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This document discusses the rapid development of Artificial Intelligence (AI) and its bioinspired applications. It highlights the benefits of bioinspired AI, such as increased accuracy in image and speech processing, reduced cost and energy usage through edge devices, and enhanced bio-signal quality. However, it also acknowledges the challenges posed by improper AI utilization, such as the generation of fake news and security issues. The document calls for research papers on bioinspired AI applications to explore its potential and address these challenges. It includes examples of research papers that utilize deep reinforcement learning for robot task sequencing, propose a real-time multi-surveillance pedestrian target detection model, develop an intelligent breast mass classification approach, and introduce a bio-inspired object detection algorithm for remote sensing images. The document concludes by emphasizing the importance of biomimetic artificial intelligence in various fields and promoting further research in this area. [Extracted from the article]

Machine part sorting is important and monotonous in smart factory. In this paper, an auto-sorting system is proposed based on the deep learning method. In the proposed system, an industrial objection detection network combined with a robotic arm controlling system is designed to automatically and efficiently complete machine part sorting. Region-based full convolutional network (R-FCN) is applied for locating and recognizing different types of images of industrial object models. After comparison and simulation analysis, it illustrated that the R-FCN model trained with enough labeled data can efficiently and accurately recognize the object from the images captured by visual sensors. Furthermore, with enough data, the network can be robust to view angle rotation both vertically and horizontally, and a small part of overlapping of object will not mislead the judgment of the network in most situations. The case study results illustrate that the position and type of objects can be successfully detected. The code will be available publicly at https://github.com/tianwangbuaa/. [ABSTRACT FROM AUTHOR]

Summary: Limited number of labeled data of surveillance video causes the training of supervised model for pedestrian re‐identification to be a difficult task. Besides, applications of pedestrian re‐identification in pedestrian retrieving and criminal tracking are limited because of the lack of semantic representation. In this paper, a data‐driven pedestrian re‐identification model based on hierarchical semantic representation is proposed, extracting essential features with unsupervised deep learning model and enhancing the semantic representation of features with hierarchical mid‐level 'attributes'. Firstly, CNNs, well‐trained with the training process of CAEs, is used to extract features of horizontal blocks segmented from unlabeled pedestrian images. Then, these features are input into corresponding attribute classifiers to judge whether the pedestrian has the attributes. Lastly, with a table of 'attributes‐classes mapping relations', final result can be calculated. Under the premise of improving the accuracy of attribute classifier, our qualitative results show its clear advantages over the CHUK02, VIPeR, and i‐LIDS data set. Our proposed method is proved to effectively solve the problem of dependency on labeled data and lack of semantic expression, and it also significantly outperforms the state‐of‐the‐art in terms of accuracy and semanteme. [ABSTRACT FROM AUTHOR]

• A practical and effective method is presented for energy prediction of machine tool. • Energy consumption features are extracted by using unsupervised deep learning. • Supervised deep learning is used to develop energy prediction model of machine tool. • This method is a generalized way for identifying energy of different machine tools. • The results show that the method could improve the energy prediction performance. Energy prediction of machine tools plays an irreplaceable role in energy planning, management, and conservation in the manufacturing industry. In the era of big machinery data, data-driven energy prediction models of machine tools have achieved remarkable results in the identification of energy consumption patterns and prediction of energy consumption conditions. However, existing data-driven studies towards the energy consumption of machine tools focus on the utilization of handcrafted-feature learning methods, which are inefficient and exhibit poor generalization. Moreover, considering variations in energy consumption characteristics among different machine tools, it is impractical to identify energy consumption features manually for energy model development. Therefore, this paper proposes a novel data-driven energy prediction approach using deep learning algorithms. Here, deep learning is used in an unsupervised manner to extract sensitive energy consumption features from raw machinery data, and in a supervised manner to develop the prediction model between the extracted features and the energy consumption of machine tools. The experiments conducted on a milling machine and a grinding machine are exploited and compared with those conducted in conventional studies. The results show that the proposed method can improve the energy prediction performance from 19.14% to 74.13% for the grinding machine and from 64.89% to 85.61% for the milling machine, and it achieves a better performance than the conventional methods in terms of effectiveness and generalization. [ABSTRACT FROM AUTHOR]