1,721,474 research outputs found
A new efficient approach for extracting the closed episodes for workload prediction in cloud
No full textThe prediction of the future workload of applications is an essential step guiding resource provisioning in cloud environments. In our previous works, we proposed two prediction models based on pattern mining. This paper builds on our previous experience and focuses on the issue of time and space complexities of the prediction model. Specifically, it presents a general approach to improve the efficiency of the pattern mining engine, which leads to improving the efficiency of the predictors. The approach is composed of two steps: (1) Firstly, to improve space complexity, redundant occurrences of patterns are defined and algorithms are suggested to identify and omit them. (2) To improve time complexity, a new data structure, called closed pattern backward tree, is presented for mining closed patterns directly. The approach not only improves the efficiency of our predictors, but also can be employed in different fields of pattern mining. The performance of the proposed approach is investigated based on real and synthetic workloads of cloud. The experimental results show that the proposed approach could improve the efficiency of the pattern mining engine significantly in comparison to common methods to extract closed patterns
A Numerical Investigation on the Effects of Vaned Diffusers on the Aerodynamic Performance of a Low Pressure-Ratio Methane Centrifugal Compressor
Get PDFVaned diffusers are widely used in centrifugal compressors due to their higher pressure-recovery coefficients compared to vaneless diffusers. In this study, the effects of the diffuser vanes’ wrap angle and number of vanes on the aerodynamic performance of an industrial Methane centrifugal compressor with a pressure-ratio of 1.288 are studied using high fidelity steady-RANS numerical simulations. Three wrap angles (WA = 19.3o, 22.3o and 25.3o) and three number of vanes (NDiff. = 16, 20 and 24) are examined, while all the other geometrical and operational parameters are kept constant. Results showed that decreasing the wrap angle can enhance the choke flow rate of the compressor, with slight reduction in pressure ratio at low flow rates. However, increasing the diffuser wrap angle, intensifies the flow separation over the diffuser vanes. On average, the best aerodynamic performance of the compressor occurred at WA=22.3o. Results also showed that reducing the number of diffuser vanes enlarges the operating range of the compressor, however, the pressure ratio will be lower at the flow rates less than the design point. Conversely, higher pressure ratios will be achieved at the flow rates greater than the design flow rate. The optimal aerodynamic efficiency of the diffuser, considering both the pressure ratio and the total-to-total efficiency, was achieved when the NDiff value was set to 20
Improving the accuracy of rainfall prediction using a regionalization approach and neural networks
Get PDFSpatial and temporal analysis of precipitation patterns has become an intense research topic in contemporary climatology. Increasing the accuracy of precipitation prediction can have valuable results for decision-makers in a specific region. Hence, studies about precipitation prediction on a regional scale are of great importance. Artificial Neural Networks (ANN) have been widely used in climatological applications to predict different meteorological parameters. In this study, a method is presented to increase the accuracy of neural networks in precipitation prediction in Chaharmahal and Bakhtiari Province in Iran. For this purpose, monthly precipitation data recorded at 42 rain gauges during 1981-2012 were used. The stations were first clustered into well-defined groupings using Principal Component Analysis (PCA) and Cluster Analysis (CA), and then one separate neural network was applied to each group of stations. Another neural network model was also developed and applied to all the stations in order to measure the accuracy of the proposed model. Statistical results showed that the presented model produced better results in comparison to the second model
Improved delivery rate-cache capacity trade-off for centralized coded caching
Get PDFCentralized coded caching problem, in which a server with N distinct files, each with the size of F bits, serves K users, each equipped with a cache of capacity MF bits, is considered. The server is allowed to proactively cache contents into user terminals during the placement phase, without knowing the particular user requests. After the placement phase, each user requests one of the N files from the server, and all the users' requests are satisfied simultaneously by the server through an error-free shared link during the delivery phase. A novel coded caching algorithm is proposed, which is shown to achieve a smaller delivery rate compared to the existing coded caching schemes in the literature for a range of N and K values; particularly when the number of files is larger than the number of users in the system
Landslide susceptibility mapping using precipitation data, Mazandaran Province, north of Iran
No full textPrecipitation is a nonlinear and complex phenomenon and varies in time and space. It is also evident that there is a link between precipitation and shallow landslides, and precipitation is always considered as a landslide-triggering factor. This study aims to investigate the relationship between the characteristics of precipitation and the historical shallow landslides in Mazandaran Province, north of Iran. For this purpose, the spatial variability of rainfall was analyzed using monthly rainfall data collected at 15 synoptic stations distributed over the region between 1981 and 2014. Monthly precipitation and other derived parameters were used, and a hybrid model combining principal component analysis and cluster analysis (CA) was applied to all the precipitation parameters to regionalize the region into well-defined clusters in terms of precipitation and prove that there is a link between precipitation and the occurred slides. Then, the rotated PCs were combined and the precipitation characteristics map was produced. Demonstrating the linkage between the precipitation characteristics and the historical slides, the combined map can be considered as landslide susceptibility map. The accuracy of prediction was tested against a random guess and obtained as 77%. It is also noticeable that only 30% of the surface area of the study region in the landslide susceptibility map covers about 80% of the known landslides. The calculated measure suggests that the developed model well predicted the location of the occurred slides using only precipitation data
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Federated learning over wireless fading channels
Get PDFWe study federated machine learning at the wirelessnetwork edge, where limited power wireless devices, each withits own dataset, build a joint model with the help of a remoteparameter server (PS). We consider a bandwidth-limited fadingmultiple access channel (MAC) from the wireless devices to thePS, and propose various techniques to implement distributedstochastic gradient descent (DSGD) over this shared noisywireless channel. We first propose a digital DSGD (D-DSGD)scheme, in which one device is selected opportunistically fortransmission at each iteration based on the channel conditions;the scheduled device quantizes its gradient estimate to a finitenumber of bits imposed by the channel condition, and transmitsthese bits to the PS in a reliable manner. Next, motivated bythe additive nature of the wireless MAC, we propose a novelanalog communication scheme, referred to as thecompressedanalogDSGD (CA-DSGD), where the devices first sparsifytheir gradient estimates while accumulating error from previousiterations, and project the resultant sparse vector into a low-dimensional vector for bandwidth reduction. We also design apower allocation scheme to align the received gradient vectorsat the PS in an efficient manner. Numerical results show thatD-DSGD outperforms other digital approaches in the literature;however, in general the proposed CA-DSGD algorithm convergesfaster than the D-DSGD scheme, and reaches a higher level ofaccuracy. We have observed that the gap between the analogand digital schemes increases when the datasets of devices arenot independent and identically distributed (i.i.d.). Furthermore,the performance of the CA-DSGD scheme is shown to be robustagainst imperfect channel state information (CSI) at the devices.Overall these results show clear advantages for the proposedanalog over-the-air DSGD scheme, which suggests that learningand communication algorithms should be designed jointly toachieve the best end-to-end performance in machine learningapplications at the wireless edge
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