Automatic Traffic Control Based on Node Network Model using Tree-type Data Analysis

2021 ◽  
Vol 31 (5) ◽  
pp. 355-360
Author(s):  
Daekeon Ha ◽  
Eun Kyeong Kim ◽  
Jin Yong Kim ◽  
Baekcheon Kim ◽  
Sungshin Kim
Author(s):  
Nuru I. Sarkar ◽  
Kashif Nisar ◽  
Layangi Babbage

The Advanced Network Technologies is research that investigates technology(s) behind today’s modern networks and network infrastructures. One part of this technology being Asynchronous Transfer Mode (ATM). A technology commonly in place in networks all around the world today. This paper focuses on ATM. Dubbed “Modelling and Performance Studies of ATM Networks”; this research seeks to look at and into the “impact of application segment length on the performance of an ATM network” and the “impact of traffic type data on the performance of an ATM network”. To be able to examine an ATM network, the authors need to be able to simulate a network. Thus, for this research, they have used the OPNET Modeler 14.0 Simulation software to create a network model that represents a ATM network. By actually simulating an ATM network at AUT University New Zealand, the authors can therefore change certain variables, and observe the effects the changes have on performance. As stated, one of the impacts that will be explored is the effect that application segment length has on an ATM network. Thus, one variable that will be changed in the authors’ simulation is the segment length. This is the length of each packet segment that is sent through the network for a particular traffic type. The second impact to be inspected is the impact of different traffic types on an ATM network. This network model is based on a campus network. An Application Configuration is setup with default parameters which specify 8 common applications used. Among them the ones that the authors will focus on are VOIP, Video and FTP. A Profile Configuration is setup that will define the 3 applications stated above. A fixed node model of 100BaseT will specify the profile configuration for each scenario and the number of work stations of each scenario.


2019 ◽  
Vol 12 (2) ◽  
pp. 20
Author(s):  
SI LUH PUTU KARTIKA DEWI

The development of bamboo handicraft business can’t be separated from the important role of capital, KUR is a working capital or investment financing specifically intended for SMEs in the field of productive and feasible business. This study aims to analyze the interest of bamboo handicraft industry business owners in KUR program, and analyze the partial and simultaneous effect of program socialization, knowledge, interest rate perception and revenue on the interest of bamboo handicraft industry owners in KUR program in Bangli regency. In this study dependent variable is dummy and primary data type. Data analysis technique used is logit model. The result of analysis shows that bamboo handicraft business owners who are interested in KUR program are 74.74 percent. Program socialization, knowledge, interest rate perception and revenue have a positive and significant effect to interest of bamboo handicraft business owners in KUR program either partially or simultaneously.


2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Yuhao Wang ◽  
Yongming Liu ◽  
Zhe Sun ◽  
Pingbo Tang

The air traffic control (ATC) system is critical in maintaining the safety and integrity of the National Airspace System (NAS). This requires the information fusion from various sources. This paper introduces a hybrid network model called the Bayesian-Entropy Network (BEN) that can handle various types of information. The BEN method is a combination of the Bayesian method and the Maximum Entropy method. The Maximum Entropy method introduces constraints and is given as an exponential term added to the classical Bayes’ theorem. The exponential term can be used to encode extra information in the form of constraints. The extra information can come from human experience, historical data etc. These knowledges, once written in a mathematical format, can be incorporated into the classical Bayesian framework. The BEN method provides an alternative way to consider common data types (e.g., point observation) and uncommon data types (e.g., linguistic description for human factors) in the NAS. The reported work is demonstrated in two example problems. The first example involves an air traffic control network model and the BEN uses information from various sources to update for the risk event probability. The second example is related to the prediction of the cause of runway incursion. A network model studying different sources of error is used to make predictions of the cause of runway incursion. The training and validation data is extracted from existing accident report in the Aviation Safety Reporting System (ASRS) database. The results are compared with that of the traditional Bayesian method. It is found that the BEN can make use of the available information to modify the distribution function of the parameter of concern.


2022 ◽  
Vol 70 (1) ◽  
pp. 1993-2011
Author(s):  
Tossapon Boongoen ◽  
Natthakan Iam-On

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