bluuebunny/crossref_metadata_2025 huggingface dataset:
Stability Enhancement in Multi-Machine Power Systems by Fuzzy-Based Coordinated AVR-PSS
Authors: Khezri Rahmat , Bevrani Hassan
Published: July 2017

This paper presents performance of intelligent fuzzy-based coordinated control for Automatic Voltage Regulator (AVR) and Power System Stabilizer (PSS), to prevent losing synchronism after major sudden faults and to achieve appropriate post-fault voltage level in multi-machine power systems. The AVR and PSS gains can adaptively change to guarantee the power system stability after faults. For change in AVR and PSS gains, at least one significant generator in each area of a multi-area power system is equipped with fuzzy logic unit. The fuzzy logic unit accepts normalized deviations of terminal voltage and phase difference of synchronous generators as inputs and generates the desirable gains for AVR and PSS. The construction of appropriate fuzzy membership functions and rules for best tuning of gains is described. The proposed fuzzy control methodology is applied to 11-bus 4-generator power system test case. Simulation results illustrate the effectiveness and robustness of the proposed fuzzy-based coordinated control strategy.
Intuitionistic Fuzzy Set Theory with Fair Share CPU Scheduler
Authors: Raheja Supriya
Published: July 2017

CPU Scheduler is one of the crucial modules of any operating system. Operating system uses CPU schedulers based on the requirements. Fair Share CPU scheduler is preferable scheduler by many operating systems. It distributes fixed processor time amongst different parts of a system like groups, users etc. Fixed distribution of processor time can distress the performance of system. Moreover, traditional CPU schedulers ignore the impreciseness and uncertainty of the tasks. This work presents the Intuitionistic Fuzzy based Fair Share scheduler (IFFS). IFFS scheduler has extended the research in the field of the impreciseness of tasks. Intuitionistic Fuzzy Inference Systems, IFIS-DP and IFIS-DCS have been designed to share the CPU time dynamically and for assigning dynamic priority to each task. Experimental results prove the better performance of IFFS scheduler over FS scheduler.
Classification of EEG Signals for Motor Imagery Based on Mutual Information and Adaptive Neuro Fuzzy Inference System
Authors: El-aal Shereen A. , Ramadan Rabie A. , Ghali Neveen
Published: July 2017

Electroencephalogram (EEG) signals based Brain Computer Interface (BCI) is employed to help disabled people to interact better with the environment. EEG signals are recorded through BCI system to translate it to control commands. There are a large body of literature targeting EEG feature extraction and classification for Motor Imagery tasks. Motor imagery task have several features can be extracted to use in classification. However, using more features consume running time and using irrelevant and redundant features affect the performance of the used classifier. This paper is dedicated to extracting the best feature vector for motor imagery task. This work suggests two feature selection methods based on Mutual Information (MI) including Minimum Redundancy Maximal Relevance (MRMR) and maximal Relevance (MaxRel). Adaptive Neuro Fuzzy Inference System (ANFIS) classifier with Subtractive clustering method is utilized for EEG signals classifications. The suggested methods are applied to BCI Competition III dataset IVa and IVb and BCI Competition II dataset III.
Fault Detection and Isolation for an Uncertain Takagi-Sugeno Fuzzy System using the Interval Approach
Authors: Bedoui Hassene , Kedher Atef , Ben Othman Kamel
Published: July 2017

This work deals with the fault detection and localization in the case of uncertain nonlinear systems. The presented method uses the diagnosis based on mathematical models. To model nonlinear systems, the multiple model approach is used. This method uses the Takagi-Sugeno fuzzy systems principle to obtain a nonlinear system named multiple models. This modeling principle has the advantage of obtaining a general model that can describe any class of nonlinear systems. This modeling principle also allows one to obtain the generalization of many results that are already obtained for linear systems to the nonlinear systems. To model the system uncertainties, the interval approach is used because the faults or disturbances are generally unknown, but it is possible to know their upper and lower bounds. The proposed technique is insensitive to measurement uncertainties and highly reliable in case of a fault affecting the outputs system.
Fuzzy Adaptive Controller for Uncertain Multivariable Nonlinear Systems with Both Sector Nonlinearities and Dead-Zones
Authors: Boulkroune Abdesselem
Published: July 2017

This chapter presents two fuzzy adaptive variable structure controllers for a class of uncertain multi-input multi-output nonlinear systems with actuator nonlinearities (i.e. with sector nonlinearities and dead-zones). The design of the first controller concerns systems with symmetric and positive definite control-gain matrix, while the design of the second one is extended to the case of non-symmetric control-gain matrix thanks to an appropriate matrix decomposition, namely the product of a symmetric positive-definite matrix, a diagonal matrix with diagonal entries +1 or -1, and a unity upper triangular matrix. An appropriate adaptive fuzzy-logic system is used to reasonably approximate the uncertain functions. A Lyapunov approach is adopted to derive the parameter adaptation laws and prove the stability of the closed-loop control system. Finally, some simulation results are carried out to show the effectiveness of the proposed controllers.
Personalized Neuro-Fuzzy Expert System for Determination of Nutrient Requirements
Authors: Sajja Priti Srinivas , Trivedi Jeegar Ashokkumar
Published: July 2017

