A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation

Vol.3, No.2, 2016

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EDITORIAL
Global Synchronization of Stochastically Disturbed Memristive Neurodynamics via Discontinuous Control Laws
Zhenyuan Guo, Shaofu Yang, Jun Wang
2016, 3(2): 121-131.
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This paper presents the theoretical results on the master-slave (or driving-response) synchronization of two memristive neural networks in the presence of additive noise. First, a control law with a linear time-delay feedback term and a discontinuous feedback term is introduced. By utilizing the stability theory of stochastic differential equations, sufficient conditions are derived for ascertaining global synchronization in mean square using this control law. Second, an adaptive control law consisting of a linear feedback term and a discontinuous feedback term is designed to achieve global synchronization in mean square, and it does not need prior information of network parameters or random disturbances. Finally, simulation results are presented to substantiate the theoretical results.
PAPERS
Semantic Similarity between Ontologies at Different Scales
Qingpeng Zhang, David Haglin
2016, 3(2): 132-140.
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In the past decade, existing and new knowledge and datasets have been encoded in different ontologies for semantic web and biomedical research. The size of ontologies is often very large in terms of number of concepts and relationships, which makes the analysis of ontologies and the represented knowledge graph computational and time consuming. As the ontologies of various semantic web and biomedical applications usually show explicit hierarchical structures, it is interesting to explore the trade-offs between ontological scales and preservation/precision of results when we analyze ontologies. This paper presents the first effort of examining the capability of this idea via studying the relationship between scaling biomedical ontologies at different levels and the semantic similarity values. We evaluate the semantic similarity between three gene ontology slims (plant, yeast, and candida, among which the latter two belong to the same kingdom - fungi) using four popular measures commonly applied to biomedical ontologies (Resnik, Lin, Jiang-Conrath, and SimRel). The results of this study demonstrate that with proper selection of scaling levels and similarity measures, we can significantly reduce the size of ontologies without losing substantial detail. In particular, the performances of Jiang- Conrath and Lin are more reliable and stable than that of the other two in this experiment, as proven by 1) consistently showing that yeast and candida are more similar (as compared to plant) at different scales, and 2) small deviations of the similarity values after excluding a majority of nodes from several lower scales. This study provides a deeper understanding of the application of semantic similarity to biomedical ontologies, and shed light on how to choose appropriate semantic similarity measures for biomedical engineering.
Traffic Flow Data Forecasting Based on Interval Type-2 Fuzzy Sets Theory
Runmei Li, Chaoyang Jiang, Fenghua Zhu, Xiaolong Chen
2016, 3(2): 141-148.
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This paper proposes a long-term forecasting scheme and implementation method based on the interval type-2 fuzzy sets theory for traffic flow data. The type-2 fuzzy sets have advantages in modeling uncertainties because their membership functions are fuzzy. The scheme includes traffic flow data preprocessing module, type-2 fuzzification operation module and long-term traffic flow data forecasting output module, in which the Interval Approach acts as the core algorithm. The central limit theorem is adopted to convert point data of mass traffic flow in some time range into interval data of the same time range (also called confidence interval data) which is being used as the input of interval approach. The confidence interval data retain the uncertainty and randomness of traffic flow, meanwhile reduce the influence of noise from the detection data. The proposed scheme gets not only the traffic flow forecasting result but also can show the possible range of traffic flow variation with high precision using upper and lower limit forecasting result. The effectiveness of the proposed scheme is verified using the actual sample application.
Coupled Cross-correlation Neural Network Algorithm for Principal Singular Triplet Extraction of a Cross-covariance Matrix
Xiaowei Feng, Xiangyu Kong, Hongguang Ma
2016, 3(2): 147-156.
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This paper proposes a novel coupled neural network learning algorithm to extract the principal singular triplet (PST) of a cross-correlation matrix between two high-dimensional data streams. We firstly introduce a novel information criterion (NIC), in which the stationary points are singular triplet of the crosscorrelation matrix. Then, based on Newton's method, we obtain a coupled system of ordinary differential equations (ODEs) from the NIC. The ODEs have the same equilibria as the gradient of NIC, however, only the first PST of the system is stable (which is also the desired solution), and all others are (unstable) saddle points. Based on the system, we finally obtain a fast and stable algorithm for PST extraction. The proposed algorithm can solve the speed-stability problem that plagues most noncoupled learning rules. Moreover, the proposed algorithm can also be used to extract multiple PSTs effectively by using sequential method.
