As data are growing rapidly in data centers,inline cluster deduplication technique has been widely used to improve storage efficiency and data reliability.However,there are some challenges faced by the cluster dedupli...As data are growing rapidly in data centers,inline cluster deduplication technique has been widely used to improve storage efficiency and data reliability.However,there are some challenges faced by the cluster deduplication system:the decreasing data deduplication rate with the increasing deduplication server nodes,high communication overhead for data routing,and load balance to improve the throughput of the system.In this paper,we propose a well-performed cluster deduplication system called AR-Dedupe.The experimental results of two real datasets demonstrate that AR-Dedupe can achieve a high data deduplication rate with a low communication overhead and keep the system load balancing well at the same time through a new data routing algorithm.In addition,we utilize application-aware mechanism to speed up the index of handprints in the routing server which has a 30%performance improvement.展开更多
Dynamometer cards are commonly used to analyze down-hole working conditions of pumping systems in actual oil production. Nowadays, the traditional supervised learning methods heavily rely on the classification accurac...Dynamometer cards are commonly used to analyze down-hole working conditions of pumping systems in actual oil production. Nowadays, the traditional supervised learning methods heavily rely on the classification accuracy of the training samples. In order to reduce the errors of manual classification, an automatic clustering algorithm is proposed and applied to diagnose down-hole conditions of pumping systems. The spectral clustering （SC） is a new clustering algorithm, which is suitable for any data distribution. However, it is sensitive to initial cluster centers and scale parameters, and needs to predefine the cluster number. In order to overcome these shortcom- ings, we propose an automatic clustering algorithm, fast black hole-spectral clustering （FBH-SC）. The FBH algo- rithm is used to replace the K-mean method in SC, and a CritC index function is used as the target function to automatically choose the best scale parameter and clus- tering number in the clustering process. Different simulation experiments were designed to define the relationship among scale parameter, clustering number, CritC index value, and clustering accuracy. Finally, an example is given to validate the effectiveness of the proposed algorithm.展开更多
Considering the nonlinea r, time-varying and ripple coupling properties in the hydraulic servo system, a two-stage Radial Basis Function （RBF） neural network model is proposed to realize the failure detection and fa...Considering the nonlinea r, time-varying and ripple coupling properties in the hydraulic servo system, a two-stage Radial Basis Function （RBF） neural network model is proposed to realize the failure detection and fault localization. The first-stage RBF neural network is adopted as a failure observer to realize the failure detection. The trained RBF observer, working concurrently with the actual system, accepts the input voltage signal to the servo valve and the measurements of the ram displacements, rebuilds the system states, and estimates accurately the output of the system. By comparing the estimated outputs with the actual measurements, the residual signal is generated and then analyzed to report the occurrence of faults. The second-stage RBF neural network can locate the fault occurring through the residual and net parameters of the first-stage RBF observer. Considering the slow convergence speed of the K-means clustering algorithm, an improved K-means clustering algorithm and a self-adaptive adjustment algorithm of learning rate arc presented, which obtain the optimum learning rate by adjusting self-adaptive factor to guarantee the stability of the process and to quicken the convergence. The experimental results demonstrate that the two-stage RBF neural network model is effective in detecting and localizing the failure of the hydraulic position servo system.展开更多
The current mathematical models for the storage assignment problem are generally established based on the traveling salesman problem（TSP）,which has been widely applied in the conventional automated storage and retri...The current mathematical models for the storage assignment problem are generally established based on the traveling salesman problem（TSP）,which has been widely applied in the conventional automated storage and retrieval system（AS/RS）.However,the previous mathematical models in conventional AS/RS do not match multi-tier shuttle warehousing systems（MSWS） because the characteristics of parallel retrieval in multiple tiers and progressive vertical movement destroy the foundation of TSP.In this study,a two-stage open queuing network model in which shuttles and a lift are regarded as servers at different stages is proposed to analyze system performance in the terms of shuttle waiting period（SWP） and lift idle period（LIP） during transaction cycle time.A mean arrival time difference matrix for pairwise stock keeping units（SKUs） is presented to