This research introduces a unique approach to segmenting breast cancer images using a U-Net-based architecture.However,the computational demand for image processing is very high.Therefore,we have conducted this resear...This research introduces a unique approach to segmenting breast cancer images using a U-Net-based architecture.However,the computational demand for image processing is very high.Therefore,we have conducted this research to build a system that enables image segmentation training with low-power machines.To accomplish this,all data are divided into several segments,each being trained separately.In the case of prediction,the initial output is predicted from each trained model for an input,where the ultimate output is selected based on the pixel-wise majority voting of the expected outputs,which also ensures data privacy.In addition,this kind of distributed training system allows different computers to be used simultaneously.That is how the training process takes comparatively less time than typical training approaches.Even after completing the training,the proposed prediction system allows a newly trained model to be included in the system.Thus,the prediction is consistently more accurate.We evaluated the effectiveness of the ultimate output based on four performance matrices:average pixel accuracy,mean absolute error,average specificity,and average balanced accuracy.The experimental results show that the scores of average pixel accuracy,mean absolute error,average specificity,and average balanced accuracy are 0.9216,0.0687,0.9477,and 0.8674,respectively.In addition,the proposed method was compared with four other state-of-the-art models in terms of total training time and usage of computational resources.And it outperformed all of them in these aspects.展开更多
The construction of island power grids is a systematic engineering task.To ensure the safe operation of power grid systems,optimizing the line layout of island power grids is crucial.Especially in the current context ...The construction of island power grids is a systematic engineering task.To ensure the safe operation of power grid systems,optimizing the line layout of island power grids is crucial.Especially in the current context of large-scale distributed renewable energy integration into the power grid,conventional island power grid line layouts can no longer meet actual demands.It is necessary to combine the operational characteristics of island power systems and historical load data to perform load forecasting,thereby generating power grid line layout paths.This article focuses on large-scale distributed renewable energy integration,summarizing optimization strategies for island power grid line layouts,and providing a solid guarantee for the safe and stable operation of island power systems.展开更多
The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe distance.To guarantee collision avoidance,the safety distance should be calcula...The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe distance.To guarantee collision avoidance,the safety distance should be calculated using the state-of-the-art space-time separation principle that separates the Emergency Braking(EB)trajectories of two successive units during the whole EB process.In this case,the minimal safety distance is usually numerically calculated without an analytic formulation.Thus,the constrained VCTS control problem is hard to address with space-time separation,which is still a gap in the existing literature.To solve this problem,we propose a Distributed Economic Model Predictive Control(DEMPC)approach with computation efficiency and theoretical guarantee.Specifically,to alleviate the computation burden,we transform implicit safety constraints into explicitly linear ones,such that the optimal control problem in DEMPC is a quadratic programming problem that can be solved efficiently.For theoretical analysis,sufficient conditions are derived to guarantee the recursive feasibility and stability of DEMPC,employing compatibility constraints,tube techniques and terminal ingredient tuning.Moreover,we extend our approach with globally optimal and distributed online EB configuration methods to shorten the minimal distance among VCTS.Finally,experimental results demonstrate the performance and advantages of the proposed approaches.展开更多
Underground mine pillars provide natural stability to the mine area,allowing safe operations for workers and machinery.Extensive prior research has been conducted to understand pillar failure mechanics and design safe...Underground mine pillars provide natural stability to the mine area,allowing safe operations for workers and machinery.Extensive prior research has been conducted to understand pillar failure mechanics and design safe pillar layouts.However,limited studies(mostly based on empirical field observation and small-scale laboratory tests)have considered pillar-support interactions under monotonic loading conditions for the design of pillar-support systems.This study used a series of large-scale laboratory compression tests on porous limestone blocks to analyze rock and support behavior at a sufficiently large scale(specimens with edge length of 0.5 m)for incorporation of actual support elements,with consideration of different w/h ratios.Both unsupported and supported(grouted rebar rockbolt and wire mesh)tests were conducted,and the surface deformations of the specimens were monitored using three-dimensional(3D)digital image correlation(DIC).Rockbolts instrumented with distributed fiber optic strain sensors were used to study rockbolt strain distribution,load mobilization,and localized deformation at different w/h ratios.Both axial and bending strains were observed in the rockbolts,which became more prominent in the post-peak region of the stress-strain curve.展开更多
In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural networks.Adversarial training is one of the most potent methods to defend against adversarial attacks.How...In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural networks.Adversarial training is one of the most potent methods to defend against adversarial attacks.However,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial training.This paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture model.The distribution centroid is built to classify samples and constrain the distribution of the sample features.The natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the model.The proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial training.This algorithm gradually increases the accuracy and robustness of the model by scaling perturbation.Finally,the proposed method outputs the predicted labels and the distance between the sample and the distribution centroid.The distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model defense.The effectiveness of the proposed method is demonstrated through comprehensive experiments.展开更多
