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The orginal code of fuzzy logic image processing (edge detection)

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The orginal code of fuzzy logic image processing (edge detection)

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Can copy command:

openExample('fuzzy/fuzzyedgedemo')

Just paste inside the matlab terminal

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This example shows how to use fuzzy logic for image processing. Specifically, this example shows how to detect edges in an image.

An edge is a boundary between two uniform regions. You can detect an edge by comparing the intensity of neighboring pixels. However, because uniform regions are not crisply defined, small intensity differences between two neighboring pixels do not always represent an edge. Instead, the intensity difference might represent a shading effect.

The fuzzy logic approach for image processing allows you to use membership functions to define the degree to which a pixel belongs to an edge or a uniform region.

Import RGB Image and Convert to Grayscale

Import the image.

Irgb is a 384 x 512 x 3 uint8 array. The three channels of Irgb (third array dimension) represent the red, green, and blue intensities of the image.

Convert Irgb to grayscale so that you can work with a 2-D array instead of a 3-D array. To do so, use the function.

Convert Image to Double-Precision Data

The evalfis function for evaluating fuzzy inference systems supports only single-precision and double-precision data. Therefore, convert Igray to a double array using the function.

Obtain Image Gradient

The fuzzy logic edge-detection algorithm for this example relies on the image gradient to locate breaks in uniform regions. Calculate the image gradient along the -axis and -axis.

Gx and Gy are simple gradient filters. To obtain a matrix containing the -axis gradients of I, you convolve I with Gx using the function. The gradient values are in the [-1 1] range. Similarly, to obtain the -axis gradients of I, convolve I with Gy.

Plot the image gradients.

You can use other filters to obtain the image gradients, such as the Sobel operator or the Prewitt operator. For information about how you can filter an image using convolution, see What Is Image Filtering in the Spatial Domain? (Image Processing Toolbox)

Alternatively, if you have the Image Processing Toolbox™ software, you can use the (Image Processing Toolbox), imgradientxy (Image Processing Toolbox), or imgradient (Image Processing Toolbox) functions to obtain the image gradients.

Define Fuzzy Inference System (FIS) for Edge Detection

Create a fuzzy inference system (FIS) for edge detection, edgeFIS.

Specify the image gradients, Ix and Iy, as the inputs of edgeFIS.

Specify a zero-mean Gaussian membership function for each input. If the gradient value for a pixel is 0, then it belongs to the zero membership function with a degree of 1.

sx and sy specify the standard deviation for the zero membership function for the Ix and Iy inputs. To adjust the edge detector performance, you can change the values of sx and sy. Increasing the values makes the algorithm less sensitive to the edges in the image and decreases the intensity of the detected edges.

Specify the intensity of the edge-detected image as an output of edgeFIS.

Specify the triangular membership functions, white and black, for Iout.

As you can with sx and sy, you can change the values of wa, wb, wc, ba, bb, and bc to adjust the edge detector performance. The triplets specify the start, peak, and end of the triangles of the membership functions. These parameters influence the intensity of the detected edges.

Plot the membership functions of the inputs and outputs of edgeFIS.

Specify FIS Rules

Add rules to make a pixel white if it belongs to a uniform region and black otherwise. A pixel is in a uniform region when the image gradient is zero in both directions. If either direction has a nonzero gradient, then the pixel is on an edge.

Evaluate FIS

Evaluate the output of the edge detector for each row of pixels in I using corresponding rows of Ix and Iy as inputs.

Plot Results

Plot the original grayscale image.

Plot the detected edges.

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You can use fuzzy logic for image processing tasks, such as edge detection.

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Here is the original picture that use in the original/example code.

source:https://media.cheggcdn.com/media%2Ff33%2Ff33813b2-0da2-47ff-884e-b1fb49e78d6b%2FphpLaPvki.png

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GUI of image edge detection via Fussy Inference System

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Here are my user interface for edge detection. It just a simple example for you guys.

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Modification that can be done for PART B(individual part) according to textbook:

1. Obtain images gradient part: filter could be sobel operator, prewit operator. Others options: imfilter, imgradientxy, imgradient

2. Values of sx and sy (standard deviation): increasing value=decreasing sensitivity=decreasing intensity

3. Values of wa, wb,wc, ba, bb and bc: can alter the intensity of detected edge because they specify the start, peak, and the end of triangle.

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1. Carol Liew Pey Xuan (79002)

2. Leong Bee Ching (79853)

3. Lim Jing Yi (79882) - Leader

4. Melissa Lee Ee Jiet (80029)

5. Soo Siew Peng (81268)

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1. Environmental Impact Assessment for Construction Projects

Context: Assessing the environmental impact of construction projects to ensure sustainable development.

Variables: Air quality index, noise levels, water quality, waste generation, energy consumption, proximity to protected areas.

Potential Decision Rules: Assess the overall environmental impact based on these variables to determine whether a project can proceed or needs modifications.

2. Traffic Congestion Management System

Context: Managing traffic congestion in urban areas to improve traffic flow and reduce delays.

Variables: Traffic volume, average speed, number of accidents, weather conditions, road capacity.

Potential Decision Rules: Determine the level of congestion and suggest optimal traffic management strategies such as signal timing adjustments or route diversions.

3. Crop Yield Prediction System

Context: Predicting crop yield to help farmers plan and manage their agricultural activities more effectively.

Variables: Soil quality, weather conditions, irrigation levels, pest infestation, crop type.

Potential Decision Rules: Provide yield predictions and recommend actions to optimize crop production based on the current conditions.

4. Patient Health Monitoring System

Context: Continuous monitoring and assessment of patient health in hospitals or at home to provide timely medical intervention.

Variables: Heart rate, blood pressure, blood glucose levels, oxygen saturation, temperature.

Potential Decision Rules: Assess the overall health status and generate alerts or recommendations for medical intervention if any parameter deviates from the normal range.

5. Financial Risk Assessment for Investment Portfolios

Context: Assessing the risk of investment portfolios to help investors make informed decisions.

Variables: Market volatility, asset correlation, investment horizon, economic indicators, diversification.

Potential Decision Rules: Determine the risk level of a portfolio and suggest adjustments to mitigate risk and optimize returns.

6. Employee Performance Evaluation System

Context: Evaluating employee performance to support human resource decisions such as promotions, raises, or training needs.

Variables: Task completion rate, quality of work, teamwork, attendance, innovation.

Potential Decision Rules: Assess overall performance and provide recommendations for rewards or development based on the evaluation.

7. Energy Consumption Optimization for Smart Homes

Context: Managing and optimizing energy consumption in smart homes to reduce costs and environmental impact.

Variables: Energy usage patterns, occupancy, appliance efficiency, weather conditions, time of day.

Potential Decision Rules: Suggest optimal energy usage patterns and control strategies for appliances to minimize energy consumption while maintaining comfort.

8. Predictive Maintenance System for Industrial Equipment

Context: Predicting the maintenance needs of industrial equipment to prevent breakdowns and optimize maintenance schedules.

Variables: Vibration levels, temperature, usage hours, maintenance history, operating conditions.

Potential Decision Rules: Predict the likelihood of equipment failure and recommend maintenance actions to prevent downtime.

9. Customer Satisfaction Analysis in Retail

Context: Analyzing customer satisfaction to improve service quality and customer experience in retail.

