Annotated Logic Research Articles

Predictive Controller Based on Paraconsistent Annotated Logic for Synchronous Generator Excitation Control

This study presents a new Model Predictive Controller (MPC), built with algorithms based on Paraconsistent Annotated Logic (PAL), with application examples in the excitation control of a synchronous generator. PAL is a non-classical evidential and propositional logic that is associated with a Hasse lattice, and which presents the property of accepting the contradiction in its foundations. In this research, the algorithm was constructed with a version of the PAL that works with two information signals in the degrees of evidence format and, therefore, is called Paraconsistent Annotated Logic with annotation of two values (PAL2v). For the validation of the algorithmic structure, the computational tool MATLAB® Release 2012b, The MathWorks, Inc., Natick, MA, United States was used. Simulations were performed which compared the results obtained with PPC-PAL2v to those obtained in essays with the AVR (Automatic Voltage Regulator) controls in conjunction with the PSS (Power System Stabilizer) and the conventional MPC of fixed weights. The comparative results showed the PPC-PAL2v to display superior performance in the action of the excitation control of the synchronous generator, with a great efficiency in response to small signals.

Model reference control by recurrent neural network built with paraconsistent neurons for trajectory tracking of a rotary inverted pendulum

This investigation presents a recurrent paraconsistent neural network (RPNN), as the main element of the model reference control (MRC) strategy for the rotary inverted pendulum (RIP). The RIP characteristics, such as nonlinearity, two-degree-of-freedom (2DoF) motion, and under-actuated system, make it an ideal device to apply and test the RPNN. The designed paraconsistent neural model reference controller (PNMRC) uses three RPNNs: two of them to model the arm and pendulum angles and the third one to control the system while tracking a reference trajectory. The hidden neurons of the RPNN use the paraconsistent annotated logic by 2-value annotations (PAL2v) rules as an activation function. PAL2v, as a member of the paraconsistent logics family, deals with uncertain and contradictory data, representing a potentially robust alternative to applications of artificial neural networks in control. The PAL2v neuron is detailed and compared with other activation functions in recurrent neural networks (RNN). With real-time experiments, the PNMRC strategy is compared with classical control methodology, presenting excellent performance.

Paraconsistent annotated logic applied to industry assets condition monitoring and failure prevention based on vibration signatures

In this study, we introduced an expert system (ESvbrPAL2v), responsible for monitoring assets based on vibration signature analysis through a set of algorithms based on the Paraconsistent Annotated Logic – PAL. Being a non-classical logic, the main feature of the PAL is to support contradictory inputs in its foundation. It is therefore suitable for building algorithmic models capable of performing out appropriate treatment for complex signals, such as those coming from vibration. The ESvbrPAL2v was built on an ATMega2560 microcontroller, where vibration signals were captured from the mechanical structures of the machines by sensors and, after receiving special treatment through the Discrete Fourier Transform (DFT), then properly modeled to paraconsistent logic signals and vibration patterns. Using the PAL fundamentals, vibration signature patterns were built for possible and known vibration issues stored in ESvbrPAL2v and continuously compared through configurations composed by a network of paraconsistent algorithms that detects anomalies and generate signals that will report on the current risk status of the machine in real time. The tests to confirm the efficiency of ESvbrPAL2v were performed in analyses initially carried out on small prototypes and, after the initial adjustments, tests were carried out on bearings of a group of medium-power motor generators built specifically for this study. The results are shown at the end of this study and have a high index of signature identification and risk of failure detection. These results justifies the method used and future applications considering that ESvbrPAL2v is still in its first version.

Characterization and classification of numerical data patterns using Annotated Paraconsistent Logic and the effect of contradiction

This work describes the development of a computational mathematical model that uses Annotated Paraconsistent Logic - APL and a concept derived from it, the effect of contradiction, to identify patterns in numerical data for pattern classification purposes. The APL admits paraconsistent and paracomplete logical principles, which allow the manipulation of inconsistent and contradictory data, and its use allowed the identification and quantization of the attribute related to the contradiction. To validate the model, series of Raman spectroscopies obtained after exposure of proteins, lipids and nucleic acids, collected from cutaneous tissue cell samples previously examined for the detection of cancerous lesions, identified as basal carcinoma, melanoma and normal, were used. Initially, the attributes related to contradiction, derivative and median obtained from spectroscopies were identified and quantified. A machine learning process with approximately 31.6% of each type of samples detects a sequence of spectroscopies capable of characterizing and classifying the type of lesion through the chosen attributes. Approximately 68.4% of the samples are used for classification tests. The proposed model identified a segment of spectroscopies where the classification of test samples had a hit rate of 76.92%. As a differential and innovation of this work, the use of APL principles in a complete process of training, learning and classification of patterns for numerical data sets stands out, with flexibility to choose the attributes used for the characterization of patterns, and a quantity of samples of about one third of the total required for characterization.

