智能化预检分诊系统在急诊患者中的应用效果

目的:观察智能化预检分诊系统在急诊患者中的应用效果。方法:选取2020年1月至2022年1月该院急诊收治的120例患者进行前瞻性研究,按照随机数字表法将其分为对照组和研究组各60例。对照组采用常规急诊预检分诊,研究组采用智能化预检分诊系统分诊,比较两组抢救成功率、分诊准确率、候诊时间、风险事件发生率、医疗纠纷发生率及就诊满意度。结果:研究组抢救成功率和分诊准确率均高于对照组,候诊时间短于对照组,差异有统计学意义(P<0.05);研究组风险事件发生率、医疗纠纷发生率均低于对照组,差异有统计学意义(P<0.05);研究组就诊满意率为96.67%(58/60),高于对照组的76.67%(46/60),差异有统计学意义(P<0.05)。结论:智能化预检分诊系统应用于急诊就诊患者可提高抢救成功率、分诊准确率和就诊满意率,缩短候诊时间,以及降低风险事件发生率和医疗纠纷发生率的效果优于常规急诊预检分诊效果。

中心渔场智能预报系统的设计与实现

应用人工智能技术设计中心渔场智能预报系统 ,包括系统的总体结构、范例库的建设、范例推理、规则库和规则修正 ,最后按设计路线给出试验性预报实例 ,预报结果与渔场实际情况比较 ,预报的准确性为76 .2 %。研究结果表明 ,采用范例推理为主、规则修正为辅的技术路线使用计算机实现中心渔场智能化预报是可行的 ,它可以为海洋渔业生产和管理部门提供快速、准确。

ISPMAT——适用于城铁列车的预知维修智能系统

大运量交通车辆的维修 ,对于确保其社会服务的质量是非常重要的。本文介绍一种名为 ISPMAT(适用于城铁列车的预知维修智能系统 )的软件工具 ,它能对城铁列车的两个主要组成部分、即压缩机和牵引行车机构中可能发生的各种故障自动进行检测和作出诊断。 ISPMAT是以人工智能技术即神经网络和专家系统为基础的。本文的内容包括对 ISPMAT结构的阐述 ,以及通过实验所取得的某些结果。

数字化、人工智能技术辅助早期筛查和早期诊断儿童孤独症谱系障碍

目的探索使用数字化、人工智能预诊系统辅助早期筛查、早期诊断儿童孤独症谱系障碍(autism spectrum disorder,ASD)的可行性,建立ASD“社区初筛-区妇幼复筛-三甲医院诊断”的三级筛查诊断模式。方法采用分层随机抽样的方法,从广州市白云区全区23个街镇按经济水平高中低随机抽取3个街镇为调查点,于2022年1月1日至2022年12月31日,在儿童保健/预防接种服务时,使用数字化、人工智能预诊系统对调查点内所有18~30月龄儿童进行ASD网络初级筛查;初级筛查阳性者转诊至广州市白云区妇幼保健院,使用CHAT⁃23量表B部分、儿心量表-Ⅱ和S-S语言发育迟缓评价法进行面对面复筛;复筛阳性病例转诊至三甲医院使用DSM⁃V进行确诊。结果共纳入3175例儿童,初筛阳性率为12.15%(386/3175);复筛阳性率为1.45%(46/3175);最终确诊ASD 39人,其中男童28人,女童11人;ASD整体患病率为1.23%。男童现患率为1.58%,女童现患率0.78%,差异有统计学意义(χ2=4.15,P=0.04)。结论应用数字化、人工智能预诊系统辅助早期筛查、早期诊断ASD儿童具有良好的临床应用价值;应推动建立ASD“社区初筛-区妇幼复筛-三甲医院诊断”的三级筛查诊断模式。

智能预诊系统结果准确性的评价研究

目的评估国内App中常见的智能预诊系统结果准确性。方法截止2018年1月1日,在Apple App Store中筛选包含智能预诊功能的、免费的、面向公众的、针对人群的、针对全病种的7个App,在标准化病人案例中筛选内科病例7例,外科病例4例,妇科病例2例,儿科病例2例。按照病例所述病情逐一输入所筛选出的智能预诊系统中,记录系统出示的预诊结果,统计正确的诊断是否列在第一项、正确的诊断是否列在前三项、是否列出正确诊断。结果①所有APP中仅AppA询问用户的身高、体重,AppC并未询问用户的身高、体重、性别、年龄就开始问诊,有3个系统允许用户在查找症状时同时输入多个症状;②预诊结果准确率为64.76%,正确诊断列在第一项占26.67%,正确诊断列在前三项的占39.05%。结论当前现有的预诊系统结果准确性仍较低,用户应理性看待智能预诊系统的预诊结果,不能盲目依赖。

The Application and Design of the Economical Nonlinear Controller

This anticle gives a design method of the economical nonlinear controller. The controller is composed of an expert intelligent coordination controller, a fuzzy prediction controller, a fuzzy feedforward controller, a nonlinear controller and so on. The consistence of a distributed control system based on this controller is also shown briefly.

Hourly traffic flow forecasting using a new hybrid modelling method

Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department to have sufficient time to formulate corresponding traffic flow control measures.In hence,it is meaningful to establish an accurate short-term traffic flow method and provide reference for peak traffic flow warning.This paper proposed a new hybrid model for traffic flow forecasting,which is composed of the variational mode decomposition(VMD)method,the group method of data handling(GMDH)neural network,bi-directional long and short term memory(BILSTM)network and ELMAN network,and is optimized by the imperialist competitive algorithm(ICA)method.To illustrate the performance of the proposed model,there are several comparative experiments between the proposed model and other models.The experiment results show that 1)BILSTM network,GMDH network and ELMAN network have better predictive performance than other single models;2)VMD can significantly improve the predictive performance of the ICA-GMDH-BILSTM-ELMAN model.The effect of VMD method is better than that of EEMD method and FEEMD method.To conclude,the proposed model which is made up of the VMD method,the ICA method,the BILSTM network,the GMDH network and the ELMAN network has excellent predictive ability for traffic flow series.

