ANFIS Etc.)

Two new predictive design methods are presented in this study. The first is a hybrid method, called neuro-fuzzy, based on neural networks with fuzzy learning. A total of 280 experimental datasets obtained from the literature concerning concentric punching shear tests of reinforced concrete slab-column connections without shear reinforcement were used to test the model (194 for experimentation and 86 for validation) and were endorsed by statistical validation criteria. The punching shear strength predicted by the neuro-fuzzy model was compared with those predicted by current models of punching shear, widely used in the design practice, such as ACI 318-08, SIA262 and CBA93. The neuro-fuzzy model showed high predictive accuracy of resistance to punching according to all of the relevant codes. A second, more userfriendly design method is presented based on a predictive linear regression model that supports all the geometric and material parameters involved in predicting punching shear. Despite its simplicity, this formulation showed accuracy equivalent to that of the neuro-fuzzy model.

This paper presents a new islanding detection strategy for low-voltage (LV) inverter-interfaced microgrids based on adaptive neuro-fuzzy inference system (ANFIS). The proposed islanding detection method exploits the pattern recognition capability of ANFIS and its nonlinear mapping of relation between inputs. The ANFIS monitors seven inputs measured at point of common coupling (PCC), namely root-mean square (RMS) of voltage and current (RMSU and RMSI), total harmonic distortion (THD) of voltage and current (THDU and THDI), frequency (f), and also active and reactive powers (P, Q) that are experimentally obtained based on practical measurement in a real-life microgrid. The proposed method is composed of passive monitoring of the mentioned inputs and therefore, does not influence power quality (PQ); but considerably decreases non detection zones (NDZs). In order to cover as much situations as possible, minimizing false tripping and still remaining selective, type and number of samples are introduced. Here, one of the main goals is reducing NDZ by still keeping PQ in order. Based on the sampled frequency and number of samples, we find that the proposed method has less detection time and better accuracy, compared to the reported methods. Simulations performed in MATLAB/Simulink software environment and several tests performed based on different active load conditions and multiple distributed generation (DG)s, prove the effectiveness, authenticity, selectivity, accuracy and precision of the proposed method with allowable impact on PQ according to UL1741 standard.

In the process control industries, one of the main works is to identify and channelize the fault sensor. The process of sensor troubleshooting is essential to increase the production flow without any interruption. In this project the level measurement in the conical tank is considered and the capacitive sensor is used to measure the level of the water inside the conical tank. First the standard sensor is placed inside and the level is measured and monitored and the history of data's are obtained. Using LABVIEW, programming is done for the standard sensor to measure the level. Now the measuring sensor is placed and the measurement of the level is done. Using any of the soft computing techniques, the fault can be identified and it is indicated in the display or alarm.

This research suggests new experimental outcomes regarding the viscosity and thermal conductivity of silver, copper and titanium oxide nanoparticles dispersed in mineral insulating oil by high-pressure homogenization process without using any additives or surfactants. Later, via employing non-linear regression, an adaptive neuro-fuzzy inference system (ANFIS) and achieved experimental data, new models were evolved to predict the viscosity besides thermal conductivity of nanofluids. For modelling, viscosity as well as thermal conductivity of nanofluids was picked as the target factor, and the volume concentration in addition to types of nanoparticles was regarded as the design (input) factors and all experimental data was classified into a train and a test data set. The model was conducted through the train set and the outcomes were contrasted with the experimental data set. Predicted thermal conductivities as well as viscosities were compared with experimental data for three different nanofluids, having nanoparticles volume concentrations of 0.00125% and 0.050%. A comparison was made between the ANFIS and regression outcomes. To evaluate the results, the coefficient of determination (R 2) and root-mean-square error (RMSE) are reported. The achieved results of this research indicate that thermal conductivity of nanofluids enhance by nanoparticles concentration increment. Thermal conductivity of silver is higher compared to thermal conductivity of titanium oxide and copper nanoparticles. According to the ANFIS and non-linear regression outputs, two sets of correlations for calculating the dynamic viscosity as well as thermal conductivity were suggested. Comparing the experimental data with suggested correlations demonstrate very good agreement between the suggested correlations and experimental data. However, equations of previous researches would not be perfectly able to predict the experimental data of present study.

