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Qualitative identification of waste textiles based on near-infrared spectroscopy and the back propagation artificial neural network

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TEXTILE RESEARCH JOURNAL 2021年第21-22期91卷 2459-2467页

作者： Li, Wenxia Wei, Zihan Liu, Zhengdong Du, Yujun Zheng, Jiahui Wang, Huaping Zhang, Shuo Beijing Inst Fash & Technol Sch Mat Design & Engn Beijing Peoples R China GTTC Testing Technol Shanghai Co Ltd Chem Inspect Shanghai Peoples R China Beijing Inst Fash & Technol Sch Fash Beijing Peoples R China China Text Acad Dev & Reform Beijing Peoples R China Donghua Univ Coll Mat Shanghai Peoples R China Zhejiang Grerial Environm Protect Co Ltd Lanxi Peoples R China

Hand sorting for different types of waste textiles is time-consuming, laborious and inaccurate. The non-destructive and efficient identification of fibers in waste fabrics is of great significance to the reuse of textile materials. In this paper, 593 samples were selected as the research objects, including polyester, cotton, wool, viscose, nylon, silk, acrylic, polyester/nylon, polyester/cotton, polyester/wool and silk/cotton waste textiles. The near-infrared spectrum of each sample was obtained by a portable near-infrared spectrometer, and the influence of environmental humidity and fabric thickness on the near-infrared spectrum of the sample was discussed to obtain the best test conditions. On this basis, the back propagation artificial neural network (BP-ANN) was applied to the qualitative classification of waste textiles to complete the automatic identification of fabric components in the sorting process. Firstly, a standard sample set was established by waveform clipping and normalization, and a BP-ANN deep web suitable for near-infrared spectroscopy was established. Then the BP network was trained according to the input near-infrared spectrum data of known sample categories and the classification results of the preset 11 types of labels, and the weights and thresholds of each layer were adjusted in the repeated training process. Finally, a 1500 x 100 x 11 network structure was established when the network error was the smallest, and the number of corresponding hidden layer nodes was 100. When the number of training steps was 500, the sum of squared errors reached 0.001, and the model recognition effect was the best. Meanwhile, the validity of the model was verified by inspecting additional 299 samples outside the model, and the recognition accuracy rate of the established model also exceeded 99%, which verified the effectiveness of the model. These results show that this near-infrared qualitative analysis model can more accurately classify and identify waste t

关键词： near-infrared spectra fabric type qualitative analysis model back propagation artificial neural network

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Runoff modelling through back propagation artificial neural network with variable rainfall-runoff data

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WATER RESOURCES MANAGEMENT 2004年第3期18卷 285-300页

作者： Agarwal, A Singh, RD Natl Inst Hydrol Roorkee Uttar Pradesh India

Multi layer back propagation artificial neural network (BPANN) models have been developed to simulate rainfall-runoff process for two sub-basins of Narmada river (India) viz. Banjar up to Hridaynagar and Narmada up to Manot considering three time scales viz. weekly, ten-daily and monthly with variable and uncertain data sets. The BPANN runoff models were developed using gradient descent optimization technique and were generalized through cross-validation. In almost all cases, the BPANN developed with the data having relatively high variability and uncertainty learned in less number of iterations, with high generalization. Performance of BPANN models is compared with the developed linear transfer function (LTF) model and was found superior.

关键词： back propagation artificial neural network rainfall runoff modelling transfer function model

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Image deinterlacing using region-based back propagation artificial neural network

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OPTICAL ENGINEERING 2013年第7期52卷

作者： Qian, Yurong Wang, Jin Jeon, Gwanggil Jeong, Jechang Xinjiang Univ Sch Software Urumqi 830008 Peoples R China Hanyang Univ Dept Elect & Comp Engn Seoul 133791 South Korea Incheon Natl Univ Dept Embedded Syst Engn Inchon 406772 South Korea

A back propagation artificial neural network (BP-ANN) has good self-learning, self-adaptation and generalization abilities, which we intend to use to interpolate an image. The interpolated pixels are classified into two regions, each region corresponding to one BP-ANN. In order to optimize the structure of the BP-ANN and the process of deinterlacing, three experiments were performed to test the architecture and parameters of region-based BP-ANN. The experimental results show that the proposed algorithm with an 8 - 16 - 1 structure provides the best balance between time consumption and visual quality. Compared to the other six advanced deinterlacing algorithms, our region-based BP-ANN method provides about an average of 0.14 to 0.64 dB higher peak signal-to-noise-ratio while maintaining high efficiency. (c) 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： deinterlacing back propagation artificial neural network image format conversion

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A back propagation artificial neural network Application in Lithofacies Identification 11

A Back Propagation Artificial Neural Network Application in ...