Reconfigurable intelligent surface(RIS)is more likely to develop into extremely large-scale RIS(XL-RIS)to efficiently boost the system capacity for future 6 G communications.Beam training is an effective way to acquir...Reconfigurable intelligent surface(RIS)is more likely to develop into extremely large-scale RIS(XL-RIS)to efficiently boost the system capacity for future 6 G communications.Beam training is an effective way to acquire channel state information(CSI)for XL-RIS.Existing beam training schemes rely on the far-field codebook.However,due to the large aperture of XL-RIS,the scatters are more likely to be in the near-field region of XL-RIS.The far-field codebook mismatches the near-field channel model.Thus,the existing far-field beam training scheme will cause severe performance loss in the XL-RIS assisted nearfield communications.To solve this problem,we propose the efficient near-field beam training schemes by designing the near-field codebook to match the nearfield channel model.Specifically,we firstly design the near-field codebook by considering the near-field cascaded array steering vector of XL-RIS.Then,the optimal codeword for XL-RIS is obtained by the exhausted training procedure.To reduce the beam training overhead,we further design a hierarchical nearfield codebook and propose the corresponding hierarchical near-field beam training scheme,where different levels of sub-codebooks are searched in turn with reduced codebook size.Simulation results show the proposed near-field beam training schemes outperform the existing far-field beam training scheme.展开更多
Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins.With the rapid development of high-throughput genomic technologies,massive protein-protein interacti...Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins.With the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them efficiently.To address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using MapReduce.To do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and prediction.Respective solutions are then devised to overcome these limitations.In particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of proteins.After that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and accuracy.Experimental results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.展开更多
The traditional distributed tactical training simulation system is limited by the availability and bandwidth of military network transmission channel and does not have the feasibility of remote interconnection and spa...The traditional distributed tactical training simulation system is limited by the availability and bandwidth of military network transmission channel and does not have the feasibility of remote interconnection and spatial sub-regional deployment.In this paper,a new communication method of distributed tactical training simulation system is proposed to solve the problem of strong business coupling between nodes and system availability under the condition of low bandwidth.The operator of federated exchange,federated queue and their concepts and design requirements are firstly proposed,and the inverted tree,triangle,ring exchange topology and circular queue structure are further constructed.Theoretically,the expected goal of high-speed interworking between nodes in the cluster and high reliable transmission between clusters is realized.The example also shows that this method can significantly improve the throughput of single switching node and federated node after using reliability confirmation mechanism.展开更多
How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classif...How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.展开更多
An antenna selection algorithm based on large-scale fading between the transmitter and receiver is proposed for the uplink receive antenna selection in distributed multiple-input multiple-output(D-MIMO) systems. By ut...An antenna selection algorithm based on large-scale fading between the transmitter and receiver is proposed for the uplink receive antenna selection in distributed multiple-input multiple-output(D-MIMO) systems. By utilizing the radio access units(RAU) selection based on large-scale fading,the proposed algorithm decreases enormously the computational complexity. Based on the characteristics of distributed systems,an improved particle swarm optimization(PSO) has been proposed for the antenna selection after the RAU selection. In order to apply the improved PSO algorithm better in antenna selection,a general form of channel capacity was transformed into a binary expression by analyzing the formula of channel capacity. The proposed algorithm can make full use of the advantages of D-MIMO systems,and achieve near-optimal performance in terms of channel capacity with low computational complexity.展开更多
This paper investigates large-scale distributed system design. It looks at features, main design considerations and provides the Netflix API, Cassandra and Oracle as examples of such systems. Moreover, the paper inves...This paper investigates large-scale distributed system design. It looks at features, main design considerations and provides the Netflix API, Cassandra and Oracle as examples of such systems. Moreover, the paper investigates the challenges of designing, developing, deploying, and maintaining such systems, in regard to the features presented. Finally, the paper discusses aspects of available solutions and current practices to challenges that large-scale distributed systems face.展开更多
The dynamic load distribution within in-service axlebox bearings of high-speed trains is crucial for the fatigue reliability assessment and forward design of axlebox bearings. This paper presents an in situ measuremen...The dynamic load distribution within in-service axlebox bearings of high-speed trains is crucial for the fatigue reliability assessment and forward design of axlebox bearings. This paper presents an in situ measurement of the dynamic load distribution in the four rows of two axlebox bearings on a bogie wheelset of a high-speed train under polygonal wheel–rail excitation. The measurement employed an improved strain-based method to measure the dynamic radial load distribution of roller bearings. The four rows of two axlebox bearings on a wheelset exhibited different ranges of loaded zones and different means of distributed loads. Besides, the mean value and standard deviation of measured roller–raceway contact loads showed non-monotonic variations with the frequency of wheel–rail excitation. The fatigue life of the four bearing rows under polygonal wheel–rail excitation was quantitatively predicted by compiling the measured roller–raceway contact load spectra of the most loaded position and considering the load spectra as input.展开更多