Variables: Customer feedback scores, wait time, product availability, service quality, return rates.

Potential Decision Rules: Assess overall customer satisfaction and suggest improvements in specific areas to enhance the customer experience.

10. Smart Irrigation System for Water Conservation

Context: Managing irrigation systems to optimize water use in agriculture and landscaping.

Variables: Soil moisture levels, weather forecast, plant type, water availability, evaporation rate.

Potential Decision Rules: Determine the optimal irrigation schedule to ensure plants receive adequate water while minimizing waste.

11.Medical Diagnosis Support System for Diabetes Risk Assessment

Context: Diabetes is a chronic condition that, if not managed properly, can lead to serious health complications such as heart disease, kidney failure, and vision loss.

Variables: Ages,Body Mass, Body Mass Index (BMI), Blood Pressure, Family History, Physical Activity, Fasting Blood Sugar.

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We start to adjust the degree of membership function

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This video is teach that how to adjust the Nmf

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Fuzzy Logic-Based Medical Expert System(Chronic Kidney Disease)

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Fuzzy Logic-Based Medical Expert System(Chronic Kidney Disease)

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This is some example that we moderate for Part A.

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Chronic kidney disease is a life-threatening complication. Primary diagnosis and active control avoid its progression. To increase the life span of a patient, it is necessary to detect such diseases in early stages. In this research paper, design and development of a fuzzy expert system (FES) to identify the current stage of chronic kidney disease is proposed. The proposed fuzzy rule-based expert system is developed with the help of clinical practice guidelines, database, and the knowledge of a team of specialists. It makes use of input variables like nephron functionality, blood sugar, diastolic blood pressure, systolic blood pressure, age, body mass index (BMI), and smoke. The normality tests are applied on different input parameters. The input variables, i.e., nephron functionality, blood sugar, and BMI have more impact on the chronic kidney disease as shown by the response of surface analysis. The output of the system shows the current stage of patient's kidney disease. Totally 80 tests were performed on the FES developed in this research work, and the generated output was compared with expected output. It is observed that this system succeeds in 93.75% of the tests. This system supports the doctors in assessment of chronic kidney disease among patients. The detection of chronic kidney disease is a serious clinical problem that comprises imprecision, and the use of fuzzy inference system is suggested to overcome this issue. The proposed FES is implemented in the MATLAB.

1. Introduction

Chronic kidney disease or chronic renal disorders arise due to the lack of functionality of nephrons in the kidney. It basically occurs when the kidney stops its functionality like the maintenance of pH value, water, and salt in the blood. The kidney is the natural filter of blood and it discharges the wastage from the body in the form of urine. This disease can also lead to the damage of other neighbor organs of a kidney.

Nowadays, due to increase in population and unhealthy lifestyle, number of patients with renal disorder is increasing [ 1- 3]. It is tough for individual to recover from every kidney disease [ 5]. The detection of kidney disease and disorder can be done by studying the features extracted from an acquired image. The image-processing techniques are applied on magnetic resonance images of total kidney volume [ 6]. The registration is performed on the selected portion of an image to increase the accuracy [ 7].The optimal path is detected during the segmentation of magnetic resonance images of kidneys for diagnosis of Chronic Kidney Disease (CKD) [ 8]. Computer aided diagnostic systems are used to examine the kidney features for the early identification of kidney diseases [ 9, 10]. Certain test, like F-test, is applied to find out the dependency of features on the kidney area [ 11]. Ultrasonography images are also used for detection of stages of CKDs. The stages of CKD are predicted by using machine learning techniques [ 12- 15]. The comparisons have been made between the performances of different systems developed by using various algorithms of machine learning [ 16]. Intelligent models are designed by using different methods of artificial neural networks, such as generalized feed forward neural network, back-propagation neural network, and modular neural network [ 17]. The Naive-Bayes method is also used to classify the various stages of CKD [ 18]. An approach is designed for detection of disease at initial stage by training the neural network with some amount of training data [ 19- 21]. Further, the data mining algorithms and techniques are used for CKD detection [ 22, 23]. Ensemble learning is used to clean the data that are acquired and to fill the values which are missing in that particular dataset to recalculate the different stages of CKD, and hence, the performance of the system is increased with this step [ 24]. The decision support systems are also built by using the information or dataset of CKD [ 25].

Early detection of the disease permits the doctor to suggest the required measure so that the risk may reduce [ 26]. The ranks are given to all the risk factors that extremely affect the kidney [ 27, 28]. Renal illness is a public health problem across the nation. In Taiwan, there are over 1,100,000 patients living with CKD and this number is increasing gradually. Moreover, the data from United States Renal Data System (USRDS) stated that Taiwan has extreme occurrence of end stage kidney disease across the world [ 29, 30].

In India, the death rate is increasing rapidly due to various chronic diseases, and CKD is one of the major diseases among them. The average age of a person that suffers from CKD is from 18 to 98 years in which the number of male patients is more than females [ 31]. The CKD is not detected easily till it grows due to its asymptomatic nature, and it results in decrease in the opportunities for prevention of the disease [ 32, 33]. It is very important to detect and treat CKD in early stages to avoid kidney failure [ 34]. It is reported that this problem can be mitigated by developing fuzzy logic-based expert system [ 35].

The main objective of this work is to develop a medical expert system for diagnosis of chronic kidney disease by using fuzzy logic. The aim is to assist doctors in diagnosis of patients suffering from chronic kidney disease. Fuzzy logic is a rule-based logic that deals with imprecise concepts. It is a many-valued logic that can combine human heuristics into computer-assisted decision-making. The expert system is a computer program which is derived from the artificial intelligence and can be used in real-time applications [ 36]. Over 70%, this domain is used to solve the real-life applications [ 37]. The expert systems are used for diagnosis of various diseases. The main idea behind expert system is that the knowledge from the experts of a specific disease will be transferred to the computer system [ 38]. The knowledge stored in the computer system can be used by the user for specific task. After that, the system will conclude the result by doing various estimations.

The fuzzy logic-based system is having three blocks and the fuzzy logic-based system used here is Mamdani type fuzzy model, all three blocks are discussed as follows:(1) Fuzzification: It is the process of transforming the crisp values into the fuzzy inputs. The crisp values are inputs measured by the sensors or the values given by the users [ 39]. (2) Inference mechanism: In the inference mechanism, the system maps rules satisfy input given by user and it gives an output. (3) Defuzzification: This is the last step in the system. In defuzzification process, a single number from the output of the aggregated fuzzy set is obtained.

Response surface methodology is a set of precise and numerical procedures that are supportive for exhibiting and analysimg issues wherever an interest response obtains the impact of various parameters, and also the aim is to maximise such response [ 39- 41].

The research works discussed above provide fruitful information about the applications of expert systems for medical diagnosis and especially for the diagnosis of chronic kidney disease. The primary risk factors or causes of the chronic kidney disease have been used as the input variables in the development of a proposed medical expert system. After the processing of all blocks of fuzzy logic, it generates an output. In this developed medical expert system, the output is obtained according to the health of a patient who is suffering from this disease.

The main limitations of this system are that the number of rules and input variables can be increased to get the more accurate result and also this system does not have capability to adapt itself according to the environment or to train itself from new examples.

The rest of the paper is organized as follows.

