Table of Content

    30 April 2015, Volume 20 Issue 2 Previous Issue    Next Issue

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    Technique of Probability Density Function Shape Control for Nonlinear Stochastic Systems
    WANG Ling-zhi1,2* (王玲芝), QIAN Fu-cai1,3 (钱富才)
    2015, 20 (2):  129-134.  doi: 10.1007/s12204-015-1600-4
    Abstract ( 542 )  
    The shape control of probability density function (PDF) of the system state is an important topic in stochastic systems. In this paper, we propose a control technique for PDF shape of the state variable in nonlinear stochastic systems. Firstly, we derive and prove the form of the controller by investigating the Fokker-Planck- Kolmogorov (FPK) equation arising from the stochastic system. Secondly, an approach for getting approximate solution of the FPK equation is provided. A special function including some parameters is taken as the approximate stationary solution of the FPK equation. We use nonlinear least square method to solve the parameters in the function, and capture the approximate solution of the FPK equation. Substituting the approximate solution into the form of the controller, we can acquire the PDF shape controller. Lastly, some example simulations are conducted to verify the algorithm.
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    Wavelet-Based Hybrid Thresholding Method for Ultrasonic Liver Image Denoising
    ZHU Hai-jiang (祝海江)
    2015, 20 (2):  135-142.  doi: 10.1007/s12204-015-1601-3
    Abstract ( 653 )  
    This paper presents a wavelet-based hybrid threshold method according to the soft- and hard-threshold functions proposed by Donoho. The wavelet-based hybrid threshold method may help doctors to know more details on the liver disease through denoising the ultrasound image of the liver. First of all, an analytical expression for the hybrid threshold function is discussed. The wavelet-based hybrid threshold method is then investigated for ultrasound image of the liver. Finally, we test the influence of this parameter on the proposed method with the real ultrasound image corrupted by speckle noise with different variances. Moreover, we compare the proposed method under the varying parameters with the soft-threshold function and the hard-threshold function. Three metrics, which are correlation coefficient, edge preservation index and structural similarity index, are measured to quantify the denoised results of ultrasound liver image. Experimental results demonstrate the potential of the proposed method for ultrasound liver image denosing.
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    Genetic Algorithm Based Feature Selection and Parameter Optimization for Support Vector Regression Applied to Semantic Textual Similarity
    SU Bai-hua1 (苏柏桦), WANG Ying-lin2* (王英林)
    2015, 20 (2):  143-148.  doi: 10.1007/s12204-015-1602-2
    Abstract ( 491 )  
    Semantic textual similarity (STS) is a common task in natural language processing (NLP). STS measures the degree of semantic equivalence of two textual snippets. Recently, machine learning methods have been applied to this task, including methods based on support vector regression (SVR). However, there exist amounts of features involved in the learning process, part of which are noisy features and irrelative to the result. Furthermore, different parameters will significantly influence the prediction performance of the SVR model. In this paper, we propose genetic algorithm (GA) to select the effective features and optimize the parameters in the learning process, simultaneously. To evaluate the proposed approach, we adopt the STS-2012 dataset in the experiment. Compared with the grid search, the proposed GA-based approach has better regression performance.
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    Application-Oriented Cloud Monitoring Data Distribution Mechanism
    LI Da-zhi1,2* (李大志), LIU Jian-hua3 (刘建华), DONG Xin1 (董鑫),LI Lu-qun2 (李鲁群), CHEN Jun-hua2 (陈军华)
    2015, 20 (2):  149-155.  doi: 10.1007/s12204-015-1603-1
    Abstract ( 691 )  
    Cloud computing system consists of private clouds and public clouds. It merges its resources on each layer (e.g. IaaS, PaaS and SaaS), which poses a challenge for resource management. The cloud monitoring system is a solution to managing cloud system data from the heterogeneous resources. This paper discusses the monitoring and collection of the heterogeneous resources, studies the adaptive system, and proposes a real-time extensible distributed framework of monitoring data processing. Based on this framework, a system of monitoring data distribution, publication and subscription is proposed. The simulation results show that the proposed mechanism can adaptively determine the distribution action of monitoring data flow, and effectively reduce the costs for data monitoring and distribution.
