Predicting the Emissive Characteristics of an IC Engine Using DNN

Pravin, M. C.; Mukilan, M.; Prakash, G. Vishnu; Nithish, P.; Kanna, B. Monish; Logesh, E.

doi:10.1088/1757-899x/995/1/012010

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Substituting the above table data into formula (7), the integrated unit price adjustment factor of solid brick wall can be calculated as 0.85. Since the integrated unit price output of the solid brick wall is $468, the cost price is…”

Section: Cost Price Of Branch Subitem Projectmentioning

confidence: 99%

“…erefore, cost forecasting for construction enterprises is an inevitable trend in the current construction industry and is an extremely important task. However, most of the engineering cost forecasting methods on the market at present have problems such as unscienti c methods and uncritical processes, which cannot e ectively improve the competitiveness of enterprises and are not conducive to the long-term development of construction enterprises [1][2][3][4][5][6][7]. In this regard, Xu et al proposed to use BP neural network to predict the project cost of substation and introduced sparrow search algorithm (SSA) to optimize the parameters of traditional BP algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Engineering Cost Prediction Model Based on DNN

Bingxin

Xin

Zhang

2022

Scientific Programming

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A DNN-based cost prediction method is proposed for the difficult problem of cost calculation in engineering cost accounting, combined with deep neural networks. Firstly, we introduce the basic information of artificial neural network and select the DNN structure to calculate the engineering cost price according to the characteristics of the data related to engineering cost price. Secondly, the DNN-based engineering cost price prediction model is constructed, and the two types of index systems, engineering characteristics and list item characteristics, are used as model inputs. In addition, the total quotation and each subitem engineering quotation and tax are used as model outputs by analyzing previous relevant studies. Based on this, simulation experiments are conducted on the DNN-based engineering cost price prediction model, and it is concluded from the training model that the DNN model has a better prediction effect. Among them, the relative error of total price forecast by DNN is 4.203%, and the relative error of integrated unit prices V1 and V2 is 2.98% and 4.52%, respectively, with small relative error. Finally, by reasonably adjusting the integrated unit price, the cost price of the integrated unit price and the cost price of the total offer can be calculated.

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Section: Cost Price Of Branch Subitem Projectmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Engineering Cost Prediction Model Based on DNN

Bingxin

Xin

Zhang

2022

Scientific Programming

View full text Add to dashboard Cite

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Prediction of Emission Characteristics of Spark Ignition (S.I.) Engines with Premium Level Gasoline-Ethanol-Alkane Blends Using Machine Learning

Kumbhar,

Khot,

Jujare

et al. 2024

Communications in Computer and Information Science

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Modeling and prediction for diesel performance based on deep neural network combined with virtual sample

Zheng

Zhou

Kang

et al. 2021

Sci Rep

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The performance models are the critical step for condition monitoring and fault diagnosis of diesel engines, and are an important bridge to describe the link between input parameters and targets. Large-scale experimental methods with higher economic costs are often adopted to construct accurate performance models. To ensure the accuracy of the model and reduce the cost of the test, a novel method for modeling the performances of marine diesel engine is proposed based on deep neural network method coupled with virtual sample generation technology. Firstly, according to the practical experience, the four parameters including speed, power, lubricating oil temperature and pressure are selected as the input factors for establishing the performance models. Besides, brake specific fuel consumption, vibration and noise are adopted to assess the status of marine diesel engine. Secondly, small sample experiments for diesel engine are performed under multiple working conditions. Moreover, the experimental sample data are diffused for obtaining valid extended data based on virtual sample generation technology. Then, the performance models are established using the deep neural network method, in which the diffusion data set is adopted to reduce the cost of testing. Finally, the accuracy of the developed model is verified through experiment, and the parametric effects on performances are discussed. The results indicate that the overall prediction accuracy is more than 93%. Moreover, power is the key factor affecting brake specific fuel consumption with a weighting of 30% of the four input factors. While speed is the key factor affecting vibration and noise with a weighting of 30% and 30.5%, respectively.

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Predicting the Emissive Characteristics of an IC Engine Using DNN

Cited by 4 publications

References 7 publications

Engineering Cost Prediction Model Based on DNN

Engineering Cost Prediction Model Based on DNN

Prediction of Emission Characteristics of Spark Ignition (S.I.) Engines with Premium Level Gasoline-Ethanol-Alkane Blends Using Machine Learning

Modeling and prediction for diesel performance based on deep neural network combined with virtual sample

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