In Section 2, the related work is explained briefly. In Section 3, some established theories about fuzzy logic and notations used in the proposed system are discussed. In Section 4, the methodology for the proposed work including architecture of input-output parameters is described. The normality tests performed on different input variables are also shown in this section. The performance of the proposed system in terms of sensitivity, specificity, precision, and accuracy is evaluated in Section 5. In Section 6, the fuzzy expert system developed in this work along with ideas about future work is discussed. Finally, the work is concluded in Section 7.

Kubota et al. [ 5] proposed a methodology by using image-processing technique in which the detection of the kidney failure of a patient has been done in the early stage based on information of kidney region. The required information is gathered from the CT image of kidney. The image of kidney is segmented into number of regions to collect correct and accurate information from it. Tang et al. [ 7] projected a method of registration known as intrasubject registration by using the local MI maximization. In this method, the registration is not done on the unified volumes that were acquired from the abdomen region of the patient's body. It will be accomplished only on the selected areas of kidney, which are affected by the disease. This method also increases the accuracy of performing registration. Bommanna Raja and Madheswaran [ 8] developed a computer aided system that examined the various features which are unconstrained and also independent of kidney's area. These examined features are used in the developed system to examine the disease. Rovenţa and Roşu [ 37] developed a medical expert system to diagnose twenty-seven different kidney diseases from nine different categories. The identification of kidney disease is done by taking the symptoms that can be seen in clinical examination and the result evaluated in the laboratory test. The system was developed by using Visual Prolog 5.2 and helps the doctor to diagnose the disease more accurately.

Shen et al. [ 29] developed a low cost method for the patients of dialysis. This method is used to identify the risks of cardiovascular disease (CVD) on the chronic kidney disease (CKD). ECG features and heart rate variability of a patient are considered in this system. It uses the decision-based neural network structure for the feature fusion. The overall accuracy of developed method for cardiovascular disease on chronic kidney disease is 71.07%. Adam and Hashim [ 19] used the artificial neural network technique for the detection of kidney problems in the initial stage. This method predicts various symptoms of kidney problem and then compares these symptoms with the mental behaviour of an individual. The artificial neural network is first trained by giving training samples, and then the testing procedure is done for the same. Chw et al. [ 17] designed intelligence models by using the various technologies of artificial neural network such as back-propagation network (BPN), generalized feed forward neural networks (GRNN), and modular neural network (MNN). These methodologies are developed to detect the chronic kidney disease at initial stages. These three models are compared with each other based on accuracy and effectiveness and also deployed on the Google Cloud platform. The constructed models help to avoid the chronic renal disorders in the beginning of the disease or in the introductory stages. Ahmed et al. [ 42] proposed a fuzzy expert system for the diagnosis of chronic kidney disease. The seven inputs were considered, and by using the values of these inputs, the membership functions are generated. The rules are further generated, and Matlab software is used to implement this research work. The output is predicted by expert system and it gave similar results as the human expert can give.

3. Notations and Preliminaries

The notations and preliminaries used in this work are as follows [ 43].

3.1. Crisp Sets

The crisp set or the classical set is defined as the assemblage or group of elements that can be finite. For example, to define the set if elements are larger than 5, the membership function can be defined as follows:where X is the set of positive integers.

3.2. Fuzzy Sets

If is the set of elements denoted by , then a fuzzy set in is the set of ordered pair and is represented bywhere  = membership function of x in A.

3.3. Fuzzy Numbers

A fuzzy number is convex. A normalized fuzzy set of the real line , such thatwhere  = membership function of fuzzy set.

There are many fuzzy numbers that exist. But in this work, only trapezoidal fuzzy numbers are used.

3.4. Trapezoidal Fuzzy Number (TrFN)

Assume an arbitrary trapezoidal fuzzy number  = ( p, q, r, s). The membership function of is represented by and is given by

4. The Proposed Methodology

The methodology applied in this research work is shown in Figure 1.

The expert system has one layer. As shown in Figure 2, this layer detects the stage of CKD based on various input variables. The proposed expert system is explained mathematically as follows:

4.1. Selection of Input and Output Parameters

The attributes should be selected carefully so that classification of patients can be done perfectly [ 44, 45]. The details of seven input variables used in this research work are described as follows.

4.1.1. Nephron Functionality (NF)

The nephron functionality is the proportion of active nephron in the kidney [ 42]. The nephron functionality is in safe zone if the value is greater than 0.47. However, the functionality is moderately risky if the value is in between 0.3 to 0.5 and it is very-risky if it is below 0.35.

4.1.2. Blood Sugar (BS)

The amount of glucose available in the blood refers to the blood sugar. It is composed of three fuzzy sets. If the level of blood sugar is below 104, then it is safe, whereas it is at borderline if it is between 104 to 150 and if the level is greater than 140 then the existence level of glucose in blood is high.

4.1.3. Diastolic Blood Pressure

The diastolic blood pressure (DBP) is the function of congesting the heart with blood in between the muscle convulsion. It consists of 4 fuzzy sets. If the range is less than 89, then the diastolic blood pressure is low. If the range is between 87 to 110, then it is medium and if the range is between 106 to 121, then it is high. Nevertheless, if the range is above 118, then the diastolic blood pressure is extremely high.

4.1.4. Systolic Blood Pressure

When the heart contracts, the systolic blood pressure (SBP) is stimulated. It is peculiarly the utmost arterial pressure during shrinkage of heart's left ventricle. The time at which ventricular contraction occurs is called systole. If the range of systolic blood pressure is below 134, then it is low; if the value is between 127 to 153, then it is medium; if the range is between 142 to 172, then it is high; and if the range is more than 162, then it is extremely high.

4.1.5. Age

Patient's kidneys are devilishly affected by age. It includes three variables. If the age is under 36, then the patient is young; if age is within 33 to 66, the patient is considered as the mid-age person; and if the age is greater than 52, the patient is classified as old.

4.1.6. Body Mass Index (BMI)

BMI is a simple and clinically useful data for CKD diagnosis. It is calculated by dividing the person's weight with square of height. It has four variables. A person is underweight if BMI is less than 19, normal weight if it is between 18.5 to 24.9, overweight if the value is 24.6 to 30, and obese if it is 29.5 or more.

4.1.7. Smoke

Smoking plays a major role in disturbing the kidney. It has three fuzzy sets. It is low if the range is less than 2.64, medium if the range lies between 1.8 to 9.5, and high if the range is greater than 8.5.

The output variable evaluates the condition of patient's kidney according to the behaviour of the input variables. The range of the output is from 0 to 10. The value 0 means that there is no hiccup in the patient's kidney and 10 means the patient is abundantly sick. This variable consists of six fuzzy sets.(i) Healthy: The patient is healthy if the value of the output variable is less than 1.7. (ii) Concerning: The condition of the patient's kidney is concerning if the value of the output variable is within 1.5 to 3.26. (iii) Very concerning: If the range of output variable falls between 2.84 to 4.5, then the condition of kidney is considered as "very concerning". (iv) Sick: Sick class is assigned to the patients who get output value between 4 to 6. (v) Very sick: The patient is considered as very sick if the range of output variable is within 5.5 to 7.98. (vi) Extremely Sick: The condition of the patient's kidney is extremely sick if the output value is above 7.5.

