In this paper, we propose the reinforcement learning based multi-robot collision avoidance approach by learning collision. Dynamical path re-planning, which is massively used in classical collision avoidance methods, needs overall information of the environment. Also, training agent robots to avoid the collision and pursue a goal point simultaneously is inefficient since the agent should learn two tasks. As the number of tasks that the agent should learn increases, it is difficult to make the performance of an algorithm consistent, which is known as reproducibility issue. To overcome these limitations, Collision Avoidance by Learning Collision (CALC), which learns collision instead of avoiding an obstacle robot is suggested. To solve the collision avoidance problem efficiently, the proposed method divides the problem into training and planning. In the training algorithm, an agent robot learns how to collide with a single obstacle robot and then generates a trained policy. With the trained policy, the agent can pursue a goal point since the policy leads the agent to `collide' with the goal. Furthermore, by taking action in a reverse way from the trained policy, the agent can avoid multiple obstacle robots in the planning algorithm at once. The proposed method is validated both in the robot simulation and real robot experiment, and compared with the existing collision avoidance method.

Kernel principal component analysis (PCA) has recently been proposed as a nonlinear extension of PCA. The basic idea is to first map the input space into a feature space via a nonlinear map and then compute the principal components in that feature space. This letter illustrates the potential of kernel PCA for texture classification. Accordingly, supervised texture classification was performed using kernel PCA for texture feature extraction. By adopting a polynomial kernel, the principal components were computed within the product space of the input pixels making up the texture patterns, thereby producing a good performance.

In this paper, we propose a model of schema versions for object-oriented databases called RiBS. At the heart of this model is the concept of the Rich Base Schema (RiBS). Each schema version is in the form of a class hierarchy view over one base schema, RiBS, which accumulates all the schema information ever defined in the schema versions. Users, insulated from RiBS layer, access databases only through schema versions. Users impose schema evolution directly on schema versions, and the effects are, if necessary, automatically propagated to RiBS. We first describe the structure of the model and then introduce a set of invariants that should be satisfied by the model structure. As the third element of our model, we give a set of schema update operations, of which semantics are defined so as to preserve all the invariants.

An unsupervised method is presented for segmenting video sequences degraded by noise. Each frame in a sequence is modeled using a Markov random field (MRF), and the energy function of each MRF is minimized by chromosomes that evolve using distributed genetic algorithms. To improve the computational efficiency, only unstable chromosomes corresponding to moving object parts are evolved. Experimental results show the effectiveness of the proposed method.

The authors investigate an infinite-impulse response (IIR) adaptive digital filter (ADF) based on the nonlinear least-squares (NLS) algorithm, and compare its convergence speed to that of a self-orthogonalizing IIR ADF which is known to have fastest convergence. By simulation, it is shown that the NLS IIR ADF converges faster than other known IIR ADFs, especially for a low-order case.>