The continuous progress in modern medicine is not only the level of medical technology, but also various high-tech medical auxiliary equipment. With the rapid development of hospital information construction, medical equipment plays a very important role in the diagnosis, treatment, and prognosis observation of the disease. However, the continuous growth of the types and quantity of medical equipment has caused considerable difficulties in the management of hospital equipment. In order to improve the efficiency of medical equipment management in hospital, based on cloud computing and the Internet of Things, this paper develops a comprehensive management system of medical equipment and uses the improved particle swarm optimization algorithm and chicken swarm algorithm to help the system reasonably achieve dynamic task scheduling. The purpose of this paper is to develop a comprehensive intelligent management system to master the procurement, maintenance, and use of all medical equipment in the hospital, so as to maximize the scientific management of medical equipment in the hospital. Scientific Management. It is very necessary to develop a preventive maintenance plan for medical equipment. From the experimental data, it can be seen that when the system simultaneously accesses 100 simulated users online, the corresponding time for submitting the equipment maintenance application form is 1228 ms, and the accuracy rate is 99.8%. When there are 1000 simulated online users, the corresponding time for submitting the equipment maintenance application form is 5123 ms, and the correct rate is 99.4%. On the whole, the medical equipment management information system has excellent performance in stress testing. It not only predicts the initial performance requirements, but also provides a large amount of data support for equipment management and maintenance.
A handy and effective method was established to obtain the cis-2,3-dihydrobenzofuranols having three stereocenters, involving asymmetric transfer hydrogenation of benzofuranones via dynamic kinetic resolution. The general applicability of this method was examined with different benzofuran-3-(2 H)-ones, and stereoselectivities of 85-99% ee and up to 98/2 dr were obtained.
At present, image recognition processing technology has been playing a decisive role in the field of pattern recognition, of which automatic recognition of bank notes is an important research topic. Due to the limitation of the size of bill layout and printing method, many invoice layouts are not clear, skewed or distorted, and even there are irregular handwritten signature contents, which lead to the problem of recognition of digital characters on bill surface. In this regard, this paper proposes a data acquisition and recognition algorithm based on improved BP neural network for ticket number identification, which is based on the theory of image processing and recognition, combined with improved bill information recognition technology. First, in the pre-processing stage of bill image, denoising and graying of bill image are processed. After binarization of bill image, the tilt detection method based on Bresenham integer algorithm is used to correct the tilted bill image. Secondly, character localization and feature extraction are carried out for par characters, and the target background is separated from the interference background in order to extract the desired target characters. Finally, the improved BP neural network-based bill digit data acquisition and recognition algorithm is used to realize the classification and recognition of bill characters. The experimental results show that the improved method has better classification and recognition effect than other data acquisition and recognition algorithms.
There are many news articles reported online everyday. Within an ongoing topic, people can find a huge amount of news articles. A topic often consists of several events, and people are interested in the whole evolution of a topic along a timeline. This requests for finding and identifying the dependent relationships between events. In order to understand the whole evolution of a topic effectively, we propose a framework of event relationship analysis. We define three kinds of event relationships which are co-occurrence dependence relationship, event reference relationship, and temporal proximity relationship for modeling how an event is dependent on another event within a topic. Through combining three kinds of relationships, we can discover an Event Evolution Graph (EEG) for users to view the evolution of a topic. Experiments conducted on a real data set show that our method outperforms baseline methods.
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