scholarly journals Application of Soft Computing Techniques for the Analysis of Tractive Properties of a Low-Power Agricultural Tractor under Various Soil Conditions

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Katarzyna Pentoś ◽  
Krzysztof Pieczarka ◽  
Krzysztof Lejman
Keyword(s):  
Neural Networks ◽  
Soil Moisture ◽  
Low Power ◽  
Soft Computing ◽  
Soil Compaction ◽  
Traction Force ◽  
Soil Conditions ◽  
Vertical Load ◽  
Soft Computing Techniques ◽  
Tractive Efficiency

Considering the fuel consumption and soil compaction, optimization of the performance of tractors is crucial for modern agricultural practices. The tractive performance is influenced by many factors, making it difficult to be modeled. In this work, the traction force and tractive efficiency of a low-power tractor, as affected by soil coefficient, vertical load, horizontal deformation, soil compaction, and soil moisture, were studied. The optimal work of a tractor is a compromise between the maximum traction force and the maximum tractive efficiency. Optimizing these factors is complex and requires accurate models. To this end, the performances of soft computing approaches, including neural networks, genetic algorithms, and adaptive network fuzzy inference system, were evaluated. The optimal performance was realized by neural networks trained by backpropagation as well as backpropagation combined with a genetic algorithm, with a coefficient of determination of 0.955 for the traction force and 0.954 for the tractive efficiency. Based on models with the best accuracy, a sensitivity analysis was performed. The results showed that the traction performance is mainly influenced by the soil type; nevertheless, the vertical load and soil moisture also exhibited a relatively strong influence.

REGRESSION ANALYSES OF GRAIN YIELD OF CORN, LEVEL OF LEAF N P K AND SOIL CONDITIONS IN A LONG-TIME ROTATION EXPERIMENT ON BROOKSTON CLAY

1980 ◽  
Vol 60 (4) ◽  
pp. 599-611 ◽  
Author(s):  
V. A. DIRKS ◽  
E. F. BOLTON
Keyword(s):  
Soil Moisture ◽  
Grain Yield ◽  
Soil Compaction ◽  
Nutrient Level ◽  
Soil Conditions ◽  
Fertilizer Treatment ◽  
Long Time ◽  
Yield Difference ◽  
Soil Measurements ◽  
Leaf N

Regression and covariance analysis of a 13-yr rotation experiment of corn on Brookston clay soil showed that grain yield of corn could be related to each of nine other plant and soil measurements. Soil compaction as measured by bulk density was negatively associated with the level of leaf K in the plants, as well as available soil moisture. The major part of the yield difference between fertilized continuous corn and fertilized corn following alfalfa could be accounted for by multiple regression of grain yield on leaf N and K nutrient levels, soil compaction and soil moisture. Soil compaction was not affected or modified by fertilizer treatment. Response of corn grain yield to soil conditions, moisture and plant nutrient level appears to vary with rotation and fertilizer input.

scholarly journals Towards Robustness in Neural Network Based Fault Diagnosis

2008 ◽  
Vol 18 (4) ◽  
pp. 443-454 ◽  
Author(s):  
Krzysztof Patan ◽  
Marcin Witczak ◽  
Józef Korbicz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Soft Computing ◽  
Analytical Techniques ◽  
Industrial Applications ◽  
Nonlinear Behaviour ◽  
Design Problems ◽  
Modelling Uncertainty ◽  
Soft Computing Techniques

Towards Robustness in Neural Network Based Fault DiagnosisChallenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as neural networks become more and more popular in industrial applications of fault diagnosis. Taking into account the two crucial aspects, i.e., the nonlinear behaviour of the system being diagnosed as well as the robustness of a fault diagnosis scheme with respect to modelling uncertainty, two different neural network based schemes are described and carefully discussed. The final part of the paper presents an illustrative example regarding the modelling and fault diagnosis of a DC motor, which shows the performance of the proposed strategy.

scholarly journals Effect of soil moisture content, dynamic load and wheel slippage in measuring traction in an indoor traction bed

2021 ◽  
Vol 46 (1) ◽  
pp. 22-30
Author(s):  
A.F. Tola ◽  
A.M. Sedara ◽  
O.B. Olatunde ◽  
A.A. Babalola
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Dynamic Load ◽  
Soil Moisture Content ◽  
Soil Conditions ◽  
Inflation Pressure ◽  
Test Rig ◽  
Wheel Load ◽  
Test Mode ◽  
Tractive Efficiency

