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Screen-printed graphite electrode on polyvinyl chloride and parchment strips integrated with genetic programming for in situ nitrate sensing of aquaponic pond water

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Information Processing in Agriculture 2024年第2期11卷 187-201页

作者： Ronnie ConcepcionⅡ Bernardo Duarte Maria Gemel Palconit Jonah Jahara BaunArgel Bandala Ryan Rhay Vicerra Elmer Dadios Department of Manufacturing Engineering and Management De La Salle UniversityManila 1004 Philippines MARE-Marine and Environmental Sciences Centre.&ARNET-Aquatic Research Infrastructure Network Associated Laboratory Faculdade de Ciencias da Universidade de LisboaCampo Grande1749-016 LisboaPortugal Departamento de Biologia Vegetal da Faculdade de Ciencias da Universidade de Lisboa Campo Grande 1749-016 LisboaPortugal Department of Electronics and Computer Engineering De La Salle UniversityManila 1004 Philppines

Nitrate is the primary water-soluble macronutrient essential for plant growth that is converted from excess fish feeds,fish effluents,and degrading biomaterials on the aquaponic pond floor,and when aquacultural malpractices occur,large amounts of it retain in the water system causing increase rate in eutrophication and toxifies fish and aquaculture *** nitrate sensor prototypes still require performing the additional steps of water sample deionization and dilution and were constructed with expensive *** response to the challenge of sensor enhancement and aquaponic water quality monitoring,this study developed sensitive,repeatable,and reproducible screen-printed graphite electrodes on polyvinyl chloride and parchment paper substrates with silver as electrode material and 60:40 graphite powder:nail polish formulated conductive ink for electrical traces,integrated with 9-gene genetic expression model as a function of peak anodic current and electrochemical test time for nitrate concentration prediction that is embedded into low-power Arduino ESP32 for in situ nitrate sensing in aquaponic pond *** SPE electrical traces were designed on the two types of *** electron microscopy with energy dispersive X-ray confirmed the electrode surface *** cyclic voltammetry using 10 to 100 mg/L KNO3 and water from three-depth regions of the actual pond established the electrochemical test time(10.5 s)and electrode potential(0.135 V)protocol necessary to produce peak current that corresponds to the strength of nitrate ions during *** findings from in situ testing revealed that the proposed sensors have strong linear predictions(R2=0.968 MSE=1.659 for nSPEv and R2=0.966 MSE=4.697 for nSPEp)in the range of 10 to 100 mg/L and best detection limit of 3.15μg/L,which are comparable to other sensors of more complex *** developed three-electrode electrochemical nitrate sensor confirms that it is reliable for bot

关键词： Aquaponic water quality Electrochemical technology genetic programming Graphite electrode Nitrate sensor Precision agriculture Printed electronics Scanning electron microscopy Screen-printed electrode Voltammetry

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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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genetic programming with Simple Loops

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Journal of Computer Science & Technology 1999年第4期14卷 429-432,F003页

作者：齐越胜王保中康立山 StateKeyLaboratoryofSoftwareEngineering WuhanUniversityWuhan430072PR.China

A kind of loop function LoopN in genetic programming (GP) isproposed. Different from other forms of loop function, such as While-Do and Repeat-Until, LoopN takes only one argument as its loop body and makes its loop bodysimply run N times, so infinite loops will never happen. The problem of how to avoidtoo many layers of loops in genetic programming is also solved. The advantage ofLoopN in GP is shown by the computational results in solving the mower problem.

关键词： loop function genetic programming mower problem

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genetic programming Approach for Predicting Surface Subsidence Induced by Mining

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Journal of China University of Geosciences 2006年第4期17卷 361-366页

作者：翟淑花高谦宋建国 Department of Civil Engineering University of Science and Technology Beijing Beijing 100083 China Key Laboratory of Ministry of Education on High Efficient and Safety Mining for Metal Mines Beijing 100083 China

The surface subsidence induced by mining is a complex problem, which is related with many complex and uncertain factors. genetic programming （GP） has a good ability to deal with complex and nonlinear problems, therefore genetic programming approach is propesed to predict mining induced surface subsidence in this article. First genetic programming technique is introduced, second, surface subsidence genetic programming model is set up by selecting its main affective factors and training relating to practical engineering data, and finally, predictions are made by the testing of data, whose results show that the relative error is approximately less than 10%, which can meet the engineering needs, and therefore, this proposed approach is valid and applicable in predicting mining induced surface subsidence. The model offers a novel method to predict surface subsidence in mining.

