As the cloud data center (CDC) landscape continues to broaden, CDC resource utilization as the benchmark for assessing scheduling methodologies. Concurrently enhancing both CPU and memory utilization stands as a param...
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As the cloud data center (CDC) landscape continues to broaden, CDC resource utilization as the benchmark for assessing scheduling methodologies. Concurrently enhancing both CPU and memory utilization stands as a paramount priority. However, prevalent algorithms tend to solely prioritize CPU utilization while neglecting memory efficiency, ultimately escalating energy expenditure. This article initiates by conducting a comprehensive examination of memory utilization repercussions on CDCs. Subsequently, it facilitates the dynamic clustering of physical machines and virtual machine deployments, ensuring a balanced utilization profile. Furthermore, it introduces the memory priority scheduling algorithm for CDC based on machine learning dynamic clustering algorithm (PMPD). Comparative evaluations against other algorithms underscore the prowess of PMPD in concurrently optimizing CPU and memory utilization, thereby minimizing the number of active PMs and diminishing energy consumption of CDCs.
This work proposes an algorithm to detect and classify railway track defects by analysing the axle-box acceleration in the vertical direction. The axle-box acceleration is generated by using an experimentally validate...
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This work proposes an algorithm to detect and classify railway track defects by analysing the axle-box acceleration in the vertical direction. The axle-box acceleration is generated by using an experimentally validated vehicle track model. The vehicle-track model is built in the commercial multibody dynamics software SIMPACK. When the vehicle moves over the track defects, each defect excites a specific band of frequencies. To separate the track defects based on the frequency content the axle-box acceleration is passed through a filter bank to decompose it into its constituents. Mean and standard deviation are calculated for each decomposed signal to form a feature matrix. On the feature matrix, principal component analysis is performed to extract the orthogonal features and select the dominant features. On these dominant features, density-based spatial clustering of application with noise (DBSCAN) is applied to group the data points into defective and perfect clusters. The effect of vehicle speed, axle load, defect features, and epsilon value is analysed for the defect detection algorithm. The track defects are classified by applying the DBSCAN algorithm on selected decomposed signals. The proposed algorithm is found to accurately detect and locate the track defects without being affected by the external parameters.
Resource reconfiguration can integrate and optimize manufacturing resources (MRs) into various resource sets, enhancing overall resource management in cloud manufacturing (CM). However, achieving efficient resource re...
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Resource reconfiguration can integrate and optimize manufacturing resources (MRs) into various resource sets, enhancing overall resource management in cloud manufacturing (CM). However, achieving efficient resource reconfiguration remains difficult due to the large data volumes, heterogeneity, and implicit relationships among MRs in CM. To improve the practicality of MR reconfiguration in cloud manufacturing (CM), a new multi-process parallel clustering algorithm (MPPCA) is proposed to categorize the multi-source heterogeneous MRs into distinct resource sets based on their functional characteristics. MPPCA can divide MRs into multiple processes and cluster them parallel to form several subclasses. Finally, similar subclasses are merged, and MRs with implicit relationships are grouped into the same resource set. In order to ensure the accurate calculation of the subclass radius during the merging process, a skewness indicator is introduced. The indicator is used to evaluate the irregular shape of the data set during the clustering process to ensure that the subclasses after division are quasi-circular. Additionally, merge criteria are proposed to merge similar resource subclasses effectively, exploring implicit relationships between MRs. The feasibility of the proposed method is validated through experiments using MR data from laboratories and artificial datasets, demonstrating that MPPCA successfully achieves resource reconfiguration.
Power system data possess many characteristics and indicators, having certain high dimensions and redundant information, which can easily increase the calculation and storage overhead. To reduce the dimension of power...
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Power system data possess many characteristics and indicators, having certain high dimensions and redundant information, which can easily increase the calculation and storage overhead. To reduce the dimension of power data, eliminate redundant information, and reduce the delay time, a data clustering algorithm is proposed. Firstly, an algorithm based on PCA and kernel local Fisher identification is used to reduce the dimension of large multidimensional samples and enhance the accuracy of subsequent clustering. Thereafter, the redundant data are processed after dimension reduction is processed to optimize the data quality by introducing a bloom filter structure. In the graph model, data clustering is completed based on the parallel processing of redundant data. Simulation results show that the correctness and stability of this method are over 85%, and the delay time is decreased, representing good application prospects.
The past decade has seen a dramatic improvement in the quality of data available at both high(HE,>10 Ge V)and very high(VHE,>100 Ge V)gamma-ray *** to the latest Pass8 data release by Fermi LAT which increases
The past decade has seen a dramatic improvement in the quality of data available at both high(HE,>10 Ge V)and very high(VHE,>100 Ge V)gamma-ray *** to the latest Pass8 data release by Fermi LAT which increases
作者：
Yu, YangChangchun Univ
Sports Teaching & Res Dept 6543 Weixing Rd Changchun 130022 Peoples R China
For athlete performance evaluation and injury risk prediction-which is increasingly crucial-traditional approaches find difficulty handling complex, multidimensional data. We introduce the PerfoRisk-KDB model to preci...
