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Abstract

This work deals with the inverse problem associated to 3D crack identification inside a conductive material using eddy current measurements. In order to accelerate the time-consuming direct optimization, the reconstruction is provided by the minimization of a last-square functional of the data-model misfit using space mapping (SM) methodology. This technique enables to shift the optimization burden from a time consuming and accurate model to the less precise but faster coarse surrogate model. In this work, the finite element method (FEM) is used as a fine model while the model based on the volume integral method (VIM) serves as a coarse model. The application of the proposed method to the shape reconstruction allows to shorten the evaluation time that is required to provide the proper parameter estimation of surface defects.

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Authors and Affiliations

Piotr Putek
Guillaume Crevecoeur
Marian Slodička
Konstanty Gawrylczyk
Roger van Keer
Luc Dupré
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Abstract

The stable supply of iron ore resources is not only related to energy security, but also to a country’s sustainable development. The accurate forecast of iron ore demand is of great significance to the industrialization development of a country and even the world. Researchers have not yet reached a consensus about the methods of forecasting iron ore demand. Combining different algorithms and making full use of the advantages of each algorithm is an effective way to develop a prediction model with high accuracy, reliability and generalization performance. The traditional statistical and econometric techniques of the Holt–Winters (HW) non-seasonal exponential smoothing model and autoregressive integrated moving average (ARIMA) model can capture linear processes in data time series. The machine learning methods of support vector machine (SVM) and extreme learning machine (ELM) have the ability to obtain nonlinear features from data of iron ore demand. The advantages of the HW, ARIMA, SVM, and ELM methods are combined in various degrees by intelligent optimization algorithms, including the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and simulated annealing (SA) algorithm. Then the combined forecast models are constructed. The contrastive results clearly show that how a high forecasting accuracy and an excellent robustness could be achieved by the particle swarm optimization algorithm combined model, it is more suitable for predicting data pertaining to the iron ore demand.
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Bibliography

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Authors and Affiliations

Min Ren
1
Jianyong Dai
2
Wancheng Zhu
3
Feng Dai
3
ORCID: ORCID

  1. Northeastern University, Shenyang, China
  2. University of South China, Hengyang, China
  3. Northeastern University, Shenyang
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Abstract

Industrial processes such as batch distillation columns, supply chain, level control etc. integrate dead times in the wake of the transportation times associated with energy, mass and information. The dead time, the cause for the rise in loop variability, also results from the process time and accumulation of time lags. These delays make the system control poor in its asymptotic stability, i.e. its lack of self-regulating savvy. The haste of the controller’s reaction to disturbances and congruence with the design specifications are largely influenced by the dead time; hence it exhorts a heed. This article is aimed at answering the following question: “How can a fractional order proportional integral derivative controller (FOPIDC) be tuned to become a perfect dead time compensator apposite to the dead time integrated industrial process?” The traditional feedback controllers and their tuning methods do not offer adequate resiliency for the controller to combat out the dead time. The whale optimization algorithm (WOA), which is a nascent (2016 developed) swarm-based meta-heuristic algorithm impersonating the hunting maneuver of a humpback whale, is employed in this paper for tuning the FOPIDC. A comprehensive study is performed and the design is corroborated in the MATLAB/Simulink platform using the FOMCON toolbox. The triumph of the WOA tuning is demonstrated through the critical result comparison of WOA tuning with Bat and particle swarm optimization (PSO) algorithm-based tuning methods. Bode plot based stability analysis and the time domain specification based transient analysis are the main study methodologies used.
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Bibliography

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Authors and Affiliations

R. Anuja
1
T.S. Sivarani
1
M. Germin Nisha
2

  1. Arunachala College of Engineering For Women, India
  2. St. Xavier’s Catholic College of Engineering, India
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Abstract

Architectural structures’ nodal coordinates are significant to shape appearance; vertical overloading causes displacement of the joints resulting in shape distortion. This research aims to reshape the distorted shape of a double-layer spherical numerical model under vertical loadings; meanwhile, the stress in members is kept within the elastic range. Furthermore, an algorithm is designed using the fmincon function to implement as few possible actuators as possible to alter the length of the most active bars. Fmincon function relies on four optimization algorithms: trust-region reflective, active set, Sequential quadratic progra mming (SQP), and interior-point. The fmincon function is subjected to the adjustment technique to search for the minimum number of actuators and optimum actuation. The algorithm excludes inactive actuators in several iterations. In this research, the 21st iteration gave optimum results, using 802 actuators and a total actuation of 1493 mm.MATLAB analyzes the structure before and after adjustment and finds the optimum actuator set. In addition, the optimal actuation found in MATLAB is applied to the modeled structure in MATLAB and SAP2000 to verify MATLAB results.
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Authors and Affiliations

