Paraconsistência em lógica híbrida

Mestrado em Matemática e AplicaçõesThe use of hybrid logics allows the description of relational structures, at the same time that allows establishing accessibility relations between states and, furthermore, nominating and making mention to what happens at speci c states. However, the information we collect is subject to inconsistencies, namely, the search for di erent information sources can lead us to pick up contradictions. Nowadays, by having so many means of dissemination available, that happens frequently. The aim of this work is to develop tools capable of dealing with contradictory information that can be described as hybrid logics' formulas. To build models, to compare inconsistency in di erent databases, and to see the applicability of this method in day-to-day life are the basis for the development of this dissertation.O uso de lógicas híbridas permite a descrição de estruturas relacionais, ao mesmo tempo que permite estabelecer relações de acessibilidade entre estados, e, para além disso, nomear e fazer referência ao que acontece em estados específicos. No entanto, a informação que recolhemos está sujeita a inconsistências, isto é, a procura de diferentes fontes de informação pode levar a recolha de contradições. O que nos dias de hoje, com tantos meios de divulgação disponíveis, acontece frequentemente. O objetivo deste trabalho e desenvolver ferramentas capazes de lidar com informação contraditória que possa ser descrita através de fórmulas de lógicas híbridas. Construir modelos e comparar a inconsistência de diferentes bases de dados e ver a aplicabilidade deste método no dia-a-dia são a base para o desenvolvimento desta dissertação

Organizational climate assessment using the paraconsistent decision method

The present study aims to show a model of organisational climate assessment through the integration of success codes and the Paraconsistent Decision Method. In this way, contributing to a previous scenario analysis that can return a more precise feedback of the organisational culture conditions of the organisation.(3ortugal

PEMANFATAAN ALGORITMA JARINGAN SYARAF TIRUAN LEVENBERG MARQUADT UNTUK MENDETEKSI PENYAKIT ALZHEIMER

Analisis visual EEG sangat berguna dalam membantu diagnosis penyakit Alzheimer (AD) ketika diagnosis yang dilakukan sesuai protocol klinis masih belum pasti. Namum, beberapa analisis bergantung pada ketidaktepatan peralatan yang melekat, patient movement, electrical record, interpretasi dokter terhadap variasi analisis visual. Jaringan syaraf tiruan (JST) dapat menjadi tool yang sangat baik dalam melakukan prediksi dan pengenalan pola. Pada penelitian ini menggunakan JST levenberg marquadt (LM) yang merupakan pengembangan JST backpropagation standar, yang mampu menangani masalah informasi yang tidak tepat, tidak pasti, dan tidak komplit untuk mengenali pola yang ditentukan EEG untuk mengkaji nilai EEG sebagai metode tambahan yang mungkin untuk mendiagnosa AD. Diperoleh EEG record dari 33 pasien dengan penyakit Alzheimer dan 34 pasien control yang diambil pada saat santai. Hasil analisis EEG dari data tersebut yang bekerja antara 8.0 dan 12.0 Hz (dengan frekuensi rata-rata 10Hz), mengizinkan range 0.5 Hz menetapkan sebagai pola pasien normal. JST LM dapat mengenali gelombang milik masing-masing band of clinical yang digunakan (teta, beta, alfa, dan delta), terutama untuk kesepakatan diagnosis klinis dengan sensitivity 88%, dan specivity 79 % serta akurasi 78

Universal Logic and the Geography of Thought - Reflections on logical pluralism in the light of culture

