Automaticthesaurus construction is accomplished by extracting term relations mechanically.A popular method uses statistical anaJysis to discover the term relations.For low frequency terms, however, the statistical information of the terms cannot be reliably used for deciding the relationship of terms.This problem is generally referred as data sparseness problem.Unfortunately, many studies have been said that the low frequency terms are of most use in information retrieval.In this paper, we formalize the statistical behaviors of terms by using inference network.A formal approach for the data sparseness problem which is crucial in constructing a thesaurus is developed.The validity of this approach is shown by experiments.

Two different grasp indices, the uncertainty grasp index and the task compatibility grasp index, are proposed as a measure of grasp quality. When a robot hand grasps an object, error can occur due to various uncertainties. The uncertainty grasp index represents the effect of grasp position error on stable grasping. Also, when the hand grasps an object, the object will usually be used to accomplish a task. For such a task-oriented grasp, the hand should grasp an object in such a manner that it is stable and at the same time can accomplish the task most effectively. The task compatibility grasp index is formulated to represent how well the identified grasp can perform the task. An optimization problem is formulated to compute the grasp index, and the solution procedure is also presented.>

A simple and efficient algorithm is presented to find the best force closure grasp on a planar polygon with a three-fingered robot hand. In searching for the best grasp, force closure grasps on each feasible combination of edges and/or vertices are constructed using computational geometry. A heuristic function formulated from the consideration of uncertainty in grasping is used to evaluate the quality of each grasp. A test of the algorithm on several different polygons shows that the resulting grasp is realistic and fast enough for real-time use.>

The authors present an efficient computational procedure to determine the finger force of a three-fingered robot hand in object manipulation. They first define initial grasping force as the force needed to hold a massless object. Then grasping force and contact normal during manipulation are determined from initial grasping force and initial contact normal by tracking the object displacement. Manipulation force, the finger force required to manipulate an object and to compensate for object weight, is computed by the generalized matrix inverse method. Optimal internal force, when necessary, is determined from the grasping force during manipulation without explicitly solving the optimization problem. The computational burden for determining finger force is less than that of previous methods.>