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

2021 Vol. 8, No. 9

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2021, 8(9): 1477-1499. doi: 10.1109/JAS.2021.1004087
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The speed and pace of the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; also referred to as novel Coronavirus 2019 and COVID-19) have resulted in a global pandemic, with significant health, financial, political, and other implications. There have been various attempts to manage COVID-19 and other pandemics using technologies such as Internet of Things (IoT) and 5G/6G communications. However, we also need to ensure that IoT devices used to facilitate COVID-19 monitoring and treatment (e.g., medical IoT devices) are secured, as the compromise of such devices can have significant consequences (e.g., life-threatening risks to COVID-19 patients). Hence, in this paper we comprehensively survey existing IoT-related solutions, potential security and privacy risks and their requirements. For example, we classify existing security and privacy solutions into five categories, namely: authentication and access control solutions, key management and cryptography solutions, blockchain-based solutions, intrusion detection systems, and privacy-preserving solutions. In each category, we identify the associated challenges. We also identify a number of recommendations to inform future research.
2021, 8(9): 1500-1522. doi: 10.1109/JAS.2021.1004105
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Robotics has aroused huge attention since the 1950s. Irrespective of the uniqueness that industrial applications exhibit, conventional rigid robots have displayed noticeable limitations, particularly in safe cooperation as well as with environmental adaption. Accordingly, scientists have shifted their focus on soft robotics to apply this type of robots more effectively in unstructured environments. For decades, they have been committed to exploring sub-fields of soft robotics (e.g., cutting-edge techniques in design and fabrication, accurate modeling, as well as advanced control algorithms). Although scientists have made many different efforts, they share the common goal of enhancing applicability. The presented paper aims to brief the progress of soft robotic research for readers interested in this field, and clarify how an appropriate control algorithm can be produced for soft robots with specific morphologies. This paper, instead of enumerating existing modeling or control methods of a certain soft robot prototype, interprets for the relationship between morphology and morphology-dependent motion strategy, attempts to delve into the common issues in a particular class of soft robots, and elucidates a generic solution to enhance their performance.
2021, 8(9): 1523-1538. doi: 10.1109/JAS.2021.1004108
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2021, 8(9): 1539-1548. doi: 10.1109/JAS.2021.1004111
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This paper deals with the consensus problem in an uncertain multi-agent system whose agents communicate with each other through a weighted undirected (primary) graph. The considered multi-agent system is described by an uncertain state-space model in which the involved matrices belong to some matrix boxes. As the main contribution of the paper, a unified optimization-based framework is proposed for simultaneously reducing the weights of the edges of the primary communication graph (optimizing the network topology) and synthesizing a controller such that the consensus in the considered uncertain multi-agent system is ensured with an adjustable convergence rate. Considering the NP-hardness nature of the optimization problem related to the aforementioned framework, this problem is relaxed such that it can be solved by regular LMI solvers. Numerical/practical-based examples are presented to verify the usefulness of the obtained results.
2021, 8(9): 1549-1566. doi: 10.1109/JAS.2020.1003444
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In view of the environment competencies, selecting the optimal green supplier is one of the crucial issues for enterprises, and multi-criteria decision-making (MCDM) methodologies can more easily solve this green supplier selection (GSS) problem. In addition, prioritized aggregation (PA) operator can focus on the prioritization relationship over the criteria, Choquet integral (CI) operator can fully take account of the importance of criteria and the interactions among them, and Bonferroni mean (BM) operator can capture the interrelationships of criteria. However, most existing researches cannot simultaneously consider the interactions, interrelationships and prioritizations over the criteria, which are involved in the GSS process. Moreover, the interval type-2 fuzzy set (IT2FS) is a more effective tool to represent the fuzziness. Therefore, based on the advantages of PA, CI, BM and IT2FS, in this paper, the interval type-2 fuzzy prioritized Choquet normalized weighted BM operators with ${{ \lambda}}$ fuzzy measure and generalized prioritized measure are proposed, and some properties are discussed. Then, a novel MCDM approach for GSS based upon the presented operators is developed, and detailed decision steps are given. Finally, the applicability and practicability of the proposed methodology are demonstrated by its application in the shared-bike GSS and by comparisons with other methods. The advantages of the proposed method are that it can consider interactions, interrelationships and prioritizations over the criteria simultaneously.
