با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
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91 |
A unified framework for big data acquisition, storage, and analytics for demand response management in smart cities
چارچوب یکپارچه برای جمع آوری، ذخیره سازی و تجزیه و تحلیل داده های بزرگ برای مدیریت پاسخ تقاضا در شهرهای هوشمند-2018 With an increased usage of information and communication technologies (ICT) in the smart cities, the
data generated from different smart devices has increased manifolds. This data is heterogeneous in nature
which varies with respect to time and exhibits the properties of all essential V’s for big data. Therefore, to
handle such an enormous amount of data, various big data processing techniques are required. To cope
up with these issues, this paper presents a tensor-based big data management technique to reduce the
dimensionality of data gathered from the Internet-of-Energy (IoE) environment in a smart city. The core
data is extracted out of the gathered data by using tensor operations such as-matricization, vectorization
and tensorization with the help of higher-order singular value decomposition. This core data is then stored
on the cloud in the reduced form. After reducing the dimensionality of data, it is used for providing many
services in smart cities; and its application to provide demand response (DR) services has been discussed
in this paper. For this purpose, support vector machine (SVM)-based classifier is used to classify the end
users (residential and commercial) into normal, overloaded and underloaded categories from the core
data. Once such users are identified to take part in DR mechanism, utilities then generate commands to
handle their DR in order to alter load requirements so that the overall load is optimized. Results obtained
on Open Energy Information and PJM dataset clearly indicate the supremacy of the proposed tensor-based
scheme over the traditional scheme for DR management.
Keywords: Data analytics ، Data representation ، Demand response ،Dimensionality reduction ، Singular value decomposition ، Smart city ، Support vector machine ، Tensor decomposition |
مقاله انگلیسی |
92 |
Approximation of discrete time tandem queueing networks with unreliable servers and blocking
تقریب شبکه های صف بندی گسسته زمانی دونفره با خدمات غیرقابل اطمینان و انسداد-2018 We consider the discrete time tandem queues with single unreliable server at each service station and a buffer of finite capacity between service stations. The blocking after service (BAS) mechanism and operation dependent failure (ODF) rule are adopted. The service time of each server is a constant unit time. A failure of each server occurs in a time slot with a fixed probability and the repair time distribution of each server is of discrete phase type. In this paper, we present an approximate analysis for the system based on the decomposition method and show that the approach can be applied to the variants of the system.
keywords: Discrete time tandem queue |Discrete phase type distribution |Decomposition |Finite buffers |Unreliable servers |Blocking |
مقاله انگلیسی |
93 |
A Bi-objective Hyper-Heuristic Support Vector Machines for Big Data Cyber-Security
یک بردار حمایتی بیش از حد حقیقی بی هدف ماشین آلات برای داده های بزرگ امنیت سایبری -2018 Cyber security in the context of big data is known to be a critical problem and presents a
great challenge to the research community. Machine learning algorithms have been suggested as candidates
for handling big data security problems. Among these algorithms, support vector machines (SVMs) have
achieved remarkable success on various classification problems. However, to establish an effective SVM,
the user needs to define the proper SVM configuration in advance, which is a challenging task that requires
expert knowledge and a large amount of manual effort for trial and error. In this paper, we formulate the
SVM configuration process as a bi-objective optimization problem in which accuracy and model complexity
are considered as two conflicting objectives. We propose a novel hyper-heuristic framework for bi-objective
optimization that is independent of the problem domain. This is the first time that a hyper-heuristic has
been developed for this problem. The proposed hyper-heuristic framework consists of a high-level strategy
and low-level heuristics. The high-level strategy uses the search performance to control the selection of
which low-level heuristic should be used to generate a new SVM configuration. The low-level heuristics
each use different rules to effectively explore the SVM configuration search space. To address bi-objective
optimization, the proposed framework adaptively integrates the strengths of decomposition- and Paretobased approaches to approximate the Pareto set of SVM configurations. The effectiveness of the proposed
framework has been evaluated on two cyber security problems: Microsoft malware big data classification and
anomaly intrusion detection. The obtained results demonstrate that the proposed framework is very effective,
if not superior, compared with its counterparts and other algorithms.
