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.5, No.2, 2018

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REVIEWS
Decomposition Methods for Manufacturing System Scheduling: A Survey
Fajun Yang, Kaizhou Gao, Simon Ian Ware, Yuting Zhu, Rong Su
2018, 5(2): 389-400. doi: 10.1109/JAS.2017.7510805
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Manufacturing is the application of labor, tools, machines, chemical and biological processing, to an original raw material by changing its physical and geometrical characteristics, in order to make finished products. Since the first industrial revolution, to accommodate the large-scale production, tremendous changes have happened to manufacturing through the innovations of technology, organization, management, transportation and communication. This work first reviews the high-volume low-mix process by focusing on the quantity production, transfer line and single model assembly line. Then, it reviews the high-volume high-mix process. For such a process type, mixed/multi model assembly line is usually adopted. Hence, two main decisions on them, i.e., balancing and, sequencing are reviewed. Thereafter, it discusses the low-volume high-mix process in detail. Then, technology gap and future work is discussed, and at last, conclusions are given.
Nonlinear Bayesian Estimation: From Kalman Filtering to a Broader Horizon
Huazhen Fang, Ning Tian, Yebin Wang, MengChu Zhou, Mulugeta A. Haile
2018, 5(2): 401-417. doi: 10.1109/JAS.2017.7510808
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This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of engineering fields, attracting decades of research effort. To date, one of the most promising and popular approaches is to view and address the problem from a Bayesian probabilistic perspective, which enables estimation of the unknown state variables by tracking their probabilistic distribution or statistics (e.g., mean and covariance) conditioned on a system's measurement data. This article offers a systematic introduction to the Bayesian state estimation framework and reviews various Kalman filtering (KF) techniques, progressively from the standard KF for linear systems to extended KF, unscented KF and ensemble KF for nonlinear systems. It also overviews other prominent or emerging Bayesian estimation methods including Gaussian filtering, Gaussian-sum filtering, particle filtering and moving horizon estimation and extends the discussion of state estimation to more complicated problems such as simultaneous state and parameter/input estimation.
Vehicle Dynamic State Estimation: State of the Art Schemes and Perspectives
Hongyan Guo, Dongpu Cao, Chen Hong, Chen Lv, Huaji Wang, Siqi Yang
2018, 5(2): 418-431. doi: 10.1109/JAS.2017.7510811
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Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the recent developments in the estimation of vehicle dynamic states. The definitions used in vehicle dynamic state estimation are first introduced, and alternative estimation structures are presented. Then, the sensor configuration schemes used to estimate vehicle velocity, sideslip angle, yaw rate and roll angle are presented. The vehicle models used for vehicle dynamic state estimation are further summarized, and representative estimation approaches are discussed. Future concerns and perspectives for vehicle dynamic state estimation are also discussed.
PAPERS
Coordinated Control Architecture for Motion Management in ADAS Systems
Tzu-Chi Lin, Siyuan Ji, Charles E. Dickerson, David Battersby
2018, 5(2): 432-444. doi: 10.1109/JAS.2017.7510814
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Advanced driver assistance systems (ADAS) seek to provide drivers and passengers of automotive vehicles increased safety and comfort. Original equipment manufacturers are integrating and developing systems for distance keeping, lane keeping and changing and other functionalities. The modern automobile is a complex system of systems. How the functionalities of advanced driver assistance are implemented and coordinated across the systems of the vehicle is generally not made available to the wider research community by the developers and manufactures. This paper seeks to begin filling this gap by assembling open source physics models of the vehicle dynamics and ADAS command models. Additionally, in order to facilitate ADAS development and testing without having access to the details of ADAS, a coordinated control architecture for motion management is also proposed for distributing ADAS motion control commands over vehicle systems. The architecture is demonstrated in a case study where motion is coordinated between the steering and the braking systems, which are typically used only for a single functionality. The integrated vehicle and system dynamics using the coordinated control architecture are simulated for various driving tasks. It is seen that improved trajectory following can be achieved by the proposed coordinated control architecture. The models, simulations and control architecture are made available for open access.
