scholarly journals Retrieval of Chemical Oxygen Demand through Modified Capsule Network Based on Hyperspectral Data

2019 ◽  
Vol 9 (21) ◽  
pp. 4620 ◽  
Author(s):  
Deng ◽  
Zhang ◽  
Cen
Keyword(s):  
Chemical Oxygen Demand ◽  
Back Propagation ◽  
Oxygen Demand ◽  
Back Propagation Neural Network ◽  
Hyperspectral Data ◽  
Bp Algorithm ◽  
A Value ◽  
Regression Relationship ◽  
Network Method ◽  
Regression Problems

This study focuses on the retrieval of chemical oxygen demand (COD) in the Baiyangdian area in North China, using a modified capsule network. Herein, the capsule model was modified to analyze the regression relationship between 1-D hyperspectral data and COD values. The results indicate there is a statistically significant correlation between COD and the hyperspectral data. The accuracy of the capsule network was compared with the results obtained from using a traditional back-propagation neural network (BP) method. The capsule network achieved superior accuracy with fewer iterations, compared with the BP algorithm. An R2 value of 0.78 was obtained against measured COD values retrieved using the capsule network method, compared with a value of 0.42 for the BP algorithm retrievals. This suggests the capsule network method has great potential to solve regression problems in the field of remote sensing.

scholarly journals Analisa Kelayakan Pemberian Kredit Mobil Dengan Menggunakan Neural Network Backpropagation

2019 ◽  
Author(s):  
Amrin Amrin
Keyword(s):  
Neural Network ◽  
Roc Curve ◽  
Confusion Matrix ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Test Results ◽  
Testing Methods ◽  
A Value ◽  
Network Method ◽  
Auc Value

Problems are often encountered in the provision of credit is to determine lendingdecisions to someone, while other issues are not all credit payments can run well.Among the causes are errors of judgment in making credit decisions. In this studywill be used back propagation neural network method to analyze the feasibility ofproviding car loans. From the test results to measure the performance of themethod is to use testing methods Confusion Matrix and ROC curve, it is knownthat the method ofback propagation neural network has a value of89% accuracyand AUC value of 0.831. This shows that the model produced, including theclassification is quite good because it has the AUC values between 0.8-0.9.

scholarly journals Classification of ETM+ Remote Sensing Image Based on Hybrid Algorithm of Genetic Algorithm and Back Propagation Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Haisheng Song ◽  
Ruisong Xu ◽  
Yueliang Ma ◽  
Gaofei Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Local Minimum ◽  
Back Propagation ◽  
Remote Sensing Image ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Improved Bp Algorithm

The back propagation neural network (BPNN) algorithm can be used as a supervised classification in the processing of remote sensing image classification. But its defects are obvious: falling into the local minimum value easily, slow convergence speed, and being difficult to determine intermediate hidden layer nodes. Genetic algorithm (GA) has the advantages of global optimization and being not easy to fall into local minimum value, but it has the disadvantage of poor local searching capability. This paper uses GA to generate the initial structure of BPNN. Then, the stable, efficient, and fast BP classification network is gotten through making fine adjustments on the improved BP algorithm. Finally, we use the hybrid algorithm to execute classification on remote sensing image and compare it with the improved BP algorithm and traditional maximum likelihood classification (MLC) algorithm. Results of experiments show that the hybrid algorithm outperforms improved BP algorithm and MLC algorithm.

scholarly journals Integrating a Hybrid Back Propagation Neural Network and Particle Swarm Optimization for Estimating Soil Heavy Metal Contents Using Hyperspectral Data

2019 ◽  
Vol 11 (2) ◽  
pp. 419 ◽  
Author(s):  
Piao Liu ◽  
Zhenhua Liu ◽  
Yueming Hu ◽  
Zhou Shi ◽  
Yuchun Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Heavy Metals ◽  
Heavy Metal ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Hyperspectral Data ◽  
Estimation Accuracy ◽  
Soil Heavy Metals ◽  
Soil Heavy Metal ◽  
Metal Contents

