Recently, multi-view learning, which is a deep learning method that uses multiple pieces of information about the same object to make predictions, has achieved significant success in various real-world applications. This paper proposes a multi-view image classification method that considers the uncertainty of classification probability using conventional Convolutional Neural Networks (CNN). This method is characterized by lower learning costs than other methods such as Bayesian Neural Networks (BNN) and is explainable and scalable by using the normalized product of multivariate Gaussian distributions. The proposed method can reduce the computational cost compared to the method of processing all images with CNN. Furthermore, by employing the concept of Grad-CAM, this study proposes a visualization technique for highlighting regions of interest within multi-view images. Experiments conducted with a 3-class multi-view image dataset demonstrate that the normalized product enables a rational calculation of multi-view image classification predictions and the proposed method enables a faster computation of multi-view image classification predictions than an existing method and BNN.

This paper addresses control methods for nonlinear dynamical systems. Optimal control is a prominent method for controlling such systems, which involves solving an optimization problem with constraints like system dynamics to determine a series of control inputs. However, nonlinear optimization problems can be time-consuming to solve. Consequently, setting new initial states requires recomputation, leading to inefficiency. To overcome this, methods using previously obtained data to quickly compute control inputs for new initial states have been proposed. Notably, using deep neural networks to learn controllers that output control inputs based on state information has gained attention. However, despite similar control objectives, solutions to the optimization problem can vary significantly with different initial conditions, making learning challenging and reducing estimation accuracy and control performance. This paper proposes a method to improve the estimation accuracy and control performance of controllers by classifying learning data using clustering and then learning based on the classified data. The effectiveness of this approach is verified through numerical experiments on a vehicle obstacle avoidance problem, comparing it with existing methods.