Paraconsistent Annotated Logic Algorithms Applied in Management and Control of Communication Network Routes.

This paper presents a computational method based on non-classical logic dedicated to routing management and information stream control in communication networks. Paraconsistent logic (PL) was used to create an algorithmic structure whose main property is to accept contradiction. Moreover, a computational structure, the denominated paraconsistent data analyzer (PDAPAL2v), was constructed to perform routing management in communication networks. Direct comparisons of PDAPAL2v with a classical logic system that simulates routing conditions were made in the laboratory. In the conventional system, the paraconsistent algorithms were considered as binary logic gates, and in the tests, the same adjustment limits of PDAPAL2v were applied. Using a database with controlled insertion of noise, we obtained an efficacy of 97% in the detection of deteriorated packets with PDAPAL2v and 72% with the conventional simulation system. Functional tests were carried out, showing that PDAPAL2v is able to assess the conditions and degradation of links and perform the analysis and correlation of various inputs and variables, even if the signals have contradictory values. From practical tests in the laboratory, the proposed method represents a new way of managing and controlling communication network routes with good performance.

Rotary Inverted Pendulum Identification for Control by Paraconsistent Neural Network

Artificial neural networks (ANNs) have been used over the last few decades to perform tasks by learning with comparisons. Fitting input-output models, system identification, control, and pattern recognition are some fields for ANN applications. However, problems involving uncertain situations could be challenging for them. The family of paraconsistent logics (PL) is a powerful tool that can deal with uncertainty and contradictory information, so getting attention from researchers for its implications and applications in artificial intelligence. This investigation describes a novel activation function reasoned on the paraconsistent annotated logic by two-value annotations (PAL2v) rules, a variation of PL, allowing the design of a new paraconsistent neural net (PNN), applied in model identification for control (I4C) of a closed-loop rotary inverted pendulum (RIP) system.

Application of Shannon Entropy in the Construction of a Paraconsistent Model of the Atom

In this paper, we present a model of the atom that is based on a nonclassical logic called paraconsistent logic (PL), which has the main property of accepting the contradiction in logical interpretations without the conclusions being annulled. The proposed model is constructed with an extension of PL called paraconsistent annotated logic with annotation of two values (PAL2v), which is associated with an interlaced lattice of four vertices. We use the logarithmic function of the Shannon entropy H(s) to construct the paraconsistent equations and thus adopt a probabilistic model for representations in quantum physics. Through analyses of the interlaced lattice, comparative values are obtained for some of the phenomena and effects of quantum mechanics, such as superposition of states, wave functions, and equations that determine the energy levels of the atomic shells of an atom. At the end of this article, we use the hydrogen atom as a basis for the representation of the PAL2v model, where the values of the energy levels in six orbital shells are obtained. As an example, we present a possible method of applying the PAL2v model to the use of Raman spectroscopy signals in the detection of lubricating mineral oil quality.

Paraconsistent logic approach for active noise reduction

The Active Noise Reduction (ANR) is widely used in aircraft, headsets, telecommunications and medicine systems, to reduce or eliminate noise, while maintaining the characteristics of the desired signal. In this article a network of paraconsistent artificial neural cells (PANC) will be presented, based on the paraconsistent annotated logic by 2 values annotations (PAL2v) that allows to operate as ANR. Simulations indicate that the results presented by ANRPAL are better than those obtained by classic filters.

On recent applications of paraconsistent logic: an exploratory literature review

ABSTRACTThis paper aims to empirically explore the state of practical applications of paraconsistent logics. To this end, we performed an exploratory literature review, analysing papers published between the years 2015 and 2018. Paraconsistent formalisms based on annotated logics are practically the sole type of approach we found to be applied in engineering applications. The engineering problems solved by paraconsistent approaches were mainly in the fields of signal and image processing and decision support. The results of our exploratory review indicate that recent developments in the theory of paraconsistency have not yet been adopted for applications.

Three decades of paraconsistent annotated logics: a review paper on some applications

In this expository work, we sketch some applications of annotated logics. Such logics were discovered in the late 1980s and nowadays have become one of the most fertile logics for applications. They constitute a two-sorted logic, and they are paraconsistent and in general paracomplete and non-alethic logics.

