Neural & Fuzzy Logic On Linguistic Variable.
Modus Ponens,Modus Tollens,Fuzzy Implication Operators
Fuzzy Inference,Fuzzy Proposition,Linguistic variable etc are described here
How can you deal with Fuzzy Logic. Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree
between 0 and 1
Natural language processing (NLP) is a subfield of artificial intelligence that aims to allow computers to understand human language. NLP involves analyzing and representing text or speech at different linguistic levels for applications like question answering or machine translation. Challenges for NLP include ambiguities in language like lexical, syntactic, semantic, and anaphoric ambiguities. Common NLP tasks include part-of-speech tagging, parsing, named entity recognition, and sentiment analysis. Applications of NLP include text processing, machine translation, speech processing, and converting text to speech.
This document provides an overview of fuzzy logic. It begins by defining fuzzy as not being clear or precise, unlike classical sets which have clear boundaries. It then explains fuzzy logic allows for partial set membership rather than binary membership. The document outlines fuzzy logic's ability to model imprecise or nonlinear systems using natural language-based rules. It details the key concepts of fuzzy logic including linguistic variables, membership functions, fuzzy set operations, fuzzy inference systems and the 5-step fuzzy inference process of fuzzifying inputs, applying fuzzy operations and implications, aggregating outputs and defuzzifying results.
Finite-state morphological parsing uses finite-state transducers to parse words into their morphological components like stems and affixes. It requires a lexicon of stems and affixes, morphotactic rules describing valid morpheme combinations, and orthographic rules for spelling changes. The parser is built as a cascade of finite-state automata representing the lexicon, morphotactics and spelling rules. It maps surface word forms onto their underlying lexical representations including stems and morphological features. This allows morphological analysis of both regular and irregular forms.
Defuzzification is the process of producing a quantifiable result in Crisp logic, given fuzzy sets and corresponding membership degrees. It is the process that maps a fuzzy set to a crisp set. It is typically needed in fuzzy control systems.
Fuzzy logic is a form of logic that accounts for partial truth and intermediate values between true and false. It is used in control systems to mimic how humans apply fuzzy concepts like "cold" or "hot" temperature. Some key applications of fuzzy logic include temperature controllers, washing machines, air conditioners, and anti-lock braking systems. Fuzzy logic controllers use if-then rules to determine outputs based on fuzzy inputs and degrees of membership rather than binary logic.
This document provides an overview of fuzzy logic, including its origins, key concepts, and applications. It discusses how fuzzy logic allows for approximate reasoning and decision making under uncertainty by using linguistic variables and fuzzy set theory. Membership functions are used to characterize fuzzy sets and assign degrees of truth between 0 and 1 rather than binary true/false values. Common fuzzy logic operations like intersection, union, and complement are also covered. Finally, some examples of fuzzy logic control systems are presented, including how they are designed using fuzzy rule bases and inference methods like Mamdani and Sugeno.
Fuzzy logic was introduced by Lotfi Zadeh in 1965 to address problems with classical logic being too precise. Fuzzy logic allows for truth values between 0 and 1 rather than binary true/false. It involves fuzzy sets, membership functions, linguistic variables, and fuzzy rules. Fuzzy logic can be applied to knowledge representation and inference using concepts like fuzzy predicates, relations, modifiers and quantifiers. It has various applications including household appliances, animation, industrial automation, and more.
How can you deal with Fuzzy Logic. Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree
between 0 and 1
Natural language processing (NLP) is a subfield of artificial intelligence that aims to allow computers to understand human language. NLP involves analyzing and representing text or speech at different linguistic levels for applications like question answering or machine translation. Challenges for NLP include ambiguities in language like lexical, syntactic, semantic, and anaphoric ambiguities. Common NLP tasks include part-of-speech tagging, parsing, named entity recognition, and sentiment analysis. Applications of NLP include text processing, machine translation, speech processing, and converting text to speech.
This document provides an overview of fuzzy logic. It begins by defining fuzzy as not being clear or precise, unlike classical sets which have clear boundaries. It then explains fuzzy logic allows for partial set membership rather than binary membership. The document outlines fuzzy logic's ability to model imprecise or nonlinear systems using natural language-based rules. It details the key concepts of fuzzy logic including linguistic variables, membership functions, fuzzy set operations, fuzzy inference systems and the 5-step fuzzy inference process of fuzzifying inputs, applying fuzzy operations and implications, aggregating outputs and defuzzifying results.
Finite-state morphological parsing uses finite-state transducers to parse words into their morphological components like stems and affixes. It requires a lexicon of stems and affixes, morphotactic rules describing valid morpheme combinations, and orthographic rules for spelling changes. The parser is built as a cascade of finite-state automata representing the lexicon, morphotactics and spelling rules. It maps surface word forms onto their underlying lexical representations including stems and morphological features. This allows morphological analysis of both regular and irregular forms.
Defuzzification is the process of producing a quantifiable result in Crisp logic, given fuzzy sets and corresponding membership degrees. It is the process that maps a fuzzy set to a crisp set. It is typically needed in fuzzy control systems.
Fuzzy logic is a form of logic that accounts for partial truth and intermediate values between true and false. It is used in control systems to mimic how humans apply fuzzy concepts like "cold" or "hot" temperature. Some key applications of fuzzy logic include temperature controllers, washing machines, air conditioners, and anti-lock braking systems. Fuzzy logic controllers use if-then rules to determine outputs based on fuzzy inputs and degrees of membership rather than binary logic.
