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11th International IEEE Conference on Intelligent Transportation Systems, ITSC 2008

作者： Jia, Xiaolan Cao, Xianbin Guo, Yuanping Qiao, Hong Zhang, Jun University of Science and Technology of China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230026 China Institute of Automation Chinese Academy of Sciences Beijing 100080 China School of Electronic and Information Engineering Beihang University Beijing 100083 China

ISBN: (纸本)9781424421121

The task of Aircraft Landing Scheduling (ALS) is to give a landing sequence and landing times for a given set of aircrafts where many constraints must be satisfied. ALS is an NP-hard problem with large-scale and multi-constraint characteristics, thus it is hard to find optimal solution efficiently. In this paper, a hybrid algorithm of Clonal Selection Algorithm (CSA) and Receding Horizon Control (RHC) was proposed for ALS problem. In details, constrained CSA based on infeasibility degree (IFD) schedules aircrafts in current receding horizon, and then RHC repeats that optimization procedure using excellent gene segment spread (EGSS) until all aircrafts have landed. Comparative experiments show that the CSA-RHC hybrid algorithm is able to obtain an optimal landing sequence and landing times rapidly and effectively. © 2008 IEEE.

关键词： Computational complexity

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NURBS model for chaotic time series

NURBS model for chaotic time series

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2011 3rd International Conference on Computer Research and Development, ICCRD 2011

作者： Shao, Chenxi Xiao, Lipeng Department of Computer Science and Technology University of Science and Technology of China Hefei China Department of Modern Physics Nonlinear Science Center University of Science and Technology of China Hefei China Anhui Province Key Laboratory of Software in Computing and Communication Hefei China

ISBN: (纸本)9781612848372

NURBS method is extensively used in the field of Geometric Modeling. Since the original NURBS method does not have time factor, it can not display the huge advantage in the dynamics field. In this article, inspired by the segmented nature of basis functions, we propose a time factor method which can be directly applied in reconstruction of missing data, with remarkable performance in matching the original data. This work does not only give an expression of chaotic time series, but also highlights a possible way for the challenge in reconstruction of missing data. © 2011 IEEE.

关键词： Time series

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A New Channel Parameter For Cognitive Radio

A New Channel Parameter For Cognitive Radio

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International Conference on Cognitive Radio Oriented Wireless Networks and communications

作者： Ping Zhu Jinglong Li Kun Han Xufa Wang Anhui Province Key Laboratory of Software in Computing and Communication Department of Computer Science and Technology University of Science and Technology Hefei Anhui China

ISBN: (纸本)9781424408146;1424408156

Spectrum leasing has made more efficient utilization for the limited spectrum resource. While increasing the spectrum utilization, spectrum leasing also brings in some problem. Spectrum switch in the leasing process not only introduces in additional system overhead, but also decreases the secondary user's Qos. In this paper, we model the wireless system as a M/G/1 queue with preemptive priority, propose a new channel parameter to analyze the switch performance. Algorithms are suggested to reduce system overhead and to improve secondary user's Qos based on the new parameter.

关键词： Cognitive radio Switches Performance analysis laboratories Computer science communication switching Queueing analysis Physics Game theory Wideband

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Fast curve segmentation in real time

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Journal of Computational Information Systems 2007年第1期3卷 17-24页

作者： Du, Yi Lu, Detang Li, Daolun Wei, Wuzhou Institute of Engineering and Science Software University of Science and Technology of China Hefei 230027 China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230027 China

Traditional segmenting algorithm of time series cannot be used in the environment of data streams effectively. A new online algorithm for segmenting time series, called SC (Sequential Clustering), is presented here. The traditional clustering methods are confronted with the large challenge in the domain of mining time series. By considering the time attribute of temporal data, a novel clustering method is designed in this present study. Through constructing an in-memory List for saving segmenting information, SC can online segments time series with a single scan of the data points. The I/O cost is linear to the size of the sequential dataset. Using several experiments, SC's time efficiency and segmentation quality are evaluated in detail. Theory analysis and experimental Results show that SC is superior to other existed segmenting algorithms.

关键词： Data communication systems

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Emotion recognition through integrating EEG and peripheral signals

Emotion recognition through integrating EEG and peripheral s...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Yangyang Shu Shangfei Wang Key Lab of Computing and Communication Software of Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei China 230027

ISBN: (纸本)9781509041183

The inherent dependencies among multiple physiological signals are crucial for multimodal emotion recognition, but have not been thoroughly exploited yet. This paper propose to use restricted Boltzmann machine (RBM) to model such ***, the visible nodes of RBM represent EEG and peripheral physiological signals, and thus the connections between visible nodes and hidden nodes capture the intrinsic relations among multiple physiological signals. The RBM generates new representation from multiple physiological signals. Then, a support vector machine is adopted to recognize users' emotion states from the generated features. Furthermore, we extend the proposed fusion method for incomplete datas, since physiological signals are often corrupted due to artifacts. Specifically, we pre-train the RBM using all the complete data, then we update missing values and RBM parameters to minimize free energy of visible vectors using both complete and incomplete data. Experiments on two benchmark databases demonstrate the effectiveness of the proposed methods.

关键词： multimodal emotion recognition RBM EEG peripheral physiological signal missing data

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AN APPROACH BASED ON TSA-TREE FOR ACCURATE TIME SERIES CLASSIFICATION

AN APPROACH BASED ON TSA-TREE FOR ACCURATE TIME SERIES CLASS...

