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Physical linguistics, which was invented by the , Boolean Logic s, Multi-valued Logic s and Fuzzy Logic s address different issues of computational nouns. While any logic system is truth-conserved, it doesn't manipulate truth directly. Instead, any logic system manipulates only Information . The truth is manipulated by the computational verb theory. Therefore, in any logic system, the truth doesn't need a system of Metrics to associate with. For example, in a logic system if we say: "a=true is twice as large as b=true", it sounds very funny and is unnecessary because all "true" is the same. Therefore "true+true"(e.g., "a=true OR b=true") is not equal to "2true", but simply "true". However, in computational verb theory where the truth is directly manipulated and information is not directly manipulated, the "true" can have different sizes. In computational verb theory, we can have a "true1" of size 1.5 UNITs and a "true2" of size 3.4 UNITs, then when the statement "true1+true2" appears, we get a "true3" of size 4.9 UNITs. After a Truth in a computational verb collapses into a piece of information in logics, we get a "truth value" of the information. However, at this point, the Truth itself already returned to the Cognition . The main goal of physical linguistics is to translate all sentences in Natural Language s into Mathematical Formula s. For example, the following sentence The Apple turns Red from Green . can be expressed by a linguistic Differential Equation as d( Color of apple)/dt = F (color of apple, t) with the following Boundary Condition s color of apple(beginning) = green, and color of apple(end) = red. APPLICATIONS Physical linguistics was successfully applied to many image understanding tasks based on its powerful, reliable and robust representation to the visual signals samples from devices such as Webcam s. Although the design of image processing systems takes an enormous effort, the mechanism of developing an image understanding application based on physical linguistics makes it easier to accumulate dynamic and static experiences and accelerate the learning curve of the design of image understanding systems. Yet, physical linguistics also makes the threshold for becoming an expert in the design of image understanding systems much lower compared to conventional methods. This is the reason that many commercial image understanding products are backed up by the Physical Linguistic Vision Technologies developed in YangSky . The known applications of physical linguistics are:
COMPUTATIONAL VERB Computational verb was invented by Tao Yang in 1997 in the Department of Electrical Engineering and Computer Sciences, University Of California, Berkeley . The purpose of computational verb is to make all Verbs in any Natural Language computable. Each computational verb consists of an Inner System and an Outer System . The inner system of a computational verb is invisible to the outer observers. For example, for the computational verb “feel”, the inner system is the status of a Brain that is invisible even to the brain itself. For a human being, the inner system is the body together with the brain. In a computer, the inner system is called Machinself (from Machine + itself). The outer system of a computational verb is the visible part of the computational verb. Therefore, the outer system can be measured and modeled by using Mathematical Function s that are called Outer Function s of computational verb. The concept of computational verb is closely related to the concept of , Computational Verb Image Processing , Computational Verb Prediction and Computational Verb Modeling . The theory of computational verbs is the computational verb theory. COMPUTATIONAL VERB LOGIC Computational verb logic is an extension of Boolean Logic and Fuzzy Logic dealing with the concept of '' Truth Of Irrationals ''. Whereas Classical Logic and fuzzy logic hold that everything can be expressed in a truth value at any moment, computational logic replaces static truth values with Dynamics Of Truth . In a word, classical logic and fuzzy logic deal with “BE true” while computational verb logic deals with “BECOME true”. For example, the following statements can be reasoned in classical logic and/or fuzzy logic: IF the temperature IS high THEN the air flow IS fast. IF the temperature IS 40 THEN the air flow IS 5m/s. However, the following statements can’t be reasoned in either classical logic or fuzzy logic: IF I understand you THEN I become smart. IF the temperature increases too high THEN the air flow will decrease too fast. These statements can be easily modeled by using computational verb logic. Computational verb logic can be applied to design industrial controllers and develop advanced image processing platforms. Bibliography
COMPUTATIONAL VERB THEORY Computational verb theory, which was invented by then-. Another important mathematical concept in computational verb theory is Computational Verb Number ( Verb Number , for short). While a Real Number , an Interval Number and a Fuzzy Number can respectively represented as “3”, “ {Link without Title} ”, and “close to 3”, a Computational Verb Number has a form such as “increase to 3”, “become old” and “remain high”. Computational verb number gives numbers dynamic lives. Many operations between dynamic processes associated with numbers can be computed by applying computational verb numbers. In the computational verb theory, the relation between Adverb s ( Adverbial s) and verbs are mathematically modeled by using a mathematical concept called Operators . In this theory, each computational verb is modeled by a Dynamical Process of which the dynamics can be modified by either Adverb s or Operating Verb such as “must”, “will” and “be”. Theory The theory of computational verb consists of the following aspects. Mathematic Theory of Computational Verbs The researches in this aspect of computational verbs are addressing the following issues:
Physical Theory of Computational Verbs
Biological Theory of Computational Verbs
Psychological Theory of Computational Verbs
Applications The applications of computational verbs are: Image Processing Computational verb theory has been successfully applied to many dynamics-related image processing applications such as: Intelligent Traffic Control Systems ( ITCS ), visual Flame Detection , card counting and Homeland Security . The advantages of applying computational verbs to image processing are:
Some representative products based on computational verb image processing technologies are: CardSeer card and paper sheet counting system, FaceID Webcam Face Login System for biometrics password, and FlameSky flame detecting systems. Industrial Controllers The control rules and experiences of human experts can be used to design different Intelligent Controller s, among them are Fuzzy Controller s. The fuzzy controllers can be further expanded into Computational Verb Controller s. A typical Fuzzy Control Rule is given by:
This rule is in fact a concise form of the following rule:
One should note that the only Verb used in fuzzy control rule is BE. If we expand BE into any kinds of verbs such as BECOME, GO, INCREASE, FEEL, we have Computational Verb Rule s such as:
Observe that the space of computational verb rules is much bigger than that of Fuzzy Rule s. Computational verb fuzzy controllers becomes a part of the standard course( EE6452 Introduction to Fuzzy Informatics and Intelligent Systems ) for undergraduates at City University Of Hong Kong . Please refer to G. Chen's homepage for the information of the instructor. Paths When Tao Yang invented computational verbs in 1997, he might not realize that this is the second step in science to implement a Measurable Linguistics , or to make linguistics as a Natural Sciences with a system of Metrics just like that used in physics. The first step was Fuzzy Theory invented by L.A. Zadeh in 1965 . The physical linguistics is such a theory for building the measurable Linguistics . Physical linguistics leads a path to a universal theory of Mind and physical world called the Theory of the Unicogse . EXTERNAL LINKS
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