Retraction Note to: ANN model to predict the effects of composition and heat treatment parameters on transformation start temperature of microalloyed steels

Neural Computing and Applications - The Editor-in-Chief has retracted this article because it significantly overlaps with a number of previously published articles.     

Artificial neural network to predict the effect of heat treatments on Vickers microhardness of low-carbon Nb microalloyed steels

In the present study, an artificial neural networks-based model (ANNs) was developed to predict the Vickers microhardness of low-carbon Nb microalloyed steels. Fourteen parameters affecting the Vickers microhardness were considered as inputs, including the austenitizing temperature, cooling rate, initial austenite grain size, different chemical compositions and Nb in solution. The network was then trained to predict the Vickers microhardness amounts as outputs. A Multilayer feed-forward back-propagation network was developed and trained using experimental data form literatures. Five low-carbon Nb microalloyed steels and one low-carbon steel without Nb were investigated. The effects of austenitizing temperature (900–1,100°C) and subsequent cooling rate (0.15–227°C/s) and initial austenite grain size (5–130 μm) on the Vickers microhardness of steels were modeled by ANNs as well. The predicted values are in very good agreement with the measured ones, indicating that the developed model is very accurate and has the great ability for predicting the Vickers microhardness.     

Artificial neural network to predict the effects of coating parameters on layer thickness of chromium carbonitride coating on pre-nitrided steels

Chromium carbonitride coatings were formed on plain carbon and alloy steels by pre-nitrocarburizing, followed by thermoreactive deposition and diffusion in a salt bath below 700 °C. In the present study, an artificial neural network-based model (ANNs) was developed to predict the layer thickness of pre-nitrided steels. Seventeen parameters affecting the layer thickness were considered as inputs, including the pre-nitriding time, salt bath compositions ratio, salt bath aging time, ferrochromium particle size, ferrochromium weight percent, salt bath temperature, coating time, and different chemical compositions of steels. The network was then trained to predict the layer thickness amounts as outputs. A 2-feed-forward back-propagation network was developed and trained using experimental data form literatures. Five steels were investigated. The effects of coating parameters on the layer thickness of steels were modeled by ANNs as well. The predicted values are in very good agreement with the measured ones indicating that the developed model is very accurate and has the great ability for predicting the layer thickness.     

Predictions of toughness and hardness by using chemical composition and tensile properties in microalloyed line pipe steels

Artificial neural networks with multilayer feed forward topology and back propagation algorithm containing two hidden layers are implemented to predict the effect of chemical composition and tensile properties on the both impact toughness and hardness of microalloyed API X70 line pipe steels. The chemical compositions in the forms of “carbon equivalent based on the International Institute of Welding equation (CE_IIW)”, “carbon equivalent based on the Ito-Bessyo equation (CE_Pcm)”, “the sum of niobium, vanadium and titanium concentrations (VTiNb)”, “the sum of niobium and vanadium concentrations (NbV)” and “the sum of chromium, molybdenum, nickel and copper concentrations (CrMoNiCu)”, as well as, tensile properties of “yield strength (YS)”, “ultimate tensile strength (UTS)” and “elongation (El)” are considered together as input parameters of networks while Vickers microhardness with 10 kgf applied load (HV10) and Charpy impact energy at ?10 °C (CVN ?10 °C) are assumed as the outputs of constructed models. For the purpose of constructing the models, 104 different measurements are performed and gathered data from examinations are randomly divided into training, testing and validating sets. Scatter plots and statistical criteria of “absolute fraction of variance (R²)”, and “mean relative error (MRE)” are used to evaluate the prediction performance and universality of the developed models. Based on analyses, the proposed models can be further used in practical applications and thermo-mechanical manufacturing processes of microalloyed steels.     

基于分段氨基酸组成预测β-琼胶酶的最适温度

为了探究氨基酸组成对β-琼胶酶催化底物时的最适温度的影响,分别统计分析了酶全长、N端、M段及C端序列中20种氨基酸出现的频次。以支持向量机回归构建了氨基酸组成与β-琼胶酶最适温度的模型,10-倍交叉验证获得最小平均绝对误差(MAE),线性核函数的M端为4.36℃,RBF核函数的M端为4.16℃,独立样本测试得到N端的MAE分别为3.12℃和4.43℃。上述结果表明,该酶N端和M端是影响其最适温度的重要因素。通过比较最适温度为60℃和30℃β-琼胶酶的高级结构,推测M端和N端中3个螺旋结构是影响该酶最适温度的重要因素。     

Modeling and verification of Web services composition based on model transformation

With the rapid development of Cloud computing, social computing, and Web of Things, an increasing number of requirements of complexity and reliability for modeling Web services composition have emerged too. As more reliable methods are needed to model and verify current complex Web services composition, this paper proposes a method to model and verify Web services composition based on model transformation. First, a modeling and verifying framework based on model transformation is established. Then, Communicating Sequential Process (CSP) is defined according to the features of Web services composition and the corresponding model checking tool Failure Divergence Refinement (FDR) is introduced. The transformation approaches between Business Process Execution Language (BPEL) and CSP are later defined in detail. Lastly, the effect of this method is evaluated by modeling and verifying the Web services composition of a Online Shopping System. The results of the experiments show that this method can greatly increase the reliability of Web services composition. Copyright © 2016 John Wiley & Sons, Ltd.     