Chaos and Combination Synchronization of a New Fractional-order System with Two Stable Node-foci
Zeeshan Alam, Liguo Yuan, Qigui Yang
2016, 3(2): 157-164.
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A new fractional-order Lorenz-like system with two stable node-foci has been thoroughly studied in this paper. Some sufficient conditions for the local stability of equilibria considering both commensurate and incommensurate cases are given. In addition, with the effective dimension less than three, the minimum effective dimension of the system is approximated as 2.8485 and is verified numerically. It should be affirmed that the linear differential equation in fractional-order Lorenzlike system appears to be less sensitive to the damping, represented by a fractional derivative, than the two other nonlinear equations. Furthermore, combination synchronization of this system is analyzed with the help of nonlinear feedback control method. Theoretical results are verified by performing numerical simulations.
Event-triggered Tracking Consensus with Packet Losses and Time-varying Delays
Mei Yu, Chuan Yan, Dongmei Xie, Guangming Xie
2016, 3(2): 165-173.
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This paper mainly investigates the event-triggered tracking control for leader-follower multi-agent systems with the problems of packet losses and time-varying delays when both the first-order and the second-order neighbors' information are used. Event-triggered control laws are adopted to reduce the frequency of individual actuation updating under the sampleddata framework for discrete-time agent dynamics. The communication graph is undirected and the loss of data across each communication link occurs at certain probability, which is governed by a Bernoulli process. One numerical example is given to demonstrate the effectiveness of the proposed methods. It is found that the tracking consensus is sped up by using the second-order neighbors' information when packet losses and time-varying delays occur.
Direct Trajectory Optimization and Costate Estimation of Infinite-horizon Optimal Control Problems Using Collocation at the Flipped Legendre-Gauss-Radau Points
Xiaojun Tang, Jie Chen
2016, 3(2): 174-183.
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A pseudospectral method is presented for direct trajectory optimization and costate estimation of infinite-horizon optimal control problems using global collocation at flipped Legendre-Gauss-Radau points which include the end point +1. A distinctive feature of the method is that it uses a new smooth, strictly monotonically decreasing transformation to map the scaled left half-open interval τ∈(-1, +1] to the descending time interval t ∈ (+∞, 0]. As a result, the singularity of collocation at point +1 associated with the commonly used transformation, which maps the scaled right half-open interval τ∈[-1, +1) to the increasing time interval [0,+∞), is avoided. The costate and constraint multiplier estimates for the proposed method are rigorously derived by comparing the discretized necessary optimality conditions of a finite-horizon optimal control problem with the Karush-Kuhn-Tucker conditions of the resulting nonlinear programming problem from collocation. Another key feature of the proposed method is that it provides highly accurate approximation to the state and costate on the entire horizon, including approximation at t = +∞, with good numerical stability. Numerical results show that the method presented in this paper leads to the ability to determine highly accurate solutions to infinite-horizon optimal control problems.
Continuous-time System Identification with Nuclear Norm Minimization and GPMF-based Subspace Method
Mingxiang Dai, Ying He, Xinmin Yang
2016, 3(2): 184-191.
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To improve the accuracy and effectiveness of continuous-time (CT) system identification, this paper introduces a novel method that incorporates the nuclear norm minimization (NNM) with the generalized Poisson moment functional (GPMF) based subspace method. The GPMF algorithm provides a simple linear mapping for subspace identification without the timederivatives of the input and output measurements to avoid amplification of measurement noise, and the NNM is a heuristic convex relaxation of the rank minimization. The Hankel matrix with minimized nuclear norm is used to determine the model order and to avoid the over-parameterization in subspace identification method (SIM). Furthermore, the algorithm to solve the NNM problem in CT case is also deduced with alternating direction methods of multipliers (ADMM). Lastly, two numerical examples are presented to evaluate the performance of the proposed method and to show the advantages of the proposed method over the existing methods.
A HMM-based Mandarin Chinese Singing Voice Synthesis System
Xian Li, Zengfu Wang
2016, 3(2): 192-202.