Hybrid PI controller constructed with paraconsistent annotated logic

In this work a new type of hybrid PI controller is presented using the Paraconsistent Logic (PL) as the basis for the mathematical and logical treatment of the signals corresponding to the control variables. This control has the conventional actions PI (Proportional and Integral) together with the logical actions structured in the concepts of Paraconsistent Logic. Compared to conventional techniques, this new type of Hybrid PI Controller with Paraconsistent Logic responded well to the various tests performed in the control loop. The tests and the technical validations were done with evidences that prove the efficiency of the paraconsistent concepts.

Syntax and semantics of multi-adjoint normal logic programming

Multi-adjoint logic programming is a general framework with interesting features, which involves other positive logic programming frameworks such as monotonic and residuated logic programming, generalized annotated logic programs, fuzzy logic programming and possibilistic logic programming. One of the most interesting extensions of this framework is the possibility of considering a negation operator in the logic programs, which will improve its flexibility and the range of real applications. This paper introduces multi-adjoint normal logic programming, which is an extension of multi-adjoint logic programming including a negation operator in the underlying lattice. Beside the introduction of the syntax and semantics of this paradigm, we will provide sufficient conditions for the existence of stable models defined on a convex compact set of an euclidean space. Finally, we will consider a particular algebraic structure in which sufficient conditions can be given in order to ensure the unicity of stable models of multi-adjoint normal logic programs.

A Probabilistic Paraconsistent Logical Model for Non-Relativistic Quantum Mechanics Using Interlaced Bilattices with Conflation and Bernoulli Distribution

In this work, we make a representation of non-relativistic quantum theory based on foundations of paraconsistent annotated logic (PAL), a propositional and evidential logic with an associated lattice FOUR. We use the PAL version with annotation of two values (PAL2v), named paraquantum logic (PQL), where the evidence signals are normalized values and the intensities of the inconsistencies are represented by degrees of contradiction. Quantum mechanics is represented through mapping on the interlaced bilattices where this logical formalization allows annotation of two values in the format of degrees of evidence of probability. The Bernoulli probability distribution is used to establish probabilistic logical states that identify the superposition of states and quantum entanglement with the equations and determine the state vectors located inside the interlaced Bilattice. In the proposed logical probabilistic paraquantum logic model (pPQL Model), we introduce the operation of logical conflation into interlaced bilattice. We verify that in the pPQL Model, the operation of logical conflation is responsible for providing a suitable model for various phenomena of quantum mechanics, mainly the quantum entanglement. The results obtained from the entanglement equations demonstrate the formalization and completeness of paraquantum logic that allows for interpretations of similar phenomena of quantum mechanics, including EPR paradox and the wave-particle theory.

Support at Decision in Electrical Systems of subtransmission through selection of Topologies by a Paraconsistent Simulator

In this work, we present a Simulator Program that identifies the topologies and supports operation staff in decision-making about loads reassignments and changes in electric network, offering a selection of the best settings. Facing a contingency situation or action of relocation of electrical system of subtransmission lines, the Simulator considers the current state and uses special algorithms that detect the current network topology, interprets results and presents for operation the possible different maneuvers with their respective degrees of reliability. For these actions the software Simulator elaborates the states investigation of circuit breakers and electric keys, analysis of load flow, makes risk prediction and the mathematical analysis of remote sensing values. The software Simulator uses in some of his actions, special algorithms that are based on Paraconsistent Annotated Logic (PAL), which is a non-classical logic whose main property is to accept contradiction in their fundamentals. These algorithms based on PAL offer greater speed of processing and allows the Paraconsistent Simulator of topologies (ParaSimTop) to be implemented in real time.

Paraconsistent analysis network applied in the treatment of Raman spectroscopy data to support medical diagnosis of skin cancer.

Paraconsistent logic (PL) is a type of non-classical logic that accepts contradiction as a fundamental concept and has produced valuable results in the analysis of uncertainties. In this work, algorithms based on a type of PL-paraconsistent annotated logic of two values (PAL2v)-are interconnected into a network of paraconsistent analysis (PANnet). PANnet was applied to a dataset comprising 146 Raman spectra of skin tissue biopsy fragments of which 30 spectra were determined to represent normal skin tissue (N), 96 were determined to represent tissue with basal cell carcinoma, and 19 were determined to be tissue with melanoma (MEL). In this database, paraconsistent analysis was able to correctly discriminate 136 out of a total of 145 fragments, obtaining a 93.793% correct diagnostic accuracy. The application of PAL2v in the analysis of Raman spectroscopy signals produces better discrimination of cells than conventional statistical processes and presents a good graphical overview through its associated lattice structure. The technique of PAL2v-based data processing can be fundamental in the development of a computational tool dedicated to support the diagnosis of skin cancer using Raman spectroscopy.

