The **losses** in **distribution** **networks** have always been key elements in predicting investment, planning work, evaluating the efficiency and effectiveness of a network. This paper elaborates on the use of **fuzzy** logic systems in analyzing the data from a particular substation area predicting **losses** in the low voltage network. The data collected from the field were obtained from the Automatic Meter Reading (AMR) and Automatic Meter Management (AMM) systems. The AMR **system** is fully implemented in EPHZHB and integrated within the network infrastructure at secondary level substations 35/10kV and 10(20)/0.4 kV. The AMM **system** is partially implemented in the areas of electrical energy consumers; precisely, in accounting meters. Daily information gathered from these systems is of great value for the calculation of **technical** and non-**technical** **losses**. **Fuzzy** logic in combination with the Artificial Neural **Networks** implemented via the **Adaptive** **Neuro**-**Fuzzy** **Inference** **System** (ANFIS) is used. Finally, FIS Sugeno, FIS Mamdani and ANFIS are compared with the measured data from smart meters and presented with their errors and graphs.

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Abstract: The railway network plays a significant role (both economically and socially) in assisting the reduction of urban traffic congestion. It also accelerates the decarbonization in cities, societies and built environments. To ensure the safe and secure operation of stations and capture the real-time risk status, it is imperative to consider a dynamic and smart method for managing risk factors in stations. In this research, a framework to develop an intelligent **system** for managing risk is suggested. The **adaptive** **neuro**-**fuzzy** **inference** **system** (ANFIS) is proposed as a powerful, intelligently selected model to improve risk management and manage uncertainties in risk variables. The objective of this study is twofold. First, we review current methods applied to predict the risk level in the flow. Second, we develop smart risk **assessment** and management measures (or indicators) to improve our understanding of the safety of railway stations in real-time. Two parameters are selected as input for the risk level relating to overcrowding: the transfer efficiency and retention rate of the platform. This study is the world’s first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural **networks** (ANNs) by integrated training processes. The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time. Such risk information is extremely critical for decision making processes in managing safety and risks, especially when uncertain disruptions incur (e.g., COVID-19, disasters, etc.). The novel insights stemmed from this study will lead to more effective and efficient risk management for single and clustered railway station facilities towards safer, smarter, and more resilient transportation systems.

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results but they are usually hampere by the fact that they consume long computing time because of the requirement for repetitive power flow calculations. Online voltage security **assessment** is a very useful but not yet becomes a widely used tool that measures the distance from the current operating condition at any time to the critical point. **ADAPTIVE** **NEURO** **FUZZY** **INFERENCE** SYSTEMhave recently received widespread attention from researchers for this application. Most of ANFIS applications have been implemented using multi-layered feed-forward neural **networks** trained by back propagation because of their robustness to input and **system** noise, their capability of handling incomplete or corrupt input data. However, in typical power systems there are voluminous amount of input data. Then, the success of ANFIS applications also depends on the systematic approach of selecting highly important features which will result in a compact and efficient ANFIS. In this part, several voltage stability indicators are calculated. It should be mentioned here that this paper aims at implementing these already proposed indicators by ANFIS. The capability of monitoring proximity to voltage collapse was tested beforehand, but unfortunately due to space limitation and scope of this paper the complete results cANFISot be presented.

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Ubeyli used ANFIS for automatic detection of breast cancer. Although all **system** input variables did not fully conform in the two studies, the findings of this study could claim a higher quality than those of Übeyli, in terms of precision and inclusion of native data [6]. In their second study, Ubeyli et al. worked on **neuro**-**fuzzy** systems, which again was performed at a lower level than ANFIS. **Neuro**-**fuzzy** systems generally resemble **adaptive** ones, yet the former puts a higher emphasis on neural **networks**. Nevertheless, the nature of breast can- cer data, and larger existence of **fuzzy** data are amongst factors that raise the expectations toward **fuzzy**-adap- tive models for better efficacy [7]. The best possible re- sult of the present study could be higher precision of na- tive data from the BCRC Clinic, which verified not only the suitable native model being developed, but also the proper number of data records being chosen for such modeling purposes (whereas higher numbers of re- cords would have jeopardized modeling precision) [4]. On the other hand, a model arising from native datasets would definitely have higher value and validity for that area or dataset. Generalizing these results to other data- sets would require similar studies on corresponding da- tabases of other health organizations.

