the
EEEA REVIEW Vol.2. Issue 5, March’ 17
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Co- editors
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Artificial Neural Networks
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● he theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages
● An artificial neuron network (ANN) is a computational model based on the structure and functions of biological neural networks. Information that flows through the network affects the structure of the ANN because a neural network changes - or learns, in a sense - based on that input and output.
● rtificial intelligence is a vast field that has the goal of creating intelligent machines, something that has been achieved many times depending on how you define intelligence.
● A computer can optimize its response by doing the same problem thousands of times and adjusting its response according to the feedback it receives. The computer can then be given a different problem, which it can approach in the same way as it learned from the previous one.
● Many of the AIs built up to this point have been built with a purpose, such as running a ping pong playing robot or dominating at "Jeopardy". This is the inevitable result when computer scientists sit down and create something to do a specific task – they end up with something that can do that task and not much else.
● Artificial neural networks try to recreate this learning system on computers by constructing a simple framework program to respond to a problem and receive feedback on how it does.
● To get around this problem of task-orientated AIs, computer scientists started playing around with artificial neural networks. Our generally intelligent brains are made up of biological neural networks that make connections based on our perceptions and outside stimulus.
● In essence, artificial neural networks are models of human neural networks that are designed to help computers learn. Artificial intelligence is the Holy Grail some computer scientists are trying to achieve using techniques like mimicking neural networks .
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It is a suitable problem for ANN application due to the availability of historical load data on the utility databases. ANN schemes using perceptron network and SOFM have been successful in short-term as well as long-term load forecasting with impressive accuracy. A combined use of Unsupervised and supervised learning was done for short-term load forecasting. Filter based an" algorithm for faster convergence and improved prediction accuracy. The RBF (radial basis function) network was found superior to MLP or BP model in terms of training time and its accuracy.
– Static and dynamic security assessment often requires online computation. In order to evaluate solution efficiently, the nonlinear mapping of MLP is utilized to reduce computational burden and &deal with the Characteristics of power systems. This allows us to carry out on-line monitoring/assessment in transient, small signal stability, and voltage instability. Though Contingency ranking and sensitivity factor methods have reduced the Number of critical contingencies to be computed, ANNs has played a challenging role in security area. The security assessment is based on,
– Fault detection/diagnosis is one of challenging problems in power systems. MLP identifies the type and location of faults with a given set of power system conditions, measurements, alarms, etc. KN (Kohonen net) is applied to handle the classification of fault patterns. The diagnosis of the power apparatus is done to judge what kinds of faults the apparatus suffered from and then accordingly it is cleared. KN is inferior to MLP in terms of the solution accuracy due to unsupervised learning. RBF and BP models were developed for fault diagnosis problem.
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A fuzzy logic function trained by an
(SNT) and link margin are two of the key metrics of system
artificial neural network was developed
performance for telemetry data return. The ability to detect
to classify the SNT of antennas in the
the signal is affected by the SNT; the lower the noise, the
NASA
were
better chance the system can detect the signal. Thus, there
classified into normal, marginal, and
is a strong interest in monitoring and classifying the SNT.
abnormal classes. In order to capture
However, there are many causes of SNT behavior patterns
various irregular patterns of SNT data, a
that are still not known and difficult to classify by simple
set of features of the SNT curves was
logic. There is a benefit to use a more sophisticated
designed:
pattern-recognition
DSN.
The
mean
SNT
data
values,
standard
deviations, peak numbers, peak-to-
method
to
detect
and
classify
abnormal SNT behaviors.
valley variations, and slope of the peaks. The SNT data from various tracking passes were processed to extract the SNT curve feature vectors; a feature vector represents each pass. Since the criteria
were
not
simple
Boolean
operations and there was possible need for adding non-threshold criteria in later
“This technique has applications in intelligent signal analysis, data mining in aerospace manufacturing, military target recognition, and intelligence gathering.�
analysis, a simple threshold approach was deemed not suitable for classifying the SNT patterns. Instead, a fuzzy logic was designed to classify the SNT patterns, and a neural network was then used to train the fuzzy logic., The communication between NASA space mission operations teams and their respective spacecraft in outer space is accomplished via the Deep Space Network (DSN). To ensure proper operations in returning telemetry data to mission operations, sending commands to spacecraft and providing radiometric
Figure : The Neural Network is a feed-forward, back-
data for navigation purposes, the DSN
propagation model composed of separate layers of
equipment generates a large set of self-
connected units called neurons. Every neuron of one layer
monitor data. System noise temperature
is connected to every neuron of the next layer.
