what is the approximate area of a blackboard ?

Breadth of the blackboard = 100 cm = 1 m. Area of the blackboard = area of a rectangle = length x breadth = 1.2 m x 1 m = 1.2 square-metres. Example 3: The length of a rectangular screen is 15 cm. Its area is 180 sq. cm. Find its width. Solution: Area of the screen = 180 sq. cm. Length of the screen = 15 cm. Area of a rectangle = length x width

Full Answer

What is the area of the blackboard?

The area of a blackboard is 1 1/3 square yards.

What is the approximate length of a blackboard?

A typical blackboard is about 2 meters in length.May 19, 2021

What is the perimeter of chalkboard?

A chalkboard has a perimeter of 20 feet and an area of 24 square feet.May 19, 2018

What is the approximate width of blackboard?

BlackboardProduct Code9090Size (cm)20 X 25Slate Thickness2.5 mmPiece per Carton200Width (cm)401 more row

What is the standard unit of length?

meterThe standard unit of length based on the metric system is a meter (m). According to the length that needs to be measured, we can convert a meter into various units like millimeters (mm), centimeter (cm), and kilometer (km).

What is the perimeter of 20 square feet?

Likewise a rectangle with dimensions 5 feet and 4 feet has area 20 square feet and perimeter 5 + 4 + 5 + 4 = 18 feet.

What is CBR in a database?

The CBR has its knowledge derived from the historical cases. It has a simple framework consisting of four phases: i) retrieve, ii) reuse, iii) revise, and iv) retain ( Fig. 6 ). In the retrieval phase, knowledge in the database (case repository) in the form of previous experiences and histories (cases) related to the application are searched for. These old cases are then retrieved based on their index and interpreted for the current problem. In the reuse phase, the old cases are adapted to the present situation in order to find the solution. Evaluation of the new cases is carried out and solutions suggested in the revise phase and the new cases are then added to the case repository for future learning, as a part of the retain phase [ 64 ]. One of the examples of CBR application is Intelligent Systems for Aircraft Conflict Resolution (ISAC) [ 65] which was developed to help the decision-making process of aircraft controllers to resolve the conflicts between aircraft. CBR is one of the most commonly used reasoning systems, as its architecture has the capability of accommodating any advanced algorithms, mainly text processing techniques. Moreover, CBR does not require prior knowledge about the system as it solely depends on past experience. Since the CBR involves learning as a part of its methodology enabling knowledge evolution, the CBR can evolve and become expert in the domain, thus having the potential of becoming the future expert systems [ 66 ]. This is an advantage over the other frameworks since the other reasoning systems require maintenance time for updating the knowledge base. Nonetheless, the CBR system is computationally demanding when compared with the PRS and simple rule-based Expert Systems.

What is model based system?

The central concept of a model-based system is a model, a description of a device, or a system using an appropriate modeling language. The model specifies the structure, functions, and behaviors of the devices for the purposes of analysis, prediction, diagnosis, and other such procedures.

What is dice in engineering?

In this paper, we have described DICE, which is a collection of computer-based tools for cooperative engineering design. DICE facilitates coordination and communication in engineering design by utilizing an object-oriented Blackboard architecture, where the various participants involved in the engineering process communicate through a global database – called Blackboard. We have demonstrated the DICE framework through a simulation of the Hyatt Regency Disaster and an implementation of a construction planning KBES. Our current research addresses the following issues in the development of a distributed object-oriented CAD system.

What is expert system architecture?

The Expert System architecture consists of a knowledge base and an inference engine ( Fig. 4 ). Its knowledge is generally represented in terms of rules. Rules represent domain knowledge in an ‘if-then-else’ format and they can be written in different programming languages like C, LISP, and OWL. For example, in the CLIPS expert system used by Siemens, rules are written in OWL 2 language in the format of concept-ontology and instance-ontology [ 56 ]. In some cases, frames are also used to represent the knowledge in expert systems. Frames are used to represent the stereotyped knowledge as a collection of attributes and their associated values. An example is the meteorological vehicle system, wherein the expert system for fault diagnosis is built using object-frame structure, with the frame being a collection of state-object, test-object, rule-object, and repair-object [ 57 ]. Most Expert systems employ Rule-Based Reasoning (RBR) methodology to solve their problem. The RBR is executed in the following two ways: i) Forward Chaining, and ii) Backward Chaining. Forward Chaining starts with the initial state of facts and applies the rules until the endpoint is reached. Backward Chaining starts from a hypothesis and looks for rules that will allow the hypothesis to be proven. In other words, it starts with an effect and looks for the possible root causes that could lead to that effect. Forward Chaining is data driven whereas Backward Chaining is goal driven [ 58 ].

What is a PRS?

The PRS is a knowledge-based reasoning system which has its knowledge in the form of procedures called the Knowledge Areas . The PRS implements the Belief-Desire-Intention concept of modeling for real-time reasoning of dynamic systems [ 10 ]. It consists of the following modules ( Fig. 5 ): i) a goal or objective set, ii) a database with domain knowledge and beliefs that update themselves with new knowledge, iii) a knowledge area which is a library of procedures for actions and tests to achieve the goals, iv) an intention graph which has partially completed procedures to run real-time, and v) an interpreter which communicates with all these modules and carries out reasoning. The interpreter receives the goal or objective for the system, chooses the correct procedure required from the knowledge area, places it on the intention graph to narrow down the set of actions, chooses the correct action based on the intention, and finally starts the procedure which will update the next goal [ 10 ]. Fig. 5 shows the general architecture of the Procedural Reasoning System [ 10 ]. The PRS has been applied to monitor the malfunctioning of the RCS of NASA's space shuttle and also to diagnose and control the overloading of telecommunication networks [ 63 ]. The PRS architecture is simple for execution and reduces the computational time as the procedures can skip unnecessary steps for a particular problem, and narrow down rules to the relevant set directly [ 63 ]. The PRS can implement real-time reasoning and handle dynamic systems, but it can handle only simple plans; any changes to the existing plans and procedures will be time-consuming and tedious.

What is computer aided design?

Computer aided design systems developed to date lack the flexibility to incorporate and reason about geometric entities. Since much of the communications of designers with the coordination system will be graphical in nature, the system must contain or interface with normal CAD facilities.

What is knowledge representation?

Knowledge representation is a substantial subfield of AI in its own right. Winston defines a representation as “a set of syntactic and semantic conventions that make it possible to describe things” ( Winston, 1984 ). The syntax of a representation specifies a set of rules for combining symbols to form expressions in the language. The semantics of a representation specify the meanings of expressions. In the field of expert systems, knowledge representation is mostly concerned with symbolically coding a large amount of domain knowledge in computer-tractable form such that it can be used to describe the task and the environment unambiguously and to reason with the knowledge efficiently toward certain goals.