In order to recommend appropriate departments and doctors to patients, this paper uses the ERNIE pre-trained language learning model to classify corresponding departments for patient’s disease descriptions, then calculates the similarity between patient’s disease descriptions and doctors’ expertise, so as to show patients the ranking of doctors in corresponding departments for patients to choose consultation. As is shown in the experimental results on Haodf.com datasets, the ERNIE model outperforms other models in terms of evaluation indexes, and the accuracy rate is improved by 3.54%-11.51%.
With the national Belt and Road strategy in place, meteorological operational systems will face domestic and international dangers, and it is very urgent and important to protect the safety of meteorological equipment and resources. The existing meteorological operational system uses a role-based access control model for permission management, whose permissions do not change during one authorized access, and cannot sense the threat of user behavior to the system in real time, much less adjust user permissions in time, thus failing to cope with the security needs of the weather business system. In this paper, we propose a Meteorological Operational System-oriented Usage Control (MS-UCON) model to address this problem. The model refines the UCON model according to industry characteristics and business needs, and adds a periodic trust assessment module and a collection of trust value assessment factors to monitor user behavior in real time, perform periodic trust assessment on user behavior, sense user risky behavior in time, dynamically update user trust value attributes, and dynamically adjust permissions. Finally, the security of the MS-UCON model is proved based on state machine theory. The results show that the MS-UCON model can solve the problem that user privileges cannot be dynamically adjusted in meteorological operational systems, and also meet the security requirements of meteorological operational systems.
LLVM is an open-source compiler framework that provides flexible, modular and reusable compiler components for building compilers, static analysis tools, code optimizers and more. It is based on a modern modular architecture and supports multiple programming languages. LLVM also provides a rich toolchain and libraries for building high-performance compilers and optimizers. Besides, LLVM has the advantages of scalability and portability, so it is widely used in the field of compilers. This paper will introduce the architecture, features, research hotspots and applications of LLVM.
The study of the state of health (SOH) of lithium-ion batteries can avert major mishaps while also grading retired batteries for secondary use. To overcome the flaws of the traditional back propagation (BP) neural network technology, which has low precision and a poor fitting effect, this study proposes a model for forecasting the SOH of lithium-ion batteries based on a novel health factor and an upgraded BP algorithm. To begin, the NASA lithium battery database is evaluated offline, and the metrics with the highest correlation in the charging process are chosen as new health variables. On the other hand, simulation experiments are carried out on various groupings of battery data, and the BP algorithm is optimized using a genetic algorithm. The experimental results show that the errors in the classic BP models are all less than 2.4%, whereas the errors in the optimized BP neural network model are all less than 0.38%, and that the fitting effect has better generalization than the traditional BP model, which improves the estimation accuracy of SOH of lithium batteries, and that the proposed health factor can be directly collected by the battery management system (BMS), which has some practical implications.
In recent years, deep neural networks have achieved tremendous success in image classification in both academic and industrial settings. However, the high hardware requirements imposed by their intensive and complex computations pose a challenge for deployment on low-storage devices. To address this challenge, lightweight networks provide a viable solution. This paper provides a detailed review of recent lightweight image classification algorithms, which can be categorized into low-redundancy network model design and neural network compression algorithms. The former reduces network computations by replacing traditional convolution with efficient lightweight convolution, while the latter reduces redundancy in the network by employing methods such as network pruning, knowledge distillation, and parameter quantization. We summarize the experimental results of some classical models and algorithms on ImageNet2012 and CIFAR-10 datasets, and analyze the characteristics, advantages and disadvantages of these models respectively. Finally, future research directions for lightweight algorithms in the field of image classification are identified.