Ahmed Manguri
1 2
ORCID: ORCID
Najmadeen Saeed
2 3
ORCID: ORCID
Aram Mahmood
4
ORCID: ORCID
Javad Katebi
4
ORCID: ORCID
Robert Jankowski
1
ORCID: ORCID

  1. Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, 80-223 Gdańsk, Poland
  2. Civil Engineering Department, University of Raparin, Rania, Kurdistan Region, Iraq
  3. Civil Engineering Department, Tishk International University, Erbil, Kurdistan Region, Iraq
  4. Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran
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Abstract

Nowadays in e-commerce applications, aspect-based sentiment analysis has become vital, and every consumer started focusing on various aspects of the product before making the purchasing decision on online portals like Amazon, Walmart, Alibaba, etc. Hence, the enhancement of sentiment classification considering every aspect of products and services is in the limelight. In this proposed research, an aspect-based sentiment classification model has been developed employing sentiment whale-optimized adaptive neural network (SWOANN) for classifying the sentiment for key aspects of products and services. The accuracy of sentiment classification of the product and services has been improved by the optimal selection of weights of neurons in the proposed model. The promising results are obtained by analyzing the mobile phone review dataset when compared with other existing sentiment classification approaches such as support vector machine (SVM) and artificial neural network (ANN). The proposed work uses key features such as the positive opinion score, negative opinion score, and term frequency-inverse document frequency (TF-IDF) for representing each aspect of products and services, which further improves the overall effectiveness of the classifier. The proposed model can be compatible with any sentiment classification problem of products and services.
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Bibliography

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Authors and Affiliations

Nallathambi Balaganesh
1
ORCID: ORCID
K. Muneeswaran
1
ORCID: ORCID

  1. Department of Computer Science & Engineering, Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamilnadu, India
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Abstract

Computer aided detection systems are used for the provision of second opinion during lung cancer diagnosis. For early-stage detection and treatment false positive reduction stage also plays a vital role. The main motive of this research is to propose a method for lung cancer segmentation. In recent years, lung cancer detection and segmentation of tumors is considered one of the most important steps in the surgical planning and medication preparations. It is very difficult for the researchers to detect the tumor area from the CT (computed tomography) images. The proposed system segments lungs and classify the images into normal and abnormal and consists of two phases, The first phase will be made up of various stages like pre-processing, feature extraction, feature selection, classification and finally, segmentation of the tumor. Input CT image is sent through the pre-processing phase where noise removal will be taken care of and then texture features are extracted from the pre-processed image, and in the next stage features will be selected by making use of crow search optimization algorithm, later artificial neural network is used for the classification of the normal lung images from abnormal images. Finally, abnormal images will be processed through the fuzzy K-means algorithm for segmenting the tumors separately. In the second phase, SVM classifier is used for the reduction of false positives. The proposed system delivers accuracy of 96%, 100% specificity and sensitivity of 99% and it reduces false positives. Experimental results shows that the system outperforms many other systems in the literature in terms of sensitivity, specificity, and accuracy. There is a great tradeoff between effectiveness and efficiency and the proposed system also saves computation time. The work shows that the proposed system which is formed by the integration of fuzzy K-means clustering and deep learning technique is simple yet powerful and was effective in reducing false positives and segments tumors and perform classification and delivers better performance when compared to other strategies in the literature, and this system is giving accurate decision when compared to human doctor’s decision.
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Authors and Affiliations

J. Maruthi Nagendra Prasad
1
S. Chakravarty
1
M. Vamsi Krishna
2

  1. Centurion University of Technology and Management, Orissa, India
  2. Chaitanya Engineering College, Kakinada, India
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Abstract

Aiming to address power consumption issues of various equipment in metro stations and the inefficiency of peak shaving and valley filling in the power supply system, this study presents an economic optimization scheduling method for the multi-modal “source-network-load-storage” system in metro stations. The proposed method, called the Improved Gray Wolf Optimization Algorithm (IGWO), utilizes objective evaluation criteria to achieve economic optimization. First, construct a mathematical model of the “sourcenetwork- load-storage” joint system with the metro station at its core. This model should consider the electricity consumption within the station. Secondly, a two-layer optimal scheduling model is established, with the upper model aiming to optimize peak elimination and valley filling, and the lower model aiming to minimize electricity consumption costs within a scheduling cycle. Finally, this paper introduces the IGWO optimization approach, which utilizes meta-models and the Improved Gray Wolf Optimization Algorithm to address the nonlinearity and computational complexity of the two-layer model. The analysis shows that the proposed model and algorithm can improve the solution speed and minimize the cost of electricity used by about 5.5% to 8.7% on the one hand, and on the other hand, it improves the solution accuracy, and at the same time effectively realizes the peak shaving and valley filling, which provides a proof of the effectiveness and feasibility of the new method.
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Authors and Affiliations

Jingjing Tian
1
Yu Qian
1
Feng Zhao
1 2
Shenglin Mo
1
Huaxuan Xiao
1
Xiaotong Zhu
1
Guangdi Liu
1

