BPNN–QSTR Modeling to Develop Isosteres as Sulfur-Free, Anti-Wear Lubricant Additives

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Xinlei Gao ◽  
Zhan Wang ◽  
Tingting Wang ◽  
Ze Song ◽  
Kang Dai ◽  
...  
Keyword(s):  
Energy Dispersive Spectrometer ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Lubricant Additives ◽  
Thiazole Ring ◽  
Wear Performance ◽  
Base Oil ◽  
Wide Range ◽  
Electron Microscope Analysis ◽  
Branched Chain

The principle of isosterism was employed to design low- or zero-sulfur anti-wear lubricant additives. Thiobenzothiazole compounds and 2-benzothiazole-S-carboxylic acid esters were employed as templates. Sulfur in the thiazole ring or in the branched chain was exchanged with oxygen, CH2, or an NH group. Similarly, the template's benzimidazole ring was replaced with a quinazolinone group. Quantitative structure tribo-ability relationship (QSTR) models by back propagation neural network (BPNN) method were used to study correlations between additive structures and their anti-wear performance. The features of rubbing pairs with different additives were identified by energy dispersive spectrometer-scanning electron microscope analysis. A wide range of samples showed that sulfur substitution in additive molecules was found to be reasonable and feasible. Combined effects of the anti-wear additive and the base oil were able to improve anti-wear performance.

scholarly journals Automatic Flood Detection in Multi-Temporal Sentinel-1 Synthetic Aperture Radar Imagery Using ANN Algorithms

2020 ◽  
Vol 15 (3) ◽  
Author(s):  
Anusha Nallapareddy ◽  
Bharathi Balakrishnan
Keyword(s):  
Neural Network ◽  
Synthetic Aperture Radar ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Synthetic Aperture ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Wide Range ◽  
Multi Temporal ◽  
Aperture Radar

Natural Calamities like floods cause wide-range of damage to human existence as well as substructures. For automatic extraction of flooded area in multi-temporal satellite imagery acquired by Sentinel-1 Synthetic Aperture Radar (SAR), this paper presents two neural network algorithms: Feed-Forward Neural Network, Cascade-forward back-propagation neural network. This work currently focuses on Uttar Pradesh in India, which was affected due to floods during August 2017. The two models are trained, validated and tested using MATLAB R2018b. The models are first trained using a variety of input data until the percentage of error with respect to water body detection is within an acceptable error limit. These models are then used to extract the water features effectively and to detect the flooded regions. Finally, flood area is calculated in sq. km in during flood and post-flood imagery using these algorithms. The results thus obtained are compared with that from the binary thresholding method from previous studies. The results show that the Feed- Forward Neural Network gives better accuracy than the Cascade-forward back propagation neural network. Based on the promising results, the proposed method may assist in our understanding of the role of machine learning in disaster detection.

scholarly journals Performance of Zinc-Difattyalkyldithyocarbamate as Anti-Friction/Anti-Wear Lubricant Additives

2014 ◽  
Vol 14 (1) ◽  
pp. 78-84
Author(s):  
Komar Sutriah ◽  
Zainal Alim Mas’ud ◽  
Tun Tedja Irawadi ◽  
Mohammad Khotib
Keyword(s):  
Vegetable Oil ◽  
Wear Test ◽  
Lubricant Additives ◽  
Extreme Pressure ◽  
Antioxidant Additive ◽  
Wear Performance ◽  
Base Oil ◽  
Index Value ◽  
Wear Index

Dithyocarbamate is an organosulphur compound that has long been known and widely applied in various fields, including in agriculture and industry. Several variants of synthesized vegetable oil-based Zinc-difattyalkyldithyocarbamate were tested its anti-friction/anti-wear performance on four ball machine using the method of ASTM-D2783. Anti-friction/anti-wear test to six of additive variants of Zinc-difattyalkyldithyocarbamate at doses of 1.2% indicated that all variants of the product has welding point value higher than the lube base oil lubricant HVI 60, and from US Steel 136 standard for Hydraulic lubricants, but only two additive variants of Zinc-bis(lauryl palmityl)dithyocarbamate and Zinc-bis(lauryl oleyl)dithyocarbamate which has a larger load wear index value than the standard, and meet the criteria as an additive extreme pressure according to US steel 136 standard. Zinc-bis(lauryl palmityl)dithyocarbamate is an additive variant with the best performance, meet bi-functional lubricant additives criteria, as anti-friction/anti-wear and antioxidant additive.

