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. 8,  No. 7, 2021

Guest Editorial for Special Issue on Cognitive Computing for Collaborative Robotics
Huimin Lu, Dongpu Cao, Dacheng Tao, Schahram Dustdar, Pinhan Ho
2021, 8(7): 1221-1221. doi: 10.1109/JAS.2021.1004042
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A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis
Ce Zhang, Azim Eskandarian
2021, 8(7): 1222-1242. doi: 10.1109/JAS.2020.1003450
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The driver’s cognitive and physiological states affect his/her ability to control the vehicle. Thus, these driver states are essential to the safety of automobiles. The design of advanced driver assistance systems (ADAS) or autonomous vehicles will depend on their ability to interact effectively with the driver. A deeper understanding of the driver state is, therefore, paramount. Electroencephalography (EEG) is proven to be one of the most effective methods for driver state monitoring and human error detection. This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades. First, the commonly used EEG system setup for driver state studies is introduced. Then, the EEG signal preprocessing, feature extraction, and classification algorithms for driver state detection are reviewed. Finally, EEG-based driver state monitoring research is reviewed in-depth, and its future development is discussed. It is concluded that the current EEG-based driver state monitoring algorithms are promising for safety applications. However, many improvements are still required in EEG artifact reduction, real-time processing, and between-subject classification accuracy.
Global-Attention-Based Neural Networks for Vision Language Intelligence
Pei Liu, Yingjie Zhou, Dezhong Peng, Dapeng Wu
2021, 8(7): 1243-1252. doi: 10.1109/JAS.2020.1003402
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In this paper, we develop a novel global-attention-based neural network (GANN) for vision language intelligence, specifically, image captioning (language description of a given image). As many previous works, the encoder-decoder framework is adopted in our proposed model, in which the encoder is responsible for encoding the region proposal features and extracting global caption feature based on a specially designed module of predicting the caption objects, and the decoder generates captions by taking the obtained global caption feature along with the encoded visual features as inputs for each attention head of the decoder layer. The global caption feature is introduced for the purpose of exploring the latent contributions of region proposals for image captioning, and further helping the decoder better focus on the most relevant proposals so as to extract more accurate visual feature in each time step of caption generation. Our GANN is implemented by incorporating the global caption feature into the attention weight calculation phase in the word predication process in each head of the decoder layer. In our experiments, we qualitatively analyzed the proposed model, and quantitatively evaluated several state-of-the-art schemes with GANN on the MS-COCO dataset. Experimental results demonstrate the effectiveness of the proposed global attention mechanism for image captioning.
Towards Collaborative Robotics in Top View Surveillance: A Framework for Multiple Object Tracking by Detection Using Deep Learning
Imran Ahmed, Sadia Din, Gwanggil Jeon, Francesco Piccialli, Giancarlo Fortino
2021, 8(7): 1253-1270. doi: 10.1109/JAS.2020.1003453
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Collaborative Robotics is one of the high-interest research topics in the area of academia and industry. It has been progressively utilized in numerous applications, particularly in intelligent surveillance systems. It allows the deployment of smart cameras or optical sensors with computer vision techniques, which may serve in several object detection and tracking tasks. These tasks have been considered challenging and high-level perceptual problems, frequently dominated by relative information about the environment, where main concerns such as occlusion, illumination, background, object deformation, and object class variations are commonplace. In order to show the importance of top view surveillance, a collaborative robotics framework has been presented. It can assist in the detection and tracking of multiple objects in top view surveillance. The framework consists of a smart robotic camera embedded with the visual processing unit. The existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and localization. The detection models are further combined with different tracking algorithms, including GOTURN, MEDIANFLOW, TLD, KCF, MIL, and BOOSTING. These algorithms, along with detection models, help to track and predict the trajectories of detected objects. The pre-trained models are employed; therefore, the generalization performance is also investigated through testing the models on various sequences of top view data set. The detection models achieved maximum True Detection Rate 93% to 90% with a maximum 0.6% False Detection Rate. The tracking results of different algorithms are nearly identical, with tracking accuracy ranging from 90% to 94%. Furthermore, a discussion has been carried out on output results along with future guidelines.
