Publications

Recently, human activity recognition (HAR) that uses wearable sensors has become a research hotspot because its wide applications in real-world scenarios. Essentially, HAR can be treated as multi-channel time series classification problem, where different channels may come from heterogeneous sensor modalities. Deep learning, especially convolutional neural networks (CNNs) have made breakthroughs in ubiquitous HAR scenario. Various normalization methods enable layers of networks to learn more independently by normalizing hybrid sensor features. However, normalization tends to produce a channel collapse phenomenon, where many channels generates tiny values. Most channels are inhibited and contribute very little to output. As a result, the network has to rely on only a few valid channels, which inevitably impair the generality ability. In this paper, we provide an alternative called Channel Equalization to reactivate these inhibited channels by performing whitening or decorrelation operation, which compels all channels to contribute more or less to feature representation. Extensive experiments are conducted on several public HAR benchmarks, which indicate that the proposed method significantly surpasses recent SOTA at negligible computational overhead. To our knowledge, the Channel Equalization is for the first time to be applied in multimodal HAR scenario. Finally, the actual operation is evaluated on an embedded platform.

The Aerial Manipulator (AM) combines the flexibility of aerial platforms with the manipulative capability of manipulators. Autonomous grasping of the AM poses challenges due to its complex kinematics/dynamics and target object pose acquisition. This paper introduces a fully-actuated AM which consists of a fully-actuated hexarotor and a 3-DoF manipulator. The fully-actuated aerial platform provides a more stable view for the camera during the AM motion. The feedback linearization controller is used in the aerial platform to ensure the stability of the AM during the motion of the manipulator. The YOLO v5 object detector combines the Oriented Bounding Box (OBB) to form a rotating object detector which can identify the target object and obtain its tilt angle. Combined with the depth camera, the position of the target object in three-dimensional (3D) space can be obtained. Within the working space, the manipulator performs autonomous planning and grasping according to the position and tilt angle of the target. Experimental demonstrates the performance of the proposed AM system.

Robotic manipulators are nowadays widely used in various underwater scenarios, but their motion control remains a challenging task due to hydrodynamic effects. This article proposes a novel adaptive fuzzy sliding mode control (AFSMC) strategy for precise and robust control of underwater manipulators. To simulate the movement of the robotic manipulators in the underwater environment, the Unified Robot Description Format (URDF) file of a custom-designed electric underwater manipulator is imported into the Simscape Multibody and the hydrodynamic disturbance is modeled according to Morison’s equation. Furthermore, the proposed control strategy takes advantage of the universal approximation capability of fuzzy systems to avoid chattering and observe disturbances by adjusting the control gains of classical sliding mode control (CSMC). And adaptive laws are designed to update the parameters of the fuzzy systems. The strong friction caused by the seal is also compensated by actual test data. In the simulation experiments, a special environment of water flow and variable loads is considered. The results demonstrate that the AFSMC strategy can achieve high precision and strong robustness against disturbances for trajectory tracking. More importantly, the chattering caused by CSMC can be eliminated and the hydrodynamic disturbance can be estimated with high precision through the proposed control strategy.

Objective. Due to individual differences, it is greatly challenging to realize the multiple types of emotion identification across subjects.Approach. In this research, a hierarchical feature optimization method is proposed in order to represent emotional states effectively based on peripheral physiological signals. Firstly, sparse learning combined with binary search is employed to achieve feature selection of single signals. Then an improved fast correlation-based filter is proposed to implement fusion optimization of multi-channel signal features. Aiming at overcoming the limitations of the support vector machine (SVM), which uses a single kernel function to make decisions, the multi-kernel function collaboration strategy is proposed to improve the classification performance of SVM.Main results. The effectiveness of the proposed method is verified on the DEAP dataset. Experimental results show that the proposed method presents a competitive performance for four cross-subject types of emotion identification with an accuracy of 84% (group 1) and 85.07% (group 2). Significance. The proposed model with hierarchical feature optimization and SVM with multi-kernel function collaboration demonstrates superior emotion recognition accuracy compared to state-of-the-art techniques. In addition, the analysis based on DEAP dataset composition characteristics presents a novel perspective to explore the emotion recognition issue more objectively and comprehensively.

This study focuses on reachability problems in differential games. An improved level set method for computing reachable tubes is proposed in this paper. The reachable tube is described as a sublevel set of a value function, which is the viscosity solution of a Hamilton-Jacobi equation with running cost. We generalize the concept of reachable tubes and propose a new class of reachable tubes, which are referred to as cost-limited one. In particular, a performance index can be specified for the system, and a cost-limited reachable tube is a set of initial states of the system's trajectories that can reach the target set before the performance index increases to a given admissible cost. Such a reachable tube can be obtained by specifying the corresponding running cost function for the Hamilton-Jacobi equation. Different non-zero sublevel sets of the viscosity solution of the Hamilton-Jacobi equation at a certain time point can be used to characterize the cost-limited reachable tubes with different admissible costs (or the reachable tubes with different time horizons), thus reducing the storage space consumption. Several examples are provided to illustrate the validity and accuracy of the proposed method.

Biometrics have attracted growing research interests as the information security and safety gain increasing attention to date. As a kind of important biomedical signal, Electroen-cephalogram (EEG) contains valuable information about identity, emotionality, personality, etc. Thus, automatically distinguishing the identities based on EEG is beneficial to the development of biometrics, forensics and informatics. Although deep learning has absorbed much research attention for the issue of EEG-based person identification, the performance enhancement of this methodology seems to have hit a bottleneck recently. Hence, by rethinking the problems haunting this issue, we plan to reinvigorate the conventional method pipeline, and put forward a novel and effective tensorial scheme away from the deep learning mainstream. Specifically, the proposed tensorial scheme extracts the effective tensorial representation from multi-channel EEG at first; then, the scheme performs the designed tensorial learning to improve the discriminability of the feature space; finally, the scheme carries out the devised tensorial measurement in the learned metric space for classification. Experimental results have demonstrated the superiority of proposed scheme over the related advanced approaches by means of the challenging benchmark databases DEAP, SEED and DREAMER.

Purpose This paper aims to present a fully actuated aerial manipulator (AM) with a robust motion/force hybrid controller for conducting contact-typed inspection tasks in industrial plants. Design/methodology/approach An AM is designed based on a hexarotor with tilted rotors and a rigidly attached end effector. By tilting the rotors, the position and attitude of the AM can be controlled independently, and the AM can actively exert forces on industrial facilities through the rigidly attached end effector. A motion/force hybrid controller is proposed to perform contact-typed inspection tasks. The contact-typed inspection task is divided into the approach phase and the contact phase. In the approach phase, the AM automatically approaches the contact surface. In the contact phase, a motion/force hybrid controller is used for contact-typed inspection. Finally, a disturbance observer (DOB) is used to estimate external disturbances and used as feedforward compensation. Findings The proposed AM can slowly approach the contact surface without significant impact in the contact phase. It can realize constant force control in the direction normal to the contact surface in the contact phase, whereas the motion of the remaining directions can be controlled by the operator. The use of the DOB ensures the robustness of the AM in the presence of external wind disturbances. Originality/value A fully actuated AM system with a robust motion/force hybrid controller is proposed. The effectiveness of the proposed AM system for conducting contact-typed industrial inspection tasks is validated by practical experiments.