卷 6 ， 期 24 ， 2023
- 摘要：It is the main method to recognize maize leaf disease by deep learning network model, but the deep learning model needs more data sets, but in fact, the sample types and data obtained artificially are limited, moreover, the model with small sample data set is prone to over-fitting and loss of generalization ability. Based on this background, this paper proposes a method of identifying maize leaf disease with small samples by migration learning. On the one hand, it solves the problem of poor generalization caused by small samples from the perspective of migration learning ,On the other hand, Alexnet, resnet 50 and mobilenet V2 models were adopted and trained from the direction of deep learning to compare the accuracy of disease recognition based on the three models. The results showed that the mobility learning could improve the generalization ability of small samples, and the mobilenet V2 model was more suitable for the identification of leaf diseases in small samples .关键词：small sample size;disease identification;Transfer learning;Deep learning171|298|0更新时间：2025-03-26
- 摘要：Existing semantic segmentation methods produce better results on clean images, but segmentation models trained on clean images applied to real-world images experience performance degradation because of the domain gap between the training and testing domains, which reduces the segmentation accuracy. To address the problem of real-world semantic segmentation, this paper proposes a joint super-resolution-semantic segmentation framework for improving semantic segmentation accuracy. Specifically, the proposed framework embeds a two-branch network that includes a super-resolution branch, a semantic segmentation branch, and a feature sharing module. The super-resolution task encourages the network to find a robust representation of features with different resolutions, so that the segmentation head can use the recovered “clean" features for better prediction. The super-resolution branch is configured only during training and can be discarded during the inference phase. Based on the constructed pseudo-real pairwise dataset CityDeg for supervised training, the proposed framework, together with the existing state-of-the-art semantic segmentation methods, is able to effectively improve the performance of semantic segmentation for low-resolution scenes without introducing additional computational cost.关键词：super-resolution;semantic segmentation;joint framework;Deep learning145|453|0更新时间：2025-03-26
- 摘要：Alzheimer's disease (AD) is one of the important challenges facing society today, however, early diagnosis is essential for effective treatment. This study explores the application of machine learning algorithms in predicting AD risk and uses a variety of models for comparison. The results show that although the model selection is rich, the prediction effect is not ideal due to missing values in the data set. SHAP profile analysis revealed the key role of depression and APOE ε4 allele in model prediction. Future studies should further explore the influence of these genetic and environmental factors on the pathogenesis of AD, and optimize predictive models with advanced technologies to improve early diagnosis and intervention capabilities, and provide more accurate and effective methods for the prevention and treatment of Alzheimer's disease.关键词：machine learning;SHAP;Model interpretability;Risk prediction204|187|0更新时间：2025-03-26
- 摘要：This article discusses the design and practice of digital home operating systems based on cloud-network-intelligent computing integration technology. First, the concept and architecture of the home operating system are introduced, including basic concepts, resource layer abstraction, family layer abstraction, and scheduling principles. Secondly, the resource scheduling model of the home operating system is discussed in detail, including the abstraction and quantification of resources, and the construction and management of the home resource scheduling model. Finally, through practical cases, the application and effectiveness of digital home operating systems in improving family life quality and optimizing resource utilization are demonstrated.关键词：cloud network intelligent computing integration;digital home;operating system;resource scheduling;distributed system108|117|0更新时间：2025-03-26
RESEARCH PAPER
- 摘要：With the rapid development of the new generation of information technology and the Internet, the boundaries of technology are breaking. Emerging information technology is integrating into the radio and television industry, bringing unprecedented changes to the traditional radio and television industry. How to deeply understand the advantages and characteristics of new technologies, and combine them with the broadcasting network itself, through innovative concepts, to create products and ecosystems that meet the development needs of broadcasting networks, so that broadcasting networks can survive and develop in the fierce market competition. This paper summarizes the application status and development trend of broadcasting and television network related technologies in China, such as IPv6, AI generated content (AIGC), satellite Internet, meta universe, information security, 5G new calls, WiFi7, Web4.0, etc..关键词：broadcasting and television network;technology application;trend185|403|0更新时间：2025-03-26
SURVEYS & TUTORIALS
- 摘要：In this paper, we study the application of big language modeling and artificial intelligence in power systems. First, we introduce the basic principles and characteristics of big language model, including its development history and application areas. Then, we discuss the current status and prospects of the application of artificial intelligence technology in power systems. We analyze three key application areas of big language models in power systems: power load forecasting, power equipment fault diagnosis, and power system security assessment. In power load forecasting, the big language model can realize accurate load forecasting by learning historical data and environmental features, providing powerful support for power system operation and scheduling. In power equipment fault diagnosis, the big language model can automatically identify and locate faults according to the operating state of the equipment and fault characteristics, improving the diagnostic efficiency of equipment faults. In power system security assessment, the Big Language Model can analyze system operation data and risk factors, quickly assess the security status of the system, and provide early warning and decision support. Finally, we discuss the challenges and countermeasures for the application of big language modeling and artificial intelligence in power systems. We believe that with the continuous development of big language modeling and the continuous innovation of AI technology, the operational efficiency and security of power systems will be significantly improved.关键词：big language modeling;artificial intelligence;power system374|555|0更新时间：2025-03-26
- 摘要：With the rapid development of technology, the construction of smart campuses has become an important issue in the field of education. Smart campuses are composed of smart classroom systems, smart library systems, campus information management systems, campus ID card systems, broadcasting systems, clock systems, security systems, fire alarm systems, campus server rooms, and more. This smart campus is flexibly designed based on the functional requirements of each system combined with the specific conditions of the project. It utilizes BIM design for key technical validations and, through detailed design, construction, and acceptance, is capable of fulfilling the functions of each system effectively. It excels particularly in cost-effectiveness, safety, and reliability, making it a valuable reference for the design and implementation of smart projects.关键词：smart classroom system;surveillance system;broadcasting system;access control system;electronic clock system181|227|0更新时间：2025-03-26
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