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In the context of regional integration development, research on the urban network in the Yangtze River Delta (YRD) has received extensive attention from academia. Existing research mainly uses listed companies to construct urban networks. However, most of China's listed companies are concentrated in large cities. Giving too much attention to the urban network composed of listed companies may cause small cities to be “off the map.” This article uses 8,061 financial companies of different sizes in the YRD from 1990 to 2017 as research data combined with complex network models to explore the evolutionary characteristics, modes, and influencing factors of urban networks. The findings show that the connection strength and resilience of the urban network are gradually increasing, and its evolutionary mode has changed from a single center to a multicenter network. The radiation and agglomeration capabilities of urban networks have strong asymmetry. Shanghai's network agglomeration capacity is only 1/22 of its radiation capacity, which implies that Shanghai has played a bridge function in urban network links. Notably, the networks of most peripheral cities continued to expand from 1990 to 2017, while Shanghai was in a state of contraction. This finding indicates that the overall urban network is developing in a balanced and coordinated direction. In addition, economic growth provides critical support for the urban network, and technological progress may improve the level of production management of companies, thereby promoting cooperation and exchanges between cities. Further, cities with similar industrial structures may have industrial synergy or complementary relationships and are likely to have spatial connections. Finally, we suggest promoting the expansion of urban networks in the YRD by forming a multicenter development model, transforming local government functions, exploring differentiated development, and strengthening infrastructure construction.

Recent trends in consumer electronics have led to a proliferation of high dynamic range (HDR) video acquisition and transmission studies. High-end video capturing systems increase the bit depth of each pixel to adapt to the high-contrast ambient light. However, the extended bit depth is pathetically meager compared with the far wider dynamic range of the incident light. What’s more, incompatibility issues for the subsequent compression and transmission system may be arisen along with the increased bit depth. In this paper, we propose an auto complementary exposure (ACE) algorithm for optimal exposure parameter estimation under Siamese Trigger (ST) mode to maximize the entropy of the output HDR image. With the proposed algorithm, neither the sensor nor other related hardware needs to be modified. Only the auto-exposure algorithm in the firmware needs to be updated. In the proposed ACE algorithm, every two consecutive images are treated as Siamese twins, and the exposure parameters for the twin images are calculated simultaneously. The target of the ACE algorithm is not to maximize the entropy of each image but to maximize that of the fused image. Compared to existing auto exposure or bracketed fusing algorithms, the proposed algorithm has three significant advantages. Firstly, no extra bit-depth is needed to embrace the details of the scene. Thus the image transmission system is not affected. Secondly, only two raw images are necessary for one output fused HDR image. The computation load is relatively low. Thirdly, the proposed method can fit video recording, which has a broader application prospect than HDR image capturing. Experiment results on a self-built database and publicly accessible database demonstrate that the algorithm works well under various types of scenarios. Furthermore, the experiment results on the real-time system show that the captured video image reveals many details under high dynamic scenes.

Automatic assembly of elastic components is difficult because of the potential deformation of parts during the assembly process. Consequently, robots cannot adapt their manipulation to dynamic changes. Designing systems that learn assembly skills can help in alleviating the uncertain factor for industrial-grade assembly robots. This study proposes a skill acquisition method based on multi-modal information description to realize the assembly of systems with elastic components. This multi-modal information includes two-dimensional images, poses, forces/torques, and robot joint parameters. In this method, robots acquire searching, location determination, and pose adjustment skills using these multi-modal information parameters. As a result, robots can reach the assembly target by analyzing two-dimensional images with no position constraint. While acquiring pose adjustment skills, the reward function with depth and assembly steps is used to improve the learning efficiency. The deep deterministic policy gradient (DDPG) algorithm is applied for acquiring skills. Experiments using a KUKA iiwa robot demonstrated the effectiveness and conciseness of our method. Our results indicate that the robot acquired searching, location determination, and pose adjustment skills that allowed it to successfully complete elastic assembly.

