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Paraconsistent Algorithm Extractor of Contradiction Effects - Paraextrctr ctr

Nowadays networks of analyses based in non-classic logics are used with success in the treatment of uncertainties. The characteristic of accepting the contradiction in his structure is the main cause of the methodologies based in Paraconsistent Logic is ideals for applications in systems of analyses and decision making. In this work we presented an algorithm based in Paraconsistent logic capable to extract in a gradual way the effects of the contradiction in originated signals of information of uncertain knowledge database. The Algorithm Paraconsistent Extractor of Contradiction effects - Paraextrctrctr is formed with base in fundamental concepts of the Paraconsistent Annotated Logic with annotation of two values (PAL2v) it can be applied in filters of networks of analyses of signal information where uncertain and contradictory signals can be present. The process of extraction of the effect of the contradiction is always begun by the largest inconsistency degree among two signals that belong to the group that is in analysis. In the end of the analysis it is found a consensus value. In this work we presented numeric example and one example of application of the Paraextrctrctr in Load Profile Forecast used in support to decision of the operation in an Electric Power System, but his application potentiality is demonstrated in several fields of the Artificial Intelligence.

Paraconsistent Artificial Neural Network as Auxiliary in Cephalometric Diagnosis

This work presents an application of the paraconsistent artificial neural network (PANN) in the analysis of cephalometric variables and provides an orthodontic diagnosis. An experts analysis is subject to the inherent imprecision of measurements, registers, and individual variability of physician visual analysis. Patient input cephalometric values are compared with means drawn from individuals considered normal in the cephalometric point of view by the PANN. This reference is constituted by individuals from 6 to 18 years old, both genders. The applied cephalometric analysis was targeted to measure skeletal and dental discrepancies and established a cephalometric diagnosis. The analysis results in degrees of skeletal, anteroposterior, and dental discrepancy, pertinent to upper and lower incisors. A sample of 120 orthodontic patients was processed by the proposed model and three orthodontic experts. Comparisons between the model and the human experts performance provided kappa indexes that varied from moderate to almost perfect agreement. The agreement between the model and specialists performance was equivalent. In addition, the model pointed out contradictions presented in the data that were not noticed by the orthodontists, which highlight the contribution that this kind of system could carry out in the orthodontics decision support.

Paraconsistent artificial neural network: an application in cephalometric analysis

Paraconsistent artificial neural network (PANN) is a mathematical structure based on paraconsistent logic, which allows dealing with uncertainties and contradictions. In this paper we propose an application of PANN to analyze cephalometric measurements in order to support orthodontics diagnostics. Orthodontic and cephalometrical analysis taking into account several uncertainties and contradictions, ideal scenario to be treated by paraconsistent approach.

An Expert System Structured in Paraconsistent Annotated Logic for Analysis and Monitoring of the Level of Sea Water Pollutants

This chapter presents the development of a Expert System which was elaborated based on the Fundamentals of Paraconsistent Annotated Logic and aimed to help in the process of detection of physiological stress in organisms exposed to water pollution. The Paraconsistent Logic is a non-classical logic present as their main characteristics the acceptance of the contradiction in their structure. It is presented in this study the algorithms extracted from a type of Paraconsistent Logic nominated Paraconsistent Annotated Logic with annotation of two values PAL2v that are capable of simulating the applied methodology in Biology known as a neutral red retention assay. This method of biomarkers prepared with specific procedures has the goal of finding rates of exposure to marine pollution through the manipulation and study of cells from mussels. It was built a configuration of Paraconsistent Artificial Neural Network (PANN) composed of algorithms based on the principals of Paraconsistent Logic to compose the Expert System with the goal of simulating the biological method and help in the presentation of the cellular response. The process of analysis elaborated by the software consists of making a comparison with pre-established patterns through the Paraconsistent Network by biochemical/biological processes consolidated in the biology area and defined in the scope on the mussels cells’ measures that presented determined behavior and biochemical reactions, as it is the biomarker of exposure and effect of marine pollution in the site of the samples collection. With this new approach of results, besides complete, they are presented as being more efficient by decreasing the points of uncertainty given by simple human observation. This way this work opens new fields for research of application of Artificial Intelligence techniques in the analysis and monitoring of the Marine Pollution.

