دانلود و نمایش مقالات مرتبط با autocorrelation::صفحه 1
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نتیجه جستجو - autocorrelation

تعداد مقالات یافته شده: 9
ردیف عنوان نوع
1 روش ردیابی خودرو بهبود یافته برای IEEE 802:11p
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 10
توسعه روش های موقعیت یابی با قابلیت تحرک- بالا با استفاده از استاندارد IEEE 802.11p در شبکه های ادهاک وسایل نقلیه (VANETs) به دلیل نقاط ضعف در ناحیه های GNSS-dark مانند جنگل ها، تونل و غیره، و اشتباهات ناشی از GNSS-dark در نتایج ، ضروری است. برآورد زمان دقیق رسیدن(TOA) مبتنی بر مدل مسافت یابی ، به عنوان یکی از چالش های سیستم پیشگیری از برخورد اتومبیل ها، توجه زیادی را به خود جلب است. در این مقاله، روش پیشنهادی TOA یا روش تخمین مسافت با راهنمای کوتاه IEEE 802.11p پیشنهاد شد تا اثربخشی اندازه گیری های وسایل نقلیه چندکاره و نسبت نویز سیگنال کم (SNR) را کاهش دهد. ابتدا، TOA با استفاده از همبستگی خودکار و همبستگی-متقاطع برآورد شد. سپس، رویکرد sum برای یافتن مبدا دقیق زمان ارائه شد. نتایج شبیه سازی در کانال اتحادیه بین المللی مخابرات خودرو (ITUA) و کانال نویز گاوسی سفید افزایشی (AWGN)، برتری الگوریتم پیشنهادی را در شرایط SNR کم و محیط چندکاره ثابت می کند.
کليدواژه: برآورد TOA | IEEE 802.11p | VANETS | دامنه | همبستگی خودکار | همبستگی- متقابل
مقاله ترجمه شده
2 Spatial cumulative sum algorithm with big data analytics for climate change detection
الگوریتم مجموع تجمعی فضایی با تجزیه و تحلیل داده های بزرگ برای تشخیص تغییرات اقلیمی-2018
Big data plays a vital role in the prediction of diseases that occur due to climate change. For such predictions, scalable data storage platforms and efficient change detection algo rithms are required to monitor the climate change. However, traditional data storage tech niques and algorithms are not applicable to process the huge amount of climate data. This paper presents a scalable data processing framework with a novel change detection al gorithm. The large volume of climate data is stored on Hadoop Distributed File System (HDFS) and MapReduce algorithm is applied to calculate the seasonal average of climate parameters. Spatial autocorrelation based climate change detection algorithm is proposed in this paper to monitor the changes in the seasonal climate. The proposed climate change detection algorithm is compared with various existing approaches such as pruned exact linear time method, binary segmentation method, and segment neighborhood method.
Keywords: Hadoop Distributed File System ، Big data ،Climate change ، Data analytics ، Weather sensor data
مقاله انگلیسی
3 An approach combining data mining and control charts-based model for fault detection in wind turbines
رویکرد ترکیب داده کاوی و مدل مبتنی بر کنترل نمودار برای تشخیص خطا در توربین های بادی-2018
Wind energy is growing to be one of main sources of renewable energy. As the operational and main tenance costs of wind turbines are adversely affected by the occurrence of faults, the early detection of potential faults can help reduce such costs. In this study, we propose a method for detecting potential faults sooner and identifying the probable variables contributing to the faults over a certain period as well as at a specific time. The proposed method uses data mining techniques to select the more important variables from the supervisory control and data acquisition (SCADA) systems of the turbine to improve the prediction accuracy and employs an exponentially weighted moving average (EWMA) model-based control chart to implement the residual approach, in order to remove the autocorrelation in the data. Both EWMA and multivariate EWMA (MEWMA) control charts are constructed so that their detection capabilities as well as the types of errors generated can be compared. We evaluated the proposed method by using both the SCADA data and the alarm log of a turbine. It was observed that the MEWMA chart is more suitable than the EWMA chart for the early detection and avoidance of errors.
Keywords: Wind power ، Fault diagnosis ، Feature extraction ، Statistical process control
مقاله انگلیسی
4 Land-Use Degree and Spatial Autocorrelation Analysis in Kunming City Based on Big Data
کاربرد زمین شناسی و تجزیه و تحلیل خودکار فضایی در شهر کونمینگ بر اساس داده های بزرگ-2018
This paper applies the theory of spatial correlation, basing on big data of ENVI remote sensing image interpretation and the land use data of 14 counties in Kunming, to analyze the spatial autocorrelation of land use structure and land use degree in Kunming in 2005, 2011 and 2015 with Moran Index I. The research shows: (1) The share of cultivated land and vegetation coverage in the land use structure of Kunming are large, but the area of cultivated land is decreasing while the area of construction land is increasing. (2)There is a global spatial autocorrelation of land use in Kunming, but the correlation is weakening. (3)There is local spatial autocorrelation clustering in land use degree in Kunming, including four types: high - high agglomerations(HH), high - low agglomerations(LL), low - low agglomerations, and low - high agglomerations(LH). But the agglomerations are weakening.
