scholarly journals Using spline-extrapolation in the research of self-similar traffic characteristics

2019 ◽  
Vol 70 (4) ◽  
pp. 310-316 ◽  
Author(s):  
Irina Strelkovskaya ◽  
Irina Solovskaya
Keyword(s):  
Traffic Management ◽  
Cubic Splines ◽  
Short Term ◽  
Traffic Forecasting ◽  
Traffic Characteristics ◽  
Packet Data ◽  
Self Similar ◽  
Comparison Of The Results ◽  
Packet Data Transmission

Abstract The problem of predicting self-similar traffic is considered, the solution of which modeling of self-similar traffic was performed using the Simulink software package in MATLAB environment. For the simulation, the queuing system WB/M/1/K with Weibull distribution was used. The use of the spline-extrapolation method made it possible to predict self-similar traffic outside the considered period of time on which packet data transmission is considered. Extrapolation of traffic for short-term and long-term forecasts is considered. Comparison of the results of the prediction of self-similar traffic using various spline functions has shown that the accuracy of the forecast can be improved through the use of cubic splines. A method is proposed for estimating the error of traffic prediction for each variant of traffic forecasting using linear, cubic splines. The results of the research will allow you to perform effective traffic management with the support of quality characteristics, by providing the required parameters of network hardware and software in order to avoid overloads in the network.

scholarly journals Spline-Extrapolation Method in Traffic Forecasting in 5G Networks

2019 ◽  
Vol 3 ◽  
pp. 8-16 ◽  
Author(s):  
Irina Strelkovskaya ◽  
Irina Solovskaya ◽  
Anastasiya Makoganiuk
Keyword(s):  
Research Work ◽  
Mobile Network ◽  
Spline Functions ◽  
B Splines ◽  
Traffic Forecasting ◽  
Short And Long Term ◽  
Self Similar ◽  
Comparison Of The Results ◽  
Packet Data Transmission

This paper considers the problem of predicting self-similar traffic with a significant number of pulsations and the property of long-term dependence, using various spline functions. The research work focused on the process of modeling self-similar traffic handled in a mobile network. A splineextrapolation method based on various spline functions (linear, cubic and cubic B-splines) is proposed to predict selfsimilar traffic outside the period of time in which packet data transmission occurs. Extrapolation of traffic for short- and long-term forecasts is considered. Comparison of the results of the prediction of self-similar traffic using various spline functions has shown that the accuracy of the forecast can be improved through the use of cubic B-splines. The results allow to conclude that it is advisable to use spline extrapolation in predicting self-similar traffic, thereby recommending this method for use in practice in solving traffic prediction-related problems.

PREDICTING CHARACTERISTICS OF SELF SIMILAR TRAFFIC BY SPLINE-EXTRAPOLATION

2019 ◽  
Author(s):  
Irina Strelkovskay ◽  
Irina Solovskaya ◽  
Anastasija Makoganjuk ◽  
Nikolaj Severin
Keyword(s):  
Quality Characteristics ◽  
Traffic Prediction ◽  
Normative Values ◽  
Spline Functions ◽  
Network Nodes ◽  
Traffic Forecasting ◽  
Network Equipment ◽  
Self Similar ◽  
Accuracy Of Prediction

The problem of forecasting self-similar traffic, which is characterized by a considerable number of ripples and the property of long-term dependence, is considered. It is proposed to use the method of spline extrapolation using linear and cubic splines. The results of self-similar traffic prediction were obtained, which will allow to predict the necessary size of the buffer devices of the network nodes in order to avoid congestion in the network and exceed the normative values ​​of QoS quality characteristics. The solution of the problem of self-similar traffic forecasting obtained with the Simulink software package in Matlab environment is considered. A method of extrapolation based on spline functions is developed. The proposed method has several advantages over the known methods, first of all, it is sufficient ease of implementation, low resource intensity and accuracy of prediction, which can be enhanced by the use of quadratic or cubic interpolation spline functions. Using the method of spline extrapolation, the results of self-similar traffic prediction were obtained, which will allow to predict the required volume of buffer devices, thereby avoiding network congestion and exceeding the normative values ​​of QoS quality characteristics. Given that self-similar traffic is characterized by the presence of "bursts" and a long-term dependence between the moments of receipt of applications in this study, given predetermined data to improve the prediction accuracy, it is possible to use extrapolation based on wavelet functions, the so-called wavelet-extrapolation method. Based on the results of traffic forecasting, taking into account the maximum values ​​of network node traffic, you can give practical guidance on how traffic is redistributed across the network. This will balance the load of network objects and increase the efficiency of network equipment.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals EFFECTS OF WELDING PARAMETERS, TIME INTERVAL AND PREHEATING ON RESIDUAL STRESS AND DISTORTION OF JOINING ST52 STIFFENER RING IN AN AISI 4130 TUBULAR SHELL

