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Development of a new methodology for estimating the amount of PPV in surface mines based on prediction and probabilistic models (GEP-MC)

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INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT 2021年第1期35卷 48-68页

作者： Zhou, Jian Li, Chuanqi Koopialipoor, Mohammadreza Jahed Armaghani, Danial Thai Pham, Binh Cent South Univ Sch Resources & Safety Engn Changsha Peoples R China Amirkabir Univ Technol Fac Civil & Environm Engn Tehran Iran Ton Duc Thang Univ Fac Elect & Elect Engn Modeling Evolutionary Algorithms Simulat & Artif Ho Chi Minh City Vietnam Duy Tan Univ Inst Res & Dev Da Nang Vietnam

Peak particle velocity (PPV) is one of the most-used parameters for assessment of ground vibration resulting from blasting and associated damage to nearby areas. To prevent and control the damages might be caused by PPV, its amount should be predicted and controlled before conducting blasting operation. This paper analyzes results of this environmental issue of blasting using a combination of predictive and probabilistic models (prediction and simulation phases). To get the right patterns, a new intelligent model (regression tree-based), known as gene expression programming (GEP), is applied and developed. Considering various conditions of data, GEP model was able to propose a predictive model that consists of mathematical relations between input and output parameters. The design of this model was carried out with different conditions, and the most optimal model with the lowest error was selected based on several performance indices. Using the selected model of GEP model, the Monte Carlo simulation technique was implemented to examine the possible risks and control them. The results showed that there is a need to have a better controlling of conditions of explosive operations before blasting operations. Using the developed models, the blast-safety-area can be obtained/determined and all workers and equipment are in safe side during blasting operations.

关键词： Ground vibration gene expression programming Mont Carlo simulation blast-safety-area

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An Evolutionary Algorithm for Non-Destructive Reverse Engineering of Integrated Circuits

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Computer Modeling in Engineering & Sciences 2021年第6期127卷 1151-1175页

作者： Huan Zhang Jiliu Zhou Xi Wu College of Computer Science Sichuan UniversityChengdu610065China School of Computer Science Chengdu University of Information TechnologyChengdu610225China

In hardware Trojan detection technology, destructive reverse engineering can restore an original integrated circuitwith the highest accuracy. However, this method has a much higher overhead in terms of time, effort, and cost thanbypass detection. This study proposes an algorithm, called mixed-feature gene expression programming, whichapplies non-destructive reverse engineering to the chip with bypass detection data. It aims to recover the originalintegrated circuit hardware, or else reveal the unknown circuit design in the chip.

关键词： Hardware Trojans Trojan detection mixed-feature gene expression programming

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Post-fire performance of steel reinforced concrete columns with different steel sections

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ADVANCES IN STRUCTURAL ENGINEERING 2021年第16期24卷 3724-3738页

作者： Hassani, Meisam Safi, Mohammad Ardakani, Reza Rasti Daryan, Amir Saedi Shahid Beheshti Univ Fac Civil Water & Environm Engn Tehran Iran

This study investigates the post-fire performance of steel reinforced concrete composite (SRC) columns used in building construction using experimental compression tests. Four conventional steel sections as I-shaped, Cross, Box, and Plus were considered as the steel core. For fire loading, all fabricated columns were subjected to five target temperatures, including 25 degrees, 250 degrees, 500 degrees, 700 degrees, and 900 degrees C, and then the cooling phase of the columns was done under natural conditions. Then using pressure jacks, the compressive behavior including strength and axial deformation of each of them was measured, and force-deformation curves were plotted and investigated for each SRC column. The results of these tests showed that the compressive strength and the elasticity modulus of the columns decrease at higher temperatures. Also, the effect of the steel core was examined on the compressive strength. Among the tested sections, SRC columns with the Box-steel core showed a more recovery in its compressive strength and elasticity modulus, and hence, its performance was better than SRC columns with the other steel cores. Moreover, SRC columns with the Plus-steel core indicated the weakest compressive strength and elasticity modulus. Finally, some equations were proposed for the prediction of the compressive strength and elasticity modulus of SRC columns at different temperatures by applying gene expression programming (GEP modeling) to the results.

关键词： Steel reinforced concrete columns compressive strength post-fire performance axial deformation gene expression programming experimental study

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gene expression programming-Fuzzy Logic Method For Crop Type Classification

Gene Expression Programming-Fuzzy Logic Method For Crop Type...