Keywords : remote sensing interpretation, land use degree, spatial autocorrelation,Kunming city, big data
مقاله انگلیسی
5 Weather and cycling: Mining big data to have an in-depth understanding of the association of weather variability with cycling on an off-road trail and an on-road bike lane
آب و هوا و دوچرخه سواری: کاوش داده های بزرگ برای درک عمیق از ارتباط تغییرات آب و هوایی با دوچرخه سواری در یک مسیر بدون درز و مسیر دوچرخه در جاده-2018
Although cycling is an easy and popular form of physical activity and urban travel, barriers exist. In particular, cycling is more likely and more severely to be affected by inclement weather than the motorized modes as the cyclists are entirely exposed to outdoor environment. Understanding the weather-cycling relationship is of great importance to academics and practitioners for cycling activity analysis and promotion. This study contributes to an in-depth understanding of how the changes in weather conditions affect cycling on an off-road trail and an on-road (bridge) bike lane at both daily and hourly scales across four seasons. The paper compares the weather-cycling relationship based on day of week and time of day combinations. The autocorrelation effect of cycling itself and the lagging effect of weather elements are also examined. The findings indicate that cycling is significantly self-dependent especially at the finer temporal scales. Weather have a very different influence on bicycle usage of off-road trails versus on-road bike lanes. When it rains its negative impact not only continues but also significantly affects the cycling within previous one hour. At the daily level, weekend cycling on the trail is less likely to be affected by weather as compared to cycling on the bike lane, whilst inverse is true for weekday cycling. Cycling is most likely to be affected by weather conditions in spring and least likely to be affected in winter. Cycling pattern which is more unrelated to weather at the aggregated level tends to be more flexibly adjusted according to the real-time weather conditions at the disaggregated level. Cyclists on weekends especially during the weekend peak hours (11 AM–4 PM) tend to have more flexibility to adjust their cycling schedule before or after the adverse weather conditions than on weekdays. In addition, cyclists with utilitarian purposes are more likely to shift from cycling to other modes (e.g., transit) due to real-time bad weather conditions in weekdays than in week ends, especially during weekday peak hours (7–9 AM and 4–6 PM). The results provide weather officials, transport agencies and research institutions with valuable information for cycling ac tivity analysis and promotion by considering the effects of weather events especially rainfall.
Keywords: Weather ، Cycling ، Off-road trail ، On-road (bridge) bike lane ، Lagging effect ، Rainfall
مقاله انگلیسی
6 Suppressing correlations in massively parallel simulations of lattice models
سرکوب همبستگی در شبیه سازی موازی انبوه از مدل های شبکه-2017
For lattice Monte Carlo simulations parallelization is crucial to make studies of large systems and long simulation time feasible, while sequential simulations remain the gold-standard for correlation-free dynamics. Here, various domain decomposition schemes are compared, concluding with one which delivers virtually correlation-free simulations on GPUs. Extensive simulations of the octahedron model for 2 + 1 dimensional Kardar–Parisi–Zhang surface growth, which is very sensitive to correlation in the site-selection dynamics, were performed to show self-consistency of the parallel runs and agreement with the sequential algorithm. We present a GPU implementation providing a speedup of about 30× over a parallel CPU implementation on a single socket and at least 180× with respect to the sequential reference.
