Study on intelligent digital welding machine with a self-learning function

A design idea was proposed that it was about intelligent digital welding machine with self-learning and self- regulation functions. The overall design scheme of software and hardware was provided. It was introduced that a parameter self-learning algorithm was based on large-step calibration and partial Newton interpolation. Furthermore, experimental verification was carried out with different welding technologies. The results show that weld bead is pegrect. Therefore, good welding quality and stability are obtained, and intelligent regulation is realized by parameters self-learning.

The Research on Hybrid Intelligent Fault-diagnosisSystem of CNC Machine Tools

After analyzing the structure and characteristics of the hybrid intelligent diagnosis system of CNC machine toolsCNC-HIDS), we describe the intelligent hybrid mechanism of the CNC-HIDS, and present the evaluation and the running instance of the system. Through tryout and validation, we attain satisfactory results.

MODULE DEFINITION MODEL FOR MODULAR DESIGN AND MANUFACTU－RING OF HEAVY DUTY MACHINE TOOLS

The key techniques of modular design of heavy duty NC mathine tools are described. Amodule definition modelfor modular design and manufacturing of heavy duty NC machine tools isbulit and the essential composition of the module definition model (MDM) is discussed in detail. Itis composed of two models: the part definition model (PDM) and the module assembly model(MAM). The PDM and MAM are built and their structures are given. Using object-oriented know-ledge representation and based on these models, an intelligent support system of modular design forheavy duty NC machine tools is developed and implemented This system has been applied to thepractical use of Wuhan Heavy Duty Machine Tool Works

Research on the Application of Intelligent Bionic Robot Horse in Juvenile Equestrian Teaching Case Study: Beijing Chaoyang Park Youth Equestrian Center

Research purposes:in this study,the intelligent bionic robotic horse is introduced into the equestrian teaching for teenagers,compared with the traditional teaching mode of using real horses.This research aims to explore the effectiveness of using intelligent bionic robotic horse in equestrian teaching for teenagers,as well as to promote the further development of equestrian teaching for teenagers in China,and to promote the introduction of new technology into the equestrian teaching area in the age of internet.Research methods:the methods used were literature method;mathematical statistics;interviewing the equestrian coaches who participated in the experiment;experimental method.The intelligent bionic robotic horse used in this research is the GETTAEN intelligent bionic robotic horse produced by Joy Game Technology Co.,Ltd.The bionic robotic horse is equipped with Internet technology,and the course is supervised and produced by senior coaches of China Equestrian Team.It also includes multiple operation modes.In this study,40 amateur students in Beijing Chaoyang Park Youth Equestrian Center were selected as the experimental subjects.Students will spend 40 h on studying how to ride a horse.Twenty(20)students in the experimental group,they are accommodated with 20 h of bionic robotic horse courses and 20 h of real horse course;20 students in the control group were taught in the traditional teaching mode with 40 h of real horse courses.Results:(1)in horseback physical fitness test,the average value of the control group was 101.9 s;325.6 s in the experimental group.Independent sample T test p<0.05 has significant difference,the horseback physical performance in experimental group is better than the control group.(2)in horseback physical balance test,the average value of the control group was 3.75,and the average value of the experimental group was 7.1.Independent sample T test p<0.05 has significant difference,the horseback physical balance test results in experimental group have significant difference,and the experimental group is better than the control group.(3)The interview method was used to interview the equestrian coaches who participated in the experiment,coaches think that the bionic robotic horse can speed up the learning progress and has a strong technical consolidation,especially for teaching amateurs;but for the time being,it cannot meet the training and improvement target of the actual horse control ability and the ability to grasp the route,and such experience is not real and good enough for senior students.Conclusion:using real horse and intelligent bionic robotic horse combined,one can improve the teaching effectiveness and promote students’adaptation to horseback and technical mastery.But for the time being,it is only suitable for students with weak foundation or zero foundation.The capability of intelligent bionic robotic horse needs to be strengthened,and technological innovation is needed to adapt to all kinds of students.