In the modern fast and stressful life, an individual does not have time to take an extra care for one's self. Support from general information about health and nutrient requirement through modern computing infrastructure is very limited and common. Generic information on the Web and other media sometime raises genuine queries about the good health. Further, typical solutions available may not interact with users in friendly way and deal with vague inputs provided by users. To resolve this issue, a system is required which knows its users, acts smartly and friendly, learns from past data & history and provides customised advisory. This chapter introduces a neuro-fuzzy architecture, based on which an expert system for determination of nutrient requirements is presented. The chapter includes in depth literature survey, concepts, implementation details with sample code, neural network structure, fuzzy membership functions used, sample input–output screens of the system and future work.
Fuzzy Logic-Based Cluster Heads Percentage Calculation for Improving the Performance of the LEACH Protocol
Authors: Banimelhem Omar , Taqieddin Eyad , Mowafi Moad Y. , Awad Fahed , Al-Ma'aqbeh Feda'
Published: July 2017

In wireless sensor networks, cluster-based routing was proven to be the most energy-efficient strategy to deal with the scaling problem. In addition, selecting the proper number of clusters is a critical decision that can impose a significant impact on the energy consumption and the network lifetime. This paper presents FL-LEACH, a variant of the well-known LEACH clustering protocol, which attempts to relax the stringent strategy of determining the number of clusters used by LEACH via fuzzy logic decision-making scheme. This relates the number of clusters to a number of network characteristics such as the number of sensor nodes, the area of the sensing field, and the location of the base station. The performance of FL-LEACH was evaluated via simulation and was compared against LEACH using standard metrics such as network lifetime and remaining network energy. The results depicted that the proposed approach has the potential to substantially conserve the sensor node energy and extend lifetime of the network.
Evaluation of Human Machine Interface (HMI) on a Digital and Analog Control Room in Nuclear Power Plants Using a Fuzzy Logic Approach
Authors: Lagari Pola Lydia , Nasiakou Antonia , Alamaniotis Miltiadis
Published: July 2017

Nuclear Power Plants (NPPs) are nowadays facing a transition from analog to digital control rooms, mainly in the form of new interfaces for displaying sensor information as of Human Machine Interface (HMI) were one-sided, taking into consideration either the human or the machine perspective. The approach presented in the present article considers human machine interfaces met in nuclear power plants as a joint system, where the performance of the NPP operator is evaluated according to the cooperation level achieved with the machine. In particular, the purpose of this study is to provide a methodology to evaluate the degree of flexibility of an operator during the transition period from an analog to a digital system. The proposed methodology has been implemented by utilizing fuzzy logic inference and realized with the fuzzy toolbox embedded in Matlab software.
An Optimal Fuzzy Load Balanced Adaptive Gateway Discovery for Ubiquitous Internet Access in MANET
Authors: Srivastava Prakash , Kumar Rakesh
Published: July 2017

A mobile ad hoc network (MANET) is an autonomous collection of independent nodes cooperating together to form an infrastructure less network spontaneously. For increasing usability of MANET domain which finds application in natural disaster such as earthquake, floods etc. it is also desired to be connected with Internet through Internet gateways. Therefore, an efficient gateway discovery mechanism is required for MANET-Internet integration. Existing schemes use one or multiple parameters for optimal selection of gateway which causes a particular gateway to be selected many times which results in higher delay latency and packet drops due to prevailing congestion at a particular gateway. To avoid this situation, the authors have utilized the potential of fuzzy logic to ascertain the decision of load balancing at the Internet gateway. Besides this, their scheme also incorporates an effective adaptive gateway discovery mechanism. Consequently, enhanced performance is achieved as compared to existing state-of-the-art related schemes. The proposed approach is evaluated by simulation and analytical validation.
A Hybrid System Based on FMM and MLP to Diagnose Heart Disease
Authors: Aggarwal Swati , Azad Venu
Published: July 2017

In the medical field diagnosis of a disease at an early stage is very important. Nowadays soft computing techniques such as fuzzy logic, artificial neural network and Neuro- fuzzy networks are widely used for the diagnosis of various diseases at different levels. In this chapter, a hybrid neural network is designed to classify the heart disease data set the hybrid neural network consist of two types of neural network multilayer perceptron (MLP) and fuzzy min max (FMM) neural network arranged in a hierarchical manner. The hybrid system is designed for the dataset which contain the combination of continuous and non continuous attribute values. In the system the attributes with continuous values are classified using the FMM neural networks and attributes with non-continuous value are classified by using the MLP neural network and to synthesize the result the output of both the network is fed into the second MLP neural network to generate the final result.
Strictness Petroleum Prediction System Based on Fuzzy Model
Authors: Ghallab Senan A. , Badr Nagwa. L. , Salem Abdel Badeeh , Tolba M. F.
Published: July 2017