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6th International Conference on genetic and Evolutionary Computing (ICGEC)

作者： Omkar, S. N. Ramaswamy, Nikhil Senthilnath, J. Bharath, S. Anuradha, N. S. Indian Inst Sci Dept Aerosp Engn Bangalore 560012 Karnataka India Dr Ambedkar Inst Technol Dept Comp Sci & Engn Bangalore 560012 Karnataka India

ISBN: (纸本)9780769547633;9781467321389

Crop type classification using remote sensing data plays a vital role in planning cultivation activities and for optimal usage of the available fertile land. Thus a reliable and precise classification of agricultural crops can help improve agricultural productivity. Hence in this paper a gene expression programming based fuzzy logic approach for multiclass crop classification using Multispectral satellite image is proposed. The purpose of this work is to utilize the optimization capabilities of GEP for tuning the fuzzy membership functions. The capabilities of GEP as a classifier is also studied. The proposed method is compared to Bayesian and Maximum likelihood classifier in terms of performance evaluation. From the results we can conclude that the proposed method is effective for classification.

关键词： crop classification gene expression programming fuzzy logic

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Rapid Prototype Development Approach for genetic programming

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Journal of Computer and Communications 2024年第2期12卷 67-79页

作者： Pei He Lei Zhang School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China School of Cyberspace Security Guangzhou University Guangzhou China

genetic programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of genetic operators, evolutionary controls and implementations of heuristic strategy, evaluations and other mechanisms. When designing genetic operators, it is necessary to consider the possible limitations of encoding methods of individuals. And when selecting evolutionary control strategies, it is also necessary to balance search efficiency and diversity based on representation characteristics as well as the problem itself. More importantly, all of these matters, among others, have to be implemented through tedious coding work. Therefore, GP development is both complex and time-consuming. To overcome some of these difficulties that hinder the enhancement of GP development efficiency, we explore the feasibility of mutual assistance among GP variants, and then propose a rapid GP prototyping development method based on πGrammatical Evolution (πGE). It is demonstrated through regression analysis experiments that not only is this method beneficial for the GP developers to get rid of some tedious implementations, but also enables them to concentrate on the essence of the referred problem, such as individual representation, decoding means and evaluation. Additionally, it provides new insights into the roles of individual delineations in phenotypes and semantic research of individuals.

关键词： genetic programming Grammatical Evolution gene expression programming Regression Analysis Mathematical Modeling Rapid Prototype Development

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Dam failure peak outflow prediction through GEP- SVM meta models and uncertainty analysis

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WATER SUPPLY 2021年第7期21卷 3387-3401页

作者： Nobarinia, Mohammad Kalateh, Farhoud Nourani, Vahid Amini, Alireaz Babaeyan Univ Tabriz Fac Civil Engn Tabriz *** Iran Near East Univ Dept Civil Engn Mersin 10 Lefkosa North Cyprus Turkey Univ Bonab Fac Civil Engn Velayat Highway Bonab *** Iran

Accurate prediction of breached dam's peak outflow is a significance factor for flood risk analysis. In this study, capability of Support Vector Machine and Kernel Extreme Learning Machine as kernel-based approaches and gene expression programming method was assessed in breached dam's peak outflow predicting. Two types of modeling were considered. First, only dam reservoir height and volume at the failure time were used as the input combinations (state 1). Then, soil characteristics were added to input combinations to investigate particularly the impact of soil characteristics (state 2). Results showed that the use of only soil characteristics did not lead to a desired accuracy;however, adding soil characteristics to input combinations (state 2) improved the models accuracy up to 40%. The outcome of the applied models also was compared with existing empirical equations and it was found the applied models yielded better results. Sensitivity analysis results showed that dam height had the most important role in the peak outflow prediction, while the strength parameters did not had significant impacts. Furthermore, for assessing the best-applied model dependability, uncertainty analysis was used and the results indicated that the SVM model had an allowable degree of uncertainty in peak outflow modelling.

关键词： empirical equation gene expression programming kernel extreme learning machine meta model peak outflow uncertainty

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Novel quantitative structure-activity relationship model to predict activities of natural products against COVID-19

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CHEMICAL BIOLOGY & DRUG DESIGN 2021年第4期97卷 978-983页

作者： Si, Yaru Xu, Xinyue Hu, Yingfeng Si, Hongzong Zhai, Honglin Qingdao Univ Coll Life Sci Qingdao Peoples R China Qingdao Univ State Key Lab Biopolysaccharide Fiber Forming & E Qingdao 266071 Peoples R China Lanzhou Univ Dept Chem Lanzhou Peoples R China

Currently, COVID-19 is spreading in a large scale while no efficient vaccine has been produced. A high-effective drug for COVID-19 is very necessary now. We established a satisfied quantitative structure-activity relationship model by gene expression programming to predict the IC50 value of natural compounds. A total of 27 natural products were optimized by heuristic method in CODESSA program to build a liner model. Based on it, only two descriptors were selected and utilized to build a nonlinear model in gene expression programming. The square of correlation coefficient and s2 of heuristic method were 0.80 and 0.10, respectively. In gene expression programming, the square of correlation coefficient and mean square error for training set were 0.91 and 0.04. The square of correlation coefficient and mean square error for test set are 0.86 and 0.1. This nonlinear model has stronger predictive ability to develop the targeted drugs of COVID-19.

