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Modeling the 3D structure and rhythmic growth responses to environment in dioecious yerba-mate

ECOLOGICAL MODELLING 2014年第C期290卷 34-44页

作者： Matsunaga, Fabio Takeshi Rakocevic, Miroslava Brancher, Jacques Duilio Agron Inst Parana IAPAR Dept Ecophysiol BR-86047902 Londrina PR Brazil State Univ Londrina UEL Dept Computat BR-86044290 Londrina PR Brazil

Environmental factors, as incident light, temperature and night/day length mainly determine the dynamics of growth and development of dioecious yerba-mate. The complex interactions among these factors and growth responses highlight the need for growth model, which describes plant modifications under natural and stress conditions, accounting for the growth unit formations in male and female individuals. The rhythmic growth of yerba-mate considers the existence of two annual growth flushes, (spring and autumn) and two annual growth pauses (summer and winter). We developed an individual-based ecological model (InterpolMateS1) that incorporates some aspects of growth and development of yerba-mate referent to two cultivation environments - monoculture and forest understory. The environmental time series, together with plant morphological time series and information about periods of rhythmic growth and respective growth pauses, were used for artificial neural network (ANN) training. The back-propagation algorithm was implemented to refine the weights generated in ANN from the monthly organized input and to adjust using expected output morphological data sets. The probability of meristem ability to continue the growth in the next growth unit and to preserve the leaf in each internode along the axes of 1st-3rd branching order was calculated and implemented into the model. The cubic splines interpolation was more accurate to define the growth parameters curves of yerba-mate. The InterpolMateS1 was daily-step programmed to calculate the growth of yerba-mate for biennial period between two subsequent prunings. The software was tested to simulate the reduction in growth and biomass production when long-term stress conditions were applied. Virtual females were found to be more sensitive to changes of environmental conditions than males, when low water availability and low temperatures occurred during spring and autumn growth flushes. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Artificial neural network back-propagation algorithm Cubic splines Growth unit VegeSTAR VPlants

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Application of a combined response surface methodology (RSM)-artificial neural network (ANN) for multiple target optimization and prediction in a magnetic coagulation process for secondary effluent from municipal wastewater treatment plants

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第24期29卷 36075-36087页

作者： Wang, Kemei Mao, Yuxuan Wang, Chuanhua Ke, Qiang Zhao, Min Wang, Qi Wenzhou Univ Coll Life & Environm Sci Wenzhou 325035 Peoples R China Wenzhou Univ Natl & Local Joint Engn Res Ctr Ecol Treatment Te Wenzhou 325600 Peoples R China

In this study, an enhanced coagulation-flocculant process incorporating magnetic powder was used to further treat the secondary effluent of domestic wastewater from a municipal wastewater treatment plant. The purpose of this work was to improve the discharged water quality to the surface water class IV standard of China. A novel approach using a combination of the response surface methodology and an artificial neural network (RSM-ANN) was used to optimize and predict the total phosphorus (TP) pollutant removal and turbidity. This work was first evaluated by RSM using the concentrations of coagulant, magnetic powder, and flocculant as the controllable operating variables to determine the optimal TP removal and turbidity. Next, an ANN model with a back-propagation algorithm was constructed from the RSM data along with the non-controllable variables, raw TP concentration, and raw water turbidity. Under the optimized experimental conditions (28.42 mg/L coagulant, 623 mg/L magnetic powder, and 0.18 mg/L flocculant), the TP and turbidity removal reached 88.79 +/- 5.45% and 63.48 +/- 9.60%, respectively, compared with 83.28% and 59.80%, predicted by the single RSM model, and 87.71 +/- 5.74% and 64.62 +/- 10.75%, predicted by the RSM-ANN model. The treated water were 0.17 +/- 6.69% mg/L of TP and 2.46 +/- 5.09% NTU of turbidity, respectively, which completely met the surface water class IV standard (TP < 0.3 mg/L;turbidity < 3 NTU). Therefore, this work demonstrated that the discharged water quality was completely improved using the magnetic coagulation process. In addition, the combined RSM-ANN approach could have potential application in municipal wastewater treatment plants.

关键词： Wastewater treatment plant Magnetic coagulation-flocculation Phosphorus removal Secondary effluent Domestic wastewater Turbidity removal Box-Behnken Design back-propagation algorithm

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Application of ELM to predict the coagulant dosing in water treatment plants

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WATER SCIENCE AND TECHNOLOGY-WATER SUPPLY 2017年第4期17卷 1053-1061页

作者： Deng, Xiaoyan Lin, Canguang Univ South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Guangdong Peoples R China

Predicting the coagulant dosage is especially crucial to the purification process in water treatment plants, directly affecting the quality of the purified water. Nowadays, several mathematical methods have been adopted for the purification process, but their predictive precision and speed still need to be improved. This study applies a novel neural network called the extreme learning machine ( ELM) to predict the coagulant dosage based on certain signification factors of the raw water. Performances are compared between ELM and back-propagation neural networks in this paper. The results show that both neural network algorithms perform well in this application and ELM can realize online prediction due to its short time consumption.