Petroleum exploration and production is an industry that provides researchers with multi-variant challenging “real world” properties. Recently, some petroleum soft computing techniques have gained a greater interest in prediction within the oil industry. This paper is interested in the analysis, classifying, mining and predictions, based on fuzzy as an intelligent system and an intelligent system called the Strictness Petroleum Prediction System (SPPS), predicted results and statues of crude oil wells and they are compared with other measurement petroleum values. The evaluation study applies test cases, regression models and time series forecasting of vague petroleum datasets to achieve more accurate results. A regression model was made to show the effect of re-testing the prediction processes of petroleum factors. Prediction in time series using a non-parametric functional technique is considered, based on data which was collected from different sources (Daqing oilfield in China and distinct oilfields in Yemen).
Fuzzy-Based Matrix Converter Drive for Induction Motor
Authors: Venugopal Chitra
Published: July 2017

In industrial applications, approximately, 60% of world's consumption of electrical energy passes through the windings of squirrel-cage induction motors. Hence it is necessary to select an efficient drive circuit for induction motor to save energy. The MC are preferred to replace VSC in industrial applications. To control the performance of the MC, fuzzy logic technique is proposed and simulated using Matlab/Simulink. In this chapter, the basic concepts of MCs are discussed. The implementation of fuzzy logic technique to improve the performance of MC in driving induction motor is discussed in detail. The design of fuzzy controllers and the closed loop control of induction motor is shown. It seen that the introduction of fuzzy controllers in the closed loop helped to reduce the overshoot at starting and maintain the reference speed when running with load torque. Also the input and output voltage of the MC is maintained sinusoidal.
Prediction of Solar and Wind Energies by Fuzzy Logic Control
Authors: Faquir Sanaa , Yahyaouy Ali , Tairi Hamid , Sabor Jalal
Published: July 2017

Nowadays, the use of renewable energy has become increasingly significant, and cost-effective. Between all existing sources of energy, solar and wind energies are the highly exploited. However, solar and wind energies are not available all the time and their performance is affected by unpredictable weather changes, therefore, it is not always feasible to obtain an accurate mathematical model of the controlled system. Various mathematical modeling methods were used to predict wind and solar powers using natural parameters but considering multiple parameters in equations makes the solution more complex. In addition to complexity, some coefficients are uncertain and based on probability. Fuzzy logic is a perfect tool to model any kind of uncertainty related to vagueness. This chapter presents a computer algorithm based on fuzzy logic control (FLC) to estimate the wind and solar energies using natural factors. As input parameters, the wind speed was used to predict the wind power and the temperature and the lightening were used to estimate the solar power.
Fuzzy Rule Based Environment Monitoring System for Weather Controlled Laboratories Using Arduino
Authors: Sasirekha S. , Swamynathan S.
Published: July 2017

Weather controlled laboratories such as blood banks, plasma centers, biomedical, research, pharmacy and healthcare always require a portable, low cost and web-based centralized wireless monitoring system. However, it has become more stringent to monitor various weather controlling devices of these laboratories in order to reduce the risk of non-compliance with accreditation requirements. In literature, it is inferred that the majority of existing event detection approaches relies only on precise value to specify event thresholds, but those values cannot adequately handle the imprecise sensor reading. Therefore, in this work, one of the soft computing technologies, fuzzy logic is adopted to demonstrate that fuzzy rule based value significantly improves the accuracy of event detection. The prototype built is tested in a physical laboratory as a personal mobile weather station for monitoring and analyzing the environmental conditions using fuzzy simulated values.
Fuzzy Labeled Transition Refinement Tree
Authors: Kouah Sofia , Saidouni Djamel-Eddine
Published: July 2017

This paper aims to provide a formal framework that supports an incremental development of dynamic systems such as multi agents systems (MAS). We propose a fuzzy labeled transition system model (FLTS for short). FLTS allows a concise action refinement representation and deals with incomplete information through its fuzziness representation. Afterward, based on FLTS model, we propose a refinement model called fuzzy labeled transition refinement tree (FLTRT for short). The FLTRT structure serves as a tree of potential concurrent design trajectories of the system. Also, we introduce bisimulation relations for both models in order to identify equivalent design trajectories, which could be assessed with respect to relevant design parameters.
Implementation of Fuzzy Technology in Complicated Medical Diagnostics and Further Decision
Authors: Bhattacharya A. B. , Bhattacharya Arkajit
Published: July 2017

This chapter presents the importance of fuzzy expert systems in the medical field. Efficient and suitable medical work becomes difficult many times without the knowledge of the rules of logic. The chapter highlights the ways of implementing both classical logic and non-classical approach (e.g. temporal and fuzzy logic) in some adverse areas of medical diagnostics. The implementation of fuzzy expert systems is supported by some examples illustrating how indispensable the cognition of logic and showing how applying logic can effectively improve work in medicine. Fuzzy Expert Systems for diagnosis of urinary incontinence, Parkinson's disease, including neurological signs in domestic animals, as well as its implementation for diagnosis of prostate cancer are elaborately discussed.
Intelligent Decision Making and Risk Analysis of B2c E-Commerce Customer Satisfaction
Authors: Mohammadian Masoud
Published: July 2017

