372272
Data-Driven Grasp Synthesis Using Shape Matching and Task-Based Pruning
Human grasps, especially whole-hand grasps, are difficult to animate because of the high number of degrees of freedom of the hand and the need for the hand to conform naturally to the object surface. Captured human motion data provides us with a rich source of examples of natural grasps. However, for each new object, we are faced with the problem of selecting the best grasp from the database and adapting it to that object. This paper presents a data-driven approach to grasp synthesis. We begin with a database of captured human grasps. To identify candidate grasps for a new object, we introduce a novel shape matching algorithm that matches hand shape to object shape by identifying collections of features having similar relative placements and surface normals. This step returns many grasp candidates, which are clustered and pruned by choosing the grasp best suited for the intended task. For pruning undesirable grasps, we develop an anatomically-based grasp quality measure specific to the human hand. Examples of grasp synthesis are shown for a variety of objects not present in the original database. This algorithm should be useful both as an animator tool for posing the hand and for automatic grasp synthesis in virtual environments.
10269891
An analytical grasp planning on given object with multifingered hand
In this paper, an analytical approach is proposed for planning finger positions of grasping an object with a multifingered hand. First, a method is given to obtain which combination of the object edges is possible to be used for grasping. Then, a graspable finger position region (GFPR) on a combination of edges is defined where the object can be held successfully. It is shown that the region is bounded by several boundary hyperplanes. By combining these boundary hyperplanes, two propositions for analytically and exactly obtaining the GFPR are proposed. An algorithm is proposed to find a stable GFPR that contains the biggest inscribed hypersphere of GFPR and has the largest volume. Finally, a numerical example is performed to show the effectiveness of the proposed grasp planning approach.
6131842
Adaptive testing of software components
Software components are popular in nowadays software industries. However, how to test software components is a problem since the source code of the software component under test may not be available for the third-party user. In this paper we show that the software component should be tested in an adaptive manner in the sense that the software defect detection rates are estimated on-line by using testing data collected during testing to improve test case selections. In doing so, we use a recursive least squares estimation method to do on-line parameter estimations. This paper further justifies the advantages of the controlled Markov chain (CMC) approach to software testing in particular, and the practicality of the idea of software cybermetics in general.
8278906
A Hybrid Method of Unsupervised Feature Selection Based on Ranking
Feature selection is a key problem to pattern recognition. So far, most methods of feature selection focus on sample data where class information is available. For sample data without class labels, however, the related methods for feature selection are few. This paper proposes a new way of unsupervised feature selection. Our method is a hybrid approach based on ranking the features according to their relevance to clustering using a new ranking index which belongs to exponential entropy. Firstly a candidate feature subset is selected using a modified fuzzy feature evaluation index (FFEI) with a new method to calculate the feature weight, which makes the algorithm to be robust and independent of domain knowledge. Then a wrapper method is used to select compact feature subset from the candidate feature set based on the clustering performance. Experimental results on benchmark data sets indicate the effectiveness of the proposed method
46557593
A New Stochastic PSO Technique for Neural Network Training
Recently, Particle Swarm Optimization(PSO) has been widely applied for training neural network. To improve the performance of PSO for high-dimensional solution space which always occurs in training NN, this paper introduces a new paradigm of particle swarm optimization named stochastic PSO (S-PSO). The feature of the S-PSO is its high ability for exploration. Consequently, when swarm size is relatively small, S-PSO performs much better than traditional PSO in training of NN. Hence if S-PSO is used to realize training of NN, computational cost of training can be reduced significantly.
14153782
SVM Based Learning System for Information Extraction
This paper presents an SVM-based learning system for information extraction (IE). One distinctive feature of our system is the use of a variant of the SVM, the SVM with uneven margins, which is particularly helpful for small training datasets. In addition, our approach needs fewer SVM classifiers to be trained than other recent SVM-based systems. The paper also compares our approach to several state-of-the-art systems (including rule learning and statistical learning algorithms) on three IE benchmark datasets: CoNLL-2003, CMU seminars, and the software jobs corpus. The experimental results show that our system outperforms a recent SVM-based system on CoNLL-2003, achieves the highest score on eight out of 17 categories on the jobs corpus, and is second best on the remaining nine.
11166931
Automatic Extraction of Hierarchical Relations from Text
Automatic extraction of semantic relationships between entity instances in an ontology is useful for attaching richer semantic metadata to documents. In this paper we propose an SVM based approach to hierarchical relation extraction, using features derived automatically from a number of GATE-based open-source language processing tools. In comparison to the previous works, we use several new features including part of speech tag, entity subtype, entity class, entity role, semantic representation of sentence and WordNet synonym set. The impact of the features on the performance is investigated, as is the impact of the relation classification hierarchy. The results show there is a trade-off among these factors for relation extraction and the features containing more information such as semantic ones can improve the performance of the ontological relation extraction task.