INDEX TERMS: Hyper-heuristics, big data, cyber security, optimisation |
مقاله انگلیسی |
94 |
Seasonality and regional productivity in the Spanish accommodation sector
فصلی بودن و بهره وری منطقه ای در بخش اقامتی اسپانیا-2018 This study quantifies the impact of peak demand and seasonality on regional productivity in the Spanish accommodation sector. We then identify factors affecting seasonal fluctuations and their relative contributions to regional variations in seasonality. The results show that demand for accommodation in the peak season mainly determines productivity. Thus, improving a regions attractiveness as a tourist destination is most effective for tourism-based regional development. In addition, reducing seasonal variations has a non-negligible impact on productivity. A decomposition analysis reveals that providing climate-independent tourist attractions and attracting business travelers are effective in reducing seasonality.
keywords: Tourism |Regional development |Productivity accounting |
مقاله انگلیسی |
95 |
Optimal Configuration of Assembly Supply Chains Based on Hybrid Augmented Lagrangian Coordination in an Industrial Cluster
پیکربندی بهینه زنجیره تامین مجمع بر اساس همبستگی لاگرانژی پیشرفته ترکیبی در یک گروه صنعتی-2017 Industrial cluster is becoming an ever more important cost-effective industry
development mode especially when enterprises are required to give more rapid responses
to the frequently changed customized demands. The explosive number of homogeneous
enterprises/suppliers with geographic proximity provides multiple options for each supply
chain stage, which thus leads to higher potential to form a more satisfactorily performed
assembly supply chain (assembly system) in industrial clusters. However, the increased
candidate options also incur inevitably higher decision complexity to the decision model
of configuring such cluster supply chains (CSC). The situation may be more challenging
if the autonomous decision requirement of individual suppliers is accommodated. A
general assembly cluster supply chain configuration (ACSCC) model is established which
considers both horizontally and vertically collaborations in a cluster, meaning it
accommodates the typical cluster relationships including subcontracting and outsourcing.
In order to achieve the complexity reduction and autonomy protection, a newly emerged
decomposition-based solution method named augmented Lagrangian coordination (ALC)
will be adopted. Especially, two classical ALC formulation variants named the centralized
coordination formulation and the distributed coordination formulation are innovatively
integrated to form a hybrid ALC solution strategy, which deals with different assembly
branches with different alliancing structures. Experimental results have proved the
effectiveness of the proposed hybrid ALC method for ACSCC problem. From the
perspective of supply chain management, a set of sensitivity analysis for profit of each
collaborative enterprise is conducted to obtain some important managerial insights.
Key words: Industrial Cluster| Supply chain configuration | Supplier selection |Multidisciplinary design optimization| Hybrid Augmented Lagrangian coordination. |
مقاله انگلیسی |
96 |
طول اقامت و هزینه های روزانه گردشگر: یک تحلیل مشترک
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 28 در سالهای اخیر، الگوهای مصرف گردشگری با کاهش طول اقامت شناخته شده اند که سعی کرده است تا ازطریق افزایش هزینه های روزانه گردشگر یا افزایش تعداد گردشگران، اثر آن را خنثی کند. برای درک تاثیر این تغییرات روی صنعت های مسافرتی و مهمانداری، این مقاله یک تحلیل تجزیه ای را بین هزینه شخصی روزانه و طول اقامت گردشگران به عهده گرفته است. جایی که هزینه اتفاق می افتد، ازطریق تمایز بین هزینه های اقامتی و غیر اقامتی، و دسته های مختلف هزینه ای شامل هزینه های سرگرمی و تفریح، رستوران ها، حمل و نقل، گردش، هدایا و سوغاتی ها و هزینه های غذا و نوشیدنی در فروشگاهها در تحلیل تجزیه ای مدنظر قرار می گیرند. هدف از نتایج، شناسایی دسته های مختلف گردشگران می باشد که روی طول اقامت و هزینه های روزانه گردشگر تاثیرات مختلف قابل انتظاری را دارند، یک نتیجه ای که می تواند برای اهداف مدیریت گردشگری جال توجه باشد.
کلیدواژه ها: هزینه های گردشگر| هزینه های اقامتی | هزینه های غیر اقامتی | طول اقامت | تاثیر اقتصادی |
مقاله ترجمه شده |
97 |
Identification of influential spreaders in online social networks using interaction weighted K-core decomposition method
شناسایی گسترش دهنده های نفوذ در شبکه های اجتماعی آنلاین با استفاده از روش تجزیه K-core با تعادل وزن-2017 Online social networks (OSNs) have become a vital part of everyday living. OSNs provide
researchers and scientists with unique prospects to comprehend individuals on a scale and
to analyze human behavioral patterns. Influential spreaders identification is an important
subject in understanding the dynamics of information diffusion in OSNs. Targeting
these influential spreaders is significant in planning the techniques for accelerating the
propagation of information that is useful for various applications, such as viral marketing
applications or blocking the diffusion of annoying information (spreading of viruses,
rumors, online negative behaviors, and cyberbullying). Existing K-core decomposition
methods consider links equally when calculating the influential spreaders for unweighted
networks. Alternatively, the proposed link weights are based only on the degree of nodes.