An Online Fault Detection Model and Strategies Based on SVM-Grid in Clouds
PeiYun Zhang, Sheng Shu, MengChu Zhou
2018, 5(2): 445-456. doi: 10.1109/JAS.2017.7510817
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Online fault detection is one of the key technologies to improve the performance of cloud systems. The current data of cloud systems is to be monitored, collected and used to reflect their state. Its use can potentially help cloud managers take some timely measures before fault occurrence in clouds. Because of the complex structure and dynamic change characteristics of the clouds, existing fault detection methods suffer from the problems of low efficiency and low accuracy. In order to solve them, this work proposes an online detection model based on asystematic parameter-search method called SVM-Grid, whose construction is based on a support vector machine (SVM). SVM-Grid is used to optimize parameters in SVM. Proper attributes of a cloud system's running data are selected by using Pearson correlation and principal component analysis for the model. Strategies of predicting cloud faults and updating fault sample databases are proposed to optimize the model and improve its performance. In comparison with some representative existing methods, the proposed model can achieve more efficient and accurate fault detection for cloud systems.
An Adaptive RBF Neural Network Control Method for a Class of Nonlinear Systems
Hongjun Yang, Jinkun Liu
2018, 5(2): 457-462. doi: 10.1109/JAS.2017.7510820
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This paper focuses on designing an adaptive radial basis function neural network (RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively. The uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives (CMD) system, which satisfies the structure of nonlinear system, is taken for simulation to confirm the effectiveness of the method. Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.
Stabilization of Uncertain Systems With Markovian Modes of Time Delay and Quantization Density
Jufeng Wang, Chunfeng Liu
2018, 5(2): 463-470. doi: 10.1109/JAS.2017.7510823
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This work studies the stabilization of a class of control systems that use communication networks as signal transmission medium. The lateral motion of independently actuated four-wheel vehicle is modeled as an uncertain-linear system. Time delay and quantization density are modeled as Markov chains. The networked control systems (NCSs) with plants being lateral motion are first transformed to switched linear systems with uncertain parameters. Sufficient and necessary conditions for the stochastic stability of closed-loop networked control systems are then established. By solving the matrix inequalities, this work presents an output-feedback controller that depends on the modes of time delay and quantization density. The controller performance is illustrated via a vehicular lateral motion system.
A Model Predictive Scheduling Algorithm in Real-Time Control Systems
Mengya Kang, Chenglin Wen, Chenxi Wu
2018, 5(2): 471-478. doi: 10.1109/JAS.2017.7510826
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The ineffective utilization of power resources has attracted much attention in current years. This paper proposes a real-time distributed load scheduling algorithm considering constraints of power supply. Firstly, an objective function is designed based on the constraint, and a base load forecasting model is established when aggregating renewable generation and non-deferrable load into a power system, which aims to transform the problem of deferrable loads scheduling into a distributed optimal control problem. Then, to optimize the objective function, a real-time scheduling algorithm is presented to solve the proposed control problem. At every time step, the purpose is to minimize the variance of differences between power supply and aggregate load, which can thus ensure the effective utilization of power resources. Finally, simulation examples are provided to illustrate the effectiveness of the proposed algorithm.
Explicit Symplectic Geometric Algorithms for Quaternion Kinematical Differential Equation
Hong-Yan Zhang, Zi-Hao Wang, Lu-Sha Zhou, Qian-Nan Xue, Long Ma, Yi-Fan Niu
2018, 5(2): 479-488. doi: 10.1109/JAS.2017.7510829
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Solving quaternion kinematical differential equations (QKDE) is one of the most significant problems in the automation, navigation, aerospace and aeronautics literatures. Most existing approaches for this problem neither preserve the norm of quaternions nor avoid errors accumulated in the sense of long term time. We present explicit symplectic geometric algorithms to deal with the quaternion kinematical differential equation by modelling its time-invariant and time-varying versions with Hamiltonian systems and adopting a three-step strategy. Firstly, a generalized Euler's formula and Cayley-Euler formula are proved and used to construct symplectic single-step transition operators via the centered implicit Euler scheme for autonomous Hamiltonian system. Secondly, the symplecticity, orthogonality and invertibility of the symplectic transition operators are proved rigorously. Finally, the explicit symplectic geometric algorithm for the time-varying quaternion kinematical differential equation, i.e., a non-autonomous and non-linear Hamiltonian system essentially, is designed with the theorems proved. Our novel algorithms have simple structures, linear time complexity and constant space complexity of computation. The correctness and efficiencies of the proposed algorithms are verified and validated via numerical simulations.
