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. 9,  No. 4, 2022

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2022, 9(4): 573-577. doi: 10.1109/JAS.2022.105443
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The Laplacian eigenvalue spectrum of a complex network contains a great deal of information about the network topology and dynamics, particularly affecting the network synchronization process and performance. This article briefly reviews the recent progress in the studies of network synchronizability, regarding its spectral criteria and topological optimization, and explores the role of higher-order topologies in measuring the optimal synchronizability of large-scale complex networks.
2022, 9(4): 578-600. doi: 10.1109/JAS.2022.105404
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Electricity theft is one of the major issues in developing countries which is affecting their economy badly. Especially with the introduction of emerging technologies, this issue became more complicated. Though many new energy theft detection (ETD) techniques have been proposed by utilising different data mining (DM) techniques, state & network (S&N) based techniques, and game theory (GT) techniques. Here, a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and limitations. Three levels of taxonomy are presented to classify state-of-the-art ETD techniques. Different types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from literature. The challenges of different ETD techniques and their mitigation are suggested for future work. It is observed that the literature on ETD lacks knowledge management techniques that can be more effective, not only for ETD but also for theft tracking. This can help in the prevention of energy theft, in the future, as well as for ETD.
2022, 9(4): 601-614. doi: 10.1109/JAS.2022.105410
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With the rapid development of computer technology, automatic control technology and communication technology, research on unmanned aerial vehicles (UAVs) has attracted extensive attention from all over the world during the last decades. Particularly due to the demand of various civil applications, the conceptual design of UAV and autonomous flight control technology have been promoted and developed mutually. This paper is devoted to providing a brief review of the UAV control issues, including motion equations, various classical and advanced control approaches. The basic ideas, applicable conditions, advantages and disadvantages of these control approaches are illustrated and discussed. Some challenging topics and future research directions are raised.
2022, 9(4): 615-623. doi: 10.1109/JAS.2022.105449
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This paper is concerned with the coordinative control problem of networked nonlinear multi-agents (NNM) with communication delays. A high-order fully actuated (HOFA) model is introduced to describe the nonlinear multi-agents. Based on this model, a HOFA predictive coordination method is proposed to compensate for the communication delays actively and achieve simultaneous stability and consensus. This method largely simplifies the design of networked nonlinear multi-agents and makes the control performance be same for networked nonlinear multi-agents with and without communication delays. The analysis on the closed-loop systems derives the simultaneous stability and consensus criteria of networked nonlinear multi-agents using the HOFA predictive coordination method. With the presented way of designing HOFA predictive coordination controllers, a simulated example demonstrates the advantages of the proposed method.
2022, 9(4): 624-634. doi: 10.1109/JAS.2022.105452
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This article presents a distributed periodic event-triggered (PET) optimal control scheme to achieve generation cost minimization and average bus voltage regulation in DC microgrids. In order to accommodate the generation constraints of the distributed generators (DGs), a virtual incremental cost is firstly designed, based on which an optimality condition is derived to facilitate the control design. To meet the discrete-time (DT) nature of modern control systems, the optimal controller is directly developed in the DT domain. Afterward, to reduce the communication requirement among the controllers, a distributed event-triggered mechanism is introduced for the DT optimal controller. The event-triggered condition is detected periodically and therefore naturally avoids the Zeno phenomenon. The closed-loop system stability is proved by the Lyapunov synthesis for switched systems. The generation cost minimization and average bus voltage regulation are obtained at the equilibrium point. Finally, switch-level microgrid simulations validate the performance of the proposed optimal controller.