The aim of this dissertation is to provide an analysis for those involved and interested in the interdisciplinary study of logic, particularly Universal Logic. While continuing to remain aware of the importance of the central issues of logic, we hope that the factor of culture is also given serious consideration. Universal Logic provides a general theory of logic to study the most general and abstract properties of the various possible logics. As well as elucidating the basic knowledge and necessary definitions, we would especially like to address the problems of motivation concerning logical investigations in different cultures. First of all, I begin by considering Universal Logic as understood by Jean-Yves Béziau, and examine the basic ideas underlying the Universal Logic project. The basic approach, as originally employed by Universal Logicians, is introduced, after which the relationship between algebras and logics at an abstract level is discussed, i.e., Universal Algebra and Universal Logic. Secondly,I focus on a discussion of the translation paradox , which will enable readers to become more familiar with the new subject of logical translation, and subsequently comprehensively summarize its development in the literature. Besides helping readers to become more acquainted with the concept of logical translation, the discussion here will also attempt to formulate a new direction in support of logical pluralism as identified by Ruldof Carnap (1934), JC Beall and Greg Restall (2005), respectively. Thirdly, I provide a discussion of logical pluralism. Logical pluralism can be traced back to the principle of tolerance raised by Ruldof Carnap (1934), and readers will gain a comprehensive understanding of this concept from the discussion. Moreover,an attempt will be made to clarify the real and important issues in the contemporary debate between pluralism and monism within the field of logic in general. Fourthly, I study the phenomena of cultural-difference as related to the geography of thought. Two general systems in the geography of thought are distinguished, which we here call thought-analytic and thought-holistic. They are proposed to analyze and challenge the universality assumption regarding cognitive processes. People from different cultures and backgrounds have many differences in diverse areas, and these differences, if taken for granted, have proven particularly problematic in understanding logical thinking across cultures. Interestingly, the universality of cognitive processes has been challenged, especially by Richard Nisbett s research in cultural psychology. With respect to these concepts, C-UniLog can also be considered in relation to empirical evidence obtained by Richard Nisbett et al. In the final stage of this dissertation, I will propose an interpretation of the concept of logical translation, i.e., translations between formal logical mode (as cognitive processes in the case of westerners) and dialectical logical mode (as cognitive processes in the case of Asians). From this, I will formulate a new interpretation of the principle of tolerance, as well as of logical pluralism

Deep learning for surface electromyography artifact contamination type detection

The quality of surface Electromyography (sEMG) signals could be an issue if highly contaminated by Power Line Interference (PLI), Electrocardiogram signal (ECG), Movement Artifact (MOA) or White Gaussian Noise (WGN), that could lead to unsafe operation of devices that is controlled by sEMG data, such as electro-mechanical prothesis. There are some mitigation methods proposed for some specifics sEMG contaminants and to use these methods in an efficient way is important to identify the contaminant in the sEMG signal. In this work we propose the use of a Recurrent Neural Network (RNN) using Long Short-Term Memory (LSTM) units in the hidden layer with no need of features extraction with the objective to classify the signal directly from sequences of the band-pass filtered data. The method proposed use the NinaPro database with amputee and non-amputee subjects. Only non-amputee subjects are used for parameters selection and then tested on both databases. The results show that 98% of the non-contaminated sEMG data was corrected classified and more than 95% of the contaminants were identified inside the training SNR range. Also, in this work is presented a SNR sensibility control and the contamination analysis in the range from −40 dB to 40 dB in 10 dB steps. The conclusion is that is possible to classify the contamination type in sEMG signals with a RNN-LSTM with a 112.5 ms time window and to predicted with a small error the classification hit rate for each SNR level in some cases

Virtual sensor of surface electromyography in a new extensive fault-tolerant classification system

A few prosthetic control systems in the scientific literature obtain pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is being used in other fields of research. The virtual sensor technique applied to surface electromyography can help to minimize these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by computational intelligent systems. This paper presents a virtual sensor in a new extensive fault-tolerant classification system to maintain the classification accuracy after the occurrence of the following contaminants: ECG interference, electrode displacement, movement artifacts, power line interference, and saturation. The Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman filter (TVK) models are compared to define the most robust model for the virtual sensor. Results of movement classification were presented comparing the usual classification techniques with the method of the degraded signal replacement and classifier retraining The experimental results were evaluated for these five noise types in 16 surface electromyography (sEMG) channel degradation case studies. The proposed system without using classifier retraining techniques recovered of mean classification accuracy was of 4% to 38% for electrode displacement, movement artifacts, and saturation noise. The best mean classification considering all signal contaminants and channel combinations evaluated was the classification using the retraining method, replacing the degraded channel by the virtual sensor TVARMA model. This method recovered the classification accuracy after the degradations, reaching an average of 5.7% below the classification of the clean signal, that is the signal without the contaminants or the original signal. Moreover, the proposed intelligent technique minimizes the impact of the motion classification caused by signal contamination related to degrading events over time. There are improvements in the virtual sensor model and in the algorithm optimization that need further development to provide an increase the clinical application of myoelectric prostheses but already presents robust results to enable research with virtual sensors on biological signs with stochastic behavior

Daftar Ebook Engineering Science Terbitan Springer Tahun 2018

Artikel ini memuat daftar judul ebook bidang ilmu teknik yang diterbitkan oleh Springer pada tahun 2018 yang dimiliki oleh Unand

Uso de técnicas de aprendizaje automatizado para predicción de morfología mandibular en clase I, II y III esquelética

