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Optimization and machinability evaluation for WEDM of austempered ductile iron

MATERIALS TESTING 2024年第12期66卷 2066-2077页

作者： Victor, Sharun Bennet, Anand Ronald Sri Sivasubramaniya Nadar Coll Engn Dept Mech Engn OMR Chennai 603110 Tamilnadu India Panimalar Inst Technol Dept Mech Engn Chennai 600123 Tamilnadu India

Wire electrical discharge machining (Wire EDM) is a non-contact CNC machining that removes material from a workpiece with electrical sparks. Optimization of parameters involved in wire EDM is essential for better operational economics and energy usage. The major goal and objective of this research are to assess the machining parameters, like surface roughness Ra, material removal rate MRR, and hardness HV by experimental investigation utilizing the wire cut EDM machine and austempered ductile iron (ADI) as the work material. An artificial neural network (ANN) has been employed to create a prediction model using experimental data. The Aquila optimization approach is then used to obtain the ideal operating parameters. With Aquila optimization, the predicted optimum values for MRR, Ra, and Hardness are 3.529 mm3/min, 1.966 mu m, and 367 HV, respectively, when the input parameters are pulse ton 16 mu s, pulse-toff time toff 14 mu s, servo voltage 50 V, and current 3 A. Finally, SEM and 3D roughness analysis have been carried out to study surface morphology and material removal mechanism.

关键词： austempered ductile iron SEM analysis lm algorithm artificial neural network aquila optimization

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LA-lmRBF: Online and Long-Term Web Service QoS Forecasting

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2021年第6期14卷 1809-1823页

作者： Zhang, Pengcheng Jin, Huiying Dong, Hai Song, Wei Wang, Liyan Hohai Univ Coll Comp & Informat Nanjing 211100 Peoples R China RMIT Univ Sch Sci Melbourne Vic 3001 Australia Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

We propose a Long-term Quality of Service (QoS) forecasting approach using Advertisement and Levenberg-Marquardt improved Radial Basis Function (LA-lmRBF)-a novel online QoS forecasting approach. LA-lmRBF aims to accurately predict QoS attributes of Web services in the form of multivariate time series via three stages. First, the phase space reconstruction theory is employed to restore multi-dimensional and nonlinear relations among the multivariate QoS attributes. Second, short-term QoS advertisement data is incorporated to enable long-term QoS forecasting. Finally, an optimized Radial Basis Function (RBF) neural network is constructed to forecast long-term multivariate QoS values, where the Affinity Propagation clustering algorithm is used to determine the number of hidden nodes and the Levenberg-Marquardt (lm) algorithm is utilized to dynamically update some parameters of the RBF neural network. A series of experiments are performed on a mixture of public and self-collected data sets. The results show that LA-lmRBF is superior to the other approaches and more suitable for long-term QoS forecasting.

关键词： Quality of service Forecasting Predictive models Time series analysis Mathematical model Web services Neural networks Multivariate time series quality of service phase-space reconstruction RBF neural network lm algorithm long-term forecasting

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Prestack AVO Inversion of Exact Zoeppritz Equation Using Adaptive Edge Preserving Filter

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PURE AND APPLIED GEOPHYSICS 2023年第1期180卷 215-242页

作者： Xu, Zihe Lu, Yongxu Peng, Suping Cui, Xiaoqin Liao, Jin Shi, Haolei China Univ Min & Technol Beijing Coll Geosci & Surveying Engn Beijing 100083 Peoples R China China Univ Min & Technol Beijing State Key Lab Coal Resources & Safe Min Beijing 100083 Peoples R China

Amplitude variation with offset (AVO) pre-stack inversion is an effective method for reservoir prediction. The traditional AVO method is based on the Zoeppritz approximate formula, which will lead to inaccurate inversion results when the difference of incident angle and formation impedance are large. In addition, it will be affected by noise and the formation boundary may be blur. To overcome these shortcomings, a new method is proposed. First, the exact Zoeppritz equation prestack inversion (EZPI) is used. Second, the L2 norm of the second order difference matrix is added to the objective function to enhance the anti-noise performance (EZL2). Finally, the adaptive edge-preserving filter (Ad-EPS) is used in every iteration (EZL2AEPS) to make the formation boundary clearer and obtain block structure. Ad-EPS scans windows automatically to find the most suitable filter window for each sampling point. Levenberg-Marquart algorithm is used to implement the inversion process. The synthetic data test shows the superiority of EZL2AEPS, which not only has excellent anti-noise ability but also can recover the formation boundary accurately. The example of the real data from a coal mine is also proposed. The parameters of the inversion need to be continuously tested. EZL2AEPS is also able to characterize thin coal seams. The boundaries of upper and lower bedrock can be distinguished, which demonstrates the proposed approach is feasible.