Soil heavy metals affect human life and the environment, and thus, it is very necessary to monitor their contents. Substantial research has been conducted to estimate and map soil heavy metals in large areas using hyperspectral data and machine learning methods (such as neural network), however, lower estimation accuracy is often obtained. In order to improve the estimation accuracy, in this study, a back propagation neural network (BPNN) was combined with the particle swarm optimization (PSO), which led to an integrated PSO-BPNN method used to estimate the contents of soil heavy metals: Cd, Hg, and As. This study was conducted in Guangdong, China, based on the soil heavy metal contents and hyperspectral data collected from 90 soil samples. The prediction accuracies from BPNN and PSO-BPNN were compared using field observations. The results showed that, 1) the sample averages of Cd, Hg, and As were 0.174 mg/kg, 0.132 mg/kg, and 9.761 mg/kg, respectively, with the corresponding maximum values of 0.570 mg/kg, 0.310 mg/kg, and 68.600 mg/kg being higher than the environment baseline values; 2) the transformed and combined spectral variables had higher correlations with the contents of the soil heavy metals than the original spectral data; 3) PSO-BPNN significantly improved the estimation accuracy of the soil heavy metal contents, with the decrease in the mean relative error (MRE) and relative root mean square error (RRMSE) by 68% to 71%, and 64% to 67%, respectively. This indicated that the PSO-BPNN provided great potential to estimate the soil heavy metal contents; and 4) with the PSO-BPNN, the Cd content could also be mapped using HuanJing-1A Hyperspectral Imager (HSI) data with a RRMSE value of 36%, implying that the PSO-BPNN method could be utilized to map the heavy metal content in soil, using both field spectral data and hyperspectral imagery for the large area.

scholarly journals Hyperspectral Inversion of Phragmites Communis Carbon, Nitrogen, and Phosphorus Stoichiometry Using Three Models

2020 ◽  
Vol 12 (12) ◽  
pp. 1998
Author(s):  
Lijuan Cui ◽  
Zhiguo Dou ◽  
Zhijun Liu ◽  
Xueyan Zuo ◽  
Yinru Lei ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Model Error ◽  
Scientific Basis ◽  
Hyperspectral Data ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Nitrogen And Phosphorus ◽  
Accuracy And Stability

Studying the stoichiometric characteristics of plant C, N, and P is an effective way of understanding plant survival and adaptation strategies. In this study, 60 fixed plots and 120 random plots were set up in a reed-swamp wetland, and the canopy spectral data were collected in order to analyze the stoichiometric characteristics of C, N, and P across all four seasons. Three machine models (random forest, RF; support vector machine, SVM; and back propagation neural network, BPNN) were used to study the stoichiometric characteristics of these elements via hyperspectral inversion. The results showed significant differences in these characteristics across seasons. The RF model had the highest prediction accuracy concerning the stoichiometric properties of C, N, and P. The R2 of the four-season models was greater than 0.88, 0.95, 0.97, and 0.92, respectively. According to the root mean square error (RMSE) results, the model error of total C (TC) inversion is the smallest, and that of C/N inversion is the largest. The SVM yielded poor predictive results for the stoichiometric properties of C, N, and P. The R2 of the four-season models was greater than 0.82, 0.81, 0.81, and 0.70, respectively. According to RMSE results, the model error of TC inversion is the smallest, and that of C/P inversion is the largest. The BPNN yielded high stoichiometric prediction accuracy. The R2 of the four-season models was greater than 0.87, 0.96, 0.84, and 0.90, respectively. According to RMSE results, the model error of TC inversion is the smallest, and that of C/P inversion is the largest. The accuracy and stability of the results were verified by comprehensive analysis. The RF model showed the greatest prediction stability, followed by the BPNN and then the SVM models. The results indicate that the accuracy and stability of the RF model were the highest. Hyperspectral data can be used to accurately invert the stoichiometric characteristics of C, N, and P in wetland plants. It provides a scientific basis for the long-term dynamic monitoring of plant stoichiometry through hyperspectral data in the future.