Lightweight Image Super-Resolution via Weighted Multi-Scale Residual Network
Long Sun, Zhenbing Liu, Xiyan Sun, Licheng Liu, Rushi Lan, Xiaonan Luo
2021, 8(7): 1271-1280. doi: 10.1109/JAS.2021.1004009
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The tradeoff between efficiency and model size of the convolutional neural network (CNN) is an essential issue for applications of CNN-based algorithms to diverse real-world tasks. Although deep learning-based methods have achieved significant improvements in image super-resolution (SR), current CNN-based techniques mainly contain massive parameters and a high computational complexity, limiting their practical applications. In this paper, we present a fast and lightweight framework, named weighted multi-scale residual network (WMRN), for a better tradeoff between SR performance and computational efficiency. With the modified residual structure, depthwise separable convolutions (DS Convs) are employed to improve convolutional operations’ efficiency. Furthermore, several weighted multi-scale residual blocks (WMRBs) are stacked to enhance the multi-scale representation capability. In the reconstruction subnetwork, a group of Conv layers are introduced to filter feature maps to reconstruct the final high-quality image. Extensive experiments were conducted to evaluate the proposed model, and the comparative results with several state-of-the-art algorithms demonstrate the effectiveness of WMRN.
Human-Swarm-Teaming Transparency and Trust Architecture
Adam J. Hepworth, Daniel P. Baxter, Aya Hussein, Kate J. Yaxley, Essam Debie, Hussein A. Abbass
2021, 8(7): 1281-1295. doi: 10.1109/JAS.2020.1003545
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Transparency is a widely used but poorly defined term within the explainable artificial intelligence literature. This is due, in part, to the lack of an agreed definition and the overlap between the connected — sometimes used synonymously — concepts of interpretability and explainability. We assert that transparency is the overarching concept, with the tenets of interpretability, explainability, and predictability subordinate. We draw on a portfolio of definitions for each of these distinct concepts to propose a human-swarm-teaming transparency and trust architecture (HST3-Architecture). The architecture reinforces transparency as a key contributor towards situation awareness, and consequently as an enabler for effective trustworthy human-swarm teaming (HST).
A Cognitive Memory-Augmented Network for Visual Anomaly Detection
Tian Wang, Xing Xu, Fumin Shen, Yang Yang
2021, 8(7): 1296-1307. doi: 10.1109/JAS.2021.1004045
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With the rapid development of automated visual analysis, visual analysis systems have become a popular research topic in the field of computer vision and automated analysis. Visual analysis systems can assist humans to detect anomalous events (e.g., fighting, walking alone on the grass, etc). In general, the existing methods for visual anomaly detection are usually based on an autoencoder architecture, i.e., reconstructing the current frame or predicting the future frame. Then, the reconstruction error is adopted as the evaluation metric to identify whether an input is abnormal or not. The flaws of the existing methods are that abnormal samples can also be reconstructed well. In this paper, inspired by the human memory ability, we propose a novel deep neural network (DNN) based model termed cognitive memory-augmented network (CMAN) for the visual anomaly detection problem. The proposed CMAN model assumes that the visual analysis system imitates humans to remember normal samples and then distinguishes abnormal events from the collected videos. Specifically, in the proposed CMAN model, we introduce a memory module that is able to simulate the memory capacity of humans and a density estimation network that can learn the data distribution. The reconstruction errors and the novelty scores are used to distinguish abnormal events from videos. In addition, we develop a two-step scheme to train the proposed model so that the proposed memory module and the density estimation network can cooperate to improve performance. Comprehensive experiments evaluated on various popular benchmarks show the superiority and effectiveness of the proposed CMAN model for visual anomaly detection comparing with the state-of-the-arts methods. The implementation code of our CMAN method can be accessed at https://github.com/CMAN-code/CMAN_pytorch.