This document provides an overview of fuzzy logic, including its origins, key concepts, and applications. It discusses how fuzzy logic allows for approximate reasoning and decision making under uncertainty by using linguistic variables and fuzzy set theory. Membership functions are used to characterize fuzzy sets and assign degrees of truth between 0 and 1 rather than binary true/false values. Common fuzzy logic operations like intersection, union, and complement are also covered. Finally, some examples of fuzzy logic control systems are presented, including how they are designed using fuzzy rule bases and inference methods like Mamdani and Sugeno.
Fuzzy logic was introduced by Lotfi Zadeh in 1965 to address problems with classical logic being too precise. Fuzzy logic allows for truth values between 0 and 1 rather than binary true/false. It involves fuzzy sets, membership functions, linguistic variables, and fuzzy rules. Fuzzy logic can be applied to knowledge representation and inference using concepts like fuzzy predicates, relations, modifiers and quantifiers. It has various applications including household appliances, animation, industrial automation, and more.
Fuzzy logic is a form of multivalued logic that allows intermediate values between conventional evaluations like true/false, yes/no, or 0/1. It provides a mathematical framework for representing uncertainty and imprecision in measurement and human cognition. The document discusses the history of fuzzy logic, key concepts like membership functions and linguistic variables, common fuzzy logic operations, and applications in fields like control systems, home appliances, and cameras. It also notes some drawbacks like difficulty in tuning membership functions and potential confusion with probability theory.
Presentation on "Knowledge acquisition & validation"Aditya Sarkar
Presentation on "Knowledge acquisition and validation made and presented by Aditya Sarkar, I took the help of different sources available on internet to make all understand how a knowledge is acquired?. I hope this presentation will help everyone.
Understanding Fuzzy Logic in Washing Machine.
How fuzzy logic control washing time based on the user inputs.
Use of Matlab for creating Fuzzy Diagrams.
An Arduino prototype to demonstrate the working of washing machine based on time input by user.
i will provide Arduino code link as soon as possible.
Fuzzy inference systems use fuzzy logic to map inputs to outputs. There are two main types:
Mamdani systems use fuzzy outputs and are well-suited for problems involving human expert knowledge. Sugeno systems have faster computation using linear or constant outputs.
The fuzzy inference process involves fuzzifying inputs, applying fuzzy logic operators, and using if-then rules. Outputs are determined through implication, aggregation, and defuzzification. Mamdani systems find the centroid of fuzzy outputs while Sugeno uses weighted averages, making it more efficient.
This document discusses fuzzy logic, beginning with its origins in ancient Greece and formalization in 1965 by Lotfi Zadeh. It explains fuzzy logic represents concepts with overlapping membership functions rather than binary logic. Fuzzy logic and neural networks both model human reasoning but fuzzy logic uses linguistic rules while neural networks learn from examples. Fuzzy logic has applications in control systems like temperature controllers and anti-lock braking systems to handle nonlinear dynamics. It is used in other fields like business and expert systems to represent subjective concepts.
Here is a MATLAB program to implement logic functions using a McCulloch-Pitts neuron:
% McCulloch-Pitts neuron for logic functions
% Inputs
x1 = 1;
x2 = 0;
% Weights
w1 = 1;
w2 = 1;
% Threshold
theta = 2;
% Net input
net = x1*w1 + x2*w2;
% Activation function
if net >= theta
y = 1;
else
y = 0;
end
% Output
disp(y)
This implements a basic AND logic gate using a McCulloch-Pitts neuron.
This document discusses embedded systems. It defines an embedded system as a microprocessor-based system designed to perform dedicated functions. Embedded systems are found in devices ranging from household appliances to spacecraft. The document discusses the history of embedded systems and how they have evolved from using microprocessors to typically using microcontrollers. It also discusses the hardware and software components of embedded systems as well as common programming languages. Examples of different types of embedded systems are provided.
This document provides an overview of predicate logic and various techniques for representing knowledge and drawing inferences using predicate logic, including:
- Representing facts as logical statements using predicates, variables, and quantifiers.
- Distinguishing between propositional logic and predicate logic and their abilities to represent objects and relationships.
- Techniques like resolution and Skolem functions that allow inferring new statements from existing ones in a logical and systematic way.
- How computable functions and predicates allow representing relationships that have infinitely many instances, like greater-than, in a computable way.
The document discusses these topics at a high-level and provides examples to illustrate key concepts in predicate logic and automated reasoning.
The document discusses the origins and evolution of fuzzy logic, beginning with fuzzy set theory proposed by Zadeh in 1965 which aimed to represent vagueness in natural language using fuzzy sets with non-crisp boundaries. It explains key concepts in fuzzy logic like membership functions, fuzzy set operations, fuzzy relations and compositions. The document also compares classical sets with crisp boundaries to fuzzy sets and contrasts crisp logic with fuzzy logic which allows for degrees of truth between 0 and 1.
This document presents a presentation on fuzzy expert systems. It introduces expert systems and how they combine human expertise with computational capabilities. It then discusses the evolution of fuzzy expert systems to handle imprecision and uncertainty. The key components of a fuzzy expert system are described, including the knowledge base, inference engine, and user interface. Steps for constructing a fuzzy expert system are outlined, from knowledge representation to testing rules. Pros and cons as well as applications in various domains like agriculture, education, and medicine are also summarized.
1. Planning involves finding a sequence of actions that achieves a goal starting from an initial state. It uses a set of operators that define the possible actions and their effects.
