Search

• Predict the intensities of basic emotions based on surface electromyography. • Emotion distribution learning predicts the intensities of basic emotions. • Apply principal component analysis to select the most representative features. • Combine emotion distribution learning and principal component analysis. Facial expression recognition plays an important role in research on human–computer interaction. The common facial expressions are mixtures of six basic emotions: anger, disgust, fear, happiness, sadness, and surprise. The current study, however, focused on a single basic emotion on the basis of physiological signals. We proposed emotion distribution learning (EDL) based on surface electromyography (sEMG) for predicting the intensities of basic emotions. We recorded the sEMG signals from the depressor supercilii, zygomaticus major, frontalis medial, and depressor anguli oris muscles. Six features were extracted in the frequency, time, time–frequency, and entropy domains. Principal component analysis (PCA) was used to select the most representative features for prediction. The key idea of EDL is to learn a function that maps the PCA-selected features to the facial expression distributions such that the special description degrees of all basic emotions for an emotion can be learned by EDL. Simultaneously, Jeffrey's divergence considered the relationship between different basic emotions. The performance of EDL was compared with that of multilabel learning based on PCA-selected features. Predicted results were measured by six indices, which could reflect the distance or similarity degree between distributions. We conducted an experiment on six different emotion distributions. Experimental results show that the EDL can predict the facial expression distribution more accurately than the other methods. [ABSTRACT FROM AUTHOR]

This article investigates the event-triggered distributed model predictive control (DMPC) for perturbed coupled nonlinear systems subject to state and control input constraints. A novel compound event-triggered DMPC strategy, including a compound triggering condition and a new constraint tightening approach, is developed. In this event-triggered strategy, two stability-related conditions are checked in a parallel manner, which relaxes the requirement of the decrease of the Lyapunov function. An open-loop prediction scheme to avoid periodic transmission is designed for the states in the terminal set. As a result, the number of triggering and transmission instants can be reduced significantly. Furthermore, the proposed constraint tightening approach solves the problem of the state constraint satisfaction, which is quite challenging due to the external disturbances and the mutual influences caused by dynamical coupling. Simulations are conducted at last to validate the effectiveness of the proposed algorithm.

Functional corticomuscular coupling (FCMC) between the cerebral motor cortex and muscle activity reflects multi-layer and nonlinear interactions in the sensorimotor system. Considering the inherent multiscale characteristics of physiological signals, we proposed multiscale transfer spectral entropy (MSTSE) and introduced the unidirectionally coupled Hénon maps model to verify the effectiveness of MSTSE. We recorded electroencephalogram (EEG) and surface electromyography (sEMG) in steady-state grip tasks of 29 healthy participants and 27 patients. Then, we used MSTSE to analyze the FCMC base on EEG of the bilateral motor areas and the sEMG of the flexor digitorum superficialis (FDS). The results show that MSTSE is superior to transfer spectral entropy (TSE) method in restraining the spurious coupling and detecting the coupling more accurately. The coupling strength was higher in the β1, β2, and γ2 bands, among which, it was highest in the β1 band, and reached its maximum at the 22-30 scale. On the directional characteristics of FCMC, the coupling strength of EEG→sEMG is superior to the opposite direction in most cases. In addition, the coupling strength of the stroke-affected side was lower than that of healthy controls' right hand in the β1 and β2 bands and the stroke-unaffected side in the β1 band. The coupling strength of the stroke-affected side was higher than that of the stroke-unaffected side and the right hand of healthy controls in the sEMG→EEG direction of γ2 band. This study provides a new perspective and lays a foundation for analyzing FCMC and motor dysfunction.

The stabilization problem for a class of switched linear systems is investigated in the network environment. Both the synchronous and asynchronous cases are considered according to the availability of the current activated system mode to the actuator. The random communication delay is assumed to be Markovian, resulting in a sampled-data synchronous or asynchronous switched system with Markovian delay as the closed-loop system. We extend the discretization approach to deal with such sampled-data system through exploring the stability conditions of the corresponding discrete-time system. For the asynchronous case, we formulate the closed-loop system as a hybrid system with the switching between its subsystems governed by a switching signal and a Markov chain. By studying the switching number and one-step reachable mode set of the constructed vector-valued switching signal, the exponential mean-square stability (EMSS) conditions and the corresponding mode-dependent controller are obtained with a more general constraint on the designed switching signal. These results are finally verified by two illustrated numerical examples., (Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.)

This paper is concerned with the distributed H ∞ consensus of leader-follower multi-agent systems with aperiodic sampling interval and switching topologies. Under the assumption that the sampling period takes values from a given set, a new discrete-time model is proposed for the tracking error system. For the multi-agent systems with time-varying sampling period, switching topologies and external disturbance, the considered tracking problem is converted to a robust H ∞ control problem. With help of the Lyapunov stability theory, a sufficient condition for the existence of mode-dependent controller is established and it guarantees the exponential stability of tracking error system and a prescribed H ∞ disturbance attenuation level. The influence of sampling period on the overall control performance is also discussed. Two simulation examples are given to show the effectiveness of the proposed control algorithm., (Copyright © 2017. Published by Elsevier Ltd.)

Objective: To evaluate the efficacy and safety of bevacizumab plus capecitabine in treating metastatic colorectal cancer(mCRC)., Methods: Eleven patients with mCRC (6 females and 5 males) were enrolled in this study. Bevacizumab was given with 5 mg/kg every two weeks in five patients, 10 mg/kg every two weeks in four patients and 15 mg/kg every three weeks in two patients. All patients received capecitabine 2000 mg/m2 per day for 14 days., Results: Five of 11 patients had partial response and five patients had stable disease and two patients had progressive disease. The disease control rate was 90.9%. The progress-free survival were 4 months and the median overall survival time were 15 months. The adverse events related to bevacizumab were grade 2 hypertension in 3 patients (27.3%) and grade 1 or 2 proteinuria in 4 patients (36.4%). Other adverse events such as mucositis, fatigue, subcutaneous haemorrhage were also observed. No thromboembolism or severe haemorrhage happened. No other grade 3 or 4 adverse events were observed.The adverse events in the combined therapy were hand-foot-syndrome (54.6%), diarrhea (27.3%), and neutropenia (18.2%), mainly due to capecitabine., Conclusions: The combination of bevacizumab plus capecitabine has definite benefit in patients with mCRC. However,these benefits can not be maintained after the withdrawal of bevacizumab. The adverse drug reactions are well tolerated.

In this paper, the packet-based control approach to networked control systems (NCSs) is extended to the continuous-time case with the use of a discretization technique for continuous network-induced delay. The derived approach can effectively simultaneously deal with network-induced delay, data packet dropout, and data packet disorder and leads to a novel model for NCSs. This model offers the designer the freedom of designing different controllers with respect to specific network conditions, which is distinct from previous results and ensues better system performance. By applying switched system theory, the stability criterion for the derived model is obtained, which is then used to obtain an linear matrix inequality-based stabilized controller design method for the packet-based control approach. A numerical example is also presented, which illustrates the effectiveness of the proposed packet-based control approach by comparison.