Advances in machine learning-and artificial intelligence-assisted material design of steels

With the rapid development of artificial intelligence technology and increasing material data,machine learning-and artificial intelligence-assisted design of high-performance steel materials is becoming a mainstream paradigm in materials science.Machine learning methods,based on an interdisciplinary discipline between computer science,statistics and material science,are good at discovering correlations between numerous data points.Compared with the traditional physical modeling method in material science,the main advantage of machine learning is that it overcomes the complex physical mechanisms of the material itself and provides a new perspective for the research and development of novel materials.This review starts with data preprocessing and the introduction of different machine learning models,including algorithm selection and model evaluation.Then,some successful cases of applying machine learning methods in the field of steel research are reviewed based on the main theme of optimizing composition,structure,processing,and performance.The application of machine learning methods to the performance-oriented inverse design of material composition and detection of steel defects is also reviewed.Finally,the applicability and limitations of machine learning in the material field are summarized,and future directions and prospects are discussed.

Building Indoor Dangerous Behavior Recognition Based on LSTM-GCN with Attention Mechanism

Building indoor dangerous behavior recognition is a specific application in the field of abnormal human recognition.A human dangerous behavior recognition method based on LSTM-GCN with attention mechanism(GLA)model was proposed aiming at the problem that the existing human skeleton-based action recognition methods cannot fully extract the temporal and spatial features.The network connects GCN and LSTMnetwork in series,and inputs the skeleton sequence extracted by GCN that contains spatial information into the LSTM layer for time sequence feature extraction,which fully excavates the temporal and spatial features of the skeleton sequence.Finally,an attention layer is designed to enhance the features of key bone points,and Softmax is used to classify and identify dangerous behaviors.The dangerous behavior datasets are derived from NTU-RGB+D and Kinetics data sets.Experimental results show that the proposed method can effectively identify some dangerous behaviors in the building,and its accuracy is higher than those of other similar methods.

Titanium microalloying of steel:A review of its effects on processing,microstructure and mechanical properties

Carbon neutrality of the steel industry requires the development of high-strength steel.The mechanical properties of low-alloy steel can be considerably improved at a low cost by adding a small amount of titanium(Ti)element,namely Ti microalloying,whose performance is related to Ti-contained second phase particles including inclusions and precipitates.By proper controlling the precipitation behaviors of these particles during different stages of steel manufacture,fine-grained microstructure and strong precipitation strengthening effects can be obtained in low-alloy steel.Thus,Ti microalloying can be widely applied to produce high strength steel,which can replace low strength steels heavily used in various areas currently.This article reviews the characteristics of the chemical and physical metallurgies of Ti microalloying and the effects of Ti microalloying on the phase formation,microstructural evolution,precipitation behavior of low-carbon steel during the steel making process,especially the thin slab casting and continuous rolling process and the mechanical properties of final steel products.Future development of Ti microalloying is also proposed to further promote the application of Ti microalloying technology in steel to meet the requirement of low-carbon economy.

A Multi-module Controller for Walking Quadruped Robots

Motion control based on biologically inspired methods,such as Central Pattern Generator(CPG)models,offers a promising technique for robot control.However,for a quadruped robot which needs to maintain balance while performing flexible movements,this technique often requires a complicated nonlinear oscillator to build a controller,and it is difficult to achieve agility by merely modifying the predefined limit cycle in real time.In this study,we tried to solve this problem by constructing a multi-module controller based on CPG.The different parallel modules will ensure the dynamic stability and agility of walking.In the proposed controller,a specific control task is accomplished by adding basic and superposed motions.The basic motions decide the basic foot end trajectories,which are generated by the predefined limit cycle of the CPG model.According to conventional kinematics-based design,the superposed motions are generated through different modules alter the basic foot end trajectories to maintain balance and increase agility.As a considerable stability margin can be achieved,different modules are designed separately.The proposed CPG-based controller is capable of stabilizing a walking quadruped robot and performing start and stop movements,turning,lateral movement and reversal in real time.Experiments and simulations demonstrate the effectiveness of the method.

What attracts China's contracts to Latin America and the Caribbean?An empirical study of the determinants of Chinese contracts

As China's economy has reached the stage of'New Normal',Chinese companies are increasingly seeking opportunities overseas.In the context of the slow recovery of world economy,China's outward economic activities have found themselves in many parts of the globe,including the Latin Ameria and the Caribbean(LAC)region,some of the farthest places away from China.While many scholars have conducted extensive studies on China's trade and foreign direct investment(FDI)in Latin America and the Caribbean,this paper focusses on Chinese contracts in the region,a topic that has been rarely studied.Using both random-effects and fixed-effects models covering 30 LAC economies during 1998-2015,the multivariate panel regressions show that among numerous determinants,Chinese companies prefer to undertake projects in the countries that are economically more advanced and more populous.In addition to the level of development and the size of population,those that have natural resources,expansionary economy,and political openness tend to have Chinese contracts completed.The above,however,is true of only the countries with which China has diplomatic relations.For the countries that recognise China's Taiwan‘diplomatically',Chinese contracts do not seem to be as much economically determined as those that recognise People's Republic of China(PRC)diplomatically.Politics appear to interfere with contractual decisions except in the following categories:mineral resources and an expansionary economy.