A China Healthcare Security Diagnosis Related Groups Script Development Based On R Language

Background: To develop a set of R scripts that could efficiently and accurately identify the home page information of medical records and perform China Healthcare Security Diagnosis Related Groups (CHS-DRG) simulating grouping.Methods: Based on the CHS-DRG grouping rules, we abstracted the DRG grouping process into a standard algorithm and compiled the R script Z-DRG. The DRG simulating groupings by Z-DRG were compared with the DRG results from the regional CHS-DRG integrated service platform to evaluate the accuracy.Results: The Z-DRG includes one function module (zdrgfun. Rc), one operation module (zdrgpro. R) and one database form (zdrgcodes.RData). The function module set 7 algorithm steps and 8 custom functions. The functions were set for multiple diagnoses, multiple operations, joint diagnosis and operation. Only (17.85±0.11) milliseconds were taken for CHS-DRG simulating grouping of one case. Compared with the regional CHS-DRG results, the accuracy rate was 99.10%. The difference in the number of other diagnoses is the main reason that affected the accuracy.Conclusions: Z-DRG is easy to operate. The CHS-DRG simulating groupings were efficient and accurate. The simulation results could be effectively applied for medical institutions to carry out CHS-DRG grouping prediction and improve the implementation effect of CHS-DRG payment work.

Design of diversified teaching platform of college aerobics course based on artificial intelligence

In order to improve the diversified teaching effect of a college aerobics course, effectively improve the accuracy of student grouping on the teaching platform, a diversified teaching platform of college aerobics course based on artificial intelligence is designed. First of all, it puts forward the construction idea and design process of the network teaching platform, then designs the interface and function module of the teaching platform, and finally designs the grouping function of teaching objects, so as to complete the design of the diversified teaching platform of a college aerobics course based on artificial intelligence. The experimental results show that the grouping accuracy of students on the diversified teaching platform of college aerobics course based on artificial intelligence is greater than 75%, and the average score of students studying on the platform is 74.66. This explains why the designed platform can effectively provide the accuracy of grouping and the students’ performance.

Design of feasibility analysis platform for college physical education based on mobile social network

In order to promote the development of sports distance network education, a college sports teaching feasibility analysis platform based on mobile social network is designed. The key technologies such as Ajax, database and XML, Java Web containers applied to mobile social networks provide a technical basis for the platform design of this paper. Design the feasibility analysis platform framework using MVC mode, three platform functional modules were designed. Among it, the member function module is responsible for the registration and modification of the platform personal information of members; The feasibility analysis interactive discussion module is based on the cloud database storage and analysis platform user discussion records; The feasibility evaluation module of mobile social network is based on mobile social network, builds the hierarchical model of feasibility analysis and completes the design of feasibility analysis of distance teaching in universities. Experimental performance tested the platform, with an average response time of 2.3 ms and an analysis time of 18 ms. The feasibility analysis results of the platform have high reliability and effectiveness to meet the practical application requirements.

Application of Data Mining in the Evaluation of Enterprise Lean Management Effect

In order to improve the effect of enterprise lean management, this study proposes a lean data mining algorithm based on the characteristics of lean data in enterprise management. This study connects data mining and lean production to study the data of enterprise management operation, proposes an intelligent data processing model suitable for modern enterprise management, and constructs the model function module in combination with the enterprise operation management process. Moreover, this study constructs an evaluation system for the effect of enterprise lean management based on data mining. The system provides a human-computer interaction interface, and operators can use various functions and services provided by the system through a visual interface. Through experimental research, it can be known that the enterprise lean effect evaluation system based on data mining proposed in this study can play an important role in enterprise lean management.

Semantic function module pipeline generation

With modular automation, modular industrial plants use a functional engineering approach, and modules enable plug & produce plant engineering. However, plant configuration is still a largely manual process and often not optimized with respect to the available information. In this contribution, we propose a system and algorithm to support the automation engineer in the process of joining together modules into process pipelines and in their optimization. Our solution is built upon an abstract semantic data model that facilitates the automated matching of pre- and post-condition of modules and of the things that are processed by these modules. The pipeline generation engine is further extended by means of an optimization and ranking algorithm, and thus enables automated inter-module pipeline generation and plant optimization. We evaluate our system by means of a simple fictional use case scenario and prove feasibility, applicability as well as the huge potential for time and cost savings.

In-Process Error-Matching Measurement and Compensation Method for Complex Mating

This study proposed an error-matching measurement and compensation method for curve mating and complex mating. With use of polynomial curve fitting and least squares methods for error analysis, an algorithm for error identification and error compensation were proposed. Furthermore, based on the proposed method, an online error-matching compensation system with an autorevising function module for autogenerating an error-compensated NC program for machining was built. Experimental verification results showed that the proposed method can effectively improve the accuracy of assembly matching. In a curve-type mating experiment, the matching error without compensation was 0.116 mm, and it decreased to 0.048 mm after compensation. The assembly accuracy was improved by 28%. In a complex-type mating experiment, the verification results showed that the error reductions after compensation for three mating shapes (straight line, triangle, and curve shape) were 81%, 87%, and 79%, respectively. It showed that the proposed method can improve the assembly accuracy for complex mating shapes, which would also be improved without losing production efficiency.