关键词： activation function artificial neural networks back-propagation algorithm coagulant dosing prediction extreme learning machine optimization problem

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Use of different artificial neural networks to resolve binary blends of monocultivar Italian olive oils

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ANALYTICA CHIMICA ACTA 2007年第2期599卷 232-240页

作者： Marini, Federico Magri, Antonio L. Bucci, Remo Magri, Andrea D. Univ Roma La Sapienza Dipartimento Chim I-00185 Rome Italy

The problem of authenticating extra virgin olive oil varieties is particularly important from the standpoint of quality control. After having shown in our previous works the possibility of discriminating oils from a single variety using chemometrics, in this study a combination of two different neural networks architectures was employed for the resolution of simulated binary blends of oils from different cultivars. In particular, a Kohonen self-organizing map was used to select the samples to include in the training, test and validation sets, needed to operate the successive calibration stage, which has been carried out by means of several multilayer feed-forward neural networks. The optimal model resulted in a validation Q(2) in the range 0.91-0.96 (10 data sets), corresponding to an average prediction error of about 5-7.5%, which appeared significantly better than in the case of random or Kennard-Stone selection. (C) 2007 Elsevier B.V. All rights reserved.

关键词： extra virgin olive oil chemometrics artificial neural networks back-propagation algorithm Kohonen self-organizing maps

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Fault identification of gearbox degradation with optimized wavelet neural network

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SHOCK AND VIBRATION 2013年第2期20卷 247-262页

作者： Chen, Hanxin Lu, Yanjun Tu, Ling Wuhan Inst Technol Sch Mech & Elect Engn Wuhan Hubei Peoples R China

A novel intelligent method based on wavelet neural network (WNN) was proposed to identify the gear crack degradation in gearbox in this paper. The wavelet packet analysis (WPA) is applied to extract the fault feature of the vibration signal, which is collected by two acceleration sensors mounted on the gearbox along the vertical and horizontal direction. The back-propagation (BP) algorithm is studied and applied to optimize the scale and translation parameters of the Morlet wavelet function, the weight coefficients, threshold values in WNN structure. Four different gear crack damage levels under three different loads and three various motor speeds are presented to obtain the different gear fault modes and gear crack degradation in the experimental system. The results show the feasibility and effectiveness of the proposed method by the identification and classification of the four gear modes and degradation.

关键词： Fault identification wavelet neural network back-propagation algorithm gearbox degradation

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Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression models

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ENERGY 2007年第8期32卷 1513-1523页

作者： Elminir, Hamdy K. Azzam, Yosry A. Younes, Farag I. Natl Res Inst Astron & Geophys Dept Solar & Space Res Cairo Egypt

For most of the locations all over Egypt the records of diffuse radiation in whatever scale are non-existent. In case that it exists, the quality of these records is not as good as it should be for most purposes and so an estimate of its values is desirable. To achieve such a task, an artificial neural network (ANN) model has been proposed to predict diffuse fraction (K-D) in hourly and daily scale. A comparison between the performances of the ANN model with that of two linear regression models has been reported. An attempt was also done to describe the ANN outputs in terms of first order polynomials relating K-D with clearness index (K-T) and sunshine fraction (S/S-0). If care is taken in considering the corresponding regional climatic differences, these correlations can be generalized and transferred to other sites. The results hint that the ANN model is more suitable to predict diffuse fraction in hourly and daily scales than the regression models in the plain areas of Egypt. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： diffuse fraction linear regression model neural network back-propagation algorithm

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Fading channel modelling using single-hidden layer feedforward neural networks

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2017年第3期28卷 885-903页

作者： Liu, Junbiao Jin, Xinyu Dong, Fang He, Liang Liu, Hong Zhejiang Univ Inst Informat Sci & Elect Engn Hangzhou 310037 Zhejiang Peoples R China Zhejiang Univ City Coll Sch Informat & Elect Engn Hangzhou 310015 Zhejiang Peoples R China Tsinghua Univ Dept Elect Engineer Beijing 100084 Peoples R China

Due to the complexity and extensive application of wireless systems, fading channel modeling is of great importance for designing a mobile network, especially for high speed environments. High mobility challenges the speed of channel estimation and model optimization. In this study, we propose a single-hidden layer feedforward neural network (SLFN) approach to modelling fading channels, including large-scale attenuation and small-scale variation. The arrangements of SLFN in path loss (PL) prediction and fading channel estimation are provided, and the information in both of them is trained with extreme learning machine (ELM) algorithm and a faster back-propagation (BP) algorithm called Levenberg-Marquardt algorithm. Computer simulations show that our proposed SLFN estimators could obtain PL prediction and the instantaneous channel transfer function of sufficient accuracy. Furthermore, compared with BP algorithm, the ability of ELM to provide millisecond-level learning makes it very suitable for fading channel modelling in high speed scenarios.

