Pengembangan Nominal Aturan Fuzzy pada Metode Fuzzy Mamdani untuk Menyeimbangkan Beban Tiga Phasa pada Saluran Distribusi Tegangan Menengah

The use of electrical equipment on the customer side with low voltage absorbs unbalanced power. The load unbalances in each phase will result in an unbalanced current, resulting in a phase voltage shift in the secondary coil of the 20 kV/380 V medium voltage transformer. Shifting the voltage in the distribution transformer phase, then causes the flow of current in the transformer neutral wire causing losses. This paper proposes a fuzzy logic method with the Mamdani fuzzy inference system (FIS) to balance three-phase load currents at seven feeders of 20 kV medium voltage distribution at PLN Rayon Taman Jawa-Timur. The feeders are Ngelom, Tawang Sari, Geluran, Bringin, Masangan Kulon, Palm Residence, and Pasar Sepanjang. There are three input variables used, namely the load current in phase R, phase S, and phase T respectively. There are three output variables in one FIS block, namely changes in load current in phase R, phase S, and phase T respectively. With the number of fuzzy rules as many as 509 rules, the proposed method is able to produce the lowest load current unbalance value of 1.6% at Palm Residence Feeders. The development of a nominal (number) of fuzzy rules in the Fuzzy Logic Method with FIS Mamdani is able to reduce the value of unbalance load current at the 20 kV medium voltage distribution feeder better than the method proposed by previous researchers.

Prediction of Unconfined Compressive Strength of Microfine Cement Injected Sands Using Fuzzy Logic Method

In this study, unconfined compressive strength values of sand soil injected with microfine cement were predicted using fuzzy logic method. Mamdani and Sugeno methods were applied in the fuzzy logic models. In addition, a regression analysis was carried out in order to compare these two methods. In the models, water/cement ratio and injection pressure were the input variables, and unconfined compressive strength was the output variable. The dataset includes 427 samples, which were experimentally injected with microfine cement. Predictions for unconfined compressive strength were obtained by creating membership functions and rule base for each input (predictive) parameter in fuzzy logic models. The coefficient of determination (R2) and Mean Square Error (MSE) were used as criteria for evaluating the performance of the developed models. The results suggested that the three applied models (i.e. Mamdani, Sugeno and regression) provided statistically significant results, and these methods could be used in the future prediction-based studies. The results showed that Sugeno model provided the best performance for predicting unconfined compressive strength. It was followed by Mamdani and Regression models, respectively. This study has suggested that the fuzzy logic method can be an alternative to the regression method which traditionally has been used in prediction process.

The Design of Directions and Shot Sound Distance Using Fuzzy Logic Method Based on Raspberry Pi

- The rise of a shooting incident that occurred in the border areas of the Republic of Indonesia is a big loss for the state in terms of personnel. Technological developments can be used as an alternative in the military world to help the role of soldiers so as to reduce personnel losses. This study aims to create a system for detecting the direction and distance of gunshots. This study uses an experimental method. This gunshot detection system also applies the Fuzzy Logic Method which is applied to the Raspberry Pi 4 and Microphone Max 4466 which is expected to detect the direction and distance of gunshots. This Fuzzy Logic method is used as an inference system or decision maker according to the input given. Fuzzy Logic broadly consists of fuzzification, rule base, and defuzzification. Fuzzification is useful for input normalization, so that the input quantity is in accordance with the fuzzy magnitude, namely the value in the range 0 to 1. After that, enter the rule base where in this step, the input set is compared with the rules or provisions of sound decibels so that it can be classified whether the distance and the direction of the captured sound is in the data range that has been programmed, in this step the signal is analyzed how much decibel sound SS2-V1 is by the MAX 4466 sensor. The conclusion is done by defuzzification, so the final result is that the closest distance to a gunshot at 1 meter is 250 Decibels.

Feature Extraction in Six Blocks to Detect and Recognize English Numbers

The Fuzzy Logic method was implemented to detect and recognize English numbers in this paper. The extracted features within this method make the detection easy and accurate. These features depend on the crossing point of two vertical lines with one horizontal line to be used from the Fuzzy logic method, as shown by the Matlab code in this study. The font types are Times New Roman, Arial, Calabria, Arabic, and Andalus with different font sizes of 10, 16, 22, 28, 36, 42, 50 and 72. These numbers are isolated automatically with the designed algorithm, for which the code is also presented. The number’s image is tested with the Fuzzy algorithm depending on six-block properties only. Groups of regions (High, Medium, and Low) for each number showed unique behavior to recognize any number. Normalized Absolute Error (NAE) equation was used to evaluate the error percentage for the suggested algorithm. The lowest error was 0.001% compared with the real number. The data were checked by the support vector machine (SVM) algorithm to confirm the quality and the efficiency of the suggested method, where the matching was found to be 100% between the data of the suggested method and SVM. The six properties offer a new method to build a rule-based feature extraction technique in different applications and detect any text recognition with a low computational cost.

Modified Fuzzy Logic Method for Materials Selection in Mechanical Engineering Design

Materials selection is one of the primary steps in designing products, including mechanical ones. As a result, researchers are continuously developing simple, accurate, and feasible techniques to enhance the performance of the designed component by selecting the optimum materials from a wide variety of candidate materials. In this research work, a modified fuzzy approach referred to as Modified Fuzzy Logic Method (MFLM) is proposed to provide some highly appreciated advantages besides resolving some of the substantial shortcomings of the existing solutions. Customizing the method based on the designer’s knowledge and level of expertise, simultaneous consideration of qualitative and quantitative properties, and high accuracy in the ranking of candidate materials are some of the significant benefits of MFLM in comparison to the available alternative methods. While, complexity, high volume computational efforts, and more are amongst the resolved drawbacks. Moreover, the produced results show the power and simplicity of the technique in solving complex problems like materials selection for a gas turbine blade which involves a high range working temperature.