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    A Short-Term Traffic Flow Forecasting Method and Its Applications
    LIU Si-yan (刘思妍), LI De-wei* (李德伟), XI Yu-geng (席裕庚), TANG Qi-feng (汤奇峰)
    2015, 20 (2):  156-163.  doi: 10.1007/s12204-015-1604-0
    Abstract ( 686 )  
    Short-term forecast of urban traffic flow is very important to intelligent transportation. Although the conventional methods have some advantages, to some extent, in improving the traffic forecast’s precision, it is still hard to achieve high accuracy. In this paper, we propose a short-term traffic flow forecasting method, which is based on the hybrid particle swarm optimization-neural network (HPSO-NN) with error compensation mechanism. In HPSO-NN, the hybrid PSO algorithm is employed to train the structures and parameters of the feed-forward advanced neural network, while the error compensation mechanism is employed to improve the accuracy. HPSONN is used to forecast the vehicle velocity in Shanghai North-South Viaduct. Experimental results show that the HPSO-NN, compared with the auto-regressive and moving average (ARMA) model, can forecast traffic flow with a higher accuracy. What’s more, we have also found that HPSO-NN with error compensation mechanism has better performance than that of HPSO-NN alone.
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    Improved Kernel Possibilistic Fuzzy Clustering Algorithm Based on Invasive Weed Optimization
    ZHAO Xiao-qiang* (赵小强), ZHOU Jin-hu (周金虎)
    2015, 20 (2):  164-170.  doi: 10.1007/s12204-015-1605-z
    Abstract ( 531 )  
    Fuzzy c-means (FCM) clustering algorithm is sensitive to noise points and outlier data, and the possibilistic fuzzy c-means (PFCM) clustering algorithm overcomes the problem well, but PFCM clustering algorithm has some problems: it is still sensitive to initial clustering centers and the clustering results are not good when the tested datasets with noise are very unequal. An improved kernel possibilistic fuzzy c-means algorithm based on invasive weed optimization (IWO-KPFCM) is proposed in this paper. This algorithm first uses invasive weed optimization (IWO) algorithm to seek the optimal solution as the initial clustering centers, and introduces kernel method to make the input data from the sample space map into the high-dimensional feature space. Then, the sample variance is introduced in the objection function to measure the compact degree of data. Finally, the improved algorithm is used to cluster data. The simulation results of the University of California-Irvine (UCI) data sets and artificial data sets show that the proposed algorithm has stronger ability to resist noise, higher cluster accuracy and faster convergence speed than the PFCM algorithm.
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    Soft-Sensing Method with Online Correction Based on Semi-Supervised Learning
    TANG Qi-feng (汤奇峰), LI De-wei* (李德伟), XI Yu-geng (席裕庚)
    2015, 20 (2):  171-176.  doi: 10.1007/s12204-015-1606-y
    Abstract ( 499 )  
    Soft sensing has been widely used in chemical industry to build an online monitor of the variables which are unmeasurable online or measurable online but with a high cost. One inherent difficulty is insufficiency of the training samples because the labeled data are limited. Besides, the traditional soft-sensing structure has no online correction mechanism. The forecasting result may be incorrect if the working condition is changed. In this work, a semi-supervised learning (SSL) method is proposed to build the soft-sensing model by use of the unlabeled data. Meanwhile, an online correction mechanism is proposed to establish a soft-sensing approach. The mechanism estimates the input variables at each step by a prediction model and calibrates the output variables by a compensation model. The experimental results show that the proposed method has better prediction accuracy and generalization ability than other approaches.
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    Stability of Switched Positive Descriptor Systems with Average Dwell Time Switching
    XIA Biao (夏彪), LIAN Jie* (连捷), YUAN Xue-hai (袁学海)
    2015, 20 (2):  177-184.  doi: 10.1007/s12204-015-1607-x
    Abstract ( 607 )  
    In this paper, the problems of stability for a class of switched positive descriptor systems (SPDSs) with average dwell time (ADT) switching are investigated. First, based on the equivalent switched system and the properties of the projector matrix, sufficient stabilities are given for the underlying systems in both continuoustime and discrete-time contexts. Then, a sufficient stability condition for the SPDS with both stable and unstable subsystems is obtained. The stability results for the SPDSs are represented in terms of a set of linear programmings (LPs) by the multiple linear co-positive Lyapunov function (MLCLF) approach. Finally, three numerical examples are given to illustrate the effectiveness of the obtained theoretical results.
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    A Logical Characterization for Linear Higher-Order Processes
    XU Xian1*(徐贤), LONG Huan2 (龙环)
    2015, 20 (2):  185-194.  doi: 10.1007/s12204-014-1554-y
    Abstract ( 409 )  
    Modal logic characterization in a higher-order setting is usually not a trivial task because higher-order process-passing is quite different from first-order name-passing. We study the logical characterization of higherorder processes constrained by linearity. Linearity respects resource-sensitiveness and does not allow processes to duplicate themselves arbitrarily. We provide a modal logic that characterizes linear higher-order processes, particularly the bisimulation called local bisimulation over them. More importantly, the logic has modalities for higher-order actions downscaled to resembling first-order ones in Hennessy-Milner logic, based on a formulation exploiting the linearity of processes.
