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Current Affairs - June 2013 Date wise
1 June 2013
Pakistans new Parliament sworn in
Fourteen years after his last stint as a parliamentarian, Prime Ministerdesignate Nawaz Sharif 1st June 2013 returned to Pakistans National
Ass
INTERNAL COMBUSTION ENGINES (ELECTIVE) (ME667)
SIXTH SEMESTER
COMBUSTION PROCESS IN SI ENGINES
Combustion may be defined as a relatively rapid chemical combination of hydrogen
and carbon in fuel with oxygen in air resulting in liberation of energy in the
COMBUSTION PROCESS IN SI ENGINES
Combustion may be defined as a relatively rapid chemical combination of hydrogen
and carbon in fuel with oxygen in air resulting in liberation of energy in the form of
heat.
Following conditions are necessary for combustio
I.E.S-(OBJ) 2003
1 of 14
CIVIL ENGINEERING
(PAPER-I)
1.
2.
3.
4.
5.
6.
7.
A well-seasoned timber has a moisture
content of about
a. 15% to 20%
b. 10% to 12%
c. 5% to 85
d. 2% to 3%
Dry rot in timber is caused by
a. Lack of ventilation
b. Lack of light
c.
REPORT ON INDUSTRIAL VISIT TO PAP ON 13.08.2010
REPORT ON
INDUSTRIAL VISIT
TO
PARAMBIKULAM ALIYAR PROJECT (PAP)
on
13 - 8 - 2010
by
III. B. E. Civil Engineering (2008 2012 batch)
Dr. Mahalingam College of Engineering and Technology
Pollachi 642 003.
Page
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INDIA & WORLD
3rd India Republic of Korea Foreign Policy and
Security Dialogue Held In Seoul
The 3rd India Republic of Korea (ROK) Foreign Policy
and Security Dialogue (FPSD) was held in Seoul on 2
September .The Indian side wa
1.204 Lecture 23
Analytic approximations
Vehicle routing
hi
ti
Transit design
Analytic approximations
First spiral in developing problem solution
Assist in requirements, prototyping, initial results,
Assist
review
Many analytic approximations are vis
1.204 Lecture 21
Nonlinear unconstrained optimization:
First order conditions: Newtons method
Estimating a logit demand model
Nonlinear unconstrained optimization
Network equilibrium was a constrained nonlinear
optimization problem
Nonnegativity const
1.204 Lecture 22
Unconstrained nonlinear optimization:
Amoeba
BFGS
Linear programming: Glpk
Multiple optimum values
G
A
C
B
E
Z
X
F
Y
D
X1
X2
Figure by MIT OpenCourseWare.
Heuristics to deal with multiple optima:
Start at many initial points. Choose bes
Note: most of this lecture has been removed due to copyright restrictions.
1.204 Lecture 24
Approximate queuing analysis
Queuing theory
Its hard to solve real problems with classical
hard to solve real problems with classical
queuing theory
Exact solut
1.204 Lecture 18
Continuous constrained nonlinear
optimization:
optimization:
Convex combinations 1:
Network equilibrium
Transportation network flows
Amount of travel on any
road or transit line is
Amount of travel on any road or transit line is
result
1.204 Lecture 17
Branch and bound:
Method
Warehouse location problem
Breadth first search
Breadth first search manages E-nodes in the branch and
bound tree
An E node is the node currently being explored
In breadth first search, E-node stays live unt
1.204 Lecture 14
Dynamic programming:
Job scheduling
scheduling
Dynamic programming formulation
To formulate a problem as a dynamic program:
Sort by a criterion that will allow infeasible combinations
to be eliminated efficiently
li
ffi
tl
Choose gr
1.204 Lecture 13
Dynamic programming:
Method
Resource allocation
Introduction
Divide and conquer starts with the entire problem, divides it
into subproblems and then combines them into a solution
This is a top-down approach
Dynamic programming start
1.204 Lecture 16
Branch and bound:
Method, knapsack problem
problem
Branch and bound
Technique for solving mixed (or pure) integer
programming problems, based on tree search
Yes/no or 0/1 decision variables, designated xi
Problem may have continuous
1.204 Lecture 15
Dynamic programming:
Knapsack
When multistage graphs dont work
If the resource has many levels:
Large range of ints
Floating point number
Then the multistage graph cant be constructed
And label correction is not a sufficient impl
1.204 Lecture 11
Greedy algorithms:
Mi
Minimum spanning trees
Minimum spanning tree
If G is an undirected, connected graph, a subgraph T of G is
a spanning tree iff T is a tree with n nodes (or, equivalently,
n-1 arcs)
A minimum spanning tree is the s
3/15/2010
1.204 Lecture 12
Greedy/dynamic programming algorithms:
Shortest paths
paths
Shortest paths in networks
Shortest path algorithm:
Builds shortest path tree
From a root node
To all other nodes in the network.
All shortest path algorithms are
1.204 Lecture 10
Greedy algorithms:
Knapsack (capital budgeting)
it
ti
Job scheduling
Greedy method
Local improvement method
Does not look at problem globally
Takes best immediate step to find a solution
Useful in many cases where
Objectives or
1.204 Lecture 7
Data structures: graphs, sets
Graphs and Networks
A graph contains:
0
Graphs can be directed or undirected
0
1
Directed
<0, 1>
<1, 0>
0
1
2
Nodes
Arcs, or pairs of nodes ij, i !=j
3
1
Undirected
(0, 1)
A network is a graph with a cos
1.204 Lecture 8
Data structures: heaps
Priority Queues or Heaps
a
Top
Highest priority element at top
Partial sort
All enter at bottom, leave at top
b
c
d
e
Bottom
Applications:
1. Simulations: event list
2. Search, decision trees
3. Minimum spanning t
1/31/2010
1.204 Lecture 1
Course introduction
Data models
Announcements
How-to install documents on Web:
Java, Eclipse, submit problem sets (1.00 Web site)
SQL Server, Visual Paradigm, JDBC (1.204 Web site)
We will give you access to 1.00 Web site
1.204 Lecture 6
Data structures: stacks, queues,
trees, dictionaries
Data structures
Correct and efficient representation of data and applicable
rules
Stack: last in, first out discipline
Queue: first in, first out discipline
Double-ended queue (de
1.204 Lecture 5
Algorithms: analysis, complexity
Algorithms
Algorithm:
Finite set of instructions that solves a given problem.
Characteristics:
Input. Zero or more quantities are supplied.
Output. At least one quantity is computed.
Definiten
1.204 Lecture 2
Data models, concluded
Normalizati
N
li tion
Keys
Primary key: one or more attributes that uniquely
key:
identify a record
Name or identifying number, often system generated
Composite keys are made up of two fields
E.g., aircraft manuf