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1 |
Drag-mode airborne wind energy vs: wind turbines: An analysis of power production, variability and geography
کشیدن حالت باد موجود در هوا توربین های بادی در مقابل انرژی: تجزیه و تحلیل تولید برق، تنوع و جغرافیا-2020 Airborne wind energy (AWE) is a wind power technology that harvests energy at high altitudes. The
performance of AWE systems relative to traditional wind power turbines (WT) is of key relevance to any
future commercialization. In particular, the power generation as well as its consistency over time will be
key performance indicators. This study aims at identifying crucial factors that will influence the
competitiveness of drag-mode AWE systems against WTs. To that end, the hourly power production of
several drag-mode AWE designs is investigated using realistic wind data, and compared to the hourly
power production of classical WTs. These results are then analyzed through three performance indicators:
total annual power production, Gini coefficient, and correlation coefficient. The results show
that AWE systems with multiple smaller wings have the highest annual production. The AWE power
production of all AWE systems correlates in time at all sites with the production of WTs, and the Gini
coefficients are similar. This observation challenges the belief that AWE systems will outcompete WTs
thanks to a more consistent power output than WTs. However, the wing design as well as the local wind
shear have a significant impact on the AWE performance. Keywords: Airborne wind energy | Wind energy | Performance study | Optimal control |
مقاله انگلیسی |
2 |
Efficient distance join query processing in distributed spatial data management systems
فاصله کارآمد عضویت پردازش پرس و جو در سیستم های مدیریت داده های فضایی توزیع شده-2020 Due to the ubiquitous use of spatial data applications and the large amounts of such data these applications use, the processing of large-scale distance joins in distributed systems is becoming increasingly popular. Distance Join Queries (DJQs) are important and frequently used operations in numerous applications, including data mining, multimedia and spa- tial databases. DJQs (e.g., k Nearest Neighbor Join Query, k Closest Pair Query, εDistance Join Query, etc.) are costly operations, since they involve both the join and distance-based search, and performing DJQs efficiently is a challenging task. Recent Big Data develop- ments have motivated the emergence of novel technologies for distributed processing of large-scale spatial data in clusters of computers, leading to Distributed Spatial Data Man- agement Systems (DSDMSs). Distributed cluster-based computing systems can be classified as Hadoop-based or Spark-based systems. Based on this classification, in this paper, we compare two of the most recent and leading DSDMSs, SpatialHadoop and LocationSpark, by evaluating the performance of several existing and newly proposed parallel and dis- tributed DJQ algorithms under various settings with large spatial real-world datasets. A general conclusion arising from the execution of the distributed DJQ algorithms studied is that, while SpatialHadoop is a robust and efficient system when large spatial datasets are joined (since it is built on top of the mature Hadoop platform), LocationSpark is the clear winner in total execution time efficiency when medium spatial datasets are com- bined (due to in-memory processing provided by Spark). However, LocationSpark requires higher memory allocation when large spatial datasets are involved in DJQs (even more so when k and εare large). Finally, this detailed performance study has demonstrated that the new distributed DJQ algorithms we have proposed are efficient, robust and scalable with respect to different parameters, such as dataset sizes, k , εand number of computing nodes. Keywords: Distance join | Spatial data processing | Space partitioning | SpatialHadoop | LocationSpark | Spatial query evaluation |
مقاله انگلیسی |
3 |
Supergraph based periodic pattern mining in dynamic social networks
استخراج الگوهای دوره ای مبتنی بر سوپرگراف در شبکه های اجتماعی پویا-2017 In dynamic networks, periodically occurring interactions express especially significant meaning. However,
these patterns also could occur infrequently, which is why it is difficult to detect while working with
mass data. To identify such periodic patterns in dynamic networks, we propose single pass supergraph
based periodic pattern mining SPPMiner technique that is polynomial unlike most graph mining prob
lems. The proposed technique stores all entities in dynamic networks only once and calculate common
sub-patterns once at each timestamps. In this way, it works faster. The performance study shows that
SPPMiner method is time and memory efficient compared to others. In fact, the memory efficiency of our
approach does not depend on dynamic network’s lifetime. By studying the growth of periodic patterns in
social networks, the proposed research has potential implications for behavior prediction of intellectual
communities.
Keywords: Periodic patterns mining | Dynamic social networks | Supergraph |
مقاله انگلیسی |
4 |
Performance analysis of a parallel algorithm for restoring large-scale CT images
تجزیه و تحلیل عملکرد یک الگوریتم موازی برای بازگرداندن تصاویر CT در مقیاس بزرگ-2017 In multiple areas of image processing, such as Computed Tomography, in which data
acquisition is based on counting particles that hit a detector surface, Poisson noise occurs.
Using variance-stabilizing transformations, the Poisson noise can be approximated by a
Gaussian one, for which classical denoising filters can be used. This paper presents an
experimental performance study of a parallel implementation of the Poissonian image
restoration algorithm, introduced in Harizanov et al. (2013). Hybrid parallelization based
on MPI and OpenMP standards is investigated. The convergence rate of the algorithm
heavily depends on both the image size and the choice of input parameters (ρ,σ), thus
maximizing its parallel efficiency is vital for real-life applications. The implementation is
tested for high-resolution radiographic images, on Linux clusters with Intel processors and
on an IBM supercomputer.