The current research on the aerodynamic performance of the train running in rainy weather is primarily concerned with the trajectory of the raindrops and the aerodynamic variation of trains caused by raindrops.In fact...The current research on the aerodynamic performance of the train running in rainy weather is primarily concerned with the trajectory of the raindrops and the aerodynamic variation of trains caused by raindrops.In fact,water film will generate on the train body when raindrops hit the train,which interacts with the flow field around the train,and would probably affect the aerodynamic performance of the train.In this paper,based on shear stress transport(SST)k-w turbulence model and Euler-Lagrange discrete phase model,the aerodynamic calculation model of a highspeed train under rainfall environment is established.The LWF(Lagrangian wall film)is used to simulate the water film distribution of the high-speed train under different rainfall intensities,and the aerodynamic performance of the train are studied.The calculation results show that raindrops will gather on the train surface and form water film under rainfall environment.With the extension of rainfall time,the thickness and coverage range of water film expand,and the maximum thickness of water film can reach 4.95 mm under the working conditions in this paper.The average thickness of water film on the train body increases with the rainfall intensity.When the rainfall intensity increases from 100 mm/h to 500 mm/h,the average water film thickness will increase by 3.26 times.The velocity of water film in the streamlined area of head car is larger than that in other areas,and the maximum velocity is 22.14 m/s.Compared with the rainless environment condition,the skin friction coefficient of the high-speed train increases and the average value will increase by 10.74%for a rainfall intensity of 500 mm/h.The positive pressure and resistance coefficient of the head car increase with the rainfall intensity.This research proposes a methodology to systematically analyze the generation of water film on the train surface and its influence on the train aerodynamic performance;the analysis can provide more practical results and can serve as a reference basis for the design and development of high-speed trains.展开更多
BACKGROUND： Elk-1 mRNA distributes extensively in the neurons of mice, rat and human brains, and the Elk-1 expression may be correlated with the synaptic plasticity, learning and memory. OBJECTIVE： To observe the di...BACKGROUND： Elk-1 mRNA distributes extensively in the neurons of mice, rat and human brains, and the Elk-1 expression may be correlated with the synaptic plasticity, learning and memory. OBJECTIVE： To observe the distribution of phosphorylated Elk-1 （pEIk-1） in whole brain of rats received Y-maze active avoidance training and the changes of pEIk-1 expression at different time points after training. DESIGN ： A randomized controlled study SETTING ： Research Room of Neurobiology, the Second Affiliated Hospital of Southern Medical University MATERIALS ： Fifty-five male clean-degree SD rats of 3-4 months old, weighing 200-250 g, were provided by the Experimental Animal Center of Southem Medical University. The rabbit anti-monoclonal pEIk-1 antibody was purchased from Cell Signal Transduction Company, and ABC kit from Vector Company. METHODS ： The experiments were carried out in the Research Room of Neurobiology, Second Affiliated Hospital of Southern Medical University from September 2004 to February 2005. ① Grouping： The rats were randomly divided into training group （n = 25）, sham-training group （n = 25） and normal control group （n = 5）, and the training and sham-training groups were observed at 0, 1, 3, 6 and 24 hours after training, which represented the five phases in the process of leaming and memory. ② Y-maze training： The rats were preconditioned in the electrical Y-maze apparatus, 20 minutes a day for 3 days continuously, and training began from the 4^th day. In the training group, the rats were trained with the combination of light and electddty. Each rat repeated for 60 times in each training, and the correct times were recorded, those correct for less than 25 times were taken as unqualified, and excluded from the training group, and supplemented by other rats in time. In the sham-training group, there was no fixed correlation between the application of light and electricity. The rats in the normal contrel group were given not any training. ③Detection of pEIk-1 expression： The rats were anesthetized after Y-maze training, brain tissue was removed to prepare coronal freezing sections, and the pEIk-1 expression was detected with routine ABC method. MATN OUTCOME MEASURES： ① Distribution of pEIk-1 immuno-positive neurons in whole brain of rats in the normal control group. ②Comparison of the expression of pEIk-1 immuno-positive neurons in whole brain at different time points after training between the training group and sham-training group. RESULTS ： All the 55 rats were involved in result analysis. ③ Distribution of pEIk-1 immuno-positive neurons in the whole brain of rats in the normal control group： Strong expressions of pEIk-1 immuno-positive neurons were observed in prefrontal lobe, granular layer of olfactory bulbs, Purkinje cell layer and granular layer of cerebellum, whole stdate cortex, temporal cortex, pre-pyriform cortex, hypothalamic supraoptic nucleus, hypothalamic paraventricular nucleus and periventricular nucleus, thalamic paraventricular nucleus, pronucleus and postnucleus of amygdala cortex, central nucleus of amygdala, medial amygdaloid nucleus, entorhinal cortex, hippocampal dentate gyros, CA1-4 regions, caudate-putamen, material division, brain stem spinal nucleus of trigeminal nerve, and superior olivary nucleus, and those in hippocampal dentate gyrus and CA1 region were the strongest.② Distribution of pEIk-1 immuno-positive neurons in the whole brain of rats at different time points after training in the training group and sham-training group： In the training group, the expressions were obviously enhanced in caudate-putamen of striatum, material division, most cortexes, hippocampal dentate gyrus, hippocampal CA regions, nucleus amygdalae, thalamic paraventricular nucleus, Purkinje cell layer of cerebellum, entorhinal cortex, hypothalamic supraoptic nucleus, hypothalamic paraventricular nucleus, and periventricular nucleus at 0 hour after training, and the enhancement lasted for 6 hours at least, and those at 24 hours were decreased to normal. In the sham-training group, obvious enhanced expressions of pEIk-1 immuno-positive neurons could be observed in most cortexes, nucleus amygdalae, entorhinal cortex, hypothalamic supraoptic nucleus, hypothalamic paraventdoular nucleus and periventricular nucleus, brain stem spinal nucleus of trigeminal nerve, Purkinje cell layer and granular layer of cerebellum at O, 1, 3 and 6 hours, and decreased to normal after 24 hours. The expressions in material division, caudate-putamen of striatum, hippocampus were not obviously enhanced as compared with those in the normal control group, but significantly different from those in the training group （0, 1, 3, 6 hours after training, material division： F= 0.576, 0.023, 0.116, 8.873, P〈 0.01; caudate-putamen： F= 0.157, 0.427, 0.030, 0.001, P〈 0.01; hippocampus： F= 6.716, 2.405, 14.137, 1.416, P 〈 0.05-0.01 ）. CONCLUSION： The expression of activated pEIk-1 can be detected in the learning related brain areas under normal status, and the perk-1 expression in the brain areas dynamically changed in a time-dependent manner after Y-maze training, and it is indicated that pEIk-1 is involved in the learning and memory process in Y-maze related brain areas.展开更多