2021, 8(9): 1567-1575. doi: 10.1109/JAS.2020.1003605
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Many mechanical parts of multi-rotor unmanned aerial vehicle (MUAV) can easily produce non-smooth phenomenon and the external disturbance that affects the stability of MUAV. For multi-MUAV attitude systems that experience output dead-zone, external disturbance and actuator fault, a leader-following consensus anti-disturbance and fault-tolerant control (FTC) scheme is proposed in this paper. In the design process, the effect of unknown nonlinearity in multi-MUAV systems is addressed using neural networks (NNs). In order to balance out the effects of external disturbance and actuator fault, a disturbance observer is designed to compensate for the aforementioned negative impacts. The Nussbaum function is used to address the problem of output dead-zone. The designed fault-tolerant controller guarantees that the output signals of all followers and leader are synchronized by the backstepping technique. Finally, the effectiveness of the control scheme is verified by simulation experiments.
2021, 8(9): 1576-1587. doi: 10.1109/JAS.2021.1003958
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To realize high-precision attitude stabilization of a flexible spacecraft in the presence of complex disturbances and measurement noises, an iterative learning disturbance observer (ILDO) is presented in this paper. Firstly, a dynamic model of disturbance is built by augmenting the integral of the lumped disturbance as a state. Based on it, ILDO is designed by introducing iterative learning structures. Then, comparative analyses of ILDO and traditional disturbance observers are carried out in frequency domain. It demonstrates that ILDO combines the advantages of high accuracy in disturbance estimation and favorable robustness to measurement noise. After that, an ILDO based composite controller is designed to stabilize the spacecraft attitude. Finally, the effectiveness of the proposed control scheme is verified by simulations.
2021, 8(9): 1588-1599. doi: 10.1109/JAS.2021.1004114
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In this paper, accelerated saddle point dynamics is proposed for distributed resource allocation over a multi-agent network, which enables a hyper-exponential convergence rate. Specifically, an inertial fast-slow dynamical system with vanishing damping is introduced, based on which the distributed saddle point algorithm is designed. The dual variables are updated in two time scales, i.e., the fast manifold and the slow manifold. In the fast manifold, the consensus of the Lagrangian multipliers and the tracking of the constraints are pursued by the consensus protocol. In the slow manifold, the updating of the Lagrangian multipliers is accelerated by inertial terms. Hyper-exponential stability is defined to characterize a faster convergence of our proposed algorithm in comparison with conventional primal-dual algorithms for distributed resource allocation. The simulation of the application in the energy dispatch problem verifies the result, which demonstrates the fast convergence of the proposed saddle point dynamics.
2021, 8(9): 1600-1613. doi: 10.1109/JAS.2019.1911534
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Hand gesture recognition is a popular topic in computer vision and makes human-computer interaction more flexible and convenient. The representation of hand gestures is critical for recognition. In this paper, we propose a new method to measure the similarity between hand gestures and exploit it for hand gesture recognition. The depth maps of hand gestures captured via the Kinect sensors are used in our method, where the 3D hand shapes can be segmented from the cluttered backgrounds. To extract the pattern of salient 3D shape features, we propose a new descriptor–3D Shape Context, for 3D hand gesture representation. The 3D Shape Context information of each 3D point is obtained in multiple scales because both local shape context and global shape distribution are necessary for recognition. The description of all the 3D points constructs the hand gesture representation, and hand gesture recognition is explored via dynamic time warping algorithm. Extensive experiments are conducted on multiple benchmark datasets. The experimental results verify that the proposed method is robust to noise, articulated variations, and rigid transformations. Our method outperforms state-of-the-art methods in the comparisons of accuracy and efficiency.
2021, 8(9): 1614-1626. doi: 10.1109/JAS.2020.1003390
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Facial attribute editing has mainly two objectives: 1) translating image from a source domain to a target one, and 2) only changing the facial regions related to a target attribute and preserving the attribute-excluding details. In this work, we propose a multi-attention U-Net-based generative adversarial network (MU-GAN). First, we replace a classic convolutional encoder-decoder with a symmetric U-Net-like structure in a generator, and then apply an additive attention mechanism to build attention-based U-Net connections for adaptively transferring encoder representations to complement a decoder with attribute-excluding detail and enhance attribute editing ability. Second, a self-attention (SA) mechanism is incorporated into convolutional layers for modeling long-range and multi-level dependencies across image regions. Experimental results indicate that our method is capable of balancing attribute editing ability and details preservation ability, and can decouple the correlation among attributes. It outperforms the state-of-the-art methods in terms of attribute manipulation accuracy and image quality. Our code is available at https://github.com/SuSir1996/MU-GAN.