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This paper is concerned with the time-varying formation tracking issue for high-order multi-agent systems based on event-triggered mechanism and adaptive control strategy. The time-varying formation configuration involved in our work is expressed as a bounded piecewise continuously differentiable vector function. The follower agents are required to achieve a pre-specified time-varying formation while tracking the state trajectory of the leader. First of all, in the light of event-triggered schema, we constructed an adaptive control protocol with adjustable time-variant parameters for achieving time-varying formation tracking. Meanwhile, the corresponding triggering conditions were designed to avoid continuous communication. Then, a four-step algorithm and feasible conditions have been provided to determine protocol parameters. Additionally, theoretically proof reveals that the specified formation tracking could be realized while excluding Zeno behavior. Two simulation examples were finally provided to illustrate the effectiveness of the obtained results.

As an emerging research topic for proximity service (ProSe), automatic emotion recognition enables the machines to understand the emotional changes of human beings which can not only facilitate natural, effective, seamless, and advanced human-robot interaction or human-computer interface but also promote emotional health. Facial expression recognition (FER) is a vital task for emotion recognition. However, significant gap between human and machine exists in FER task. In this paper, we present a conditional generative adversarial network-based approach to alleviate the intra-class variations by individually controlling the facial expressions and learning the generative and discriminative representations simultaneously. The proposed framework consists of a generator G and three discriminators (Di, Da, and Dexp). The generator G transforms any query face image into another prototypic facial expression image with other factors preserved. Armed with action units condition, the generator G pays more attention to information relevant to facial expression. Three loss functions ($L_{I}$ , La, and Lexp) corresponding to the three discriminators (Di, Da, and Dexp) were designed to learn generative and discriminative representations. Moreover, after rendering the generated expression back to its original facial expression, cycle consistency loss is also applied to guarantee the identity and produce more constrained visual representations. Optimized by combining both synthesis and classification loss functions, the learnt representation is explicitly disentangled from other variations such as identity, head pose, and illumination. Qualitative and quantitative experimental results demonstrate the proposed FER system is effective for expression recognition.

The recognition of human emotions from facial expression images is one of the most important topics in the machine vision and image processing fields. However, recognition becomes difficult when dealing with non-frontal faces. To alleviate the influence of poses, we propose an encoder-decoder generative adversarial network that can learn pose-invariant and expression-discriminative representations. Specifically, we assume that a facial image can be divided into an expressive component, an identity component, a head pose component and a remaining component. The encoder encodes each component into a feature representation space and the decoder recovers the original image from these encoded features. A classification loss on the components and an ℓ1 pixel-wise loss are applied to guarantee the rebuilt image quality and produce more constrained visual representations. Quantitative and qualitative evaluations on two multi-pose datasets demonstrate that the proposed algorithm performs favorably compared to state-of-the-art methods.

Surface electromyography signal plays an important role in hand function recovery training. In this paper, an IoT-enabled stroke rehabilitation system was introduced which was based on a smart wearable armband (SWA), machine learning (ML) algorithms, and a 3-D printed dexterous robot hand. User comfort is one of the key issues which should be addressed for wearable devices. The SWA was developed by integrating a low-power and tiny-sized IoT sensing device with textile electrodes, which can measure, pre-process, and wirelessly transmit bio-potential signals. By evenly distributing surface electrodes over user's forearm, drawbacks of classification accuracy poor performance can be mitigated. A new method was put forward to find the optimal feature set. ML algorithms were leveraged to analyze and discriminate features of different hand movements, and their performances were appraised by classification complexity estimating algorithms and principal components analysis. According to the verification results, all nine gestures can be successfully identified with an average accuracy up to 96.20%. In addition, a 3-D printed five-finger robot hand was implemented for hand rehabilitation training purpose. Correspondingly, user's hand movement intentions were extracted and converted into a series of commands which were used to drive motors assembled inside the dexterous robot hand. As a result, the dexterous robot hand can mimic the user's gesture in a real-time manner, which shows the proposed system can be used as a training tool to facilitate rehabilitation process for the patients after stroke.

In this paper, a formation collision forecast and coordination algorithm (FCFCA) is proposed to avoid collisions in the configuration control of missile autonomous formation (MAF). First, the conditions are derived to forecast collisions of the MAF. Second, the transition target configuration is designed to coordinate collisions. Third, by minimizing the formation group cost, we obtain the optimal transition vector, which can avoid collisions and guarantee the efficiency, safety, and stability of the MAF. Finally, simulation and experiment results are presented to verify the effectiveness of the FCFCA.