Social Manufacturing for High-end Apparel Customization
Xiuqin Shang, Fei-Yue Wang, Gang Xiong, Timo R. Nyberg, Yong Yuan, Sheng Liu, Chao Guo, Sen Bao
2018, 5(2): 489-500. doi: 10.1109/JAS.2017.7510832
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Social manufacturing (SM), a novel distributed, collaborative and intelligent manufacturing mode, is proposed and developed for high-end apparel customization. The main components of SM cloud are designed, and its research topics are summarized. Then, SM's key technologies are studied. 3D technologies for apparel customization, like 3D modeling, 3D fitting mirror and 3D customization, are developed to improve the customization precision and user experience. Information based collaborative management is realized to share, communicate, and handle the information efficiently among all groups and individuals of SM cloud. Suppliers' evaluation mechanism is designed to support the optimal decisions making. Next, SM cloud is constructed in five layers for high-end apparel customization. By using SM cloud based crowd-sourcing, social resources can be allocated rationally and utilized efficiently, consumer can customize the product in any processes like innovation, design, making, marketing and service, and traditional apparel enterprise can be upgraded into SM mode for keeping it competitive in the future customization markets.
Research on the Low-order Control Strategy of the Power System With Time Delay
Xinyi Yu, Xuejinfeng Hong, Jun Qi, Linlin Ou, Yanlin He
2018, 5(2): 501-508. doi: 10.1109/JAS.2017.7510835
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In this paper, the design problem of the low-order controller is considered for the power system with a fixed time delay. A linear model of the power system with time delay is firstly established. Then the proportional-integral-differential (PID) controller, which is the typical low-order controller, is designed to improve the stability of the power system. The stabilizing region of the PID controller is obtained. The control parameters chosen arbitrarily in the resultant region can ensure the stability of the power system. Finally, based on the stabilizing result, the PID controller satisfying the H performance index is designed, which improves the robustness of the whole power system. The main advantage of the proposed method lies in that there is no need to approximate the model of the power system. The method can be further extended to the power system which is more complex.
LMI Consensus Condition for Discrete-time Multi-agent Systems
Magdi S. Mahmoud, Gulam Dastagir Khan
2018, 5(2): 509-513. doi: 10.1109/JAS.2016.7510016
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This paper examines a consensus problem in multi-agent discrete-time systems, where each agent can exchange information only from its neighbor agents. A decentralized protocol is designed for each agent to steer all agents to the same vector. The design condition is expressed in the form of a linear matrix inequality. Finally, a simulation example is presented and a comparison is made to demonstrate the effectiveness of the developed methodology.
Constructing Multicast Routing Tree for Inter-cloud Data Transmission: An Approximation Algorithmic Perspective
Jun Huang, Shihao Li, Qiang Duan
2018, 5(2): 514-522. doi: 10.1109/JAS.2017.7510460
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Networking plays a crucial role in cloud computing especially in an inter-cloud environment, where data communications among data centers located at different geographical sites form the foundation of inter-cloud federation. Data transmissions required for inter-cloud federation in the complex inter-cloud networking system are often point-to-multi points, which calls for a more effective and efficient multicast routing algorithm in complex networking systems. In this paper, we investigate the multicast routing problem in the inter-cloud context with K constraints where K ≥ 2. Unlike most of existing algorithms that are too complex to be applied in practical scenarios, a novel and fast algorithm for establishing multicast routing tree for inter-clouds is proposed. The proposed algorithm leverages an entropy-based process to aggregate all weights into a comprehensive metric, and then uses it to search a multicast tree (MT) on the basis of the shortest path tree (SPT). We conduct complexity analysis and extensive simulations for the proposed algorithm from the approximation perspective. Both analytical and experimental results demonstrate that the algorithm is more efficient than a representative multi-constrained multicast routing algorithm in terms of both speed and accuracy, and thus we believe that the proposed algorithm is applicable to the inter-cloud environment.