Application of Paraconsistent Artificial Neural Network in Statistical Process Control acting on voltage level monitoring in Electrical Power Systems

In this paper, we present an application of the Paraconsistent Artificial Neural Network (PANnet) in a Statistical Process Control to trigger alarms in an electrical power System. The PANnet is based on Paraconsistent Annotated Logic (PAL) which is a non-classical logic with properties of accepting contradictions in their fundamentals. In this work, we make a joint application technique of Paraconsistent Annotated logic, in the form of PANnet, with the concepts of Statistical Process Control (SPC). The Statistical Process Control Paraconsistent (SCP-PAL) is used in this paper to make a dynamic monitoring of the conditions of the electrical voltage in an electrical power system. To test and validate the functioning of SPC-PAL Controller we use data obtained by random computational processes and we also use a database with actual values generated by a power electrical System of a company installed in Brazil. The SPC-PAL Controller, due to the use of the algorithms of PAL in its construction, has some features that does afford optimized electrical voltage monitoring. The result of the analysis made with the SPC-PAL Controller provides three types of alarms: (1) rapid Variation in the voltage; (2) Imbalance of Mean in the Voltage data; (3) High dispersion index in the voltage data. Based on these three types of alarms we can obtain a better understanding about the operating state of the electrical power system. In the various tests performed, the SPC-PAL Controller presented a good performance and detected changes in Mean and in the variation of tension, as well as sudden changes in voltage data.

Paraconsistent Artificial Neural Network for Structuring Statistical Process Control in Electrical Engineering

In this study, we present an algorithmic structure based on paraconsistent annotated logic (PAL) that can simulate the calculi of average values present in a dataset and detect the variations of the average using only PAL concepts. We call the structure as paraconsistent artificial neural network for extraction of moving average (PANnet\(_\mathrm{{MovAVG}})\). As an example of its application, we use PANnet\(_\mathrm{{MovAVG}}\) to assist in the analysis of a final product quality index related to electrical engineering. To obtain the final result, we applied PANnet\(_\mathrm{{MovAVG}}\) to simulate the statistical behavior of the Statistical Process Control (SPC) by comparing values obtained with a ranking that establishes quality index standards based on electrical power distribution. First, tests were conducted using data with random values to verify the behavior of PANnet\(_\mathrm{{MovAVG}}\) and to set the optimum number of algorithms to form an optimized computational structure. Then, we used a database with actual electric voltage values generated by an electrical power system of an electrical power utility grid in Brazil. In the various tests, PANnet\(_\mathrm{{MovAVG}}\) appropriately detected changes and identified variations of electric voltage in 220-V transmission lines. The results show that PANnet\(_\mathrm{{MovAVG}}\) can be used to construct an efficient architecture for determining and monitoring quality scores with applications in various areas of engineering, especially for detecting quality index in an electricity distribution network.

An Algorithmic Method Supported by Paraconsistent Annotated Logic Applied to the Determination of Friction Factors for Turbulent Flow in Smooth Pipes

The high complexity of the study of fluid flow is due to the existence of an excessive number of formulas to determine analytically the friction factor in pipelines. Currently, with more than a dozen formulas and the obligation of using graphics with readings on logarithmic scales for this purpose, the results are obtained with some degree of uncertainty. Recent work, with treatment of uncertainties, suggests that these complex calculations can be better performed with the basis of non-classical logic, such as the paraconsistent annotated logic (PAL) which has as a fundamental property the acceptance of contradictions. In this chapter we present a method that uses algorithms of PAL to make analysis in tests of fluid flow in smooth pipes. The PAL algorithms select and classify various results originating from the various equations for the obtaining of friction factor and, according to the Reynolds number, they optimize the calculation application of hydraulic projects in smooth pipes.

Paraconsistent Logic Algorithms Applied to Seasonal Comparative Analysis with Biomass Data Extracted by the Fouling Process

In ecology, extracting information about species that live in ecosystems and to find new ways to make the analysis of this data is very important to get the levels of contamination of the marine environment and other information about ecosystems. The values of biomass taken from ecological process is called Fouling, which are considered complex, bring usually incomplete or even inconsistent information and therefore can lead to conclusions far from the reality. Recently, paraconsistent logics have emerged as an innovative proposal to make the data processing which brings contradictory or uncertain information and therefore can offer better response under these conditions. In this chapter, we present a method that uses paraconsistent annotated logic (PAL) to find degrees of evidence resulting from seasonal comparison among analytical descriptor values of biomass type in the Fouling process.