A study was conducted to determine the effect of soil moisture content, dynamic load and wheel slippage in measuring traction. A single wheel test rig was developed to enable fundamental research on traction studies on tropical soil at the department of Agricultural and Environmental Engineering of Federal University of Technology, Akure. This facility consists of a moving carriage with a trolley that moves in either forward or reverse directions on rails well above a soil bin powered by 2.2 kW, three phase induction motor. The present facility set up was able to operate in either towing test mode for tire motion resistance studies or driving test mode for tire net traction and tractive efficiency studies. The test tire on the moving carriage was powered to rotate by a motor with additional pull provided by a cable-pulley mechanism connected to a tower with hanging dead weights. All controls on the moving carriage were activated from the main control console. The developed facility was successfully tested to determine tractive efficiency for narrow wheels at a particular inflation pressure on clay soil. The single wheel test rig facility worked well and the research indicates that wheel load, wheel slippage, soil moisture content and inflation pressure has great effect on traction efficiency. Traction efficiency decreased as the wheel load and wheel slippage increased. The developed single wheel testing facility can perform traction tests in controlled soil conditions to study the effect of soil, tire and moisture parameters on the performance of the system.

scholarly journals Managing business processes using soft computing techniques – a literature review

2017 ◽  
Vol 23 (1) ◽  
pp. 63 ◽  
Author(s):  
Nikola D. Vojtek
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Process Management ◽  
Business Processes ◽  
External Factors ◽  
Business Operations ◽  
Soft Computing Techniques ◽  
Internal And External Factors ◽  
And Behavior

Non-ideal and turbulent environment is affecting the business operations of today's companies. The influence and behavior of external factors is not possible or very difficult to predict, but necessary to consider in the decision making process at all organizational levels. Development of computer technologies allowed companies processing large amounts of data in order to make decisions which take into account both internal and external factors. Due to their tolerance of imprecision and uncertainty, Soft computing techniques (SCT) are increasingly used in solving those problems. They are characterized by the ability to adapt quickly to the changes in the environment, stability when processing large amounts of inaccurate data and real time responsiveness. The basic and most commonly used Soft Computing techniques are Fuzzy logic (FL) and neural networks (NN). This paper gives a review of 78 papers referring to the development of theory and practical application of soft computing techniques in business process management. Papers were selected based on the journals impact factor and year of publication (with the focus on recent papers). Approximately, equal number of both Fuzzy logic and Neural Network techniques was tried to be captured. Although this review is not final, it can be considered as a valid guide to explore the opportunities and possibilities which fuzzy logic and neural networks offer process owners in managing their business processes. In conclusion of this paper, fields for further research have been identified.

A Soft Computing Overview

2010 ◽  
pp. 1-11
Author(s):  
Marcos Gestal ◽  
Daniel Rivero
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soft Computing ◽  
Computing Methods ◽  
Testing System ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Computation Algorithms ◽  
Brain Behavior ◽  
The Brain

Nature has proved to be the best testing system, where we can analyze the effectiveness of any method of solving problems. It provides one of the most complex problems to be resolved: the survival. Analyzing how the species behave to achieve that survival, soft computing methods try to mimic this behavior to provide meaningful solutions to diverse problems. This chapter offers an introduction the fundamentals that the different soft computing techniques translate from Nature. It includes an approach of the brain behavior (Artificial Neural Networks) or the evolution ideas taken from Darwin’ laws (Evolutionary Computation algorithms).

A survey on the soil penetration resistance and soil moisture content in field experiments

2011 ◽  
Vol 59 (4) ◽  
pp. 349-359
Author(s):  
P. Földesi ◽  
C. Gyuricza
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Physical Condition ◽  
Soil Compaction ◽  
Field Experiments ◽  
Soil Moisture Content ◽  
Penetration Resistance ◽  
Soil Conditions ◽  
Environmental Damage ◽  
Soil Penetration Resistance

Various forms of physical soil degradation, including soil compaction, have been widely investigated both in Hungary and abroad. Soil compaction is a world-scale problem, which may be triggered by both natural and artificial factors and may adversely influence the effectiveness of crop production. In the long run this unfavourable change in the physical condition of the soil may result in extra expenses, higher energy consumption and excessive environmental damage. The effect of conventional tillage on the physical condition of the soil was assessed on six farms for three years in the framework of field experiments. In this study the physical condition of the soil was examined in terms of soil penetration resistance and moisture content. Significant differences between the experiments were revealed when penetration resistance (PR) was examinated at a depth of 20–30 cm, but not at other depths. In 2004 the mean PR values exceeded 3 MPa in experiments A, B and E. In 2005 significant differences were observed between the experiments at a depth of 30–40 cm, but no adverse compaction was detected at any depth in any of the experiments. In 2006 significant differences were observed between the experiments at depths of 0–10 cm and 10–20 cm, though even in that year no adverse soil compaction was detected. In the first year significant differences in soil moisture content were revealed at depths of 10–20 cm, 20–30 cm, 30–40 cm and 40–50 cm, and in 2005 at depths of 20–30 cm and 30–40 cm. In 2006 no significant differences were found between the experiments at any depth. The highest soil moisture contents were recorded in all the experiments at a depth of 30–40 cm. All in all, the results of both penetration resistance and moisture content were indicative of favourable soil conditions. During the period investigated adversely compact layers that would hamper moisture transport were not found in any of the experiments.

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