钢轨火焰热处理温度场数值模拟

为在钢轨火焰热处理时使钢轨接头达到规定的温度和合理均匀分布的温度场,在Abaqus中建立PD3钢轨的三维有限元模型,通过调整加热火焰模型的热流密度,模拟钢轨的温度场,获得钢轨异形结构对钢轨温度场的影响规律.结果表明：在采用均匀热流密度加热火焰模型时,钢轨底部升温速度最快,钢轨头部最慢;加热完成后钢轨底部平均温度最高,钢轨头部最低,钢轨温度场分布均匀性较差,而对于钢轨心部,钢轨腰部升温速度最快、平均温度最高,钢轨头部升温速度最慢、平均温度最低.经过对加热火焰模型热流密度的优化,钢轨表面和心部温度场分布的均匀性得到很好改善,可为实际生产中火焰热处理设计提供参考.     

基于粗糙集人工神经网络的锅炉故障诊断模型

针对锅炉这种大型特种设备,提出了一种基于粗糙集和人工神经网络集成的智能故障诊断方法.该方法先利用Rs理论建立故障决策表,对原始数据进行约简,并按照一定的原则选取多个约简;然后建立神经网络故障诊断子系统,使用粗糙集处理后的数据计算出故障发生程度,研究结果表明:该方法能够正确而且高效地诊断出锅炉中各种部件的故障发生的严重程度.     

Artificial neural networks application to predict the ultimate tensile strength of X70 pipeline steels

The paper presents some results of the research connected with the development of new approach based on the artificial neural network (ANN) of predicting the ultimate tensile strength of the API X70 steels after thermomechanical treatment. The independent variables in the model are chemical compositions (carbon equivalent), based upon the International Institute of Welding equation (CE_IIW), the carbon equivalent, based upon the chemical portion of the Ito-Bessyo carbon equivalent equation (CE_Pcm), the sum of the niobium, vanadium and titanium concentrations (VTiNb), the sum of the niobium and vanadium concentrations (NbV), the sum of the chromium, molybdenum, nickel and copper concentrations (CrMoNiCu), Charpy impact energy at ?10 °C (CVN) and yield strength at 0.005 offset (YS). For purpose of constructing these models, 104 different data were gathered from the experimental results. The data used in the ANN model is arranged in a format of seven input parameters that cover the chemical compositions, yield stress and Charpy impact energy, and output parameter which is ultimate tensile strength. In this model, the training, validation and testing results in the ANN have shown strong potential for prediction of relations between chemical compositions and mechanical properties of API X70 steels.     

Addressing the ability of a land biosphere model to predict key biophysical vegetation characterisation parameters with Global Sensitivity Analysis

Sensitivity Analysis (SA) of the SimSphere Soil Vegetation Atmosphere Transfer (SVAT) model has been performed in this study using a cutting edge and robust Global Sensitivity Analysis (GSA) approach, based on the use of the Gaussian Emulation Machine for Sensitivity Analysis (GEM-SA) tool. The sensitivity of the following model outputs was evaluated: the ambient CO₂ concentration, the rate of CO₂ uptake by the plant, the ambient O₃ concentration, the flux of O₃ from the air to the plant/soil boundary and the flux of O₃ taken up by the plant alone. The most sensitive model inputs for the majority of outputs were: The Leaf Area Index (LAI), Fractional Vegetation Cover (Fr), Cuticle Resistance (CR) and Vegetation Height (VH). The influence of the external CO₂ on the leaf and O₃ concentration in the air as input parameters was also significant. Our study provides an important step forward in the global efforts towards SimSphere verification given the increasing interest in its use as an independent modelling or educational tool. Results of this study are also timely given the ongoing global efforts focused on deriving, at an operational level, spatio-temporal estimates of energy fluxes and soil moisture content using SimSphere synergistically with Earth Observation (EO) data.     

