Intelligent Data Mining of Computer-Aided Extension Residential Building Design Based on Algorithm Library

In recent years, with the development of construction industry, more scientific, systematic, fast, and intelligent calculation methods are needed to coordinate urban development and fierce market competition, and mathematical algorithm library plays an important role in artificial intelligence. Therefore, the author uses computer mathematical algorithm and extension theory to study and analyze the residential building design and intelligent data mining. It is found that the research of the computer-aided expression method of extension building planning is mainly the expression of the input and output system of extension building planning. It includes knowledge representation, system outline design, system flow, and interface expression based on the mathematical database.

A Process Ontology for a Confectionery Service Robot

Confectionery items play a vital role in our lives. A plethora of confectionery items are ordered and consumed during various social gatherings, by a wide range of customers. The order may vary in terms of quantity, type of event and level of specificity. Moreover, some order may contain all the information needed while some may only contain the name of the festival for which suitable items need to be inferred and packaged accordingly. To understand all these intricacies for an automated agent involved in picking and packaging of items requires background knowledge. An automated system can perform these tasks only if, it comprises of a robust internal knowledge representation system. We, thus, propose an ontology for a service robot, SweetBot that defines pertinent concepts applicable to the confectionery domain needed by the robot in order to perform the picking and packaging of sweet items efficiently. We further evaluated the efficiency or the robustness of the model by using various SPARQL queries.

Application of RS-SVM in Coal Mine Fire Warning System

In order to reduce the false alarm rate in coal mine fire warning system, we apply information fusion technology to the system and propose a fire forecast algorithm based on Rough Set Support Vector Machine ( RS-SVM ). Firstly, we map the feature description of coal mine fires to the knowledge representation system described by rough set; Secondly, we discrete the continuous attributes and eliminate the redundant information for attribute reduction to form a rule set of this knowledge representation system; At last, we use the above rule set as the training sample to optimize the parameters for the fire warning support vector machine. The experimental results show that the accuracy of the algorithm is very high. It can make timely and accurate prediction of coal mine fire.

The Pattern Recognition Method for Wear Particle Image Based on Rough Sets — Neural Network Theory

By analysis the difference of applying the rough set method and the neural network method to pattern recognition, a improved recognition method that the rough set method is the front system of neural network was produced. the advantages of this method is that the knowledge representation system is reduced without affecting the recognition precision, so the complexity of neural network system and the time of calculating the attribute value is declined ; at the same time ,the neural network as the postpositional system has the tolerance and anti-jamming capability, but it is difficult to do this with rough set method. The example about how to combine these two methods and conclusions from this combination was given.