Identification of Abnormal Noise Temperature Patterns in Deep Space Network Antennas Using Fuzzy Logic By: Pratyush Tipathi
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In the advent of 2017 Champions trophy and 2019 WC, England wicket keeping Coach Bruce French along with Raph Brandon, the ECB’s Head of Science and Medicine conducted some tests at National Cricket Performance Centre on different possibilities of collecting a throw during a run out scenario. They basically tested on two possibilities, one when the player is standing in front of the stumps and other case being when the player is standing behind the stumps. The volunteers for this experiments were Ben Cox (Worcestershire wicketkeeper) and James Tredwell ( Kent and England off spinner). After a series of experiments it was found out that Ben Cox was significantly quicker in intercepting the ball and in making a run out attempt when he was standing in front of the stumps. James Tredwell being a bowler was a little bit inconsistent when making a run out attempt by standing in front of the stumps. So the conclusion can be that by standing in front of the stumps and intercepting the ball results in making quicker run out attempts as compared to standing behind the stumps provided the player has practised his technique. “It could gain up to a yard in terms of the batsman running in,” said Raph Brandon. The difference in standing behind and in front of the stumps is in the fact being when behind the stumps, the ball will take a fraction of a second more to reach the wicket-keeper/player before he can make an attempt to hit the stumps and making
that attempt a fraction of second before by standing in front of the stumps can make a lot difference in crucial matches involving the finals of some big events or matches of high repute.
Run out study to aid England by SAI ROHIT
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Nike Sport Research: The Art of Science By SAI ROHIT "If you take science and design by themselves, they have no power. They're not going to do beautiful things but the overlap and the convergence of those two together, that's when beautiful things happen."
Various other testing methods which are used before the final product is launched are accelerometers, metabolic cart, electrocardiogram, and blood chemistry analysis among others.
The Nike sport research lab (NSRL) is an institute of Data is the new voice of the athlete. And by listening research for the development of new technologies for to this voice, the current paradigm to do change the athletes. It takes about 14-18 months from when game and an athlete's performance can be challenged. research and design begins until the end product reaches the market. Quantification of athletes' movements by using different products in accordance Scan the QR code to know more. with the flexibility, fit, traction, and cushioning help to compare the various situations encountered by the in. athlete and reduce the chances of an injury. Markers are used on athletes to give 200 fps or higher frames of information which is usually invisible to the eyes of the audience. Biomechanics is the study of the mechanical laws relating to the movement of the body. NSRL uses a track comprising of sensors on the athlete's body and force plates to determine data’s recorded by the computer screens. The force plates present take thousands of measurements per second and forces in three dimensional space is observed. Ground sensors measure the forces, loading and the pressure of the foot on the ground. Not only that, it also gives the pressure in different areas like the ball of the foot or the ankle. Also present in the lab is a zero gravity treadmill wherein the athlete trains without the impact of the actual environment which in turn shows us how the athletes train during recovery. The copper sweaty mannequin helps to understand the thermal and evaporative properties of garments which will be later used by the athlete. It helps determining how to create a performance gear which cools the body effectively by adding ventilation or by reduction of fabric to provide a better movement of the body. Another notable development is of the spark suit. It is a wearable generator based on nanotechnology. It converts the mechanical movement of the player to electrical energy with the help of nano-ion pumps. The products developed are tested in an environmental chamber with varying physical conditions of temperature, pressure, and humidity and replicate weather from anywhere around the globe.
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By: Deepesh Jain Akshat Kukreti
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By: Deepesh Jain Akshat Kukreti
Automated comparison of faces in the photographs is a well established discipline. The main aim of this is to describe an approach whereby face recognition can be used in suggestion of a new contacts. The new contact suggestion is a common technique used across all main social networks. Social networks seem to be the ideal place where extracted information about relationships can be used. The process of new contact suggestions is currently based only on text data; such as place of birth of a user, his or her favorite topics in magazines or on the Internet, and so on. It is obvious that there is also a number of problems that must be resolved, especially those related to reliability of face recognition algorithms. The face recognition could be performed on real world photos captured by robot‘s cameras or photos downloaded from Facebook. After the analysis the robot can ‘decide’ who is friend of whom.
compare the positions and sizes of the objects and then this data is used for face comparison. Many problems, such as obstruction of the part of the face, can occur. Another problem that the algorithms have to deal with is the fact that the size and distance between primary objects may vary during diff er ent e moti ons of t he exa m i ned object . Success in dealing with these problems depends on how each algorithm can adapt in less than ideal conditions.
Luxand is not provided as full-service ready for use but it provides SDK that enables its functionality for face recognition. Their algorithms detect and use 66 special points (equivalent of the primary objects mentioned above). Analysis of the face based on this special point is then performed. There is a freeware test application that can be used via web page, the complete SDK is available only as a payware service. There is also no additional information about the algorithms that are used for face recognition.
Nowadays a specific primary objects of the face are used for face recognition in most of the current algorithms . The primary objects are for example eyes, the nose or ears. When these objects are found in a face the algorithms usually
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ELEKTRA 2K17
Glimpses
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ELEKTRA 2K17 Elektra 2k17 was held with the motto -
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Diys
By Pratyush Tripathi
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