determine the mean waiting time and queue length to optimize the storage assignment problem on the basis of SKU correlation.The decomposition method is applied to analyze the interactions among outbound task time,SWP,and LIP.The ant colony clustering algorithm is designed to determine storage partitions using clustering items.In addition,goods are assigned for storage according to the rearranging permutation and the combination of storage partitions in a 2D plane.This combination is derived based on the analysis results of the queuing network model and on three basic principles.The storage assignment method and its entire optimization algorithm method as applied in a MSWS are verified through a practical engineering project conducted in the tobacco industry.The applying results show that the total SWP and LIP can be reduced effectively to improve the utilization rates of all devices and to increase the throughput of the distribution center.展开更多
This article studies the pod layout problem in the Kiva mobile fulfillment system which adopts the synchronized zoning strategy. An integer programming model for the pod layout problem is formulated under the premise ...This article studies the pod layout problem in the Kiva mobile fulfillment system which adopts the synchronized zoning strategy. An integer programming model for the pod layout problem is formulated under the premise of knowing the relationship of the pods and items. A three-stage algorithm is proposed based on the Spectral Clustering algorithm. Firstly, the pod similarity matrix and the Laplacian matrix are constructed according to the relationship of the pods and items. Secondly, the pods are clustered by the Spectral Clustering algorithm and assigned to each zone based on the cluster results. Finally, the exact locations of pods in each zone are determined by the historical retrieval frequency of items, using the real data of a large-scale Kiva mobile fulfillment system to simulate and calculate the order picking efficiency before and after the adjustment of the pod layout. The results showed that the pod layout using synchronized zoning strategy can effectively improve the picking efficiency.展开更多
为防止船舶违规安装虚假船舶通信系统软件并用其模拟发送干扰船舶自动识别系统(automatic identification system,AIS)的信号,提出一种基于运动特征的双阶段虚假船舶通信系统检测算法(two-stage false ship communication system detect...为防止船舶违规安装虚假船舶通信系统软件并用其模拟发送干扰船舶自动识别系统(automatic identification system,AIS)的信号,提出一种基于运动特征的双阶段虚假船舶通信系统检测算法(two-stage false ship communication system detection algorithm based on motion features,FSMF)。FSMF算法分析虚假船舶通信系统与AIS同时开启和间隔开启两种干扰情况下的船舶运动轨迹特征,通过计算轨迹之间的时间重叠度将识别过程分为两个阶段。在第一阶段,将动态时间规整(dynamic time warping,DTW)方法应用于时间重叠度为0的轨迹,利用地理距离和动态距离判断轨迹相似性,识别出两者间隔开启时的异常船舶。在第二阶段,将聚类算法应用于时间重叠度大于0的轨迹,把聚成同一簇的轨迹识别为两者同时开启时的运动轨迹,进而识别出异常船舶。通过三种不同类型的数据集对FSMF算法进行性能验证。随机海域的AIS数据分析结果表明,该算法能够有效识别安装虚假船舶通信系统软件的异常船舶,提升船舶监控能力,降低潜在的安全风险。展开更多
基金the National High Technology Research and Development Program(863)of China(No.2013AA013201)the National Natural Science Foundation of China(Nos.61025009,61232003,61170288 and 61332003)
文摘As data are growing rapidly in data centers,inline cluster deduplication technique has been widely used to improve storage efficiency and data reliability.However,there are some challenges faced by the cluster deduplication system:the decreasing data deduplication rate with the increasing deduplication server nodes,high communication overhead for data routing,and load balance to improve the throughput of the system.In this paper,we propose a well-performed cluster deduplication system called AR-Dedupe.The experimental results of two real datasets demonstrate that AR-Dedupe can achieve a high data deduplication rate with a low communication overhead and keep the system load balancing well at the same time through a new data routing algorithm.In addition,we utilize application-aware mechanism to speed up the index of handprints in the routing server which has a 30%performance improvement.
基金the National Natural Science Foundation of China (Grant No. 61403040)
文摘Dynamometer cards are commonly used to analyze down-hole working conditions of pumping systems in actual oil production. Nowadays, the traditional supervised learning methods heavily rely on the classification accuracy of the training samples. In order to reduce the errors of manual classification, an automatic clustering algorithm is proposed and applied to diagnose down-hole conditions of pumping systems. The spectral clustering （SC） is a new clustering algorithm, which is suitable for any data distribution. However, it is sensitive to initial cluster centers and scale parameters, and needs to predefine the cluster number. In order to overcome these shortcom- ings, we propose an automatic clustering algorithm, fast black hole-spectral clustering （FBH-SC）. The FBH algo- rithm is used to replace the K-mean method in SC, and a CritC index function is used as the target function to automatically choose the best scale parameter and clus- tering number in the clustering process. Different simulation experiments were designed to define the relationship among scale parameter, clustering number, CritC index value, and clustering accuracy. Finally, an example is given to validate the effectiveness of the proposed algorithm.