As deep neural networks (DNNs) have been successfully adopted in various domains, the training of these large-scale models becomes increasingly difficult and is often deployed on compute clusters composed of many devi...As deep neural networks (DNNs) have been successfully adopted in various domains, the training of these large-scale models becomes increasingly difficult and is often deployed on compute clusters composed of many devices like GPUs. However, as the size of the cluster increases, so does the possibility of failures during training. Currently, faults are mainly handled by recording checkpoints and recovering, but this approach causes large overhead and affects the training efficiency even when no error occurs. The low checkpointing frequency leads to a large loss of training time, while the high recording frequency affects the training efficiency. To solve this contradiction, we propose BAFT, a bubble-aware fault tolerant framework for hybrid parallel distributed training. BAFT can automatically analyze parallel strategies, profile the runtime information, and schedule checkpointing tasks at the granularity of pipeline stage depending on the bubble distribution in the training. It supports higher checkpoint efficiency and only introduces less than 1% time overhead, which allows us to record checkpoints at high frequency, thereby reducing the time loss in error recovery and avoiding the impact of fault tolerance on training.展开更多
This work aims to analyse the actions that companies working in large-scale distribution carry along their value chains to minimise impacts on climate change.Companies operating in this field are aware that it is less...This work aims to analyse the actions that companies working in large-scale distribution carry along their value chains to minimise impacts on climate change.Companies operating in this field are aware that it is less effective to act directly on the core processes and need to involve the upstream value chain in their carbon reduction strategy.These businesses,in fact,need to focus on the indirect GHG(Greenhouse Gases)emissions and depend on how suppliers manage their impacts.In this sector,virtuous companies collaborate with their suppliers to get involved in a common path of quantifying and cutting said impacts together.This aspect is particularly relevant in the case of large-scale retailers.However,the process is not immediate since the supply chain is usually very dense and diverse,for instance,adopting various approaches that do not always coincide.In any case,the key aspect is mapping these suppliers:one of the tools mostly used for this purpose is the survey,as a quick instrument able to reach hundreds of suppliers at the same time,receiving a fast and standardized response,which can easily be processed to form a comprehensive and harmonized mapping of the results as the first step for the subsequent implementation of mitigation strategies.展开更多
Edge machine learning creates a new computational paradigm by enabling the deployment of intelligent applications at the network edge.It enhances application efficiency and responsiveness by performing inference and t...Edge machine learning creates a new computational paradigm by enabling the deployment of intelligent applications at the network edge.It enhances application efficiency and responsiveness by performing inference and training tasks closer to data sources.However,it encounters several challenges in practice.The variance in hardware specifications and performance across different devices presents a major issue for the training and inference tasks.Additionally,edge devices typically possess limited network bandwidth and computing resources compared with data centers.Moreover,existing distributed training architectures often fail to consider the constraints of resources and communication efficiency in edge environments.In this paper,we propose DSparse,a method for distributed training based on sparse update in edge clusters with various memory capacities.It aims at maximizing the utilization of memory resources across all devices within a cluster.To reduce memory consumption during the training process,we adopt sparse update to prioritize the updating of selected layers on the devices in the cluster,which not only lowers memory usage but also reduces the data volume of parameters and the time required for parameter aggregation.Furthermore,DSparse utilizes a parameter aggregation mechanism based on multi-process groups,subdividing the aggregation tasks into AllReduce and Broadcast types,thereby further reducing the communication frequency for parameter aggregation.Experimental results using the MobileNetV2 model on the CIFAR-10 dataset demonstrate that DSparse reduces memory consumption by an average of 59.6%across seven devices,with a 75.4%reduction in parameter aggregation time,while maintaining model precision.展开更多
In the large-scale logistics distribution of single logistic center,the method based on traditional genetic algorithm is slow in evolution and easy to fall into the local optimal solution.Addressing at this issue,we p...In the large-scale logistics distribution of single logistic center,the method based on traditional genetic algorithm is slow in evolution and easy to fall into the local optimal solution.Addressing at this issue,we propose a novel approach of exploring hybrid genetic algorithm based large-scale logistic distribution for BBG supermarket.We integrate greedy algorithm and hillclimbing algorithm into genetic algorithm.Greedy algorithm is applied to initialize the population,and then hill-climbing algorithm is used to optimize individuals in each generation after selection,crossover and mutation.Our approach is evaluated on the dataset of BBG Supermarket which is one of the top 10 supermarkets in China.Experimental results show that our method outperforms some other methods in the field.展开更多
基金the Researchers Supporting Project,King Saud University,Saudi Arabia,for funding this research work through Project No.RSPD2025R951.
