16340504
Research on Telerobotic System Based on ARM Core Processor
This paper discusses an embedded and teleoperation technology-based, the use of Internet to achieve real-time remote device control system,embedded ARM processor S3C2410 as a network server, CAN bus as a multi-robot remote control bus operating system.It realizes bilateral communication between control command and feedback data of the master-slave robot, and enhances the performance of stability. The simulation experimental results demonstrate that the system is accurate and available.
18482512
Remote equipment monitoring system based on ARM9 and CAN bus
In this paper, the remote monitoring system based on ARM9 and CAN bus is studied. It focuses on the ARM9 and Ethernet interface methods, CAN / Ethernet protocol conversion methods, and the construction of the network database. Adopting the technology, which is embedded Network interface device in the network environment of self-identification, self-description and interoperability, it will make the network sensor dynamic configuration and reconfiguration. Test results show that the system meets the requirements of automation equipment maintenance, real-time monitoring and reliability, so that it makes the effective realization of the remote monitoring equipment come true.
553829
Energy-Efficient Evaluation of Multiple Skyline Queries over a Wireless Sensor Network
Though skyline queries in wireless sensor networks have been intensively studied in recent years, existing solutions are not optimized for multiple skyline queries as they focus on single full space skyline queries. It is not efficient to individually evaluate skyline queries especially in a wireless sensor network environment where power consumption should be minimized. In this paper, we propose an energy-efficient multi-skyline evaluation (EMSE) algorithm to effectively evaluate multiple skyline queries in wireless sensor networks. EMSE first utilizes a global optimization mechanism to reduce the number of skyline queries and save on query propagation cost and parts of redundant result transmission cost as a consequence. Then, it utilizes a local optimization mechanism to share the skyline results among skyline queries and uses some filtering policies to further eliminate unnecessary data transmission and save the skyline result transmission cost as a consequence. The experimental results show that the proposed algorithm is energy-efficient when evaluating multiple skyline queries over wireless sensor networks.
27024485
A new design of multiple classifier system and its application to the classification of time series data
In this paper, we propose the scheme of multiple input representation-adaptive ensemble generation and aggregation(MIR-AEGA) for the classification of time series data. MIR-AEGA employs a set of heterogeneous classifiers, each of which takes a different representation of time series data as the input. MIR-AEGA adopts an "overfitting and selection" strategy. In the training phase, different ensembles of classifiers are adaptively generated by fitting the validation data ' globally in different degrees. The test data are then classified by each of the generated ensembles. The final decision is made by taking consideration into both the ability of each ensemble to fit the validation data locally and the possible overfitting effects. We claim that MIR-AEGA has two advantages (1) By using multiple representations, it exploits the temporal information of time series data as much as possible, thus could improve the overall performance (2) By tweaking the trade-off between the ability to fit the validation data and the overfitting effects, we expect the performance of this method is reliable in different situations. In this paper, the performance of MIR- AEGA is also assessed experimentally in comparison with other benchmark techniques. The experimental results demonstrate the good performance and the reliability of MIR-AEGA for the classification of time series data.
2155976
Maximal Subspace Coregulated Gene Clustering
Clustering is a popular technique for analyzing microarray data sets, with n genes and m experimental conditions. As explored by biologists, there is a real need to identify coregulated gene clusters, which include both positive and negative regulated gene clusters. The existing pattern-based and tendency-based clustering approaches cannot directly be applied to find such coregulated gene clusters, because they are designed for finding positive regulated gene clusters. In this paper, in order to cluster coregulated genes, we propose a coding scheme that allows us to cluster two genes into the same cluster if they have the same code, where two genes that have the same code can be either positive or negative regulated. Based on the coding scheme, we propose a new algorithm for finding maximal subspace coregulated gene clusters with new pruning techniques. A maximal subspace coregulated gene cluster clusters a set of genes on a condition sequence such that the cluster is not included in any other subspace coregulated gene clusters. We conduct extensive experimental studies. Our approach can effectively and efficiently find maximal subspace coregulated gene clusters. In addition, our approach outperforms the existing approaches for finding positive regulated gene clusters.
728817
Study of Coupling Based on Genetic Neural Network Multivariable Nonlinear Complex System
In this paper a method of decoupling has been put forward. Combine genetic neural network with multivariable nonlinear system adaptive decoupling control algorithm. Establish a control model of multivariable parameter in order to cancel the couplings of various variables. This method improves the stability and precision. According to the METLAB emulator proving, genetic neural network multivariable nonlinear decoupling model can cancel multivariable coupling problem preferably, make the system optimized.
24767232
The Research and Application of Human Detection Based on Support Vector Machine Using in Intelligent Video Surveillance System
This paper presented a special design and implementation of human detection based on SVM (support vector machine) and this method is used in intelligent video surveillance system. In order to simplify the design of the SVM classifier and improve efficiency of machine learning, both a grid vector representation and a center radiating vector representation are proposed to abstract features of the object. The sample data is obtained through processing and analysis including human and no-human which forms the training input to SVM. Finally, we used the trained recognizer to identify whether there is somebody broken into the object region. If there is, the automatic warning device gives the alarm, which guarantees a real-time surveillance.