2. A plan is a sequence of operator instances that transforms the initial state into a goal state. Classical planning assumes fully observable, deterministic environments.
3. Planning problems can be represented using a logical language that describes states, goals, actions and their preconditions and effects. This representation allows planning algorithms to operate over problems.
This document discusses the application of fuzzy logic to optimal capacitor placement in distribution systems. It begins with definitions of fuzzy logic and fuzzy sets. It then describes the key components of a fuzzy logic system including fuzzification, fuzzy inference rules, and defuzzification. It proposes using power loss reduction index and bus voltage as input variables, and capacitor placement suitability index as the output variable, to determine the optimal locations and sizes of capacitors. The goal is to minimize power losses and maximize annual savings using fuzzy logic techniques.
The document discusses various concepts related to finite automata. It begins by defining a finite automaton as a mathematical model of a system with discrete inputs and outputs that can be in a finite number of states. A finite automaton consists of a finite set of states and transitions between states based on input symbols. The document then discusses formal languages, the functions of a head pointer and finite control, the two main types of finite automata (DFA and NFA), ways to represent automata, definitions of languages and transitions, regular expressions and languages, two-way finite automata, epsilon closure, equivalence of NFAs and DFAs, Moore and Mealy machines, and applications of finite automata such as lexical analysis.
The document discusses planning and problem solving in artificial intelligence. It describes planning problems as finding a sequence of actions to achieve a given goal state from an initial state. Common assumptions in planning include atomic time steps, deterministic actions, and a closed world. Blocks world examples are provided to illustrate planning domains and representations using states, goals, and operators. Classical planning approaches like STRIPS are summarized.
This document provides an overview of genetic algorithms. It discusses that genetic algorithms are a type of evolutionary algorithm inspired by biological evolution that is used to find optimal or near-optimal solutions to problems by mimicking natural selection. The document outlines the basic concepts of genetic algorithms including encoding, representation, search space, fitness functions, and the main operators of selection, crossover and mutation. It also provides examples of applications in bioinformatics and highlights advantages like being easy to understand while also noting potential disadvantages like requiring more computational time.
Fuzzy logic provides a means of calculating intermediate values between absolute true and absolute false. It allows partial set membership and handles imprecise data. Fuzzy logic systems use membership functions to determine the degree to which inputs belong to sets and fuzzy inference systems to map inputs to outputs. Fuzzy logic has applications in devices like washing machines and cameras that require handling imprecise variables.
This document presents information on fuzzy arithmetic and operations. It discusses fuzzy numbers, linguistic variables, and arithmetic operations on fuzzy intervals and fuzzy numbers. Some key points:
- Fuzzy numbers are fuzzy sets with certain properties like being normal, having closed interval alpha-cuts, and bounded support.
- Linguistic variables assign linguistic values like "young" or "old" to numerical variables. They are represented as fuzzy sets.
- Arithmetic operations on fuzzy intervals are defined based on the corresponding operations on their alpha-cuts, which are closed intervals. Properties like commutativity and distributivity are discussed.
- Operations on fuzzy numbers are similarly defined based on the alpha-cuts of the resulting fuzzy
This document provides an overview of natural language processing and planning topics including:
- NLP tasks like parsing, machine translation, and information extraction.
- The components of a planning system including the planning agent, state and goal representations, and planning techniques like forward and backward chaining.
- Methods for natural language processing including pattern matching, syntactic analysis, and the stages of NLP like phonological, morphological, syntactic, semantic, and pragmatic analysis.
The document describes qualitative research conducted on the first draft of a trailer. The researcher interviewed 4-6 people and asked them 6 questions to gather feedback on the trailer. The questions covered understanding, music, genre, shots, likes/dislikes, and interest in the full film. Interviews provided varied feedback, with some finding it confusing and others understanding. Feedback noted shots could be clearer and narrative straighter. The researcher concluded the narrative needs to be clearer through more voiceover and shots to make it easier to understand, which will be the focus of improving the trailer.
ARC 2615 INTERNSHIP TRAINING FINAL REPORTRyan Kerry Jy
The intern gained knowledge of the planning and building submission process through working on various projects at an architecture firm over 8 weeks. They were involved in 2 main projects - an interior exhibition design for a heritage shophouse, and renovations for a terrace house. They also assisted with other projects such as housing developments, office renovations, and submission drawings. The intern learned about project documentation, 3D modeling, site analysis, and the workflow within an architecture office.
Fuzzy logic is a form of multivalued logic that allows intermediate values between conventional evaluations like true/false, yes/no, or 0/1. It provides a mathematical framework for representing uncertainty and imprecision in measurement and human cognition. The document discusses the history of fuzzy logic, key concepts like membership functions and linguistic variables, common fuzzy logic operations, and applications in fields like control systems, home appliances, and cameras. It also notes some drawbacks like difficulty in tuning membership functions and potential confusion with probability theory.
Presentation on "Knowledge acquisition & validation"Aditya Sarkar
Presentation on "Knowledge acquisition and validation made and presented by Aditya Sarkar, I took the help of different sources available on internet to make all understand how a knowledge is acquired?. I hope this presentation will help everyone.
Understanding Fuzzy Logic in Washing Machine.
How fuzzy logic control washing time based on the user inputs.
Use of Matlab for creating Fuzzy Diagrams.
An Arduino prototype to demonstrate the working of washing machine based on time input by user.
i will provide Arduino code link as soon as possible.
