shapes only change the local structure, but have no effects on the global structure of the formed fractal duster. The local compactness decreases as the number of polygon edges increases.展开更多
The developments in the field of construction raise the need for concrete with less weight. This is beneficial for different applications starting from the less load applied to foundations and soil till the reduction ...The developments in the field of construction raise the need for concrete with less weight. This is beneficial for different applications starting from the less load applied to foundations and soil till the reduction of carnage capacity required for lifting precast units. In this paper, the production of light weight concrete from light local weight aggregate is investigated. Three candidate materials are used: crushed fired brick, vermiculite and light exfoliated clay aggregate (LECA). The first is available as the by-product of brick industry and the later two types are produced locally for different applications. Nine concrete mixes were made with same proportions and different aggregate materials. Physical and mechanical properties were measured for concrete in fresh and hardened states. Among these measured ones are unit weight, slump, compressive and tensile strength, and impact resistance. Also, the performance under elevated temperature was measured. Results show that reduction of unit weight up to 45%, of traditional concrete, can be achieved with 50% reduction in compressive strength. This makes it possible to get structural light weight concrete with compressive strength of 130 kg/cm2. Light weight concrete proved also to be more impact and fire resistant. However, as expected, it needs separate calibration curves for non-destructive evaluation. Following this experimental effort, the Artificial Neural Network (ANN) technique was applied for simulating and predicting the physical and mechanical properties of light weight aggregate concrete in fresh and hardened states. The current paper introduced the (ANN) technique to investigate the effect of light local weight aggregate on the performance of the produced light weight concrete. The results of this study showed that the ANN method with less effort was very efficiently capable of simulating the effect of different aggregate materials on the performance of light weight concrete.展开更多
Stereo matching is a fundamental and crucial problem in computer vision. In the last decades, many researchers have been working on it and made great progress. Generally stereo algorithms can be classified into local ...Stereo matching is a fundamental and crucial problem in computer vision. In the last decades, many researchers have been working on it and made great progress. Generally stereo algorithms can be classified into local methods and global methods. In this paper, the challenges of stereo matching are first introduced, and then we focus on local approaches which have simpler structures and higher efficiency than global ones. Local algorithms generally perform four steps： cost computation, cost aggregation, disparity computation and disparity refinement. Every step is deeply investigated, and most work focuses on cost aggregation. We studied most of the classical local methods and divide them into several classes. The classification well illustrates the development history of local stereo correspondence and shows the essence of local matching along with its important and difficult points. At the end we give the future development trend of local methods.展开更多
Background Despite the recent progress in 3D point cloud processing using deep convolutional neural networks,the inability to extract local features remains a challenging problem.In addition,existing methods consider ...Background Despite the recent progress in 3D point cloud processing using deep convolutional neural networks,the inability to extract local features remains a challenging problem.In addition,existing methods consider only the spatial domain in the feature extraction process.Methods In this paper,we propose a spectral and spatial aggregation convolutional network(S^(2)ANet),which combines spectral and spatial features for point cloud processing.First,we calculate the local frequency of the point cloud in the spectral domain.Then,we use the local frequency to group points and provide a spectral aggregation convolution module to extract the features of the points grouped by the local frequency.We simultaneously extract the local features in the spatial domain to supplement the final features.Results S^(2)ANet was applied in several point cloud analysis tasks;it achieved stateof-the-art classification accuracies of 93.8%,88.0%,and 83.1%on the ModelNet40,ShapeNetCore,and ScanObjectNN datasets,respectively.For indoor scene segmentation,training and testing were performed on the S3DIS dataset,and the mean intersection over union was 62.4%.Conclusions The proposed S^(2)ANet can effectively capture the local geometric information of point clouds,thereby improving accuracy on various tasks.展开更多