4.2. Architecture for Input and Output Parameters

There are two ways to develop the architecture of input and output parameters:(1) The first way is to determine the ranges of variables such as nephron functionality, blood sugar, and BMI used in fuzzy set using the confidence intervals (CI) [ 46]. (2) The second way is to get them from the experts or Clinical Practice Guidelines (CPG).

4.2.1. Development of the Parameters Using CIs

CIs are used on the sample of individuals from the target population because it is not possible to perform a study on entire population [ 39]. Sample data of 102 patients are taken, and to further obtain some important information from the given sample, it is crucial to apply tests. Figures 3- 5 depict the normality tests applied on the dataset with Minitab 18 software. The normality tests used for this analysis are Anderson Darling and Ryan-Joiner normality test. To accept the null hypothesis, the -value must be greater than 0.05, whereas value of correlation coefficient must be near to 1.

After the normality test, the next step is to apply confidence intervals to these three variables, i.e., Nephron Functionality, Blood sugar, and BMI. For example, in Table 1 the case of nephron functionality is mentioned, and the various parameters like mean are described as per interest.

4.2.2. Ranges of Input and Output Parameters

An efficient analysis of each variable range determines the parameters corresponding to each variable. Figure 6 shows the membership function of the nephron functionality. Figure 7 shows the membership function of blood sugar by CIs and Figure 8 shows the membership function for BMI by CIs. With the information of the nephron functionality variable, the mathematical representation of the interval used in this model can be described as follows:

4.3. Knowledge Base (IF-THEN Rules Formulation)

In this phase, a group of rules is related to the fuzzy variables with the output classification. A knowledge base contains these fuzzy rules. The precedent-consequential way is followed for these rules. In this model, the Mamdani fuzzy rule-based model is applied, and these rules are stated as follows:

Table 2 shows the variables in FES, and the abovementioned rules are interpreted into fuzzy rules as presented in Table 3.

In the fuzzy inference system, the input-output rules play an important role. The efficiency of a proposed system is directly proportional to the I/O rules generated and stored in the knowledge base of expert system. Figure 9 presents the framework of knowledge base in which the rules are stored.

In the proposed model, there are seven variables; hence, it gives 5184 rules, but it is difficult to handle so many rules. Therefore, in this work, 83 rules are used. These rules have no omission because each of them has probability of its occurrence. The variables are chosen in such a way that the redundancy in rules can be avoided and impeded the dependency of one variable on other so that it will not affect the inference mechanism. The rules are also revised to find out the inconsistent rules, and the result of analysis showed positive outcomes. The abovementioned seven input variables are mixed and according to the reference rules the output is determined.where IRT stands for Inference rules total.

The defuzzification method computes the centre of gravity of images that is driven at the breakdown time of every input linguistic parameter. It implies that this method inspects the relationship between the degrees of membership of two joint sets and enumerates the outside of each subsequent image. The classic structure is utilized in the centroid method corresponding to the abscissa axis. The centre of gravity is in the middle of its base, in rectangles. Further, for triangles, it is set at the third part of its base as indicated by inverse side of the point inducted by hypotenuse and base. Finally, to obtain the total centroid, the summation of surface product of every figure is divided by its centroid into the total surface. [ 46].

4.4. Tests

Here, the need was to interview the patients suffering from CKD. The proposed fuzzy expert system is validated. This validation is done by various specialist doctors and experts from the nephrology and urology department. To evaluate confidence of the system, eighty tests are performed. Results are of a high certainty degree.

The main aim of the evaluation of system is to identify the error percentage and imperfections which could be possibly found in the fuzzy expert system. It also helped to demonstrate the expectations that will be fulfilled by system and the possible error which further helped to calculate performance of system and where the performance measure was lacking behind, it helped to improve that problem.

The various tests that were done with this fuzzy expert system are given in Table 4. In these tests, doctors evaluated suffering patients by this fuzzy expert system with various input variables that are used in the system, and then the obtained results were compared with the actual results. In Test 1, Nephron functionality is equal to 0.458, blood sugar is 132.2, Diastolic Blood Pressure is equal to 105.5, systolic Blood Pressure equal to 128.9, age is 38, BMI have the value 29.55, and smoke value is 2.037, and then control assessment by expert system for that particular patient's kidney condition is concerning. The result as per doctor is also that the kidney of the patient is in concerning phase.

4.5. Response Surface Analysis

The system also gives the three-dimensional plot which describes the influence of input variables on output and output can also be calculated at particular inputs from this three-dimensional plot. These surfaces are helping to depict the level of clinical variables that influence results. It can be seen as if the combinations of variables are in light colour, then it means the optimal zone, whereas if the colour of combination is dark, then the variable levels activate a nonoptimal zone, which indicates a possibility of having CKD to a particular patient.

5. Experimental Results and Analysis

Eighty tests were carried out to analyse CKD using developed fuzzy expert system. The team of doctors also diagnosed each patient and gave result for same. After that, the results given by experts are compared with obtained results from the fuzzy expert system. By correlating the two results, 75 out of 80 tests were correctly classified, and hence the system showed 5 errors. A sample of tests is shown in Table 4.

In Table 5, the confusion matrix is given. 80 cases of different patients are carried out. In the first column, 15 cases of healthy patients are considered and all of them are classified into correct class, i.e., healthy class. In the second column, the data of 10 patients at the concerning level is considered, but as a result out of 10 cases, 8 cases are exactly classified into concerning class and rest of 2 are classified into sick class. Similarly, in the third column, 16 cases very concerning are carried out, and from those 16 cases 15 are classified correctly and one is classified into class sick. In the fourth column, out of 11 diagnosed patients, 1 is classified incorrectly and 10 are in exact class. Likewise, fifth column illustrates that 14 diagnosed patients of class very sick are classified in correct class. At last, the sixth column shows that there are 14 cases of extremely sick patient, but out of 14, 1 is misclassified.where number of successes = 75 and total number of tests = 80.

It was examined that 93.75% of the results given by fuzzy expert system are classified correctly by calculating the confidence indicator using above formula. Therefore, by observing the confidence indicator, it can be said that the developed fuzzy expert system can work as the supporting tool for doctors in diagnosis of CKD.

The results show that the categorization by fuzzy inference system for CKD is 06.25% wrong. Now, the first three classes that is healthy, concerning, and very concerning are taken as "no". Similarly, the rest of classes sick, very sick, and extremely sick are taken as "yes". Hence, the confusion matrix shown in Table 4 is reduced to 22 matrix and shown in Table 6.

The performance of developed medical expert system is evaluated by considering various parameters like accuracy, precision, specificity, and sensitivity.

These calculated parameters are shown in the form of graph in Figure 10.

The main aim to develop this expert system is that it will help doctors to identify the CKD in patients. This medical expert system is able to diagnose the disease and can help specialist to provide an appropriate and suitable treatment for disease.

To portray the behaviour of two given variables in 3-dimensional surface, the response surface is used. It explains the probability of risk with which a person suffers on two considered variables. Three plots are considered in this work. First in Figure 11, it is the plot between BMI and nephron functionality. The response surface plot of variable BMI and nephron functionality is analysed by test 2 in which the value of BMI is 35 and similarly value of nephron functionality is 0.1. From plot, it can be estimated easily that at these values, the result is high i.e. extremely sick.