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2012 IEEE 2nd International Conference on Cloud computing and Intelligence Systems

作者： Xiaoxu He Chenxi Shao School of Computer Science and Technology University of Science and Technology of China Anhui Province Key Laboratory of Software in Computing and Communication

In order to improve the performance of time series classification, we introduce a new approach of time series classification. The first step of the approach is to design a feature exaction model based on Trend and Surprise Abstraction tree （TSA-tree）. The second step of the approach is to combine the exacted global feature and 1 nearest neighbor to classify time series. The proposed approach is compared with a number of known classifiers by experiments in artificial and real-world data sets. The experimental results show it can reduce the error rates of time series classification, so it is highly competitive with previous approaches.

关键词： Feature exaction dimension reduction classification TSA-tree

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A hormone based tracking strategy for wireless sensor network

A hormone based tracking strategy for wireless sensor networ...

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IEEE International Conference on Intelligent computing and Intelligent Systems (ICIS)

作者： Yipeng Zhang Haifeng You Xufa Wang Key Laboratory of Software in Computing and Communication Anhui Province School of Computer Science and Technology University of Science and Technology Hefei China

In this paper, we present a hormone based tracking strategy called Hormone based Activation (HA) for mobile target tracking in wireless sensor networks inspired by the bio-endocrine system. Hormone messages are used in sensor networks to coordinate sensor nodes for mobile target tracking in a self-adaptive way. Three kinds of hormones are introduced into sensor network for mobile target tracking: Base Hormone (BH),Wakeup Hormone (WH) and Sleep Hormone (SH).Based on different hormone messages, sensor nodes can choose to be asleep or awake in order to get good tracking results with less energy consumed. Comparative experiment shows that our strategy needs fewer nodes to wake up and has a much higher usage ratio of sensor nodes than other tracking strategies.

关键词： Biochemistry Wireless sensor networks Target tracking Biosensors Intelligent sensors Computer networks Mobile communication Sleep Energy efficiency communication system control

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A Clustering System for Gene Expression Data Based upon Genetic Programming and the HS-Model

A Clustering System for Gene Expression Data Based upon Gene...

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The Third International Joint Conference on Computational Science and Optimization(第三届计算科学与优化国际大会 CSO 2010)

作者： Guiquan Liu Xiufang Jiang Lingyun Wen Key Laboratory of Software in Computing and Communication Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei Anhui 230027 China

ISBN: (纸本)9781424468126;9780769540306

Cluster analysis is a major method to study gene function and gene regulation information for there is a lack of prior knowledge for gene data. Many clustering methods existed at present usually need manual operations or predetermined parameters, which are difficult for gene data. Besides, gene data possess their own characteristics, such as large scale, high-dimension, and noise. Therefore, a systematic clustering algorithm should be proposed to effectively deal with gene data. In this paper, a novel genetic programming (GP) clustering system for gene data based on hierarchical statistical model (HS-model) is proposed. And an appropriate fitness function is also proposed in this system. This clustering system can largely eliminate the infection of data scale and dimension. The proposed GP clustering system is applied to cluster the whole intact yeast gene data without dimensionality reduction. The experimental results indicate that the algorithm is highly efficient and can effectively deal with missing values in gene dataset

关键词： genetic programming cluster analysis missing value fitness function

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Evolutionary repair for evolutionary design of combinational logic circuits

Evolutionary repair for evolutionary design of combinational...

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Congress on Evolutionary Computation

作者： Xin Zhang Wenjian Luo School of Computer Science and Technology Anhui Province key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Hefei Anhui China

Evolutionary algorithms have been adopted to design logic circuits for many years. However, it takes too much time for evolutionary algorithms to generate circuits directly. Recently, the repair technique has been introduced into evolutionary design of the circuit to significantly decrease the time cost. And yet, the repair technique costs a lot of gate resource. In this paper, the evolutionary repair for evolutionary design of the combinational circuit is proposed, which generates the repair circuits with an evolutionary algorithm. The evolutionary repair could reduce the gate resource cost and does not spend much more time. The evolutionary repair is merged into the traditional evolutionary algorithm to form a novel evolutionary design algorithm, i.e. the erEDA. The experimental results demonstrate that the erEDA could balance the time cost and the gate resource consumption.

关键词： Maintenance engineering Logic gates Evolutionary computation Vectors Pins Adders Layout

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A fast algorithm for finding community structure based on community closeness

A fast algorithm for finding community structure based on co...

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The Third International Joint Conference on Computational Science and Optimization(第三届计算科学与优化国际大会 CSO 2010)

作者： Xiufang Jiang Guiquan Liu Zhiting Lin Key Laboratory of Software in Computing and Communication Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei Anhui 230027 China

ISBN: (纸本)9781424468126;9780769540306

Recently, the characterization of community structures in complex networks has received a considerable amount of attentions. Effective identification of these communities or clusters is a general problem in the field of data mining. In this paper we present a /ast hierarchical agglomerative algorithm based on community closeness (FHACC) algorithm, for detecting community structure which is very efficient and faster than many other competing algorithms. FHACC tends to agglomerate such communities that share the most common vertices into larger ones. Its running time on a sparse network with n vertices and m edges is O(mk + mt), where k denotes the mean vertex degree, and t is the iteration times of community agglomeration in FHACC algorithm. The algorithm was tested on several real-world networks and proved to be high efficient and effective in community finding.

关键词： community structure complex network data mining community closeness

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