scholarly journals Support Vector Machine and Back Propagation Neural Network Approach for Text Classification

2017 ◽  
Vol 3 (2) ◽  
pp. 869
Author(s):  
Yaqeen Saad ◽  
Khaled Shaker
Keyword(s):  
Support Vector Machine ◽  
Text Classification ◽  
Text Categorization ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Features Selection ◽  
Neural Network Approach ◽  
Wide Range ◽  
Important Approach

Text classification is the process of inserting text into one or additional categories. Text categorization has many of significant application, Mostly in the field of organization, and for browsing within great groups of document. It is sometimes  completed by means of  "machine learning.". Since the system is built based on a wide range of document features."Feature selection." is an important approach within this process, since there are typically several thousand possible features terms. Within text categorization, The target goal of features selection is to improve the efficiency of procedures and reliability of classification by deleting features that have no relevance and non-essential terms. While keeping terms which hold enough data that facilitate with the classification task. The target goal of this work is to increase the efficient  text categorization models. Within  the "text mining" algorithms, a document is appearing as "vector" whose dimension is that the range of special keywords in it, which can be very large. Classic document categorization may be computationally costly. Therefore, feature extraction through the singular valued decomposition is employed for decrease the dimensionality of the documents, we are applying classification  algorithms based on "Back propagation" and "Support Vector Machine." methodology. before the classification we applied "Principle Component Analysis." technique  in order to improve  the result accuracy . We then compared  the performance  of these two algorithms via computing  standard precision and recall for the documentscollection.                                                                                                           

scholarly journals Development of Back-Propagation Neural Network for Transient Stability Assessment of Power Systems

2019 ◽  
Vol 8 (4) ◽  
pp. 11637-11641
Keyword(s):  
Neural Network ◽  
Power System ◽  
Transient Stability ◽  
Research Work ◽  
Back Propagation ◽  
Stability Index ◽  
Back Propagation Neural Network ◽  
Stability Assessment ◽  
Target Feature ◽  
Wide Range

Transient Stability Assessment (TSA) is aspect of the power system dynamic stability assessment, which includes measuring the capacity of the system to stay synchronized under extreme disturbances. This research work shows the transient stability status of the power system following a major disturbance, such as a faults, line switchings, generator voltages. It can be predicted early based on response trajectories of rotor angle. This early prediction of transient stability is achieved by training a Back Propagation Neural Network (BPNN) taking trajectory of rotor angles as training features. Transient stability index (TSI) proposed in [4] is utilized as a target feature. The proposed methodology is tested with wide range of fault data collected from simulated IEEE 39-Benchmark system. The simulation results shows, utilization of BPNN for transient stability prediction resulted in better performance when compared to Radial Basis Neural Network (RBFNN) [4]..

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

Classroom Lighting Energy-Saving Control System Based on Machine Vision Technology

2018 ◽  
pp. 143-149 ◽  
Author(s):  
Ruijie CHENG
Keyword(s):  
Control System ◽  
Machine Vision ◽  
Energy Saving ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Lighting Control ◽  
Lighting Energy ◽  
Neural Network Algorithm ◽  
Energy Saving Control ◽  
Model Algorithm

In order to further improve the energy efficiency of classroom lighting, a classroom lighting energy saving control system based on machine vision technology is proposed. Firstly, according to the characteristics of machine vision design technology, a quantum image storage model algorithm is proposed, and the Back Propagation neural network algorithm is used to analyze the technology, and a multi­feedback model for energy­saving control of classroom lighting is constructed. Finally, the algorithm and lighting model are simulated. The test results show that the design of this paper can achieve the optimization of the classroom lighting control system, different number of signals can comprehensively control the light and dark degree of the classroom lights, reduce the waste of resources of classroom lighting, and achieve the purpose of energy saving and emission reduction. Technology is worth further popularizing in practice.

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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