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For athlete performance evaluation and injury risk prediction-which is increasingly crucial-traditional approaches find difficulty handling complex, multidimensional data. We introduce the PerfoRisk-KDB model to precisely estimate athlete performance and injury risk by combining K-means and DBSCAN clustering techniques. By combining these two clustering techniques, the idea of this work surpasses the constraints of a single technique and increases accuracy and robustness for complex and high-dimensional data. This work tests the performance assessment and injury risk prediction of a real athlete dataset against conventional models. Based on tests, the PerfoRisk-KDB model shows good performance on several evaluation criteria and shows good application possibilities.
Curved beams are irregular composite structures commonly used in aircraft and ships. The complexity of their material properties and irregular geometries make it challenging to define their failure accurately based on...
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Curved beams are irregular composite structures commonly used in aircraft and ships. The complexity of their material properties and irregular geometries make it challenging to define their failure accurately based on the failure phenomenon. Moreover, few full-size composite laminate curved beam tests have been reported in previous publications because of their high cost and difficulty. This work proposes a 3-order network-free renormalization method based on the thermodynamic-based failure definition to reveal the failure law of curved beams more accurately, which is suitable for characterizing the stressing state of full-size irregular composite structures. Applying 3-order network-free renormalization and clustering algorithms to eight full-size composite curved beams can reveal the elastoplastic branching (EPB), failure starting (FS), and progressive failure (PF) points. We can also use the phase transition definition of Wilson's theory to verify the stability of phase transition loads. Unlike failure loads based on buckling or fracture phenomena, phase transition loads in composite curved beams are based on catastrophes in the relative deformation distribution, which is more physically significant.
A variety of techniques and tools exist to parallelize software systems on different parallel architectures (SIMD, MIMD). With the advances in high-speed networks, there has been a dramatic increase in the number of c...
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A variety of techniques and tools exist to parallelize software systems on different parallel architectures (SIMD, MIMD). With the advances in high-speed networks, there has been a dramatic increase in the number of client/server applications. A variety of client/server applications are deployed today, ranging from simple telnet sessions to complex electronic commerce transactions. Industry standard protocols. like Secure Socket Layer (SSL), Secure Electronic Transaction (SET), etc., are in use for ensuring privacy and integrity of data, as well as for authenticating the sender and the receiver during message passing. Consequently, a majority of applications using parallel processing techniques are becoming synchronization-centric, i.e., for every message transfer, the sender and receiver must synchronize. However, more effective techniques and tools are needed to automate the clustering of such synchronization-centric applications to extract parallelism. In this paper, we present a new clustering algorithm to facilitate the parallelization of software systems in a multiprocessors environment. The new clustering algorithm achieves traditional clustening objectives (reduction in parallel execution time, communication cost, etc.). Additionally, our approach 1) reduces the performance degradation caused by synchronizations, and 2) avoids deadlocks during clustering. The effectiveness of our approach is depicted with the help of simulation results.
The synthetic aperture radar (SAR) auto target recognition (ATR) system developed at Lincoln Laboratory is a standard system for target detection/recognition. It has three main stages: a prescreener, a discriminator a...
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The synthetic aperture radar (SAR) auto target recognition (ATR) system developed at Lincoln Laboratory is a standard system for target detection/recognition. It has three main stages: a prescreener, a discriminator and a classifier. The clustering algorithm between the prescreener stage and the discriminator stage is used to cluster the multiple detections of a single target to form a region of interest (ROI). This paper introduces the steps of the common clustering algorithm and analyzes its disadvantages. We improve the common clustering algorithm from two aspects of the read sequence of image data and the calculation means of clustering quasi-center coordinates. The clustering results based on two actual images testify efficiency of clustering algorithm improvement.
Many experiments show that outliers have important implications for clustering. However, Most of the clustering algorithm ignores to compute outliers, or does not detect outliers well. In this paper, we present a loca...
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Many experiments show that outliers have important implications for clustering. However, Most of the clustering algorithm ignores to compute outliers, or does not detect outliers well. In this paper, we present a local deviation factor graph-based (LDFGB) algorithm. We measure the effectiveness of the algorithm by detection rate, false positive rate, false negative rate, time overhead, and so on. This algorithm can accurately detect outliers by calculating the relative distance between the data nodes. It can detect any shape of the cluster and still keep high detection rate for detecting known and unknown attacks. Using KDD CUP99 data sets, the experimental results show that this method is effective for improving the detection rates and false positive rates.
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