Wednesday, 29 March 2017

24 LATEST(2017) RESEARCH TOPIC YOU CAN WORK ON USING FUZZY LOGIC

24 LATEST(2017) RESEARCH TOPIC YOU CAN WORK ON USING FUZZY LOGIC
  1. Application of fuzzy logic to student performance in calculation subjects.
  2. Fuzzy logic based approach for controlling of a vehicle in its longitudinal motion.
  3. Fuzzy logic based process control strategy for effective sheeting of wheat dough  in small and medium-sized enterprises.
  4. Fuzzy logic based mppt controller for high conversion ratio quadratic boost converter.
  5. Evaluation of websites’ compliance to legal and ethical guidelines: a fuzzy logic–based methodology.
  6. Comparative study of a fuzzy logic based controller and a neuro-fuzzy logic based controller for computer fan.
  7. Development of a fuzzy logic-based quantitative risk assessment model subject to HSE hazards.
  8. Stability studies of fuzzy logic based power system stabilizer in enhancing dynamic stability of a two generators tie-line system.
  9. Fuzzy-logic-based solution to dynamic target interception and landing with a small multi-rotor aircraft.
  10. A fuzzy logic-based prediction model for fracture force using low-power fiber laser beam welding.
  11. A fuzzy logic based technical indicator for bist 30 index and islamic index.
  12. Fuzzy logic-based operation of battery energy storage systems (besss) for enhancing the resiliency of hybrid microgrids.
  13. Fuzzy logic-based analogue forecasting and hybrid modelling of horizontal visibility.
  14. Direct power control of a grid-connected photovoltaic system using a fuzzy-logic based controller.
  15. Fuzzy logic based fine-tuning approach for robust load frequency control in a multi-area power system.
  16. Control response of electric demand by means of fuzzy logic using programmable logic controller (PLC).
  17. Controlling the navigation of automatic guided vehicle (agv) using integrated fuzzy logic controller with programmable logic controller (iflplc) - stage 1.
  18. Design and implementation of a programmable logic controller based day lighting control system.
  19. Programmable logic controller based design and implementation of multiple axes solar tracking system.
  20. Programmable logic controller based implementation of supervisory control for sugar refinery.
  21. Development of programmable logic controller-based supervisory system for group production machine.
  22. Design of the miniature programmable logic controller based on stm32.
  23. A new intelligent programmable logic controller based on switched networks
  24. Development and hardware-in-the-loop analysis of commercial marine-nuclear propulsion plant programmable logic controller-based control.

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DO IT YOURSELF: INSTALLATION OF MATLAB


INTRODUCTION TO MATLAB
Matlab, short for 'Matrix Laboratory,' is among the most powerful mathematics programs available for commercial use, particularly for numerical work. Matlab come equipped with a substantial library for doing most operations you could ever think of and the ability to create your own functions and programs to provide an infinitely customizable mathematics interface. Matlab is used by all profession. 

Installation of Matlab
There is various version of Matlab released over the years like MATLAB 6.1, 6,5, 6.5.1 etc. The latest Matlab version released is MATLAB 9.2 (R2017a) released on the 9th of March 2017. But will would be using MATLAB 8.6 to demonstrate the installation in this tutorial.

STEP 1: Copy the raw file of Matlab into a folder (from any source you are copying it from either from a flash or compact disk).
STEP 2: Open the folder and run the setup as administrator as shown in Fig. 1
Fig. 1
STEP 3: Fig. 2 will display, then select ‘use a file installation key’ after that you click next as shown in Fig. 2.

                                                    Fig. 2
STEP 4: Fig. 3 will be display, click ‘Yes’ then Fig. 4 will be display.
                                                       Fig. 3
STEP 5: Click ‘I have the file installation key for my license’ as shown in Fig. 4
Fig. 4
STEP 6: Go back to the raw file folder and open ‘crack folder’ as shown in Fig 5
                                               Fig. 5
STEP 7: Open the folder, Fig. 6 will display.
                                                  Fig. 6
STEP 8: Open the text document named ‘install’ as shown in Fig. 7 and ‘copy’ the serial number and paste it in the Column for serial number in Fig. 4. As shown in Fig. 8 then click next, Fig. 9 will display
                                                                Fig. 7


                                                                             Fig. 8
                                                             Fig. 9
STEP 9: Click Next, Fig. 10 and 11 will display, click Next again then fig. 12 will display.
                                                               Fig. 10
                                                                      Fig. 11
                                                                      
STEP 10: in Fig. 12 click ‘install’
Fig. 12
STEP 11: Then Fig. 13 will display.  Matlab is installing!!!! , exercise a little patient while Matlab is installing.