This study presents the development of high strength concrete (HSC) that has been made more sustainable by using both local materials from central Texas and recycled concrete aggregate (RCA), which has also been obtai...This study presents the development of high strength concrete (HSC) that has been made more sustainable by using both local materials from central Texas and recycled concrete aggregate (RCA), which has also been obtained locally. The developed mixtures were proportioned with local constituents to increase the sustainable impact of the material by reducing emissions due to shipping as well as to make HSC more affordable to a wider variety of applications. The specific constituents were: limestone, dolomite, manufactured sand (limestone), locally available Type I/II cement, silica fume, and recycled concrete aggregate, which was obtained from a local recycler which obtains their product from local demolition. Multiple variables were investigated, such as the aggregate type and size, concrete age (7, 14, and 28-days), the curing regimen, and the water-to-cement ratio (w/c) to optimize a HSC mixture that used local materials. This systematic development revealed that heat curing the specimens in a water bath at 50℃ (122oF) after demolding and then dry curing at 200℃ (392oF) two days before testing with a w/c of 0.28 at 28-days produced the highest compressive strengths. Once an optimum HSC mixture was identified a partial replacement of the coarse aggregate with RCA was completed at 10%, 20%, and 30%. The results showed a loss in compressive strength with an increase in RCA replacement percentages, with the highest strength being approximately 93.0 MPa (13,484 psi) at 28-days for the 10% RCA replacement. The lowest strength obtained from an RCA-HSC mixture was approximately 72.9 (MPa) (10,576 psi) at 7-days. The compressive strengths obtained from the HSC mixtures containing RCA developed in this study are comparable to HSC strengths presented in the literature. Developing this innovative material with local materials and RCA ultimately produces a novel sustainable construction material, reduces the costs, and produces mechanical performance similar to prepackaged, commercially, available construction building materials.展开更多
针对当前两阶段的点云目标检测算法PointRCNN:3D object proposal generation and detection from point cloud在点云降采样阶段时间开销大以及低效性的问题,本研究基于PointRCNN网络提出RandLA-RCNN(random sampling and an effectivel...针对当前两阶段的点云目标检测算法PointRCNN:3D object proposal generation and detection from point cloud在点云降采样阶段时间开销大以及低效性的问题,本研究基于PointRCNN网络提出RandLA-RCNN(random sampling and an effectivelocal feature aggregator with region-based convolu-tional neural networks)架构。首先,利用随机采样方法在处理庞大点云数据时的高效性,对大场景点云数据进行下采样;然后,通过对输入点云的每个近邻点的空间位置编码,有效提高从每个点的邻域提取局部特征的能力,并利用基于注意力机制的池化规则聚合局部特征向量,获取全局特征;最后使用由多个局部空间编码单元和注意力池化单元叠加形成的扩展残差模块,来进一步增强每个点的全局特征,避免关键点信息丢失。实验结果表明,该检测算法在保留PointRCNN网络对3D目标的检测优势的同时,相比PointRCNN检测速度提升近两倍,达到16 f/s的推理速度。展开更多
基金supported by the National Natural Science Foundation of China(Nos.12072027,62103052,61603346 and 62103379)the Henan Key Laboratory of General Aviation Technology,China(No.ZHKF-230201)+3 种基金the Funding for the Open Research Project of the Rotor Aerodynamics Key Laboratory,China(No.RAL20200101)the Key Research and Development Program of Henan Province,China(Nos.241111222000 and 241111222900)the Key Science and Technology Program of Henan Province,China(No.232102220067)the Scholarship Funding from the China Scholarship Council(No.202206030079).
文摘In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.
文摘Neurons are highly polarized cells with axons reaching over a meter long in adult humans.To survive and maintain their proper function,neurons depend on specific mechanisms that regulate spatiotemporal signaling and metabolic events,which need to be carried out at the right place,time,and intensity.Such mechanisms include axonal transport,local synthesis,and liquid-liquid phase separations.Alterations and malfunctions in these processes are correlated to neurodegenerative diseases such as amyotrophic lateral sclerosis(ALS).
基金This work was supported,in part,by the National Nature Science Foundation of China under Grant Numbers 61502240,61502096,61304205,61773219in part,by the Natural Science Foundation of Jiangsu Province under grant numbers BK20201136,BK20191401+1 种基金in part,by the Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant Numbers SJCX21_0363in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund.