Furthermore, by Figures 12 and 13, the output can be estimated by considering blood sugar with nephron functionality and BMI with blood sugar, respectively. With a complete analysis of all the potential instances of test two response surface plots, and with grouping of seven parameters, it is found that the parameters with most extreme impact in diagnosis of CKD are nephron functionality, blood sugar, and BMI. These three variables have the highest effect on patient's health in case of CKD. Therefore, doctors must give appropriate recommendation to patients to make their health stable and be cured in time from this CKD.

Figure 14 shows the input values of various input variables used in medical expert system for diagnosis of CKD and output corresponding to given inputs. Image-processing techniques such as registration of an image, segmentation of an image, and extraction of features have also been applied on acquired magnetic resonance images and computed tomography images.

Additionally, the artificial neural network techniques such as backpropagation neural network and modular neural network are also used for the detection of CKD. Moreover, many systems have been proposed by using machine learning algorithms for the diagnosis of CKD such as k star and k nearest neighbor classifier to classify the stage of the disease into different classes according to the symptoms and risk factors.

All these abovementioned techniques deal with the crisp numbers. The developed system in this work deals with the fuzzy values, and it has also given more accuracy than these techniques.

6. Discussion

The developed fuzzy logic-based medical expert system is a supporting tool in decision-making about diagnosis of CKD. Although, specialist doctor's decision will be considered as final, the developed medical expert system can also make decision as a specialist doctor. Hence, it could be a very useful tool for assisting medical doctors for the robust decision regarding the CKD.

The fuzzy expert system can also be suitable in remote areas, such as villages, and small towns, as there is always scarcity of specialists in such places. This expert system can also help to train the new doctors who do not have enough experience to diagnose the CKD, because this fuzzy expert system has knowledge and information stored in knowledge base that was acquired from specialists of the disease. Hence, it can be used as a training tool as well.

The accuracy obtained from the developed system is 93.75%. It means the system is successful in identification of 93.75% of the total cases considered in this research work. Another performance parameter considered in this work is precision. The precision obtained from this developed system is 92.5%. It means the developed system is having high quality of giving exact and clear results. Similarly, the sensitivity and specificity of developed system are 94.87% and 92.68%. It means developed system is capable of measuring the proportion of actual positives and negatives which are correctly determined as such.

The whole work done in this study is new and original as the clinical tests, and risk factors considered in this study have not been incorporated in the systems similar to it. The various research works are discussed in Introduction to understand more about risk factors, prevention, and treatment of CKD. Competitive advantage is that the proposed expert system is able to diagnose the disease with various variations in input variables. Variation states considered in this work are healthy, concerning, very concerning, sick, very sick and extremely sick.

The main contribution of this work is that it is a supporting tool which is capable to make decisions, or it supports the decision-making process according to the given inputs to system by the user to identify whether the patient is suffering from CKD or not. To improve the success rate of fuzzy expert system, the recommendation is to gather more knowledge about the disease from specialist doctor and make rules as enough as possible. One can also increase the number of variables used as input to recognize the CKD and make their range accordingly so that the result can be calculated more accurately.

7. Conclusion

The diagnosis of chronic kidney disease is a difficult task. There is always some possibility of misdiagnosis that leads to wrong treatment. This work proposes a fuzzy expert system that can help a doctor and nonspecialist to examine the chronic kidney patient.

The fuzzy expert system is based on clinical test ranges. It works on two types of information, that is, the knowledge acquired from specialist doctors and experts from the nephrology and urology departments. This study also gives response surface plot of various input variables used to recognize the disease. This system can give correct results about the health status of a patient.

In the current state, it would be inappropriate to say that the developed fuzzy expert system will replace the specialist doctors or knowledge of the team of doctors. However, this could be a supporting tool for doctors that could assist in decision-making and does not replace their admirable work. In addition, only computer and software are required to implement this medical expert system. Therefore, this system can be used in hospitals, where there is lack of resources and can be used in those geographic areas where hospitals are not available.

Data Availability

The data are available on request on demand through the first author [email protected] . or via phone +9779288330.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This work was supported by Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korea Government (MSIT) (no.2018-0-01369).

# data[].attributes.attachment_link.title
A Novel Fuzzy Logic-Based Medical Expert System for Diagnosis of Chronic Kidney Disease

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Chronic kidney disease is a life-threatening complication. Primary diagnosis and active control avoid its progression. To increase the life span of a patient, it is necessary to detect such diseases in early stages. In this research paper, design and development of a fuzzy expert system (FES) to identify the current stage of chronic kidney disease is proposed.

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Add on MF

https://www.mathworks.com/help/pdf_doc/fuzzy/fuzzy_ug.pdf (page 78)

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Define Fuzzy Rules Using Fuzzy Logic Designer

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Define Fuzzy Rules Using Fuzzy Logic Designer

Once you create a fuzzy inference system (FIS) using Fuzzy Logic Designer and define the input and output variables along with their respective membership functions, you can create a fuzzy rule base for your system.

Each if-then rule in a fuzzy system has two components.

Antecedent - The if portion of the rule, which specifies the input variable linguistic terms

Consequent - The then portion of the rule, which specifies the output variable linguistic terms

For more information on fuzzy rule structure, see Foundations of Fuzzy Logic.

Once you define your rules, you can analyze the behavior of your FIS, including the rule inference process. For more information, see Analyze Fuzzy System Using Fuzzy Logic Designer.

Add Rules

To add a rule to your FIS, first open the Rule Editor document.

To add a rule, click . The app adds a default rule to the rule table and shows the rule properties in the Property Editor. You can then edit the rule properties using the Property Editor.

You can copy an existing rule by selecting the rule in the Rule Editor and clicking .

You can also add a rule by selecting either the FIS name or Rules in the System Browser. Then, on the Design tab, in the Add Components gallery, click Rule.

Add All Possible Rules

You can add AND-based rules to your FIS for all possible input combinations using one of the following methods:

In the Rule Editor, click Add All Possible Rules.

Select the FIS name or Rules in the System Browser. Then, in the Add Components gallery, click Add All Rules.

Edit Rules

To configure a rule, first select the rule in the System Browser or the Rule Editor, which displays the rule in the Property Editor.

To define the rule antecedent, first specify the connection operator. To do so, set the Connection parameter to one of the following values.

Then, under If, select a linguistic term for each input variable. To do so, in the right-hand drop-down list, select a membership function name. To ignore an input variable, select .

Then, indicate whether to apply a NOT operation to the selected membership function. To apply a NOT operation, in the left-hand drop-down list, select . Otherwise, select .

Similarly, under Then configure each output variable in the consequent.

Typically, you weight the importance of all the rules in a FIS equally. However, you can decrease the relative weighting of a rule. To do so, specify a value for the Weight parameter that is less than 1.

Delete Rules

To delete a rule from your FIS, in the Rule Editor, select the rule and click

Alternatively, in the System Browser, under Rules, right-click the rule and select .

To delete all of the rules from your FIS, in the Rule Editor, click Clear All Rules.

Alternatively, in the System Browser, right-click Rules, and select all rules.

See Also

Fuzzy Logic Designer

Related Topics

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Define Fuzzy Rules Using Fuzzy Logic Designer - MATLAB & Simulink

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Interactively define rules for a fuzzy inference system using the Fuzzy Logic Designer app.

