Бидний тухай
Багш ажилтан
Outliers can affect misspecification of models, biased estimation of parameters, incorrect results and poor forecasts. Outliers in weather data arise due to human error and instrument error. Outliers can be classified into three categories: point outliers, collective outliers, and contextual outliers. In this paper, we propose a point outlier detection method based on a moving median filter. The method consists of three key steps. They are autocorrelation, moving median filter, and threshold determination. The autocorrelation step gives the information of similarity between immediate neighbors. The result of the autocorrelation step defines window width of the median filter. The output of the median filter gives candidate outliers and exact outliers detected based on the threshold values. Out analysis of the results demonstrate in this paper is successful and effective in detecting outliers in meteorological data.
Радио хугарлын илтгэгч нь радио долгионы тархалт болон радио системийн төлөвлөлтөд чухал параметр болдог. Энэ судалгааны ажлаар газрын гадаргуу орчмын радио хугарлын илтгэгчийн улирлын явцын загварыг Улаанбаатар хотын хувьд Гауссын функцийг ашиглан гаргасан. Бид радио хугарлын илтгэгчийн улирлын явцын загварыг сонгоход үндсэн хоёр шаардлага тавьсан. Нэгдүгээрт, загвар нь улирлын явцын ерөнхий шинж төрхтэй таарах ёстой. Хоёрдугаарт, загвар нь цөөн параметртэй байх ёстой. Ингэснээр загвар энгийн бөгөөд тооцоолол хялбар болно. Энэ үндсэн шаардлагуудаас гадна статистик хэмжүүрийг тохирох загвар сонгохдоо ашигласан. Үр дүнд нь есөн параметртэй Гауссын функцийг сонгож загварчилсан. Ингэж загварчилснаар тухайн жилийн аль ч өдрийн радио хугарлын илтгэгчийн утгыг таамаглах боломжтой болсон.
This paper presents a random walk based heuristic algorithm for detecting critical nodes in networks. There are a number of methods that are proposed for solving the problem. However, most of them are focused on sparse networks, and therefore studies on methods focusing on denser graphs are still needed. In this paper, we introduce two simple random walk based heuristic algorithms. The performance of the variants is conducted with extensive experiments on datasets from the literature. The result of experiments shows that the proposed algorithms can be competitive with the existing other methods.
In this paper, we analyzed the diurnal cycle of the hybrid model of surface radio refractivity in Ulaanbaatar, Mongolia. It is seen that diurnal variations vary in different seasons. The original hybrid model did not consider these changes. In this paper, diurnal model has determined in four different seasons for both deterministic and probabilistic model parts. Also, the methodology of the hybrid modeling for seasonal and diurnal models is presented.
In this paper, we analyzed the diurnal cycle of the hybrid model of surface radio refractivity in Ulaanbaatar, Mongolia. It is seen that diurnal variations vary in different seasons. The original hybrid model did not consider these changes. In this paper, diurnal model has determined in four different seasons for both deterministic and probabilistic model parts. Also, the methodology of the hybrid modeling for seasonal and diurnal models is presented.
This paper presents a random walk based heuristic algorithm for detecting critical nodes in networks. There are a number of methods that are proposed for solving the problem. However, most of them are focused on sparse networks, and therefore studies on methods focusing on denser graphs are still needed. In this paper, we introduce two simple random walk based heuristic algorithms. The performance of the variants is conducted with extensive experiments on datasets from the literature. The result of experiments shows that the proposed algorithms can be competitive with the existing other methods.