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11th International Conference on Natural Computation (ICNC) / 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Dong, Yue Hou, Jiagen Liu, Yuming Wang, Ye Zhao, Jing Shi, Yanqing Zou, Jingyun China Univ Petr Dept Geol Sci Beijing Peoples R China

ISBN: (纸本)9781467376792

The traditional lithofacies identification by the geology method usually has the flaw of strong subjectivity, high randomness and high requirement of the geological interpreter. In order to accurately identify the lithofacies in each well and thus provide a better guidance for the plan of further exploration and exploitation, an integrated lithofacies identification method based on ANN (artificial neural network) is presented. Take the YJ Oilfield as an example, on the basis of well log data preprocessing, choose appropriate training samples and identify the lithofacies in single well taking advantage of the generalization and self-learning ability of the ANN algorithm, and compare the result to the lithofacies identification from core data. It shows that this method has relatively high accuracy when applied to lithofacies identification of clastic reservoirs which normally have complicated lithological sequences. Compared to the traditional identification method, the ANN method avoid the subjectivity in the well log interpretation and don't have to set up interpretation models for the district which usually calls for abundant experience;what's more, the interpreter could make a balance between accuracy and efficiency by shifting the neuron number of the hidden layer. In general, this method is of high practical value.

关键词： back propagation artificial neural network Well Log Data Core Data Lithofacies Identification

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Investigation of Multistage Oxidation Behavior of Al4SiC4 Powders with Aid of back propagation artificial neural network

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STEEL RESEARCH INTERNATIONAL 2023年第5期94卷

作者： Guo, Chunyu Shi, Jinzhuo Ru, Feng Zhao, Chunyang Wang, Enhui Yang, Tao Hou, Xinmei Univ Sci & Technol Beijing Collaborat Innovat Ctr Steel Technol Beijing 10083 Peoples R China China Acad Launch Vehicle Technol Beijing 100076 Peoples R China

In order to investigate the multistage oxidation behavior of aluminum silicon carbide (Al4SiC4), back propagation artificial neural network (BP-ANN) has been trained and employed considering the oxidation temperature, time, and aspect ratio. The results denote that the BP-ANN model can accurately and efficiently simulate the oxidation behavior of Al4SiC4 powders with different reaction laws. In addition, the extrapolation ability suggests that the BP-ANN model can maintain a high accuracy with the coefficient of determination >= 0.801 to expand the experimental data to 1.2 times the original range. By incorporating a real physical picture model developed by the research group, the experimental data can be further expanded to 2.2 times. This study can provide a new path to recognize the oxidation behavior of materials with multistage oxidation.

关键词： Al4SiC4 back propagation artificial neural network extrapolation ability multistage oxidation

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Online detection of tobacco combustion components based on laser-induced breakdown spectroscopy

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SPECTROSCOPY LETTERS 2025年第5期58卷 453-464页

作者： Ji, Hang Ye, Yanpeng Gao, Wenhan Liu, Yuzhu Nanjing Univ Informat Sci & Technol State Key Lab Cultivat Base Atmospher Optoelect De Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Jiangsu Int Joint Lab Meteorol Photon & Optoelect Nanjing Peoples R China

The impacts of tobacco combustion on the atmosphere and human health are very enormous. In order to study the tobacco smoke in the local air, an online observation experimental system based on LIBS (laser-induced breakdown spectroscopy) was designed. This paper conducts an online analysis of the smoke generated by tobacco combustion. Moreover, it compares the ash composition resulting from tobacco combustion with the composition of the smoke. By comparing the spectral line distributions of smoke and ash, it can be observed that the types of elements in the ash are more than those in the smoke. The study found that the composition of ash is more complex than that of smoke. Comparing the spectral intensities of smoke and ash reveals that some elements in the smoke are volatile. Moreover, the CN radicals detected in the experiment were simulated by LIFBASE, and the vibrational and rotational temperatures of the CN radicals were obtained. Additionally, comparisons were made to detect whether substances emitted from burning different parts of tobacco contain heavy metal components. PCA (Principal Component Analysis) was employed to perform dimensionality reduction and analysis of smoke generated from burning different parts of tobacco and cigarettes. By comparing different types of machine learning models, the machine learning with the highest accuracy is selected for classification and tracking. Combined with error back propagation training of artificial neural networks, the classification and traceability models of the smoke from tobacco are performed. This was undertaken to develop a system capable of detecting and categorizing these substances. The online analysis of tobacco combustion components based on LIBS has the advantages of being able to perform online in situ detection. At the same time, it also verifies the feasibility of LIBS application in the tobacco industry.

