pearson correlation coefficient
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فرونشست پدیده ای است بسیار مخرب و خطرناک که علاوه بر خطرات جانی برای انسان ها، می تواند به تاسیسات زیربنایی شهرها نیز آسیب برساند. یکی از دلایل ایجاد آن استخراج بی رویه آب زیرزمینی می باشد که به طور گسترده در دشت های ایران اتفاق می افتد. تداخل سنجی سری زمانی تصاویر راداری یکی از روش های مهم برای بررسی دقیق و پیوسته فرونشست است. اما مشکل اصلی این روش حذف پیکسل ها با همبستگی پایین در چرخه پردازش است. در این تحقیق برای غلبه بر این مشکل، فرونشست دشت رفسنجان با استفاده از روش سری زمانی SBAS بهبود یافته برپایه همدوسی بررسی شده است. داده های مورد استفاده 15 تصویر ماهواره SENTINEL-1 مربوط به محدوده زمانی مهرماه 1394 تا مهرماه 1395 است و50 تداخل نگاشت تولید شده است. نتایج حاصله توانایی این روش در استفاده از پیکسل ها با همبستگی پایین مربوط به مناطق پوشش گیاهی را نشان می دهد. بیشترین مقدار نرخ فرونشست 284میلی متر در سال برای محدوه دشت رفسنجان-بهرمان و 252میلی متر درسال برای محدوده دشت رفسنجان-کشکوییه در راستای خط دید ماهواره بدست آمد. برای بررسی رابطه بین نتایج SBAS بهبود یافته و سطح آب چاه های منطقه از ضریب همبستگی پیرسون و جهت مدل کردن رابطه از مدل رگرسیون خطی استفاده شد که نتایج بیانگر رابطه خطی مستقیم قوی است. همچنین مدل رگرسیون خطی قابلیت مدل کردن رابطه را با سطح اطمینان 95% دارا می باشد. برای بررسی معنی دار بودن مدل رگرسیون خطی از آزمون تحلیل واریانس (ANOVA) و به منظور بررسی خودهمبستگی باقی ماندها از آزمون دوربین- واتسون استفاده شد که نتایج آن معنی دار بودن مدل و استقلال مشاهدات را تایید می کند.کلید واژگان: فرونشست، تداخل سنجی، سری زمانی، SBAS بهبود یافته، Sentinel-1، مدل رگرسیون خطی، ضریب همبستگی پیرسونSubsidence is a very destructive and dangerous phenomenon that, in addition to endangering human life, can also damage the infrastructure of cities. One of the reasons for its creation is the uncontrolled extraction of groundwater, which occurs widely in the plains of Iran. The time Series InSAR method is one of the important methods for accurate and continuous monitoring of subsidence. But the main problem with this method is the removal of pixels with low correlation in the processing cycle. In this study, to overcome this problem, subsidence of Rafsanjan plain has been investigated using the improved SBAS time series method based on coherence. The data used are 15 images of SENTINEL-1 satellite related to the period from October 2015 to October 2016 and 50 interferograms are generated. The results show the ability of this method to use all pixels of the interferogram, even pixels related to vegetation areas with low correlation. The highest subsidence rate was 284 mm per year for Rafsanjan-Bahrman plain and 252 mm per year for Rafsanjan-Kashkoyeh plain along the satellite line of sight. To investigate the relationship between the improved SBAS results and the water level of wells in the region, Pearson correlation coefficient was used, and to model the relationship, a linear regression model was used. The results indicate a strong direct linear relationship. Also, the linear regression model has the ability to model the relationship with a 95% confidence level. Analysis of variance (ANOVA) was used to test the significance of the linear regression model and Durbin–Watson test was used to evaluate the autocorrelation in the residuals. The results confirm the significance of the model and the independence of the observations.Keywords: Subsidence, Time-Series, InSAR, Improved SBAS, Sentinel-1, Linear Regression Model, Pearson Correlation Coefficient
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Power system flexibility is the ability of power system to cope with the uncertainty and variability both in generation and load sides. This ability should be quantified and measured by a suitable index to show the level of system flexibility in different situations. Flexibility area index, proposed by the authors is a suitable metric for power system flexibility evaluation especially in the presence of renewable sources as large scale wind and solar farms. Similar other system flexibility indices, this index is defined at first for one generation unit and then extended to the power system by combination the unit indices. In this way an accurate and meaningful combination routine should be established to reflect the effect of each unit flexibility index in the combined system flexibility index correctly.This paper proposes a suitable and justified method to combine the unit flexibility indices achieving the system flexibility index. The performance of the proposed index is verified by the wind/load curtailment in economic load dispatch incorporated wind power. Achieving this purpose, the mentioned index is decomposed into two components, one for ramp up and maximum generation system capabilities (upper component) and another for ramp down and minimum generation system capabilities (lower component) each of them related to the load or wind curtailment respectively which is another contribution of this paper. Finally by establishment a correlation between upper/lower component and load/wind curtailment, a suitable validity evaluation for the proposed system flexibility index is done which is another contribution of this paper.
Keywords: Power system flexibility, Flexibility area index, System flexibility index, wind, load curtailment, Pearson correlation coefficient -
Journal of Artificial Intelligence and Data Mining, Volume:3 Issue: 1, Winter-Spring 2015, PP 39 -46With rapid development in information gathering technologies and access to large amounts of data, we always require methods for data analyzing and extracting useful information from large raw dataset and data mining is an important method for solving this problem. Clustering analysis as the most commonly used function of data mining, has attracted many researchers in computer science. Because of different applications, the problem of clustering the time series data has become highly popular and many algorithms have been proposed in this field. Recently Swarm Intelligence (SI) as a family of nature inspired algorithms has gained huge popularity in the field of pattern recognition and clustering. In this paper, a technique for clustering time series data using a particle swarm optimization (PSO) approach has been proposed, and Pearson Correlation Coefficient as one of the most commonly-used distance measures for time series is considered. The proposed technique is able to find (near) optimal cluster centers during the clustering process. To reduce the dimensionality of the search space and improve the performance of the proposed method, a singular value decomposition (SVD) representation of cluster centers is considered. Experimental results over three popular data sets indicate the superiority of the proposed technique in comparing with fuzzy C-means and fuzzy K-medoids clustering techniques.Keywords: Clustering, Time Series, Particle Swarm Optimization, singular value decomposition, Pearson Correlation Coefficient
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