Las técnicas de aprendizaje automatizado se emplean principalmente para clasificar y predecir datos en diferentes aplicaciones. El objetivo de esta investigación fue predecir a través de estos métodos la morfología mandibular en maloclusiones Clase I, Clase II y Clase III esquelética, empleando medidas craneomaxilares. Se recolectaron 229 radiografías posteroanteriores y de perfil de adultos jóvenes colombianos de ambos sexos. Se emplearon coordenadas de landmarks óseos para formar variables craneomaxilares y mandibulares. Se probó inicialmente la clasificación de maloclusiones esqueléticas por medio de una máquina de vectores de soporte con un kernel lineal, excluyendo las variables mandibulares. En las radiografías posteroanteriores tuvo una precisión del 66%, clasificando en 71.43%, 70% y 60.87% para la Clase I, II y III. Para las radiografías de perfil, la precisión fue de 74.51%, con un 62.50%, 77.78% y 82.35% en la Clase I, II y III, definida por los atributos ENP-A-Pr, Zm-A-Pr, Te-Pr-A, Pr-A-Te, RhiA-Pr, A-Pr-Rhi, A-Te-Pr, A-N-Pr, N-Pr-A, Pr-A-N. En predicción, se usaron variables mandibulares específicas a partir de medidas craneofaciales seleccionadas evaluándose por medio de un coeficiente de correlación a través de una ridge regression; las variables Cdd-God, Cdd-Me, Cdi-Cdd, Cdi-Me y Goi-God tuvieron un r de 0.72, 0.82, 0.77, 0.86 y 0.76 con las redes neuronales en las radiografías posteroanteriores. Y en las radiografías de perfil, las medidas Gn-Id, Cd-Go-Gn, Gn-B, Gn-Pg, Go-Gn, Go-Me, Id-Gn-Go, Pg-B, Cd-Go-Gn, Gn-B, Gn-Pg, Go-Gn, Go-Me, Id-Gn-Go y Pg-B obtuvieron coeficientes de 0.95, 0.99, 0.95, 0.84, 0.91, 0.89, 0.93, 0.84, 0.95, 0.93, 0.98, 0.86, 0.84, 0.88, 0.96, 0.96 y 0.92 respectivamente. Las técnicas de aprendizaje automatizado en especial las redes neuronales, demostraron una precisión relevante que podría tener importancia en la reconstrucción facial para el proceso de individualización.Abstract. Learning machine techniques are used primarily to classify and predict data in different applications. The aim of this study was to predict through these techniques mandibular morphology in skeletal malocclusion Class I, Class II and Class III, by using craniomaxillary measurements. 229 posteroanterior and lateral cephalograms of Colombian young adults of both sexes were collected. Coordinates of landmarks were used to create mandibular and cranio-maxillary attributes. Skeletal malocclusions classification using a support vector machine with a linear kernel was initially performed. An accuracy of 66% was found in posteroanterior cephalograms, with a classification percentage of 71.43%, 70% and 60.87% in Class I, Class II and Class III respectively. An accuracy of 74.51% was found in lateral cephalograms with a classification percentage of 62.50%, 77.78% and 82.35% in Class I, Class II and Class III respectively, defined by ENP-A-Pr, Zm-APr, Te-Pr-A, Pr-A-Te, Rhi-A-Pr, A-Pr-Rhi, A-Te-Pr, A-N-Pr, N-Pr-A and Pr-A-N angles. In prediction, specific mandibular variables from selected craniofacial measurements were used, an artificial neural network was applied, and a ridge regression was employed in order to access the prediction. Cdd-God, Cdd-Me, Cdi-Cdd, Cdi-Me and Goi-God had the biggest coefficient correlations of 0.72, 0.82, 0.77, 0.86 and 0.76 respectively in posteroanterior cephalograms. In lateral cephalograms the best coefficient correlations were 0.95, 0.99, 0.95, 0.84, 0.91, 0.89, 0.93, 0.84, 0.95, 0.93, 0.98, 0.86, 0.84, 0.88, 0.96, 0.96 and 0.92 in the attributes: Gn-Id, Cd-Go-Gn, Gn-B, Gn-Pg, Go-Gn, Go-Me, IdGn-Go, Pg-B, Cd-Go-Gn, Gn-B, Gn-Pg, Go-Gn, Go-Me, Id-Gn-Go and Pg-B, respectively. Automated learning techniques especially artificial neural networks demonstrated a significant performance, which could become important in facial reconstruction for the individualization process.Maestrí