Customer satisfaction and relationship is complex, demanding, and yet crucial to an organization success and its competitive position in the marketplace. Due to rapid changes in emerging technologies there is a need for constant improvement and adjustment of product and services offered using e-commerce systems. Customer satisfaction is dependent on a large number of organizational as well as product and services attributes. These attributes require continuous development, improvement and monitoring. The interdependencies of these attributes make it very difficult for managers and product and services development teams to comprehend and be aware of effect (, Rohm et al 2004, , ) of inefficiencies that may exist in development and offering of their products and services. This paper considers the implementation of an intelligent decision making system using Fuzzy Cognitive Maps (FCMs) to provide facilities to capture and represent complex relationships in a customer satisfaction management and modelling to improve the understanding of managers and product developers about their customers and associated risks related to products and services that are offered online using e-commerce sites. By using FCMs, customer satisfactions can regularly be reviewed and improved. Managers can perform what-if analyses to better understand vulnerabilities and pitfalls in the way their product and services are provided to customers.
Fuzzy Logic Based Approach for Power System Fault Section Analysis
Authors: Dugaya Neeti , Shandilya Smita
Published: July 2017

In this chapter, a fuzzy expert system is developed to assist the operators in fault detection. It requires much less memory to store the database (power system topology and the post fault status of circuit breakers and protective relays). The fuzzy expert system identifies two basic network section sets, Shealthy for the healthy sub network and Sisland for the fault islands, using the post fault status of circuit breakers and relays. It then calculates membership function for each possible fault section. The objective of this calculation is to determine the likelihood of each candidate fault section as the actual fault section. Moreover membership functions provide a convenient means of ranking among possible (or candidate) fault sections, and are the most important factors in decision making. During decision making, the most possible fault section is determined by maximum selection method. In this method most possible fault section is the one which is having highest membership grade. MATLAB code for the proposed scheme is developed and the results obtained in four cases for a power- system network.
Some Recent Defuzzification Methods
Authors: Kumar Harendra
Published: July 2017

Defuzzification is a process that converts a fuzzy set or fuzzy number into a crisp value or number. Defuzzification is used in fuzzy modeling and in fuzzy control system to convert the fuzzy outputs from the systems to crisp values. This process is necessary because all fuzzy sets inferred by fuzzy inference in the fuzzy rules must be aggregated to produce one single number as the output of the fuzzy model.There are numerous techniques for defuzzifying a fuzzy set; some of the more popular techniques are included in fuzzy logic system. In the present chapter some recent defuzzification methods used in the literature are discussed with examples.
Predicting Uncertain Behavior and Performance Analysis of the Pulping System in a Paper Industry Using PSO and Fuzzy Methodology
Authors: Garg Harish , Rani Monica , Sharma S.P.
Published: July 2017

The main objective of the present study is to permit the reliability analyst or system manager to analyze the failure behavior of the system in a more consistent and logical manner. As the collected or available data from various resources are uncertain and imprecise due to various practical constraints and hence the performance of the system cannot be made up to desired levels. To cope with such situations and subjective information in a consistent and logical manner, fuzzy methodology is one of the most vital and effective tool. To this effect a structural framework has been developed by the authors for analyzing and predicting the system behavior. The pulping unit of paper industry has been taken as an illustration. The failure rates and repair times for all the constituent components are obtained by solving availability-cost optimization model using particle swarm optimization and genetic algorithm. To increase the performance of the system, various reliability parameters are computed with the obtained results using a confidence interval based fuzzy lambda-tau methodology. Sensitivity as well as performance analysis of the system performance has been done for ranking the critical component of the system as per preferential order. The computed results are compared with existing fuzzy lambda-tau and traditional (crisp) methodology results.
Nonlinear System Identification of Smart Buildings
Authors: Mohammadzadeh Soroush , Kim Yeesock
Published: July 2017

In this book chapter, a system identification method for modeling nonlinear behavior of smart buildings is discussed that has a significantly low computation time. To reduce the size of the training data used for the adaptive neuro-fuzzy inference system (ANFIS), principal component analysis (PCA) is used, i.e., PCA-based adaptive neuro-fuzzy inference system: PANFIS. The PANFIS model is evaluated on a seismically excited three-story building equipped with a magnetorheological (MR) damper. The PANFIS model is trained using an artificial earthquake that contains a variety of characteristics of earthquakes. The trained PANFIS model is tested using four different earthquakes. It was demonstrated that the proposed PANFIS model is effective in modeling nonlinear behavior of a smart building with significant reduction in computational loads.
Improvement of JXTA-Overlay P2P Platform
Authors: Liu Yi , Sakamoto Shinji , Matsuo Keita , Ikeda Makoto , Barolli Leonard , Xhafa Fatos
Published: July 2017