Paraconsistents artificial neural networks applied to the study of mutational patterns of the F subtype of the viral strains of HIV-1 to antiretroviral therapy.

The high variability of HIV-1 as well as the lack of efficient repair mechanisms during the stages of viral replication, contribute to the rapid emergence of HIV-1 strains resistant to antiretroviral drugs. The selective pressure exerted by the drug leads to fixation of mutations capable of imparting varying degrees of resistance. The presence of these mutations is one of the most important factors in the failure of therapeutic response to medications. Thus, it is of critical to understand the resistance patterns and mechanisms associated with them, allowing the choice of an appropriate therapeutic scheme, which considers the frequency, and other characteristics of mutations. Utilizing Paraconsistents Artificial Neural Networks, seated in Paraconsistent Annotated Logic Et which has the capability of measuring uncertainties and inconsistencies, we have achieved levels of agreement above 90% when compared to the methodology proposed with the current methodology used to classify HIV-1 subtypes. The results demonstrate that Paraconsistents Artificial Neural Networks can serve as a promising tool of analysis.

Undulatory Theory with Paraconsistent Logic (Part I): Quantum Logical Model with Two Wave Functions

Paraconsistent logic (PL) is a non-classical logic that accepts contradiction in its foundations. It can be represented in the form of paraconsistent annotated logic with annotation of two values (PAL2v). When used to model quantum phenomena, PAL2v is called paraquantum logic (PQL). In this work, the concept of PQL is applied to create a logical model presenting the fundamental principles of quantum mechanics that support particle-wave theory. This study uses the well-known Young’s double-slit experiment, wherein quantum phenomena appear when a monochromatic light beam passes through the two slits. We focused on a reference point located between the slits, where we observed the effects of two types of wave interferences in a region defined as a two-wave region (2W region). Considering that the effect in this 2W region is very similar to that studied by Huygens, we adopt a paraquantum logical model in which a particle (or quantum) is represented by two wave functions. The two wave functions result in four State Vectors (Ket, Bra,,) in the PQL Lattice that express the symmetry and the entanglement of Quantum Mechanics. The constructed model adapts well to the quantum phenomena, is strongly consistent, and can be considered as an innovative form of analysis in the field of quantum mechanics. Based on this model, we present in two parts (Part I and Part II) the comparative analysis of values found in SchrÖdinger’s equation and probabilistic models of wave-particle theory using Bonferroni inequality.

An Ontology-based Problem-logic Driven Approach towards the Activity Awareness for Elderly Care

The same activity under different user situations in the elderly care system may lead to different intelligent system response, since user needs vary with the changing situations. Activity awareness (AA) emphasizes that systems intelligently respond to user needs by aware the user-performed activities which is denoted and comprehended according to various user situations. But most current AA technics use similar patterns retrieved from the growing historical data, neglecting the situation change, called dataset driven. Thus, an aptly approach is needed in support of AA systems to handle various activity denotations in different user situations. Responding to the problem, this paper proposes an Ontology-based Problem-logic driven approach (OPL) to enhance the AA system by denoting user activities with the problem logic according to user-oriented denoted problem domains, where the AA system can seamlessly integrate with inferring for intelligent system response. Specifically, denoted problem domain and logic are proposed to support the OPL concepts, while activity graph is formed to support the intelligent system response based on the annotated problem logic. With the OPL, systems can directly target at the changing situations with rule-based inferring. A case study is performed upon the scenario retrieved from a European elderly care project, where a proof-of-concept prototype is established to confirm the validity of the OPL approach.

The paraconsistent process order control method

We have already developed some kinds of paraconsistent annotated logic programs. In this paper we propose the paraconsistent process order control method based on a paraconsistent annotated logic program called before–after extended vector annotated logic program with strong negation (bf-EVALPSN) with a small example of pipeline process order verification. Bf-EVALPSN can deal with before–after relations between two processes (time intervals) in its annotations, and its reasoning system consists of two kinds of inference rules called the basic bf-inference rule and the transitive bf-inference rule. We introduce how the bf-EVALPSN-based reasoning system can be applied to the safety verification for process order.

Application of paraconsistent annotated logic program bf-EVALPSN to intelligent control

A paraconsistent annotated logic program called EVALPSN has been developed for dealing with defeasible deontic reasoning and plausible reasoning, and applied to various kinds of intelligent control and safety verification. Moreover, in order to deal with before-after relation between processes(time intervals), bf(before-after)-EVALPSN has also been developed recently. In this paper, we review the reasoning system for before-after relation between processes based on bf- EVALPSN. The system consists of two groups of inference rules in bf-EVALPSN called basic and transitive bf-inference rules. The application of the reasoning system to real-time process order control is introduced with simple examples.