Artificial Intelligence Fuzzy Logic Research Articles

Model of the Performance Based on Artificial Intelligence-Fuzzy Logic Description of Physical Activity.

The aim of the study was to build a fuzzy model of lower limb peak torque in an isokinetic mode. The study involved 93 male participants (28 male deaf soccer players, 19 hearing soccer players and 46 deaf untraining male). A fuzzy computational model of different levels of physical activity with a focus on the lower limbs was constructed. The proposed fuzzy model assessing lower limb peak torque in an isokinetic mode demonstrated its effectiveness. The novelty of our research lies in the use of hierarchical fuzzy logic to extract computational rules from data provided explicitly and then to determine the corresponding physiological and pathological mechanisms. The contribution of our research lies in complementing the methods for describing physiology, pathology and rehabilitation with fuzzy parameters, including the so-called dynamic norm embedded in the model.

Artificial Intelligence Fuzzy Logic Modeling of Surface Roughness in Plasma Jet Cutting Process of Shipbuilding Aluminium Alloy 5083

In this paper the influence of different process parameters on surface roughness responses in plasma jet cutting process was investigated. Experimentations were conducted on shipbuilding aluminium 5083 sheet thickness 8 mm. Experimental work was performed according to Taguchi L27 orthogonal array by varying four parameters such as gas pressure, cutting speed, arc current and cutting height. Due to complexity of manufacturing process and aim to cover wide experimental space few constraints regarding cutting area were defined. Surface roughness parameters Ra and Rz were analysed as cut quality responses. In order to define mathematical model that will be able to describe effects of process parameters on surface roughness artificial intelligence (AI) fuzzy logic (FL) technique was applied. After functional relations between input parameters and surface roughness responses were defined prediction accuracy of developed fuzzy logic model was checked by comparison between experimental and predicted data. Mean absolute percentage error (MAPE) as well as coefficient of determination (R2) were used as validation measures. Finally, optimal process conditions that lead to minimal surface roughness were defined by creating response surface plots.

Implementation of Quality of Service-Oriented Distributed Routing Protocol using Fuzzy Logic in Mobile Ad Hoc Networks

A mobile ad hoc network (MANET) is a type of wireless network made up of nodes that are both wireless and mobile. This type of wireless network is a non-infrastructure network (without a central manager such as router, servers, etc.) that is hard to manage; providing the Quality of Service (QoS) is also difficult. The fuzzy logic is one of the effective methods required to achieve high QoS in MANET. We propose an Ant-colony Based Cluster-head (ABC) selection a protocol based on artificial intelligence fuzzy logic techniques in this paper. With the proposed protocol, we divide the network into clusters, then, choose a cluster head which is a node having high residual energy, the most trusted; this is achieved using Ant-colony optimization (ACO) algorithm thanks to the use of a probability function which determines the likelihood of various nodes to be selected as cluster-heads. Using the Network Simulator Version-2 (NS-2), we evaluated our Proposed Protocol with ones existing in literature; Quality of Service-Ad-hoc On-Demand Distance Vector (QoS-AODV), Bandwidth aware Multi-path Routing protocol in mobile Ad Hoc networks (BMR) and Dynamic Multi-Path Source Routing Method (MSR). We conducted various experimental evaluations varying both the number of the total mobile nodes in the network and the number of receivers from a single sender; all simulation outcomes revealed that the Proposed protocol; Quality of Service-Oriented Distributed Routing Protocol using Fuzzy Logic and clustering techniques in Mobile Ad Hoc Networks (QODFL) outperformed the existing ones as compared to existing protocols, it was able to increase both the Packet Delivery Ratio by 10.5% and throughput by 6.7 % , and the End-to-End Delivery ratio by 7.7%.