Keywords: Primal–dual algorithm | Anscombe transform | Image restoration | Parallel algorithm | Epigraphical projection |
مقاله انگلیسی |
5 |
Efficient query processing on large spatial databases: A performance study
پردازش پرس و جو موثر در پایگاه داده های فضایی بزرگ: یک مطالعه عملکردی-2017 Processing of spatial queries has been studied extensively in the literature. In most cases, it is accom
plished by indexing spatial data using spatial access methods. Spatial indexes, such as those based on
the Quadtree, are important in spatial databases for efficient execution of queries involving spatial con
straints and objects. In this paper, we study a recent balanced disk-based index structure for point data,
called xBR+-tree, that belongs to the Quadtree family and hierarchically decomposes space in a regular
manner. For the most common spatial queries, like Point Location, Window, Distance Range, Nearest Neigh
bor and Distance-based Join, the R-tree family is a very popular choice of spatial index, due to its excellent
query performance. For this reason, we compare the performance of the xBR+-tree with respect to the
R∗-tree and the R+-tree for tree building and processing the most studied spatial queries. To perform this
comparison, we utilize existing algorithms and present new ones. We demonstrate through extensive ex
perimental performance results (I/O efficiency and execution time), based on medium and large real and
synthetic datasets, that the xBR+-tree is a big winner in execution time in all cases and a winner in I/O
in most cases.
Keywords: Spatial databases | Spatial access methods | Quadtrees | xBR-trees | R-trees | Query processing | Performance evaluation |
مقاله انگلیسی |
6 |
A large-scale web QoS prediction scheme for the Industrial Internet of Things based on a kernel machine learning algorithm
یک برنامه پیش بینی QoS در مقیاس بزرگ برای اینترنت اشیاء صنعتی بر اساس یک الگوریتم یادگیری ماشین هسته-2016 Cloud computing plays an essential role in enabling practical applications based on the
Industrial Internet of Things (IIoT). Hence, the quality of these services directly impacts
the usability of IIoT applications. To select or recommend the best web and cloud based
services, one method is to mine the vast data that are pertinent to the quality of service
(QoS) of such services. To enable dynamic discovery and composition of web services, one
can use a set of well-defined QoS criteria to describe and distinguish functionally similar
web services. In general, QoS is a nonfunctional performance index of web services, and
it might be user-dependent. Hence, to fully assess the QoS of all available web services, a
user normally would have to invoke every one of them. This implies that the QoS values
for services that the user has not invoked would be missing. If the number of web services
available is large, it is virtually inevitable for this to happen because invoking every sin
gle service would be prohibitively expensive. This issue is typically resolved by employing
some predication algorithms to estimate the missing QoS values. In this paper, a data
driven scheme of predicting the missing QoS values for the IIoT based on a kernel least
mean square algorithm (KLMS) is proposed. During the data prediction process, the Pear
son correlation coefficient (PCC) is initially introduced to find the relevant QoS values from
similar service users and web service items for each known QoS entry. Next, KLMS is used
to analyze the hidden relationships between all the known QoS data and corresponding
QoS data with the highest similarities. We therefore can apply the derived coefficients for
the prediction of missing web service QoS values. An extensive performance study based
on a public data set is conducted to verify the prediction accuracy of our proposed scheme.
This data set includes 200 distributed service users on 500 web service items with a to
tal of 1,858,260 intermediate data values. The experiment results show that our proposed
KLMS-based prediction scheme has better prediction accuracy than traditional approaches.
Keywords: Kernel least mean square | Quality of services (QoS) | QoS prediction | Pearson correlation coefficient (PCC) | Industrial Internet of Things (IIoT) |
مقاله انگلیسی |
7 |
Performance study of a synchronization algorithm for a 3-phase photovoltaic grid-connected system under harmonic distortions and unbalances
بررسی عملکرد یک الگوریتم هماهنگ سازی برای فتوولتائیک سیستم 3 فاز-شبکه متصل تحت تحریف هارمونیک و نامتعادل-2014 In a distributed generation (DG) system, several renewable agents are connected to the low-voltage 3-
phase utility grid through an inverter which is used as power condition and must guarantee the higher
efficiency of the renewable agent. To attain this level of efficiency, a unitary power factor (FP) between
the inverter currents and the utility grid voltages is necessary, and a synchronization algorithm is needed
for the perfect synchronization between the renewable agent and the 3-phase utility grid. Within this
context, this paper gives a performance study of the Positive Sequence Detector plus a Synchronous Ref
erence Frame Phase-Locked Loop (PSD + dqPLL) as the synchronization algorithm, evaluating its accuracy
under different conditions and studying their advantages and drawbacks. A grid-connected photovoltaic
system with a nominal power of 6 kW is used so as to evaluate the behavior of the synchronization
algorithm when the 3-phase utility grid is affected by some disturbances such as voltage unbalances,
frequency variations and harmonic distortions. Firstly, several simulations with a disturbed 3-phase util
ity grid using MATLAB/SIMULINK from The MathWorks, Inc. are shown, and secondly, the previous tests
are run in a Real-Time Digital Simulation (RTDS) platform in order to validate the obtained results with
simulations.
Keywords:
Photovoltaic
Voltage Source Inverter
Positive Sequence Detector
Real-Time Digital Simulation
Distributed Generation
Grid-Connected system |
مقاله انگلیسی |