Model-based knowledge acquisition for heuristic classification systems

Vehicle detection and classification information is invaluable in many transportation issues. Vehicle feature extraction and detection are the preprocesses required for vehicle classification. Current automatic vehicle classification systems have deficiencies: low accuracy, special requirements, fixed orientation of the camera, or additional hardware and devices. This paper discusses a vehicle detection and classification system using model-based and fuzzy logic approaches. The system was tested with the use of a variety of images captured by the highway traffic control center of the Utah Department of Transportation. In comparison with existing systems, major advantages of the proposed system are ( a) no special orientation of the camera is required, ( b) no additional devices are needed, and ( c) high classification accuracy is provided. Experimental results show that the performance of the proposed system exceeds that of the existing video-based vehicle classification systems.

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A Knowledge Acquisition Model Based on Formal Concept Analysis in Complex Information Systems

Lecture Notes in Computer Science - Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing ◽

10.1007/978-3-319-25783-9_26 ◽

2015 ◽

pp. 286-297 ◽

Cited By ~ 1

Author(s):

Xiangping Kang ◽

Duoqian Miao ◽

Na Jiao

Keyword(s):

Information Systems ◽

Knowledge Acquisition ◽

Formal Concept Analysis ◽

Concept Analysis ◽

Formal Concept ◽

Model Based ◽

Complex Information

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Constraint mechanisms for knowledge acquisition from computer-aided design data

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060400000871 ◽

1993 ◽

Vol 7 (3) ◽

pp. 181-188

Author(s):

Harley R. Myler ◽

Avelino J. Gonzalez ◽

Massood Towhidnejad

Keyword(s):

Process Control ◽

Knowledge Acquisition ◽

Computer Aided Design ◽

Structural Information ◽

Knowledge Bases ◽

Knowledge Generation ◽

Model Based ◽

Reasoning System ◽

Computer Aided ◽

Aided Design

A number of automated reasoning systems find their basis in process control engineering. These programs are often model-based and use individual frames to represent component functionality. This representation scheme allows the process system to be dynamically monitored and controlled as the reasoning system need only simulate the behavior of the modeled system while comparing its behavior to real-time data. The knowledge acquisition task required for the construction of knowledge bases for these systems is formidable because of the necessity of accurately modeling hundreds of physical devices. We discuss a novel approach to the capture of this component knowledge entitled automated knowledge generation (AKG) that utilizes constraint mechanisms predicated on physical behavior of devices for the propagation of truth through the component model base. A basic objective has been to construct a complete knowledge base for a model-based reasoning system from information that resides in computer-aided design (CAD) databases. If CAD has been used in the design of a process control system, then structural information relating the components will be available and can be utilized for the knowledge acquisition function. Relaxation labeling is the constraint-satisfaction method used to resolve the functionality of the network of components. It is shown that the relaxation algorithm used is superior to simple translation schemes.

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