文摘This research introduces a unique approach to segmenting breast cancer images using a U-Net-based architecture.However,the computational demand for image processing is very high.Therefore,we have conducted this research to build a system that enables image segmentation training with low-power machines.To accomplish this,all data are divided into several segments,each being trained separately.In the case of prediction,the initial output is predicted from each trained model for an input,where the ultimate output is selected based on the pixel-wise majority voting of the expected outputs,which also ensures data privacy.In addition,this kind of distributed training system allows different computers to be used simultaneously.That is how the training process takes comparatively less time than typical training approaches.Even after completing the training,the proposed prediction system allows a newly trained model to be included in the system.Thus,the prediction is consistently more accurate.We evaluated the effectiveness of the ultimate output based on four performance matrices:average pixel accuracy,mean absolute error,average specificity,and average balanced accuracy.The experimental results show that the scores of average pixel accuracy,mean absolute error,average specificity,and average balanced accuracy are 0.9216,0.0687,0.9477,and 0.8674,respectively.In addition,the proposed method was compared with four other state-of-the-art models in terms of total training time and usage of computational resources.And it outperformed all of them in these aspects.
文摘The construction of island power grids is a systematic engineering task.To ensure the safe operation of power grid systems,optimizing the line layout of island power grids is crucial.Especially in the current context of large-scale distributed renewable energy integration into the power grid,conventional island power grid line layouts can no longer meet actual demands.It is necessary to combine the operational characteristics of island power systems and historical load data to perform load forecasting,thereby generating power grid line layout paths.This article focuses on large-scale distributed renewable energy integration,summarizing optimization strategies for island power grid line layouts,and providing a solid guarantee for the safe and stable operation of island power systems.
基金supported by the National Natural Science Foundation of China(52372310)the State Key Laboratory of Advanced Rail Autonomous Operation(RAO2023ZZ001)+1 种基金the Fundamental Research Funds for the Central Universities(2022JBQY001)Beijing Laboratory of Urban Rail Transit.
文摘The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe distance.To guarantee collision avoidance,the safety distance should be calculated using the state-of-the-art space-time separation principle that separates the Emergency Braking(EB)trajectories of two successive units during the whole EB process.In this case,the minimal safety distance is usually numerically calculated without an analytic formulation.Thus,the constrained VCTS control problem is hard to address with space-time separation,which is still a gap in the existing literature.To solve this problem,we propose a Distributed Economic Model Predictive Control(DEMPC)approach with computation efficiency and theoretical guarantee.Specifically,to alleviate the computation burden,we transform implicit safety constraints into explicitly linear ones,such that the optimal control problem in DEMPC is a quadratic programming problem that can be solved efficiently.For theoretical analysis,sufficient conditions are derived to guarantee the recursive feasibility and stability of DEMPC,employing compatibility constraints,tube techniques and terminal ingredient tuning.Moreover,we extend our approach with globally optimal and distributed online EB configuration methods to shorten the minimal distance among VCTS.Finally,experimental results demonstrate the performance and advantages of the proposed approaches.
基金the funding support from Alpha Foundation for the Improvement of Mine Safety and Health Inc.(ALPHAFOUNDATION,Grant No.AFC820-52)。
文摘Underground mine pillars provide natural stability to the mine area,allowing safe operations for workers and machinery.Extensive prior research has been conducted to understand pillar failure mechanics and design safe pillar layouts.However,limited studies(mostly based on empirical field observation and small-scale laboratory tests)have considered pillar-support interactions under monotonic loading conditions for the design of pillar-support systems.This study used a series of large-scale laboratory compression tests on porous limestone blocks to analyze rock and support behavior at a sufficiently large scale(specimens with edge length of 0.5 m)for incorporation of actual support elements,with consideration of different w/h ratios.Both unsupported and supported(grouted rebar rockbolt and wire mesh)tests were conducted,and the surface deformations of the specimens were monitored using three-dimensional(3D)digital image correlation(DIC).Rockbolts instrumented with distributed fiber optic strain sensors were used to study rockbolt strain distribution,load mobilization,and localized deformation at different w/h ratios.Both axial and bending strains were observed in the rockbolts,which became more prominent in the post-peak region of the stress-strain curve.
基金supported by the National Natural Science Foundation of China(No.U21B2003,62072250,62072250,62172435,U1804263,U20B2065,61872203,71802110,61802212)the National Key R&D Program of China(No.2021QY0700)+4 种基金the Key Laboratory of Intelligent Support Technology for Complex Environments(Nanjing University of Information Science and Technology),Ministry of Education,and the Natural Science Foundation of Jiangsu Province(No.BK20200750)Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness(No.HNTS2022002)Post Graduate Research&Practice Innvoation Program of Jiangsu Province(No.KYCX200974)Open Project Fund of Shandong Provincial Key Laboratory of Computer Network(No.SDKLCN-2022-05)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)Fund and Graduate Student Scientific Research Innovation Projects of Jiangsu Province(No.KYCX231359).
文摘In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural networks.Adversarial training is one of the most potent methods to defend against adversarial attacks.However,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial training.This paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture model.The distribution centroid is built to classify samples and constrain the distribution of the sample features.The natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the model.The proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial training.This algorithm gradually increases the accuracy and robustness of the model by scaling perturbation.Finally,the proposed method outputs the predicted labels and the distance between the sample and the distribution centroid.The distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model defense.The effectiveness of the proposed method is demonstrated through comprehensive experiments.