Heat wave hazard classification and risk assessment using artificial intelligence fuzzy logic

The average summer temperatures as well as the frequency and intensity of hot days and heat waves are expected to increase due to climate change. Motivated by this consequence, we propose a methodology to evaluate the monthly heat wave hazard and risk and its spatial distribution within large cities. A simple urban climate model with assimilated satellite-derived land surface temperature images was used to generate a historic database of urban air temperature fields. Heat wave hazard was then estimated from the analysis of these hourly air temperatures distributed at a 1-km grid over Athens, Greece, by identifying the areas that are more likely to suffer higher temperatures in the case of a heat wave event. Innovation lies in the artificial intelligence fuzzy logic model that was used to classify the heat waves from mild to extreme by taking into consideration their duration, intensity and time of occurrence. The monthly hazard was subsequently estimated as the cumulative effect from the individual heat waves that occurred at each grid cell during a month. Finally, monthly heat wave risk maps were produced integrating geospatial information on the population vulnerability to heat waves calculated from socio-economic variables.

ENGLISH

Automatic Generation Control (AGC) or Load Frequency Control is a very important issue in power system operation and control for supplying sufficient and reliable electric power with good quality. AGC is a feedback control system adjusting a generator output power to remain defined frequency. One of the objectives of AGC is to maintain the system frequency at nominal value In the steady state operation of power system .An extended power system can be divided into a number of load frequency control areas interconnected by means of tie lines. Without loss of generality one can consider a three- area case connected by tie line. Here we are considering system, which is integration of two thermal power systems with hydro power system. That is area-1 and area-2 consists of thermal reheat power plant whereas area-3 consists of hydro power plant. The performance analysis of load frequency control for multi area inter connected system will be done in MATLAB/SIMULINK environment. As integration of multi area effects performance of system study of different characteristics using controllers like conventional PI ,PID, artificial intelligence FUZZY LOGIC controlling techniques are necessary in order to determine the effective gains of controller in efficient manner. And this knowledge about dynamic response characteristics will give us idea of controlling techniques that we need to implement for obtaining required frequency response at tie line stations.

A Fuzzy-Active Force Control Architecture Based in Characterizing Nonlinear Systems’ Behavior

This paper presents Active Force Control (AFC) based architecture in characterizing the twin rotor multi-input multi-output (MIMO) system (TRMS). The proposed architecture is expected to produce an optimum control gains in both pitch and yaw responses by introducing decoupling function between pitch and yaw responses. The internal change corresponds to coupling effects, gust and wind turbulence are very difficult to compensate by the classical PID control, but both of them are stamped out as AFC scheme is implemented into the control strategy. The performance of TRMS is further optimized by the realization of hybrid strategy in which an artificial intelligence Fuzzy Logic is integrated into the control architecture.

The evaluation of Learning Management Systems using an artificial intelligence fuzzy logic algorithm

There are many open source and commercially available Learning Management System (LMS) on the Internet and one of the important problems in this field is how to choose an LMS that will be the most effective one and that will satisfy the requirements. In order to help in the solution of this problem, the author has developed a computer program to aid in the selection of an LMS. The developed system is web-based and can easily be used over the Internet any where over the world at any time. The developed system is basically a web-based decision support system used to evaluate LMSs by using a flexible and smart algorithm derived from artificial intelligent concepts with fuzzy logic values. The paper describes the development of the LMS evaluation system. The individuals who are most likely to be interested in the LMS evaluation process are teachers, students, and any educational organizations such as: universities, schools, institutes, and anyone else who seeks to have a LMS.

Automatic identification of oil spills on satellite images

A fully automated system for the identification of possible oil spills present on Synthetic Aperture Radar (SAR) satellite images based on artificial intelligence fuzzy logic has been developed. Oil spills are recognized by experts as dark patterns of characteristic shape, in particular context. The system analyzes the satellite images and assigns the probability of a dark image shape to be an oil spill. The output consists of several images and tables providing the user with all relevant information for decision-making. The case study area was the Aegean Sea in Greece. The system responded very satisfactorily for all 35 images processed. The complete algorithmic procedure was coded in MS Visual C++ 6.0 in a stand-alone dynamic link library (dll) to be linked with any sort of application under any variant of MS Windows operating system.