JXTA-Overlay is a middleware built on top of the JXTA specification, which defines a set of protocols that standardize how different devices may communicate and collaborate among them. JXTA-Overlay provides a set of basic functionalities, primitives, intended to be as complete as possible to satisfy the needs of most JXTA-based applications. In this paper, the authors improve the reliability of our JXTA-Overlay P2P platform by implementing a new fuzzy-based Peer Reliability (PR) system. In the system, the authors considered three input parameters: Local Score (LS), Security (S) and Number of Interactions (NI). They evaluate JXTA-Overlay platform for medical applications and reliability. The experimental results show that by using JXTA-Overlay is possible to decide the situation of the patients. The simulation results have shown that the proposed system has a good performance and can choose reliable peers to connect in JXTA-Overlay platform.
Artificial Intelligence Methods and Their Applications in Civil Engineering
Authors: Martínez-Barrera Gonzalo , Gencel Osman , Beycioglu Ahmet , Subaşı Serkan , González-Rivas Nelly
Published: July 2017

Simulation of material properties generally involves the development of a mathematical model derived from experimental data. In structural mechanics and construction materials contexts, recent experiments have reported that fuzzy logic (FL), artificial neural networks (ANNs), genetic algorithm (GA), and fuzzy genetic (FG) may offer a promising alternative. They are known as artificial intelligence (AI). In civil engineering, AI methods have been extensively used in the fields of civil engineering applications such as construction management, building materials, hydraulic, optimization, geotechnical and transportation engineering. Many studies have examined the applicability of AI methods to estimate concrete properties. This chapter described the principles of FL methods that can be taught to engineering students through MATLAB graphical user interface carried out in a postgraduate course on Applications of Artificial Intelligence in Engineering, discussed the application of Mamdani type in concrete technology and highlighted key studies related to the usability of FL in concrete technology.
A Study on Hybridization of Intelligent Techniques in Bioinformatics
Authors: Bhargavi Peyakunta , Jyothi S. , Mamatha D. M.
Published: July 2017

This chapter aims to study the use of Hybridization of intelligent techniques in the areas of bioinformatics and computational molecular biology. These areas have risen from the needs of biologists to utilize and help interpret the vast amounts of data that are constantly being gathered in genomic research. Also describes the kind of methods which were developed by the research community in order to search, classify and mine different available biological databases and simulate biological experiments. This chapter also presents the hybridization of intelligent systems involving neural networks, fuzzy systems, neuro-fuzzy system, rough set theory, swam intelligence and genetic algorithm. The key idea was to demonstrate the evolution of intelligence in bioinformatics. The developed hybridization of intelligent techniques was applied to the real world applications. The hybridization of intelligent systems performs better than the individual approaches. Hence these approaches might be extremely useful for hardware implementations.
Dynamic Behaviour and Crack Detection of a Multi Cracked Rotating Shaft using Adaptive Neuro-Fuzzy-Inference System
Authors: Ranjan Rajeev
Published: July 2017

The presence of crack changes the physical characteristics of a structure which in turn alter its dynamic response characteristics. So it is important to understand dynamics of cracked structures. Crack depth and location are the main parameters influencing the vibration characteristics of the rotating shaft. In the present study, a technique based on the measurement of change of natural frequencies has been employed to detect the multiple cracks in rotating shaft. The model of shaft was generated using Finite Element Method. In Finite Element Analysis, the natural frequency of the shaft was calculated by modal analysis using the software ANSYS. The Numerical data were obtained from FEA, then used to train through Adaptive Neuro-Fuzzy-Inference System. Then simulations were carried out to test the performance and accuracy of the trained networks. The simulation results show that the proposed ANFIS estimate the locations and depth of cracks precisely.
Fuzzy Opinion
Authors: Boudia Mohamed Amine , Hamou Reda Mohamed , Amine Abdelmalek
Published: July 2017

In this paper, the authors propose a new approach to detect opinion by SentiWorNet with the introduction of the concept of fuzzy logic. In this vein, the authors will build detection system fuzzy opinion “Fuzzy Opinion.” To give flexibility to their system, they will use a threshold of opinion. The texts are represented by a vector of word (bag of words) which will be reduced to vector the word bearer opinion by filtering with SentiWordNet. Consequently, the heart of their approach is to associate each text into two scores (bi- scoring): Sp represents the positivity of text and Sn represents the negativity of text; this is the stage of Fuzzification. To identify opinion of a text and to ensure flexibility, the authors have used a threshold of opinion. Further, they have adapted the defuzzification step for identifying opinion. Finally, they compared the results of this approach with the results of the same approach without fuzzy logic in using the same corpus.
Bidder Selection in Public Procurement using a Fuzzy Decision Support System
Authors: Bobar Vjekoslav , Mandic Ksenija , Suknovic Milija
Published: July 2017