Fuzzy inference systems use fuzzy logic to map inputs to outputs. There are two main types:
Mamdani systems use fuzzy outputs and are well-suited for problems involving human expert knowledge. Sugeno systems have faster computation using linear or constant outputs.
The fuzzy inference process involves fuzzifying inputs, applying fuzzy logic operators, and using if-then rules. Outputs are determined through implication, aggregation, and defuzzification. Mamdani systems find the centroid of fuzzy outputs while Sugeno uses weighted averages, making it more efficient.
This document discusses fuzzy logic, beginning with its origins in ancient Greece and formalization in 1965 by Lotfi Zadeh. It explains fuzzy logic represents concepts with overlapping membership functions rather than binary logic. Fuzzy logic and neural networks both model human reasoning but fuzzy logic uses linguistic rules while neural networks learn from examples. Fuzzy logic has applications in control systems like temperature controllers and anti-lock braking systems to handle nonlinear dynamics. It is used in other fields like business and expert systems to represent subjective concepts.
Here is a MATLAB program to implement logic functions using a McCulloch-Pitts neuron:
% McCulloch-Pitts neuron for logic functions
% Inputs
x1 = 1;
x2 = 0;
% Weights
w1 = 1;
w2 = 1;
% Threshold
theta = 2;
% Net input
net = x1*w1 + x2*w2;
% Activation function
if net >= theta
y = 1;
else
y = 0;
end
% Output
disp(y)
This implements a basic AND logic gate using a McCulloch-Pitts neuron.
This document discusses embedded systems. It defines an embedded system as a microprocessor-based system designed to perform dedicated functions. Embedded systems are found in devices ranging from household appliances to spacecraft. The document discusses the history of embedded systems and how they have evolved from using microprocessors to typically using microcontrollers. It also discusses the hardware and software components of embedded systems as well as common programming languages. Examples of different types of embedded systems are provided.
This document provides an overview of predicate logic and various techniques for representing knowledge and drawing inferences using predicate logic, including:
- Representing facts as logical statements using predicates, variables, and quantifiers.
- Distinguishing between propositional logic and predicate logic and their abilities to represent objects and relationships.
- Techniques like resolution and Skolem functions that allow inferring new statements from existing ones in a logical and systematic way.
- How computable functions and predicates allow representing relationships that have infinitely many instances, like greater-than, in a computable way.
The document discusses these topics at a high-level and provides examples to illustrate key concepts in predicate logic and automated reasoning.
The document discusses the origins and evolution of fuzzy logic, beginning with fuzzy set theory proposed by Zadeh in 1965 which aimed to represent vagueness in natural language using fuzzy sets with non-crisp boundaries. It explains key concepts in fuzzy logic like membership functions, fuzzy set operations, fuzzy relations and compositions. The document also compares classical sets with crisp boundaries to fuzzy sets and contrasts crisp logic with fuzzy logic which allows for degrees of truth between 0 and 1.
This document presents a presentation on fuzzy expert systems. It introduces expert systems and how they combine human expertise with computational capabilities. It then discusses the evolution of fuzzy expert systems to handle imprecision and uncertainty. The key components of a fuzzy expert system are described, including the knowledge base, inference engine, and user interface. Steps for constructing a fuzzy expert system are outlined, from knowledge representation to testing rules. Pros and cons as well as applications in various domains like agriculture, education, and medicine are also summarized.
1. Planning involves finding a sequence of actions that achieves a goal starting from an initial state. It uses a set of operators that define the possible actions and their effects.
2. A plan is a sequence of operator instances that transforms the initial state into a goal state. Classical planning assumes fully observable, deterministic environments.
3. Planning problems can be represented using a logical language that describes states, goals, actions and their preconditions and effects. This representation allows planning algorithms to operate over problems.
This document discusses the application of fuzzy logic to optimal capacitor placement in distribution systems. It begins with definitions of fuzzy logic and fuzzy sets. It then describes the key components of a fuzzy logic system including fuzzification, fuzzy inference rules, and defuzzification. It proposes using power loss reduction index and bus voltage as input variables, and capacitor placement suitability index as the output variable, to determine the optimal locations and sizes of capacitors. The goal is to minimize power losses and maximize annual savings using fuzzy logic techniques.
The document discusses various concepts related to finite automata. It begins by defining a finite automaton as a mathematical model of a system with discrete inputs and outputs that can be in a finite number of states. A finite automaton consists of a finite set of states and transitions between states based on input symbols. The document then discusses formal languages, the functions of a head pointer and finite control, the two main types of finite automata (DFA and NFA), ways to represent automata, definitions of languages and transitions, regular expressions and languages, two-way finite automata, epsilon closure, equivalence of NFAs and DFAs, Moore and Mealy machines, and applications of finite automata such as lexical analysis.
The document discusses planning and problem solving in artificial intelligence. It describes planning problems as finding a sequence of actions to achieve a given goal state from an initial state. Common assumptions in planning include atomic time steps, deterministic actions, and a closed world. Blocks world examples are provided to illustrate planning domains and representations using states, goals, and operators. Classical planning approaches like STRIPS are summarized.
This document provides an overview of genetic algorithms. It discusses that genetic algorithms are a type of evolutionary algorithm inspired by biological evolution that is used to find optimal or near-optimal solutions to problems by mimicking natural selection. The document outlines the basic concepts of genetic algorithms including encoding, representation, search space, fitness functions, and the main operators of selection, crossover and mutation. It also provides examples of applications in bioinformatics and highlights advantages like being easy to understand while also noting potential disadvantages like requiring more computational time.
