切割机在切割块石时的性能评估

Ataei, Mohammad; Mohammadi, Sadjad; Mikaeil, Reza

doi:10.1007/s11771-019-4144-1

Cited by 8 publications

(5 citation statements)

References 20 publications

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“…Although there are no specific relationships to determine these parameters, based on previous studies and experts' opinions, a range was determined for each of them, such as the number of grasshopper populations (5, 10, 15, 20, 30, and 40) and the number of iterations (10, 20, 40, 50, and 100). Then, the most appropriate ones were selected by a trial-and-error approach [107].…”

Section: Goa-svm Modelingmentioning

confidence: 99%

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Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Guido

Haghshenas

et al. 2022

Safety

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The evaluation of road safety is a critical issue having to be conducted for successful safety management in road transport systems, whereas safety management is considered in road transportation systems as a challenging task according to the dynamic of this issue and the presence of a large number of effective parameters on road safety. Therefore, the evaluation and analysis of important contributing factors affecting the number of vehicles involved in crashes play a key role in increasing the efficiency of road safety. For this purpose, in this research work, two machine learning algorithms, including the group method of data handling (GMDH)-type neural network and a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA), are employed. Hence, the number of vehicles involved in an accident is considered to be the output, and the seven factors affecting transport safety, including Daylight (DL), Weekday (W), Type of accident (TA), Location (L), Speed limit (SL), Average speed (AS), and Annual average daily traffic (AADT) of rural roads in Cosenza, southern Italy, are selected as the inputs. In this study, 564 data sets from rural areas were investigated, and the relevant, effective parameters were measured. In the next stage, several models were developed to investigate the parameters affecting the safety management of road transportation in rural areas. The results obtained demonstrated that the “Type of accident” has the highest level and “Location” has the lowest importance in the investigated rural area. Finally, although the results of both algorithms were the same, the GOA-SVM model showed a better degree of accuracy and robustness than the GMDH model.

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Section: Goa-svm Modelingmentioning

confidence: 99%

“…There is no specific method for determining the amount of k-fold, and it is determined according to the number of data and the opinion of experts. Hence, k-fold was considered to be equal to 3 in this study [107]. In addition, the three different types of kernel functions, including RBF, POL, and LIN, were used.…”

Section: Goa-svm Modelingmentioning

confidence: 99%

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Guido

Haghshenas

et al. 2022

Safety

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show abstract

“…Testing Accuracy (%) 1 0. After constructing different models and determining the accuracy performance of each model, all models were ranked based on a simple ranking method suggested by Zorlu et al, (2008) and Table 4 indicates the ranking results [103]. According to Table 4, all models were ranked and the seven developed models had the lowest rank with a rank equal to 31 among 25 developed models.…”

Section: Sp Mnl Mnnl Training Accuracy (%)mentioning

confidence: 99%

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Guido¹,

Haghshenas²,

Haghshenas³

et al. 2021

Preprint

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Evaluation of road safety is a critical issue having to be conducted for successful safety management in road transport systems, whereas safety management is considered in road transportation systems as a challenging task according to the dynamic of this issue and the presence of a large number of effective parameters on road safety. Therefore, evaluation and analysis of important contributing factors affecting the number of crashes play a key role in increasing the efficiency of road safety. For this purpose, in this research work, two machine learning algorithms including the group method of data handling (GMDH)-type neural network and a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA) are employed for evaluating the number of vehicles involved in the accident based on the seven factors affecting transport safety including the Daylight (DL), Weekday (W), Type of accident (TA), Location (L), Speed limit (SL), Average speed (AS) and Annual average daily traffic (AADT) of rural roads of Cosenza in southern Italy. In this study, 564 data sets of rural areas were investigated and relevant effective parameters were measured. In the next stage, several models were developed to investigate the parameters affecting the safety management of road transportation for rural areas. The results obtained demonstrated that "Average speed" has the highest level and "Weekday" has the lowest level of importance in the investigated rural area. Finally, although the results of both algorithms were the same, the GOA-SVM model showed a better degree of accuracy and robustness than the GMDH model.

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“…Akhyani et al [26] predicted the circular diamond saw wear by combining the fuzzy rock engineering system (Fuzzy RES) and genetic algorithm (GA). Ataei et al [27] evaluated the energy consumption and vibration of cutting machine by incorporating a combination of multi-layered perceptron ANN and genetic algorithm (GANN-BP) and the support vector regression method and Cuckoo optimization algorithm (COA-SVR). Hosseini et al [28] investigated the effect of cooling and lubricant fluid on the cutting performance (as a maximum electrical current) with ANN and the hybrid genetic algorithmartificial neural networks (hybrid GA-ANN).…”

Section: Introductionmentioning

confidence: 99%

Comparing the reliability of classical statistics and data mining techniques in unit energy prediction in circular stone cutting

Bayram

2023

Scientia Iranica

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Energy efficiency is one of the critical parameters affecting production in the natural stone sector, as it is in every industrial sector. High energy consumption negatively affects production costs, especially in stone cutting and surface treatments. Nowadays, it is crucial to predetermine energy consumption with reliable predictive techniques to produce with the lowest energy possible and sustain sectorial competition. This study conducted stone cutting tests with a computer-assisted circular cutting machine at different peripheral speeds (PSs) and advance rates (ARs). Unit energy (UE) consumptions were measured in stone cuttings.UE was evaluated regarding the circular stone cutting machine's (CSCM's) operating parameters, some stone characteristics, vibration amplitude (VA), and sound level (SL) measured during cutting. Classical statistics (CS) and data mining (DM) techniques were used to predict UE. 287 and 24 cutting data sets were selected as training and testing data for CS and DM techniques, respectively. These techniques were also compared and provided more significant and reliable results of DM techniques than CS. DM techniques predicted the UE with high correlation coefficients obtained in the range of R 2 =0.963 and 0.973. DM models for UE prediction before stone cutting have been introduced for stone processing researchers and those interested.

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切割机在切割块石时的性能评估

Cited by 8 publications

References 20 publications

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Comparing the reliability of classical statistics and data mining techniques in unit energy prediction in circular stone cutting

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