scholarly journals Clock Skew Based Computer Identification on Different Types of Area Networks

2019 ◽  
Vol 3 (1) ◽  
pp. 25
Author(s):  
Nola Verli Herlian ◽  
Komang Oka Saputra ◽  
I Gst A. Komang Diafari Djuni Hartawan
Keyword(s):  
Linear Programming ◽  
Linear Regression ◽  
Linear Regression Method ◽  
Clock Skew ◽  
Device Identification ◽  
Identification Method ◽  
Measurement Results ◽  
Different Types ◽  
True Time ◽  
Computer Identification

The increase of client devices along with the growth of internet access currently affects to security threats at the user's identity. Identifiers that commonly used today, such as SSID, IP address, MAC address, cookies, and session IDs have a weakness, which is easy to duplicate. Computer identification based on clock skew is an identification method that is not easily duplicated because it is based on the hardware characteristics of the device. Clock skew is the deviation of the clock to the true time which causes each clock to run at a slightly different speed. This study aims to determine the effect of network types to the clock skew stability as a reliable device identification method. This research was conducted on five client computers which running windows and linux operating systems. The measurement was conducted based on three different types of area networks, i.e., LAN, MAN, and WAN. The skew estimation was done using two linear methods i.e., linear programming and linear regression. The measurement results show that the most stable clock skew is found on the LAN measurement because it meets the threshold tolerance limit i.e., ±1 ppm. Skew estimation using linear programming method has better accuracy than linear regression method.

scholarly journals Identifying High Significance Input Factors in Strawberry Production using Linear Model

2020 ◽  
Vol 9 (2) ◽  
pp. 1680-1684
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Nutrient Solution ◽  
Linear Regression Model ◽  
Ph Value ◽  
Correlation Method ◽  
Linear Regression Method ◽  
High Significance ◽  
Different Types ◽  
Growth Production

This study is focused on identifying the high significance of input factors in strawberry growth and production using a linear regression model. Greenhouse strawberry cultivation is increasing so fast due to the high demand for strawberry and farmers are also taking different types of technics for greenhouse cultivation to get high productions of strawberry. This study aims to increase the production of strawberries in order to maximize the profits from the cultivation of strawberries and also to fulfill the demand for strawberries. The strawberry data consist of average strawberry productions (AvgSP), electric conductivity (EC), potential of Hydrogen (PH) value, greenhouse inside temperature (Temp), greenhouse inside humidity, CO2 , nutrient solution with water, and supply of water nutrient solution. To find out the relationship among each input factor we use the correlation method and after that based on the correlation we make different types of combination of input factors. In this study, we use the linear regression method to find out the R2 value and significance factors of different combinations of input factors. For the linear regression model we take average strawberry production as output and different combination of input factors as input. In result and discussion, we concluded the high significance input factors in strawberry growth production.

scholarly journals Analisis Peramalan Penjualan dan Penggunaan Metode Linear Programming dan Decision Tree Guna Mengoptimalkan Keuntungan pada PT Primajaya Pantes Garment

The Winners ◽  
2013 ◽  
Vol 14 (2) ◽  
pp. 113
Author(s):  
Inti Sariani Jianta Djie
Keyword(s):  
Linear Programming ◽  
Linear Regression ◽  
Decision Tree ◽  
Production Capacity ◽  
Exponential Smoothing ◽  
Programming Method ◽  
Linear Regression Method ◽  
Moving Averages ◽  
Linear Programming Method ◽  
Naive Method

Primajaya Pantes Garment is a company that runs its business in garment sector. However, due to various numbers of requests each month, the company is difficult to determine the amount of production per month that is appropriate to maximize profits. The purpose of this study is to determine the appropriate forecasting method that can be used as a reference to determine the amount of production in the next period and to find a combination of products to maximize profits. Research used forecasting methods, including naive method, moving averages, weighted moving averages, exponential smoothing, exponential smoothing with trend, and linear regression. In addition, this study also used Linear Programming method with Simplex method to determine the best combination of products for the company and to choose a decision using a decision tree to determine which alternative should be done by the company. Results of this study found that the linear regression method is the most appropriate method in determining the forecast demand in the next period. While in the Linear Programming method, constraints used were the constraints of raw materials, labor hours, and limited demand for the product. The result of the decision tree is to increase production capacity.

Rainfall Prediction in South-Eastern Part of Bangladesh by Linear Regression Method

2018 ◽  
Vol 6 (6) ◽  
pp. 119
Author(s):  
Mohammad Shohidul Islam ◽  
Sultana Easmin Siddika ◽  
S M Injamamul Haque Masum
Keyword(s):  
Linear Regression ◽  
Correlation Coefficients ◽  
Rain Gauge ◽  
Regression Method ◽  
Linear Regression Method ◽  
Climate Data ◽  
Rainfall Forecasting ◽  
Statistical Parameters ◽  
Time Duration ◽  
Rainfall Prediction

Rainfall forecasting is very challenging task for the meteorologists. Over the last few decades, several models have been utilized, attempting the successful analysing and forecasting of rainfall. Recorded climate data can play an important role in this regard. Long-time duration of recorded data can be able to provide better advancement of rainfall forecasting. This paper presents the utilization of statistical techniques, particularly linear regression method for modelling the rainfall prediction over Bangladesh. The rainfall data for a period of 11 years was obtained from Bangladesh Meteorological department (BMD), Dhaka i.e. that was surface-based rain gauge rainfall which was acquired from 08 weather stations over Bangladesh for the years of 2001-2011. The monthly and yearly rainfall was determined. In order to assess the accuracy of it some statistical parameters such as average, meridian, correlation coefficients and standard deviation were determined for all stations. The model prediction of rainfall was compared with true rainfall which was collected from rain gauge of different stations and it was found that the model rainfall prediction has given good results.

