Climate change poses significant challenges to agricultural management,particularly in adapting to extreme weather conditions that impact agricultural production.Existing works with traditional Reinforcement Learning(...Climate change poses significant challenges to agricultural management,particularly in adapting to extreme weather conditions that impact agricultural production.Existing works with traditional Reinforcement Learning(RL)methods often falter under such extreme conditions.To address this challenge,our study introduces a novel approach by integrating Continual Learning(CL)with RL to form Continual Reinforcement Learning(CRL),enhancing the adaptability of agricultural management strategies.Leveraging the Gym-DSSAT simulation environment,our research enables RL agents to learn optimal fertilization strategies based on variable weather conditions.By incorporating CL algorithms,such as Elastic Weight Consolidation(EWC),with established RL techniques like Deep Q-Networks(DQN),we developed a framework in which agents can learn and retain knowledge across diverse weather scenarios.The CRL approach was tested under climate variability to assess the robustness and adaptability of the induced policies,particularly under extreme weather events like severe droughts.Our results showed that continually learned policies exhibited superior adaptability and performance compared to optimal policies learned through the conventional RL methods,especially in challenging conditions of reduced rainfall and increased temperatures.This pioneering work,which combines CL with RL to generate adaptive policies for agricultural management,is expected to make significant advancements in precision agriculture in the era of climate change.展开更多
The predominant method for smart phone accessing is confined to methods directing the authentication by means of Point-of-Entry that heavily depend on physiological biometrics like,fingerprint or face.Implicit continuou...The predominant method for smart phone accessing is confined to methods directing the authentication by means of Point-of-Entry that heavily depend on physiological biometrics like,fingerprint or face.Implicit continuous authentication initiating to be loftier to conventional authentication mechanisms by continuously confirming users’identities on continuing basis and mark the instant at which an illegitimate hacker grasps dominance of the session.However,divergent issues remain unaddressed.This research aims to investigate the power of Deep Reinforcement Learning technique to implicit continuous authentication for mobile devices using a method called,Gaussian Weighted Cauchy Kriging-based Continuous Czekanowski’s(GWCK-CC).First,a Gaussian Weighted Non-local Mean Filter Preprocessing model is applied for reducing the noise pre-sent in the raw input face images.Cauchy Kriging Regression function is employed to reduce the dimensionality.Finally,Continuous Czekanowski’s Clas-sification is utilized for proficient classification between the genuine user and attacker.By this way,the proposed GWCK-CC method achieves accurate authen-tication with minimum error rate and time.Experimental assessment of the pro-posed GWCK-CC method and existing methods are carried out with different factors by using UMDAA-02 Face Dataset.The results confirm that the proposed GWCK-CC method enhances authentication accuracy,by 9%,reduces the authen-tication time,and error rate by 44%,and 43%as compared to the existing methods.展开更多
The flexural behaviors of continuous fully and partially prestressed steel fiber reinforced high strength concrete beams are studied by experiment and nonlinear finite element analysis. Three levels of partial prestre...The flexural behaviors of continuous fully and partially prestressed steel fiber reinforced high strength concrete beams are studied by experiment and nonlinear finite element analysis. Three levels of partial prestress ratio （PPR） are considered, and three pairs of two-span continuous beams with box sections varying in size are designed. The major parameters involved in the study include the PPR and the fiber location. It is concluded that the prestressed high strength concrete beam exhibits satisfactory ductility; the influences of steel fiber on the crack behaviors for partially prestressed beams are not as obvious as those for fully prestressed ones; steel fibers can improve the structural stiffness after cracking for fully prestressed high strength concrete beams; the moment redistribution from mid-span to intermediate support in the first stage should be mainly considered in practical design.展开更多
The dry friction and wear behavior of 7075 Al alloy reinforced with SiC 3D continuous ceramic network against Cr12 steel was studied with oscillating dry friction and wear tester under the testing conditions of 70 ℃,...The dry friction and wear behavior of 7075 Al alloy reinforced with SiC 3D continuous ceramic network against Cr12 steel was studied with oscillating dry friction and wear tester under the testing conditions of 70 ℃, 30 min, and the load range of 40-100 N. The experimental result shows that the characteristic of abrasive wear and oxidation wear mechanisms are present for 3D continuous SiC/7075 Al composite. 3D continuous network ceramic as the reinforcement can avoid composite from the third body wear that usually occurs in traditional particle reinforced composite. Under low load, the composite with low volume fraction of ceramic reinforcement exhibits better wear resistance due to the homogeneous reinforcement distribution with small pore size; on the contrary, under high load, the composite with high reinforcement volume fraction exhibits better wear resistance because of the coarse frame size. Hard SiC frame leads to the wear of Cr12 steel mainly. The frame with high volume fraction corresponds to the high Fe content.展开更多