Students and instructors’ performance analyses are one of the major issues in today’s education programs due to the quality evaluation and management approaches. Today, design program course targets are usually related with the program outputs intuitively and subjectively based on the past experiences of the instructors. Current proof methods are not very reliable and have difficulties adapting changes, in addition the new accreditation criteria on the agenda such as Bologna Process makes the requirement of more reliable proof methods inevitable. The aim of this article is to investigate the potential of the proposed adaptive neuro-fuzzy inference system (ANFIS) with a Sugeno type engine that can be flexibly trained to analyze and learn the relations between the selected program inputs and output, namely, the pre-design studio courses and the design studio relation for instructors’ evaluation performance analyses. The ANFIS method is applied in two institutions and the results of both applications are showing that the ANFIS model has performed well and is an optimal and reliable way of measuring and analyzing the correlations of the inputs and outputs of the program.

El mercado de energía mayorista es uno de los sectores industriales más competitivos de Colombia, y representa uno de los ejes principales en la economía del país. Este mercado ha sido objeto de estudio de varias áreas de conocimiento como la ingeniería eléctrica, economía, finanzas y otros. Aquí se presenta un análisis de los posibles comportamientos
estratégicos de los principales agentes de la industria, desde la perspectiva de la inteligencia artificial orientada a la inteligencia de mercados, es decir, un trabajo multidisciplinar centrado en la explicación y emulación de la conducta inteligente y posiblemente estratégica de los agentes involucrados en la actividad de generación de energía en Colombia.

The aim of this paper is to design a unified knowledge economy competitiveness index using fuzzy clustering, to aggregate four of the most reputable and famous knowledge economy indicators into a unified index that reflects the overall rate of knowledge in an economy, to serve many purposes for the decision makers and foreign investors interested in such economy. The four selected indices are: Knowledge Economy Index (KEI) from World Bank, Information and Communication Technologies Development Index (IDI) from United Nations agency for information and communication technology issues (ITU), Global Competitiveness Index (GCI) from the World Economic Forum, and World Competitiveness Yearbook (WCY) from Institute for Management Development (IMD). To achieve this unified index, a four steps framework is proposed. The first step utilizes a Correlation analysis, the second step is to carry a Principle Component Analysis (PCA) analysis and the third step employs training an Adaptive Neural Fuzzy Inference Systems (ANFIS) and the forth step is to create a unified index based on all existing indices. The purpose of the first step was to test the relationship between the selected indices and how strong it is. The PCA is employed to test the similarity amongst existing indices and whether they can be reduced in any form. ANFIS was used to generate rules to create trained submodel that determine which of the input indices make efficient contribution to the new unified knowledge indicator. Then, the fuzzy c-means clustering technique is used to construct the new Unified Knowledge Competitiveness and Progress Indicator (UKPI) which combines the four selected aggregate indices into a new single meaningful index that reflects the overall rate of Knowledge competitiveness and progress in a nation.

During past few years, brain tumor segmentation in CT has become an emergent research area in the field of medical imaging system. Brain tumor detection helps in finding the exact size and location of tumor. An efficient algorithm is proposed in this project for tumor detection based on segmentation and morphological operators. Firstly quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. The problem with biopsy is that the patient has to be hospitalized and also the results (around 15%) give false negative. Scan images are read by radiologist but it's a subjective analysis which requires more experience. In the proposed work we segment the renal region and then classify the tumors as benign or malignant by using ANFIS, which is a non-invasive automated process. This approach reduces the waiting time of the patient.

In this paper, we present an application of soft
computing into a decision support system RETS:
Rheumatic Evaluation and Treatment System in Oriental Medicine (OM). Inputs of the system are severities of observed symptoms on patients and outputs are
a diagnosis of rheumatic states, its explanations and
herbal prescriptions. First, an outline of the proposed
decision support system is described after considering
rheumatic diagnoses and prescriptions by OM doctors. Next, diagnosis by fuzzy inference and prescription by neural networks are described. By fuzzy inference, RETS diagnoses the most appropriate rheumatic
state in which the patient appears to be infected, then
it gives a prescription written in suitable herbs with
reasonable amounts based on neural networks. Training data for the neural networks is collected from experienced OM physicians and OM text books. Finally,
we describe evaluations and restrictions of RETS