热处理温度对氧化铟薄膜特性的影响

利用离子束溅射沉积的方式在Al2O3陶瓷基片表面制备了In2O3薄膜,并分别研究了热处理温度对薄膜相结构、电阻-温度特性及气敏特性的影响。结果表明:薄膜相结构受热处理温度影响显著,热处理温度高于600℃时,薄膜相结构由无定形状态变为立方相状态;随着热处理温度的升高,薄膜电阻率显著增大;薄膜对CO气体具有良好的气敏特性,热处理温度为600℃时,薄膜对CO气体灵敏度达到最大值。     

基于传热原理的热电偶测温误差模型及应用

应用传热学原理研究了热电偶静态测温误差和动态响应模型及减少测温误差的方法。热电偶测温误差是由热电偶与周围环境净热辐射引起,对流传热系数大小决定性地影响测温误差。带遮热套热电偶高速抽气时能有效减少测温误差,裸装和不抽气的热电偶不能直接应用于气体温度在线检测;动态响应过程误差还受时间常数0τ影响,30τ可视为动态响应结束时间。此研究结果不仅可用于裸偶信号校正、高精度温度信号获取及动态读数时间选取,也可为温度在线控制提供科学依据。     

Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling

The application of Artificial Neural Networks (ANNs) in the field of environmental and water resources modelling has become increasingly popular since early 1990s. Despite the recognition of the need for a consistent approach to the development of ANN models and the importance of providing adequate details of the model development process, there is no systematic protocol for the development and documentation of ANN models. In order to address this shortcoming, such a protocol is introduced in this paper. In addition, the protocol is used to critically review the quality of the ANN model development and reporting processes employed in 81 journal papers since 2000 in which ANNs have been used for drinking water quality modelling. The results show that model architecture selection is the best implemented step, while greater focus should be given to input selection considering input independence and model validation considering replicative and structural validity.     

The development of a model to predict the effects of worker and task factors on foot placements in manual material handling tasks

Accurate prediction of foot placements in relation to hand locations during manual materials handling tasks is critical for prospective biomechanical analysis. To address this need, the effects of lifting task conditions and anthropometric variables on foot placements were studied in a laboratory experiment. In total, 20 men and women performed two-handed object transfers that required them to walk to a shelf, lift an object from the shelf at waist height and carry the object to a variety of locations. Five different changes in the direction of progression following the object pickup were used, ranging from 45° to 180° relative to the approach direction. Object weights of 1.0 kg, 4.5 kg, 13.6 kg were used. Whole-body motions were recorded using a 3-D optical retro-reflective marker-based camera system. A new parametric system for describing foot placements, the Quantitative Transition Classification System, was developed to facilitate the parameterisation of foot placement data. Foot placements chosen by the subjects during the transfer tasks appeared to facilitate a change in the whole-body direction of progression, in addition to aiding in performing the lift. Further analysis revealed that five different stepping behaviours accounted for 71% of the stepping patterns observed. More specifically, the most frequently observed behaviour revealed that the orientation of the lead foot during the actual lifting task was primarily affected by the amount of turn angle required after the lift (R(2) = 0.53). One surprising result was that the object mass (scaled by participant body mass) was not found to significantly affect any of the individual step placement parameters. Regression models were developed to predict the most prevalent step placements and are included in this paper to facilitate more accurate human motion simulations and ergonomics analyses of manual material lifting tasks. STATEMENT OF RELEVANCE: This study proposes a method for parameterising the steps (foot placements) associated with manual material handling tasks. The influence of task conditions and subject anthropometry on the foot placements of the most frequently observed stepping pattern during a laboratory study is discussed. For prospective postural analyses conducted using digital human models, accurate prediction of the foot placements is critical to realistic postural analyses and improved biomechanical job evaluations.     

The development of a model to predict the effects of worker and task factors on foot placements in manual material handling tasks

Accurate prediction of foot placements in relation to hand locations during manual materials handling tasks is critical for prospective biomechanical analysis. To address this need, the effects of lifting task conditions and anthropometric variables on foot placements were studied in a laboratory experiment. In total, 20 men and women performed two-handed object transfers that required them to walk to a shelf, lift an object from the shelf at waist height and carry the object to a variety of locations. Five different changes in the direction of progression following the object pickup were used, ranging from 45° to 180° relative to the approach direction. Object weights of 1.0 kg, 4.5 kg, 13.6 kg were used. Whole-body motions were recorded using a 3-D optical retro-reflective marker-based camera system. A new parametric system for describing foot placements, the Quantitative Transition Classification System, was developed to facilitate the parameterisation of foot placement data. Foot placements chosen by the subjects during the transfer tasks appeared to facilitate a change in the whole-body direction of progression, in addition to aiding in performing the lift. Further analysis revealed that five different stepping behaviours accounted for 71% of the stepping patterns observed. More specifically, the most frequently observed behaviour revealed that the orientation of the lead foot during the actual lifting task was primarily affected by the amount of turn angle required after the lift (R ² = 0.53). One surprising result was that the object mass (scaled by participant body mass) was not found to significantly affect any of the individual step placement parameters. Regression models were developed to predict the most prevalent step placements and are included in this paper to facilitate more accurate human motion simulations and ergonomics analyses of manual material lifting tasks.

Statement of Relevance: This study proposes a method for parameterising the steps (foot placements) associated with manual material handling tasks. The influence of task conditions and subject anthropometry on the foot placements of the most frequently observed stepping pattern during a laboratory study is discussed. For prospective postural analyses conducted using digital human models, accurate prediction of the foot placements is critical to realistic postural analyses and improved biomechanical job evaluations.