From tunnel boring machine to tunnel boring robot: perspectives on intelligent shield machine and its smart operation

Advances in intelligent shield machines reflect an evolving trend from traditional tunnel boring machines(TBMs)to tunnel boring robots(TBRs).This shift aims to address the challenges encountered by the conventional shield machine industry arising from construction environment and manual operations.This study presents a systematic review of intelligent shield machine technology,with a particular emphasis on its smart operation.Firstly,the definition,meaning,contents,and development modes of intelligent shield machines are proposed.The development status of the intelligent shield machine and its smart operation are then presented.After analyzing the operation process of the shield machine,an autonomous operation framework considering both stand-alone and fleet levels is proposed.Challenges and recommendations are given for achieving autonomous operation.This study offers insights into the essence and developmental framework of intelligent shield machines to propel the advancement of this technology.

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.

Information Models for Forecasting Nonlinear Economic Dynamics in the Digital Era

The aim of this study was to develop an adequate mathematical model for long-term forecasting of technological progress and economic growth in the digital age (2020-2050). In addition, the task was to develop a model for forecast calculations of labor productivity in the symbiosis of “man + intelligent machine”, where an intelligent machine (IM) is understood as a computer or robot equipped with elements of artificial intelligence (AI), as well as in the digital economy as a whole. In the course of the study, it was shown that in order to implement its goals the Schumpeter-Kondratiev innovation and cycle theory on forming long waves (LW) of economic development influenced by a powerful cluster of economic technologies engendered by industrial revolutions is most appropriate for a long-term forecasting of technological progress and economic growth. The Solow neoclassical model of economic growth, synchronized with LW, gives the opportunity to forecast economic dynamics of technologically advanced countries with a greater precision up to 30 years, the time which correlates with the continuation of LW. In the information and digital age, the key role among the main factors of growth (capital, labour and technological progress) is played by the latter. The authors have developed an information model which allows for forecasting technological progress basing on growth rates of endogenous technological information in economics. The main regimes of producing technological information, corresponding to the eras of information and digital economies, are given in the article, as well as the Lagrangians that engender them. The model is verified on the example of the 5th information LW for the US economy (1982-2018) and it has had highly accurate approximation for both technological progress and economic growth. A number of new results were obtained using the developed information models for forecasting technological progress. The forecasting trajectory of economic growth of developed countries (on the example of the USA) on the upward stage of the 6th LW (2018-2042), engendered by the digital technologies of the 4th Industrial Revolution is given. It is also demonstrated that the symbiosis of human and intelligent machine (IM) is the driving force in the digital economy, where man plays the leading role organizing effective and efficient mutual work. Authors suggest a mathematical model for calculating labour productivity in the digital economy, where the symbiosis of “human + IM” is widely used. The calculations carried out with the help of the model show: 1) the symbiosis of “human + IM” from the very beginning lets to realize the possibilities of increasing work performance in the economy with the help of digital technologies;2) the largest labour productivity is achieved in the symbiosis of “human + IM”, where man labour prevails, and the lowest labour productivity is seen where the largest part of the work is performed by IM;3) developed countries may achieve labour productivity of 3% per year by the mid-2020s, which has all the chances to stay up to the 2040s.

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.

Towards autonomous and optimal excavation of shield machine:a deep reinforcement learning-based approach

Autonomous excavation operation is a major trend in the development of a new generation of intelligent tunnel boring machines(TBMs).However,existing technologies are limited to supervised machine learning and static optimization,which cannot outperform human operation and deal with ever changing geological conditions and the long-term performance measure.The aim of this study is to resolve the problem of dynamic optimization of the shield excavation performance,as well as to achieve autonomous optimal excavation.In this study,a novel autonomous optimal excavation approach that integrates deep reinforcement learning and optimal control is proposed for shield machines.Based on a first-principles analysis of the machine-ground interaction dynamics of the excavation process,a deep neural network model is developed using construction field data consisting of 1.1 million samples.The multi-system coupling mechanism is revealed by establishing an overall system model.Based on the overall system analysis,the autonomous optimal excavation problem is decomposed into a multi-objective dynamic optimization problem and an optimal control problem.Subsequently,a dimensionless multi-objective comprehensive excavation performance measure is proposed.A deep reinforcement learning method is used to solve for the optimal action sequence trajectory,and optimal closed-loop feedback controllers are designed to achieve accurate execution.The performance of the proposed approach is compared to that of human operation by using the construction field data.The simulation results show that the proposed approach not only has the potential to replace human operation but also can significantly improve the comprehensive excavation performance.

