Introduction to Relational algebra in DBMS - The relational algebra is explained with all the operations. Some of the examples from the textbook is also solved and explained.
Introduction to Relational algebra in DBMS - The relational algebra is explained with all the operations. Some of the examples from the textbook is also solved and explained.
These slides cover basic introduction to Relational Algebra which is a part of Relational Database Management System(RDBMS). The content includes basic RA symbols, operations with visualization.
These slides cover basic introduction to Relational Algebra which is a part of Relational Database Management System(RDBMS). The content includes basic RA symbols, operations with visualization.
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
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.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
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.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
1. Relational Model
Asst.Prof. Rupali Lohar
Dept. of Computer Science & Engineering
B. R. Harne College Of Engineering & Technology, Karav, Post Vangani (W Tal
Ambernath, Mumbai, Maharashtra 421503
2. S# SNAME STATUS CITY
S1 Smith 20 London
S2 Jones 10 Paris
S3 Blake 30 Paris
S4 Clark 20 London
S5 Adams 30 Athens
2
Relational Databases
represent data as a collection of tables
each row in a table represents a collection of related values
Example
Supplier S
4. Relational Data Structure
4
Table is the Data Structure for relational model
Relation = Table
name of this relation is S.
Tuple = Row
This relation has 5 tuples
Each tuple represents a record of one supplier, so tuples
are seldom called records also.
S# SNAME STATUS CITY
S1 Smith 20 London
S2 Jones 10 Paris
S3 Blake 30 Paris
S4 Clark 20 London
S5 Adams 30 Athens
Supplier S
5. Relational Data Structure
5
Cardinality = # of rows with data in the relation
so for S, cardinality = 5.
Attribute = Columns or fields.
Degree = # of columns or fields of a relation.
The relation S has a degree of 4.
S# SNAME STATUS CITY
S1 Smith 20 London
S2 Jones 10 Paris
S3 Blake 30 Paris
S4 Clark 20 London
S5 Adams 30 Athens
Supplier S
6. Relational Data Structure
6
Domain = A pool of legal values
For example:
For S# we have S followed by a positive number.
For Year we might have a four digit positive number between
1000 and 2003.
Primary Key = A unique identifier, used to identify one
specific record from among all other records.
S# SNAME STATUS CITY
S1 Smith 20 London
S2 Jones 10 Paris
S3 Blake 30 Paris
S4 Clark 20 London
S5 Adams 30 Athens
Supplier S
7. Relations
7
A relation consists of 2 parts
Heading:
Consists of a fixed set of attributes or columns or fields.
Body
Consists of the tuples or rows or records.
Time varying set, i.e., at different intervals of time
there may be different contents in the body of a
relation.
8. Key Properties of Relations
8
No Duplicate tuples
In other words, not all fields are same
All cells have a single value
A relation which follows this rule is said to be in the
first normal form.
9. Structure of Relational Database
• Basic Structure
– The relational model uses a collection of tables.
– These tables have multiple columns, and each column has a unique name called attributes.
– The set of allowed values for each attribute is called the domain of the attribute
– whether the tuples of a relation are listed in sorted order, as in Figure 1, or are unsorted,
as in Figure 2, does not matter
The account relation.
9
The account relation with unordered tuples.
10. Structure of Relational Database
• Database Schema
The customer relation The branch relation.. The account relation. The depositor relation.
The loan relation.
10
The borrower relation.
12. 12
Structure of Relational Database
• Schema Diagram
– A database schema, along with primary key and foreign key dependencies, can be
depicted pictorially by schema diagrams.
• Each relation appears as a box, with the attributes listed inside it and the relation name above
it.
• If there are primary key attributes, a horizontal line crosses the box, with the primary key
attributes listed above the line.
• Foreign key dependencies appear as arrows from the foreign key attributes of the referencing
relation to the primary key of the referenced relation.
13. • Schema Diagram
– Figure shows the schema diagram for our banking enterprise.
13
14. ER Model to Relational Model
• ER diagrams can be mapped to relational schema, that is, it is
possible to create relational schema using ER diagram. We cannot
import all the ER constraints into relational model, but an
approximate schema can be generated.