2022, 9(4): 635-651. doi: 10.1109/JAS.2022.105455
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The number of available control sources is a limiting factor to many network control tasks. A lack of input sources can result in compromised controllability and/or sub-optimal network performance, as noted in engineering applications such as the smart grids. The mechanism can be explained by a linear time-invariant model, where structural controllability sets a lower bound on the number of required sources. Inspired by the ubiquity of time-varying topologies in the real world, we propose the strategy of spatiotemporal input control to overcome the source-related limit by exploiting temporal variation of the network topology. We theoretically prove that under this regime, the required number of sources can always be reduced to 2. It is further shown that the cost of control depends on two hyperparameters, the numbers of sources and intervals, in a trade-off fashion. As a demonstration, we achieve controllability over a complex network resembling the nervous system of Caenorhabditis elegans using as few as 6% of the sources predicted by a static control model. This example underlines the potential of utilizing topological variation in complex network control problems.
2022, 9(4): 652-667. doi: 10.1109/JAS.2022.105458
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Data with missing values, or incomplete information, brings some challenges to the development of classification, as the incompleteness may significantly affect the performance of classifiers. In this paper, we handle missing values in both training and test sets with uncertainty and imprecision reasoning by proposing a new belief combination of classifier (BCC) method based on the evidence theory. The proposed BCC method aims to improve the classification performance of incomplete data by characterizing the uncertainty and imprecision brought by incompleteness. In BCC, different attributes are regarded as independent sources, and the collection of each attribute is considered as a subset. Then, multiple classifiers are trained with each subset independently and allow each observed attribute to provide a sub-classification result for the query pattern. Finally, these sub-classification results with different weights (discounting factors) are used to provide supplementary information to jointly determine the final classes of query patterns. The weights consist of two aspects: global and local. The global weight calculated by an optimization function is employed to represent the reliability of each classifier, and the local weight obtained by mining attribute distribution characteristics is used to quantify the importance of observed attributes to the pattern classification. Abundant comparative experiments including seven methods on twelve datasets are executed, demonstrating the out-performance of BCC over all baseline methods in terms of accuracy, precision, recall, F1 measure, with pertinent computational costs.
2022, 9(4): 668-685. doi: 10.1109/JAS.2022.105461
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In this paper, we study the system performance of mobile edge computing (MEC) wireless sensor networks (WSNs) using a multiantenna access point (AP) and two sensor clusters based on uplink nonorthogonal multiple access (NOMA). Due to limited computation and energy resources, the cluster heads (CHs) offload their tasks to a multiantenna AP over Nakagami-m fading. We proposed a combination protocol for NOMA-MEC-WSNs in which the AP selects either selection combining (SC) or maximal ratio combining (MRC) and each cluster selects a CH to participate in the communication process by employing the sensor node (SN) selection. We derive the closed-form exact expressions of the successful computation probability (SCP) to evaluate the system performance with the latency and energy consumption constraints of the considered WSN. Numerical results are provided to gain insight into the system performance in terms of the SCP based on system parameters such as the number of AP antennas, number of SNs in each cluster, task length, working frequency, offloading ratio, and transmit power allocation. Furthermore, to determine the optimal resource parameters, i.e., the offloading ratio, power allocation of the two CHs, and MEC AP resources, we proposed two algorithms to achieve the best system performance. Our approach reveals that the optimal parameters with different schemes significantly improve SCP compared to other similar studies. We use Monte Carlo simulations to confirm the validity of our analysis.
2022, 9(4): 686-698. doi: 10.1109/JAS.2022.105464
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It is crucial to predict the outputs of a thickening system, including the underflow concentration (UC) and mud pressure, for optimal control of the process. The proliferation of industrial sensors and the availability of thickening-system data make this possible. However, the unique properties of thickening systems, such as the non-linearities, long-time delays, partially observed data, and continuous time evolution pose challenges on building data-driven predictive models. To address the above challenges, we establish an integrated, deep-learning, continuous time network structure that consists of a sequential encoder, a state decoder, and a derivative module to learn the deterministic state space model from thickening systems. Using a case study, we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental results. The results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system trajectories. The proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.
