In the pattern recognition domain, deep architectures are currently widely used and they have achieved fine results. However, these deep architectures make particular demands, especially in terms of their requirement for big datasets and GPU. Aiming to gain better results without deep networks, we propose a simplified algorithm framework using fusion features extracted from the salient areas of faces. Furthermore, the proposed algorithm has achieved a better result than some deep architectures. For extracting more effective features, this paper firstly defines the salient areas on the faces. This paper normalizes the salient areas of the same location in the faces to the same size; therefore, it can extracts more similar features from different subjects. LBP and HOG features are extracted from the salient areas, fusion features’ dimensions are reduced by Principal Component Analysis (PCA) and we apply several classifiers to classify the six basic expressions at once. This paper proposes a salient areas definitude method which uses peak expressions frames compared with neutral faces. This paper also proposes and applies the idea of normalizing the salient areas to align the specific areas which express the different expressions. As a result, the salient areas found from different subjects are the same size. In addition, the gamma correction method is firstly applied on LBP features in our algorithm framework which improves our recognition rates significantly. By applying this algorithm framework, our research has gained state-of-the-art performances on CK+ database and JAFFE database.

There are two main categories of force control schemes: hybrid position-force control and impedance control. However, the former does not take into account the dynamic interaction between the robot’s end effector and the environment. In contrast, impedance control includes regulation and stabilization of robot motion by creating a mathematical relationship between the interaction forces and the reference trajectories. It involves an energetic pair of a flow and an effort, instead of controlling a single position or a force. A mass-spring-damper impedance filter is generally used for safe interaction purposes. Tuning the parameters of the impedance filter is important and, if an unsuitable strategy is used, this can lead to unstable contact. Humans, however, have exceptionally effective control systems with advanced biological actuators. An individual can manipulate muscle stiffness to comply with the interaction forces. Accordingly, the parameters of the impedance filter should be time varying rather than value constant in order to match human behavior during interaction tasks. Therefore, this paper presents an overview of impedance control strategies including standard and extended control schemes. Standard controllers cover impedance and admittance architectures. Extended control schemes include admittance control with force tracking, variable impedance control, and impedance control of flexible joints. The categories of impedance control and their features and limitations are well introduced. Attention is paid to variable impedance control while considering the possible control schemes, the performance, stability, and the integration of constant compliant elements with the host robot.

Indoor temperature prediction of multiple zones in near future horizons is vital in developing an optimal regulation strategy of heating, ventilation, and air conditioning systems in large-scale complex buildings. This is, however, challenging due to the spatial–temporal correlation and multivariable coupling characteristics. This article proposes a novel deep learning framework incorporating the distributed long short-term memory and graph convolution network namely DL-GCN for indoor temperature prediction in large public buildings, aiming to learn the spatial–temporal correlation and multivariable coupling features. First, the indoor temperature and humidity data from different zones are handled by GCN networks to extract the temperature spatial features. Then, in the distributed LSTM module, other data, such as light and ac power consumption, are fused with the outputs of the GCN module, respectively, in a distributed way to learn the coupling interactions and temporal characteristics between these variables. Comparison study and ablation experiments are conducted using real datasets from a large-scale building to verify its effectiveness and superior performance in multizone indoor temperature prediction.

A proton exchange membrane fuel cell (PEMFC) has great application prospects due to its low emission and high efficiency. An accurate model to predict the dynamic output voltage is essential for the optimal control of the PEMFC for the applications on vehicles and power stations. In this article, a novel deep learning framework with the long short-term memory (LSTM) and artificial neural network (ANN) fusion is proposed to develop the PEMFC dynamic model by extracting both the historical and current information. The LSTM extracts the temporal information from the past PEMFC states with its order determined with autocorrelation and partial autocorrelation functions, while the influence of the system current inputs is learnt by the ANN. Then, the outputs of LSTM and ANN are concatenated with the multiple information fused to predict the PEMFC dynamic output voltage. After validated by the operating data from a laboratory-scale PEMFC system, the LSTM and ANN fusion model is compared with the existing models, such as support vector regression, ANN, and LSTM methods. The comparison results show that the proposed LSTM and ANN fusion model can provide the best prediction performance with the lowest mean square error of 1.303. The proposed LSTM and ANN fusion model can be helpful to develop the optimal control strategy of the PEMFC.

In this article presents a trajectory planning approach toward safe and smooth robot motion in dynamic environments. We develop a hierarchical planning framework with a global planner generating the shortest path between the robot and the navigation target. Specially, a virtual target (VT) is set to run on the global path with a designed velocity. At the local level, the robot chases the VT and tracks the global path when traveling through the dynamic environment. We employ the model predictive control (MPC) framework for the local path generation. In particular, the prediction horizon of the MPC is adaptively changed concerning the distance between the robot and the VT. It implicitly reflects the crowdedness of the environment, which helps reduce the environmental uncertainty. Besides, we develop an event-triggered mechanism that executes the local plan aperiodically to release the computational burden. Based on the local chasing and tracking performance, we develop a global path replanning scheme in response to the untraversable area emerging in the dense environment. The developed framework is validated through extensive experiments in dynamic environments, demonstrating that the robot can reach the target faster and showcase a safer and smoother trajectory in the navigation.