  1. School of Automation and Electrical Engineering, Lanzhou Jiaotong University Lanzhou, China
  2. Key Laboratory of Opto-Technology and Intelligent Control Ministry of Education Lanzhou, China
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Abstract

Cost prediction for construction projects provides important information for project feasibility studies and design scheme selection. To improve the accuracy of early-stage cost estimation for construction projects, an improved neural network prediction model was proposed based on BP (back propagation) neural network and Snake Optimizer algorithm (SO). SO algorithm is adopted to optimize the initial weights and thresholds of the BP neural network. Cost data for 50 construction projects undertaken by Shandong Tianqi Real Estate Group in China was collected, and the data samples were clustered into three categories using cluster analysis. 18 engineering feature indicators were determined through a literature review and 10 feature indicators were selected using Boruta algorithm for the input set. Compared to BP neural network and PSO–BP neural network, the results show that the improved SO–BP model has higher prediction accuracy, stability, better generalization ability and applicability. Therefore, based on reasonable feature indicators, the method proposed in this paper has certain guiding significance for predicting engineering costs.
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Authors and Affiliations

Hao Cui
1
ORCID: ORCID
Junjie Xia
1
ORCID: ORCID

  1. College of Civil Engineering, Jiangxi Science and TechnologyNormalUniversity,No. 605 Fenglin Avenue,330013, Nanchang, China
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Abstract

The selection of a reference model (RM) for a Model-Reference Adaptive Control is one of the most important aspects of the synthesis process of the adaptive control system. In this paper, the four different implementations of RM are developed and investigated in an adaptive PMSM drive with variable moment of inertia. Adaptation mechanisms are based on the Widrow-Hoff rule (W-H) and the Adaptation Procedure for Optimization Algorithms (APOA). Inadequate order or inaccurate approximation of RM for the W-H rule may provide poor behavior and oscillations. The results prove that APOA is robust against an improper selection of RM and provides high-performance PMSM drive operation.
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Authors and Affiliations

Rafał Szczepański
Tomasz Tarczewski
Lech Grzesiak
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Abstract

Blasting cost prediction and optimization is of great importance and significance to achieve optimal fragmentation through controlling the adverse consequences of the blasting process. By gathering explosive data from six limestone mines in Iran, the present study aimed to develop a model to predict blasting cost, by gene expression programming method. The model presented a higher correlation coefficient (0.933) and a lower root mean square error (1088) comparing to the linear and nonlinear multivariate regression models. Based on the sensitivity analysis, spacing and ANFO value had the most and least impact on blasting cost, respectively. In addition to achieving blasting cost equation, the constraints such as fragmentation, fly rock, and back break were considered and analyzed by the gene expression programming method for blasting cost optimization. The results showed that the ANFO value was 9634 kg, hole diameter 76 mm, hole number 398, hole length 8.8 m, burden 2.8 m, spacing 3.4 m, hardness 3 Mhos, and uniaxial compressive strength 530 kg/cm2 as the blast design parameters, and blasting cost was obtained as 6072 Rials/ton, by taking into account all the constraints. Compared to the lowest blasting cost among the 146-research data (7157 Rials/ton), this cost led to a 15.2% reduction in the blasting cost and optimal control of the adverse consequences of the blasting process.

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Authors and Affiliations

Reza Bastami
Abbas Aghajani Bazzazi
Hadi Hamidian Shoormasti
Kaveh Ahangari
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Abstract

Existing scientific studies devoted to the design of eddy-current probes with a priori given configuration of the electromagnetic excitation field, which provide a uniform eddy current density distribution, consider a wide class of such, but are limited to the case when the probe is stationary relative to the testing object. Therefore, the actual problem is the synthesis of moving tangential eddy current probes with a frame excitation system that provides a uniform eddy current density distribution in the testing object, the solution of which is proposed in this study.
A mathematical method for nonlinear surrogate synthesis of excitation systems for frame moving tangential surface eddy current probes, which implements a uniform eddy current density distribution of the testing zone object, is proposed. A metamodel of the volumetric structure of the excitation system of the frame tangential eddy current probe, applied in the process of surrogate optimal parametric synthesis, has been created. The examples of nonlinear synthesis of excitation systems using modern metaheuristic stochastic algorithms for finding the global extremum are considered. The numerical results of the obtained solutions of the problems are presented. The efficiency of the synthesized structures of excitation systems in comparison with classical analogs is shown on the graphs of the eddy current density distribution on the object surface in the testing zone.
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Authors and Affiliations

Volodymyr Yakovych Halchenko
1
ORCID: ORCID
Ruslana Volodymyrivna Trembovetska
1
ORCID: ORCID
Volodymyr Volodymyrovych Tychkov
1
ORCID: ORCID

  1. Cherkasy State Technological University, Ukraine

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