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A Service-Oriented Remanufacturing Framework With Recovery Timing Prediction Based on Remote Condition Monitoring

IEEE ACCESS 2019年 7卷 70046-70058页

作者： Liu, Qingtao Shang, Ziyu Ding, Kai Lv, Jingxiang Changan Univ Key Lab Rd Construct Technol & Equipment MoE Xian 710064 Shaanxi Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Cardiff Univ Sch Engn Inst Energy Cardiff CF24 3AA S Glam Wales

Remanufacturing is generally regarded as a key technology to implement cleaner production. However, in traditional remanufacturing, scrap products are recycled and remanufactured after their performance declines sharply. This passive approach easily arises many problems such as increases of remanufacturing cost, unstable product quality, and unsatisfactory customer demand, which brought great challenges to the remanufacturing industry. To address these challenges, a novel framework, namely service-oriented remanufacturing (SORM), is proposed to improve the overall efficiency of remanufacturing. Contrast to the traditional mode, SORM actively recovers in-service products at the optimal recovery time based on their real-time performance obtained by remote monitoring. The operational logic and implementation path of SORM is firstly discussed. Then the recovery timing prediction (RTP) model, as the core issue of the SORM, is presented to figure out the optimal recovery time of in-service products. Moreover, a comprehensive method combining a two-parameter Weibull distribution (TPWD) and gene expression programming (GEP) is developed to solve the model. The example of excavator remanufacturing illustrates the feasibility of the SORM. Finally, the key findings and managerial implications from application results and discussion are summarized, which provides the theoretical guidance and technical support for better sustainable development.

关键词： Service-oriented remanufacturing recovery timing prediction remote condition monitoring Weibull distribution gene expression programming

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A new methodology for multi-objective multidisciplinary design optimization problems based on game theory

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第3期42卷 1602-1612页

作者： Xiao, Mi Shao, Xinyu Gao, Liang Luo, Zhen Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Univ Technol Sch Mech & Mech Engn Ultimo NSW 2007 Australia

The design of engineering systems often involves multiple disciplines and competing objectives, which requires coordination, information exchange and share amongst the disciplines. However, in practical design environments, designers have to make decisions in isolation due to organization barriers, time schedules and geographical constraints. This paper will propose a new approach for the multi-objective multidisciplinary design optimization (MDO) problems in non-cooperative environments based on gene expression programming (GEP) and Nash equilibrium in the game theory. In this approach, the GEP method is used as a surrogate to construct the approximate rational reaction sets (RRSs) in the Nash model. The effectiveness of the proposed method is demonstrated by the design of a thin-walled pressure vessel and the hull form parameter design of a small waterplane area twin hull (SWATH) ship. The results show that this approach can fully explore and provide the explicit functional relationship between the strategy of an isolated player and the control variables of the other players, thus able to obtain a better Nash equilibrium solution. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Multi-objective multidisciplinary design optimization Game theory gene expression programming Nash equilibrium

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Deep gene selection method to select genes from microarray datasets for cancer classification

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BMC BIOINFORMATICS 2019年第1期20卷 1-15页

作者： Alanni, Russul Hou, Jingyu Azzawi, Hasseeb Xiang, Yong Deakin Univ Sch Informat Technol Geelong Vic Australia

Background: Microarray datasets consist of complex and high-dimensional samples and genes, and generally the number of samples is much smaller than the number of genes. Due to this data imbalance, gene selection is a demanding task for microarray expression data analysis. Results: The gene set selected by DGS has shown its superior performances in cancer classification. DGS has a high capability of reducing the number of genes in the original microarray datasets. The experimental comparisons with other representative and state-of-the-art gene selection methods also showed that DGS achieved the best performance in terms of the number of selected genes, classification accuracy, and computational cost. Conclusions: We provide an efficient gene selection algorithm can select relevant genes which are significantly sensitive to the samples' classes. With the few discriminative genes and less cost time by the proposed algorithm achieved much high prediction accuracy on several public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.