2020 ◽  
Vol 3 (1) ◽  
pp. 11-15
Author(s):  
Alireza M. Haghighi ◽  
Farhad S. Samani
Keyword(s):  
Residual Stress ◽  
Welding Current ◽  
Welding Parameters ◽  
Time Interval ◽  
Negative Effects ◽  
Short Term ◽  
Welding Line ◽  
Aisi 4130 ◽  
Comparison Of The Results

Stiffener rings and stringers are used commonly in offshore and aerospace structures. Welding the stiffener to the structure causes the appearance of residual stress and distortion that leads to short-term and long-term negative effects. Residual stress and distortion of welding have destructive effects such as deformation, brittle fracture, and fatigue of the welded structures. This paper aims to investigate the effects of preheating, time interval and welding parameters such as welding current and speed on residual stress and distortion of joining an ST52-3N (DIN 1.0570) T-shape stiffener ring to an AISI 4130 (DIN 1.7218) thin-walled tubular shell by eleven pairs of welding line in both sides of the ring by means of finite element method (FEM). Results in tangent (longitudinal), axial and radial directions have been compared and the best welding methods proposed. After the comparison of the results, simultaneous welding both sides of the ring with preheating presented as the best method with less distortion and residual stresses among the studied conditions. The correctness of the FEM confirmed by the validation of the results.

DEEP LEARNING WITH LONG-TERM SHORT-TERM MEMORY FOR IOT TRAFFIC PREDICTION

2021 ◽  
Author(s):  
А.С. БОРОДИН ◽  
А.Р. АБДЕЛЛАХ ◽  
А.Е. КУЧЕРЯВЫЙ
Keyword(s):  
Deep Learning ◽  
Communication Networks ◽  
Short Term Memory ◽  
Traffic Prediction ◽  
Short Term ◽  
Term Memory ◽  
Traffic Forecasting ◽  
Long Short Term Memory ◽  
Iot Devices

Использование искусственного интеллекта в сетях связи пятого (5G) и последующих поколений дает новые возможности, в том числе для прогнозирования трафика. Это особенно важно для трафика интернета вещей (IoT - Internet of Things), поскольку число устройств IoT очень велико. Предлагается для прогнозирования трафика IoT применить глубокое обучение с использованием нейронной сети долговременной краткосрочной памяти LSTM (Long Short-Term Memory). The use of artificial intelligence in communication networks of the 5G and subsequent generations provides completely new opportunities, including for traffic forecasting. This is especially important for IoT traffic because the number of IoT devices is very large. The article proposes to apply deep learning to predict IoT traffic using a neural network of longterm short-term memory (LSTM).

Long-Term and Short-Term Traffic Forecasting Using Holt-Winters Method

2018 ◽  
pp. 1758-1772
Author(s):  
Aditya R. Raikwar ◽  
Rahul R. Sadawarte ◽  
Rishikesh G. More ◽  
Rutuja S. Gunjal ◽  
Parikshit N. Mahalle ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Data Science ◽  
Forecasting Model ◽  
Short Term ◽  
Traffic Forecasting ◽  
Important Solution ◽  
System Data ◽  
Long Term Forecasting ◽  
Access To Data

The need of faster life has caused the exponential growth in No. of vehicles on streets. The adverse effects include frequent traffic congestion, less time efficiency, unnecessary fuel consumption, pollution, accidents, etc. One of most important solution for resolving these problems is efficient transportation management system. Data science introduces different techniques and tools for overcoming these problems and to improve the data quality and forecasting inferences. The proposed long-term forecasting model can predict numerical values of effective attributes for a particular day on half-hourly basis, at least 24 hours prior to the time of prediction. The proposed forecasting model for short-term analysis will be having access to data as close as 30-minute difference from the time of prediction. Our proposed solution has integrated use of Holt-Winters (HW) method along with comparability schemes for seasonal approach.