Disassembly Sequence Planning: A Survey
Xiwang Guo, MengChu Zhou, Abdullah Abusorrah, Fahad Alsokhiry, Khaled Sedraoui
2021, 8(7): 1308-1324. doi: 10.1109/JAS.2020.1003515
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It is well-recognized that obsolete or discarded products can cause serious environmental pollution if they are poorly be handled. They contain reusable resource that can be recycled and used to generate desired economic benefits. Therefore, performing their efficient disassembly is highly important in green manufacturing and sustainable economic development. Their typical examples are electronic appliances and electromechanical/mechanical products. This paper presents a survey on the state of the art of disassembly sequence planning. It can help new researchers or decision makers to search for the right solution for optimal disassembly planning. It reviews the disassembly theory and methods that are applied for the processing, repair, and maintenance of obsolete/discarded products. This paper discusses the recent progress of disassembly sequencing planning in four major aspects: product disassembly modeling methods, mathematical programming methods, artificial intelligence methods, and uncertainty handling. This survey should stimulate readers to be engaged in the research, development and applications of disassembly and remanufacturing methodologies in the Industry 4.0 era.
A Fully Distributed Approach to Optimal Energy Scheduling of Users and Generators Considering a Novel Combined Neurodynamic Algorithm in Smart Grid
Chentao Xu, Xing He
2021, 8(7): 1325-1335. doi: 10.1109/JAS.2021.1004048
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A fully distributed microgrid system model is presented in this paper. In the user side, two types of load and plug-in electric vehicles are considered to schedule energy for more benefits. The charging and discharging states of the electric vehicles are represented by the zero-one variables with more flexibility. To solve the nonconvex optimization problem of the users, a novel neurodynamic algorithm which combines the neural network algorithm with the differential evolution algorithm is designed and its convergence speed is faster. A distributed algorithm with a new approach to deal with the inequality constraints is used to solve the convex optimization problem of the generators which can protect their privacy. Simulation results and comparative experiments show that the model and algorithms are effective.
Distributed MPC for Reconfigurable Architecture Systems via Alternating Direction Method of Multipliers
Ting Bai, Shaoyuan Li, Yuanyuan Zou
2021, 8(7): 1336-1344. doi: 10.1109/JAS.2020.1003195
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This paper investigates the distributed model predictive control (MPC) problem of linear systems where the network topology is changeable by the way of inserting new subsystems, disconnecting existing subsystems, or merely modifying the couplings between different subsystems. To equip live systems with a quick response ability when modifying network topology, while keeping a satisfactory dynamic performance, a novel reconfiguration control scheme based on the alternating direction method of multipliers (ADMM) is presented. In this scheme, the local controllers directly influenced by the structure realignment are redesigned in the reconfiguration control. Meanwhile, by employing the powerful ADMM algorithm, the iterative formulas for solving the reconfigured optimization problem are obtained, which significantly accelerate the computation speed and ensure a timely output of the reconfigured optimal control response. Ultimately, the presented reconfiguration scheme is applied to the level control of a benchmark four-tank plant to illustrate its effectiveness and main characteristics.
Remaining Useful Life Prediction for a Roller in a Hot Strip Mill Based on Deep Recurrent Neural Networks
Ruihua Jiao, Kaixiang Peng, Jie Dong
2021, 8(7): 1345-1354. doi: 10.1109/JAS.2021.1004051
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Accurate estimation of the remaining useful life (RUL) and health state for rollers is of great significance to hot rolling production. It can provide decision support for roller management so as to improve the productivity of the hot rolling process. In addition, the RUL prediction for rollers is helpful in transitioning from the current regular maintenance strategy to conditional-based maintenance. Therefore, a new method that can extract coarse-grained and fine-grained features from batch data to predict the RUL of the rollers is proposed in this paper. Firstly, a new deep learning network architecture based on recurrent neural networks that can make full use of the extracted coarsegrained fine-grained features to estimate the heath indicator (HI) is developed, where the HI is able to indicate the health state of the roller. Following that, a state-space model is constructed to describe the HI, and the probabilistic distribution of RUL can be estimated by extrapolating the HI degradation model to a predefined failure threshold. Finally, application to a hot strip mill is given to verify the effectiveness of the proposed methods using data collected from an industrial site, and the relatively low RMSE and MAE values demonstrate its advantages compared with some other popular deep learning methods.