Bidder selection in public procurement is a decision making problem whose primary purpose is to achieve the cost effectiveness and efficiency in the expenditure of public money. This principle is also known as the principle of “value for money”. This selection is based on many alternatives and many quantitative and qualitative criteria where qualitative criteria are often expressed as linguistic uncertain variables. The theory of fuzzy sets is a tool suitable to model uncertainty when applied to a variety of problems in real life. However, many fuzzy methods require complex calculation and they are not appropriate for using in public procurement because they slow down this process. In this paper, in order to make a quick decision in public procurement, a Decision Support System based on the fuzzy extent analysis method is developed. In order to demonstrate the usefulness of this system, a real-life case scenario of public procurement is presented.
Fuzzy Dynamic Load Balancing in Virtualized Data Centers of SaaS Cloud Provider
Authors: Nine Md. S. Q. Zulkar , Azad Abul Kalam , Abdullah Saad , Rahman Rashedur M.
Published: July 2017

Cloud computing provides a robust infrastructure that can facilitate computing power as a utility service. All the virtualized services are made available to end users in a pay-as-you-go basis. Serving user requests using distributed network of Virtualized Data Centers is a challenging task as response time increases significantly without a proper load balancing strategy. As the parameters involved in generating load in the Virtualized Data Center has imprecise effect on the overall load of Virtual Machine, a fuzzy load balancing strategy is required. This paper proposes two efficient fuzzy load balancing methods - Fuzzy Active Monitoring Load Balancer (FAM-LB) and Fuzzy Throttled Load Balancer (FT-LB) for the distributed SaaS cloud provider. The authors implemented a cloud model in simulation environment and compared the results of otheir novel approach with the existing techniques. Among them FT-LB has provided better performance compared to other scheduling algorithms.
Emerging Application of Fuzzy Expert System in Medical Domain
Authors: Kumar A. V. Senthil , Kalpana M.
Published: July 2017

Fuzzy expert system is an artificial intelligence tool that helps to resolve the decision-making problem with the existence of uncertainty and plays an important role in medicine for symptomatic diagnostic remedies. In this chapter, construction of Fuzzy expert system is the focused, which helps to diagnosis disease. Fuzzy expert system is constructed by using the fuzzification to convert crisp values into fuzzy values. Fuzzy expert system consists of fuzzy inference, implication, and aggregation. The system contains a set of rules with fuzzy operators T-norm and of T-Conorm. By applying the fuzzy inference mechanism, diagnosis of disease becomes simple for medical practitioners and patients. Defuzzification method is adopted to convert the fuzzy values into crisp values. With crisp values, the knowledge regarding the disease is given to medical doctors and patients. Application of Fuzzy expert system to diagnosis of disease is mainly focused on in this chapter.
Fuzzy Critical Path Method Based on a New Approach of Ranking Fuzzy Numbers Using Centroid of Centroids
Authors: Shankar N. Ravi , Saradhi B. Pardha , Babu S. Suresh
Published: July 2017

The Critical Path Method (CPM) is useful for planning and control of complex projects. The CPM identifies the critical activities in the critical path of an activity network. The successful implementation of CPM requires the availability of clear determined time duration for each activity. However, in practical situations this requirement is usually hard to fulfil since many of activities will be executed for the first time. Hence, there is always uncertainty about the time durations of activities in the network planning. This has led to the development of fuzzy CPM. In this paper, a new approach of ranking fuzzy numbers using centroid of centroids of fuzzy numbers to its distance from original point is proposed. The proposed method can rank all types of fuzzy numbers including crisp numbers with different membership functions. The authors apply the proposed ranking method to develop a new fuzzy CPM. The proposed method is illustrated with an example.
Advances in QoS/E Characterization and Prediction for Next Generation Mobile Communication Systems
Authors: Pitas Charalampos N. , Fertis Apostolos G. , Charilas Dimitris E. , Panagopoulos Athanasios D.
Published: July 2017

The scope of this work is to present a holistic approach in quality of service (QoS) and quality of experience (QoE) characterization and prediction in modern mobile communication networks. Analytically, multi radio access technologies have been deployed in order to deliver mobile services to quality demanded consumers. Quality of Experience (QoE) parameters describe the End-to-End (E2E) quality as experienced by the mobile users. These parameters are difficult to be measured and quantified. System Quality of Service (SQoS) parameters are metrics that are closely related to the network status, and defined from the viewpoint of the service provider rather than the service user. Moreover, E2E Service Quality of Service (ESQoS) parameters describe the QoS of the services and they are obtained directly from the QoE parameters by mapping them into parameters more relevant to network operators, service providers and mobile users. A useful technique for mobile network planning and optimization is to build reliable quality estimation models for mobile voice and video telephony service.
PCA as Dimensionality Reduction for Large-Scale Image Retrieval Systems
Authors: Belarbi Mohammed Amin , Mahmoudi Saïd , Belalem Ghalem
Published: July 2017

Dimensionality reduction in large-scale image research plays an important role for their performance in different applications. In this paper, we explore Principal Component Analysis (PCA) as a dimensionality reduction method. For this purpose, first, the Scale Invariant Feature Transform (SIFT) features and Speeded Up Robust Features (SURF) are extracted as image features. Second, the PCA is applied to reduce the dimensions of SIFT and SURF feature descriptors. By comparing multiple sets of experimental data with different image databases, we have concluded that PCA with a reduction in the range, can effectively reduce the computational cost of image features, and maintain the high retrieval performance as well
CARMIE
Authors: Lobo Joana , Ferreira Liliana , Ferreira Aníbal JS
Published: July 2017