关键词： Zoeppritz equation AVO inversion Ad-EPS filtering lm algorithm

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Improved extreme learning machine for multivariate time series online sequential prediction

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2015年 40卷 28-36页

作者： Wang, Xinying Han, Min Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian Liaoning Peoples R China

Multivariate time series has attracted increasing attention due to its rich dynamic information of the underlying systems. This paper presents an improved extreme learning machine for online sequential prediction of multivariate time series. The multivariate time series is first phase-space reconstructed to form the input and output samples. Extreme learning machine, which has simple structure and good performance, is used as prediction model. On the basis of the specific network function of extreme learning machine, an improved Levenberg-Marquardt algorithm, in which Hessian matrix and gradient vector are calculated iteratively, is developed to implement online sequential prediction. Finally, simulation results of artificial and real-world multivariate time series are provided to substantiate the effectiveness of the proposed method. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Online prediction Multivariate time series Extreme Learning Machine lm algorithm

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Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model

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COMPUTERS & GEOSCIENCES 2010年第6期36卷 735-745页

作者： Panda, Rabindra K. Pramanik, Niranjan Bala, Biplab Indian Inst Technol Agr & Food Engn Dept Kharagpur 721302 W Bengal India

Simulation of water levels at different sections of a river using physically based flood routing models is quite cumbersome, because it requires many types of data such as hydrologic time series, river geometry, hydraulics of existing control structures and channel roughness coefficients. Normally in developing countries like India it is not easy to collect these data because of poor monitoring and record keeping. Therefore, an artificial neural network (ANN) technique is used as an effective alternative in hydrologic simulation studies. The present study aims at comparing the performance of the ANN technique with a widely used physically based hydrodynamic model in the MIKE 11 environment. The MIKE 11 hydrodynamic model was calibrated and validated for the monsoon periods (June-September) of the years 2006 and 2001, respectively. Feed forward neural network architecture with Levenberg-Marquardt (lm) back propagation training algorithm was used to train the neural network model using hourly water level data of the period June-September 2006. The trained ANN model was tested using data for the same period of the year 2001. Simulated water levels by the MIKE 11HD were compared with the corresponding water levels predicted by the ANN model. The results obtained from the ANN model were found to be much better than that of the MIKE 11HD results as indicated by the values of the goodness of fit indices used in the study. The Nash-Sutcliffe index (E) and root mean square error (RMSE) obtained in case of the ANN model were found to be 0.8419 and 0.8939 m, respectively, during model testing, whereas in case of MIKE 11HD, the values of E and RMSE were found to be 0.7836 and 1.00 m, respectively, during model validation. The difference between the observed and simulated peak water levels obtained from the ANN model was found to be much lower than that of MIKE 11HD. The study reveals that the use of Levenberg-Marquardt algorithm with eight hidden neurons in the hidden layer is

关键词： Artificial neural network Hydrodynamic model Back propagation training algorithms lm algorithm

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X-ray radiographs reconstruction based on nonlinear least squares with deconvolution

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2019年 929卷 134-141页

作者： Wang, Zhongmiao Jing, Yuefeng Kang, Xu Ding, Tong Liu, Jun China Acad Engn Phys Inst Fluid Phys POB 919-105 Mianyang 621900 Peoples R China

High energy X-ray radiographs reconstruction is a typical nonlinear process. However, most of the current reconstruction methods are based on a linear approximation model, which uses optical depth images for reconstruction. As radiographs are convolved by the system blur, images reconstructions with these algorithms are blurred, especially at edges. In this paper, a nonlinear least squares reconstruction model for transmittance images is established and optimized by the Levenberg-Marquardt algorithm assuming that the system blur is known. The numerical experiments show that the reconstruction error of this approach is greatly reduced and the edges sharpness is significantly improved, compared with the traditional constrained conjugate gradient method and the trust region reflective method. For images contaminated by blur and low noise, the reconstruction is still improved and acceptable even when an inaccurate system blur is used. Furthermore, we propose an idea of rectifying the system blur measured by experiments. Our reconstruction method is practical in deconvolution and shows a promising prospect in high energy X-ray radiographs reconstruction.