基金supported in part by the National Key Research and Development Program of China(Grant No.2020YFB1807205)in part by the National Natural Science Foundation of China(Grant No.62031019)in part by the European Commission through the H2020-MSCA-ITN META WIRELESS Research Project under Grant 956256。
文摘Reconfigurable intelligent surface(RIS)is more likely to develop into extremely large-scale RIS(XL-RIS)to efficiently boost the system capacity for future 6 G communications.Beam training is an effective way to acquire channel state information(CSI)for XL-RIS.Existing beam training schemes rely on the far-field codebook.However,due to the large aperture of XL-RIS,the scatters are more likely to be in the near-field region of XL-RIS.The far-field codebook mismatches the near-field channel model.Thus,the existing far-field beam training scheme will cause severe performance loss in the XL-RIS assisted nearfield communications.To solve this problem,we propose the efficient near-field beam training schemes by designing the near-field codebook to match the nearfield channel model.Specifically,we firstly design the near-field codebook by considering the near-field cascaded array steering vector of XL-RIS.Then,the optimal codeword for XL-RIS is obtained by the exhausted training procedure.To reduce the beam training overhead,we further design a hierarchical nearfield codebook and propose the corresponding hierarchical near-field beam training scheme,where different levels of sub-codebooks are searched in turn with reduced codebook size.Simulation results show the proposed near-field beam training schemes outperform the existing far-field beam training scheme.
基金This work was supported in part by the National Natural Science Foundation of China(61772493)the CAAI-Huawei MindSpore Open Fund(CAAIXSJLJJ-2020-004B)+4 种基金the Natural Science Foundation of Chongqing(China)(cstc2019jcyjjqX0013)Chongqing Research Program of Technology Innovation and Application(cstc2019jscx-fxydX0024,cstc2019jscx-fxydX0027,cstc2018jszx-cyzdX0041)Guangdong Province Universities and College Pearl River Scholar Funded Scheme(2019)the Pioneer Hundred Talents Program of Chinese Academy of Sciencesthe Deanship of Scientific Research(DSR)at King Abdulaziz University(G-21-135-38).
文摘Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins.With the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them efficiently.To address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using MapReduce.To do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and prediction.Respective solutions are then devised to overcome these limitations.In particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of proteins.After that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and accuracy.Experimental results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.
基金Supported by the National Natural Science Foundation of China(61401496)。
文摘The traditional distributed tactical training simulation system is limited by the availability and bandwidth of military network transmission channel and does not have the feasibility of remote interconnection and spatial sub-regional deployment.In this paper,a new communication method of distributed tactical training simulation system is proposed to solve the problem of strong business coupling between nodes and system availability under the condition of low bandwidth.The operator of federated exchange,federated queue and their concepts and design requirements are firstly proposed,and the inverted tree,triangle,ring exchange topology and circular queue structure are further constructed.Theoretically,the expected goal of high-speed interworking between nodes in the cluster and high reliable transmission between clusters is realized.The example also shows that this method can significantly improve the throughput of single switching node and federated node after using reliability confirmation mechanism.
基金supported by the National Natural Science Foundation of China(U1435220)
文摘How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.
基金Supported by the National Natural Science Foundation of China(No.61201086,61272495)the China Scholarship Council(No.201506375060)+1 种基金the Planned Science and Technology Project of Guangdong Province(No.2013B090500007) the Dongguan Project on the Integration of Industry,Education and Research(No.2014509102205)
文摘An antenna selection algorithm based on large-scale fading between the transmitter and receiver is proposed for the uplink receive antenna selection in distributed multiple-input multiple-output(D-MIMO) systems. By utilizing the radio access units(RAU) selection based on large-scale fading,the proposed algorithm decreases enormously the computational complexity. Based on the characteristics of distributed systems,an improved particle swarm optimization(PSO) has been proposed for the antenna selection after the RAU selection. In order to apply the improved PSO algorithm better in antenna selection,a general form of channel capacity was transformed into a binary expression by analyzing the formula of channel capacity. The proposed algorithm can make full use of the advantages of D-MIMO systems,and achieve near-optimal performance in terms of channel capacity with low computational complexity.
文摘This paper investigates large-scale distributed system design. It looks at features, main design considerations and provides the Netflix API, Cassandra and Oracle as examples of such systems. Moreover, the paper investigates the challenges of designing, developing, deploying, and maintaining such systems, in regard to the features presented. Finally, the paper discusses aspects of available solutions and current practices to challenges that large-scale distributed systems face.
基金supported by the National Natural Science Foundation of China (Grant No. 12302238)the National Key Research and Development Program of China (Grant Nos. 2021YFB3400701, 2022YFB3402904)。
文摘The dynamic load distribution within in-service axlebox bearings of high-speed trains is crucial for the fatigue reliability assessment and forward design of axlebox bearings. This paper presents an in situ measurement of the dynamic load distribution in the four rows of two axlebox bearings on a bogie wheelset of a high-speed train under polygonal wheel–rail excitation. The measurement employed an improved strain-based method to measure the dynamic radial load distribution of roller bearings. The four rows of two axlebox bearings on a wheelset exhibited different ranges of loaded zones and different means of distributed loads. Besides, the mean value and standard deviation of measured roller–raceway contact loads showed non-monotonic variations with the frequency of wheel–rail excitation. The fatigue life of the four bearing rows under polygonal wheel–rail excitation was quantitatively predicted by compiling the measured roller–raceway contact load spectra of the most loaded position and considering the load spectra as input.