                                                     Fig. 13
STEP 12: After installation Fig. 14 will display, then click 'Next'
                                                                    Fig. 14
STEP 13: Fig. 15 will display then click 'finish'
                                                           Fig. 15
Step 14: activating Matlab. Open the Crack folder as shown in Fig. 6 Copy ‘bin, java and toolbox folder inside the crack folder’
STEP 15: Go to the install directory (window - program files – Matlab - Matlab production server- R2015a) then paste what you copied in Step 14 inside the R-2015a folder. As shown in Fig. 16 -20
                                                                       Fig. 16

                                                                               Fig. 17

                                                                               Fig. 18



                                                          Fig. 19
      
                                         Fig. 20
STEP 16: Create Matlab shortcut on desktop: Go to the install directory again (crack - bin – Matlab.exe) as shown in fig. 21.
                                                   Fig. 21

STEP 17: Run the Matlab by double clicking on the shortcut created on the desktop. Fig. 22 will display then click ‘activate manually without internet’ then click ‘Next’ , Fig. 23 will display.
                                                                             Fig. 22


                                                           Fig. 23
STEP 18: Go back to the crack folder as shown in fig. 6 and copy the crack path: ‘C:\Users\Engr. Abayomi\Desktop\Cleaned Desktop\my software\Matlab\MATLAB 2015 1\crack’ then paste it in box below ‘enter the full path to your file, including the file name’. then click browse and select ‘lic_standalone.dat’ as shown in Fig. 24. Click ‘select’ then click ‘next’
STEP 19: fig. 25 will be displayed then click finish. ENJOY!!!!!!!!!!!!!!!!!!!
                                                                               Fig. 24
                                                                            Fig. 25

                                                                      Fig. 26


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Sunday, 26 February 2017

20 RESEARCH TOPICS YOU CAN WORK ON USING PYTHON


A four-part working bibliography of neuroethics: parts3_ ‘second tradition neuroethics’ – ethical issues in neuroscience.

The effects of an editor serving as one of the reviewers during the peer-review process.

Implementation of a python based hierarchical clustering program on hi-index data set of journals using normalization and priority queue elements.

A mobile phone App to enhance communication between ID physicians and members of the public health community.

Comparison of using scopus and pubmed for compiling publications with siriraj affiliation
Enabling computationally intensive bioinformatics applications on the Grid platform.

Design of a universal tool for annotation, visualization and analysis in functional genomics research.

An inexpensive Arduino-based LED stimulator system for vision research.

Integrated land use and transportation planning and modelling: addressing challenges in research and practice.

A standards-based low-power wireless development environment.

Numerical simulation of optical wave propagation with examples in MATLAB.

Rotation mechanism of enterococcus hirae V1-ATPase based on asymmetric crystal structures.

Modelling landscape dynamics with python.

Development of a rapid and highly automatable program for processing powder diffraction data into total scattering pair distribution functions.

Development of a visualization system for exploratory research and analysis.

Microcrystalline silicon solar cells: effect of substrate temperature on cracks and their role in post-oxidation.

Datamonkey: rapid detection of selective pressure on individual sites of condon alignments.
IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content.

Integration of geographic information system and agent-based models.

Data science, predictive analytic, and big data: a revolution that will transform supply chain design and management.

For training and help regarding any projects on python. feel free to contact Abataysoftwarewizard Research Institute.

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WEEKLY TIPS FOR SPSS USERS: WHEN IS A HYPOTHESIS NOT A HYPOTHESIS?