关键词： back propagation artificial neural network laser-induced breakdown spectroscopy smoke principal component analysis tobacco

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Retrieval of spinach crop parameters by microwave remote sensing with back propagation artificial neural networks: A comparison of different transfer functions

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ADVANCES IN SPACE RESEARCH 2012年第3期50卷 363-370页

作者： Prasad, Rajendra Pandey, A. Singh, K. P. Singh, V. P. Mishra, R. K. Singh, D. BHU IT Dept Appl Phys Varanasi Uttar Pradesh India BHU IT Dept Elect Engn Varanasi Uttar Pradesh India Texas A&M Univ Dept Biol & Agr Engn College Stn TX 77843 USA BHU IT Dept Elect Engn Varanasi Uttar Pradesh India Indian Inst Technol Dept Elect & Comp Engn Roorkee 247667 Uttar Pradesh India

back propagation artificial natural network (BPANN) is a well known and widely used machine learning methodology in the field of remote sensing. In this paper an attempt is made to retrieve the spinach crop parameters like biomass, leaf area index, average plant height and soil moisture content by using the X-band scattering coefficients with BPANN at different growth stages of this crop. The maturity age of this crop was found to be 45 days from the date of sowing. After 45 days from the date of sowing, this crop was cut at a certain height for production. Then, it is a point of interest to investigate the microwave response of variation in production. Significant variations in all the crop parameters were observed after cutting the crop and consequently made the problem more critical. Our work confirms the utility of BPANN in handling such a non-linear data set. The BPANN is essentially a network of simple processing nodes arranged into different layers as input, hidden and the output. The input layer propagates components of a particular input vector after weighting these with synaptic weights to each node in the hidden layer. At each node, these weighted input vector components are added. Each hidden layer computes output corresponding to these weighted sum through a non-linear/linear function (e.g. LOGSIG, TANSIG and PURLIN). These functions are known as transfer functions. Thus, each of the hidden layer nodes compute output values, which become inputs to the nodes of the output layer. At nodes of output layer also a weighted sum of outputs of previous layer (hidden layer) are obtained and processed through a transfer function. Thus, the output layer nodes compute the network output for the particular input vector. In this paper, output nodes use linear transfer function. Different transfer functions e.g. TANSIG, LOGSIG and PURELIN were used and the performance of the ANN was optimized by changing the number of neurons in the hidden layers. The present analysis

关键词： back propagation artificial neural network Transfer function Scattering coefficient Biomass Leaf area index

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Egg freshness during storage: the effect of laying hen age and shelf life prediction using a novel hybrid modeling method

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JOURNAL OF FUTURE FOODS 2025年第6期5卷 614-627页

作者： Lu, Yifeng Li, Jing He, Zihao Chen, Linyun Tian, Huixin Xu, Chen Xu, Xinglian Han, Minyi Nanjing Agr Univ Key Lab Meat Proc & Qual Control Jiangsu Synerget Innovat Ctr Meat Prod & Proc Key Lab Anim Prod ProcMinist AgrMinist EducColl Nanjing 210095 Jiangsu Peoples R China Wens Foodstuff Grp Co Ltd Yunfu 527400 Peoples R China Univ Ghent Fac Biosci Engn Dept Food Technol Safety & Hlth Res Unit Food Microbiol & Food Preservat FMFP B-9000 Ghent Belgium

Changes in the quality of eggs during storage relate to their shelf life and economic value. Factors such as temperature, relative humidity, the operation of cleaning and microorganisms have been shown to play a role in the storage quality of eggs. This study thus aimed at investigating the effect of hen age on the storage quality of egg, and predicting egg shelf life using back propagation artificial neural network (BP-ANN) based models. Eggs laid by Jingfen No.1 (27 and 58 weeks of age) and Jingfen No.6 (26 and 57 weeks of age) hens were stored under ambient conditions and evaluated by physicochemical properties. It was found that the shelf life of the lower age group was significantly longer than that of the higher age group. A novel hybrid model combining BP-ANN, cuckoo search and adaptive boosting (CS-BP-AdaBoost) was proposed for predicting the remaining egg shelf life, with the input being Haugh unit, yolk index, air cell depth, albumen pH, hen age and breed. The tuning process of hyperparameters such as learning rate, training function, and transfer function was presented in detail. Results show that CS-BP-AdaBoost had satisfactory performance on the test set with root mean square error (RMSE) and coefficient of determination (R2) of 0.68 and 0.97, respectively. And it outperformed BP-ANN by reducing RMSE by 0.39 and improving R2 by 0.05. The model used solved the problem that the traditional BP-ANN tends to fall into local minima. The removal of hen age from the input parameters caused a decrease in prediction accuracy (R2 = 0.95, RMSE = 1.00), suggesting an important role of hen age in shelf life prediction. This study demonstrates the great potential of applying combinatorial modeling approaches to predict egg shelf life and the crucial impact of hen age on egg shelf life prediction. (c) 2025 The authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://cre

