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Generating robotic grasps for given tasks is a difficult problem. This paper proposes a learning-based approach to generate suitable partial power grasp for a set of tool-using tasks. First a number of valid partial power grasps are sampled in simulation and encoded as a probabilistic model, which encapsulates the relations among the task-specific contact, the graspable object feature and the finger...
Nowadays massive log streams are generated from many Internet and cloud services. Storing log streams consumes a large amount of disk space and incurs high cost. Traditional compression methods can be applied to reduce storage cost, but are inefficient for log analysis, because fetching relevant log entries from compressed data often requires retrieval and decompression of large blocks of data. We...
The growth of E-commerce which can be seen in recent years, has contributed a lot to global economy. Prediction of trade, especially in C2C market, can help decision-makers obtain the information from the online transactions and find the knowledge underlying the data. This paper facilities the traditional search index prediction system with ANFIS model. By using purchasing transactions from Taobao,...
For financial investment, the problem that we often encounter is how to extract information hidden in the volatile and noise data and forecast it into future. This study proposes a novel three-stage neural-network-based nonlinear weighted ensemble model. In proposed model, three different types neural-network base models, i.e., Elman network, generalized regression neural network (GRNN) and wavelet...
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