Search

As a novel economic form, the digital economy plays an important role in promoting urban low-carbon sustainable development (LCSD). Based on panel data from 270 cities in China from 2011 to 2021, this study used principal component analysis and the global entropy weight method to calculate the level of digital economy development and LCSD as well as to empirically discuss any correlated effects and mechanisms. The research showed that the digital economy has significantly promoted urban LCSD and has become an important source for promoting green low-carbon development. The digital economy can indirectly promote LCSD by accelerating industrial structure upgrading and promoting technological innovation; Moreover, this promotion can show significant threshold characteristics with these factors. When industrial structure upgrading and technological innovation reach a certain threshold, the digital economy can further enhance urban LCSD. The promotion of the digital economy is positively influenced by marketization level and environmental regulation, and it exhibits distinct regional characteristics. During the critical period of economic development and transformation, the government must continuously promote the development of the digital economy, fully release its dividends on LCSD, and implement a differentiated digital development strategy to promote low-carbon economic development according to local conditions.

Abstract Traditional warehousing typically needs machine learning or manual tagging to classify objects. However, this method is less robust and consumes a lot of labour and material resources. Based on DenseNet, this work proposes a feature weighting convolutional network recognition model and designs a set of software and hardware for data acquisition, which is applied to the efficient classification of industrial parts in warehouse management. Firstly, this work modifies DenseNet by embedding SE‐Block, and replaces the cross‐entropy loss function with the focus loss function to optimize the model structure. Secondly, a multi‐view hardware and software acquisition system is designed to complete the functions of part image acquisition, image preprocessing, model training and part recognition. Finally, an industrial parts sorting experiment was designed. Compared with the original DenseNet model, the proposed weighted convolutional network identification model showed that the accuracy of the modified model was increased by 3.09% and the convergence rate was significantly improved. The modified model proposed in this work aims to improve the recognition accuracy of industrial parts in modern warehouse management, so as to modify the classification efficiency of warehouse parts in production.

High-density 3D warehousing is the future cornerstone of modern logistics storage. It aims to efficiently manage and store various types of goods by utilizing 3D space and automation technology. The accurate and fast docking algorithm of the four-way shuttles, as a core method of carrying the goods in high-density 3D warehousing, has become an increasingly important technical challenge. Inaccurate docking will result in a failure to change direction, while long docking time will affect the operation efficiency of the four-way shuttle. To overcome these obstacles, this paper presents an accurate and rapid docking algorithm for four-way shuttles. Firstly, the deceleration and jerk of a four-way shuttle in the braking stage are initialized according to the motion parameter set calculated by the docking motion model. Then, the four-way shuttle starts to move until the stop signal has been detected. If the system fails to detect the stop signal, the distance compensation is needed to ensure that the four-way shuttle can arrive at the stop point. Finally, the four-way shuttle stops immediately by way of amplifying the deceleration when reaching the stop point. The result shows that the accuracy of rapid docking is superior to that of direct docking. Compared with crawling docking, rapid docking reduces the braking time by 3.99s and stops at a speed lower than the crawling speed. The rapid docking algorithm not only enhances the throughput of high-density 3D warehousing but also improves the service life of four-way shuttles.

Generalized target detection algorithms perform well for large- and medium-sized targets but struggle with small ones. However, with the growing importance of aerial images in urban transportation and environmental monitoring, detecting small targets in such imagery has been a promising research hotspot. The challenge in small object detection lies in the limited pixel proportion and the complexity of feature extraction. Moreover, current mainstream detection algorithms tend to be overly complex, leading to structural redundancy for small objects. To cope with these challenges, this paper recommends the PCSG model based on yolov5, which optimizes both the detection head and backbone networks. (1) An enhanced detection header is introduced, featuring a new structure that enhances the feature pyramid network and the path aggregation network. This enhancement bolsters the model’s shallow feature reuse capability and introduces a dedicated detection layer for smaller objects. Additionally, redundant structures in the network are pruned, and the lightweight and versatile upsampling operator CARAFE is used to optimize the upsampling algorithm. (2) The paper proposes the module named SPD-Conv to replace the strided convolution operation and pooling structures in yolov5, thereby enhancing the backbone’s feature extraction capability. Furthermore, Ghost convolution is utilized to optimize the parameter count, ensuring that the backbone meets the real-time needs of aerial image detection. The experimental results from the RSOD dataset show that the PCSG model exhibits superior detection performance. The value of mAP increases from 97.1% to 97.8%, while the number of model parameters decreases by 22.3%, from 1,761,871 to 1,368,823. These findings unequivocally highlight the effectiveness of this approach.

Collaborative robots face barriers to widespread adoption due to the complexity of programming them to achieve human-like movement. Learning from demonstration (LfD) has emerged as a crucial solution, allowing robots to learn tasks directly from expert demonstrations, offering versatility and an intuitive programming approach. However, many existing LfD methods encounter issues such as convergence failure and lack of generalization ability. In this paper, we propose: (1) a generative adversarial network (GAN)-based model with multilayer perceptron (MLP) architecture, coupled with a novel loss function designed to mitigate convergence issues; (2) an affine transformation-based generalization method aimed at enhancing LfD tasks by improving their generalization performance; (3) a data preprocessing method tailored to facilitate deployment on robotics platforms. We conduct experiments on a UR5 robotic platform tasked with handwritten digit recognition. Our results demonstrate that our proposed method significantly accelerates generation speed, achieving a remarkable processing time of 23 ms, which is five times faster than movement primitives (MPs), while preserving key features from demonstrations. This leads to outstanding convergence and generalization performance.