Keywords: Lattice Monte Carlo | Kardar–Parisi–Zhang | GPU | Autocorrelation
مقاله انگلیسی
7 Geographically weighted negative binomial regression applied to zonal level safety performance models
اعمال رگرسیون دو جانبه منفی با توجه به جغرافیایی به مدل های عملکرد ایمنی سطح منطقه ای -2017
Generalized Linear Models (GLM) with negative binomial distribution for errors, have been widely used to estimate safety at the level of transportation planning. The limited ability of this technique to take spatial effects into account can be overcome through the use of local models from spatial regression techniques, such as Geographically Weighted Poisson Regression (GWPR). Although GWPR is a system that deals with spatial de pendency and heterogeneity and has already been used in some road safety studies at the planning level, it fails to account for the possible overdispersion that can be found in the observations on road-traffic crashes. Two approaches were adopted for the Geographically Weighted Negative Binomial Regression (GWNBR) model to allow discrete data to be modeled in a non-stationary form and to take note of the overdispersion of the data: the first examines the constant overdispersion for all the traffic zones and the second includes the variable for each spatial unit. This research conducts a comparative analysis between non-spatial global crash prediction models and spatial local GWPR and GWNBR at the level of traffic zones in Fortaleza/Brazil. A geographic database of 126 traffic zones was compiled from the available data on exposure, network characteristics, socioeconomic factors and land use. The models were calibrated by using the frequency of injury crashes as a dependent variable and the results showed that GWPR and GWNBR achieved a better performance than GLM for the average residuals and likelihood as well as reducing the spatial autocorrelation of the residuals, and the GWNBR model was more able to capture the spatial heterogeneity of the crash frequency.
Keywords: Safety performance models | Spatial dependency | Local spatial models | Geographically weighted poisson regression | Geographically weighted negative binomial | regression
مقاله انگلیسی
8 Global investigation of return autocorrelation and its determinants
تحقیق و بررسی جهانی از بازگشت همبستگی خودکار و عوامل تعیین کننده ان-2017
We estimate global return autocorrelation by using the quantile autocorrelation model and investigate its determinants across 43 stock markets from 1980 to 2013. Although our results document a decline in autocorrelation across the entire sample period for all countries, return autocorrelation is significantly larger in emerging markets than in developed markets. The results further document that larger and liquid stock markets have lower return autocorrelation. We also find that price limits in most emerging markets result in higher return autocorrelation. We show that the disclosure requirement, public enforcement, investor psychology, and market character istics significantly affect return autocorrelation. Our results document that investors from different cultural backgrounds and regulation regimes react differently to corporate disclosers, which affects return autocorrelation.
Keywords: Return autocorrelation | Global stock markets | Quantile autoregression model | Legal environment | Investor psychology | Hofstedes cultural dimensions
مقاله انگلیسی
9 دستورالعمل ها برای استفاده از روش کنترل کیفیت آماری به منظور نظارت بر فرایندهای خود همبستگی
سال انتشار: 2014 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 20
اجرای نمودارهای کنترل آماری در شرایط خود همبستگی یک مسئله حیاتی است چرا که تاثیر قابل توجهی در قابلیت نظارت پروسه های تولیدی دارد. هدف از این مطالعه ارزیابی عملکرد نمودارهای کنترل زیر سناریو های مختلف و بهینه سازی طراحی نمودارهای کنترل به بهترین معامله همراه با فرآیندهای خود همبسته , میباشد . به منظور رسیدن به هدف ارائه شده , از دو اتورگرسیو (خود برگشتگی ) یکپارچه که در حال حرکت به سوی مدل های میانگین هستند , یعنی ( ARIMA (1, 0, 1 و( ARIMA (0, 1, 1 , برای توصیف فرآیندهای ثابت و غیر ثابت استفاده شد . این مدل های فرایند برای رسیدن به پاسخ , به طور متوسط طول اجرا (ARL) , که جز اقدامات عملکردی این مطالعه است, شبیه سازی شدند. فاکتورهای آزمایش برای تعیین کمیت اثر عوامل بحرانی, به عنوان مثال, ضریب ARIMA , انواع نمودارها ( میانگین نمایی متحرک EWMA و دامنه حرکت: MR) و تغییر اندازه در ARL , استفاده شدند . نتایج تجربی نشان می دهد که نمودار EWMA , مناسبترین نمودار کنترل برای نظارت بر مشاهدات خود همبستگی میباشند . علاوه بر این , هر دو پارامتر AR و MA همراه با اندازه تغییر , دارای اثر قابل توجهی بر روی عملکرد نمودارهای کنترل هستند. بنابراین ,این مطالعه اشاره به یک ابزار مناسب برای استفاده از سناریوهای (طرح راهنمای ) مختلف همبستگی کرده است . اعتبار نتایج تجربی فوق در مدلی دیگر از ARIMA و ARIMA (1, 0, 0) اعمال شد . اگر عملکرد نمودارهای کنترل که تحت اختلالات خود همبستگی هستند به درستی مشخص شوند , محققین و دست اندرکاران , راهنماهایی برای دستیابی به بالاترین پتانسیل عملکرد ممکن , هنگام استقرار SPC خواهند داشت .
واژه های کلیدی: همبستگی | میانگین متحرک اتورگرسیو یکپارچه (ARIMA) | میانگین طول اجرا (ARL) | معدل متحرک موزون نمایی (EWMA) نمودار | محدوده حرکت (MR) نمودار.
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