Fuzzy logic provides a means of calculating intermediate values between absolute true and absolute false. It allows partial set membership and handles imprecise data. Fuzzy logic systems use membership functions to determine the degree to which inputs belong to sets and fuzzy inference systems to map inputs to outputs. Fuzzy logic has applications in devices like washing machines and cameras that require handling imprecise variables.
This document presents information on fuzzy arithmetic and operations. It discusses fuzzy numbers, linguistic variables, and arithmetic operations on fuzzy intervals and fuzzy numbers. Some key points:
- Fuzzy numbers are fuzzy sets with certain properties like being normal, having closed interval alpha-cuts, and bounded support.
- Linguistic variables assign linguistic values like "young" or "old" to numerical variables. They are represented as fuzzy sets.
- Arithmetic operations on fuzzy intervals are defined based on the corresponding operations on their alpha-cuts, which are closed intervals. Properties like commutativity and distributivity are discussed.
- Operations on fuzzy numbers are similarly defined based on the alpha-cuts of the resulting fuzzy
This document provides an overview of natural language processing and planning topics including:
- NLP tasks like parsing, machine translation, and information extraction.
- The components of a planning system including the planning agent, state and goal representations, and planning techniques like forward and backward chaining.
- Methods for natural language processing including pattern matching, syntactic analysis, and the stages of NLP like phonological, morphological, syntactic, semantic, and pragmatic analysis.
The document describes qualitative research conducted on the first draft of a trailer. The researcher interviewed 4-6 people and asked them 6 questions to gather feedback on the trailer. The questions covered understanding, music, genre, shots, likes/dislikes, and interest in the full film. Interviews provided varied feedback, with some finding it confusing and others understanding. Feedback noted shots could be clearer and narrative straighter. The researcher concluded the narrative needs to be clearer through more voiceover and shots to make it easier to understand, which will be the focus of improving the trailer.
ARC 2615 INTERNSHIP TRAINING FINAL REPORTRyan Kerry Jy
The intern gained knowledge of the planning and building submission process through working on various projects at an architecture firm over 8 weeks. They were involved in 2 main projects - an interior exhibition design for a heritage shophouse, and renovations for a terrace house. They also assisted with other projects such as housing developments, office renovations, and submission drawings. The intern learned about project documentation, 3D modeling, site analysis, and the workflow within an architecture office.
1) O estudo estimou a prevalência de Transtorno de Estresse Pós-Traumático (TEPT) em bombeiros de Belo Horizonte como 6,9% e encontrou associação entre o TEPT e variáveis ocupacionais como eventos traumáticos no trabalho, fatores psicossociais, tempo de serviço e absenteísmo.
2) Fatores não-ocupacionais como idade, problemas de saúde mental e eventos de vida adversos também se associaram ao TEPT.
3) Os resultados sugerem que tanto estressores ocupacionais
This document discusses several basic source documents used for payroll, including time cards, time books, electronic clock-in cards, and fingerprint time clocks. Time cards are used to record an employee's arrival and departure times each day and are used by HR to prepare payroll. Time books record the time workers spend on jobs. Electronic clock-in cards and fingerprint time clocks also track employee hours worked. Additionally, an Employee Earnings Record Card is kept for each employee and summarizes gross pay, deductions, and net pay after each payroll period.
El documento resume varios lugares de interés en Cataluña, incluyendo el delta del Ebro en Tarragona, ruinas romanas en la capital, una exposición del pintor Sorolla en Barcelona, playas en Salou y Cambrils, el mercado de la Boquería en Barcelona, la catedral de Santa Eulalia y su claustro, fiestas populares con gigantes y castellers, y la basílica de la Sagrada Familia de Gaudí.
This document discusses several basic source documents used for payroll, including time cards, time books, electronic clock-in cards, and fingerprint time clocks. Time cards are used to record an employee's arrival and departure times each day and are used by HR to prepare payroll. Time books record the time workers spend on jobs. Electronic clock-in cards and fingerprint time clocks also track employee hours worked. Additionally, an Employee Earnings Record Card is kept for each employee and summarizes gross pay, deductions, and net pay after each payroll period.
This document contains slides from a lecture on C# programming. It discusses the history and evolution of the C# language, including details about different versions from C# 1.0 to the proposed C# 7.0 features. It also covers core C# concepts like data types, variables, operators, and structures. The slides are intended to teach a CSC 313 course on visual programming using the C# language.
Linguistic and Applied linguistic contribution to English TeachingKing Saud University
Linguistics is the scientific study of language, divided into theoretical and applied fields. Theoretical linguistics includes phonetics, phonology, morphology, syntax, semantics, and pragmatics. Applied linguistics applies linguistic theories to solve practical problems and is interdisciplinary, drawing from fields like psychology and education. It is concerned with language teaching, learning, and use. Key areas include second language acquisition, teaching methodology, assessment, translation, and forensic linguistics. Applied linguists use theories but are consumers not producers of theories.
Phonetics is the study of how sounds are produced using parts of the body like lips, teeth, tongue, pharynx and lungs. It focuses on the relationship between these articulators in producing sounds. There are three main branches of phonetics: articulatory phonetics studies sound production, auditory phonetics studies sound transmission from speaker to listener, and acoustics phonetics studies sound perception by the listener. The document then provides examples of vowels and consonant sounds in the International Phonetic Alphabet and their occurrence in everyday English words.