关键词： gene selection Microarray Evolutionary algorithms gene expression programming

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Experimental Study to Estimate Hyporheic Velocity Using Wavelet-Hybrid Soft-Computing Model

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WATER RESOURCES MANAGEMENT 2024年第3期38卷 915-933页

作者： Kabiri, Fazeleh Tabatabai, Mohammad Reza Majdzadeh Mozaffari, Sevda Shayannejad, Mohammad Shahid Beheshti Univ Fac Civil Water & Environm Engn Tehran 167651719 Iran Urmia Univ Dept Water Engn Orumiyeh 57561 Iran Isfahan Univ Technol Coll Agr Dept Water Sci & Engn Esfahan *** Iran

The hyporheic zone represents the interface between surface and subsurface flow. An experimental investigation was conducted in a laboratory flume featuring a maximum flow rate of 50 L per second, utilizing artificial grass completely submerged in water. Two sets of experiments were carried out, one with vegetation cover and the other without. The findings revealed that vegetation cover led to a reduction in hyporheic velocity, whereas the absence of vegetation increased hyporheic velocity. The study also noted that the absence of a hyporheic zone in vegetation, compared to gravel, could be attributed to the formation of a separation zone. Additionally, it was observed that vegetation cover facilitated the supply of more nutrients around the divide line, owing to upwelling flows from both upstream and downstream directions. Given the limited dataset, Soft Computing (SC) techniques, namely Wavelet-GEP (WGEP) and gene expression programming (GEP), were employed to formulate mathematical equations for estimating hyporheic velocity under steady-state conditions for a given discharge. These equations, incorporating hydraulic and geomorphic variables, proved effective in estimating hyporheic velocity under stable bed conditions during steady flow. The models were trained and tested using 70% and 30% of the collected data, respectively. The performance of the models was assessed using statistical criteria. The results indicated a strong correlation between noise reduction in data and improved performance of the WGEP model compared to the GEP model in estimating velocity.

关键词： Hyporheic velocity Vegetation cover gene expression programming Wavelet analysis

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Impurity effect on clear water evaporation: toward modelling wastewater evaporation using ANN, ANFIS-SC and GEP techniques

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HYDROLOGICAL SCIENCES JOURNAL 2017年第11期62卷 1856-1866页

作者： Izady, A. Sanikhani, H. Abdalla, O. Chen, M. Kisi, O. Sultan Qaboos Univ Water Res Ctr Muscat Oman Univ Kurdistan Agr Fac Water Engn Dept Sanandaj Iran Int Black Sea Univ Ctr Interdisciplinary Res Tbilisi Georgia

The growing shortage of freshwater resources and increasing environmental awareness give rise to the use of treated wastewater as an alternative resource for water supply. Accurate estimation of wastewater evaporation (WWE), as the main cause of water losses, is necessary for proper water resources management. Unfortunately, few studies have focused on modelling WWE despite its vital importance. This study investigates the ability of gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANN) techniques to estimate WWE as a function of variables including wastewater properties, clear water evaporation and climatic factors. The study uses measured data from an experiment conducted in Neishaboor municipal wastewater treatment plant, Iran. Results indicate that the ANN model is superior among the three methods, and also demonstrates higher accuracy when compared with those of a dimensional analysis model using the F-test statistic.

关键词： wastewater evaporation neural networks subtractive clustering gene expression programming

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Quantitative combination load forecasting model based on forecasting error optimization

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 101卷

作者： Deng, Song Chen, Fulin Wu, Di He, Yi Ge, Hui Ge, Yuan Nanjing Univ Post & Telecommun Inst Adv Technol Nanjing 210003 Peoples R China SouthEast Univ Sch Cyber Sci & Engn Nanjing 211189 Peoples R China Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing 400714 Peoples R China Old Dominion Univ Norfolk VA 23462 USA Nanjing Univ Post & Telecommun Collegetemp Automation & Artificial Intelligence Nanjing 210003 Peoples R China Anhui Polytech Univ Key Lab Adv Percept & Intelligent Control High en Minist Educ Wuhu 241000 Peoples R China

Accurate load forecasting is indispensable in various applications of the electric power industry. Although existing load forecasting methods perform well, they cannot handle complicated scenarios where load-related data are highly random and uncertain. To deal with this issue, A Quantitative Combination Load Forecasting model(QCLF) is proposed. Its main idea is to incorporate the load forecasting errors into the forecasting process as an optimization problem, which can significantly reduce the adverse impacts of random and uncertain load-related data. First, we propose an improved K-Means and Least Square-based Load Forecasting Error Model(LFEM-KLS) to improve the availability and effectiveness of load-related data. Second, we employ gene expression programming (GEP) to optimize the proposed LFEM-KLS to achieve highly accurate load forecasting. Experimental results on three load datasets demonstrate that a QCLF model significantly outperforms other related load forecasting models.