Long-Term and Short-Term Traffic Forecasting Using Holt-Winters Method

2017 ◽  
Vol 8 (2) ◽  
pp. 38-50 ◽  
Author(s):  
Aditya R. Raikwar ◽  
Rahul R. Sadawarte ◽  
Rishikesh G. More ◽  
Rutuja S. Gunjal ◽  
Parikshit N. Mahalle ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Data Science ◽  
Forecasting Model ◽  
Short Term ◽  
Traffic Forecasting ◽  
Important Solution ◽  
System Data ◽  
Long Term Forecasting ◽  
Access To Data

The need of faster life has caused the exponential growth in No. of vehicles on streets. The adverse effects include frequent traffic congestion, less time efficiency, unnecessary fuel consumption, pollution, accidents, etc. One of most important solution for resolving these problems is efficient transportation management system. Data science introduces different techniques and tools for overcoming these problems and to improve the data quality and forecasting inferences. The proposed long-term forecasting model can predict numerical values of effective attributes for a particular day on half-hourly basis, at least 24 hours prior to the time of prediction. The proposed forecasting model for short-term analysis will be having access to data as close as 30-minute difference from the time of prediction. Our proposed solution has integrated use of Holt-Winters (HW) method along with comparability schemes for seasonal approach.

Conceptual short-term memory (CSTM) supports core claims of Christiansen and Chater

2016 ◽  
Vol 39 ◽  
Author(s):  
Mary C. Potter
Keyword(s):  
Working Memory ◽  
Rapid Serial Visual Presentation ◽  
Language Comprehension ◽  
Short Term Memory ◽  
Visual Presentation ◽  
Long Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Use Of Knowledge

AbstractRapid serial visual presentation (RSVP) of words or pictured scenes provides evidence for a large-capacity conceptual short-term memory (CSTM) that momentarily provides rich associated material from long-term memory, permitting rapid chunking (Potter 1993; 2009; 2012). In perception of scenes as well as language comprehension, we make use of knowledge that briefly exceeds the supposed limits of working memory.

Ultrastructural investigations of MDL 19,660-induced effects on the canine platelet and megakaryocyte

1990 ◽  
Vol 48 (3) ◽  
pp. 374-375
Author(s):  
D.E. Loudy ◽  
J. Sprinkle-Cavallo ◽  
J.T. Yarrington ◽  
F.Y. Thompson ◽  
J.P. Gibson
Keyword(s):  
Antidepressant Drug ◽  
Beagle Dogs ◽  
Long Term Effects ◽  
Short Term ◽  
Platelet Counts ◽  
Toxicological Studies ◽  
Induced Aggregation ◽  
Internal Architecture

Previous short term toxicological studies of one to two weeks duration have demonstrated that MDL 19,660 (5-(4-chlorophenyl)-2,4-dihydro-2,4-dimethyl-3Hl, 2,4-triazole-3-thione), an antidepressant drug, causes a dose-related thrombocytopenia in dogs. Platelet counts started to decline after two days of dosing with 30 mg/kg/day and continued to decrease to their lowest levels by 5-7 days. The loss in platelets was primarily of the small discoid subpopulation. In vitro studies have also indicated that MDL 19,660: does not spontaneously aggregate canine platelets and has moderate antiaggregating properties by inhibiting ADP-induced aggregation. The objectives of the present investigation of MDL 19,660 were to evaluate ultrastructurally long term effects on platelet internal architecture and changes in subpopulations of platelets and megakaryocytes.Nine male and nine female beagle dogs were divided equally into three groups and were administered orally 0, 15, or 30 mg/kg/day of MDL 19,660 for three months. Compared to a control platelet range of 353,000- 452,000/μl, a doserelated thrombocytopenia reached a maximum severity of an average of 135,000/μl for the 15 mg/kg/day dogs after two weeks and 81,000/μl for the 30 mg/kg/day dogs after one week.

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