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In recent years, image perception such as enhancement, classification and object detection with deep learning has achieved significant successes. However, in real world under extreme conditions, the training of a deep learning model often yields low accuracy, low efficiency in feature extraction and generalizability, due to the inner uncourteous and uninterpretable characteristics. In this paper, a maximal-entropy-attention-based convolutional neural network (MEA-CNN) framework is proposed. A maximum entropy algorithm is first used for image feature pre-extraction. An attention mechanism is then proposed by combining the extracted features on original images. By applying the mechanism, the key areas of an image are enhanced, and noised area can be ignored. Afterward, the processed images are transferred into region convolutional neural network, which is a well-known pre-trained CNN model, for further feature learning and extraction. Finally, two real-world experiments on traffic sign recognition and road surface condition monitoring are designed. The results show that the proposed framework has high testing accuracy, with improvements of 17% and 2.9%, compared with some other existing methods. In addition, the features extracted by the model are more easily interpretable. [ABSTRACT FROM AUTHOR]

The target of this article is to study almost periodic dynamical behaviors for complex-valued recurrent neural networks with discontinuous activation functions and time-varying delays. We construct an equivalent discontinuous right-hand equation by decomposing real and imaginary parts of complex-valued neural networks. Based on differential inclusions theory, diagonal dominant principle and nonsmooth analysis theory of generalized Lyapunov function method, we achieve the existence, uniqueness and global stability of almost periodic solution for the equivalent delayed differential network. In particular, we derive a series of results on the equivalent neural networks with discontinuous activation functions, constant coefficients as well as periodic coefficients, respectively. Finally, we give a numerical example to demonstrate the effectiveness and feasibility of the derived theoretical results. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of exponential stability and stabilization for a class of delayed distributed parameter systems, which is modeled by partial integro-differential equations (PIDEs). By employing the vector-valued Wirtinger's inequality, the sufficient condition of exponential stability of the PIDE system with a given decay rate is investigated. The condition is presented by linear matrix inequality (LMIs). After that, we develop a spatial proportional-integral-derivative state-feedback controller that ensures the exponential stabilization of the PIDE system in terms of LMIs. Finally, numerical examples are presented to verify the effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]