文摘Vehicle re-identification(ReID)aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera scenario.It has gradually become a core technology of intelligent transportation system.Most existing vehicle re-identification models adopt the joint learning of global and local features.However,they directly use the extracted global features,resulting in insufficient feature expression.Moreover,local features are primarily obtained through advanced annotation and complex attention mechanisms,which require additional costs.To solve this issue,a multi-feature learning model with enhanced local attention for vehicle re-identification(MFELA)is proposed in this paper.The model consists of global and local branches.The global branch utilizes both middle and highlevel semantic features of ResNet50 to enhance the global representation capability.In addition,multi-scale pooling operations are used to obtain multiscale information.While the local branch utilizes the proposed Region Batch Dropblock(RBD),which encourages the model to learn discriminative features for different local regions and simultaneously drops corresponding same areas randomly in a batch during training to enhance the attention to local regions.Then features from both branches are combined to provide a more comprehensive and distinctive feature representation.Extensive experiments on VeRi-776 and VehicleID datasets prove that our method has excellent performance.
基金supported in part by the National Natural Science Foundation of China under Grant No.61972371Youth Innovation Promotion Association of Chinese Academy of Sciences(CAS)under Grant No.Y202093.
文摘By integrating the traditional power grid with information and communication technology, smart grid achieves dependable, efficient, and flexible grid data processing. The smart meters deployed on the user side of the smart grid collect the users' power usage data on a regular basis and upload it to the control center to complete the smart grid data acquisition. The control center can evaluate the supply and demand of the power grid through aggregated data from users and then dynamically adjust the power supply and price, etc. However, since the grid data collected from users may disclose the user's electricity usage habits and daily activities, privacy concern has become a critical issue in smart grid data aggregation. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring a trusted third party.
基金supported by the Italian Ministry of University and Research(MIUR) through the Career Reintegration grant scheme(RLM Program for Young Researcher)and from Scuola Normale Superiore
文摘For more than a decade numerous evidence has been reported on the mechanisms of toxicity of α-synuclein（αS） oligomers and aggregates in α-synucleinopathies.These species were thought to form freely in the cytoplasm but recent reports of αS multimer conformations when bound to synaptic vesicles in physiological conditions,have raised the question about where αS aggregation initiates.In this review we focus on recent literature regarding the impact on membrane binding and subcellular localization of αS toxic species to understand how regular cellular function of αS contributes to pathology.Notably αS has been reported to mainly associate with specific membranes in neurons such as those of synaptic vesicles,ER/Golgi and the mitochondria,while toxic species of αS have been shown to inhibit,among others,neurotransmission,protein trafficking and mitochondrial function.Strategies interfering with αS membrane binding have shown to improve αS-driven toxicity in worms and in mice.Thus,a selective membrane binding that would result in a specific subcellular localization could be the key to understand how aggregation and pathology evolves,pointing out to αS functions that are primarily affected before onset of irreversible damage.
基金Project(XK100070532)supported by Beijing Education Committee Cooperation Building Foundation,China
文摘To tackle the problem of simultaneous localization and mapping(SLAM) in dynamic environments, a novel algorithm using landscape theory of aggregation is presented. By exploiting the coherent explanation how actors form alignments in a game provided by the landscape theory of aggregation, the algorithm is able to explicitly deal with the ever-changing relationship between the static objects and the moving objects without any prior models of the moving objects. The effectiveness of the method has been validated by experiments in two representative dynamic environments: the campus road and the urban road.
基金supported by the National Natural Science Foundation of China(61304097)the Projects of Major International(Regional)Joint Research Program NSFC(61120106010)the Foundation for Innovation Research Groups of the National National Natural Science Foundation of China(61321002)
文摘A hierarchical particle filter（HPF） framework based on multi-feature fusion is proposed.The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a complicated environment.In this approach,the Harris algorithm is introduced to detect the corner points of the object,and the corner matching algorithm based on singular value decomposition is used to compute the firstorder weights and make particles centralize in the high likelihood area.Then the local binary pattern（LBP） operator is used to build the observation model of the target based on the color and texture features,by which the second-order weights of particles and the accurate location of the target can be obtained.Moreover,a backstepping controller is proposed to complete the whole tracking system.Simulations and experiments are carried out,and the results show that the HPF algorithm with the backstepping controller achieves stable and accurate tracking with good robustness in complex environments.
