A face-to-face human robot interaction from the robot's first-person perspective.

A new computational framework for enabling social robots to understand human personality traits.

Performance evaluation of different nonverbal social cues using machine learning techniques.

Based on the neural network approximation model, this controller is a data-driven strategy.

The controller is proven to be asymptotically stable.

Experimental results indicate the effectiveness and robustness of this controller.

A data pre-processing method is raised before data fusion.

A data fusion approach based on reinforcement learning is designed by multi-sensors weight adjustment.

In the case without prior knowledge, the reinforcement learning based data fusion is realized by the Fisher information of observations.

A new problem of group role assignment with constraints is proposed and formalized.

A practical solution based on an optimization platform is provided and the sufficient condition is proved.

Asynchronous clock, mobility and privacy preservation are together considered.

Asynchronous localization protocol can effectively eliminate asynchronous clock.

Asynchronous localization algorithm can effectively hide privacy information.

Integrate a variety of machine learning models. 

Build a comprehensive model of user behavior.

Realize the iterative generation of feature vectors from the most primitive sensor data to realize the training of machine learning models.

Design a kind of vehicle tire longitudinal forces optimization distribution strategy.

Proposed a kind of compensation allocation strategy based on the axle load distribution method.

Adopt the hierarchical control structure for controller design.

The results solve the stabilization problem of the systems with complex coefficients, and provide the basis for the design of distributed PID controllers satisfying different performance criteria.

The multi-agent system can be decoupled into several subsystems with respect to the eigenvalues of the Laplacian matrix based on the matrix theory. 

Prior information from an accurate tracking SLAM is used to associate dense vertices between keyframes based on multithreaded processing and multi-threaded priority settings.

An adaptive weight is assigned to each inlier and the weighted fusion is implemented as the update process of the Kalman filter.

Calculate and use the real-time data of the carrier coordinate system (pedal) relative to the geographic coordinate system.

Build a second-order low pass filter with a special cut-off frequency , so that the majority of disturbances can be observed and compensated.

A permanent magnet brushless DC motor is used for adaptively assisted riding.

When the smallest eigenvalues are equal, the dynamic characteristic analysis of Moller algorithm is finished.

Some conditions are drawn to guarantee the convergence of the Moller algorithm.

The range of the learning rate is expanded.

The influence of frequency drift on the separation of collision tag signals is considered.

An adaptive RBF neural network is introduced to separate the collision tag signals in a UHF RFID system.

An FM0 decoding algorithm is proposed to decode separated tag signals by RBF.

An observable measurement model is constructed under the BMU for absolute clock in the large-scale network from the viewpoint of networked control theory.

The observability measurement model of MMSE-like equivalence is proposed to calculate the positive definite invariant set. 

Phase generation is designed for self-detector by using a small number of self-detector instead of a large number of anomaly detectors.

Self-adapt radius threshold is put forward based on self samples distribution.

A new sliding mode variable is constructed, containing the message of DoS attack.

A sliding mode control algorithm is developed under DoS attack.

The developed sliding mode control algorithm can effectively deal with DoS attack.

An important intrinsic natural feature of outdoor scenes free of sand/sandstorm is found.

A tensor least square optimization model is presented for the decomposition of edge-preserving base layers and details.

The tensor least square optimization model based image enhancement scheme is discussed for the sandstorm weather images.

Optimal parallel tracking control for general nonlinear systems via ADP is studied. 

An augmented system and an augmented performance index function are proposed.

It is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index function.