As we enter the age of artificial intelligence, the need for intelligent home appliances has become very important for what this smart equipment can provide in the provision of electrical energy and water resources that are treasures should human pre
servation, in addition to the contribution of this equipment to protect the environment from pollution, where we face the challenges next:
High prices of electrical equipment.
The number of hours of electricity supply in many areas is low
because of the current conditions in our country.
- Water shortage.
- The rise in prices of materials used in daily life in general and
household detergents in particular
- Great waste of electricity.
- Pollution of the environment and groundwater with detergents used in the laundry process.
Moreover, the unjust economic blockade imposed on our country is pushing us to work to produce low-cost national housing equipment that competes with foreign products in order to alleviate the material burden on the citizens and promote the national economy.
In order to accomplish this smart washing machine, we have written a code for f type-2 fuzzy microcontroller, using the Python programming language. This controller has received four entries, which are: The first income (clothing color), obtained by taking a picture of the clothes that we need to wash by a camera with a resolution of 8 megapixels, analyzed using OpenCV library, and the second income (clothing type), determined by the local binary pattern algorithm, which is common digital image processing algorithm that widely used to identify shapes that follow specific pattern and structure, the third income (degree of dirt), and was identified by taking a picture of the clothes after soaking them with water for two minutes. The image was then analyzed by the OpenCV library and the fourth (washing weight) that getting From the Load Cell, which measures the physical weights. The readings were converted to digital values via the HX711 digital analogue converter and then sent to Arduino UNO to determine the weight. The weight values were eventually sent to the Raspberry PI for use in the controller. The system generates three exits: washing time (the length of time the laundry was washed), the temperature required for washing, and the amount of detergent required.
After selecting all the previous values, we transferred to control Wattar washing machine model 402, where the water valve was controlled to allow the water to pass into the powder box and from it to the washing basin. The water heater was controlled, which heated the water to the temperature determined by the Fuzzy algorithm, The temperature was monitored by the DS18B20 temperature sensor, which gives a signal to the Raspberry PI at the arrival of the temperature to the required value, and the washing machine engine is controlled for a third of the time specified in the Fuzzy algorithm and we controlled the pump Water to empty basin Washing from water, the process repeated for three consecutive times, we control using a software interface designed using TKinter library
We have been able to design a smart Fuzzy logic type-2 controller with the following advantages:
o save electricity consumption o Provide quantity of detergents o Shortenwashingtime
We have been able to control the following physical components within the washing machine:
o Control the water pump
o control Water valve
o controlMotor
o controlTemperaturesensor o controlLCDscreen
We have built a smart washing machine with the following characteristics:
o Have the ability to recognize the condition of clothes o Identify the type of clothing
o Identify the color of clothes
o Dothewashingwithoutusingapredefinedprogram.
The controller we designed gives good results to calculate the following:
o Washingtime
o Quantity of detergents o Temperature
All diagrams appear in the case of the incremental gradient with an increased degree of dirt and as values correspond to each type of clothing.
Keywords: smart washing machine, saving electricity, saving detergent, shortening washing time, color and clothing distinction, artificial intelligence, fuzzy logic type-2, Raspberry PI, control, Python programming language, HX711.
The environment is the effective framework in which man lives and interacts with him, and with the development of the human being and the growth and increase of his requirements, this led to various changes in the ecosystem, which can be positive or negative, and the need to reduce the negative impact of the change on the ecosystem and raise the level of the positive impact.
This paper represents a study of all the major and sub influential
factors that affect the process of placing concrete which has
arbitrary nature and has not been stated clearly before; and the
impact of these factors on the cost of a cubic meter of placed
concrete.
This research aims to produce a diagnosis system for breast cancer by using Neural
Network depending on Back Propagation algorithm(BPNN) and Adaptive Neuro Fuzzy
Inference System ‘ANFIS’, the both of studies was done using structural features of
b
iopsies in “Wisconson Breast Cancer “data base.
In the end a comparison was made between the two studies of malignant- benign
classification of breast masses of breast cancer which has accuracy 95,95% with BPNN
and 91.9% with ANFIS system, this results can be consider very important if they
compared with researches depending on image features that obtained of various devises
like mammography, magnetic resonance.