关键词： gene expression programming Data noise reduction Load forecasting error Combination load forecasting

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A comprehensive GEP and MEP analysis of a cement-based concrete containing metakaolin

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STRUCTURES 2023年 53卷 937-948页

作者： Faraz, Muhammad Iftikhar Ul Arifeen, Siyab Amin, Muhammad Nasir Nafees, Afnan Althoey, Fadi Niaz, Akbar King Faisal Univ Coll Engn Dept Mech Engn Al Hasa 31982 Saudi Arabia COMSATS Univ Islamabad Dept Civil Engn Abbottabad 22060 Pakistan King Faisal Univ Coll Engn Dept Civil & Environm Engn Al Hasa 31982 Saudi Arabia Najran Univ Coll Engn Dept Civil Engn Najran Saudi Arabia

Due to the detrimental environmental consequence of cement formation, studies has concentrated on decreasing the environmental influence and expense of cement containing products. It is crucial to conduct research on the mechanical characteristics of cement additive/replacement products such as metakaolin (MK). To serve the said purpose, the importance of developing predictive machine learning (ML) models is growing as ecological con-cerns intensify. Since there are few ML approaches for them, it is imperative to develop techniques to improve the mechanical properties of such products. In order to overcome these issues, this research examines ML methods for forecasting the compressive strength (fc') of concrete containing MK. gene expression programming (GEP) and multigene expression programming (MEP), two distinct ML predictive models, are provided in detail along with the most useful variables, enabling effective analysis and forecasting of the fc' of concrete containing MK. An important component of any prediction or simulation endeavor is model generalization, and the re-searchers of this study used a data set of MK concrete mechanical properties for this purpose. The database has 405 data points with pertinent model parameters to calculate the fc' of concrete containing MK. Cement, per-centage of metakaolin by binder, coarse and fine aggregate by binder, water by binder, coarse aggregates by fine aggregates, percentage of superplasticizer by binder, and age are among the factors in the database that influence concrete's fc' yet still previously infrequently regarded as crucial input attributes. The effectiveness of the models is then examined in order to choose and implement the best predicted model for the mechanical properties of MK-based concrete. According to the findings of this research, the optimal ML algorithms for forecasting MK -based concrete fc' is MEP with R2 value of 0.96. Additionally, the sensitivity analysis shows that water-by -binder, percent

关键词： Metakaolin Sensitivity analysis Machine learning gene expression programming Multigene expression programming

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A machine learning-based genetic programming approach for the sustainable production of plastic sand paver blocks

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JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T 2023年 25卷 5705-5719页

作者： Iftikhar, Bawar Alih, Sophia C. Vafaei, Mohammadreza Javed, Muhammad Faisal Ali, Mujahid Gamil, Yaser Rehman, Muhammad Faisal Univ Teknol Malaysia Sch Civil Engn Johor Baharu 81310 Johor Malaysia Univ Teknol Malaysia Inst Noise & Vibrat Sch Civil Engn Johor Baharu 81310 Johor Malaysia COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Islamabad 22060 Pakistan Silesian Tech Univ Fac Transport & Aviat Engn Dept Transport Syst Traff Engn & Logist Krasinskiego 8 St Katowice Poland Lulea Univ Technol Dept Civil Environm & Nat Resources Engn Lulea Sweden Monash Univ Malaysia Sch Engn Dept Civil Engn Jalan Lagoon Selatan Bandar Sunway 47500 Selangor Malaysia Univ Engn & Technol Peshawar Dept Architecture Abbottabad Campus Peshawar Pakistan

Plastic sand paver blocks (PSPB) provide a sustainable alternative by reprocessing plastic waste and decreasing reliance on environmentally hazardous materials such as concrete. They promote waste management and environmentally favorable building practices. This paper presents a novel method for estimating the compressive strength (CS) of plastic sand paver blocks based on gene expression programming (GEP) techniques. The database collected from the experimental work comprises 135 compressive strength results. Seven input parameters were involved in predicting the CS of PSPB, namely, plastic, sand, sand size, fiber percentage, fibre length, fibre diameter, and tensile strength of the fibre. Simplified mathematical expressions were used to figure out the CS. The results of GEP formulations showed that they were better in line with the experimental data, with R2 values for CS of 0.89 (training) and 0.88 (testing). The models' performance was evaluated using sensitivity analysis and statistical checks. The statistical evaluations show that the actual and predicted values are closer together, which lends credence to the GEP model's capacity to forecast PSPB CS. The sensitivity analysis showed that sand size and fibre percentage contribute more than 50% of the CS in PSPB. In addition, the results demonstrate that the proposed models are accurate and have a robust capacity for generalizationand prediction. This research can improve environmental protection and economic benefit by enhancing the reuse of PSPB in producing green ecosystems.(c) 2023 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Plastic waste gene expression programming Plastic sand paver blocks Sustainable Compressive strength Mathematical expression