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New membership functions of the inputs and outputs of edge FIS

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New membership functions of the inputs and outputs of edge FIS

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Ix and Iy showed the bell shape graph, the graph of the output illustrated the degree of membership of black, gray and white respectively.

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This is the same explanation of impact on Distribution Transformer Life Using Electric Vechiles with long-range battery capacity

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Faculty of Electrical Engineering, Federal University of Para, Belem 66075-110, PA, Brazil

Author to whom correspondence should be addressed.

1. Introduction

Global warming and climate change have been driving a global decarbonization movement, seeking to reduce the use of fossil fuel and greenhouse gas emissions. The transport sector accounts for a huge part of total gas emissions, achieving 8 Gt. of CO ₂ emissions in 2022 [ 1]. The use of electric vehicles (EVs) is essential to decarbonize this segment. Thus, governments in several countries have been promoting public politics with subsidies and incentives for the use of EVs. For example, Norway adopted a national goal that all new cars sold by 2025 should be zero-emission [ 2]. Germany agrees to ban internal combustion engines by 2030, and France and Great Britain plan to end the sales of fossil fuel-powered cars by 2040 [ 2].

Electric vehicles market sales have grown rapidly in the last years. The main barrier to widespread adoption of EVs is the battery, which still must overcome the high costs and low charging speed and range to dominate the market. To address the consumers' concern, the EV industry has been launching vehicle models with higher battery capacity that charge at higher power levels, increasing vehicle range and reducing the charging time [ 3]. EV models from 2014 have short-range batteries with capacity around 24 kWh. However, the latest EV models such as Tesla 3 have long-range batteries of 75 kWh and charge at 7-11 kW.

The increased use of EVs can have negative impacts on electrical energy distribution systems. EVs need to be frequently charged and consume a large amount of energy, causing excessive and undesirable peaks in energy demand. This can lead distribution transformers, which were sized before EVs' integration, to overload [ 4]. When transformers are exposed to high ambient temperatures and loading above the nominal value, transformers' windings may overheat, leading to deterioration and loss-of-life, incurring extra costs. Moreover, EV demand has uncertainties and randomness associated with user's behavior and battery specification, which brings extra challenge to this problem.

Numerous studies have been conducted analyzing EVs' impact on transformer loss-of-life. In [ 5], authors propose a reactive power compensation strategy during EV charging for transformer overloading mitigation in a residential feeder. In [ 6], authors analyze the impact of reactive power from public EV charging stations on transformer aging and active power losses. Simulations considered vehicles with battery capacity from 40 kWh to 80 kWh, charging at 3.4 kW and 7.2 kW.

Authors in [ 7] analyze the effects of EVs' charging demand on distribution transformer attending residential customers and propose demand side management with time-of-use (ToU) tariffs to minimize transformer aging. Vehicles with 16 kWh and 24 kWh charging at 3.7 kW are considered, and several EVs' penetration levels are analyzed. Authors in [ 8] propose a smart charging strategy to minimize electricity consumption costs and avoid transformer overloading by considering a charging station in a commercial building integrated with photovoltaic generation and a battery energy storage system. They adopt vehicles of 16 kWh and 24 kWh charging at 3.3 kW and 6.6 kW. Reference [ 9] proposes a smart charging algorithm with variable-rate to mitigate transformer overloading and loss-of-life. The method is compared with other strategies, such as uncontrolled charging and fixed-rate controlled charging, with promising results. EV demand is modeled based on a Bureau of Transportation Statistics survey, and simulations consider eight different types of EVs charging at different power levels varying from 1.9 kW to 11.5 kW.

In [ 10], authors evaluate the damaging impact of several EVs on distribution transformers life considering different penetration levels. Simulations consider EVs with 11 kWh battery capacity charging at 4 kW. Results show the loss-of-life rate during evening peak is 12.21 for a scenario with 40% EV penetration level. In [ 11], authors propose a risk assessment to quantify the severity and likelihood of transformer overload conditions due to high levels of EVs' demand coupled with rooftop solar generation. Probabilistic analysis is performed, and multiple EV penetration scenarios are analyzed. In [ 12], authors propose a fuzzy system to estimate distribution transformer aging and mitigation strategies combining battery energy storage systems and photovoltaic generation. Authors in [ 13] evaluate distribution transformer overload and aging in a residential feeder using probabilistic analysis. Simulations considered fast chargers with vehicles with 100 kWh charging at 50 kW, and vehicles with 16 kWh and 32 kWh charging at 3.7 kW and 6.6 kW. In [ 14], authors propose an approach to support EV charging that allows the transformer to operate beyond nameplate rating without compromising its life based on the knowledge of thermal inertia. Results show transformers can operate above the nameplate rating for periods without exceeding any relevant IEEE or IEC standards. In [ 15], authors propose a framework to assess distribution transformer aging using Time Series Decomposition and the Hidden Markov Model as forecasting tools. However, EV demand is modeled based on travel surveys and several assumptions instead of real data. In [ 16], authors analyze the potential impact of EV charging on transformer lifetime using a real distribution system from USA. Although a probabilistic analysis is adopted, EV demand is modeled based on projections, assumptions and travel surveys instead of real data.

Most studies mentioned above model EVs' demand based on national surveys or projection scenarios, instead of using real data collected from trial projects, which directly impacts the estimation of transformer life. Another important aspect is that these studies do not capture stochasticity in transformer life due to EV charging demand, because only EVs' profile is generated probabilistically. Moreover, despite some studies adopting long-range EV models, to the best of the author's knowledge, no study has been reported in the literature comparing the effects of long-range and short-range EVs on transformer life. Table 1 provides an overview of the most recent studies in this area.

This paper addresses this knowledge gap in the literature, comparing the effects of long-range and short-range EVs' charging on transformer life. A probabilistic analysis is employed using the Monte Carlo (MC) Simulation considering uncertainties from residential and EV demand. Residential demand is modeled based on real data from the UK Data Service [ 17], and EVs' charging demand is modeled based on real data from the Electric Nation Project developed in the United Kingdom [ 18]. The deterioration of the transformer is investigated through the analysis of transformer hottest-spot winding temperature and equivalent aging factor, according to the thermal model presented in IEEE Guide [ 19]. Simulations are conducted considering seasonal variations, adopting winter and summer profiles for residential demand, EVs' demand and ambient temperature. Besides, several EV penetration levels are analyzed. The key contributions of this work are as follows:

Models EVs' demand based on real data collected from trial projects;

Employs a probabilistic analysis on transformer life due to EV charging demand under different EVs' penetration levels;

Compares long-range and short-range EVs' charging impact on transformer life.

This work is organized as follows. Section 2 presents the proposed methodology and the probabilistic models adopted for residential and EVs' demand. The results are presented in Section 3, followed by main conclusions in Section 4.

2. Proposed Methodology

Figure 1 shows the proposed method to quantify the impacts that long-range and short-range EVs' charging demand cause on transformer life. The Monte Carlo Simulation is used to consider model uncertainties [ 20]. First, residential and EVs' demand are sampled according to probabilistic models developed based on real data. Then, the transformer load is computed for summer and winter, accounting for residential load and EVs' charging demand. Based on transformer demand and ambient temperature, transformer indices are evaluated for summer and winter to estimate its loss of life.