Developing cognition is difficult to achieve yet crucial for robots. Infants can gradually improve their cognition through parental guidance and self-exploration. However, conventional learning methods for robots often focus on a single modality and train a pre-defined model by large datasets in an offline way. In this paper, we propose a hierarchical autonomous cognitive architecture for robots to learn object concepts online by interacting with humans. Two pathways for audio-visual information are devised. Each pathway has three layers based on the self-organizing incremental neural networks. Visual features and names of objects are incrementally learned and self-organized in an unsupervised way in sample layers, respectively, in which we propose a dynamically adjustable similarity threshold strategy to allow the network itself to control cluster rather than using a pre-defined threshold. Two symbol layers abstract the cluster results from the corresponding sample layer to form concise symbols and transmit them to an associative layer. An associative relationship between two modalities can be built in real time by binding activated visual and auditory symbols simultaneously in the associative layer. In this layer, a top-down response strategy is proposed to let robots autonomously recall another associative modality, solve conflicting associative relationships, and adjust learned knowledge from the top down. The experimental results on two objects datasets and a real task show that our architecture is efficient to learn and associate object view and name in an online way. What is more, the robot can autonomously improve its cognitive level by utilizing its own experience without enquiring with humans.

The aim of this paper is to propose a robust registration algorithm to reduce the impact of the marker-recognition error on image-to-physical registration in the robot-assisted cranio and maxillofacial (CMF) surgery. Since the image-guided technology has been widely used in the CMF surgery, the surgical robot based on remote or cloud plan data has come into focus. As a critical procedure of the image-guided surgical robot, the image-to-physical registration has become a decisive factor of the operation result. The recognition error of the reference points is a main challenge for the registration. Therefore, we propose an improved method to cope with the noise in the image space. The image-to-physical registration is accomplished via a group of implanted reference markers. Firstly, the reference markers in the image space are picked out manually and extended to a fuzzy point set via a directional region growing algorithm. Then, the reference markers in the physical space are localized by the navigation cameras. At last, the transfer matrix is calculated using an improved registration algorithm based on the geometrical features of reference points in the two spaces. The experimental results demonstrate that the proposed method is less sensitive to the number of the reference points, and the influence of recognition noise can be decreased effectively. This paper is a foundational link of the cloud-based surgical robots, and the results of every case will be collected to the cloud for further data mining and analyzing.

Translation is a complex activity in which two language systems form complex network structures. This paper aims to examine the similarities and differences between English language and Chinese language publicity material networks and to reveal the core factors manifested in the publicity materials by choosing 1000 pieces of Chinese and English publicity materials as the study objects (500 pieces in each language). The core factors of 10 contemporary translation theories identified by the Delphi method are considered nodes; then, we link the nodes in accordance with their relevance among the core factors embodied in the translated publicity materials to draw network diagrams (the English language and Chinese language publicity material networks). Finally, we discuss appropriate contemporary translation theories that are suitable for guiding China’s publicity translations and explore a new research perspective for the study of China’s publicity translations.

This paper investigates the cooperative control problem of multiple nonholonomic robots for an escorting and patrolling mission with respect to a target with a time-varying velocity. The goal of the multi-robot system is to orbit a moving target in a common circle with a prescribed radius and circular velocity, while maintaining even spacing along the perimeter of the circle. This paper proposes a distributed control strategy based on the vector field that does not require each robot to know the full state of the target. A distributed estimation law is developed to enable each robot to estimate the position of the target, and a tracking differentiator is used to estimate the velocity of the target. Based on these estimates of the target, the distributed control law is designed to ensure the asymptotic convergence of the multi-robot system to achieve the desired motion. Under some mild assumptions about the interaction graph among the target and the robots, and the velocity of the target, explicit stability and convergence analyses are presented using Lyapunov tools. Simulation results from test cases of a group of nonholonomic mobile robots verify the effectiveness of the proposed distributed control algorithms.