A good theory should allow us to make statements about the state of the world. Statements about the world are good things: they allow us to make sense of our world, and to make decisions that affect our future. One current example is global warming. Being able to make a definitive statement that global warming is happening, and that it is caused by certain practices in society, allows us to change these practices and, hopefully, avert catastrophe. However, not all statements are ones that can be tested using science. Scientific statements are ones that can be verified with reference to empirical evidence, whereas non-scientific statements are ones that cannot be empirically tested. So, statements such as ‘Lindt chocolate is the best food’, and ‘This is the worst writ up in the world’ are all non-scientific; they cannot be proved or disproved. Scientific statements can be confirmed or disconfirmed empirically. ‘Watching Curb Your Enthusiasm makes you happy’, ‘having sex increases levels of the neurotransmitter dopamine’ and ‘Velociraptors ate meat’ are all things that can be tested empirically (provided you can quantify and measure the variables concerned). Non-scientific statements can sometimes be altered to become scientific statements, so ‘The Beatles were the most influential band ever’ is non-scientific (because it is probably impossible to quantify ‘influence’ in any meaningful way) but by changing the statement to ‘The Beatles were the best-selling band ever’ it becomes testable (we can collect data about worldwide record sales and establish whether The Beatles have, in
fact, sold more records than any other music artist). Karl Popper, the famous philosopher of science, believed that non-scientific statements were nonsense, and had no place in science. Good theories should, therefore, produce hypotheses that are scientific statements.


Some Important Terms

When doing research there are some important generic terms for variables that you will encounter:
Independent variable: A variable thought to be the cause of some effect. This term is usually used in experimental research to denote a variable that the experimenter has manipulated.
Dependent variable: A variable thought to be affected by changes in an independent variable. You can think of this variable as
an outcome.
Predictor variable: A variable thought to predict an outcome variable. This is basically another term for independent variable (although some people won’t like me saying that; I think life would be easier if we talked only about predictors and outcomes).

Outcome variable: A variable thought to change as a function of changes in a predictor variable. This term could be synonymous with ‘dependent variable’ for the sake of an easy life.
for training and help on SPSS, feel free to contact abataysoftwarewizard Research Institute.
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ARTIFICIAL INTELLIGENCE MADE EASY VIA FUZZY LOGIC


What is Fuzzy Logic?
A computational paradigm that is based on how humans think Fuzzy Logic looks at the world in imprecise terms, in much the same way that our brain takes in information (e.g. temperature is hot, speed is slow), then responds with precise actions.
The human brain can reason with uncertainties, vagueness, and judgments. Computers can only manipulate precise valuations. Fuzzy logic is an attempt to combine the two techniques. “Fuzzy” – a misnomer, has resulted in the mistaken suspicion that FL is somehow less exacting than traditional logic

History of fuzzy logic
In 1965, Lotfi A. Zadeh of the University of California at Berkeley published
"Fuzzy Sets," which laid out the mathematics of fuzzy set theory and, by extension, fuzzy logic. Zadeh had observed that conventional computer logic couldn't manipulate data that represented subjective or vague ideas, so he created fuzzy logic to allow computers to determine the distinctions among data with shades of gray, similar to the process of human reasoning.

20 years later after its conception
Interest in fuzzy systems was sparked by Seiji Yasunobu and Soji Miyamoto of Hitachi, who in 1985 provided simulations that demonstrated the superiority of fuzzy control systems for the Sendai railway. Their ideas were adopted, and fuzzy systems were used to control accelerating and braking when the line opened in 1987.

Also in 1987, during an international meeting of fuzzy researchers in Tokyo, Takeshi Yamakawa demonstrated the use of fuzzy control, through a set of simple dedicated fuzzy logic chips, in an "inverted pendulum" experiment. This is a classic control problem, in which vehicle tries to keep a pole mounted on its top by a hinge upright by moving back and forth.

Observers were impressed with this demonstration, as well as later experiments by Yamakawa in which he mounted a wine glass containing water or even a live mouse to the top of the pendulum. The system maintained stability in both cases. Yamakawa eventually went on to organize his own fuzzy-systems research lab
to help exploit his patents in the field.

Introduction of Fuzzy Logic in Engineering World
Fuzzy Logic is one of the most talked-about technologies to hit the embedded control field in recent years. It has already transformed many product markets in Japan and Korea, and has begun to attract a widespread following. In the United States. Industry watchers predict that fuzzy technology is on its way to becoming a multibillion-dollar business.

Fuzzy Logic enables low cost microcontrollers to perform functions traditionally
performed by more powerful expensive machines enabling lower cost products
to execute advanced features.

What is artificial intelligence?
“The study and design of intelligent agents, where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.”

Application 0f Fuzzy Logic to Artificial Intelligence
In the city of Sendai in Japan, a 16-station subway system is controlled by a fuzzy computer (Seiji Yasunobu and Soji Miyamoto of Hitachi) – the ride is so smooth, riders do not need to hold straps.