This document summarizes a research study on the linguistic choices of teachers who teach young English language learners. The study examined the use of the teachers' first language compared to the target language of English. It found that teachers can be divided into three categories based on their language use: those who use mostly their first language, those who use a combination of first language and English, and those who use mostly English. The study also looked at whether teachers' language use patterns could be explained by their beliefs about teaching young language learners.
CrossMark: Trust and the Stewardship of Scholarly ContentCrossref
Ed Pentz's presentation about CrossMark at the APE 2010 - The Fifth International Conference "Academic Publishing in Europe"
19 - 20 January 2010
Berlin-Brandenburg Academy of Sciences
Berlin, Berlin, Germany
The document discusses central angles and circumference angles in circles. A central angle is an angle inside a circle whose vertex is the center of the circle. A circumference angle is an angle whose vertex is located on the circumference of the circle. The central angle is twice the size of the circumference angle that subtends the same arc. Circumference angles that are subtended by the same arc are equal. Angles subtended by the diameter of a circle, forming a semicircle, are right angles. The document also discusses cyclic quadrilaterals, which have all four vertices lying on the circumference of a single circle, meaning the opposite angles of a cyclic quadrilateral are supplementary.
Using Applied Linguistic to English as a Second Language to Criolle Fourth St...Princess Lover
This document summarizes a thesis presented for a degree in education sciences. Specifically, it examines using applied linguistics to teach English as a second language to 4th grade Creole students in Limon, Costa Rica. The thesis aims to 1) observe students' problems acquiring English and solutions, 2) develop and apply methods to help students learn English, and 3) determine the effectiveness of applied linguistics methods. It reviews theories of language acquisition and analyzes factors affecting students' language learning such as their first language, environment, age, and motivation. Recommendations include respecting students' culture, incorporating their first language, using varied teaching methods, increasing English instruction time, and fostering student engagement and confidence.
This document discusses different approaches to language teaching including characteristics of optimal input for language acquisition. It summarizes several common language teaching methods such as grammar translation, audio-lingualism, cognitive-code method, direct method, natural approach, total physical response, and suggestopedia. For each method, it describes the learning procedure, goals, and how they align with optimal input characteristics. It suggests considering students' interests, providing comprehensible input, and not focusing too much on grammatical accuracy or sequences. Later sections discuss alternatives like conversation, pleasure reading, using subject matter, and considerations for test evaluation, material selection, and extra activities.
This document summarizes language variation seen in the film script for Knight and Day. It presents research analyzing the descriptive qualitative data extracted from the film's dialogue. The analysis finds examples of language variation between characters including differences in dialect and accent. These variations occur at different dramatic points in the film labeled as pre-climax, climax, and anti-climax. The conclusion is that the language variations found in the film script are consistent with the theoretical framework of dialect and accent variations between speakers.
Quality, Relevance and Importance in Information Retrieval with Fuzzy Semanti...tmra
We propose a framework for ranking information based on quality, relevance and importance, and argue that a socio-semantic contextual approach that extends topicality can lead to increased value of information retrieval systems. We use Topic Maps to implement our framework, and discuss procedures for calculating the resource ranking. A fuzzy neural network approach is envisioned to complement the process of manual metadata creation.
This module discusses plane coordinate geometry concepts including the distance formula, midpoint formula, and coordinate proofs. It will teach students to derive the distance formula using the Pythagorean theorem, apply the distance and midpoint formulas to find lengths and midpoints, and use coordinate proofs to verify properties of figures on the coordinate plane. The module aims to enhance understanding of distances between points, lengths of line segments, and properties of polygons with vertices defined by coordinates.
Descriptive english linguistic By David hernandezdvd_h
The document discusses the key characteristics and branches of linguistics. It outlines the main features of language, including speech and its classification into different speech acts. The branches of linguistics are also examined, with phonology provided as an example branch. Linguistics is established as the scientific study of human language and communication.
This module discusses coordinate proofs and properties of circles on the coordinate plane. It introduces coordinate proofs as an analytical method of proving geometric theorems by using the coordinates of points and algebraic relationships. Examples demonstrate proving properties of triangles and quadrilaterals analytically. The standard form of the equation of a circle is derived from the distance formula as (x - h)2 + (y - k)2 = r2, where (h, k) is the center and r is the radius. Finding the center, radius, and equation of circles in various forms are illustrated.
Dr. Lotfi Ali Asker Zadeh is considered the father of fuzzy logic. In the 1960s and 1970s, he developed the concept of fuzzy sets and fuzzy logic to deal with imprecise data and approximations. Fuzzy logic uses membership values between 0 and 1 rather than binary logic of true and false. It allows partial truth values to model uncertainty. Fuzzy logic has been applied in areas like control systems, decision making, and pattern recognition to handle imprecise concepts.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This chapter discusses semantic discourse analysis, which involves assigning meanings and references to sequences of sentences in a discourse. Semantically, discourses are linked to sequences of underlying propositions derived from the individual sentences. Pragmatically, these propositions are in turn linked to configurations of facts in possible worlds. A full semantic analysis of discourse requires considering both intensional meanings and extensional references, and relating these to people's real-world knowledge and interpretations.
The document introduces the Word-Sensibility Model as a way to represent commonsense knowledge for AI. It consists of several components, including quadranyms, micro-topics, and an ecological perspective. Quadranyms are four-part constructs that represent virtual units of orientation and constraint. Micro-topics help organize lexical information and abstract human contextual expectations. The model takes an ecological view of representing dynamic relationships between an agent's internal responses and external occurrences across different contextual levels.