Reinforcement Learning is a commonly used technique for learning tasks in robotics, however, traditional algorithms are unable to handle large amounts of data coming from the robot’s sensors, require long training ti...Reinforcement Learning is a commonly used technique for learning tasks in robotics, however, traditional algorithms are unable to handle large amounts of data coming from the robot’s sensors, require long training times, and use dis-crete actions. This work introduces TS-RRLCA, a two stage method to tackle these problems. In the first stage, low-level data coming from the robot’s sensors is transformed into a more natural, relational representation based on rooms, walls, corners, doors and obstacles, significantly reducing the state space. We use this representation along with Behavioural Cloning, i.e., traces provided by the user;to learn, in few iterations, a relational control policy with discrete actions which can be re-used in different environments. In the second stage, we use Locally Weighted Regression to transform the initial policy into a continuous actions policy. We tested our approach in simulation and with a real service robot on different environments for different navigation and following tasks. Results show how the policies can be used on different domains and perform smoother, faster and shorter paths than the original discrete actions policies.展开更多
The overall research in Reinforcement Learning (RL) concentrates on discrete sets of actions, but for certain real-world problems it is important to have methods which are able to find good strategies using actions dr...The overall research in Reinforcement Learning (RL) concentrates on discrete sets of actions, but for certain real-world problems it is important to have methods which are able to find good strategies using actions drawn from continuous sets. This paper describes a simple control task called direction finder and its known optimal solution for both discrete and continuous actions. It allows for comparison of RL solution methods based on their value functions. In order to solve the control task for continuous actions, a simple idea for generalising them by means of feature vectors is presented. The resulting algorithm is applied using different choices of feature calculations. For comparing their performance a simple measure is展开更多
Using a genetic algorithm owing to high nonlinearity of constraints, this paper first works on the optimal design of two-span continuous singly reinforced concrete beams. Given conditions are the span, dead and live l...Using a genetic algorithm owing to high nonlinearity of constraints, this paper first works on the optimal design of two-span continuous singly reinforced concrete beams. Given conditions are the span, dead and live loads, compressive strength of concrete and yield strength of steel;design variables are the width and effective depth of the continuous beam and steel ratios for positive and negative moments. The constraints are built based on the ACI Building Code by considering the strength requirements of shear and the maximum positive and negative moments, the development length of flexural reinforcement, and the serviceability requirement of deflection. The objective function is to minimize the total cost of steel and concrete. The optimal data found from the genetic algorithm are divided into three groups: the training set, the checking set and the testing set for the use of the adaptive neuro-fuzzy inference system (ANFIS). The input vector of ANFIS consists of the yield strength of steel, compressive strength of concrete, dead load, span, width and effective depth of the beam;its outputs are the minimum total cost and optimal steel ratios for positive and negative moments. To make ANFIS more efficient, the technique of Subtractive Clustering is applied to group the data to help streamline the fuzzy rules. Numerical results show that the performance of ANFIS is excellent, with correlation coefficients between the three targets and outputs of the testing data being greater than 0.99.展开更多
In this paper the analysis of tensile stress distribution in flexural continuous T- beam has been presented. The observed damages in carrying deck of RC bridge over the Wieprz River in Baranow indicate that over pilla...In this paper the analysis of tensile stress distribution in flexural continuous T- beam has been presented. The observed damages in carrying deck of RC bridge over the Wieprz River in Baranow indicate that over pillar zones are not protected enough. The results of numerical analysis have shown that tensile stress in T- section beam appears not only in a web but in flanges as well. Thus reinforcing bars should be distributed within the whole effective width. This fact is mentioned in building codes, for example, in Eurocode 2： ＂Design of concrete structures＂, both in part 1.1 ＂General rules and rules for building＂ and in part 2 ＂Reinforced and prestressed concrete bridges＂, but there are not detailed rules how to place the bars in flanges of T-section.展开更多
In order to study the critical load position that causes cavities beneath the continuously reinforced concrete pavement（ CRCP） slab under vehicle loading, the elliptical load is translated into the square load based...In order to study the critical load position that causes cavities beneath the continuously reinforced concrete pavement（ CRCP） slab under vehicle loading, the elliptical load is translated into the square load based on the equivalence principle.The CRCP slab is analyzed to determine the cavity position beneath the slab under vehicle loading. The influences of cavity size on the CRCP slab＇s stress and vertical displacement are investigated. The study results showthat the formation of the cavity is unavoidable under traffic loading, and the cavity is located at the edge of the longitudinal crack and the slab corner.The cavity size exerts an obvious influence on the largest horizontal tensile stress and vertical displacement. The slab corner is the critical load position of the CRCP slab. The results can be used to assist the design of CRCP in avoiding cavities beneath slabs subject to vehicle loading.展开更多
基金support from the University of Iowa OVPR Interdisciplinary Scholars Program and the US Department of Education(ED#P116S210005)for this study.Kishlay Jha’s work is supported in part by the US National Institute of Health(NIH)and National Science Foundation(NSF)under grants R01LM014012-01A1 and ITE-2333740.