关键词： mining induced surface subsidence genetic programming parameters.

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genetic programming-based Self-reconfiguration Planning for Metamorphic Robot

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International Journal of Automation and computing 2018年第4期15卷 431-442页

作者： Tarek Ababsa Noureddine Djedi Yves Duthen LESIA Laboratory University of BiskraPO Box 145 RPBiskra 07000Algerla IRIT Laboratory University of Toulouse 1 Capitole21 Alles de BrinneToulouse 31032France

This paper presents a genetic programming based reconfiguration planner for metamorphic modular robots. Initially used for evolving computer programs that can solve simple problems, genetic programming （GP） has been recently used to handle various kinds of problems in the area of complex systems. This paper details how genetic programming can be used as an automatic programming tool for handling reconfiguration-planning problem. To do so, the GP evolves sequences of basic operations which are required for transforming the robot＇s geometric structure from its initial configuration into the target one while the total number of modules and their connectedness are preserved. The proposed planner is intended for both Crystalline and TeleCube modules which are achieved by cubical compressible units. The target pattern of the modular robot is expressed in quantitative terms of morphogens diffused on the environment. Our work presents a solution for self recontlguration problem with restricted and unrestricted free space available to the robot during reconfiguration, The planner outputs a near optimal explicit sequence of low-level actions that allows modules to move relative to each other in order to form the desired shape.

关键词： Modular robots unit-compressible modules self-reconfiguration genetic programming reconfiguration planning.

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FORECASTING TIME SERIES WITH genetic programming BASED ON LEAST SQUARE METHOD

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Journal of Systems Science & Complexity 2014年第1期27卷 117-129页

作者： YANG Fengmei LI Meng HUANG Anqiang LI Jian Faculty of Science Beijing University of Chemical Technology Beijing 100029 China. School of Economics and Management Beihang University Beijing 100083 China School of Economics and Management Beijing University of Chemical Technology Beijing 100029 China.

Although time series are frequently nonlinear in reality, people tend to use linear models to fit them under some assumptJLons unnecessarily in accordance with the truth, which unsurprisingly leads to unsatisfactory performance. This paper proposes a forecast method： genetic programming based on least square method （GP-LSM）. Inheriting the advantages of genetic algorithm （GA）, without relying on the particular distribution of the data, this method can improve the prediction accuracy because of its ability of fitting nonlinear models, and raise the convergence speed benefitting from the least square method （LSM）. In order to verify the vMidity of this method, the authors compare this method with seasonal auto regression integrated moving average （SARIMA） and back propagation artificial neural networks （BP-ANN）. The results of empirical analysis show that forecast accuracy and direction prediction accuracy of GP-LSM are obviously better than those of the others.

关键词： Forecast genetic programming least square method time series.

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Data-Driven Prediction of Sintering Burn-Through Point Based on Novel genetic programming

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Journal of Iron and Steel Research International 2010年第12期17卷 1-5,10页

作者： SHANG Xiu-qin LU Jian-gang SUN You-xian LIU Jun YING Yu-qian State Key Laboratory of Industrial Control Technology Department o{ Central Science and EngineeringZhejiang University Hangzhou 310027 Zhejiang China

An empirical dynamic model of burn-through point（BTP）in sintering process was *** K-means clustering was used to feed distribution according to the cold bed permeability,which was estimated by the superficial gas velocity in the cold *** each clustering,a novel genetic programming（NGP）was proposed to construct the empirical model of the waste gas temperature and the bed pressure drop in the sintering *** least square method（LSM）and M-estimator were adopted in NGP to improve the ability to compute and resist *** results show the superiority of the proposed method.

关键词： burn-through point genetic programming K-means clustering

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Defocus Blur Segmentation Using genetic programming and Adaptive Threshold

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Computers, Materials & Continua 2022年第3期70卷 4867-4882页

作者： Muhammad Tariq Mahmood Future Convergence Engineering School of Computer Science and EngineeringKorea University of Technology and EducationCheonan31253Byeongcheon-myeonKorea

Detection and classification of the blurred and the non-blurred regions in images is a challenging task due to the limited available information about blur type,scenarios and level of *** this paper,we propose an effective method for blur detection and segmentation based on transfer learning *** proposed method consists of two separate *** the first step,genetic programming(GP)model is developed that quantify the amount of blur for each pixel in the *** GP model method uses the multiresolution features of the image and it provides an improved blur *** the second phase,the blur map is segmented into blurred and non-blurred regions by using an adaptive threshold.A model based on support vector machine(SVM)is developed to compute adaptive threshold for the input blur *** performance of the proposed method is evaluated using two different datasets and compared with various state-of-the-art *** comparative analysis reveals that the proposed method performs better against the state-of-the-art techniques.