Models of Parsing: Two-Stage Models
Models of Parsing: Constraint-Based Models
Story context effects
Subcategory frequency effects
Cross-linguistic frequency data
Semantic effects
Prosody
Visual context effects
Interim Summary
Argument Structure Hypothesis
Limitations, Criticisms, and Some Alternative Parsing Theories
Construal
Race-based parsing
Good-enough parsing
Parsing Long-Distance
Dependencies
Summary and Conclusions
Test Yourself
When people speak, they produce sequences of words. When people listen or read, they also deal with sequences of words. Speakers systematically organize those sequences of words into phrases, clauses, and sentences.
The study of syntax involves discovering the cues that languages provide that show how words in sentences relate to one another.
The study of syntactic parsing involves discovering how comprehenders use those cues to determine how words in sentences relate to one another during the process of interpreting sentence.
Parsing means to breaking down a sentence into its component parts so that the meaning of the sentence can be understood.
This can either be the category of words (Nouns, Pronouns, verbs, adjectives. Etc.)
Or other elements such as verbs tense (present, past, future)
In a phrase structure tree, the labels, like NP, VP, and S, are called nodes and the connections between the different nodes form branches.
The patterns of nodes and branches show how the words in the sentence are grouped together to form phrases and clauses.
T H E B E H A V I O R A N A L Y S T T O D A Y .docxdeanmtaylor1545
This document provides an introduction to Relational Frame Theory (RFT) by comparing it to Lang's cognitive model of a fear network. It summarizes RFT's key principles of relational responding and framing relationships between stimuli. The document introduces an RFT account of Lang's fear network model to highlight how RFT analyzes explicit and implicit relationships between thoughts, emotions, behaviors, and physiological responses. It explains how discriminating relational frames allows one to glean more information from stimuli than looking at them individually, but can also lead to psychological problems if relational responding gets out of control.
Theoretical Issues In Pragmatics And Discourse AnalysisLouis de Saussure
The document discusses theoretical issues in pragmatics and discourse analysis from both cognitive and social perspectives. It notes key differences in how each views the nature and study of discourse - as static wholes shaped by social forces versus dynamic processes of information exchange. While acknowledging valid insights from both, it argues for a mechanistic "discourse as process" approach that models understanding as an incremental, parallel adjustment of representations over time. Coherence is seen as an emergent property of thought rather than an inherent feature of language.
This document discusses the computation of presuppositions and entailments from natural language text. It begins by defining presuppositions and entailments, and explaining how they can be computed using tree transformations on semantic representations. The paper then provides examples of elementary presuppositions and entailments. It describes a system that computes presuppositions and entailments while parsing sentences using an augmented transition network. The system applies tree transformations specified in the lexicon to the semantic representation to derive inferences. The paper concludes that presuppositions and entailments exhibit computational properties not shown by the general class of inferences, such as being tied to the semantic and syntactic structure of language.
The document discusses several theories of semantics, including truth-conditional semantics, generative semantics, and semantic competence. Truth-conditional semantics claims that the meaning of a sentence is identical to the conditions under which it is true. Generative semantics aims to give rules to predict which word combinations form grammatical sentences. Semantic competence refers to a native speaker's ability to recognize utterances as meaningless even if grammatically correct.
This document provides an introduction to Systemic Functional Grammar (SFG). It discusses the following key points:
1) SFG views language as a system of choices and was developed based on the work of Malinowski, Firth, and Halliday. It examines language from a functional perspective rather than just a structural perspective.
2) SFG represents grammar as system networks that show the paradigmatic choices available and realization rules that map choices to syntactic structures. This models the relationship between semantic choices and surface structures.
3) In SFG, language is analyzed in terms of three metafunctions - the ideational to represent experience, the interpersonal to enact social relationships, and the textual to organize messages
The document discusses the systems and choices that exist in English grammar related to mood, verbal groups, and nominal groups. For mood, there is a choice between indicative, imperative, declarative, interrogative, wh-interrogative, and non-wh interrogative. The verbal group involves choices around finiteness, modality, tense, polarity, and voice. Nominal groups involve number, case, and gender systems.
The document discusses the systems and choices that exist in English grammar related to mood, verbal groups, and nominal groups. For mood, there is a choice between indicative, imperative, declarative, interrogative, wh-interrogative, and non-wh interrogative. The verbal group involves choices around finiteness, modality, tense, polarity, and voice. Nominal groups have systems for number, case, and gender.
This document provides an introduction to First Order Predicate Logic (FOPL). It discusses the differences between propositional logic and FOPL, the parts and syntax of FOPL including terms, atomic sentences, quantifiers and rules of inference. The semantics of FOPL are also explained. Pros and cons are provided, such as FOPL's ability to represent individual entities and generalizations compared to propositional logic. Applications include using FOPL as a framework for formulating theories.
This document discusses research methodology concepts related to variables and hypotheses. It defines key terms like variables, independent and dependent variables, and different types of hypotheses. The document provides examples and explanations of these concepts. It outlines the objectives of understanding variables, hypothesis sources and types, and characteristics of a good hypothesis.
The document discusses clause complexes and their role in expressing logical connections between events in language. A clause complex links two or more clauses together through either parataxis (equal, independent clauses) or hypotaxis (one main clause with dependent clauses). The relationship between clauses can be one of projection (quoting or reporting speech/thoughts) or expansion (developing or extending the meaning of another clause through elaboration, extension, or enhancement).