文摘Considering the nonlinea r, time-varying and ripple coupling properties in the hydraulic servo system, a two-stage Radial Basis Function （RBF） neural network model is proposed to realize the failure detection and fault localization. The first-stage RBF neural network is adopted as a failure observer to realize the failure detection. The trained RBF observer, working concurrently with the actual system, accepts the input voltage signal to the servo valve and the measurements of the ram displacements, rebuilds the system states, and estimates accurately the output of the system. By comparing the estimated outputs with the actual measurements, the residual signal is generated and then analyzed to report the occurrence of faults. The second-stage RBF neural network can locate the fault occurring through the residual and net parameters of the first-stage RBF observer. Considering the slow convergence speed of the K-means clustering algorithm, an improved K-means clustering algorithm and a self-adaptive adjustment algorithm of learning rate arc presented, which obtain the optimum learning rate by adjusting self-adaptive factor to guarantee the stability of the process and to quicken the convergence. The experimental results demonstrate that the two-stage RBF neural network model is effective in detecting and localizing the failure of the hydraulic position servo system.
基金Supported by National Natural Science Foundation of China(Grant No.661403234)Shandong Provincial Science and Techhnology Development Plan of China(Grant No.2014GGX106009)
文摘The current mathematical models for the storage assignment problem are generally established based on the traveling salesman problem（TSP）,which has been widely applied in the conventional automated storage and retrieval system（AS/RS）.However,the previous mathematical models in conventional AS/RS do not match multi-tier shuttle warehousing systems（MSWS） because the characteristics of parallel retrieval in multiple tiers and progressive vertical movement destroy the foundation of TSP.In this study,a two-stage open queuing network model in which shuttles and a lift are regarded as servers at different stages is proposed to analyze system performance in the terms of shuttle waiting period（SWP） and lift idle period（LIP） during transaction cycle time.A mean arrival time difference matrix for pairwise stock keeping units（SKUs） is presented to determine the mean waiting time and queue length to optimize the storage assignment problem on the basis of SKU correlation.The decomposition method is applied to analyze the interactions among outbound task time,SWP,and LIP.The ant colony clustering algorithm is designed to determine storage partitions using clustering items.In addition,goods are assigned for storage according to the rearranging permutation and the combination of storage partitions in a 2D plane.This combination is derived based on the analysis results of the queuing network model and on three basic principles.The storage assignment method and its entire optimization algorithm method as applied in a MSWS are verified through a practical engineering project conducted in the tobacco industry.The applying results show that the total SWP and LIP can be reduced effectively to improve the utilization rates of all devices and to increase the throughput of the distribution center.
文摘This article studies the pod layout problem in the Kiva mobile fulfillment system which adopts the synchronized zoning strategy. An integer programming model for the pod layout problem is formulated under the premise of knowing the relationship of the pods and items. A three-stage algorithm is proposed based on the Spectral Clustering algorithm. Firstly, the pod similarity matrix and the Laplacian matrix are constructed according to the relationship of the pods and items. Secondly, the pods are clustered by the Spectral Clustering algorithm and assigned to each zone based on the cluster results. Finally, the exact locations of pods in each zone are determined by the historical retrieval frequency of items, using the real data of a large-scale Kiva mobile fulfillment system to simulate and calculate the order picking efficiency before and after the adjustment of the pod layout. The results showed that the pod layout using synchronized zoning strategy can effectively improve the picking efficiency.
文摘为防止船舶违规安装虚假船舶通信系统软件并用其模拟发送干扰船舶自动识别系统(automatic identification system,AIS)的信号,提出一种基于运动特征的双阶段虚假船舶通信系统检测算法(two-stage false ship communication system detection algorithm based on motion features,FSMF)。FSMF算法分析虚假船舶通信系统与AIS同时开启和间隔开启两种干扰情况下的船舶运动轨迹特征,通过计算轨迹之间的时间重叠度将识别过程分为两个阶段。在第一阶段,将动态时间规整(dynamic time warping,DTW)方法应用于时间重叠度为0的轨迹,利用地理距离和动态距离判断轨迹相似性,识别出两者间隔开启时的异常船舶。在第二阶段,将聚类算法应用于时间重叠度大于0的轨迹,把聚成同一簇的轨迹识别为两者同时开启时的运动轨迹,进而识别出异常船舶。通过三种不同类型的数据集对FSMF算法进行性能验证。随机海域的AIS数据分析结果表明,该算法能够有效识别安装虚假船舶通信系统软件的异常船舶,提升船舶监控能力,降低潜在的安全风险。