关键词： Fading channel Neural network back-propagation algorithm Extreme learning machine High speed

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Artificial Neural Network System for the Design of Airbag Fabrics

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JOURNAL OF INDUSTRIAL TEXTILES 2009年第1期39卷 45-55页

作者： Behera, B. K. Goyal, Y. Indian Inst Technol Dept Text Technol New Delhi 110016 India

Engineered fabric manufacturing needs a thorough understanding of the functional properties and their key control construction parameters. When the relationship between a set of interrelated properties goes out of the complete comprehension of human brain, neural networks could be used to find the unknown function. This article describes the method of applying the artificial neural network for the prediction performance parameters for airbag fabrics. The results of the ANN performance prediction had low prediction error i.e., 12% with all the samples and the artificial neural network based on Error back-propagation were found promising for a new domain of design prediction technique. The prediction performance of the neural network was based on the amount of training given to it, i.e., the diversity of the data and the amount of data;resulting in better the mapping of the network, and better predictions. Airbag fabrics could be successfully engineered using artificial neural network.

关键词： airbag fabric design engineering neural network back-propagation algorithm edgecomb resistance specific packability

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Inverse estimation of location of internal heat source in conduction

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INVERSE PROBLEMS IN SCIENCE AND ENGINEERING 2011年第3期19卷 337-361页

作者： Ghosh, S. Pratihar, D. K. Maiti, B. Das, P. K. Indian Inst Technol Dept Mech Engn Kharagpur 721302 W Bengal India

In this study, an inverse method is proposed for estimating the boundary conditions in a heat conduction problem using a regression analysis, neural network trained by a local optimizer and lastly, that trained by the local and global optimizers simultaneously. The test problem consists of a square slab with an internal heat source of circular shape. Once the boundary conditions of the square slab and periphery of the heat source are specified, the temperature can be estimated for two-dimensional heat conduction problems. This constitutes the forward heat transfer problem. Reverse heat transfer problem is formulated to determine the location of the heat source from some known temperature values elsewhere in the slab. A reasonably good solution is obtained from the said inverse problem using the proposed approach, whose performance is also compared with that of other two approaches.

关键词： inverse problem heat conduction neural network back-propagation algorithm genetic algorithm

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A combined neural network and decision trees model for prognosis of breast cancer relapse

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2003年第1期27卷 45-63页

作者： Jerez-Aragonés, JM Gómez-Ruiz, JA Ramos-Jiménez, G Muñoz-Pérez, J Alba-Conejo, E Univ Malaga Dept Languajes & Ciencias Computac E-29071 Malaga Spain Univ Malaga Hosp Clin Serv Oncol E-29071 Malaga Spain

The prediction of clinical outcome of patients after breast cancer surgery plays an important role in medical tasks such as diagnosis and treatment planning. Different prognostic factors for breast cancer outcome appear to be significant predictors for overall survival, but probably form part of a bigger picture comprising many factors. Survival estimations are currently performed by clinicians using the statistical techniques of survival analysis. In this sense, artificial neural networks are shown to be a powerful tool for analysing datasets where there are complicated non-linear interactions between the input data and the information to be predicted. This paper presents a decision support tool for the prognosis of breast cancer relapse that combines a novel algorithm TDIDT (control of induction by sample division method, CIDIM), to select the most relevant prognostic factors for the accurate prognosis of breast cancer, with a system composed of different neural networks topologies that takes as input the selected variables in order for it to reach good correct classification probability. In addition, a new method for the estimate of Bayes' optimal error using the neural network paradigm is proposed. Clinical-pathological data were obtained from the Medical Oncology Service of the Hospital Clinico Universitario of Malaga, Spain. The results show that the proposed system is an useful tool to be used by clinicians to search through large datasets seeking subtle patterns in prognostic factors, and that may further assist the selection of appropriate adjuvant treatments for the individual patient. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： back-propagation algorithm Bayes error survival analysis breast cancer decision trees inductive learning

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