The incidence of chronic diseases is increasing and monitoring patients in a home environment is recommended. Noncompliance with prescribed medication regimens is a concern, especially among older people. Heart failure is a chronic disease that requires patients to follow strict medication plans permanently. With the objective of helping these patients managing information about their medicines and increasing adherence, the personal medication advisor CARMIE was developed as a conversational agent capable of interacting, in Portuguese, with users through spoken natural language. The system architecture is based on a language parser, a dialog manager, and a language generator, integrated with already existing tools for speech recognition and synthesis. All modules work together and interact with the user through an Android application, supporting users to manage information about their prescribed medicines. The authors also present a preliminary usability study and further considerations on CARMIE.
Research on Intelligent Optimization Algorithm for the Hot Press Insulation of Generator Stator Bar
Authors: Tan Xiao Dong , Kong Ling Hao , Li Dao Ru
Published: July 2017

As the heart of generator, stator bar’s production quality will directly influence the whole generator’s quality. We researched heating and solidification process of stator bar at the beginning to clarify the relationship between heating time and temperature. BP neural network toolbox in MATLAB platform was used to build neural network model which will relatively accurate estimate bar’s resistance with four process parameters. Through the resistance, people can find out the best technological parameters before actually production. We used some data collected from one factory as learning data. Others were test data, used to illustrate the rationality of the model, which can forecast insulation resistance value in later production, finding the optimal process parameters.
The STAT4/MLL1 Epigenetic Axis Regulates the Antimicrobial Functions of Murine Macrophages
Authors: Carson William F. , Cavassani Karen A. , Soares Elyara M. , Hirai Soichiro , Kittan Nicolai A. , Schaller Matthew A. , Scola Melissa M. , Joshi Amrita , Matsukawa Akihiro , Aronoff David M. , Johnson Craig N. , Dou Yali , Gallagher Katherine A. , Kunkel Steven L.
Published: July 2017

Macrophages are critical immune cells for the clearance of microbial pathogens and cellular debris from peripheral tissues. Macrophage inflammatory responses are governed by gene expression patterns, and these patterns are often subject to epigenetic control. Chromatin modifications, such as histone methylation, regulate gene accessibility in macrophages, and macrophage polarization is governed in part by the expression and function of chromatin-modifying enzymes. The histone methyltransferase mixed-lineage leukemia 1 (MLL1) preferentially modifies lysine residue 4 on the unstructured protein tail of histone H3. MLL1 expression and function have been shown to be governed by signal transduction pathways that are activated by inflammatory stimuli, such as NF-κB. Therefore, we sought to investigate the role of MLL1 in mediating macrophage inflammatory responses. Bone marrow–derived macrophages from mice with a targeted MLL1 gene knockout (Lys2-Cre+/− MLL1fx/fx) exhibited decreased proinflammatory gene expression with concurrent decreases in activating histone methylation. However, MLL1-deficient macrophages also exhibited increased phagocytic and bacterial killing activity in vitro. RNA profiling of MLL1-knockout macrophages identified numerous genes involved with inflammatory responses whose expression was altered in response to TLR ligands or proinflammatory cytokines, including STAT4. STAT4-dependent cytokines, such as type I IFNs were able to drive MLL1 expression in macrophages, and MLL1-knockout macrophages exhibited decreased activating histone methylation in the STAT4 promoter. These results implicate an important role for MLL1-dependent epigenetic regulation of macrophage antimicrobial functions.
IL-17 Promotes Differentiation of Splenic LSK− Lymphoid Progenitors into B Cells followingPlasmodium yoeliiInfection
Authors: Ghosh Debopam , Brown Susie L. , Stumhofer Jason S.
Published: July 2017

Lineage−Sca-1+c-Kit− (LSK−) cells are a lymphoid progenitor population that expands in the spleen and preferentially differentiates into mature B cells in response to Plasmodium yoelii infection in mice. Furthermore, LSK− derived B cells can subsequently contribute to the ongoing immune response through the generation of parasite-specific Ab-secreting cells, as well as germinal center and memory B cells. However, the factors that promote their differentiation into B cells in the spleen postinfection are not defined. In this article, we show that LSK− cells produce the cytokine IL-17 in response to Plasmodium infection. Using Il-17ra−/− mice, IL-17R signaling in cells other than LSK− cells was found to support their differentiation into B cells. Moreover, primary splenic stromal cells grown in the presence of IL-17 enhanced the production of CXCL12, a chemokine associated with B cell development in the bone marrow, by a population of IL-17RA–expressing podoplanin+CD31− stromal cells, a profile associated with fibroblastic reticular cells. Subsequent blockade of CXCL12 in vitro reduced differentiation of LSK− cells into B cells, supporting a direct role for this chemokine in this process. Immunofluorescence indicated that podoplanin+ stromal cells in the red pulp were the primary producers of CXCL12 after P. yoelii infection. Furthermore, podoplanin staining on stromal cells was more diffuse, and CXCL12 staining was dramatically reduced in Il-17ra−/− mice postinfection. Together, these results identify a distinct pathway that supports lymphoid development in the spleen during acute Plasmodium infection.
α-Toxin Regulates Local Granulocyte Expansion from Hematopoietic Stem and Progenitor Cells in Staphylococcus aureus–Infected Wounds
Authors: Falahee Patrick C. , Anderson Leif S. , Reynolds Mack B. , Pirir Mauricio , McLaughlin Bridget E. , Dillen Carly A. , Cheung Ambrose L. , Miller Lloyd S. , Simon Scott I.
Published: July 2017