To guarantee simulation's reproducibility, Mersenne Twister pseudorandom number generator with a seed equal to 3000 was used, as suggested in [ 21], to perform 1000 simulations and achieve convergence, using Random Number Generation toolbox of MATLAB 2021. Simulation period of 24 h is considered with sampling interval of 10 min.

2.1. Residential Demand

The database used to generate residential load profiles comprehends real electricity measurements from 22 residences in the East Midlands, United Kingdom, from 2008 to 2009 [ 17]. This data is divided into 2 sets according to the season: summer (June to September) and winter (December to March). Since no parametric distribution function fits the original data, a non-parametric Gaussian kernel with 0.01 bandwidth is considered for each 10-min interval. The Kolmogorov-Smirnov statistic test (KS-test) is applied to the data generated with the obtained probabilistic models. Figure 2 shows the results which indicate a good fit.

2.2. Electric Vehicles Demand

EVs' demand is modeled based on a real database from Electric Nation Project, which collected data from 673 smart chargers installed at participants' homes in United Kingdom from January 2017 to July 2018 [ 18]. The database contains information, such as participant identification, day of the week, start charging time, battery capacity (kWh) and energy consumed (kWh).

Then, the start charging time and collected from the database were fitted to a non-parametric distribution function, with Gaussian kernel and 0.01 of bandwidth, to obtain their corresponding probabilistic distribution function (PDF). The goodness-of-fit is evaluated employing the KS-test and results indicate a good fit as shown in Figure 3 and Figure 4.

The EV charging demand profiled is obtained for a period of 24 h with 10-min resolution according to the flowchart shown in Figure 5. Two EV models are considered varying the EV battery capacity and the charging power level:

short-range vehicles with 24 kWh charging at 7.4 kW.

long-range vehicles with 75 kWh charging at 11 kW.

2.3. Transformer Aging Model

Transformers are one of the most important and expensive devices in the distribution grid, and the efficiency of these assets is vital to ensure reliability and power delivery. Transformer service life is mainly related to insulation degradation. Under normal operating conditions, core and coil losses generate significant internal heat, which if not dissipated, can shorten the life of transformers [ 19].

Insulation aging or deterioration is a function of temperature, moisture content and oxygen content over time. With modern oil preservation systems, moisture and oxygen contributions to insulation deterioration can be minimized by leaving insulation temperature as the control parameter. Since, in most appliances, the temperature distribution is not uniform, that part which is operating at the highest temperature will normally suffer the most deterioration. Therefore, in aging studies it is usual to consider the effects of aging produced by the higher temperature (hottest-spot temperature).

In this paper, transformer hottest-spot temperature () is evaluated based on the classic thermal model presented in IEEE Standard C57.91 [ 19]. All equations used in this section are from this reference.

The ambient temperature is an important parameter in determining the transformer's load capacity. This paper adopted ambient temperatures' curves over a period of 24 h, with average values extracted from the National Solar Radiation Database [ 22] for the location of the United Kingdom. Two curves are considered as shown in Figure 6, one for summer season and the other for winter season.

The rise of the winding hottest-spot over the upper oil is evaluated according to Equation (6):

where ΔΘ is the winding hottest-spot temperature at rated load in °C and m is an empirical exponent whose value depends on transformer cooling mode.

An equivalent aging factor equal to 1 means that the transformer aged 1 day (24 h) in a cycle of 24 h, early deterioration not occurring. On the other side, an equivalent aging factor equal to 1.5 means that the transformer aged 36 h in a cycle of 24 h, implying premature aging. Transformer normal insulation life is 20.55 years, equivalent to 180,000 h. However, if this transformer operates continuously under the foregoing conditions with daily aging of 36 h, its useful life will be significantly reduced. The distribution transformer used in this paper has 200 kVA and supplies 100 residences. The thermal parameters provided by the manufacturer are listed in Table 2.

3. Simulation Results

In this section, the impact of EV charging on transformer life is analyzed considering different penetration levels and vehicles with different battery capacities: 75 kWh (long-range) and 24 kWh (short-range). In addition, the effects of winter and summer seasons are verified.

3.1. Base Scenario

The base scenario considers no household carries EVs. Results are separated according to winter and summer seasons. Figure 7 shows the boxplot of transformer load and hottest-spot temperature due to residential demand for a period of 24 h. In the boxplot, the horizontal line inside the box is used to mark the median, while the upper and bottom of the box represent 75th and 25th percentiles. The two horizontal lines outside of the box represent the maximum and minimum of the data. Note that residential demand is below the transformer nameplate rating of 200 kVA. Therefore, the hottest-spot temperature does not violate the reference temperature of 110 °C, and the transformer does not experience loss-of-life.

3.2. Penetration Level Impact

This section evaluates the impact of different EVs' penetration levels on transformer aging. In this study, the penetration level is defined as the ratio between the number of EVs and the number of households, which is 100. EVs' penetration level gradually increases from 0% up to 60% in steps of 10%.

Figure 8 shows the boxplot of maximum transformer load and hottest-spot temperature to each penetration level. During winter, residential demand is higher and transformer violates its rated capacity limit for lower penetration levels compared to summer. Also, the impacts caused by EVs with 75 kWh batteries are more severe because they charge at higher power levels, increasing peak demand. The total load already exceeds transformer-rated capacity for penetration levels of 20% in both winter and summer with long-range batteries.

For EVs with 24 kWh, the maximum hottest-spot temperature remains below the reference value until the penetration level of 50%. For EVs with 75 kWh, the maximum hottest-spot temperature reaches undesirable values for penetration levels as from 20%.

It is important to analyze the evolution of EVs' penetration level to evaluate transformer life. Compared to the base scenario, there is a considerable increase in transformer load and hottest-spot temperature as the penetration levels of EVs increase, especially for vehicles with 75 kWh, where low penetration levels above 20% already lead to severe violations. To better understand the impact of EV charging on transformer hottest-spot temperature and loss-of-life, the penetration level of 50% is investigated in the following section.

3.3. Case Study (Penetration Level of 50%)

This scenario assumes that out of 100 households, 50 have an electric vehicle. Figure 9 shows the boxplot of transformer load for winter and summer seasons, considering EVs with battery capacity of 24 kWh and 75 kWh. In a residential area, users typically charge their vehicles when returning home at the end of the day. Residential demand is also high during this time, causing a considerable increase in peak demand overloading the transformer. Results show the transformer is more likely to exceed its rated capacity for EVs with higher battery capacity during the winter season.

Figure 10 shows the cumulative distribution function of transformer hottest-spot temperature, and the same behavior is observed. The worst scenario is when using vehicles with higher battery capacity during the winter season. For vehicles with 24 kWh, the probability of transformer hottest-spot temperature achieving values above the reference temperature of 110 °C is 64.5% during winter and 17.6% during summer. For vehicles with battery capacity of 75 kWh, the probability increases to 100% for both winter and summer seasons. The main factor that affects the transformer is its insulation temperature, which in turn is mainly related to overload operation and exposure to high ambient temperatures. Since the ambient temperature at this location (England) is not very high during summer season (maximum of 21 °C), transformer operating life is mainly affected by load, which is higher during winter.

