The task offloading in space-aerial-ground integrated network (SAGIN) has been envisioned as a challenging issue. In this paper, we investigate space/aerial-assisted edge computing architecture considering whether to take advantage of server mounted on the unmanned aerial vehicle and satellite for or not. By optimizing energy consumption completion delay, formulate NP-hard non-convex optimizati...

Scientific investigations have to deal with rapidly growing amounts of data from simulations and experiments. During data analysis, scientists typically want to extract subsets of the data and perform computations on them. In order to speed up the analysis, computations are performed on distributed systems such as computer clusters, or Grid systems. A well-known difficult problem is to build sy...

End effector tracking control of robot manipulators subject to dynamical uncertainties is the main objective this article. Direct task space that aims minimizing end error directly preferred. In open loop system, vector depends on uncertain terms modeled via a fuzzy logic network and self-adjusting adaptive component designed as part nonlinear proportional derivative based input torque. The sta...

Multi-task learning has received increasing attention in the past decade. Many supervised multi-task learning methods have been proposed, while unsupervised multitask learning is still a rarely studied problem. In this paper, we propose to learn a kernel for multi-task clustering. Our goal is to learn a Reproducing Kernel Hilbert Space, in which the geometric structure of the data in each task ...

This paper studies the construction of a refinement kernel for a given operator-valued reproducing kernel such that the vector-valued reproducing kernel Hilbert space of the refinement kernel contains that of the given kernel as a subspace. The study is motivated from the need of updating the current operator-valued reproducing kernel in multi-task learning when underfitting or overfitting occu...

Many real-world problems exhibit dualheterogeneity. A single learning task might have features in multiple views (i.e., feature heterogeneity); multiple learning tasks might be related with each other through one or more shared views (i.e., task heterogeneity). Existing multi-task learning or multi-view learning algorithms only capture one type of heterogeneity. In this paper, we introduce Mult...