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The availability of phishing kits makes it easy for cyber criminals, even those with minimal **technical** skills, to launch phishing campaigns. A phishing kit bundles phishing website resources and tools that need only be installed on a server. Once installed, all the attacker needs to do is send out emails to potential victims. Phishing kits as well as mailing lists are available on the dark web. A couple of sites, Phishtank and OpenPhish, keep crowd-sourced lists of known phishing kits. Analyzing phishing kits allows security teams to track who is using them. “One of the most useful things we can learn from analyzing phishing kits is where credentials are being sent. By tracking email addresses found in phishing kits, we can correlate actors to specific campaigns and even specific kits,” said Wright in the report. “It gets even better. Not only can we see where credentials are sent, but we also see where credentials claim to be sent from. Creators of phishing kits commonly use the ‘From’ header like a signing card, letting us find multiple kits created by the same author.”

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Due to the continuous development of applications and services in wireless communications, the requirement for access to supplementary frequency spectrum has been growing considerably. Given that almost the entire frequency spectrums are allocated, reacting to the requirement has turn out to be one of the chief challenges in wireless communications. On the other hand, various spectrum occupancy measurement surveys, carried out by the Federal Communications Commission (FCC) [1] and Shared Spectrum Company (SSC) [2], have exposed that most of the allocated spectrum is either unexploited or under-utilized. As a result, spectrum insufficiency in wireless communications is believed to be owing to the inadequacy of static frequency **distribution** rather than the intense usage of the spectrum.

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According to the recent researches, data driven induc- tive methods are popular. Because first, they have been extracted from the data and relation between them. Sec- ond, they tend to perform better in reproducing exist- ing spatial patterns (Overmars et al. 2007; Koomen et al. 2015). Artificial neural **networks** with capacity of nonlinear, parallel and highly complex processing have been employed in many fields such as climate fore- casting (Panagoulia 2006), agricultural land suitability **assessment** (Wang 1994), remote sensing (Morris et al. 2005) and land use change and urban growth modeling (Tayyebi et al. 2011; Pijanowski et al. 2002, 2009, 2014). Artificial neural network is a powerful tool in environ- mental modeling (Li and Yeh 2001). The ability to learn is the most important feature of this method. In the other words, the network uses data to identify patterns and relationships among the data. According to Almeida et al. (2008), Li and Yeh (2002), ANN method has the abil- ity to capture the non-linear relationships presented in many geographic phenomena (Li and Yeh 2002; Li et al. 2003). Thus, it can be used due to this ability to com- pute the conversion probabilities for competing multiple land uses. There is a general consensus among research- ers in the field of urban modeling that empiricism is a

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For decades, due to the increase in electrical power demand, we are in need of enlarging transmission capacity by installing 500-kV extra high-voltage power transmission lines in both AC and DC. The impact of electric fields surrounding the transmission line depends strongly on conductor surface potentials, while load currents flowing through the transmission line result in magnetic field **distribution**. For the 500 kV systems, high current density transmission is the main purpose. It can cause electrical hazards to people or their livestock nearby. Especially when the power **system** was faulted, the short-circuit current is much higher than the normal current loading. This paper focuses on utilization of efficient computing techniques to estimate the magnetic field **distribution**. Obtained estimate solutions can lead to **assessment** of electrical hazards for 500-kV power transmission systems. This paper presents an online estimation of magnetic fields for live transmission line right of way worker using Generalized Regression Artificial Neural Network.

