Intelligent Forecasting of Sintered Ore’s Chemical Components Based on SVM

Using object mathematical model of traditional control theory can not solve the forecasting problem of the chemical components of sintered ore.In order to control complicated chemical components in the manufacturing process of sintered ore,some key techniques for intelligent forecasting of the chemical components of sintered ore are studied in this paper.A new intelligent forecasting system based on SVM is proposed and realized.The results show that the accuracy of predictive value of every component is more than 90%.The application of our system in related companies is for more than one year and has shown satisfactory results.

Big Data Analytics Using Swarm-Based Long Short-Term Memory for Temperature Forecasting

In the past few decades,climatic changes led by environmental pollution,the emittance of greenhouse gases,and the emergence of brown energy utilization have led to global warming.Global warming increases the Earth’s temperature,thereby causing severe effects on human and environmental conditions and threatening the livelihoods of millions of people.Global warming issues are the increase in global temperatures that lead to heat strokes and high-temperature-related diseases during the summer,causing the untimely death of thousands of people.To forecast weather conditions,researchers have utilized machine learning algorithms,such as autoregressive integrated moving average,ensemble learning,and long short-term memory network.These techniques have been widely used for the prediction of temperature.In this paper,we present a swarm-based approach called Cauchy particle swarm optimization(CPSO)to find the hyperparameters of the long shortterm memory(LSTM)network.The hyperparameters were determined by minimizing the LSTM validationmean square error rate.The optimized hyperparameters of the LSTM were used to forecast the temperature of Chennai City.The proposed CPSO-LSTM model was tested on the openly available 25-year Chennai temperature dataset.The experimental evaluation on MATLABR2020a analyzed the root mean square error rate and mean absolute error to evaluate the forecasted output.The proposed CPSO-LSTM outperforms the traditional LSTM algorithm by reducing its computational time to 25 min under 200 epochs and 150 hidden neurons during training.The proposed hyperparameter-based LSTM can predict the temperature accurately by having a root mean square error(RMSE)value of 0.250 compared with the traditional LSTM of 0.35 RMSE.

A new hybrid method with data‑characteristic‑driven analysis for artificial intelligence and robotics index return forecasting

Forecasting returns for the Artificial Intelligence and Robotics Index is of great significance for financial market stability,and the development of the artificial intelligence industry.To provide investors with a more reliable reference in terms of artificial intelligence index investment,this paper selects the NASDAQ CTA Artificial Intelligence and Robotics(AIRO)Index as the research target,and proposes innovative hybrid methods to forecast returns by considering its multiple structural characteristics.Specifically,this paper uses the ensemble empirical mode decomposition(EEMD)method and the modified iterative cumulative sum of squares(ICSS)algorithm to decompose the index returns and identify the structural breakpoints.Furthermore,it combines the least-square support vector machine approach with the particle swarm optimization method(PSO-LSSVM)and the generalized autoregressive conditional heteroskedasticity(GARCH)type models to construct innovative hybrid forecasting methods.On the one hand,the empirical results indicate that the AIRO index returns have complex structural characteristics,and present time-varying and nonlinear characteristics with high complexity and mutability;on the other hand,the newly proposed hybrid forecasting method(i.e.,the EEMD-PSO-LSSVM-ICSS-GARCH models)which considers these complex structural characteristics,can yield the optimal forecasting performance for the AIRO index returns.

Phenomenological Models of the Global Demographic Dynamics and Their Usage for Forecasting in 21st Century

A great discovery made by H. von Foerster, P. M. Mora and L. W. Amiot was published in a 1960 issue of “Science”. The authors showed that existing data for calculating the Earth’s population in the new era (from 1 to 1958) could be described with incredibly high proximity by a hyperbolic function with the point of singularity on 13 November 2026. Thus, empirical regularity of the rise of the human population was established, which was marked by explosive demographic growth in the 20th century when during only one century it almost quadrupled: from 1.656 billion in 1900 to 6.144 billion in 2000. Nowadays, the world population has already overcome 7.8 billion people. Immediately after 1960, an active search for phenomenological models began to explain the mechanism of the hyperbolic population growth and the following demographic transition designed to stabilize its population. A significant role in explaining the mechanism of the hyperbolic growth of the world population was played by S. Kuznets (1960) and E. Boserup (1965), who found out that the rates of technological progress historically increased in proportion to the Earth’s population. It meant that the growth of the population led to raising the level of life-supporting technologies, and the latter in its turn enlarged the carrying capacity of the Earth, making it possible for the world population to expand. Proceeding from the information imperative, we have developed the model of the demographic dynamics for the 21st century for the first time. The model shows that with the development and spread of Intelligent Machines (IM), the number of the world population reaching a certain maximum will then irreversibly decline. Human depopulation will largely touch upon the most developed countries, where IM is used intensively nowadays. Until a certain moment in time, this depopulation in developed countries will be compensated by the explosive growth of the population in African countries located south of the Sahara. Calculations in our model reveal that the peak of the human population of 8.52 billion people will be reached in 2050, then it will irreversibly go down to 7.9 billion people by 2100, if developed countries do not take timely effective measures to overcome the process of information depopulation.

应用自动化支持药学实践,精准快预警用药风险

目的:研发模块式系列信息化辅助工具,支持精准快的药品保障与药学服务实践。方法:总结信息化智能化多年研发经验,归纳提升工作效率、实现ADR/ADE自动化监测、构建用药风险防控体系的有效举措。结果:针对工作需求,从提升保障效率、强化信息支持、统筹人员管理等方面,系统性的研发模块式信息化自动化辅助工具;进而深化药品风险监测关键技术研究,实现临床用药风险的自动监测与智能评估预警,帮助药学人员精准快的评估预测药源性疾病,实施用药风险的全方位有效防控。结论:贴合医院药学需求的信息化研究,提供药师便捷高效的支撑工具,有助于提升药师的工作效率与服务水准。

板材拉深成形性能智能化预测系统

对板料成形性能智能化技术的特点及其开发和应用的意义进行简要的介绍,并详细阐述了板料成形性能智能化系统的整体结构和功能,详细论述了该系统开发的关键技术部分:用户信息输入部分、推理和评定部分的设计思路和具体内容。

海上风电场运行控制维护关键技术综述

海上风电是目前和未来几十年我国可再生能源发展的重点。然而,与陆上相比,海上风电面临全寿命周期度电成本偏高、大规模并网冲击大等挑战。数字化、智能化是解决上述问题的关键。为此,该文围绕海上风电场智能运行控制维护关键技术进行了介绍,包括海上风电功率预测技术、海上风电运行控制技术、海上风电设备维护管理技术、海上风电与海域综合利用。首先,针对海上天气环境、资源特性、地理位置、设备运行等因素,分析了海上风电与陆上的差异,据此归纳了上述技术领域所面临的挑战及可能的解决方案,并对这些技术的研究现状及成果进行了分析和总结;最后,指出海上风电场智能运行控制维护关键技术领域的发展趋势及需要进一步研究的方面,为海上风电降本增效和大规模安全经济并网提供支撑和参考。