Operator-Based Robust Nonlinear Control for SISO and MIMO Nonlinear Systems With PI Hysteresis
Shuhui Bi, Lei Wang, Shengjun Wen, Mingcong Deng
2018, 5(2): 523-530. doi: 10.1109/JAS.2016.7510175
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In this paper, operator based robust nonlinear control for single-input single-output (SISO) and multi-input multi-output (MIMO) nonlinear uncertain systems preceded by generalized Prandtl-Ishlinskii (PI) hysteresis is considered respectively. In detail, by using operator based robust right coprime factorization approach, the control system design structures including feedforward and feedback controllers for both SISO and MIMO nonlinear uncertain systems are given, respectively. In which, the controller design includes the information of PI hysteresis and its inverse, and some sufficient conditions for the controllers in both SISO and MIMO systems should be satisfied are also derived respectively. Based on the proposed conditions, influence from hysteresis is rejected, the systems are robustly stable and output tracking performance can be realized. Finally, the effectiveness of the proposed method is confirmed by numerical simulations.
A Novel Design Framework for Smart Operating Robot in Power System
Qiang Wang, Xiaojing Yang, Zhigang Huang, Shiqian Ma, Qiao Li, David Wenzhong Gao, Fei-Yue Wang
2018, 5(2): 531-538. doi: 10.1109/JAS.2017.7510838
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This paper proposes the concept and framework of smart operating system based on the artificial intelligence (AI) techniques. The demands and the potential applications of AI technologies in power system control centers is discussed in the beginning of the paper. The discussion is based on the results of a field study in the Tianjin Power System Control Center in China. According to the study, one problem in power systems is that the power system analysis system in the control center is not fast and powerful enough to help the operators in time to deal with the incidents in the power system. Another issue in current power system control center is that the operation tickets are compiled manually by the operators, so that it is less efficient and human errors cannot be avoided. Based on these problems, a framework of the smart operating robot is proposed in this paper, which includes an intelligent power system analysis system and a smart operation ticket compiling system to solve the two problems in power system control centers. The proposed framework is mainly based on the AI techniques, especially the neural network with deep learning, since it is faster and more capable of dealing with the highly nonlinear and complex power system.
Training and Testing Object Detectors With Virtual Images
Yonglin Tian, Xuan Li, Kunfeng Wang, Fei-Yue Wang
2018, 5(2): 539-546. doi: 10.1109/JAS.2017.7510841
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In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world is too demanding in terms of labor and money investments, and is usually inflexible to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of Parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations. A virtual dataset named ParallelEye is built, which can be used for several computer vision tasks. Then, by training the DPM (Deformable parts model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining ParallelEye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors.
Local Robust Sparse Representation for Face Recognition With Single Sample per Person
Jianquan Gu, Haifeng Hu, Haoxi Li
2018, 5(2): 547-554. doi: 10.1109/JAS.2017.7510658
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The purpose of this paper is to solve the problem of robust face recognition (FR) with single sample per person (SSPP). In the scenario of FR with SSPP, we present a novel model local robust sparse representation (LRSR) to tackle the problem of query images with various intra-class variations, e.g., expressions, illuminations, and occlusion. FR with SSPP is a very difficult challenge due to lacking of information to predict the possible intra-class variation of the query images. The key idea of the proposed method is to combine a local sparse representation model and a patch-based generic variation dictionary learning model to predict the possible facial intra-class variation of the query images. The experimental results on the AR database, Extended Yale B database, CMU-PIE database and LFW database show that the proposed method is robust to intra-class variations in FR with SSPP, and outperforms the state-of-art approaches.
Fractional-order Sparse Representation for Image Denoising
Leilei Geng, Zexuan Ji, Yunhao Yuan, Yilong Yin
2018, 5(2): 555-563. doi: 10.1109/JAS.2017.7510412
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Sparse representation models have been shown promising results for image denoising. However, conventional sparse representation-based models cannot obtain satisfactory estimations for sparse coefficients and the dictionary. To address this weakness, in this paper, we propose a novel fractional-order sparse representation (FSR) model. Specifically, we cluster the image patches into K groups, and calculate the singular values for each clean/noisy patch pair in the wavelet domain. Then the uniform fractional-order parameters are learned for each cluster. Then a novel fractional-order sample space is constructed using adaptive fractional-order parameters in the wavelet domain to obtain more accurate sparse coefficients and dictionary for image denoising. Extensive experimental results show that the proposed model outperforms state-of-the-art sparse representation-based models and the block-matching and 3D filtering algorithm in terms of denoising performance and the computational efficiency.