文摘Climate change poses significant challenges to agricultural management,particularly in adapting to extreme weather conditions that impact agricultural production.Existing works with traditional Reinforcement Learning(RL)methods often falter under such extreme conditions.To address this challenge,our study introduces a novel approach by integrating Continual Learning(CL)with RL to form Continual Reinforcement Learning(CRL),enhancing the adaptability of agricultural management strategies.Leveraging the Gym-DSSAT simulation environment,our research enables RL agents to learn optimal fertilization strategies based on variable weather conditions.By incorporating CL algorithms,such as Elastic Weight Consolidation(EWC),with established RL techniques like Deep Q-Networks(DQN),we developed a framework in which agents can learn and retain knowledge across diverse weather scenarios.The CRL approach was tested under climate variability to assess the robustness and adaptability of the induced policies,particularly under extreme weather events like severe droughts.Our results showed that continually learned policies exhibited superior adaptability and performance compared to optimal policies learned through the conventional RL methods,especially in challenging conditions of reduced rainfall and increased temperatures.This pioneering work,which combines CL with RL to generate adaptive policies for agricultural management,is expected to make significant advancements in precision agriculture in the era of climate change.
文摘The predominant method for smart phone accessing is confined to methods directing the authentication by means of Point-of-Entry that heavily depend on physiological biometrics like,fingerprint or face.Implicit continuous authentication initiating to be loftier to conventional authentication mechanisms by continuously confirming users’identities on continuing basis and mark the instant at which an illegitimate hacker grasps dominance of the session.However,divergent issues remain unaddressed.This research aims to investigate the power of Deep Reinforcement Learning technique to implicit continuous authentication for mobile devices using a method called,Gaussian Weighted Cauchy Kriging-based Continuous Czekanowski’s(GWCK-CC).First,a Gaussian Weighted Non-local Mean Filter Preprocessing model is applied for reducing the noise pre-sent in the raw input face images.Cauchy Kriging Regression function is employed to reduce the dimensionality.Finally,Continuous Czekanowski’s Clas-sification is utilized for proficient classification between the genuine user and attacker.By this way,the proposed GWCK-CC method achieves accurate authen-tication with minimum error rate and time.Experimental assessment of the pro-posed GWCK-CC method and existing methods are carried out with different factors by using UMDAA-02 Face Dataset.The results confirm that the proposed GWCK-CC method enhances authentication accuracy,by 9%,reduces the authen-tication time,and error rate by 44%,and 43%as compared to the existing methods.
文摘The flexural behaviors of continuous fully and partially prestressed steel fiber reinforced high strength concrete beams are studied by experiment and nonlinear finite element analysis. Three levels of partial prestress ratio （PPR） are considered, and three pairs of two-span continuous beams with box sections varying in size are designed. The major parameters involved in the study include the PPR and the fiber location. It is concluded that the prestressed high strength concrete beam exhibits satisfactory ductility; the influences of steel fiber on the crack behaviors for partially prestressed beams are not as obvious as those for fully prestressed ones; steel fibers can improve the structural stiffness after cracking for fully prestressed high strength concrete beams; the moment redistribution from mid-span to intermediate support in the first stage should be mainly considered in practical design.
基金Project(50575076)supported by the National Natural Science Foundation of ChinaProject(36547) supported by the Natural Science Foundation of Guangdong Province, China
文摘The dry friction and wear behavior of 7075 Al alloy reinforced with SiC 3D continuous ceramic network against Cr12 steel was studied with oscillating dry friction and wear tester under the testing conditions of 70 ℃, 30 min, and the load range of 40-100 N. The experimental result shows that the characteristic of abrasive wear and oxidation wear mechanisms are present for 3D continuous SiC/7075 Al composite. 3D continuous network ceramic as the reinforcement can avoid composite from the third body wear that usually occurs in traditional particle reinforced composite. Under low load, the composite with low volume fraction of ceramic reinforcement exhibits better wear resistance due to the homogeneous reinforcement distribution with small pore size; on the contrary, under high load, the composite with high reinforcement volume fraction exhibits better wear resistance because of the coarse frame size. Hard SiC frame leads to the wear of Cr12 steel mainly. The frame with high volume fraction corresponds to the high Fe content.
