scholarly journals How to correctly apply Gaussian statistics in a non-stationary climate?

2021 ◽  
Author(s):  
Reinhold Steinacker
Keyword(s):  
Time Series ◽  
Actual Data ◽  
Temperature Record ◽  
Positive Trend ◽  
Positive Temperature ◽  
Climatic Trend ◽  
Filter Methods ◽  
Periodic Time Series ◽  
Past Data

AbstractTime series with a significant trend, as is now being the case for the temperature in the course of climate change, need a careful approach for statistical evaluations. Climatological means and moments are usually taken from past data which means that the statistics does not fit to actual data anymore. Therefore, we need to determine the long-term trend before comparing actual data with the actual climate. This is not an easy task, because the determination of the signal—a climatic trend—is influenced by the random scatter of observed data. Different filter methods are tested upon their quality to obtain realistic smoothed trends of observed time series. A new method is proposed, which is based on a variational principle. It outperforms other conventional methods of smoothing, especially if periodic time series are processed. This new methodology is used to test, how extreme the temperature of 2018 in Vienna actually was. It is shown that the new annual temperature record of 2018 is not too extreme, if we consider the positive trend of the last decades. Also, the daily mean temperatures of 2018 are not found to be really extreme according to the present climate. The real extreme of the temperature record of Vienna—and many other places around the world—is the strongly increased positive temperature trend over the last years.

The Microbiological Quality of Honey as Determined by Aerobic Colony Counts

1993 ◽  
Vol 56 (4) ◽  
pp. 336-337 ◽  
Author(s):  
JOSEP SERRA BONVEHI ◽  
ROSSEND ESCOLÁ JORDÁ
Keyword(s):  
Membrane Filter ◽  
Microbiological Quality ◽  
Plate Count ◽  
Filter Method ◽  
Filter Methods ◽  
Plate Counts ◽  
Multifloral Honey ◽  
Plate Count Method ◽  
Membrane Filter Method

The number of mesophilic aerobic colonies was determined in 72 samples of mono- and multifloral honey from various sources by the plate count and the membrane filter methods. The presence of motile colonies made the plate counts unreliable. The microorganism producing these colonies was identified as Bacillus alvei. Colony counts could only be carried out in 27 of the samples when using the plate count method, while with the membrane filter method the number of colonies was counted in all the samples.

scholarly journals Thermal anomalies detection before strong earthquakes (<i>M</i> > 6.0) using interquartile, wavelet and Kalman filter methods

2011 ◽  
Vol 11 (4) ◽  
pp. 1099-1108 ◽  
Author(s):  
M. R. Saradjian ◽  
M. Akhoondzadeh
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Wavelet Transform ◽  
Anomaly Detection ◽  
Land Surface ◽  
Seasonal Effects ◽  
Filter Method ◽  
Strong Earthquakes ◽  
Thermal Anomalies ◽  
Filter Methods

Abstract. Thermal anomaly is known as a significant precursor of strong earthquakes, therefore Land Surface Temperature (LST) time series have been analyzed in this study to locate relevant anomalous variations prior to the Bam (26 December 2003), Zarand (22 February 2005) and Borujerd (31 March 2006) earthquakes. The duration of the three datasets which are comprised of MODIS LST images is 44, 28 and 46 days for the Bam, Zarand and Borujerd earthquakes, respectively. In order to exclude variations of LST from temperature seasonal effects, Air Temperature (AT) data derived from the meteorological stations close to the earthquakes epicenters have been taken into account. The detection of thermal anomalies has been assessed using interquartile, wavelet transform and Kalman filter methods, each presenting its own independent property in anomaly detection. The interquartile method has been used to construct the higher and lower bounds in LST data to detect disturbed states outside the bounds which might be associated with impending earthquakes. The wavelet transform method has been used to locate local maxima within each time series of LST data for identifying earthquake anomalies by a predefined threshold. Also, the prediction property of the Kalman filter has been used in the detection process of prominent LST anomalies. The results concerning the methodology indicate that the interquartile method is capable of detecting the highest intensity anomaly values, the wavelet transform is sensitive to sudden changes, and the Kalman filter method significantly detects the highest unpredictable variations of LST. The three methods detected anomalous occurrences during 1 to 20 days prior to the earthquakes showing close agreement in results found between the different applied methods on LST data in the detection of pre-seismic anomalies. The proposed method for anomaly detection was also applied on regions irrelevant to earthquakes for which no anomaly was detected, indicating that the anomalous behaviors can be related to impending earthquakes. The proposed method receives its credibility from the overall capabilities of the three integrated methods.