基金Supported by Shandong Provincial Natural Science Foundation of China(Grant No.ZR2022ME180)Guangdong Provincial Basic and Applied Basic Research Fund of China(Grant No.2019A1515111005)National Natural Science Foundation of China(Grant No.51705267)。
文摘The current research on the aerodynamic performance of the train running in rainy weather is primarily concerned with the trajectory of the raindrops and the aerodynamic variation of trains caused by raindrops.In fact,water film will generate on the train body when raindrops hit the train,which interacts with the flow field around the train,and would probably affect the aerodynamic performance of the train.In this paper,based on shear stress transport(SST)k-w turbulence model and Euler-Lagrange discrete phase model,the aerodynamic calculation model of a highspeed train under rainfall environment is established.The LWF(Lagrangian wall film)is used to simulate the water film distribution of the high-speed train under different rainfall intensities,and the aerodynamic performance of the train are studied.The calculation results show that raindrops will gather on the train surface and form water film under rainfall environment.With the extension of rainfall time,the thickness and coverage range of water film expand,and the maximum thickness of water film can reach 4.95 mm under the working conditions in this paper.The average thickness of water film on the train body increases with the rainfall intensity.When the rainfall intensity increases from 100 mm/h to 500 mm/h,the average water film thickness will increase by 3.26 times.The velocity of water film in the streamlined area of head car is larger than that in other areas,and the maximum velocity is 22.14 m/s.Compared with the rainless environment condition,the skin friction coefficient of the high-speed train increases and the average value will increase by 10.74%for a rainfall intensity of 500 mm/h.The positive pressure and resistance coefficient of the head car increase with the rainfall intensity.This research proposes a methodology to systematically analyze the generation of water film on the train surface and its influence on the train aerodynamic performance;the analysis can provide more practical results and can serve as a reference basis for the design and development of high-speed trains.
文摘BACKGROUND： Elk-1 mRNA distributes extensively in the neurons of mice, rat and human brains, and the Elk-1 expression may be correlated with the synaptic plasticity, learning and memory. OBJECTIVE： To observe the distribution of phosphorylated Elk-1 （pEIk-1） in whole brain of rats received Y-maze active avoidance training and the changes of pEIk-1 expression at different time points after training. DESIGN ： A randomized controlled study SETTING ： Research Room of Neurobiology, the Second Affiliated Hospital of Southern Medical University MATERIALS ： Fifty-five male clean-degree SD rats of 3-4 months old, weighing 200-250 g, were provided by the Experimental Animal Center of Southem Medical University. The rabbit anti-monoclonal pEIk-1 antibody was purchased from Cell Signal Transduction Company, and ABC kit from Vector Company. METHODS ： The experiments were carried out in the Research Room of Neurobiology, Second Affiliated Hospital of Southern Medical University from September 2004 to February 2005. ① Grouping： The rats were randomly divided into training group （n = 25）, sham-training group （n = 25） and normal control group （n = 5）, and the training and sham-training groups were observed at 0, 1, 3, 6 and 24 hours after training, which represented the five phases in the process of leaming and memory. ② Y-maze training： The rats were preconditioned in the electrical Y-maze apparatus, 20 minutes a day for 3 days continuously, and training began from the 4^th day. In the training group, the rats were trained with the combination of light and electddty. Each rat repeated for 60 times in each training, and the correct times were recorded, those correct for less than 25 times were taken as unqualified, and excluded from the training group, and supplemented by other rats in time. In the sham-training group, there was no fixed correlation between the application of light and electricity. The rats in the normal contrel group were given not any training. ③Detection of pEIk-1 expression： The rats were anesthetized after Y-maze training, brain tissue was removed to prepare coronal freezing sections, and the pEIk-1 expression was detected with routine ABC method. MATN OUTCOME MEASURES： ① Distribution of pEIk-1 immuno-positive neurons in whole brain of rats in the normal control group. ②Comparison of the expression of pEIk-1 immuno-positive neurons in whole brain at different time points after training between the training group and sham-training group. RESULTS ： All the 55 rats were involved in result analysis. ③ Distribution of pEIk-1 immuno-positive neurons in the whole brain of rats in the normal control group： Strong expressions of pEIk-1 immuno-positive neurons were observed in prefrontal lobe, granular layer of olfactory bulbs, Purkinje cell layer and granular layer of cerebellum, whole stdate cortex, temporal cortex, pre-pyriform cortex, hypothalamic supraoptic nucleus, hypothalamic paraventricular nucleus and periventricular nucleus, thalamic paraventricular nucleus, pronucleus and postnucleus of amygdala cortex, central nucleus of amygdala, medial amygdaloid nucleus, entorhinal cortex, hippocampal dentate gyros, CA1-4 regions, caudate-putamen, material division, brain stem spinal nucleus of trigeminal nerve, and superior olivary nucleus, and those in hippocampal dentate gyrus and CA1 region were the strongest.② Distribution of pEIk-1 immuno-positive neurons in the whole brain of rats at different time points after training in the training group and sham-training group： In the training group, the expressions were obviously enhanced in caudate-putamen of striatum, material division, most cortexes, hippocampal dentate gyrus, hippocampal CA regions, nucleus amygdalae, thalamic paraventricular nucleus, Purkinje cell layer of cerebellum, entorhinal cortex, hypothalamic supraoptic nucleus, hypothalamic paraventricular nucleus, and periventricular nucleus at 0 hour after training, and the enhancement lasted for 6 hours at least, and those at 24 hours were decreased to normal. In the sham-training group, obvious enhanced expressions of pEIk-1 immuno-positive neurons could be observed in most cortexes, nucleus amygdalae, entorhinal cortex, hypothalamic supraoptic nucleus, hypothalamic paraventdoular nucleus and periventricular nucleus, brain stem spinal nucleus of trigeminal nerve, Purkinje cell layer and granular layer of cerebellum at O, 1, 3 and 6 hours, and decreased to normal after 24 hours. The expressions in material division, caudate-putamen of striatum, hippocampus were not obviously enhanced as compared with those in the normal control group, but significantly different from those in the training group （0, 1, 3, 6 hours after training, material division： F= 0.576, 0.023, 0.116, 8.873, P〈 0.01; caudate-putamen： F= 0.157, 0.427, 0.030, 0.001, P〈 0.01; hippocampus： F= 6.716, 2.405, 14.137, 1.416, P 〈 0.05-0.01 ）. CONCLUSION： The expression of activated pEIk-1 can be detected in the learning related brain areas under normal status, and the perk-1 expression in the brain areas dynamically changed in a time-dependent manner after Y-maze training, and it is indicated that pEIk-1 is involved in the learning and memory process in Y-maze related brain areas.
