Differential flatness-based distributed control of underactuated robot swarms

This paper proposes a distributed control method based on the differential flatness(DF) property of robot swarms. The swarm DF mapping is established for underactuated differentially flat dynamics, according to the control objective. The DF mapping refers to the fact that the system state and input of each robot can be derived algebraically from the flat outputs of the leaders and the cooperative errors and their finite order derivatives. Based on the proposed swarm DF mapping, a distributed controller is designed. The distributed implementation of swarm DF mapping is achieved through observer design. The effectiveness of the proposed method is validated through a numerical simulation of quadrotor swarm synchronization.

Construction of Domain Web Classification Ontology Based on Suggested Upper Merged Ontology

Aiming at the problem of merging heterogeneous semantic taxonomy emerged in Web information integration, a method of building Web classification ontology （WCO） has been proposed. A WCO that is logically consistent with the suggested upper merged ontology （SUMO） is defined, together with axioms needed to classify Web pages. WCO can be used as a foundation of merging heterogeneous semantic taxonomy, and could be used to support Web information integration and classification based Web information retrieval.

A Routing Optimization Algorithm with Quality of Multi- Service in Wireless Sensor Network

According to the problem of energy consumption in wireless sensor network （WSN）,this paper puts forward a routing optimization algorithm with quality of multi-service, using the function of routing optimization with quality of multi-service and membership function of satisfaction, which integrates the energy consumption of communication and residual and the information of time delay into the membership function of satisfaction to solve the equilibrium factor, so that it can become the optimal routing that balances the network lifetime, transmission delay of data, and node energy consumption of nodes. Simulation experiment shows that adopting the algorithm can make lifecycle of nodes longer and network transmit more data packets at the same time. Experimental results verify the algorithm can effectively balance the network energy, reduce the energy consumption and prolong the network lifetime.

Optical polarization imaging for underwater target detection with non-scatter background

For conventional optical polarization imaging of underwater target,the polarization degree of backscatter should be pre-measured by averaging the pixel intensities in the no target region of the polarization images,and the polarization property of the target is assumed to be completely depolarized.When the scattering background is unseen in the field of view or the target is polarized,conventional method is helpless in detecting the target.An improvement is to use lots of co-polarization and cross polarization detection components.We propose a polarization subtraction method to estimate depolarization property of the scattering noise and target signal.And experiment in a quartz cuvette container is performed to demonstrate the effectiveness of the proposed method.The results show that the proposed method can work without scattering background reference,and further recover the target along with smooth surface for polarization preserving response.This study promotes the development of optical polarization imaging systems in underwater environments.

Fast implementation of kernel simplex volume analysis based on modified Cholesky factorization for endmember extraction

Endmember extraction is a key step in the hyperspectral image analysis process. The kernel new simplex growing algorithm （KNSGA）, recently developed as a nonlinear alternative to the simplex growing algorithm （SGA）, has proven a promising endmember extraction technique. However, KNSGA still suffers from two issues limiting its application. First, its random initialization leads to inconsistency in final results; second, excessive computation is caused by the iterations of a simplex volume calculation. To solve the first issue, the spatial pixel purity index （SPPI） method is used in this study to extract the first endrnember, eliminating the initialization dependence. A novel approach tackles the second issue by initially using a modified Cholesky fac- torization to decompose the volume matrix into triangular matrices, in order to avoid directly computing the determinant tauto- logically in the simplex volume formula. Theoretical analysis and experiments on both simulated and real spectral data demonstrate that the proposed algorithm significantly reduces computational complexity, and runs faster than the original algorithm.

Valuable Data Extraction for Resistivity Imaging Logging Interpretation

Imaging logging has become a popular means of well logging because it can visually represent the lithologic and structural characteristics of strata.The manual interpretation of imaging logging is affected by the limitations of the naked eye and experiential factors.As a result,manual interpretation accuracy is low.Therefore,it is highly useful to develop effective automatic imaging logging interpretation by machine learning.Resistivity imaging logging is the most widely used technology for imaging logging.In this paper,we propose an automatic extraction procedure for the geological features in resistivity imaging logging images.This procedure is based on machine learning and achieves good results in practical applications.Acknowledging that the existence of valueless data significantly affects the recognition effect,we propose three strategies for the identification of valueless data based on binary classification.We compare the effect of the three strategies both on an experimental dataset and in a production environment,and find that the merging method is the best performing of the three strategies.It effectively identifies the valueless data in the well logging images,thus significantly improving the automatic recognition effect of geological features in resistivity logging images.

Clapping and Broadcasting Synchronization in Wireless Sensor Networks

The Clapping and Broadcasting Synchronization （CBS） algorithm, which is specifically designed for large-scale sensor networks with low communication overhead and high synchronization accuracy, is introduced. The CBS protocol uses broadcasting rather than pairwise communication to accomplish synchronization. In the CBS scheme, the initial offset of local clocks can be successfully eliminated by the operation of clapping nodes, which leads to significant improvement in synchronization accuracy. The CBS protocol was implemented on the TelosB platform and its performance was evaluated in a variety of experiments. The results demonstrate that the CBS protocol outperforms the current state-of-the-art approach, the Flooding Time Synchronization Protocol （FTSP）, in both single-hop and multi-hop scenarios in terms of synchronous precision and energy consumption. In multi-hop scenarios, the CBS algorithm keeps about 50% of its synchronization errors within 1 ms. In comparison, the FTSP keeps less than 7% of its synchronization errors within this range. In both single-hop and multi-hop scenarios, the CBS protocol is over 3.2 times more energy-efficient than the FTSP.