2022, 9(4): 699-708. doi: 10.1109/JAS.2021.1004383
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This paper investigates the atomic spin polarization controllability of spin-exchange relaxation-free co-magnetometers (SERFCMs). This is the first work in the field of controllability analysis for the atomic spin ensembles systems, whose dynamic behaviors of spin polarization are described by the Bloch equations. Based on the Bloch equations, a state-space model of the atomic spin polarization for SERFCM is first established, which belongs to a particular class of nonlinear systems. For this class of nonlinear systems, a novel determination method for the global state controllability is proposed and proved. Then, this method is implemented in the process of controllability analysis on the atomic spin polarization of an actual SERFCM. Moreover, a theoretically feasible and reasonable solution of the control input is proposed under some physical constraints, with whose limitation of realistic conditions, the controller design can be accomplished more practically and more exactly. Finally, the simulation results demonstrate the feasibility and validation of the proposed controllability determination method.
2022, 9(4): 709-718. doi: 10.1109/JAS.2021.1004389
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In this paper, the Kalman filter (KF) and the unbiased finite impulse response (UFIR) filter are fused in the discrete-time state-space to improve robustness against uncertainties. To avoid the problem where fusion filters may give up some advantages of UFIR filters by fusing based on noise statistics, we attempt to find a way to fuse without using noise statistics. The fusion filtering algorithm is derived using the influence function that provides a quantified measure for disturbances on the resulting filtering outputs and is termed as an influence finite impulse response (IFIR) filter. The main advantage of the proposed method is that the noise statistics of process noise and measurement noise are no longer required in the fusion process, showing that a critical feature of the UFIR filter is inherited. One numerical example and a practice-oriented case are given to illustrate the effectiveness of the proposed method. It is shown that the IFIR filter has adaptive performance and can automatically switch from the Kalman estimate to the UFIR estimates according to operating conditions. Moreover, the proposed method can reduce the effects of optimal horizon length on the UFIR estimate and can give the state estimates of best accuracy among all the compared methods.
2022, 9(4): 719-727. doi: 10.1109/JAS.2022.105467
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With the increasing intelligence and integration, a great number of two-valued variables (generally stored in the form of 0 or 1) often exist in large-scale industrial processes. However, these variables cannot be effectively handled by traditional monitoring methods such as linear discriminant analysis (LDA), principal component analysis (PCA) and partial least square (PLS) analysis. Recently, a mixed hidden naive Bayesian model (MHNBM) is developed for the first time to utilize both two-valued and continuous variables for abnormality monitoring. Although the MHNBM is effective, it still has some shortcomings that need to be improved. For the MHNBM, the variables with greater correlation to other variables have greater weights, which can not guarantee greater weights are assigned to the more discriminating variables. In addition, the conditional probability ${P( {{{x}_{j}}| {{{x}_{j'}},{y} = k} } )}$ must be computed based on historical data. When the training data is scarce, the conditional probability between continuous variables tends to be uniformly distributed, which affects the performance of MHNBM. Here a novel feature weighted mixed naive Bayes model (FWMNBM) is developed to overcome the above shortcomings. For the FWMNBM, the variables that are more correlated to the class have greater weights, which makes the more discriminating variables contribute more to the model. At the same time, FWMNBM does not have to calculate the conditional probability between variables, thus it is less restricted by the number of training data samples. Compared with the MHNBM, the FWMNBM has better performance, and its effectiveness is validated through numerical cases of a simulation example and a practical case of the Zhoushan thermal power plant (ZTPP), China.
2022, 9(4): 728-731. doi: 10.1109/JAS.2022.105470
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2022, 9(4): 732-734. doi: 10.1109/JAS.2022.105473
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2022, 9(4): 735-737. doi: 10.1109/JAS.2022.105476
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2022, 9(4): 738-740. doi: 10.1109/JAS.2022.105479
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2022, 9(4): 741-744. doi: 10.1109/JAS.2022.105482
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2022, 9(4): 745-748. doi: 10.1109/JAS.2022.105485
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