卷 7 ， 期 1 ， 2024
- 摘要：As the degree of automation in modern logistics and warehousing systems increases, AGV (Automated Guided Vehicle) carts are widely used in manufacturing enterprises with high intelligent manufacturing levels. In large unmanned warehouses, the efficient, safe and stable coordinated operation of a large number of AGV carts places high demands on the network transmission and computing speed of industrial networks. Based on the analysis of the requirements of network and computing power for the operation of a large number of AGV carts, this paper proposes an AGV cart task scheduling network model in a computing and networking integrated environment. In this network system architecture model, the network nodes have the functions of computing power calculation, storage, and network task distribution and scheduling, integrating computing power and network, and improving the performance of network routing nodes in terms of bandwidth, delay, and computing power, to more efficiently meet the coordinated operation requirements of a large number of AGV carts.关键词：Industrial Internet of Things （IIoT）;AGV cart scheduling planning;arithmetic network fusion;network architecture model;edge computing48|34|0更新时间：2025-02-13
- 摘要：Through image processing and deep learning technology, DenseNet201 network is used to analyze and classify breast pathological images. The dataset included 162 breast cancer specimens, of which DenseNet121 is used for experimental comparison. Experimental results show that DenseNet201 performed better in breast cancer detection than DenseNet121. Through 20 training epochs, the overall accuracy reaches 82%, the accuracy of category without invasive ductal carcinoma is 90%, the recall rate is 84%, and the F1 score is 0.87. With invasive ductal carcinoma category accuracy of 66%, recall rate of 77%, F1 score of 0.71. Compared to other DenseNet networks, DenseNet201 improves accuracy by about 5%. When processing large-scale breast cancer image data sets, DenseNet201 has more powerful feature extraction capabilities and can better adapt to complex data patterns and relationships, thus improving the accuracy and efficiency of breast cancer detection.关键词：breast cancer;DenseNet;Pathological image;Deep learning104|155|0更新时间：2025-02-13
- 摘要：Without considering the coplanarity or nearly coplanarity of space targets, there exist an intersection line on the orbital planes of two space targets, and the possible intersection of two space targets occurs near the orbital intersection line. In addition, the orbit of space targets is affected by various perturbation forces, and the distance between two space targets has certain fluctuations, but the overall approach distance has a gradual trend when time changes. Therefore, a spatial target approach analysis method with additional time windows is proposed. The results show that this method can quickly and accurately analyze the approach between space targets, and obtain the parameters such as the closet approach time and approach distance between two space targets, especially for the global collision warning, which will effectively reduce the calculation time and improve computation efficiency by more than 10%.关键词：spatial target;collision warning;orbit perturbation;orbit selection;approach analysis16|27|0更新时间：2025-02-13
- 摘要：The popularity of smart home devices requires accurate spatial modeling of the devices. This paper proposes an automatic device location modeling method based on the Zigbee communication protocol. This method uses a custom Zigbee distance vector cluster (ZDV cluster) to achieve distance measurement and location modeling between devices, and can automatically build digital models of each device in the home based on the signal strength between devices. By combining modeling, signal processing and enhanced positioning algorithms, the positioning accuracy can be stabilized in the range of 1-3 meters, which is suitable for most smart home application scenarios. This paper introduces the workflow, implementation details and practical application scenarios of this method in detail.关键词：zigbee protocol;device location modeling;smart home;distance vector clustering18|16|0更新时间：2025-02-13
RESEARCH PAPER
- 摘要：Deep learning technology has shown great potential in medical image analysis, greatly improving the accuracy and efficiency of medical diagnosis. This paper discusses the major technological developments and applications of deep learning in the field of medical image processing, with a focus on the key roles of deep learning based segmentation, registration, fusion, and federated learning methods in enhancing diagnostic accuracy and assisting treatment. Finally, this article explores the limitations of current technology and future development directions, aiming to provide valuable references for research and application in the field of medical image processing.关键词：Deep learning;image analysis;medical imaging analysis;application of analytical techniques523|162|0更新时间：2025-02-13
- 摘要：With the rapid development of the digital economy, the digital twin has gradually become a key technology for digital transformation in the industrial field with its integration and interactivity, which can in parallel sense, monitor and control physical equipment and production systems across the entire manufacturing plant network infrastructure. This paper summarizes the basic characteristics and development stage of digital twin, discusses the practical application of digital twin technology in different life cycles of the industry digital transformation, introduces the latest progress of Nvidia Omniverse platform, and looks forward to the future development trend. Digital twin will accelerate the integration with artificial intelligence, and form integrated innovation through the open design of the platform on the basis of data collection and modeling standardization, in order to promote the application of digital twin in the field of industrial digital transformation.关键词：digital twin;Digital Transformation;life cycle;Omniverse platform;integrated innovation195|72|0更新时间：2025-02-13
- 摘要：In the ongoing global technological revolution and industrial transformation, artificial intelligence (AI) stands at the forefront. This paper reviews and discusses the current status and trends of various key technologies in the field of AI. Specifically, AI chips enhance computing power through process optimization and architectural innovation; Chip-enabling technologies optimize software architectures, thereby improving heterogeneous integration capabilities; AI development frameworks bolster compatibility, flexibility, and portability; Large models have made breakthroughs in handling complex tasks and integrating multimodal information; The integration of cloud computing and AI promotes the deep fusion of computing power, algorithms, and data, facilitating the formation of an AI industrial ecosystem. These technologies collectively propel AI towards more efficient, intelligent, and widespread applications. Through this research, we aspire to provide valuable reference and insights for policymakers, researchers, and industry practitioners.关键词：AI chips;chips enabling;AI development frameworks;large model;cloud-AI integration2670|204|0更新时间：2025-02-13
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