Discovery of key function module in complex mechanical 3D CAD assembly model for design reuse

Purpose Complex mechanical 3D computer-aided design (CAD) model embodies rich implicit design knowledge. Through discovering the key function parts and key function module in 3D CAD assembly model in advance, it can promote the designers’ understanding and reuse efficiency of 3D assembly model in design reuse. Design/methodology/approach An approach for discovering key function module in complex mechanical 3D CAD assembly model is proposed. First, assembly network for 3D CAD assembly model is constructed, where the topology structure characteristics of 3D assembly model are analyzed based on complex network centrality. The degree centrality, closeness centrality, betweenness centrality and mutual information of node are used to evaluate the importance of the parts in 3D assembly model. Then, a multi-attribute decision model for part-node importance is established, and the comprehensive evaluation for key function parts in 3D assembly model is accomplished by combining Analytic Hierarchy Process and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Subsequently, a community discovery of function module in assembly model-based Clauset–Newman–Moore (CNM)-Centrality is given in details. Finally, 3D CAD assembly model of worm gear reducer is taken as an example to verify the effectiveness and feasibility of proposed method. Findings The key function part in CAD assembly model is evaluated comprehensively considering assembly topology more objectively. In addition, the key function module containing key function part is discovered from CAD assembly model by using CNM-Centrality-based community discovery. Practical implications The approach can be used for discovering important design knowledge from complex CAD assembly model when reusing the assembly model. It can help designers capture and understand the design thinking and intent, improve the reuse efficiency and quality. Originality/value The paper first proposes an approach for discovering key function module in complex mechanical 3D CAD assembly model taking advantage of complex network theory, where the key function part is evaluated using node centrality and TOPSIS, and the key function module is identified based on community discovery.

Design and Research of Intelligent Educational Administration Management System Based on Mobile Edge Computing Internet

Educational administration management is the primary link in the teaching management of colleges and universities. Mobile edge computing can create a carrier-class service environment with high performance, low latency, and high bandwidth and accelerate the rapid download of various contents, services, and applications in the network, which greatly promotes the upgrade of the educational administration system. Using educational administration management system to manage educational administration can promote the teaching work of colleges and universities better. This paper aims to design and develop a set of educational management information systems, using mobile edge computing (MEC) technology to combine the IT service environment and cloud computing technology at the edge of the network to improve the computing and performance of the edge network. Storage capacity reduces network operation and service delivery delay, improves user service quality experience, and helps universities improve the efficiency of educational administration management. This paper first discusses the implementation mode and related technologies of educational administration management system, then discussing the demand analysis of each functional module of the system; in the nonfunctional demand analysis part, the system needs to meet the security and performance. According to the function modules included in the system, using the way of running interface screenshot, implementation code, and flowchart, the paper analyzes the realization process of the function module and also completes the function test of the function module, as well as the performance test of the whole system. The experimental results show that the rule 4 of teaching level evaluation data mining reveals that the support degree of excellent teaching effect is 18% and the confidence degree is 53% in the age of 50–60. Rule 5 shows that the degree of support is 16% and the degree of confidence is 52%. Rule 10 shows that the degree of support for excellent teaching effect is 22% and the confidence level is 71%. The high confidence level of backbone teachers aged 50–60 indicates that the old teachers are more experienced and popular with students, while the young teachers under 30 need to focus on training to help young teachers improve their professional level. From the above data, it can be seen that through the application of mobile edge technology the educational administration system is more efficient in processing and analyzing data in terms of teacher management and teaching level, which once again shows the impact of this network technology on the construction and development of educational administration systems highly feasible.

Obstacle Detection Based on 3D Lidar Euclidean Clustering

Environment perception is the basis of unmanned driving and obstacle detection is an important research area of environment perception technology. In order to quickly and accurately identify the obstacles in the direction of vehicle travel and obtain their location information, combined with the PCL (Point Cloud Library) function module, this paper designed a euclidean distance based Point Cloud clustering obstacle detection algorithm. Environmental information was obtained by 3D lidar, and ROI extraction, voxel filtering sampling, outlier point filtering, ground point cloud segmentation, Euclide clustering and other processing were carried out to achieve a complete PCL based 3D point cloud obstacle detection method. The experimental results show that the vehicle can effectively identify the obstacles in the area and obtain their location information.

Research on News Recommendation System Based on Deep Network and Personalized Needs

In order to solve the problems of poor performance of the recommendation system caused by not considering the needs of users in the process of news recommendation, a news recommendation system based on deep network and personalized needs is proposed. Firstly, it analyzes the news needs of users, which is the basis of designing the system. The functions of the system module mainly include the network function module, database module, user management module, and news recommendation module. Among them, the user management module uses the deep network to set the user news interest model, inputs the news data into the model, completes the personalized needs of the news, and realizes the design of the news recommendation system. The experimental results show that the proposed system has good effect and certain advantages.