scholarly journals People counter on CCTV video using histogram of oriented gradient and Kalman filter methods

2020 ◽  
Vol 8 (3) ◽  
pp. 222-227
Author(s):  
Faisal Dharma Adhinata ◽  
Muhammad Ikhsan ◽  
Wahyono Wahyono
Keyword(s):  
Kalman Filter ◽  
Mutual Information ◽  
Public Service ◽  
Information Entropy ◽  
Support Service ◽  
Entropy Method ◽  
Histogram Of Oriented Gradient ◽  
Filter Methods ◽  
Human Object ◽  
Long Time

CCTV cameras have an important function in the field of public service, especially for convenience. The objects recorded through CCTV cameras are processed into information to support service satisfaction in the community. This study uses the function of CCTV for people counting from objects recorded by a camera. Currently, the process of detecting and tracking people takes a long time to detect all frames. In this study, the frame selection into keyframes uses the mutual information entropy method. The keyframes processing uses the Histogram of Oriented Gradient (HOG) and Kalman filter methods. The proposed method results F1 value of 0.85, recall of 76 %, and precision of 97 % with winStride parameter (12,12), scale 1.05, and the distance of the human object to CCTV 4 meters.

Examining Swarm Intelligence-based Feature Selection for Multi-Label Classification

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Awder Mohammed Ahmed ◽  
◽  
Adnan Mohsin Abdulazeez ◽  
Keyword(s):  
Feature Selection ◽  
Swarm Intelligence ◽  
Real World ◽  
Computational Cost ◽  
Computing Methods ◽  
Open Problems ◽  
Label Data ◽  
Filter Methods ◽  
Wide Range ◽  
Classification Tasks

Multi-label classification addresses the issues that more than one class label assigns to each instance. Many real-world multi-label classification tasks are high-dimensional due to digital technologies, leading to reduced performance of traditional multi-label classifiers. Feature selection is a common and successful approach to tackling this problem by retaining relevant features and eliminating redundant ones to reduce dimensionality. There is several feature selection that is successfully applied in multi-label learning. Most of those features are wrapper methods that employ a multi-label classifier in their processes. They run a classifier in each step, which requires a high computational cost, and thus they suffer from scalability issues. Filter methods are introduced to evaluate the feature subsets using information-theoretic mechanisms instead of running classifiers to deal with this issue. Most of the existing researches and review papers dealing with feature selection in single-label data. While, recently multi-label classification has a wide range of real-world applications such as image classification, emotion analysis, text mining, and bioinformatics. Moreover, researchers have recently focused on applying swarm intelligence methods in selecting prominent features of multi-label data. To the best of our knowledge, there is no review paper that reviews swarm intelligence-based methods for multi-label feature selection. Thus, in this paper, we provide a comprehensive review of different swarm intelligence and evolutionary computing methods of feature selection presented for multi-label classification tasks. To this end, in this review, we have investigated most of the well-known and state-of-the-art methods and categorize them based on different perspectives. We then provided the main characteristics of the existing multi-label feature selection techniques and compared them analytically. We also introduce benchmarks, evaluation measures, and standard datasets to facilitate research in this field. Moreover, we performed some experiments to compare existing works, and at the end of this survey, some challenges, issues, and open problems of this field are introduced to be considered by researchers in the future.

Anti-interference Test for the New SEP Instrument: CSAMT Study at Dongguashan Copper Mine, China

2017 ◽  
Vol 22 (4) ◽  
pp. 339-352
Author(s):  
Da Lei ◽  
Qing-yun Di ◽  
Jun-jie Wu ◽  
Xing-chun Wang ◽  
Yun-xiang Liu ◽  
...  
Keyword(s):  
Electromagnetic Interference ◽  
Signal To Noise Ratio ◽  
Field Work ◽  
Noisy Environment ◽  
Quality Of Data ◽  
Copper Mine ◽  
Filter Methods ◽  
Temporal Filter ◽  
Interference Test

In this paper, an independently developed device system called Surface Electromagnetic Prospecting (SEP) system was introduced through a CSAMT test at Dongguashan copper mine, east of Tonglin, Anhui province. In this area exists a strong electromagnetic interference including mineral and other human interferences, so there is a big challenge for electromagnetic exploration field work. In order to test the anti-interference ability of our system and ensure the quality of data, we applied both spectral analysis and temporal filter methods to improve the signal-to-noise ratio. After data processing, we obtained better results in our geophysical models and showed that the SEP system is able to obtain stable and reliable data in a complex and noisy environment. Consequently, the anti-interference capability of the SEP system is capable to undertake complicated exploration tasks.

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