基金supported by the National Key R&D Program of China(2021ZD0110104)the National Natural Science Foundation of China(Grant Nos.62222210,U21B2017,61832006,and 62072297).
文摘As deep neural networks (DNNs) have been successfully adopted in various domains, the training of these large-scale models becomes increasingly difficult and is often deployed on compute clusters composed of many devices like GPUs. However, as the size of the cluster increases, so does the possibility of failures during training. Currently, faults are mainly handled by recording checkpoints and recovering, but this approach causes large overhead and affects the training efficiency even when no error occurs. The low checkpointing frequency leads to a large loss of training time, while the high recording frequency affects the training efficiency. To solve this contradiction, we propose BAFT, a bubble-aware fault tolerant framework for hybrid parallel distributed training. BAFT can automatically analyze parallel strategies, profile the runtime information, and schedule checkpointing tasks at the granularity of pipeline stage depending on the bubble distribution in the training. It supports higher checkpoint efficiency and only introduces less than 1% time overhead, which allows us to record checkpoints at high frequency, thereby reducing the time loss in error recovery and avoiding the impact of fault tolerance on training.
文摘This work aims to analyse the actions that companies working in large-scale distribution carry along their value chains to minimise impacts on climate change.Companies operating in this field are aware that it is less effective to act directly on the core processes and need to involve the upstream value chain in their carbon reduction strategy.These businesses,in fact,need to focus on the indirect GHG(Greenhouse Gases)emissions and depend on how suppliers manage their impacts.In this sector,virtuous companies collaborate with their suppliers to get involved in a common path of quantifying and cutting said impacts together.This aspect is particularly relevant in the case of large-scale retailers.However,the process is not immediate since the supply chain is usually very dense and diverse,for instance,adopting various approaches that do not always coincide.In any case,the key aspect is mapping these suppliers:one of the tools mostly used for this purpose is the survey,as a quick instrument able to reach hundreds of suppliers at the same time,receiving a fast and standardized response,which can easily be processed to form a comprehensive and harmonized mapping of the results as the first step for the subsequent implementation of mitigation strategies.
基金supported by the National Natural Science Foundation of China under Grant Nos.62072434 and U23B2004the Innovation Funding of Institute of Computing Technology,Chinese Academy of Sciences,under Grant Nos.E361050 and E361030.
文摘Edge machine learning creates a new computational paradigm by enabling the deployment of intelligent applications at the network edge.It enhances application efficiency and responsiveness by performing inference and training tasks closer to data sources.However,it encounters several challenges in practice.The variance in hardware specifications and performance across different devices presents a major issue for the training and inference tasks.Additionally,edge devices typically possess limited network bandwidth and computing resources compared with data centers.Moreover,existing distributed training architectures often fail to consider the constraints of resources and communication efficiency in edge environments.In this paper,we propose DSparse,a method for distributed training based on sparse update in edge clusters with various memory capacities.It aims at maximizing the utilization of memory resources across all devices within a cluster.To reduce memory consumption during the training process,we adopt sparse update to prioritize the updating of selected layers on the devices in the cluster,which not only lowers memory usage but also reduces the data volume of parameters and the time required for parameter aggregation.Furthermore,DSparse utilizes a parameter aggregation mechanism based on multi-process groups,subdividing the aggregation tasks into AllReduce and Broadcast types,thereby further reducing the communication frequency for parameter aggregation.Experimental results using the MobileNetV2 model on the CIFAR-10 dataset demonstrate that DSparse reduces memory consumption by an average of 59.6%across seven devices,with a 75.4%reduction in parameter aggregation time,while maintaining model precision.
基金This project was funded by the National Natural Science Foundation of China(41871320,61872139)the Provincial and Municipal Joint Fund of Hunan Provincial Natural Science Foundation of China(2018JJ4052)+2 种基金Hunan Provincial Natural Science Foundation of China(2017JJ2081)the Key Project of Hunan Provincial Education Department(19A172)the Scientific Research Fund of Hunan Provincial Education Department(18K060).
文摘In the large-scale logistics distribution of single logistic center,the method based on traditional genetic algorithm is slow in evolution and easy to fall into the local optimal solution.Addressing at this issue,we propose a novel approach of exploring hybrid genetic algorithm based large-scale logistic distribution for BBG supermarket.We integrate greedy algorithm and hillclimbing algorithm into genetic algorithm.Greedy algorithm is applied to initialize the population,and then hill-climbing algorithm is used to optimize individuals in each generation after selection,crossover and mutation.Our approach is evaluated on the dataset of BBG Supermarket which is one of the top 10 supermarkets in China.Experimental results show that our method outperforms some other methods in the field.