关键词： COVID-19 gene expression programming heuristic method model natural compounds quantitative structure-activity relationship

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Fully Automated Construction of a Deep U-Net Network Model for Medical Image Segmentation

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SENSORS AND MATERIALS 2023年第10期35卷 4633-4652页

作者： Gong, Daoqing Yang, Jiayan Gan, Xinyan Gao, Xiang Zhang, Yuanxia Guangxi Univ Chinese Med Sch Publ Hlth & Management Nanning 530299 Peoples R China Yulin Normal Univ Sch Comp & Informat Engn Nanning 537000 Peoples R China

In recent years, the use of deep learning technology for image processing has become mainstream, and the U-Net network has received widespread attention owing to its unique U-shaped structure, which has achieved excellent results in the field of image segmentation, especially in medical image segmentation. To enhance the performance of the U-Net network model and establish better U-Net design variables, in this paper, we propose a fuzzy-controlled multicellular gene expression programming algorithm to automatically build and optimize the U-Net. The algorithm creates an efficient variable-length gene code, generates chromosomes for the optimization of U-Net design variables, decodes the chromosomes to construct the U-Net model, dynamically calculates population fitness and fuzzy affiliation values, and achieves the optimal U-Net network through continuous evolution. The experimental results indicate that the proposed algorithm outperforms U-Net, Fully Convolutional Networks32, and VanillaUnet in image recognition segmentation, especially in the segmentation of COVID-19 CT medical images.

关键词： fully automatic gene expression programming U-Net Model medical images

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Predicting quality parameters of wastewater treatment plants using artificial intelligence techniques

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JOURNAL OF CLEANER PRODUCTION 2023年第1期405卷

作者： Aghdam, Ehsan Mohandes, Saeed Reza Manu, Patrick Cheung, Clara Yunusa-Kaltungo, Akilu Zayed, Tarek Hong Kong Polytech Univ Fac Construct & Environm FCE Dept Bldg & Real Estate BRE Hung HomKowloon Hong Kong Peoples R China Univ Manchester Sch Engn Dept Mech Aerosp & Civil Engn Manchester M13 9PL England

Estimating wastewater treatment plants' (WWTPs) influent parameters such as 5-day biological oxygen demand (BOD5) and chemical oxygen demand (COD) is vital for optimizing electricity and energy consumption. Against this backdrop, the existing body of knowledge is bereft of a study employing Artificial Intelligence-based techniques for the prediction of BOD5 and COD. Thus, in this study, gene expression programming (GEP), multilayer perception neural networks, multi-linear regression, k-nearest neighbors, gradient boosting, and regression trees -based models were trained for predicting BOD5 and COD, using monthly data collected from the inflow of 7 WWTPs over a three-year period in Hong Kong. Based on different statistical parameters, GEP provides more accurate estimations, with R2 values of 0.784 and 0.861 for BOD5 and COD respectively. Furthermore, results of sensitivity analysis undertaken by monte Carlo simulation revealed that both BOD5 and COD were mostly affected by concentrations of total suspended solids, and a 10% increase in the value of TSS resulted in a 7.94% and 7.92% increase in the values of BOD5 and COD, respectively. It is seen that the GEP modeling results complied with the fundamental chemistry of the wastewater quality parameters and can be further applied on other sewage sources such as industrial sewage and leachate. The promising results obtained pave the way for forecasting the operational parameters during sludge processing, leading to an extensive energy savings during the wastewater treatment processes.

关键词： gene expression programming AI models Wastewater quality parameters Artificial neural networks Monte Carlo simulation

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gene expression programming for Static Security Assessment of Power Systems

Gene Expression Programming for Static Security Assessment o...

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general Meeting of the IEEE-Power-and-Energy-Society

作者： Khattab, H. M. Abdelaziz, A. Y. Mekhamer, S. F. Badr, M. A. L. El-Saadany, E. F. Engn Petr & Proc Ind ENPPI Cairo Egypt Ain Shams Univ Fac Engn Dept Elect Power & Machine Cairo Egypt Univ Waterloo Elect & Comp Engn Dept Waterloo ON Canada

ISBN: (纸本)9781467327299

In this paper, a novel gene expression programming (GEP) algorithm is introduced for power system static security assessment. The GEP algorithms as evolutionary algorithms for pattern classification have recently received attention for classification problems because they can perform global searches. The proposed methodology introduces the GEP for the first time in static security assessment problems. The proposed algorithm is examined using different IEEE standard test systems. Different contingency case studies have been used to test the proposed methodology. The GEP based algorithm formulates the problem as a multi-class classification problem using the one-against-all binarization method. The algorithm classifies the security of the power system into three classes, normal, alert and emergency. Performance of the algorithm is compared with other neural network based algorithm classifiers to show its superiority in static security assessment.

关键词： Static security gene expression programming probabilistic neural network radial basis function neural network line outage voltage level power system classifier

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