Figure 11 shows the boxplot of the transformer equivalent aging factor. Under normal operating conditions, the transformer should have a maximum equivalent aging factor of 1 day for a 24-h load cycle to prevent premature degradation. The results show the occurrence of transformer aging is very rare when EVs with battery capacity of 24 kWh are adopted both in winter and summer scenarios. However, when EVs with battery capacity of 75 kWh are used, transformer aging always occurs, with minimum values of 127.8 during winter and 5.2 during summer. As an example, an equivalent aging factor equal to 5.2 means that the transformer aged 124.8 h (5.2 × 24) in a cycle of 24 h, implying premature aging. If this transformer is subjected under the foregoing conditions during the whole year, its useful life will be shortened from 20.55 years (normal insulation life) to 4 years, which is not acceptable.

Figure 12 shows the boxplot of transformer overload duration during a day. The results clearly show the negative effect of using EVs with higher battery capacity as transformer overload duration increases significantly. For EVs with battery capacity of 24 kWh, overload duration has median value of 130 min (approximately 2 h) during winter and 0 min during summer. For EVs with battery capacity of 75 kWh, overload duration has median value of 410 min (approximately 7 h) during winter and 380 min (approximately 6 h) during summer. This confirms that, when the transformer operates overloaded for long periods, it will accelerate transformer aging and shorten its service life.

4. Conclusions

This paper analyzed the impacts of long-range EVs on transformer life and compared results with the effects caused by short-range EVs. A probabilistic analysis is employed using the Monte Carlo Simulation. Residential demand is modeled based on real household curves from the UK Data Service, and EVs' pattern is modeled based on data collected from the Electric Nation Project developed in the United Kingdom. Several EVs' penetration levels are analyzed from 0% to 60% in steps of 10% for winter and summer seasons. From the results, important conclusions can be addressed:

The impacts caused by EVs with 75 kWh batteries are more severe because they charge at higher power levels, increasing total peak demand;

Transformer life is mainly affected by load and ambient temperature. Since ambient temperature at the site of the study is not very high during summer and residential demand is higher in winter, transformer is more likely to exceed its rated capacity during winter;

Transformer overload duration is significantly higher when EVs with higher battery capacity are used, accelerating transformer aging and shortening its service life;

As EVs' penetration level increases, there is a considerably increase in transformer load and hottest-spot temperature, especially for vehicles with 75 kWh, where low penetration levels above 20% already lead to severe violations on hottest-spot temperature;

For penetration level of 50% during summer, the use of long-range EVs leads to a minimum equivalent aging factor of 5.2, which means a transformer aged 124.8 h in a cycle of 24 h, decreasing its lifetime.

As in most studies, EV demand is modeled in this paper assuming an everyday plug-in charging behavior. More research should be carried out modeling EVs' charging pattern for a week-long period since some studies already demonstrated that vehicles with higher battery capacity do not charge every day [ 24]. This behavior directly affects the number of vehicles charging simultaneously and, therefore, EV peak load. Besides, as high ambient temperatures directly impact transformer loss-of-life, more analysis should be conducted using data from a different location with warmer weather.

Author Contributions

Conceptualization, C.M.A. and J.H.A.; methodology, C.M.A.; software, J.H.A. and A.M.P.B.; validation, A.M.P.B.; formal analysis, C.M.A.; investigation, C.M.A. and J.H.A.; resources, A.M.P.B.; data curation, A.M.P.B.; writing-original draft preparation, C.M.A.; writing-review and editing, J.H.A. and C.M.A.; visualization, A.M.P.B.; supervision, C.M.A.; project administration, C.M.A.; funding acquisition, C.M.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported in part by PROPESP/UFPA (PAPQ) and CNPQ, Brazil.

Data Availability Statement

Conflicts of Interest

The authors declare no conflict of interest.

References

1. IEA. CO₂ Emissions in 2022. Paris. 2023. Available online: https://iea.blob.core.windows.net/assets/3c8fa115-35c4-4474-b237-1b00424c8844/CO2Emissionsin2022.pdf (accessed on 27 March 2023).

2. Cabtree, G. The coming electric vehicle transformation: A future electric transportation market will depend on battery innovation. Science 2019, 366, 422-424. [Google Scholar] [CrossRef] [PubMed] [Green Version]

3. Electric Vehicle Database. Available online: https://ev-database.org (accessed on 27 March 2023).

4. Usman, H.M.; ElShatshat, R.; El-Hag, A.H. Distribution Transformer Remaining Useful Life Estimation Considering Electric Vehicle Penetration. IEEE Trans. Power Deliv. 2023, 1-12. [Google Scholar] [CrossRef]

5. Richardson, I.; Thomson, M. One-Minute Resolution Domestic Electricity Use Data, 2008-2009. 2010. Available online: https://dss.princeton.edu/catalog/resource1162 (accessed on 21 May 2023).

6. WPD Smart Charging Project|Electric Nation: Electric Nation. Available online: https://www.westernpower.co.uk/electric-nation-data (accessed on 27 March 2023).

7. C57.91-2011; IEEE Guide for Loading Mineral-Oil-Immersed Transformers and Step-Voltage Regulators. IEEE/PES: New York, NY, USA, 2012.

8. Rubinstein, R.Y.; Kroese, D.P. Simulation and the Monte Carlo Method; Wiley Series in Probability and Statistics; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2016; ISBN 9781118631980. [Google Scholar]

9. NSRDB. National Solar Radiation Database. Available online: https://nsrdb.nrel.gov/data-viewerel.gov (accessed on 27 March 2023).

10. Union Transformers Company. 3-Phase Hermetically Sealed Oil Immersed Distribution Transformers Technical Specification. Available online: http://union-transformer.com/transformers/200kva (accessed on 6 October 2022).

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Impact on Distribution Transformer Life Using Electric Vehicles with Long-Range Battery Capacity

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This paper presents a comparative analysis of the effects of short-range and long-range electric vehicles charging on transformer life. Long-range vehicles are expected to become more common in the future. They have higher battery capacity and charge at higher power levels, modifying demand profile.

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Due to unsatisfactory results with the three-dimensional surface output for kidney disease, we changed our topic to control actions for electric vehicles

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the fuzzy model have 3 inputs which are PV generation, power factor and aggregated apparent power and one ouput which is control action

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The PV power membership functions categorize PV generation from 0% to 140% of the transformer's rated power as low, medium, or high.

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The power factor membership functions categorize the power factor as poor and high

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The aggregated apparent power membership functions categorized the transformer's rated power as normal and high.

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The membership function for control action categorizes values from 0 to 0.5 as necessitating subtle tuning, values from 0.25 to 0.75 as requiring active tuning, and values from 0.75 to 1 as demanding intense tuning.

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Here is the 3D surface of the fuzzy system which congtains 3 key variable: PV generation, power factor and control action.

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Rule 1 - Rule 5 are referred to the reference, our group try to add new rules (Rule 6 - Rule 8) to improve the system performance and facilitate decision-making.

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This is Original grayscale image

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This is the plot result after detect edges processing

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GUI For Image Edge Detection (Before & After)

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GUI For Image Edge Detection (Before & After)

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This the GUI design for edge detection for before and after

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In order to evaluate the performance of the system, we have set up 2 case studies with different inputs. Here is what was discussed:

Case Study 1:

PV generation: 0.499 (low)

Power factor: 0.743 (poor)

Aggregated apparent power: 96.4 (normal)

Output: 0.403

Case study 2:

PV generation: 0.941727 (high)

Power factor: 0.634058 (high)

Aggregated apparent power: 110.87 (high)

Output: 0.708