Nissan – fuzzy automatic transmission, fuzzy anti-skid braking system

CSK, Hitachi – Hand-writing Recognition

Sony - Hand-printed character recognition

Ricoh, Hitachi – Voice recognition

Tokyo’s stock market has had at least one stock-trading portfolio based on Fuzzy Logic that outperformed the Nikkei exchange average.

NASA has studied fuzzy control for automated space docking: simulations show that a fuzzy control system can greatly reduce fuel consumption.

Canon developed an auto-focusing camera that uses a charge-coupled device (CCD) to measure the clarity of the image in six regions of its field of view and use the information provided to determine if the image is in focus. It also tracks the rate of change of lens movement during focusing, and controls its speed to prevent overshoot. The camera's fuzzy control system uses 12 inputs: 6 to obtain the current clarity data provided by the CCD and 6 to measure the rate of change of lens movement. The output is the position of the lens. The fuzzy control system uses 13 rules and requires 1.1 kilobytes of memory.

For washing machines, Fuzzy Logic control is almost becoming a standard feature Others: Samsung, Toshiba, National, Matsushita, etc. fuzzy controllers to load-weight, fabric-mix, and dirt sensors and automatically set the wash cycle for the best use of power, water, and detergent.

What is control system?
This is a device which produce a set of desired outputs for a given set of inputs.

For example:
A household thermostat takes a temperature input and sends a control
signal to a furnace.

A car engine controller responds to variables such as engine position, manifold pressure and cylinder temperature to regulate fuel flow and spark timing.

Conventional Control Vs Fuzzy
Look up table
In the simplest case, a controller takes its cues from a look-up table, which tells
what output to produce for every input or combination of inputs.

Sample
The table might tell the controller,
“IF temperature is 85, THEN increase furnace fan speed to 300 RPM.”

Drawback
The problem with the tabular approach is that the table can get very long, especially in situations where there are many inputs or outputs. And that, in turn, may require more memory than the controller can handle, or more than is cost-effective. Tabular control mechanisms may also give a bumpy, uneven response, as the controller jumps from one table-based value to the next.

Mathematical Formula
The usual alternative to look-up tables is to have the controller execute a mathematical formula – a set of control equations that express the output
as a function of the input. Ideally, these equations represent an accurate model of the system behaviour. The formulas can be very complex, and working them out in real-time may be more than an affordable controller (or machine) can manage.

Downside of Mathematical Modelling
It may be difficult or impossible to derive a workable mathematical model in the
first place, making both tabular and formula-based methods impractical. Though an automotive engineer might understand the general relationship between say, ignition timing, air flow, fuel mix and engine RPM, the exact math that underlies those interactions may be completely obscure.

Why use Fuzzy logic?
FL overcomes the disadvantages of both table-based and formula-based control.

Fuzzy has no unwieldy memory requirements of look-up tables, and no heavy number-crunching demands of formula-based solutions.

FL can make development and implementation much simpler. It needs no intricate mathematical models, only a practical understanding of the overall system behaviour.

FL mechanisms can result to higher accuracy and smoother control as well. FL differs from orthodox logic in that it is multivalued. Fuzzy deals with degrees of truth and degrees of membership.

NOTE: Fuzzy logic is one of the add-in of Matlab. It means that if you have Matlab installed on you PC then you automatically have access to Fuzzy logic interface.
For training and help regarding your project, feel free to contact abataysoftwarewizard Research Institute.
08130582034


Sunday, 19 February 2017

LATEST JOB ADVERT FOR MATLAB USERS APPLY NOW!!!!!


Graduate engineer
Rainex Group-Lagos

Mechanical engineer

Control engineer for mechatronic system
Atmen s.r.l- Benue state

Mechanical Engineer
CAMP CRAFT- lagos

Mechanical Engineer
Vinlandresources –Lagos

Machine learning Expert
SAP – Nigeria

Mechanical Engineer
Fresco Ventures –Lagos

Financial Engineer senior consultant(m/f)
EY-Enugu State

Creative mechanical Engineer
Epe

Data scientist intern
EHealth System Africa-Kano

Engineering
Greatminds Resources –Lagos

Mechanical Engineer
Rotex –Lagos

Researcher control system for vehicle of smart machines
Atmen S.r.l- Benue state

Mechanical engineering
Rotex – Lagos

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