T-distribution is the most famous theoretical probability distribution in continuous family of distributions. T distribution is used in estimation where normal distribution cannot be used to estimate population parameters. Copy the link given below and paste it in new browser window to get more information on T distribution:- http://www.transtutors.com/homework-help/statistics/t-distribution.aspx
Descriptive statistics, central tendency, measures of variability, measures of dispersion, skewness, kurtosis, range, standard deviation, mean, median, mode, variance, normal distribution
T-distribution is the most famous theoretical probability distribution in continuous family of distributions. T distribution is used in estimation where normal distribution cannot be used to estimate population parameters. Copy the link given below and paste it in new browser window to get more information on T distribution:- http://www.transtutors.com/homework-help/statistics/t-distribution.aspx
Descriptive statistics, central tendency, measures of variability, measures of dispersion, skewness, kurtosis, range, standard deviation, mean, median, mode, variance, normal distribution
Presentation on methods to analyse student's performance. The presentation includes - Measures of central tendencies (Mean, Median, Mode), Percentile and Percentile rank, Standard scores - Z and T scores
A measure of central tendency (also referred to as measures of centre or central location) is a summary measure that attempts to describe a whole set of data with a single value that represents the middle or centre of its distribution.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
3. Central Tendency
• Mean is the average or arithmetic mean of the data.
• Median is the middle value of the data set, when the
data set is arranged in the ascending or descending order.
• Mode is the most frequently observed value(s).
4. • Range is the difference between highest and lowest observations in the data set
• Variance is a measure of spread of a data set
It is calculated as the average squared deviation of each number from mean of a data set.
• Standard Deviation is the Square Root of the Variance.
Dispersion
6. The marks obtained by 10 students in a subject are as
given below
9,31,35,34,37,32,100,33,30,100
Average Marks:
=(9+31+35+34+37+32+100+33+30+100)/10
=44
Effect of outliers on Mean
7. The marks obtained by 8 students in a subject are as
given below
31,35,34,37,32,38,33,30
Average Marks:
=(31+35+34+37+32+38+33+30)/8
=33.75
Mean
8. The marks obtained by 10 students in a subject are as
given below
9,31,35,34,37,32,100,33,30,100
Average Marks:
=(9+31+35+34+37+32+100+33+30+100)/10
=44.1
Effect of outliers on Mean
9. • The middle value when a variable’s values are ranked in
ascending/ descending order
If n is odd;
Median is ((n-1)/2)+1th value
If n is even;
Median is Average of {(n/2), (n/2)+1} values
Median
10. Median = 60
(six cases above, six below)
Marks scored by 13 students in an exam is as give below.
Find the median marks
35,90,40,62,54,95,38,60,73,92,51,32,74
Median
32
35
38
40
51
54
60
62
73
74
90
92
95
When “n” is odd
11. Median = (60+64) /2
= 62
(Average of 5th and 6th values)
Marks scored by 10 students in an exam is as give below.
Find the median marks
38,84,42,64,55,96,39,60,73,92
Median
38
39
42
55
60
64
73
84
92
96
When “n” is even
12. The marks obtained by 10 students in a subject are as
given below
9,31,35,34,37,32,100,33,30,100
Median: 9,30,31,32,33,34,35,37,100,100
Effect of outliers on Median
= (33+34)/2
=33.5
Median is not influenced by outliers
13. The mode for a data set is the element that occurs the most often.
More than one mode is possible for a data set.
It is also possible that a data set may not have a mode at all.
Example-1:
Data set: 2,5,4,7,8,9,4,3,6,4,7,8,6,4,9,2
Mode: 4
Example-2:
Data set: 10,25,15,30,25,45,50,15,40,35
Modes: 25, 15
Mode
14. Range
Range = Largest value – Smallest value
The spread, or the difference, between the lowest and highest values of a variable.
31,35,34,37,32,38,33,30
Data set
Range = 38 - 30
= 8
15. Variance
A measure of the spread of the recorded values on a variable.
The larger the variance, the further the individual values are from the mean.
The smaller the variance, the closer the individual values are to the mean.
Mean
Mean
16. The square root of the variance (Standard deviation) reveals the
average deviation of the observations from the mean.
Standard deviation
1
1
2
n
xx
s
n
i
i
17. • The larger the standard deviation, the more
spread out the data is.
Interpretation of standard deviation