In this paper, we propose a distributed global connectivity maintenance and control framework of multi-robot networks, which can adapt connectivity online. Based on a series of energy functions, the control framework can make each robot autonomously move along the direction of gradient descent of the energy functions in order to reach the desired global connectivity of the multi-robot networks. This framework has enhanced adaptability and flexibility of multi-robot systems, because adapting connectivity online enables the system to maintain, promote or degrade connectivity at different stages of an integrated mission which require different desired connectivity values. Online connectivity adaptation can also help to change connectivity when the environment becomes more complicated, unknown and dynamic. Theorems are proposed to prove that the framework can not only maintain global connectivity, but can also effectively adapt global connectivity to desired values online, which has rarely been studied. Furthermore, connectivity promotion in the presence of bounded external control terms, such as collision avoidance, is also investigated, which helps to expand the scope of the framework. Finally, simulation and experiment results are presented so as to validate our unified control framework.

A key challenge for self-driving vehicle researchers is to curate massive instrumented vehicle datasets. A common task in their development workflow is to extract video segments that meet particular criteria, such as a particular road scenario or vehicle maneuver. We present a novel approach for detecting vehicle maneuvers from monocular dashboard camera video building upon a deep learning visual odometry model (DeepV2D) to estimate frame-accurate ego-vehicle movement. We leverage image classification and lane line estimation to extend our technique. We classify movement sequences against reference maneuvers using dynamic time warping. We describe, implement, and evaluate classifiers to recognize maneuvers such as turns, lane changes, and deceleration. We show that using deep learning visual odometry to estimate location is superior to consumer-grade high-resolution GPS for this application. We describe and implement a greedy approach to classify maneuvers and evaluate our approach on common road maneuvers. We find an overall AUROC value of 0.91 for turns and 0.84 for all maneuvers in our dataset. [ABSTRACT FROM AUTHOR]

The article presents 2021 Reviewers List of the periodical, which include Tor Aamodt, Davide Abati, and Rameen Abdal.

Traditional loss-minimization strategies of model-based control, copper loss minimization (CLM), and finite-element-based control suffer from respective problems of iron loss mismatch with a physical view, pseudominimization and amounts of efforts when they are applied to permanent magnet synchronous motor with nonsinusoidal back electromotive force (NS-PMSM). This article combines theories of multiple synchronous reference frames and iron loss resistance to develop a new model of NS-PMSM, based on which the stator iron loss is evaluated to match well with the physical view. Then, a current strategy is proposed to minimize the total stator loss under constraints of ripple-free torque and finite dc supply. The optimal current solution is solved in closed form and implemented effortlessly. A good robustness to phase resistance variation is also achieved. Compared to CLM, the proposed strategy realizes a true minimization with the stator loss reduced by 9% and the motor efficiency enhanced by 0.7% at rated operation of a 3.8-kW NS-PMSM. Precision of the iron loss evaluation and performances of the strategy are validated experimentally on the 3.8-kW motor setup. [ABSTRACT FROM AUTHOR]

The article lists various reviewers of the journal in 2018 including Alessandro Achille, Michael Bloodgood and Chao Chen.

This paper develops and characterizes a three-dimensional (3-D) nanofabrication process using ultrasonic vibration assisted nanomachining based on an atomic force microscope (AFM). The superiorities of height control over force control in the process are explained and are demonstrated by the fabrication results. Three factors impacting actual feature depths are investigated, including the ultrasonic z-vibrational amplitude, the assigned base feature depth, and the machining speed. 3-D nanostructures with continuous height variations were successfully fabricated on polymethyl methacrylate (PMMA) films with the feature height manipulated through controlling the absolute height of the cantilever tip in AFM. By selecting machining parameters based on characterizations, feature dimensions can be controlled as desired values within small variances. The capability of transferring 3-D nanostructures from PMMA films to silicon substrates is further explored in this paper. After selecting recipes of the reactive ion etching process, 3-D nanostructures are successfully transferred to silicon substrates with controllable selectivity. The reported ultrasonic vibration assisted nanomachining process in height control provides a robust approach of fabricating 3-D nanostructures. [ABSTRACT FROM AUTHOR]

The article lists several reviewers for manuscripts submitted for publication in the vol. 40 of the journal including Austin Abrams, Jonathan Barron, and Justin Bedo.