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Data-driven evolutionary programming for evaluating the mechanical properties of concrete containing plastic waste

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CASE STUDIES IN CONSTRUCTION MATERIALS 2024年 21卷

作者： Asif, Usama Javed, Muhammad Faisal Alsekait, Deema Mohammed Aslam, Fahid Elminaam, Diaa Salama Abd Nazarbayev Univ Dept Civil Engn Astana Kazakhstan COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad 22060 Pakistan Ghulam Ishaq Khan Inst Engn Sci & Technol Dept Civil Engn Topi Pakistan Western Caspian Univ Baku Azerbaijan Princess Nourah Bint Abdulrahman Univ Appl Coll Dept Comp Sci & Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia Middle East Univ MEU Res Unit Amman 11831 Jordan Jadara Univ Jadara Res Ctr Irbid 21110 Jordan

Plastic waste (PW) has emerged as a global environmental concern due to its detrimental impact on ecosystems and human health. Traditional concrete heavily relies on natural aggregates like sand, gravel, and crushed stone, whose extraction leads to environmental degradation, including habitat destruction and resource depletion. Recently, the use of PW in concrete has gained attention as a sustainable alternative to these conventional aggregates. By incorporating PW as a partial replacement for natural aggregates, the construction industry can reduce its reliance on finite resources while also addressing the issue of PW. However, despite its potential environmental benefits, the incorporation of PW into concrete has primarily been explored through experimental studies, which are often time-consuming and resource-intensive. Therefore, this study aims to optimize the utilization of waste plastic in concrete through machine learning (ML) techniques, specifically Multi-expression programming (MEP) and gene expression programming (GEP). A comprehensive literature review was conducted to compile a database for evaluating the compressive strength (CS) and tensile strength (TS) of PW concrete. The most influential parameters, such as plastic (P), gravel (G), water (W), cement (C), sand (S), and age (A), were considered as inputs in the models' development. The models developed were thoroughly evaluated using multiple statistical measures. Additionally, sensitivity analysis was conducted to discern and highlight influential factors that have a significant impact on the predicted outcomes. The findings indicate that both MEP (CS_R-2 = 0.88, and TS_R-2 = 0.89) and GEP (CS_R-2 = 0.87, and TS_R-2 = 0.88) models performed well, with MEP demonstrating slightly superior performance. Sensitivity analysis highlights the significant influence of cement (25.63 % and 24.53 %) and plastic (22.4 % and 23.44 %) on concrete strength properties. Furthermore, the equations provided by GEP and

关键词： Plastic waste concrete Compressive strength gene expression programming Multi-expression programming Tensile strength Sustainable development

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Optimal shape design of an autonomous underwater vehicle based on multi-objective particle swarm optimization

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NATURAL COMPUTING 2020年第4期19卷 733-742页

作者： Tang, Qirong Li, Yinghao Deng, Zhenqiang Chen, Di Guo, Ruiqin Huang, Hai Tongji Univ Sch Mech Engn Lab Robot & Multibody Syst Shanghai 201804 Peoples R China Harbin Engn Univ Natl Key Lab Sci & Technol Underwater Vehicle Harbin 150001 Peoples R China

Optimization of autonomous underwater vehicle's shape is usually a multi-objective optimization problem, which is essential for autonomousunderwater navigation and manipulation. To overcome the inefficiency of computational fluid dynamics software during the optimization process and thelimitations of traditional single-objective optimization, a novel strategy combining genetic expression programming and crowding distance basedmulti-objective particle swarm algorithm is presented. Its central idea is as follows, several underwater vehicle shapes are analysed to obtain their waterresistances and determine the best underwater robot shape. Shape factor of the bow and shape factor of the stern are employed as design variables, and sample points are selected by the optimal latin hypercube design. Then gene expression programming method is used to establish the surrogate model of resistance and surrounded volume. After that, the surrogate model based on the gene expression programming method is compared with that based on the surface respond method. The results show the superiority of the GEP method. Then the resistance and surrounded volume are set as two optimized variables and Pareto optimal solutions are obtained by using multi-objective particle swarm algorithm. Finally, the optimization results are compared with the hydrodynamic calculations, which shows the method proposed in the paper can greatly reduce the cost of computation and improve the efficiency of optimal shape design for underwater vehicle.

关键词： Autonomous underwater vehicle Shape optimization gene expression programming Multi-objective particle swarm optimization

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