Modeling, Optimization, and Control of Solution Purification Process in Zinc Hydrometallurgy
Bei Sun, Chunhua Yang, Hongqiu Zhu, Yonggang Li, Weihua Gui
2018, 5(2): 564-576. doi: 10.1109/JAS.2017.7510844
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The solution purification process is an essential step in zinc hydrometallurgy. The performance of solution purification directly affects the normal functioning and economical benefits of zinc hydrometallurgy. This paper summarizes the authors' recent work on the modeling, optimization, and control of solution purification process. The online measurable property of the oxidation reduction potential (ORP) and the multiple reactors, multiple running statuses characteristic of the solution purification process are extensively utilized in this research. The absence of reliable online equipment for detecting the impurity ion concentration is circumvented by introducing the oxidation-reduction potential into the kinetic model. A steady-state multiple reactors gradient optimization, unsteady-state operational-pattern adjustment strategy, and a process evaluation strategy based on the oxidation-reduction potential are proposed. The effectiveness of the proposed research is demonstrated by its industrial experiment.
U-model Enhanced Dynamic Control of a Heavy Oil Pyrolysis/Cracking Furnace
Quanmin Zhu, Dongya Zhao, Shuzhan Zhang, Pritesh Narayan
2018, 5(2): 577-586. doi: 10.1109/JAS.2017.7510847
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This paper proposes a case study in the control of a heavy oil pyrolysis/cracking furnace with a newly extended U-model based pole placement controller (U-PPC). The major work of the paper includes:1) establishing a control oriented nonlinear dynamic model with Naphtha cracking and thermal dynamics; 2) analysing a U-model (i.e., control oriented prototype) representation of various popular process model sets; 3) designing the new U-PPC to enhance the control performance in pole placement and stabilisation; 4) taking computational bench tests to demonstrate the control system design and performance with a user-friendly step by step procedure.
Causality Diagram-based Scheduling Approach for Blast Furnace Gas System
Feng Jin, Jun Zhao, Chunyang Sheng, Wei Wang
2018, 5(2): 587-594. doi: 10.1109/JAS.2017.7510715
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Rational use of blast furnace gas (BFG) in steel industry can raise economic profit, save fossil energy resources and alleviate the environment pollution. In this paper, a causality diagram is established to describe the causal relationships among the decision objective and the variables of the scheduling process for the industrial system, based on which the total scheduling amount of the BFG system can be computed by using a causal fuzzy C-means (CFCM) clustering algorithm. In this algorithm, not only the distances among the historical samples but also the effects of different solutions on the gas tank level are considered. The scheduling solution can be determined based on the proposed causal probability of the causality diagram calculated by the total amount and the conditions of the adjustable units. The causal probability quantifies the impact of different allocation schemes of the total scheduling amount on the BFG system. An evaluation method is then proposed to evaluate the effectiveness of the scheduling solutions. The experiments by using the practical data coming from a steel plant in China indicate that the proposed approach can effectively improve the scheduling accuracy and reduce the gas diffusion.
Relief Materials Vehicles Planning in Natural Disasters
Qun Shi, Wen Yang, Zhen-Ming Yang, Qian-Chuan Zhao
2018, 5(2): 595-601. doi: 10.1109/JAS.2017.7510850
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A variety of relief materials needs to be transported to the disaster hit areas immediately after natural disasters. This paper studies the problem of planning relief materials vehicles in order to maximize their total weight. We propose a new method named substitution equilibrium point which can be used to plan relief materials vehicles. One feature of our model is to consider the substitution among vehicles which is usually omitted in the literature. The simulation experiments show that the transportation fleets are increased effectively considering the substitution between vehicles. Substitution equilibrium point has the same results as integer programming but has much lower time complexity.
Cyber Attack Protection and Control of Microgrids
Md Masud Rana, Li Li, Steven W. Su
2018, 5(2): 602-609. doi: 10.1109/JAS.2017.7510655
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Recently, the smart grid has been considered as a next-generation power system to modernize the traditional grid to improve its security, connectivity, efficiency and sustainability. Unfortunately, the smart grid is susceptible to malicious cyber attacks, which can create serious technical, economical, social and control problems in power network operations. In contrast to the traditional cyber attack minimization techniques, this paper proposes a recursive systematic convolutional (RSC) code and Kalman filter (KF) based method in the context of smart grids. Specifically, the proposed RSC code is used to add redundancy in the microgrid states, and the log maximum a-posterior is used to recover the state information, which is affected by random noises and cyber attacks. Once the estimated states are obtained by KF algorithm, a semidefinite programming based optimal feedback controller is proposed to regulate the system states, so that the power system can operate properly. Test results show that the proposed approach can accurately mitigate the cyber attacks and properly estimate and control the system states.
