scholarly journals Influence of Meteorological Parameters on Explosive Charge and Stemming Length Predictions in Clay Soil during Blasting Using Artificial Neural Networks

2021 ◽  
Vol 11 (16) ◽  
pp. 7317
Author(s):  
Karlo Leskovar ◽  
Denis Težak ◽  
Josip Mesec ◽  
Ranko Biondić
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Temperature ◽  
Explosive Charge ◽  
Meteorological Parameters ◽  
Subject Area ◽  
Process Time ◽  
Geotechnical Characteristics ◽  
The Subject ◽  
Artificial Neural

The influence of the meteorological parameters (precipitation and air temperature) during blasting in clay has a direct impact on the success of blasting. In the case of large amounts of precipitation (rain and snow) recorded in the subject area, blasting in clays cannot be carried out due to the grain of the clay and the inability to access the subject area. Moreover, the air temperature in the subject area affects the blasting performance. The most ideal temperature for blasting in clays is between 15 and 25 °C because then the clay has the best geotechnical characteristics. The research was conducted on the exploitation field Cukavec II, which is located near the city of Varaždin in the Republic of Croatia. Amount of precipitation and air temperature were considered to obtain the best blasting effect. Influence of meteorological parameters on the amount of the explosive charge and stemming length when blasting in clays was demonstrated via models based on Artificial Neural Networks (ANN). The ANN model network consists of a Long Short-term Memory (LSTM) part to process time dependent meteorological data, and fully connected layers to process blasting input data. Two types of explosive charges were compared, Pakaex and Permonex V19.

Evaluation and Modeling of Passenger Vehicle Celeration Performance Based on Artificial Neural Networks

2015 ◽  
Vol 744-746 ◽  
pp. 1938-1942
Author(s):  
Yi He ◽  
Duan Feng Chu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Comfort Level ◽  
Subjective Rating ◽  
Acceleration Data ◽  
Acceleration And Deceleration ◽  
The Subject ◽  
Artificial Neural ◽  
The Relationship ◽  
Subjective Sensation

As the siginificant factors influence passengers comfort, the vehicle celebration performance may easy to cause accidents, such as hard acceleration and deceleration performance. In order to find the relationship between passengers comfort and celebration performance, 35 passengers and three professional drivers were recruited in the field experiment. The passengers’ comfort feelings were analysed by subject questionnaires, the acceleration and deceleration data were received by CAN bus.The Artificial Neural Networks (ANNs) model was elaborated to estimate and predict the passengers comfort level of driver unsafe acceleration behavior situations. Therefore, the subject views of the passengers could be compared to object acceleration data. An ANN is applied to interconnect output data (subjective rating) with input data (objective parameters). Finally, it is found the investigatioin have demonstrated that the objective values are efficiently correlated with the subjective sensation. Thus, the presented approach can be effectively applied to support the drive train development of bus.

scholarly journals Dewpoint Temperature Prediction Using Artificial Neural Networks

2008 ◽  
Vol 47 (6) ◽  
pp. 1757-1769 ◽  
Author(s):  
D. B. Shank ◽  
G. Hoogenboom ◽  
R. W. McClendon
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Temperature ◽  
Heat Waves ◽  
Weather Data ◽  
Temperature Prediction ◽  
Related Data ◽  
Dewpoint Temperature ◽  
Artificial Neural ◽  
Selection Of

Abstract Dewpoint temperature, the temperature at which water vapor in the air will condense into liquid, can be useful in estimating frost, fog, snow, dew, evapotranspiration, and other meteorological variables. The goal of this study was to use artificial neural networks (ANNs) to predict dewpoint temperature from 1 to 12 h ahead using prior weather data as inputs. This study explores using three-layer backpropagation ANNs and weather data combined for three years from 20 locations in Georgia, United States, to develop general models for dewpoint temperature prediction anywhere within Georgia. Specific objectives included the selection of the important weather-related inputs, the setting of ANN parameters, and the selection of the duration of prior input data. An iterative search found that, in addition to dewpoint temperature, important weather-related ANN inputs included relative humidity, solar radiation, air temperature, wind speed, and vapor pressure. Experiments also showed that the best models included 60 nodes in the ANN hidden layer, a ±0.15 initial range for the ANN weights, a 0.35 ANN learning rate, and a duration of prior weather-related data used as inputs ranging from 6 to 30 h based on the lead time. The evaluation of the final models with weather data from 20 separate locations and for a different year showed that the 1-, 4-, 8-, and 12-h predictions had mean absolute errors (MAEs) of 0.550°, 1.234°, 1.799°, and 2.280°C, respectively. These final models predicted dewpoint temperature adequately using previously unseen weather data, including difficult freeze and heat stress extremes. These predictions are useful for decisions in agriculture because dewpoint temperature along with air temperature affects the intensity of freezes and heat waves, which can damage crops, equipment, and structures and can cause injury or death to animals and humans.

scholarly journals Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

2017 ◽  
Author(s):  
Sara C. Pryor ◽  
Ryan C. Sullivan ◽  
Justin T. Schoof
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soil Moisture ◽  
Air Temperature ◽  
Energy Content ◽  
Daily Maximum ◽  
Synoptic Scale ◽  
Near Surface ◽  
Artificial Neural ◽  
Static Energy

