Development of accurate and reliable models for predicting the strength of rocks and rock masses is one of the most common interests of geologists,civil and mining engineers and many others.Due to uncertainties in eva...Development of accurate and reliable models for predicting the strength of rocks and rock masses is one of the most common interests of geologists,civil and mining engineers and many others.Due to uncertainties in evaluation of effective parameters and also complicated nature of geological materials,it is difficult to estimate the strength precisely using theoretical approaches.On the other hand,intelligent approaches have attracted much attention as novel and effective tools of solving complicated problems in engineering practice over the past decades.In this paper,a new method is proposed for mining descriptive Mamdani fuzzy inference systems to predict the strength of intact rocks and anisotropic rock masses containing well-defined through-going joint.The proposed method initially employs a genetic algorithm(GA)to pick important rules from a preliminary rule base produced by grid partitioning and,subsequently,selected rules are given weights using the GA.Moreover,an information criterion is used during the first phase to optimize the models in terms of accuracy and complexity.The proposed hybrid method can be considered as a robust optimization task which produces promising results compared with previous approaches.展开更多
This paper proposes an approach to detecting diseases in neem leaf that uses a Fuzzy-Higher Order Biologically Inspired Neuron Model(F-HOBINM)and adaptive neuro classifier(ANFIS).India exports USD 0.28-million worth o...This paper proposes an approach to detecting diseases in neem leaf that uses a Fuzzy-Higher Order Biologically Inspired Neuron Model(F-HOBINM)and adaptive neuro classifier(ANFIS).India exports USD 0.28-million worth of neem leaf to the UK,USA,UAE,and Europe in the form of dried leaves and powder,both of which help reduce diabetesrelated issues,cardiovascular problems,and eye disorders.Diagnosing neem leaf disease is difficult through visual interpretation,owing to similarity in their color and texture patterns.The most common diseases include bacterial blight,Colletotrichum and Alternaria leaf spot,blight,damping-off,powdery mildew,Pseudocercospora leaf spot,leaf web blight,and seedling wilt.However,traditional color and texture algorithms fail to identify leaf diseases due to irregular lumps and surfaces,and rough ridges,as the classification time involved takes as long as a week.The proposed F-HOBINM algorithm recognizes the leaf intensity through the leaky capacitor,and uses subjective intensity and physical stimulus to interpret the diagnosis.Further,the processed leaf images from the HOBINM algorithm are applied to the ANFIS classifier to identify neem leaf diseases.The experimental results show 92.18%accuracy from a database of 1,462 neem leaves.展开更多
Sowing depth has an important impact on the performance of no-tillage planters,it is one of the key factors to ensure rapid germination.However,the consistency of sowing depth is easily affected by the complex environ...Sowing depth has an important impact on the performance of no-tillage planters,it is one of the key factors to ensure rapid germination.However,the consistency of sowing depth is easily affected by the complex environment of no-tillage operation.In order to improve the performance of no-tillage planters and improve the control precision of sowing depth,an intelligent depth regulation system was designed.Three Flex sensors installed on the inner surface of the gauge wheel at 120°intervals were used to monitor the downward force exerted by the seeding row unit against ground.The peak value of the output voltage of the sensor increased linearly with the increase of the downward force.In addition,the pneumatic spring was used as a downforce generator,and its intelligent regulation model was established by the Mamdani fuzzy algorithm,which can realize the control of the downward force exerted by the seeding row unit against ground and ensure the proper seeding depth.The working process was simulated based on MATLAB-Simulink,and the results showed that the Mamdani fuzzy model performed well in changing the pressure against ground.Field results showed that when the operating speed was 6 km/h,8 km/h and 10 km/h,the error of the system’s control of sowing depth was±9 mm,±12 mm,and±22 mm,respectively,and its sowing performance was significantly higher than that of the unadjusted passive operation.展开更多
文摘Development of accurate and reliable models for predicting the strength of rocks and rock masses is one of the most common interests of geologists,civil and mining engineers and many others.Due to uncertainties in evaluation of effective parameters and also complicated nature of geological materials,it is difficult to estimate the strength precisely using theoretical approaches.On the other hand,intelligent approaches have attracted much attention as novel and effective tools of solving complicated problems in engineering practice over the past decades.In this paper,a new method is proposed for mining descriptive Mamdani fuzzy inference systems to predict the strength of intact rocks and anisotropic rock masses containing well-defined through-going joint.The proposed method initially employs a genetic algorithm(GA)to pick important rules from a preliminary rule base produced by grid partitioning and,subsequently,selected rules are given weights using the GA.Moreover,an information criterion is used during the first phase to optimize the models in terms of accuracy and complexity.The proposed hybrid method can be considered as a robust optimization task which produces promising results compared with previous approaches.
文摘This paper proposes an approach to detecting diseases in neem leaf that uses a Fuzzy-Higher Order Biologically Inspired Neuron Model(F-HOBINM)and adaptive neuro classifier(ANFIS).India exports USD 0.28-million worth of neem leaf to the UK,USA,UAE,and Europe in the form of dried leaves and powder,both of which help reduce diabetesrelated issues,cardiovascular problems,and eye disorders.Diagnosing neem leaf disease is difficult through visual interpretation,owing to similarity in their color and texture patterns.The most common diseases include bacterial blight,Colletotrichum and Alternaria leaf spot,blight,damping-off,powdery mildew,Pseudocercospora leaf spot,leaf web blight,and seedling wilt.However,traditional color and texture algorithms fail to identify leaf diseases due to irregular lumps and surfaces,and rough ridges,as the classification time involved takes as long as a week.The proposed F-HOBINM algorithm recognizes the leaf intensity through the leaky capacitor,and uses subjective intensity and physical stimulus to interpret the diagnosis.Further,the processed leaf images from the HOBINM algorithm are applied to the ANFIS classifier to identify neem leaf diseases.The experimental results show 92.18%accuracy from a database of 1,462 neem leaves.
基金by the National Key R&D Plan Project(Grant No.2016YFD070030201)。
文摘Sowing depth has an important impact on the performance of no-tillage planters,it is one of the key factors to ensure rapid germination.However,the consistency of sowing depth is easily affected by the complex environment of no-tillage operation.In order to improve the performance of no-tillage planters and improve the control precision of sowing depth,an intelligent depth regulation system was designed.Three Flex sensors installed on the inner surface of the gauge wheel at 120°intervals were used to monitor the downward force exerted by the seeding row unit against ground.The peak value of the output voltage of the sensor increased linearly with the increase of the downward force.In addition,the pneumatic spring was used as a downforce generator,and its intelligent regulation model was established by the Mamdani fuzzy algorithm,which can realize the control of the downward force exerted by the seeding row unit against ground and ensure the proper seeding depth.The working process was simulated based on MATLAB-Simulink,and the results showed that the Mamdani fuzzy model performed well in changing the pressure against ground.Field results showed that when the operating speed was 6 km/h,8 km/h and 10 km/h,the error of the system’s control of sowing depth was±9 mm,±12 mm,and±22 mm,respectively,and its sowing performance was significantly higher than that of the unadjusted passive operation.