Robust H Load Frequency Control of Multi-area Power System With Time Delay: A Sliding Mode Control Approach
Yonghui Sun, Yingxuan Wang, Zhinong Wei, Guoqiang Sun, Xiaopeng Wu
2018, 5(2): 610-617. doi: 10.1109/JAS.2017.7510649
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This paper is devoted to investigate the robust H sliding mode load frequency control (SMLFC) of multi-area power system with time delay. By taking into account stochastic disturbances induced by the integration of renewable energies, a new sliding surface function is constructed to guarantee the fast response and robust performance, then the sliding mode control law is designed to guarantee the reach ability of the sliding surface in a finite-time interval. The sufficient robust frequency stabilization result for multi-area power system with time delay is presented in terms of linear matrix inequalities (LMIs). Finally, a two-area power system is provided to illustrate the usefulness and effectiveness of the obtained results.
An Exploration on Adaptive Iterative Learning Control for a Class of Commensurate High-order Uncertain Nonlinear Fractional Order Systems
Jianming Wei, Youan Zhang, Hu Bao
2018, 5(2): 618-627. doi: 10.1109/JAS.2017.7510361
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This paper explores the adaptive iterative learning control method in the control of fractional order systems for the first time. An adaptive iterative learning control (AILC) scheme is presented for a class of commensurate high-order uncertain nonlinear fractional order systems in the presence of disturbance. To facilitate the controller design, a sliding mode surface of tracking errors is designed by using sufficient conditions of linear fractional order systems. To relax the assumption of the identical initial condition in iterative learning control (ILC), a new boundary layer function is proposed by employing Mittag-Leffler function. The uncertainty in the system is compensated for by utilizing radial basis function neural network. Fractional order differential type updating laws and difference type learning law are designed to estimate unknown constant parameters and time-varying parameter, respectively. The hyperbolic tangent function and a convergent series sequence are used to design robust control term for neural network approximation error and bounded disturbance, simultaneously guaranteeing the learning convergence along iteration. The system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapnov-like composite energy function (CEF) containing new integral type Lyapunov function, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.
A Novel Approach for Enhancement of Geometric and Contrast Resolution Properties of Low Contrast Images
Koushlendra Kumar Singh, Manish Kumar Bajpai, Rajesh Kumar Pandey
2018, 5(2): 628-638. doi: 10.1109/JAS.2017.7510670
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The present work encompasses a new image enhancement algorithm using newly constructed Chebyshev fractional order differentiator. We have used Chebyshev polynomials to design Chebyshev fractional order differentiator. We have generated the high pass filter corresponding to it. The designed filters are applied for decomposing the input image into four bands and low-low (L-L) sub-band is updated using correction coefficients. Reconstructed image with updated L-L sub-band provides the enhanced image. The visual results obtained are encouraging for image enhancement. The applicability of the developed algorithm is illustrated on three different test images. The effects of order of differentiation on the edges of images have also been presented and discussed.
Relationship Between Integer Order Systems and Fractional Order Systems and Its Two Applications
Xuefeng Zhang
2018, 5(2): 639-643. doi: 10.1109/JAS.2016.7510205
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Existence of periodic solutions and stability of fractional order dynamic systems are two important and difficult issues in fractional order systems (FOS) field. In this paper, the relationship between integer order systems (IOS) and fractional order systems is discussed. A new proof method based on the above involved relationship for the non existence of periodic solutions of rational fractional order linear time invariant systems is derived. Rational fractional order linear time invariant autonomous system is proved to be equivalent to an integer order linear time invariant non-autonomous system. It is further proved that stability of a fractional order linear time invariant autonomous system is equivalent to the stability of another corresponding integer order linear time invariant autonomous system. The examples and state figures are given to illustrate the effects of conclusion derived.
LETTER TO THE EDITOR
Letter to the Editor Re "Fractional Modeling and SOC Estimation of Lithium-ion Battery"
Rahat Hasan, Jonathan Scott
2018, 5(2): 644-644. doi: 10.1109/JAS.2017.7510853
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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)