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    A Distributed Power Allocation Scheme in Green Cognitive Radio Ad Hoc Networks
    WANG Rui (王睿), JI Hong (纪红), LI Xi*(李曦)
    2015, 20 (2):  195-201.  doi: 10.1007/s12204-014-1533-3
    Abstract ( 361 )  

    For the realization of green communications in cognitive radio ad hoc networks (CRAHNs), selfadaptive and efficient power allocation for secondary users (SUs) is essential. With the distributed and timevarying network topology, it needs to consider how to optimize the throughput and power consuming, avoid the interference to primary users (PUs) and other SUs, and pay attention to the convergence and fairness of the algorithm. In this study, this problem is modeled as a constraint optimization problem. Each SU would adjust its power and corresponding strategy with the goal of maximizing its throughput. By studying the interactions between SUs in power allocation and strategy selection, we introduce best-response dynamics game theory and prove the existence of Nash equilibrium (NE) point for performance analysis. We further design a fully distributed algorithm to make the SUs formulate their strategy based on their utility functions, the strategy and number of neighbors in local area. Compared with the water-filling (WF) algorithm, the proposed scheme can significantly increase convergent speed and average throughput, and decrease the power consuming of SUs.

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    An Iterative Algorithm for Computed Tomography Image Reconstruction from Limited-Angle Projections
    SUN Yu-li (孙玉立), TAO Jin-xu* (陶进绪), CHEN Hao (陈浩), LIU Cong-gui (刘从桂)
    2015, 20 (2):  202-208.  doi: 10.1007/s12204-015-1608-9
    Abstract ( 575 )  
    In application of tomography imaging, limited-angle problem is a quite practical and important issue. In this paper, an iterative reprojection-reconstruction (IRR) algorithm using a modified Papoulis-Gerchberg (PG) iterative scheme is developed for reconstruction from limited-angle projections which contain noise. The proposed algorithm has two iterative update processes, one is the extrapolation of unknown data, and the other is the modification of the known noisy observation data. And the algorithm introduces scaling factors to control the two processes, respectively. The convergence of the algorithm is guaranteed, and the method of choosing the scaling factors is given with energy constraints. The simulation result demonstrates our conclusions and indicates that the algorithm proposed in this paper can obviously improve the reconstruction quality.
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    Design and Analysis of an Adaptive Handover Protocol for 4G Networks
    BHASKAR Subbe Gowda1*, SATISH KUMAR Gandluru Arthur Edwin2, RAMANA REDDY Patil3
    2015, 20 (2):  209-217.  doi: 10.1007/s12204-015-1609-8
    Abstract ( 564 )  
    The future generation networks or 4G networks constitute of varied technologies converged over the Internet protocol version 6 (IPv6) core. The 4G networks offer varied services over different interfaces to the user nodes. Mobility management in 4G networks is an issue that exists. The handover protocols for mobility management in 4G networks that currently exist, do not consider wireless signal degradation during handover operations. This paper introduces the Noise Resilient Reduced Registration Time Care of Mobile IP (NR RRTC: MIP) protocol for handover management. A handover decision algorithm based on the signal strength measured by the user nodes is considered in the NR RRTC: MIP protocol. A simulation study is discussed in the paper to evaluate the performance of the NR RRTC: MIP protocol. The results obtained from the simulation study prove that the NR RRTC: MIP protocol effectively reduces handover latencies and improves network performance.
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    Improved Real-Coded Genetic Algorithm Solution for Unit Commitment Problem Considering Energy Saving and Emission Reduction Demands
    PAN Qian1*(潘谦), HE Xing1 (何星), CAI Yun-ze1 (蔡云泽),WANG Zhi-hua2 (王治华), SU Fan2 (苏凡)
    2015, 20 (2):  218-223.  doi: 10.1007/s12204-015-1610-2
    Abstract ( 368 )  
    Unit commitment (UC), as a typical optimization problem in electric power system, faces new challenges as energy saving and emission reduction get more and more important in the way to a more environmentally friendly society. To meet these challenges, we propose a UC model considering energy saving and emission reduction. By using real-number coding method, swap-window and hill-climbing operators, we present an improved real-coded genetic algorithm (IRGA) for UC. Compared with other algorithms approach to the proposed UC problem, the IRGA solution shows an improvement in effectiveness and computational time.
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    Neural Network Prediction Model for Ship Hydraulic Pressure Signal Under Wind Wave Background
    LI Song1 (李松), ZHANG Chun-hua2* (张春华), SHI Min1 (石敏)
    2015, 20 (2):  224-227.  doi: 10.1007/s12204-015-1611-1
    Abstract ( 366 )  
    The ship hydraulic pressure signal is one of the important characters for the target detection and recognition. At present, most of the researches on the detection focus on the ways in the time domain. The ways are usually invalid in the large wind wave background. In order to solve the problem efficiently, we present an effectual way to detect the ship using the ship hydraulic pressure signal. Firstly, the signature in the proposed method is decomposed by wavelet-transform technique and reconstructed at the low-frequency region. Then, a predictive model is set up by using the radial basis function (RBF) neural network. Finally, the signature predictive error is regarded as the testing signal which can be used to judge whether the target exists or does not. The practical result shows that the method can improve the signal to noise ratio (SNR) obviously.
