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.
1356080
Obstacle Avoidance for Redundant Nonholonomic Mobile Modular Manipulators via Neural Fuzzy Approaches
This paper addresses an obstacle avoidance issue for redundant nonholonomic mobile modular manipulators. On the basis of modular robot concept, an integrated dynamic modeling method is proposed, which takes both the mobile platform and the onboard modular manipulator as an integrated structure. A new obstacle avoidance algorithm is proposed which is mainly composed of two parts: a self-motion planner (SMP) and a robust adaptive neural fuzzy controller (RANFC). One important feature of this algorithm lies in that obstacles are avoided by online adjusting self-motions so that the end-effector task will not be affected unless the obstacles are just on the desired trajectory. The RANFC does not rely on exact aprior dynamic parameters and can suppress bounded external disturbance effectively. The effectiveness of the proposed algorithm is verified by simulations.
16004582
Formation Control for a Multiple Robotic System Using Adaptive Neural Network
Due to the limitations of sensors, each member of a decentralized system can only deal with local information respectively. A description of local relationship within formation pattern is proposed in this paper. Furthermore, a NN control approach with robust term is proposed to control individual motion. By using such individual control method, all robots will finally form a unique formation. Based on properties of such control strategy, we propose a modified artificial potential approach to realize obstacle avoidance.
12730309
Adaptive Neural-Network Control for Redundant Nonholonomic Mobile Modular Manipulators
This paper discusses the trajectory following issue for redundant nonholonomic mobile modular manipulators. Dynamic model is established and an adaptive neural-network controller is developed to control the end-effector to follow a desired spacial trajectory. The proposed algorithm doesn't need any priori dynamics and provides a new solution for stabilization of redundant robotic self-motions. Simulation results for a real robot demonstrate the proposed algorithm is effective.
6924123
Publicly verifiable ownership protection for relational databases
Today, watermarking techniques have been extended from the multimedia context to relational databases so as to protect the ownership of data even after the data are published or distributed. However, all existing watermarking schemes for relational databases are secret key based, thus require a secret key to be presented in proof of ownership. This means that the ownership can only be proven once to the public (e.g., to the court). After that, the secret key is known to the public and the embedded watermark can be easily destroyed by malicious users. Moreover, most of the existing techniques introduce distortions to the underlying data in the watermarking process, either by modifying least significant bits or exchanging categorical values. The distortions inevitably reduce the value of the data. In this paper, we propose a watermarking scheme by which the ownership of data can be publicly proven by anyone, as many times as necessary. The proposed scheme is distortion-free, thus suitable for watermarking any type of data without fear of error constraints. The proposed scheme is robust against typical database attacks including tuple/attribute insertion/deletion, random/selective value modification, data frame-up, and additive attacks.
11801606
Efficient communication in metacube: a new interconnection network
This paper introduces a new interconnection network for very large parallel computers called metacube (MC). An MC network has a 2-level cube structure. An MC(k,m) network connects 2(m2/sup k/+k) nodes with m + k links per node, where k is the dimension of a high-level cube and m is the dimension of low-level cubes (clusters). An MC network is a symmetric network with short diameter, easy and efficient routing similar to that of hypercubes. However, an MC network can connect more than one hundred of millions of nodes with only 6 links per node. Design of efficient routing algorithms for collective communications is the key issue for any interconnection network. In this paper we also show that total exchange (all-to-all personalized communication) can be done efficiently in metacube.
15288744
Cost/performance tradeoff of n-select square root implementations
Hardware square-root units require large numbers of gates even for iterative implementations. In this paper we present four low-cost high-performance fully-pipelined n-select implementations (nS-Root) based on a non-restoring-remainder square root algorithm. The nS-Root uses a parallel array of carry-save adders (CSAs). For a square root bit calculation, a CSA is used once. This means that the calculations can be fully pipelined. It also uses the n-way root-select technique to speedup the square root calculation. The cost/performance evaluation shows that n=2 or n=2.5 is a suitable solution for designing a high-speed fully pipelined square root unit while keeping the low-cost.
18109263
Automatically Acquiring Training Sets for Web Information Gathering
The traditional techniques rely on human effort to acquire training sets, which is expensive and inefficient. In this paper we present an alternative method to automatically acquire training sets without heavy investment of user efforts. The proposed method tends to fill a gap for effectiveness of using Web data in Web mining, and contributes to Web information gathering. The evaluation shows that the method is adequate to yield an promising achievement
14671442
An Approach to Outsourcing Data Mining Tasks while Protecting Business Intelligence and Customer Privacy
Data mining is playing an important role in decision making. It is beneficial to outsource data mining tasks if an organization does not have required expertise in-house. However, the organization may lose business intelligence and customer privacy during this outsourcing process. In this paper, we present a Bloom filter based solution to enable organizations to outsource their tasks of mining association rules while protecting their business intelligence and customer privacy. Our approach can achieve high precision in data mining by trading-off storage requirements