Thus, if a node is linked to high-degree nodes, then this node will receive high weight and
is treated as an important node. Conversely, the degree of nodes in OSN context does not
always provide accurate influence of users. In the present study, we improve the K-core
method for OSNs by proposing a novel link-weighting method based on the interaction
among users. The proposed method is based on the observation that the interaction of users
is a significant factor in quantifying the spreading capability of user in OSNs. The tracking
of diffusion links in the real spreading dynamics of information verifies the effectiveness
of our proposed method for identifying influential spreaders in OSNs as compared with
degree centrality, PageRank, and original K-core.
Keywords: Online social networks | Complex networks | Influential spreaders | K-shell decomposition | Social media | Twitter |
مقاله انگلیسی |
98 |
MPARD: A high-frequency wave-based acoustic solver for very large compute clusters
MPARD: یک حل کننده صوتی مبتنی بر موج فرکانس بالا برای خوشه های محاسباتی بسیار بزرگ-2017 We present a parallel time-domain wave solver designed for large and high frequency acoustic domains.
Our approach is based on a novel scalable method for dividing acoustic field computations specifically for
large-scale distributed memory clusters using parallel Adaptive Rectangular Decomposition (ARD).
In order to efficiently compute the acoustic field for large or high frequency domains, we need to take
full advantage of the compute resources of large clusters. This is done with new algorithmic contribu
tions, including a hypergraph partitioning scheme to reduce the communication cost between the cores
on the cluster, a novel domain decomposition scheme that reduces the amount of numerical dispersion
error introduced by the load balancing algorithm, and a revamped pipeline for parallel ARD computation
that increases memory efficiency and reduces redundant computations.
Our resulting parallel algorithm makes it possible to compute the sound pressure field for high fre
quencies in large environments that are thousands of cubic meters in volume. We highlight the perfor
mance of our system on large clusters with 16,000 cores on homogeneous indoor and outdoor
benchmarks up to 10 kHz. To the best of our knowledge, this is the first time-domain parallel acoustic
wave solver that can handle such large domains and frequencies.
Keywords: Large-scale | Wave-based methods | Massively parallel |
مقاله انگلیسی |
99 |
Identification of influential spreaders in online social networks using interaction weighted K-core decomposition method
شناسایی گسترش نفوذ در شبکه های اجتماعی آنلاین با استفاده از تعامل روش تجزیه وزنی K هسته ای-2017 Online social networks (OSNs) have become a vital part of everyday living. OSNs provide
researchers and scientists with unique prospects to comprehend individuals on a scale and
to analyze human behavioral patterns. Influential spreaders identification is an important
subject in understanding the dynamics of information diffusion in OSNs. Targeting
these influential spreaders is significant in planning the techniques for accelerating the
propagation of information that is useful for various applications, such as viral marketing
applications or blocking the diffusion of annoying information (spreading of viruses,
rumors, online negative behaviors, and cyberbullying). Existing K-core decomposition
methods consider links equally when calculating the influential spreaders for unweighted
networks. Alternatively, the proposed link weights are based only on the degree of nodes.
Thus, if a node is linked to high-degree nodes, then this node will receive high weight and
is treated as an important node. Conversely, the degree of nodes in OSN context does not
always provide accurate influence of users. In the present study, we improve the K-core
method for OSNs by proposing a novel link-weighting method based on the interaction
among users. The proposed method is based on the observation that the interaction of users
is a significant factor in quantifying the spreading capability of user in OSNs. The tracking
of diffusion links in the real spreading dynamics of information verifies the effectiveness
of our proposed method for identifying influential spreaders in OSNs as compared with
degree centrality, PageRank, and original K-core.
Keywords: Online social networks | Complex networks | Influential spreaders | K-shell decomposition | Social media | Twitter |
مقاله انگلیسی |
100 |
Parallel multiphase field simulations with OpenPhase
شبیه سازی زمینه چند فازی موازی با OpenPhase-2017 The open-source software project OpenPhase allows the three-dimensional simulation of microstructural
evolution using the multiphase field method. The core modules of OpenPhase and their implementation
as well as their parallelization for a distributed-memory setting are presented. Especially communication
and load-balancing strategies are discussed. Synchronization points are avoided by an increased halo-size,
i.e. additional layers of ghost cells, which allow multiple stencil operations without data exchange. Load
balancing is considered via graph-partitioning and sub-domain decomposition. Results are presented
for performance benchmarks as well as for a variety of applications, e.g. grain growth in polycrystalline
materials, including a large number of phase fields as well as Mg–Al alloy solidification.
Keywords: Material science | Phase field | Parallel computing | Load-balancing |
مقاله انگلیسی |