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    Conceptual Process and Analysis of Water-Gas-Shift Membrane Reactor
    PANG Ting* (庞婷), LIN Jerry Y. S. (林跃生)
    2015, 20 (2):  228-233.  doi: 10.1007/s12204-015-1612-0
    Abstract ( 400 )  
    A new conceptual water-gas-shift (WGS) process is designed for integrated gasification combined cycle (IGCC), using membrane reactor (MR) equipped with H2-permselective zeolite membranes for the WGS reaction. The new process makes it possible to capture CO2 before power generation process by converting CO in the syngas to CO2 which can be collected after WGS reaction. The new process also produces purer H2 for combustion in gas turbine. Conceptual design of the MR, mass and heat balance analysis, and cost estimation of the new process are also provided in this paper.
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    Coupled Element Modeling Scheme for the Global Dynamic Analysis of Unbonded Flexible Risers
    YANG He-zhen*(杨和振), JIANG Hao (姜豪), YANG Qi (杨启), DING Jin-hong (丁金鸿)
    2015, 20 (2):  234-242.  doi: 10.1007/s12204-015-1613-z
    Abstract ( 396 )  
    A coupled element modeling method is proposed for global dynamic analyses of unbonded flexible risers. Owing to the multi-layer structure of unbonded flexible risers, the global-dynamic-analysis method applied to the steel rigid risers is insufficient for flexible risers. The main challenges lie in the enormous difference between the anti-tension and anti-binding capacity of unbonded flexible risers which results in serious ill-conditional calculation in global dynamic analysis. In order to solve this problem, the coupled element modeling approach was proposed in this study. A time domain fatigue analysis was applied to illustrate the necessity of the proposed approach. A dynamic benchmark case is used to demonstrate the accuracy of the coupled element method respectively. Subsequently the validated coupling element method is employed to conduct the global dynamic analyses for a free hanging flexible riser. The results demonstrate that the proposed approach can give the accurate global dynamic response under the guidance of the fatigue failure mode for unbonded flexible riser. The parametric influence analyses also provide a practical and effective way for predicting the global dynamic response.
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    The Modified Ensemble Empirical Mode Decomposition Method and Extraction of Oceanic Internal Wave from Synthetic Aperture Radar Image
    WANG Jing-tao1,3 (王静涛), XU Xiao-ge2,3* (许晓革), MENG Xiang-hua3? (孟祥花)
    2015, 20 (2):  243-250.  doi: 10.1007/s12204-015-1614-y
    Abstract ( 513 )  
    In this paper a modified ensemble empirical mode decomposition (EEMD) method is presented, which is named winning-EEMD (W-EEMD). Two aspects of the EEMD, the amplitude of added white noise and the number of intrinsic mode functions (IMFs), are discussed in this method. The signal-to-noise ratio (SNR) is used to measure the amplitude of added noise and the winning number of IMFs (which results most frequency) is used to unify the number of IMFs. By this method, the calculation speed of decomposition is improved, and the relative error between original data and sum of decompositions is reduced. In addition, the feasibility and effectiveness of this method are proved by the example of the oceanic internal solitary wave.
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    Role of Liver Progenitor Cell in Liver Regeneration:Cellular Cross-Talks and Signals
    CHEN Li-li (陈黎黎), ZHANG Qian-fei (张倩飞), KONG Xiao-ni*(孔晓妮)
    2015, 20 (2):  251-256.  doi: 10.1007/s12204-015-1615-x
    Abstract ( 498 )  
    The liver is well known for its ability to regenerate in response to injury. After partial hepatectomy and some chemicals induced acute liver injury, existing hepatocytes can expand to repair the liver function. While adult liver stem/progenitor cells (LPCs) are evoked and differentiate into functional hepatocytes and cholangiocytes to compensate the damaged liver once hepatocyte proliferation is severely impaired. A number of evidences suggest that adjacent hepatic stellate cells (HSCs) or invading leukocytes may be involved in LPCs directed regeneration through governning two major events including fibrogenic and inflammatory responses respectively or simultaneously. As such, a microenvironment (or “niche”) composed of different cell sources or factors presents diversity, which eventually mediates LPCs response to biliary or hepatocellular regeneration. This mini review aims at summarizing the latest development on the roles of HSCs, macrophages and lymphocytes as well as corresponding signaling pathways in liver progenitor cells mediated biliary and hepatocellular regeneration, and discussing therapeutic potential of liver progenitor cells in hepatic diseases.
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