An alternative model of the vibronic coupling in octahedral complexes

1988 ◽  
Vol 53 (6) ◽  
pp. 1134-1140
Author(s):  
Martin Breza ◽  
Peter Pelikán
Keyword(s):  
Linear Regression ◽  
Alternative Model ◽  
Potential Surface ◽  
Analytical Formula ◽  
Coupling Model ◽  
Regression Method ◽  
Vibronic Coupling ◽  
Linear Regression Method ◽  
Active Systems ◽  
Jahn Teller

It is suggested that for some transition metal hexahalo complexes, the Eg-(a1g + eg) vibronic coupling model is better suited than the classical T2g-(a1g + eg) model. For the former, alternative model, the potential constants in the analytical formula are evaluated from the numerical map of the adiabatic potential surface by using the linear regression method. The numerical values for 29 hexahalo complexes of the 1st row transition metals are obtained by the CNDO/2 method. Some interesting trends of parameters of such Jahn-Teller-active systems are disclosed.

scholarly journals Research of the Photo-Optical Method Application for Measuring Selected Data on the Movement of a Parachute for Type M-282

2021 ◽  
Vol 11 (12) ◽  
pp. 5637
Author(s):  
Peter Kaľavský ◽  
Róbert Rozenberg ◽  
Peter Korba ◽  
Martin Kelemen ◽  
Matej Antoško ◽  
...  
Keyword(s):  
Optical Method ◽  
Training Programs ◽  
Performance Standard ◽  
Motion Data ◽  
Innovative Methods ◽  
Measurement Results ◽  
Different Types ◽  
Vertical Speed ◽  
Individualized Training ◽  
Minimum Performance

Testing in the field of parachute technology provides space for the application of new and innovative methods of measuring operating and functional parameters. The main aim of the paper is to present the results of research for the verification of the photo-optical method of measuring the vertical speed of the M-282 parachutes, and for its use in testing, collecting, and investigating motion data in parachuting. As part of this measuring technology, twelve jumps were performed. It was verified that the experiment was completed for the M-282 parachute according to the regulation of SAE AS 8015B “Minimum Performance Standard Parachute Assemblies and Components”. An analysis of the influencing factors and quantification of their influence on the uncertainty of the measurement results was also performed. The results of the measurement achieved by using the photo-optical method were compared with the measurement with the electronic variometer FLYTEC 4030. The vertical speed of the M-282 parachute (4.655 m·s−1) defined by the photo-optical method is significantly similar to the vertical speed of the M-282 parachute (4.662 m·s−1) defined by FLYTEC 4030. We can state that the process of identifying the vertical speed of the parachute by the photo-optical method was correct. This is a suitable method of evaluating motion data in the operation of M-282 type parachutes. In the following research for generalization of the methodology, we assume the performance of more than 60 experimental jumps using different types of parachutes, digital sensors (cameras), and a photo-optical method to examine motion data and formulate recommendations for testing, investigative applications, individualized training programs, and aspects of parachuting injury prevention.

The Accuracy Assessment of Weapon System Using Grey Linear Regression Method

2012 ◽  
Vol 268-270 ◽  
pp. 1809-1813
Author(s):  
Dai Yu Zhang ◽  
Bao Wei Song ◽  
Zhou Quan Zhu
Keyword(s):  
Linear Regression ◽  
Fuzzy Theory ◽  
Accuracy Assessment ◽  
Regression Method ◽  
Small Sample ◽  
Easy Access ◽  
Linear Regression Method ◽  
Grey Theory ◽  
Weapon System ◽  
Complex Engineering

The accuracy assessment of weapon system is always a complex engineering. How to make the most of the information given in only a few tests and obtain reasonable estimate is always a problem. Based on the fuzzy theory and grey theory, a grey linear regression method is presented. From the numerical example, we can see that this method provides an easy access to deal with data in small sample case and may have potential use in the analysis of weapon performance.

scholarly journals Demineralization Depth Using QLF and a Novel Image Processing Software

2010 ◽  
Vol 2010 ◽  
pp. 1-7 ◽  
Author(s):  
Jun Wu ◽  
Zachary R. Donly ◽  
Kevin J. Donly ◽  
Steven Hackmyer
Keyword(s):  
Image Processing ◽  
Linear Regression ◽  
Polarized Light ◽  
Region Of Interest ◽  
Linear Regression Method ◽  
Percent Loss ◽  
Significant Linear Correlation ◽  
Processing Software ◽  
Image Processing Software ◽  
Imagej Software

Quantitative Light-Induced fluorescence (QLF) has been widely used to detect tooth demineralization indicated by fluorescence loss with respect to surrounding sound enamel. The correlation between fluorescence loss and demineralization depth is not fully understood. The purpose of this project was to study this correlation to estimate demineralization depth. Extracted teeth were collected. Artificial caries-like lesions were created and imaged with QLF. Novel image processing software was developed to measure the largest percent of fluorescence loss in the region of interest. All teeth were then sectioned and imaged by polarized light microscopy. The largest depth of demineralization was measured by NIH ImageJ software. The statistical linear regression method was applied to analyze these data. The linear regression model wasY=0.32X+0.17, whereXwas the percent loss of fluorescence andYwas the depth of demineralization. The correlation coefficient was 0.9696. The two-tailed t-test for coefficient was 7.93, indicating theP-value=.0014. TheFtest for the entire model was 62.86, which shows theP-value=.0013. The results indicated statistically significant linear correlation between the percent loss of fluorescence and depth of the enamel demineralization.

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