文摘Medical image fusion plays an important role in clinical applications such as image-guided surgery, image-guided radiotherapy, noninvasive diagnosis, and treatment planning. In order to retain useful information and get more reliable results, a novel medical image fusion algorithm based on pulse coupled neural networks (PCNN) and multi-feature fuzzy clustering is proposed, which makes use of the multi-feature of image and combines the advantages of the local entropy and variance of local entropy based PCNN. The results of experiments indicate that the proposed image fusion method can better preserve the image details and robustness and significantly improve the image visual effect than the other fusion methods with less information distortion.
文摘With time concrete / reinforced concrete has become the popular material for construction. Modern industry utilizes this material a lot and has produced various beautiful, eye catching and amazing structures. Due to modern requirements for living and developed construction industries, the old buildings (usually constructed with brick masonry) are demolished and are replaced with new modern buildings. Demolition of buildings results in waste materials which can create waste related problems and environmental issues. By using recycled aggregates weight of concrete can also be reduced, which can also solve problems related to self-weight of concrete. In this paper attempt has been made to use local used bricks from vicinity of Nawabshah, Pakistan, as coarse aggregate. Concrete cubes made with local recycled bricks are cast and tested for overall weight of concrete, moisture content, dynamic modulus of elasticity and compressive strength (nondestructive and destructive methods). The results showed that concrete derived from recycled aggregates attained lower strength than regular concrete. More detailed elaborated work is recommended with different mix ratios and different proportions recycled aggregates for better conclusions.
基金Supported by the Hundred Talent Program of the Chinese Academy of Sciences (CAS)the National Natural Science Foundation of China under Grant Nos. 10974208, 11121403, 1083401401, and 91027045
文摘Diffusion-limited aggregation （DLA） assumes that particles perform pure random walk at a finite tem- perature and aggregate when they come close enough and stick together. Although it is well known that DLA in two dimensions results in a ramified fractal structure, how the particle shape influences the formed morphology is still un- clear. In this work, we perform the off-lattice two-dimensional DLA simulations with different particle shapes of triangle, quadrangle, pentagon, hexagon, and octagon, respectively, and compare with the results for circular particles. Our results indicate that different particle shapes only change the local structure, but have no effects on the global structure of the formed fractal duster. The local compactness decreases as the number of polygon edges increases.
文摘The developments in the field of construction raise the need for concrete with less weight. This is beneficial for different applications starting from the less load applied to foundations and soil till the reduction of carnage capacity required for lifting precast units. In this paper, the production of light weight concrete from light local weight aggregate is investigated. Three candidate materials are used: crushed fired brick, vermiculite and light exfoliated clay aggregate (LECA). The first is available as the by-product of brick industry and the later two types are produced locally for different applications. Nine concrete mixes were made with same proportions and different aggregate materials. Physical and mechanical properties were measured for concrete in fresh and hardened states. Among these measured ones are unit weight, slump, compressive and tensile strength, and impact resistance. Also, the performance under elevated temperature was measured. Results show that reduction of unit weight up to 45%, of traditional concrete, can be achieved with 50% reduction in compressive strength. This makes it possible to get structural light weight concrete with compressive strength of 130 kg/cm2. Light weight concrete proved also to be more impact and fire resistant. However, as expected, it needs separate calibration curves for non-destructive evaluation. Following this experimental effort, the Artificial Neural Network (ANN) technique was applied for simulating and predicting the physical and mechanical properties of light weight aggregate concrete in fresh and hardened states. The current paper introduced the (ANN) technique to investigate the effect of light local weight aggregate on the performance of the produced light weight concrete. The results of this study showed that the ANN method with less effort was very efficiently capable of simulating the effect of different aggregate materials on the performance of light weight concrete.