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We propose a mandarin Chinese singing voice synthesis system, in which hidden Markov model (HMM)-based speech synthesis technique is used. A mandarin Chinese singing voice corpus is recorded and musical contextual features are well designed for training. F0 and spectrum of singing voice are simultaneously modeled with context-dependent HMMs. There is a new problem, F0 of singing voice is always sparse because of large amount of context, i.e., tempo and pitch of note, key, time signature and etc. So the features hardly ever appeared in the training data cannot be well obtained. To address this problem, difference between F0 of singing voice and that of musical score (DF0) is modeled by a single Viterbi training. To overcome the over-smoothing of the generated F0 contour, syllable level F0 model based on discrete cosine transforms (DCT) is applied, F0 contour is generated by integrating two-level statistical models. The experimental results demonstrate that the proposed system outperforms the baseline system in both objective and subjective evaluations. The proposed system can generate a more natural F0 contour. Furthermore, the syllable level F0 model can make singing voice more expressive.
Pose Robust Low-resolution Face Recognition via Coupled Kernel-based Enhanced Discriminant Analysis
Xiaoying Wang, Haifeng Hu, Jianquan Gu
2016, 3(2): 203-212.
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Most face recognition techniques have been successful in dealing with high-resolution (HR) frontal face images. However, real-world face recognition systems are often confronted with the low-resolution (LR) face images with pose and illumination variations. This is a very challenging issue, especially under the constraint of using only a single gallery image per person. To address the problem, we propose a novel approach called coupled kernel-based enhanced discriminant analysis (CKEDA). CKEDA aims to simultaneously project the features from LR non-frontal probe images and HR frontal gallery ones into a common space where discrimination property is maximized. There are four advantages of the proposed approach: 1) by using the appropriate kernel function, the data becomes linearly separable, which is beneficial for recognition; 2) inspired by linear discriminant analysis (LDA), we integrate multiple discriminant factors into our objective function to enhance the discrimination property; 3) we use the gallery extended trick to improve the recognition performance for a single gallery image per person problem; 4) our approach can address the problem of matching LR non-frontal probe images with HR frontal gallery images, which is difficult for most existing face recognition techniques. Experimental evaluation on the multi-PIE dataset signifies highly competitive performance of our algorithm.
Face Super-resolution Reconstruction and Recognition Using Non-local Similarity Dictionary Learning Based Algorithm
Ningbo Hao, Haibin Liao, Yiming Qiu, Jie Yang
2016, 3(2): 213-224.
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One of the challenges of face recognition in surveillance is the low resolution of face region. Therefore many superresolution (SR) face reconstruction methods are proposed to produce a high-resolution face image from one or a set of low-resolution face images. However, existing dictionary learning based algorithms are sensitive to noise and very time-consuming. In this paper, we define and prove the multi-scale linear combination consistency. In order to improve the performance of SR, we propose a novel SR face reconstruction method based on nonlocal similarity and multi-scale linear combination consistency (NLS-MLC). We further proposed a new recognition approach for very low resolution face images based on resolution scale invariant feature (RSIF). A series of experiments are conducted on two public face image databases to test feasibility of our proposed methods. Experimental results show that the proposed SR method is more robust and computationally effective in face hallucination, and the recognition accuracy of RSIF is higher than some state-of-art algorithms.
Distributed Filtering Algorithm Based on Tunable Weights Under Untrustworthy Dynamics
Shiming Chen, Xiaoling Chen, Zhengkai Pei, Xingxing Zhang, Huajing Fang
2016, 3(2): 225-232.
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Aiming at effective fusion of a system state estimate of sensor network under attack in an untrustworthy environment, distributed filtering algorithm based on tunable weights is proposed. Considering node location and node influence over the network topology, a distributed filtering algorithm is developed to evaluate the certainty degree firstly. Using the weight reallocation approach, the weights of the attacked nodes are assigned to other intact nodes to update the certainty degree, and then the weight composed by the certainty degree is used to optimize the consensus protocol to update the node estimates. The proposed algorithm not only improves accuracy of the distributed filtering, but also enhances consistency of the node estimates. Simulation results demonstrate the effectiveness of the proposed algorithm.

IEEE/CAA Journal of Automatica Sinica

  • CiteScore 2018: 5.31
    Rank:Top 9% (Category of Control and Systems Engineering), Top 10% (Categories of Information System and Artificial Intelligence)