关键词： Blur measure blur segmentation sharpness measure genetic programming support vector machine

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Data-driven approach to learning salience models of indoor landmarks by using genetic programming

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International Journal of Digital Earth 2020年第11期13卷 1230-1257页

作者： Xuke Hu Lei Ding Jianga Shang Hongchao Fan Tessio Novack Alexey Noskov Alexander Zipfa GIScience Research Group Institute of GeographyHeidelberg UniversityHeidelbergGermany School of Geography and Information Engineering China University of GeosciencesWuhanPeople’s Republic of China National Engineering Research Center for Geographic Information System WuhanPeople’s Republic of China Department of Civil and Environmental Engineering Norwegian University of Science and TechnologyTrondheimNorway

In landmark-based way-finding,determining the most salient landmark from several candidates at decision points is *** overcome this problem,current approaches usually rely on a linear model to measure the salience of ***,linear models are not always able to establish an accurate quantitative relationship between the attributes of a landmark and its perceived ***,the numbers of evaluated scenes and of volunteers participating in the testing of these models are often *** the aim of overcoming these gaps,we propose learning a non-linear salience model by means of genetic *** compared our proposed approach with conventional algorithms by using photographs of two hundred test scenes collected from two shopping *** hundred volunteers who were not in these environments were asked to answer questionnaires about the collected *** results from this experiment showed that in 76%of the cases,the most salient landmark(according to the volunteers’perception)was correctly predicted by our proposed *** accuracy rate is considerably higher than the ones achieved by conventional linear models.

关键词： Indoor navigation landmarks salience model genetic programming

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Aquaphotomics determination of nutrient biomarker for spectrophotometric parameterization of crop growth primary macronutrients using genetic programming

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Information Processing in Agriculture 2022年第4期9卷 497-513页

作者： Ronnie Concepcion II Sandy Lauguico Jonnel Alejandrino Elmer Dadios Edwin Sybingco Argel Bandala Department of Manufacturing Engineering and Management De La Salle UniversityManila 1004Philippines Department of Electronics and Computer Engineering De La Salle UniversityManila 1004Philippines Center for Engineering and Sustainable Development Research De La Salle UniversityManila 1004Philippines

Water quality assessment is currently based on time-consuming and costly laboratory pro-cedures and numerous expensive physicochemical sensors *** response to the trend of device minimization and reduced outlays in sustainable aquaponic water monitoring,the integration of aquaphotomics and computational intelligence is presented in this *** study used the combination of temperature,pH,and electrical conductivity sensors in predicting crop growth primary macronutrient concentration(nitrate,phos-phate,and potassium(NPK)),thus,limiting the number of deployed sensors.A total of 220 water samples collected from an outdoor artificial aquaponic pond were temperature perturbed from 16 to 36℃ with 2℃ increments to mimic ambient range,which varies water compositional *** was applied on ultraviolet,visible light,and near-infrared spectral regions,100 to 1000 nm,to determine NPK ***-pal component analysis emphasized nutrient dynamics through selecting the highly corre-lated water absorption bands resulting in 250 nm,840 nm,and 765 nm for nitrate,phosphate,and potassium *** activated water bands were used as wave-length protocols to spectrophotometrically measure macronutrient ***-iments have shown that multigene symbolic regression genetic programming(MSRGP)obtained the optimal performance in parameterizing and predicting nitrate,phosphate,and potassium concentrations based on water physical properties with an accuracy of 87.63%,88.73%,and 99.91%,*** results have shown the established 4-dimensional nutrient dynamics map reveals that temperature significantly strengthens nitrate and potassium above 30℃ and phosphate below 25℃ with pH and electrical con-ductivity ranging between 7 and 8 and 0.1 to 0.2 mS cm^(-1) *** novel approach of developing a physicochemical estimation model predicted macronutrient concentra-tions in real-time using physical limnological sensors with a

关键词： Aquaphotomics genetic programming Plant nutrients Physicochemical composition Spectrophotometry Water quality monitoring

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