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With the popularization of wind energy, the further reduction of power generation cost became the critical problem. As to improve the efficiency of control for variable speed Wind Turbine Generation **System** (WTGS), the data-driven **Adaptive** **Neuro**-**Fuzzy** **Inference** **System** (ANFIS) was used to establish a sensorless wind speed estimator. Moreover, based on the Supervisory Control and Data Acquisition (SCADA) **System**, the optimum setting strategy for the maxi- mum energy capture was proposed for the practical operation process. Finally, the simulation was executed which sug- gested the effectiveness of the approaches.

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This paper provides the overview on liver cancer using **Adaptive** **Neuro** **Fuzzy** **Inference** **System** (ANFIS) data mining technique. This paper proposed by taking 2-D CT images as input. In data preprocessing step, the noise removal in the CT image, segmentation process, morphological operation and the feature extraction techniques has been discussed. We have also discussed the study of **Adaptive** **Neuro** **Fuzzy** **Inference** **System** for early detection of Liver Cancer in human. Implementations of these technique or combination of ANFIS with other data mining techniques can be made to help the medical field at early diagnosis of liver cancer.

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ANFIS is a multilayered feedforward Artificial Neural Network (ANN) which applies NN learning algorithms and **fuzzy** reasoning. ANFIS uses Takagi–Sugeno **fuzzy** **inference** **system** (FIS) to map from input space to output space. Sugeno output membership functions are either linear or constant as given below:

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The properties of MANET include Neighbor Discovery, Data routing abilities, and limited wireless connectivity range and resource constraints. The important characteristic of MANET Distributed operation, multihop routing, and Dynamic topology, light weight terminals and shared physical medium. Various Multimedia applications such as Video on Demand, video conferencing require stringent QOS requirements which are sensitive to delay and bandwidth. The Quality of Service (QOS) is the guarantee to satisfy the user requirements provided by the **networks**. . This paper provides the study of **fuzzy** models such as mamdani model, takagi-sugeno model, and tsukamoto model and ANFIS based IF-THEN rules for optimizing the QoS parameters.

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present study to develop ANFIS model, past discharge and rainfall data of study area are used to forecast flood in a river **system**. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate the models. Statistical parameters such as Root Mean Square Error (RMSE), Correlation Coefficient (R), Coefficient of Determination (R 2 ) and Discrepancy Ratio (D) are used to evaluate performance of the ANFIS models in forecasting flood. To show the accuracy and reliability of the ANFIS model; the model is compared with the statistical method i.e. Log Pearson type-III.

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associated with the membership functions change through the learning process. The computation of these parameters (or their adjustment) is facilitated by a gradient vector. This gradient vector provides a measure of how well the **fuzzy** **inference** **system** is modeling the input/output data for a given set of parameters. When the gradient vector is obtained, any of several optimization routines can be applied in order to adjust the parameters to reduce some error measure. This error measure is usually defined by the sum of the squared differences between actual and desired outputs. ANFIS uses either back propagation or a combination of least squares estimation and back propagation for membership function parameter estimation. In this work, genfis3 function of **FUZZY** Toolbox of Matlab was used to generate a FIS using **fuzzy** c-means (FCM) clustering by extracting a set of rules that models the data behavior. The function requires separate sets of input and output data as input arguments. The rule extraction method first uses the fcm function to determine the number of rules and membership functions for the antecedents and consequents. The number of clusters determines the number of rules and membership functions in the generated FIS. For this work, the number of clusters was selected automatically by the command. The input membership function was selected to be 'gaussmf', and the output membership function was selected to be 'linear'. The input and output was given to genfis3 using the database generated in the first phase of the approach. The number of iterations for genfis3 was selected to be 1000 and the tolerance to be 0.001 after certain trials. In this way, the training process of ANFIS was executed.