The role of linguistic information for shallow language processingConstantin Orasan
The document discusses shallow language processing and summarization. It argues that while deep language understanding is limited, shallow methods can be improved by adding linguistic information. As an example, it shows how term frequency, anaphora resolution, discourse cues and genetic algorithms can select extractive summaries that better match human abstracts, without requiring full text comprehension.
New Quantitative Methodology for Identification of Drug Abuse Based on Featur...Carrie Wang
This project developed a new quantitative methodology using feature-based context-free grammar to analyze discourse semantics from social media discussions in order to identify potential drug abuse. The methodology was able to parse YouTube comments about recreational cough syrup use and perform anaphora resolution. This computational representation of discourse contributes to understanding human language structure and has applications in public health monitoring and clinical research.
The document discusses propositional logic as a knowledge representation language. It defines key concepts in propositional logic including: syntax, semantics, validity, satisfiability, interpretation, models, and entailment. It explains that propositional logic uses symbols to represent facts about the world and connectives to combine symbols into sentences. Sentences can then be evaluated based on the truth values assigned to symbols to determine if the overall sentence is true or false. Propositional logic allows new sentences to be deduced from existing sentences through inference rules while maintaining logical validity.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
3. • Fuzzy Sets
• Fuzzy Relations
• Implication Operators
• Compositions
Analytical
Form
• Variables
• Propositions
• If/then Rules
• Algorithms
• Inference
Linguistic
Form
4. Linguistic Variable:
Linguistic variable is an important concept in fuzzy logic and plays a key role in its applications, especially
in the fuzzy expert system.
Linguistic variable is a variable whose values are words in a natural language.
For example, “speed” is a linguistic variable, which can take the values as “slow”, “fast”, “very fast” and
so on.
Linguistic variables collect elements into similar groups where we can deal with less precisely and hence
we can handle more complex systems.
A linguistic variable is a variable whose values are words or sentences in a natural or artificial language.
It is a mathematical representation of semantic concepts that includes more than one term (fuzzy set).
It is a variable made up of a number of words (linguistic terms) with associated degrees of membership.
5. More About Linguistic Variable:
Linguistic variable is a variable of higher order than fuzzy variable, and it take fuzzy variable as its values
A linguistic variable is characterized by: (x, T(x), U, M), x; name of the variable
T(x); the term set of x, the set of names or linguistic values assigned to x, with each value is a fuzzy variable
defined in U
M; Semantic rule associate with each variable (membership)
For Example: x : “age” is defined as a linguistic variable
T(age) = {young, not young, very young, more or less old, old}
U: U={0, 100}
M: Defines the membership function of each fuzzy variable for example; M (young) = the fuzzy set for age
below 25 years with membership of µyoung
6. Fuzzy Variable:
A fuzzy variable is characterized by (X, U, R(X)), X is the name of the variable; U is the universe of
discourse; and R(X) is the fuzzy set of U.
For example: X = “old” with U = {10, 20, ..,80}, and R(X) = 0.1/20 + 0.2/30 + 0.4/40 + 0.5/50 + ….+ 1/80
is called a fuzzy membership of “old”
7. Fuzzy Proposition:
A specific evaluation of a fuzzy variable is called fuzzy proposition.
Individual fuzzy propositions on either LHS or RHS of a rule may be connected by connectives such as
AND & OR.
Individual if/then rules are connected with connective ELSE to form a fuzzy algorithm.
Propositions and if/then rules in classical logic are supposed to be either true or false.
In fuzzy logic they can be true or false to a degree.
8. Fuzzy Inference:
Fuzzy proposition is computational procedures used for evaluating linguistic descriptions.
Two important inferring procedures are:
i. Generalized Modus Ponens(GMP)
ii. Generalized Modus Tollens(GMT)
(See Details From Book)
9. Modus Ponens vs Modus Tollens
Modus Ponens and Modus Tollens are forms of valid inferences.
By Modus Ponens, from a conditional statement and its antecedent, the consequent of the conditional
statement is inferred: e.g. from “If John loves Mary, Mary is happy” and “John loves Mary,” “Mary is happy”
is inferred.
By Modus Tollens, from a conditional statement and the negation of its consequent, the negation of the
antecedent of the conditional statement is inferred: e.g. from “If today is Monday, then tomorrow is
Tuesday” and “Tomorrow is not Tuesday,” “Today is not Monday” is inferred.
The validity of these inferences is widely recognized and they are incorporated into many logical systems.
10. Application Of Fuzzy Inference:
Fuzzy inference systems have been successfully applied in fields such as:
Automatic control
Data classification
Decision analysis
Expert systems
Computer vision.
Because of its multidisciplinary nature, fuzzy inference systems are associated with a number of names, such
as:
Fuzzy-rule-based systems
Fuzzy expert systems
Fuzzy modeling
Fuzzy associative memory
Fuzzy logic controllers
Simply (and ambiguously) fuzzy systems.
13. SEE TOPICS FROM BOOK
Linguistic Values
Linguistic Variables
Primary Values
Compound Values
Implication Relation
Fuzzy Inference & Composition
Degree Of Fulfillment
Area Cum Point
Crisp Point
Rules Of Inferences
Fuzzy Algorithm
Modus Ponens And Modus Tollens (More Details Read From Discrete Mathematics)