文摘Reinforcement Learning is a commonly used technique for learning tasks in robotics, however, traditional algorithms are unable to handle large amounts of data coming from the robot’s sensors, require long training times, and use dis-crete actions. This work introduces TS-RRLCA, a two stage method to tackle these problems. In the first stage, low-level data coming from the robot’s sensors is transformed into a more natural, relational representation based on rooms, walls, corners, doors and obstacles, significantly reducing the state space. We use this representation along with Behavioural Cloning, i.e., traces provided by the user;to learn, in few iterations, a relational control policy with discrete actions which can be re-used in different environments. In the second stage, we use Locally Weighted Regression to transform the initial policy into a continuous actions policy. We tested our approach in simulation and with a real service robot on different environments for different navigation and following tasks. Results show how the policies can be used on different domains and perform smoother, faster and shorter paths than the original discrete actions policies.
文摘The overall research in Reinforcement Learning (RL) concentrates on discrete sets of actions, but for certain real-world problems it is important to have methods which are able to find good strategies using actions drawn from continuous sets. This paper describes a simple control task called direction finder and its known optimal solution for both discrete and continuous actions. It allows for comparison of RL solution methods based on their value functions. In order to solve the control task for continuous actions, a simple idea for generalising them by means of feature vectors is presented. The resulting algorithm is applied using different choices of feature calculations. For comparing their performance a simple measure is
文摘Using a genetic algorithm owing to high nonlinearity of constraints, this paper first works on the optimal design of two-span continuous singly reinforced concrete beams. Given conditions are the span, dead and live loads, compressive strength of concrete and yield strength of steel;design variables are the width and effective depth of the continuous beam and steel ratios for positive and negative moments. The constraints are built based on the ACI Building Code by considering the strength requirements of shear and the maximum positive and negative moments, the development length of flexural reinforcement, and the serviceability requirement of deflection. The objective function is to minimize the total cost of steel and concrete. The optimal data found from the genetic algorithm are divided into three groups: the training set, the checking set and the testing set for the use of the adaptive neuro-fuzzy inference system (ANFIS). The input vector of ANFIS consists of the yield strength of steel, compressive strength of concrete, dead load, span, width and effective depth of the beam;its outputs are the minimum total cost and optimal steel ratios for positive and negative moments. To make ANFIS more efficient, the technique of Subtractive Clustering is applied to group the data to help streamline the fuzzy rules. Numerical results show that the performance of ANFIS is excellent, with correlation coefficients between the three targets and outputs of the testing data being greater than 0.99.
文摘In this paper the analysis of tensile stress distribution in flexural continuous T- beam has been presented. The observed damages in carrying deck of RC bridge over the Wieprz River in Baranow indicate that over pillar zones are not protected enough. The results of numerical analysis have shown that tensile stress in T- section beam appears not only in a web but in flanges as well. Thus reinforcing bars should be distributed within the whole effective width. This fact is mentioned in building codes, for example, in Eurocode 2： ＂Design of concrete structures＂, both in part 1.1 ＂General rules and rules for building＂ and in part 2 ＂Reinforced and prestressed concrete bridges＂, but there are not detailed rules how to place the bars in flanges of T-section.
基金The Science Foundation of Ministry of Transport of the People's Republic of China(No.200731822301-7)
文摘In order to study the critical load position that causes cavities beneath the continuously reinforced concrete pavement（ CRCP） slab under vehicle loading, the elliptical load is translated into the square load based on the equivalence principle.The CRCP slab is analyzed to determine the cavity position beneath the slab under vehicle loading. The influences of cavity size on the CRCP slab＇s stress and vertical displacement are investigated. The study results showthat the formation of the cavity is unavoidable under traffic loading, and the cavity is located at the edge of the longitudinal crack and the slab corner.The cavity size exerts an obvious influence on the largest horizontal tensile stress and vertical displacement. The slab corner is the critical load position of the CRCP slab. The results can be used to assist the design of CRCP in avoiding cavities beneath slabs subject to vehicle loading.