15. Mapping Entity
Mapping Process (Algorithm)
• Create table for each entity.
• Entity's attributes should become fields of tables with their respective
data types.
• Declare primary key.
16. Mapping Relationship
Mapping Process
• Create table for a relationship.
• Add the primary keys of all participating Entities as fields of table with
their respective data types.
• If relationship has any attribute, add each attribute as field of table.
• Declare a primary key composing all the primary keys of participating
entities.
• Declare all foreign key constraints.
17. Mapping Weak Entity Sets
Mapping Process
• Create table for weak entity set.
• Add all its attributes to table as field.
• Add the primary key of identifying entity set.
• Declare all foreign key constraints.
18. Mapping Hierarchical Entities
ER specialization or generalization comes in the form of hierarchical entity
sets.
Mapping Process
• Create tables for all higher-level entities.
• Create tables for lower-level entities.
• Add primary keys of higher-level entities in the table of lower-level entities.
• In lower-level tables, add all other attributes of lower-level entities.
• Declare primary key of higher-level table and the primary key for lower-
level table.
• Declare foreign key constraints.
20. Reduction of an E-R Diagram to Relational
Schemas
• Representation of Strong Entity Sets
Example
customer
customer_name customer_city
customer_street
customer_name customer_street customer_city
2
0
The relation customer
21. Reduction of an E-R Diagram to Relational
Schemas
loan_pay
ment
loan payment
• Representation of Weak Entity Sets
Example
branch_name payment_date
loan_number amount payment_number payment_amount
loan_number payment_number payment_date payment_amount
2
1
The relation payment
22. Reduction of an E-R Diagram to Relational
Schemas
customer depositor account
• Tabular Representation of Relationship Sets
Example
access_date
customer_street branch_name
customer_name customer_city account_number balance
customer_name account_number access_date
8TMhrse.SrueniltaaMtioDonl,WdeIT,pSoolaspiutror
23. Reduction of an E-R Diagram to Relational
Schemas
• Tabular Representation of Relationship Sets
Combination of Tables
Example : we can combine the table for
account-branch with the table for account and
require only the following two tables:
account, with attributes account-number,
balance, and branch-name
branch, with attributes branch-name,
branch-city, and assets
account_number
account
branch_name
branch_name
branch
branch_city
assets
account_
brach
balance
14Mrs. Sunita M Dol, WIT,Solapur
24. Reduction of an E-R Diagram to Relational
Schemas
• Composite Attributes
We handle composite attributes by creating a separate attribute for each of
the component attributes; we do not create a separate column for the
composite attribute itself.
Suppose address is a composite attribute of entity set customer, and the
components of address are street and city. The table generated from
customer would then contain columns address-street and address-city;
there is no separate column for address
15Mrs. Sunita M Dol, WIT,Solapur
25. Reduction of an E-R Diagram to Relational
Schemas
• Composite Attributes
Example
customer_name
customer
address
phone_number
Street city
customer_name customer_street customer_city phone_number
16TMhrse.SrueniltaaMtioDonl,WcuIT,sStoolampurer
26. Reduction of an E-R Diagram to Relational
Schemas
• Multivalued Attributes
For a multivalued attribute M, we create a table T with a column C that
corresponds to M and columns corresponding to the primary key of the entity
set or relationship set of which M is an attribute.
17
27. Reduction of an E-R Diagram to Relational
Schemas• MultivaluedAttributes
Example: For the multivalued attribute dependent-name, we create a table
dependent-name, with columns dname, referring to the dependent-name
attribute of employee, and employee-id, representing the primary key of the
entity set employee. Each dependent of an employee is represented as a
unique row in the table.
dname employee_id
The relation dependent_name 18
28. Advantages of Relational Model
• Simple
• Scalable
• Structural Independence
Disadvantages of Relational Model
• Hardware Overheads: more powerful hardware computers and data
storage devices.
• Bad Design: As the relational model is very easy to design and use. So
the users don't need to know how the data is stored in order to
access it.
