18717814
Research on Distributed Data Mining Tool Used in Control System
In this paper, A distributed data mining tool that suitable for distributed and isomeric database environment of intelligent control system by borrowing ideas from architecture of grid is introduced. It can also perform incremental data mining based on history mining result. This tool makes full use of Metadata of each kind of resources. The resources associated in the data mining are all packed into form of web service, which is give a description by respective attribute document of the XML format. SOAP takes charge of the communication between the separated parts of the whole data mining process.
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.
9319216
Distance-Based Sparse Associative Memory Neural Network Algorithm for Pattern Recognition
A sparse two-Dimension distance weighted approach for improving the performance of exponential correlation associative memory (ECAM) and modified exponential correlation associative memory (MECAM) is presented in this paper. The approach is inspired by biological visual perception mechanism and extensively existing sparse small-world network phenomenon. By means of the approach, the two new associative memory neural networks, i.e., distance-based sparse ECAM (DBS-ECAM) and distance-based sparse MECAM (DBS-MECAM), are induced by introducing both the decaying two-Dimension distance factor and small-world architecture into ECAM and MECAM’s evolution rule for image processing application. Such a new configuration can reduce the connection complexity of conventional fully connected associative memories so that makes AM’ VLSI implementation easier. More importantly, the experiments performed on the binary visual images show DBS-ECAM and DBS-MECAM can learn and recognize patterns more effectively than ECAM and MECAM, respectively.
7600762
Automatic Scoring of Children's Read-Aloud Text Passages and Word Lists
Assessment of reading proficiency is typically done by asking subjects to read a text passage silently and then answer questions related to the text. An alternate approach, measuring reading-aloud proficiency, has been shown to correlate well with the aforementioned common method and is used as a paradigm in this paper. 
 
We describe a system that is able to automatically score two types of children's read speech samples (text passages and word lists), using automatic speech recognition and the target criterion "correctly read words per minute". Its performance is dependent on the data type (passages vs. word lists) as well as on the relative difficulty of passages or words for individual readers. Pearson correlations with human assigned scores are around 0.86 for passages and around 0.80 for word lists.
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.
18800487
Pattern Matching over Cloaked Time Series
In many privacy preserving applications such as Location-Based Services (LBS), medical data analysis, and data sequence matching, users often deliberately disturb the original data in order to avoid the release of their private information. Although these disturbed cloaked data cannot reveal the privacy information of individual users, they can still help perform some data mining tasks such as data classification. In this paper, we study one important and fundamental query predicate, that is, to find the cloaked time series that are similar to a query pattern. In this paper, we formalize such similarity search problem over the cloaked time series, and propose a novel approach to index the cloaked series, which can facilitate the similarity query.
38065195
Aggregation of Multiple Reinforcement Learning Algorithms
Reinforcement learning (RL) has been successfully used in many fields. With the increasing complexity of environments and tasks, it is difficult for a single learning algorithm to cope with complicated problems with high performance. This paper proposes a new multiple learning architecture, "Aggregated Multiple Reinforcement Learning System (AMRLS)", which aggregates different RL algorithms in each learning step to make more appropriate sequential decisions than those made by individual learning algorithms. This architecture was tested on a Cart-Pole system. The presented simulation results confirm our prediction and reveal that aggregation not only provides robustness and fault tolerance ability, but also produces more smooth learning curves and needs fewer learning steps than individual learning algorithms.