This paper proposes a new prediction-based dynamic multi-objective optimization (PBDMO) method, which combines a new prediction-based reaction mechanism and a popular regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for solving dynamic multi-objective optimization problems. Whenever a change is detected, PBDMO reacts effectively to it by generating three sub-populations based on different strategies. The first sub-population is created by moving non-dominated individuals using a simple linear prediction model with different step sizes. The second sub-population consists of some individuals generated by a novel sampling strategy to improve population convergence as well as distribution. The third sub-population comprises some individuals generated using a shrinking strategy based on the probability distribution of variables. These sub-populations are tailored to form a population for the new environment. Experimental results carried out on a variety of bi-and three-objective benchmark functions demonstrate that the proposed technique has competitive performance compared with some state-of-the art algorithms.

A quadrotor is a rotorcraft capable of hover, forward flight, and VTOL and is emerging as a fundamental research and application platform at present with flexibility, adaptability, and ease of construction. Since a quadrotor is basically considered an unstable system with the characteristics of dynamics such as being intensively nonlinear, multivariable, strongly coupled, and underactuated, a precise and practical model is critical to control the vehicle which seems to be simple to operate. As a rotorcraft, the dynamics of a quadrotor is mainly dominated by the complicated aerodynamic effects of the rotors. This paper gives a tutorial of the platform configuration, methodology of modeling, comprehensive nonlinear model, the aerodynamic effects, and model identification for a quadrotor.

The overall performance of complex industrial systems using control and optimization strategies is often hampered by the lack of available measurements of key quality variables. The development of a soft sensor is a solution that allows for the necessary process information to be made available. Due to the nonlinear characteristics of industrial processes, a new soft sensor development approach that incorporates infrequent, variable time delayed measurements is proposed. Based on the fast-sampled measurement and the nonlinear, first principles model, the frequent and delay-free key performance indicator (KPI) estimation is realized using the square-root unscented Kalman filter (SR-UKF). Then, a modified Kalman Filter (MKF) algorithm is proposed to deal with those infrequent, but accurate measurements, whose values are variably delayed in time. These two types of estimates are fused and optimized to give an optimal, reliable KPI estimate based on the distributed state fusion UKF filter algorithm. The performance and effectiveness of the proposed approach are demonstrated by an experimental application for alumina concentration monitoring in the aluminum electrolysis industry.

Stably grasping objects for a specific task is a hot research topic in robotics due to multiple degrees of freedom of hand kinematics, various shapes of objects, and incomplete visual sensing of objects (partial point clouds). This paper proposes an effective grasp planning method by integrating the crucial grasp cues (positions and orientations of thumb fingertips and the wrist) from humans' grasp experience. This approach has multiple advantages: greatly reducing the search space of the hand kinematics; no reconstruction or registration; being able to directly perform on the partial point cloud of objects. Meanwhile, for various shapes of objects which are partially observable in the single-view visual sensing, the presented approach learns the ``thumb'' grasp point employing a signature of histograms of orientations shape descriptor based on objects' category level. This method recognizes the grasp point according to the shape affordance at each point on the object, which performs the grasp point generalization on the familiar objects. Finally, we verify the developed methods via both simulations and experiments by grasping various shapes of objects.