关键词： Radiographs reconstruction Nonlinear least squares Point spread function Deconvolution lm algorithm

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Evaluation of water quality based on artificial intelligence: performance of multilayer perceptron neural networks and multiple linear regression versus water quality indexes

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2024年 1-24页

作者： Palabiyik, Selda Akkan, Tamer Giresun Univ Sebinkarahisar Hlth Serv Vocat Sch Sebinkarahisar Giresun Turkiye Giresun Univ Fac Arts & Sci Dept Biol Giresun Turkiye

A significant problem in the sustainable management of water resources is the lack of funding and long-term monitoring. Today, this problem has been greatly reduced by innovative, adaptive, and sustainable learning methods. Therefore, in this study, a sample river was selected and 14 variables observed at 5 different points for 12 months, traditionally reference values, were calculated by multivariate statistical analysis methods to obtain the water quality index (WQI). The WQI index was estimated using different algorithms including the innovatively used multiple linear regression (MLR), multilayer perceptron artificial neural networks (MLP-ANN) and various machine learning estimation algorithms including neural networks (NN), support vector machine (SVM), gaussian process regression (GPR), ensemble and decision tree approach. By comparing the results, the most appropriate method was selected. The determination of water quality was best estimated by the multiple linear regression (MLR) model. As a result of this MLR modeling, high prediction performance was obtained with accuracy values of R2 = 1.0, RMSE = 0.0025, and MAPE = 0.0296. The root mean square error (RMSE), percent mean absolute error (MAE), and coefficient of determination (R2) were used to determine the accuracy of the models. These results confirm that both MLR model can be used to predict WQI with very high accuracy. It seems that it can contribute to strengthening water quality management. As a result, as with the powerful results of the innovative approaches (MLR and MLP-ANN) and other assessments, it was found that the presence of intense anthropogenic pressure in the study area and the current situation needs immediate remediation.

关键词： Water quality index Artificial neural networks MLR lm algorithm

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A Control algorithm for the Optimization of Batch Reactor-Based Processes

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Fluid Dynamics & Materials Processing 2019年第4期15卷 307-319页

作者： Yanling Bai Feng Liu Artificial Intelligence Institute Zhengzhou Railway Vocational&Technical CollegeZhengzhouChina Cyber Security Guard Henan Provincial Public Security DepartmentZhengzhouChina

Levenberg-Marquardt(lm)algorithm is applied for the optimization of the heat transfer of a batch *** validity of the approach is verified through comparison with experimental *** is found that the mathematical model can properly describe the heat transfer relationships characterizing the considered system,with the error being kept within±2℃.Indeed,the difference between the actual measured values and the model calculated value curve is within±1.5℃,which is in agreement with the model assumptions and demonstrates the reliability and effectiveness of the algorithm applied to the batch reactor heat transfer ***,the present work provides a theoretical reference for the conversion of practical problems in the field of chemical production into mathematical models.

关键词： Batch reactor lm algorithm parameter estimation model

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Using deep belief networks to extract Chinese entity attribute relation in domain-specific

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2016年第2期7卷 144-155页

作者： Xian, Yantuan Shao, Fa Guo, Jianyi Zhou, Lanjiang Yu, Zhengtao Chen, Wei Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming Yunnan Peoples R China Yunnan Coll Key Lab Pattern Recognit & Intelligent Comp Kunming Yunnan Peoples R China

The state-of-the-art methods used for entity attribute relation extraction are primarily based on statistical machine learning, and the performance strongly depends on the quality of the extracted features. Deep belief networks (DBN) has been successful in the high dimensional feature space information extraction task, which can without complicated pre-processing. In this paper, the DBN, which consists of one or more restricted Boltzmann machine (RBM) layers and a back-propagation (BP) layer, is presented to extract Chinese entity attribute relation in domain-specific. First, the word tokens are transformed to vectors by looking up word embeddings. Then, the RBM layers maintain as much information as possible when feature vectors are transferred to next layer. Finally, the BP layer is trained to classify the features generated by the last RBM layer, and adopting Levenberg-Marquard (lm) optimisation algorithm to do the training. The experimental results show that the proposed method outperforms state-of-the-art learning models in specific domain entity attribute relation extraction.

关键词： relation extraction Chinese entity attribute deep belief nets combination features lm algorithm

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A Parallel Levenberg-Marquardt algorithm for Recursive Neural Network in a Robot Control System

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2018年第2期12卷 32-47页

作者： Wang, Wei Pu, Yunming Li, Wang Jimei Univ Coll Comp Engn Xiamen Peoples R China

This article has the purpose of overcoming the shortcomings of the recursive neural network learning algorithm and the inherent delay problem on the manipulator master control system. This is by analyzing the shortcomings of lm learning algorithms based on DRNN network, an improved parallel lm algorithm is proposed. The parallel search of the damping coefficient p is found in order to reduce the number of iterations of the loop, and the algorithm is used to decompose the parameter operation and the matrix operation into the processor (core), thereby improve the learning convergence speed, and control the scale of the delay. The simulation results show that the pro-posed algorithm is feasible.

关键词： Damping Coefficient DRNN lm algorithm Manipulator Control System

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