Abstract: An object search method and an object search system which reduce the time needed for coordinate transformation of a plurality of objects to be displayed as three-dimensional view data through viewing transformation. The system determines a reference box which circumscribes a view volume, created according to an eyepoint. The system determines a bounding box for each object. Each bounding box circumscribes the corresponding object. A 6-d tree, composed of a plurality of nodes each having keys composed of the coordinate components of each bounding box, is prepared beforehand. With the coordinate components of the reference box as a search condition, the system searches the 6-d tree for bounding boxes included in the reference box. Then, the system performs coordinate transformation only on the objects corresponding to the obtained bounding boxes.

Abstract: A three-dimensional object data processing method and system reduces the number of coordinate calculations of the objects when an eyepoint changes during CG processing. A workstation (2) comprises a space searching module (6) which searches for the objects included in a view volume, a rasterizing module (12) which displays the view volume, and a texture data generating module (10) which groups a plurality of objects according to a user's request and generates two-dimensional texture data. Each object is drawn as texture data.

Abstract: In a search method and system for reducing the number of memory page-in/page-out operations to display, three dimensional objects are extracted through space search. The system comprises a parameter accepting module (22) which accepts parameters from a user to set up a view volume, a space searching module 24 which searches for an object which is included in the view volume, a tree holding module (28) which holds a multi-dimensional tree describing the three-dimensional coordinates of the object, and a file reading module (29) which loads the object data into memory in file units.

Abstract: A plant malfunction diagnostic method is characterized by determining by simulation a change in a plant state variable, forming a pattern among plant state variables obtained by autoregressive analysis of the change in plant state variable, inserting the formed pattern among the plant state variables in a neural network, performing learning until a preset precision is obtained, and identifying the cause of the malfunction by inserting, in the neural network, a pattern which indicates the pattern among plant state variables formed by data gathered from the plant. This makes possible early identification of the cause of a malfunction. Plant rate of operation and safety are improved by allowing the operator to perform the appropriate recovery operation with a sufficient time margin.