A novel recurrent neural network forecasting model for power intelligence center

In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was created through three steps. First, by combining with the general project uncertain element transmission theory (GPUET), the basic definitions of stochastic, fuzzy, and grey uncertain elements were given based on the principal types of uncertain information. Second, a power dynamic alliance including four sectors: generation sector, transmission sector, distribution sector and customers was established. The key factors were amended according to the four transmission topologies of uncertain elements, thus the new factors entered the power intelligence center as the input elements. Finally, in the intelligence handing background of PIC, by performing uncertain and recursive process to the input values of network, and combining unascertained mathematics, the novel load forecasting model was built. Three different approaches were put forward to forecast an eastern regional power grid load in China. The root mean square error (ERMS) demonstrates that the forecasting accuracy of the proposed model UMRNN is 3% higher than that of BP neural network (BPNN), and 5% higher than that of autoregressive integrated moving average (ARIMA). Besides, an example also shows that the average relative error of the first quarter of 2008 forecasted by UMRNN is only 2.59%, which has high precision.

A two-stage short-term traffic flow prediction method based on AVL and AKNN techniques

Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.

Traffic Assignment Forecast Model Research in ITS

As an important role in the development of ITS, traffic assignment forecast is always the research focus. Based on the analysis of classic traffic assignment forecast models, an improved traffic assignment forecast model, multi-ways probability and capacity constraint (MPCC) is presented. Using the new traffic as- signment forecast model to forecast the traffic volume will improve the rationality and veracity of traffic as- signment forecast.

Research on Car Reversing Warning System based on CAN Bus

The paper study the software and hardware principles of ultrasonic ranging reversing radar, design the vehicle reversing radar based on intelligent node of CAN bus, realize data communication and resource sharing with other intelligent nodes of the reversing radar. The experimental results show that, the design can achieve the expected technical indicators design, measurement precision and the reliability is high, which has certain guiding significance in the design of the car reversing radar, has wide application prospect and practical significance in the function of the extended CAN bus communication messages design and automotive CAN network.

Application of computational intelligence platform in coal and gas outburst prediction

The present situation of lacking fast and effective coal and gas outburst prediction techniques will lead to long out- burst prevention cycles and poor accurate prediction effects and slows down coal roadway drive speed seriously. Also, due to historical and economic reasons, some coal mines in China are equipped with poor safety equipment, and the staff professional capability is low. What＇s worse, artificial and mine geological conditions have great influences on the traditional technologies of coal and gas outburst prediction. Therefore, seeking a new fast and efficient coal and gas outburst prediction method is nec- essary. By using system engineering theory, combined with the current mine production conditions and based on the coal and gas outburst composite hypothesis, a coal and gas outburst spatiotemporal forecasting system was established. This system can guide forecasting work schedule, optimize prediction technologies, carry out step-by-step prediction and eliminate hazard hier- archically. From the point of view of application, the proposed system improves the prediction efficiency and accuracy. On this basis, computational intelligence methods to construct disaster information analysis platform were used. Feed-back results pro- vide decision support to mine safety supervisors.

Real Time Application of Bearing Wear Prediction Model Using Intelligent Drilling Advisory System

The real-time prediction of bearing wear for roller cone bits using the Intelligent Drilling Advisory system （IDAs） may result in better performance in oil and gas drilling operations and reduce total drilling cost. IDAs is a real time engineering software and being developed for the oil and gas industry to enhance the performance of complex drilling processes providing meaningful analysis of drilling operational data. The prediction of bearing wear for roller cone bits is one of the most important engineering modules included into IDAs to analyze the drilling data in real time environment. The Bearing Wear Prediction module in IDAs uses a newly developed wear model considering drilling parameters such as weight on bit （WOB）, revolution per minute （RPM）, diameter of bit and hours drilled as a function of International Association of Drilling Contractors （IADC） bit bearing wear. The drilling engineers can evaluate bearing wear status including cumulative wear of roller cone bit in real time while drilling, using this intelligent system and make a decision on when to pull out the bit in time to avoid bearing failure. The wear prediction module as well as the intelligent system has been successfully tested and verified with field data from different wells drilled in Western Canada. The estimated cumulative wears from the analysis match close with the corresponding field values.

Modern Control and Diagnostic System of Traction Vehicle with Hybrid Drive System

In paper it introduced a review of modem traction vehicle drive system with induction motor drive system （PMSM with single or dual rotor drive system） or BLDC motor with different configuration of magnetic circuits. For particular part of drive system proposed a quasi intelligent control system version smart control enables multi criteria predictive control of vehicle work. In the paper presented also a selected diagnostic procedure, enables monitoring exploitation parameters, and prediction of probable failure state. For different vehicle work state realized a simulation models and crash test of exploitations failure models.

INTELLIGENT EARLY-WARNING SUPPORT SYSTEM FOR ENTERPRISE FINANCIAL CRISIS BASED ON CASE-BASED REASONING

Case-based reasoning （CBR） is an important reasoning technique of expert system. In this paper, the authors introduce CBR to intelligent early-warning support system, which could warn quantitatively for enterprise financial crisis and could warn qualitatively by expert＇s knowledge and experience. Furthermore, genetic algorithm is applied to case-based reasoning in CBR-IEWSS, which improves accuracy and efficiency of case retrieval. Last, the structure of CBR-IEWSS is given.