The immune response to Staphylococcus aureus infection in skin involves the recruitment of polymorphonuclear neutrophils (PMNs) from the bone marrow via the circulation and local granulopoiesis from hematopoietic stem and progenitor cells (HSPCs) that also traffic to infected skin wounds. We focus on regulation of PMN number and function and the role of pore-forming α-toxin (AT), a virulence factor that causes host cell lysis and elicits inflammasome-mediated IL-1β secretion in wounds. Infection with wild-type S. aureus enriched in AT reduced PMN recruitment and resulted in sustained bacterial burden and delayed wound healing. In contrast, PMN recruitment to wounds infected with an isogenic AT-deficient S. aureus strain was unimpeded, exhibiting efficient bacterial clearance and hastened wound resolution. HSPCs recruited to infected wounds were unaffected by AT production and were activated to expand PMN numbers in proportion to S. aureus abundance in a manner regulated by TLR2 and IL-1R signaling. Immunodeficient MyD88-knockout mice infected with S. aureus experienced lethal sepsis that was reversed by PMN expansion mediated by injection of wild-type HSPCs directly into wounds. We conclude that AT-induced IL-1β promotes local granulopoiesis and effective resolution of S. aureus–infected wounds, revealing a potential antibiotic-free strategy for tuning the innate immune response to treat methicillin-resistant S. aureus infection in immunodeficient patients.
Leitura homeostásica dos homeóstatos de José-Alberto Marques
Authors: Ministro Bruno
Published: July 2017

http://dx.doi.org/10.5007/1807-9288.2016v12n1p45Este curto texto é uma tentativa de esboçar uma linha de leitura para as múltiplas potenciais interpretações de Homeóstatos, de José-Alberto Marques. O texto parte da premissa de que é a leitura dinâmica que dá movimento a um texto construído numa dinâmica material que extrapola os protocolos de escrita e leitura convencionados.
Escrita coreográfica digital: novas perspectivas sobre o processo criativo em dança
Authors: Castro Daniele
Published: July 2017

http://dx.doi.org/10.5007/1807-9288.2016v12n1p99Nas últimas décadas do século XX, coreógrafos e bailarinos presenciaram o surgimento de softwares para notação e criação em dança. Uma tecnologia cuja promessa é facilitar a escrita coreográfica e preservar sequências de movimento que antes estariam sob a guarda apenas da memória de seus criadores e intérpretes. Porém, para além de seus benefícios mais evidentes, é preciso investigar as implicações de tais tecnologias na compreensão do processo criativo de dança e do estatuto do corpo do bailarino. Recorrendo a estudos específicos sobre o tema e considerações dos principais criadores e utilizadores de tais tecnologias, este artigo procura confrontar diferentes perspectivas sobre a relação entre a escrita da dança e a tecnologia, tendo como eixo norteador o pensamento sobre o papel do corpo e da sensibilidade humana no processo criativo.
Sentido do trabalho: uma análise sobre a percepção dos coordenadores de cursos em uma instituição universitária
Authors: Ronchi Carlos César , Bandeira Nehemias Pinto , Mendes Braga Carlos Henrique , Oliveira Ricardo Daher , Miranda Melo Jr José Samuel
Published: July 2017

O presente artigo é resultado de uma pesquisa que buscou compreender o trabalho e seu sentido, tendo em vista os aspectos que influenciam o ambiente organizacional. O objetivo desta pesquisa é diagnosticar o que os coordenadores de curso de uma universidade maranhense, atribuem ao sentido do trabalho. Para que fosse possível o alcance dos resultados, foi utilizado um questionário, que envolve dimensões relacionadas ao sentido do trabalho e questões socioeconômicas. Os resultados expressam que os coordenadores de curso atribuem uma concepção positiva quanto ao trabalho, sociedade e às organizações. Ao analisar os dados, percebe-se que os pesquisados não consideram o dinheiro um fator essencial para realizarem suas atividades, e que o trabalho oferece uma contribuição para a sociedade, assim beneficiando os outros e permitindo o aperfeiçoamento. Constata-se que o trabalho na percepção dos pesquisados, é considerado mentalmente exigente. Ao analisar a pesquisa, destaca-se que os investigados consideram o sentido do trabalho como algo fundamental e agregador de valor, pois contribui para o crescimento pessoal e coletivo, tornando-se significante em suas vidas.