文摘Stereo matching is a fundamental and crucial problem in computer vision. In the last decades, many researchers have been working on it and made great progress. Generally stereo algorithms can be classified into local methods and global methods. In this paper, the challenges of stereo matching are first introduced, and then we focus on local approaches which have simpler structures and higher efficiency than global ones. Local algorithms generally perform four steps： cost computation, cost aggregation, disparity computation and disparity refinement. Every step is deeply investigated, and most work focuses on cost aggregation. We studied most of the classical local methods and divide them into several classes. The classification well illustrates the development history of local stereo correspondence and shows the essence of local matching along with its important and difficult points. At the end we give the future development trend of local methods.
文摘Background Despite the recent progress in 3D point cloud processing using deep convolutional neural networks,the inability to extract local features remains a challenging problem.In addition,existing methods consider only the spatial domain in the feature extraction process.Methods In this paper,we propose a spectral and spatial aggregation convolutional network(S^(2)ANet),which combines spectral and spatial features for point cloud processing.First,we calculate the local frequency of the point cloud in the spectral domain.Then,we use the local frequency to group points and provide a spectral aggregation convolution module to extract the features of the points grouped by the local frequency.We simultaneously extract the local features in the spatial domain to supplement the final features.Results S^(2)ANet was applied in several point cloud analysis tasks;it achieved stateof-the-art classification accuracies of 93.8%,88.0%,and 83.1%on the ModelNet40,ShapeNetCore,and ScanObjectNN datasets,respectively.For indoor scene segmentation,training and testing were performed on the S3DIS dataset,and the mean intersection over union was 62.4%.Conclusions The proposed S^(2)ANet can effectively capture the local geometric information of point clouds,thereby improving accuracy on various tasks.
文摘This study presents the development of high strength concrete (HSC) that has been made more sustainable by using both local materials from central Texas and recycled concrete aggregate (RCA), which has also been obtained locally. The developed mixtures were proportioned with local constituents to increase the sustainable impact of the material by reducing emissions due to shipping as well as to make HSC more affordable to a wider variety of applications. The specific constituents were: limestone, dolomite, manufactured sand (limestone), locally available Type I/II cement, silica fume, and recycled concrete aggregate, which was obtained from a local recycler which obtains their product from local demolition. Multiple variables were investigated, such as the aggregate type and size, concrete age (7, 14, and 28-days), the curing regimen, and the water-to-cement ratio (w/c) to optimize a HSC mixture that used local materials. This systematic development revealed that heat curing the specimens in a water bath at 50℃ (122oF) after demolding and then dry curing at 200℃ (392oF) two days before testing with a w/c of 0.28 at 28-days produced the highest compressive strengths. Once an optimum HSC mixture was identified a partial replacement of the coarse aggregate with RCA was completed at 10%, 20%, and 30%. The results showed a loss in compressive strength with an increase in RCA replacement percentages, with the highest strength being approximately 93.0 MPa (13,484 psi) at 28-days for the 10% RCA replacement. The lowest strength obtained from an RCA-HSC mixture was approximately 72.9 (MPa) (10,576 psi) at 7-days. The compressive strengths obtained from the HSC mixtures containing RCA developed in this study are comparable to HSC strengths presented in the literature. Developing this innovative material with local materials and RCA ultimately produces a novel sustainable construction material, reduces the costs, and produces mechanical performance similar to prepackaged, commercially, available construction building materials.
文摘针对当前两阶段的点云目标检测算法PointRCNN:3D object proposal generation and detection from point cloud在点云降采样阶段时间开销大以及低效性的问题,本研究基于PointRCNN网络提出RandLA-RCNN(random sampling and an effectivelocal feature aggregator with region-based convolu-tional neural networks)架构。首先,利用随机采样方法在处理庞大点云数据时的高效性,对大场景点云数据进行下采样;然后,通过对输入点云的每个近邻点的空间位置编码,有效提高从每个点的邻域提取局部特征的能力,并利用基于注意力机制的池化规则聚合局部特征向量,获取全局特征;最后使用由多个局部空间编码单元和注意力池化单元叠加形成的扩展残差模块,来进一步增强每个点的全局特征,避免关键点信息丢失。实验结果表明,该检测算法在保留PointRCNN网络对3D目标的检测优势的同时,相比PointRCNN检测速度提升近两倍,达到16 f/s的推理速度。