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METHODOLOGY 3.1 Overview 3.2 Design of a Modified Adaptive Fuzzy Inference Engine MAFIE 3.2.1 Fuzzy Inference System FIS 3.2.2 Hybrid Fuzzy clustering algorithm for Automatic Generation [r]

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For each considered utterance, the features extractor cycled through the syllables and selected the phone with the highest intensity as the nucleus, following the sonority sequencing principle. Both the duration of the entire syllable and the du- ration of the syllable nucleus are considered as features in this work as it is generally not clear whether one is more important than the other or if they interact in some way. Also, from the syllable nucleus the average intensity and the average pitch were extracted. As perceptual prominence is a phenomenon linked to the specific syllable context, a windowing procedure with zero-padding at the extremes was adopted to provide the ANFIS **system** with this specific knowledge. In this work, we used a one-sized window to present a set of first results, so the acoustic features of the considered syllable and of its immediate neighbours are included in each features vector of the dataset. Previous work [8] has shown that context may extend up to two neighbouring syllables, at least for Italian and English, but this kind of analysis is left for future work. B. ANFIS

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B. There are over 200 different types of cancer [11], each of which has a unique set of clinical characteristics, a specific treatment regime and a different chance of being cured (Khan et al. (2001)). Unfortunately, it is sometimes difficult for even the experienced specialists to tell the difference among particular cancer and their subtypes. **Neuro**-**Fuzzy** technology provides a much more robust diagnosis than traditional approaches available.

engineers, and easiness to design and implement. The PID and its variations (P, PI, PD) still are widely applied in the motion control because of its simple structure and robust performance in a wide range of operating conditions. Unfortunately, it has been quite difficult to tune properly the gains of PID controllers because many industrial plants are often burdened with problems such as high order, time delays, and nonlinearities. Therefore, when the search space complexity increases the exact algorithms can be slow to find global optimum. Linear and nonlinear programming, brute force or exhaustive search and divide and conquer methods are some of the exact optimization methods. Over the years, several heuristic methods have been proposed for the tuning of PID controllers. These methods have several advantages compared to other algorithms as follows: (a) Heuristic algorithms are generally easy to implement; (b) They can be used efficiently in a multiprocessor environment; (c) They do not require the problem definition function to be continuous; (d) They generally can find optimal or near-optimal solutions. Particle swarm optimization (PSO) is an efficient and well known stochastic algorithm which has found many successful applications in engineering problems [1-4]. Signal to noise ratio algorithm doesn’t require a wide solution space, and the large number of searching and iterations were susceptible to related control parameters. On the other hand, this method has an effective appliance and better result for uncertainties conditions and different operation points. SNR has a responsible result in the nonlinear systems optimization. The integral performance criteria in frequency domain were often used to evaluate the controller performance, but these criteria have their own advantages and disadvantages [5-6]. In this study a novel design method for determining the optimal signal to noise ratio algorithm and particle swarm optimization (SNR-PSO) parameters for design the optimal proportional-integral-derivative (PID) controller of an automatic voltage regulator (AVR) **system** using the hybrid SNRPSO approach such that the controlled **system** could obtain a good step response output for some event and case that may be happen in the power **system**. After that we use ANFIS for training and obtained the **fuzzy** membership function (MF) for **fuzzy** **inference** **system** with result of our optimization. In this paper a **fuzzy** **inference** **system** models which takes K G and g as inputs and K p , K i and K d as output. Therefore

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for 3-story building showed that the proposed control **system**, ANFIS, significantly reduced normed drift, acceleration and base shear and also peak of inter story drift, and slightly reduced the peak acceleration and base shear. Results also showed that the passive on the **system** performed better than other systems in most criteria, except for normed acceleration. Both Passive off and SOFLC schemes were poor control systems, especially SOFLC that increased normed acceleration and base shear, and peak base shear. ANFIS and LQG controllers were between the best (passive on) and the worst (passive off and SOFLC) controllers. Figure 8 showed that the inverse NN model of MR damper could estimate the control voltage very well to produce the desired force. Results for 76-story building, which were presented in Table 4, showed that the active LQG controller reduced responses more than semi-active ANFIS, and semi-active ANFIS controller reduced them more than PTMD.

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