Optimization of the Hot Pressing Parameters of Nanocomposite Ceramic Tool and Die Materials: I. with BP Neural Network Method

2010 ◽  
Vol 154-155 ◽  
pp. 1114-1118
Author(s):  
Jing Jie Zhang ◽  
Chong Hai Xu ◽  
Ming Dong Yi ◽  
Hui Fa Zhang ◽  
Xing Hai Wang
Keyword(s):  
Neural Network ◽  
Hot Pressing ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Ceramic Tool ◽  
Network Method ◽  
Die Materials ◽  
Die Material ◽  
Set Up ◽  
The Relationship

In this paper, back propagation neural network was used in the optimum design of the hot pressing parameters of an advanced ZrO2/TiB2/Al2O3 nanocomposite ceramic tool and die material. The BP algorithm could set up the relationship well between the hot pressing parameters and mechanical property of nanocomposite ceramic tool and die materials. After analyzed the predicted results, the best predicted results were the sintering temperature was 1420°C and the holding time was 60min. Under these hot pressing parameters, the best flexural strength and the best fracture toughness of the material could be obtained.

scholarly journals An investigation on treatment of tannery wastewater by electrocoagulation

2013 ◽  
Vol 11 (4) ◽  
pp. 546-555 ◽  
Keyword(s):  
Chemical Oxygen Demand ◽  
Suspended Solids ◽  
Oxygen Demand ◽  
Electrical Current ◽  
The Other ◽  
Tannery Wastewater ◽  
Contact Duration ◽  
A Value ◽  
Removal Efficiencies ◽  
Electro Coagulation

Treatability by the electro-coagulation (EC) and electro-Fenton (EF) methods have been applied to the tannery wastewater from an organized industrial region consisting mostly of tannery plants and compared with each other in this study. Iron plates were used as the anode and cathode. Electrical current was applied at a value of 33.3 mA m-2 for all processes in order to determine the electricity consumptions for chemical oxygen demand (COD) and sulfide removal. The optimal contact duration for each process was discovered at the end of the first five minutes. During the EC process, the removal efficiencies of COD and sulfide were 46% and 90%, respectively. Electricity consumptions were also obtained as 1.8 kWh kg- 1 COD removed and 27.7 kWh kg-1 sulfide removed. During the EF process, on the other hand, the removal efficiencies of COD and sulfide parameters were 54% and 85%, respectively, and electricity consumptions were also obtained as 1.5 kWh kg-1 COD removed and 8.3 kWh kg-1 sulfide removed. Furthermore, the removal efficiencies of total Chrome and suspended solids were determined to be 97% and 70%, respectively.

scholarly journals Neural network method for control valve cost estimation on the EPC project bidding

2018 ◽  
Vol 49 ◽  
pp. 02004 ◽  
Author(s):  
Gilang Almaghribi Sarkara Putra ◽  
Rendra Agus Triyono
Keyword(s):  
Neural Network ◽  
Cost Estimation ◽  
Back Propagation ◽  
Control Valve ◽  
Back Propagation Neural Network ◽  
Neural Network Method ◽  
Price Quotation ◽  
Network Method ◽  
Alternative Choice ◽  
The Cost

Cost estimation on the bidding phase is a crucial stage that determines the success of the Engineering, Procurement and Construction (EPC) project. If the cost offered to the client is too high then it could not compete with the other bidder, but if the cost offered are too low it can reduce profit margins and result in losses for the EPC companies. This paper describe the use of Back Propagation Neural Network method to help determine cost estimation. This method is applied specifically to determine control valve cost estimation on the bidding phase so that the retrieved costs will be accurate. When there is no technical and price quotation from vendors as well as the narrowness of the bidding processing time, this method can be an alternative choice to determine the price based on previous vendor quotation. In the future, this method could be developed and applied for other instrumentation equipment such as transmitter, switch, analyzer, control system and others to achieve total cost estimation of instrumentation equipment in EPC bidding proposal.

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