The fluctuation of voltage cannot be tolerant for equipment in modern industrial
plants such as lighting loads, PLC, robots, and another equipment, which exist in
transmission and distribution systems, so we should use proper aids to regulate volta
ge and
control it.
In this study a (± 25Mvar) Static Synchronous Compensator (STATCOM) is used to
enhance voltage stability in a (66 kv, 1500MV.A) power transmission network. The
STATCOM in this study regulates the voltage of the transmission network for changing in
voltage (± 7%) from the nominal value. A model of the power transmission system and
another model of the STATCOM device, which will enhance the stability of voltage are
designed in MATLAB/Simulink. And the control of (STATCOM) is achieved by using a
Proportional Integrative (PI) controller with Fuzzy Logic Supervisor to adjust the
parameters in PI controller in DC voltage regulator during transient states of load changing
which gives more stability in DC voltage. The results of the simulation are shown. This
study demonstrates the ability of STATCOM for regulating the voltage of the transmission
system by injecting and absorbing reactive power from the power system, and the DC
voltage be more stability by using Fuzzy Logic supervisor.
This paper presents the proposed Method for designing fuzzy
supervisory controller model for Proportional Integral Differential
controller (PID) by Fuzzy Reasoning Petri Net (FRPN),the Features
of Method shows the fuzzification value for each prop
erty of
membership function for each input of fuzzy supervisory controller,
and determine the total number of rules required in designing the
controller before enter the appropriate rules in the design phase of
the rules, and determine the value of the inputs of the rule that has
been activated, and assembly variables that have the same property
and show the value for each of them programmatically, and
determine the deffuzification value using deffuzification methods.
The aim of this study is to develop a fuzzy inference model to
estimate the impact of change orders on the duration of
construction projects in Syria. This can help in obtaining the
optimal estimation of the increasing of the project duration caus
ed
by the changes. The capability of gradient estimation of the fuzzy
logic approach reduces the distrust of estimation using factor
assessment. Also it estimates the duration of the project after any
change.via the experts evaluation or according to the crisp logic.
This research
aims to develop overlay functions methodology based on fuzzy
logic, reclassify the objects into fuzzy classes, and study the
usability of this method to integrate data of specific phenomenon
to help make optimal decisions.
The overlay functions in Geographic Information Systems (GIS) are considered as one of the basic functions of these systems, and often, a variety of data stored in layers may be integrated together to generate new layers that contain useful informati
on for decision-makers. All geographic objects are stored in layers and usually rely on crisp set theory, whether, stored in a vector or raster format. In many cases, boundaries of classes or objects are not clearly defined, or when we perform classification of features into classes, the geographic objects located in the boundaries of classes could be classified into the wrong class. This research aims to develop overlay functions methodology based on fuzzy logic, reclassify the objects into fuzzy classes, and study the usability of this method to integrate data of specific phenomenon to help make optimal decisions. To implement and examine this idea, a set of Fuzzy Membership Functions was developed using the Python programming language embedded within the ArcGIS environment. Through this Fuzzy Membership Functions, the user can generate fuzzy sets and combine with each other according to one of fuzzy operations, and thus the generation of fuzzy sets allows supporting the right and reliable decision. To test the proposed fuzzy model capabilities, it has been applied in Tartous governorate to select suitable tourist facility sites in accordance with groups of factors. In summary, data integration using proposed fuzzy overlay functions can improve the reliability of data representation and thus the reliability of make the best decisions.
This study has been done to develop scientific research and select talented people of
post-graduate students (master students) to continue and get doctoral degrees (degree in
PHD) at Tishreen University. The research has been prepared, which aims t
o suggest a
model for measuring the degree of creativity and talent for post-graduate students by using
one of artificial intelligence techniques such as Fuzzy Logic.
An expert system has been built that contains an inference rule which consists of
three types of tests: (Theory Test, TT), (Practice Test, PT), and (Creativity Test, CT) for
each course.
This intelligent system has also aimed to determine the ability to make decisions
which gives the rate of talent for post- graduate students.
The study has reached to an important set of results, and the most important is:
Results have shown high strength and reliability shows the validity of this proposed
model, the validity of the results have reached to 85% and 100%, by using two different
methods to defuzzificate of proposed model.