Abstract. The static energy content of the atmosphere is increasing at the global scale, but exhibits important sub-global and sub-regional scales of variability and is a useful parameter for integrating the net effect of changes in the partitioning of energy at the surface and for improving understanding of the causes of so-called warming-holes (i.e. locations with decreasing daily maximum air temperatures (T) or increasing trends of lower magnitude than the global mean). Further, measures of the static energy content (herein the equivalent potential temperature, θe) are more strongly linked to excess human mortality and morbidity than air temperature alone, and have great relevance in understanding causes of past heat-related excess mortality and making projections of possible future events that are likely to be associated with negative human health and economic consequences. A new non-linear statistical model for summertime daily maximum and minimum θe is developed and used to advance understanding of drivers of historical change and variability over the eastern USA. It is shown that soil moisture (SM) is particularly important in determining the magnitude of θe over regions that have previously been identified as exhibiting warming holes confirming the key importance of SM in dictating the partitioning of net radiation into sensible and latent heat and dictating trends in near-surface T and θe. Consistent with our a priori expectations, models built using Artificial Neural Networks (ANN) out-perform linear models that do not permit interaction of the predictor variables (global T, synoptic-scale meteorological conditions and SM). This is particularly marked in regions with high variability in min- and max-θe, where more complex models built using ANN with multiple hidden layers are better able to capture the day-to-day variability in θe and the occurrence of extreme max-θe. Over the entire domain the ANN with 3 hidden layers exhibits high accuracy in predicting max-θe > 347 K. The median hit rate for max-θe > 347 K is > 0.60, while the median false alarm rate ≈ 0.08.

Winter surface air temperature prediction over Japan using artificial neural networks

2021 ◽  
Author(s):  
J. V. Ratnam ◽  
Masami Nonaka ◽  
Swadhin K. Behera
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Correlation Coefficients ◽  
Ann Model ◽  
Anomaly Correlation ◽  
The North ◽  
Skill Scores ◽  
Artificial Neural

AbstractThe machine learning technique, namely Artificial Neural Networks (ANN), is used to predict the surface air temperature (SAT) anomalies over Japan in the winter months of December, January and February for the period 1949/50 to 2019/20. The predictions are made for the four regions Hokkaido, North, Central and West of Japan. The inputs to the ANN model are derived from the anomaly correlation coefficients among the SAT anomalies over the regions of Japan and the global SAT and sea surface temperature anomalies. The results are validated using anomaly correlation coefficient (ACC) skill scores with the observation. It is found that the ANN predictions over Hokkaido have higher ACC skill scores compared to the ACC scores over the other three regions. The ANN predicted SAT anomalies are compared with that of ensemble mean of 8 of the North American Multi-Model Ensemble (NMME) models besides comparing them with the persistent anomalies. The ANN predictions over all the four regions have higher ACC skill scores compared to the NMME model skill scores in the common period of 1982/83 to 2018/19. The ANN predicted SAT anomalies also have higher Hit rate and lower False alarm rate compared to the NMME predicted SAT anomalies. All these indicate that the ANN model is a promising tool for predicting the winter SAT anomalies over Japan.

Wavelet-Based Recognition of Handwritten Characters Using Artificial Neural Network

Biometrics ◽  
2017 ◽  
pp. 1043-1060
Author(s):  
D. K. Patel ◽  
T. Som ◽  
M. K. Singh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Character Recognition ◽  
Process Time ◽  
Discrete Wavelet ◽  
Training Time ◽  
Handwritten Character ◽  
Artificial Neural

In the present chapter, the widely common problem of handwritten character recognition has been tackled with multiresolution technique using discrete wavelet transform and artificial neural networks. The technique has been tested and found to be more accurate and economic in respect of the recognition process time of the system. Features of the handwritten character images are extracted by discrete wavelet transform used with appropriate level of multiresolution technique, then the artificial neural networks is trained by extracted features. The unknown input handwritten character images are recognized by trained artificial neural networks system. The proposed method provides good recognition accuracy for handwritten characters with less training time, less no. of samples and less no. of iterations.

scholarly journals Integrated approach to the planning of energy consumption by non-traction railway consumers

2018 ◽  
Vol 239 ◽  
pp. 01056 ◽  
Author(s):  
Alexander Galkin ◽  
Alexey Kovalev ◽  
Timur Shayuhov
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Correlation Analysis ◽  
Power Consumption ◽  
Systematic Approach ◽  
Integrated Approach ◽  
Expert Assessments ◽  
The Subject ◽  
Artificial Neural ◽  
High Degree

Non-traction railway load consumes a significant amount of electricity. Russian Railways supplies electricity not only to its structural units but also to other consumers. Private houses, individual entrepreneurs and small production facilities located near the railway are powered by the RZD. It is very important for the company to plan consumption for non- traction needs. The subject of the study in the paper is a systematic approach to the planning of power consumption using the mathematical apparatus of artificial neural networks, correlation analysis and the method of expert assessments. The method of expert assessments allows identifying the most significant factors that affect the consumption of electricity. It is necessary to attract experienced professionals in the field of electricity, working at a particular enterprise. They are able to determine with a high degree of accuracy those factors that have a significant impact on the consumption of the organization. Correlation analysis allows you to mathematically check the degree of influence of a single factor on the resulting value. The apparatus of artificial neural networks allows building a forecast of power consumption, taking into account the influence of external factors. The authors propose to use a systematic approach to the planning of power consumption. It is necessary to combine three tools: the method of expert assessments, correlation analysis and artificial neural networks. The combination of these tools will improve the accuracy of power consumption planning and, as a result, will lead to increased economic efficiency due to the rational consumption of electricity.

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