Neural Network


 An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process.

"x: [Roman(x)L know(x, Marcus)]® [hate(x ,Caesar) V ("y:$z: hate(y, z) ® think crazy(x ,y))]

1.     Convert the following to clausal form.

"x: [Roman(x)L know(x, Marcus)]® [hate(x ,Caesar) V ("y:$z: hate(y, z) ® think crazy(x ,y))]

Answer: "All Romans who know Marcus either hate Caesar or think that anyone who hates anyone is crazy."

There are 9 simple steps to convert from Predicate logic to clause form.

We will use the following example :
" All Romans who know Marcus either hate Caesar or think that anyone who hates anyone is crazy."

The well formed formula for this statement is :
"x [ Roman(x) and know(x,Marcus)] ® [hate(x,Caesar) Ú ("y($z hate(y,z)) ® thinkcrazy(x,y))]
Lets see each of the covnersion steps in detail :

Well formed formula [ wff] : "x [ Roman(x) and know(x,Marcus)] ® [hate(x,Caesar) Ú ("y($z hate(y,z)) ® think crazy(x,y))]
1. Eliminate ®

We will eliminate implication [ ®] by substituting it with its equivalent.
for e.g. a ® b  =  ~a v b .
Here 'a' and 'b' can be any predicate logic expression.

For the above statement we get :
"x ~[Roman(x) Ù know(x,Marcus)] Ú [hate(x,Caesar) Ú("y~($z hate(y,z)) Ú thinkcrazy(x,y))]
2. Reduce the scope of ~
To reduce the scope we can use 3 rules :
~(~p) = p           
DeMorgans Laws :    ~(aÚb) = ~aÙ~b
                                    ~(aÙb) = ~aÚ~b
Applying this reduction on our example yields :
"x[~Roman(x)Ú~know(x,Marcus)]Ú[hate(x,Caesar)Ú("y"z ~hate(y,z)Úthinkcrazy(x,y))]
3.Change variable names such that, each quantifier has a unique name.
We do this in preparation for the next step. As variables are just dummy names, changing a variable name doesnot affect the truth value of the wff.
Suppose we have
"xP(x) Ú "xQ(x) will be converted to  "x(P(x) Ú "yQ(y) 
4.Move all the quantifiers to the left of the formula without changing their relative order.
As we already have unique names for each quantifier in the previous step, this will not cause a problem.
Performing this on our example we get :
"x"y"z [ ~Roman(x) Ú ~know(x,Marcus)] Ú [ hate(x,Caesar)Ú(~hate(y,z)Úthinkcrazy(x,y))]
5. Eliminate existential quantifiers [ $ ]
We can eliminate the existential quantifier by simply replacing the variable with a reference to a function that produces the desired value.
for eg.
$y President(y) can be transformed into the formula President(S1)
If the existential quantifiers occur within the scope of a universal wuantifier, then the value that satisfies the predicate may depend on the values of the unviersally quantified variables.
For eg..   "x$y fatherof(y,x) will be converted to  "x fatherof( S2(x),x )
6. Drop the Prefix
As we have eliminated all existential quantifiers, all the variables present in the wff are unversally quantified, hence for simplicity we can just drop the prefix, and assume that every variable is universally quantified.
We have form our example :
[ ~Roman(x) Ú ~know(x,Marcus)] Ú [ hate(x,Caesar)Ú(~hate(y,z)Úthinkcrazy(x,y))]
7. Convert into conjunction of disjuncts
As we have no ANDs we will just have to use the associative property to get rid of the brackets.
Incase of ANDs we will need to use the distributive property.
We have :
 ~Roman(x) Ú ~know(x,Marcus) Ú  hate(x,Caesar) Ú ~hate(y,z) Ú thinkcrazy(x,y)
8. Separate each conjunct into a new clause.
As we did not have ANDs in out example, this step is avoided for our example and the final output of the conversion is :
 ~Roman(x) Ú ~know(x,Marcus) Ú  hate(x,Caesar) Ú ~hate(y,z) Ú thinkcrazy(x,y)

Various areas where real time simulation will be used


Real time simulation is the new era of simulation and what are the various areas where real time simulation will be used and what are the various requirements of achieving the real time simulation?

Ans: Real-time Simulation refers to a computer model of a physical system that can execute at the same rate as actual "wall clock" time. In other words, the computer model runs at the same rate as the actual physical system. For example if a tank takes 10 minutes to fill in the real-world, the simulation would take 10 minutes as well.
The Telemedicine and Advanced Technology Research Center and the U.S. Army’s Simulation, Training, and Instrumentation Command (STRICOM) co-hosted a conference at the Morningside Inn, a remote facility ensconced among the rolling pastureland of Frederick County, Maryland, USA.  Actual and potential end users, researchers, and representatives from materiel developers and other government agencies – thirty-three of them, presented their concepts, needs, and challenges about how modeling and simulation should be developed to meet military – and private sector -- medical needs. 

IN the keynote address delivered by Major General John Parker, Commanding General, Medical Research and Materiel Command, MG Parker compared the potential impact of the Medical Modeling & Simulation (MM&S) field to that of the human genome.  By meeting’s end, there was a strong feeling that the MM&S potential to improve healthcare training was higher than originally envisioned.  The skyrocketing growth of the MM&S community of interest since that time has confirmed that belief.

The mission of the Telemedicine and Advanced Technology Research Center is to “explore medical science and engineering technologies ahead of programmed research, leveraging other programs to maximize benefits to military medicine.” One of its major research portfolios is Medical Simulation and Training Technology.  The vision for this portfolio is to facilitate a paradigm shift in medical training, from a subjective mode of skills assessment to a curriculum-aligned, metrics-driven, objective system to assess proficiency of skills -- both cognitive and psychomotor – from the foxhole to the operating room and beyond.

Differences between expression based languages and block structured languages


Frame out the differences between expression based languages and block structured languages and for which type of systems both the languages will be used?
Ans: BLOCK STRUCTURED LANGUAGES:  A block-structured control system is a coupling of functional blocks. Each of these functional blocks stands for a subsystem in the original system. In control systems, a functional block can be classified into two categories: linear and nonlinear. In a block-structured programming language, the names of variables and other objects such as procedures which are declared in outer blocks are visible inside other inner blocks, unless they are shadowed by an object of the same name.
EXPRESSION ORIENTED CONTINUOUS LANGUAGES: are based on writing expressions that represent the mathematical model. So the system simulated must be expressed by a set of equations. Then the user adds statements and/or directives that control the simulation. Some languages enable both block and expression based ways of system definition. Simulation control means selection of: the integration method because some languages offer more, the integration step, the variables outputs of blocks that should be observed, the intervals for collecting data for printing and/or plotting, scaling of outputs that may be also done automatically, duration of the simulation runs, number of repetitions and the way certain values are changed in them, etc.