Machine Knowledge and Human Cognition

Intelligent machines are knowledge systems with unique knowledge structure and function.In this paper,we discuss issues including the characteristics and forms of machine knowledge,the relationship between knowledge and human cognition,and the approach to acquire machine knowledge.These issues are of great significance to the development of artificial intelligence.

FUTURE OF LAW CONFERENCE： THE INTERNET OF THINGS, SMART CONTRACTS AND INTELLIGENT MACHINES

From 26 to 27 October 2017, the Centre for Cross-Border Commercial Law in Asia of Singapore Management University （SMU） Law School held an international conference entitled ＂Future of Law Conference： The Internet of Things, Smart Contracts and Intelligent Machines＂ in Singapore. The conference brought together the leading thinkers in academia and practice in the field of information technology law to discuss the legal and regulatory implications of recent technological developments. Associate Professor ZHANG Jiyu and Associate Professor DING Xiaodong of the Law and Technology Institute of Renmin Law School were invited to attend the conference.

Film identification method based on improved deeplabv3+ for full-film double-ditch corn seedbed

This study aimed to investigate the task demand of intelligent unmanned fertilizer application in seedling stage of corn planted in full-film double-ditch seedbed,a film identification method based on improved DeepLabv3+ identification method for full-film double-ditch corn seedbed was proposed.The differences in performance indicators of the original Deeplabv3+ network taking Xception as the backbone network and the network model that replaced three lightweight backbone networks,MobileNetV2,MobileNetV3 and GhostNet were tested.At the same time,the network models,classical semantic segmentation was introduced to PSPNet and UNet for comparative test.The MIoU of DeepLabv3+ network model that replaced its backbone network increased by 5.01%,and FPS improved by 206%compared with original network,and the model size reduced by 90.3%.The three DeepLabv3+ models after replacing the backbone network were further compressed,and the two-layer expansion convolution with low expansion rate in ASPP was deleted,and the common convolution after feature fusion was replaced by the depthwise separable convolution to obtain a lightweight network model.After testing the improved network model,it was found that the average decline of precision indicators was only 0.17%,FPS raised to 66.5,with an average increase of 25.5%,and the size of the model was compressed to 10.53 MB.Test results showed that,the improved model showed excellent performance,and could provide important technology and method support for the research and development of intelligent topdressing and field management on full-film double-ditch corn seedbed during seedling stage.

Intelligent Machining NC Program and Its Automatic Generation in Integrated Environment

A processing environment based intelligent machining method, which utilizes an intelligent machining NC(IMNC) program,is described for use on machining centers. A new type of IMNC for box-shaped parts has been developed which can achieve various intelligent processing functions such as automatic adjustment of the part's roughlocalization;blank remainder based automatic determination of cutting times in rough machining;quality control supervision, measurement and machining compensation in fine processing, and breakdown anticipation in drilling,reaming and tapping. The IMNC is integrated with the CAD/CAPP/CAM environment and can be automatically generated on the CAM console. The flexibility of the manufacturing system's facilities layer has been greatly improved.

Tool path strategy and cutting process monitoring in intelligent machining

Intelligent machining is a current focus in advanced manufacturing technology, and is characterized by high accuracy and efficiency. A central technology of intelligent machining--the cutting process online monitoring and optimization--is urgently needed for mass production. In this research, the cutting process online monitoring and optimization in jet engine impeller machining, cranio-maxillofacial surgery, and hydraulic servo valve deburring are introduced as examples of intelligent machining. Results show that intelligent tool path optimization and cutting process online monitoring are efficient techniques for improving the efficiency, quality, and reliability of machining.