关键词： Egg Hen age Shelf life back propagation artificial neural network Hybrid model Hyperparameter tuning

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Development of Temperature Dependent Retention Models in Ion Chromatography by the Cascade Forward and back propagation artificial neural networks

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JOURNAL OF LIQUID CHROMATOGRAPHY & RELATED TECHNOLOGIES 2009年第19期32卷 2765-2778页

作者： Bolanca, Tomislav Stefanovic, Stefica Cerjan Ukic, Sime Rogosic, Marko Univ Zagreb Fac Chem Engn & Technol Zagreb 10000 Croatia

The most important part of the complex ion chromatography method development process is retention modeling. It tries to integrate the demands for high quality ion chromatography with the demands for low consumption of chemicals, fast analysis, and the short time of method development. This work compares the properties of the cascade forward and back propagation artificial neural network in the development of temperature dependent retention models. The retention times of bromate, bromide, nitrite, iodide, and perchlorate were modeled in relation with temperature of separation process, concentration of hydroxide eluent competing ion, and eluent flow rate. artificial neural networks were optimized in term of selecting the optimal training algorithm, optimal number of hidden layer neurons, activation function, and number of experiments needed for modeling procedure. The retention model based on cascade forward methodology exhibited superior predictive ability and, therefore, should be the method of first choice for the temperature dependent optimization in ion chromatography.

关键词： back propagation artificial neural network Cascade forward artificial neural network Ion chromatography Retention model Temperature of separation process

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The genetic algorithm based back propagation neural network for MMP prediction in CO₂-EOR process

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FUEL 2014年 126卷 202-212页

作者： Chen, Guangying Fu, Kaiyun Liang, Zhiwu Sema, Teerawat Li, Chen Tontiwachwuthikul, Paitoon Idem, Raphael Hunan Univ Coll Chem & Chem Engn Joint Int Ctr Capture & Storage iCCS CO2 Changsha 410082 Hunan Peoples R China Univ Regina Fac Engn & Appl Sci Regina SK S4S 0A2 Canada Univ Regina Fac Engn & Appl Sci Int Test Ctr Capture ITC CO2 Regina SK S4S 0A2 Canada

In this study, a genetic algorithm based back propagation artificial neural network model was developed and used to predict the minimum miscibility pressure, i.e. MMP, for both pure and impure CO2 injection cases. Ten parameters that affecting the MMP were chosen as input variables, while the MMP was selected as output parameter. These parameters were reservoir temperature-T-R, mole fraction of volatile oil components-x(vol), mole fraction of intermediate oil components-x(int), mole fraction of C-5-C-6 oil components-x(C5-C6), molecular weight of C7+ components-MWC7+, mole fraction of CO2 in solvent-x(CO2), mole fraction of C-1 in solvent-x(C1), mole fraction of N-2 in solvent-x(N2), mole fraction of H2S in solvent-x(H2S), and mole fraction of C-2-C-4 in solvent-x(C2-C4). The performance of the newly developed model was evaluated by calculating the deviations between the predicted and the target values, and was compared with four well known correlations in published literature. Through the comparison, it can be found that our new model outperformed those four correlations with the lowest average absolute relative error of 5.51% and mean square error of 7.01%. The influence degrees of each factor on MMP were also analyzed qualitatively and quantitatively by sensitivity analysis. It was found that x(int), x(C5-C6), and x(H2S) have positive effects on MMP, while T-R, x(vol), MWC7+, x(CO2), x(C1), and x(N2) have negative effects on MMP. In addition, the effect of x(C2-C4) on MMP can be neglected. Furthermore, the variations of MMP with the increase of each factor were also considered, and it can be found that the slopes of these curves are not constant all the time and change with the variations of the influence factors. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： CO2 enhanced oil recovery Minimum miscibility pressure back propagation artificial neural network Genetic algorithm

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