Growth in urban population,urbanisation,and economic development has increased the demand for water,especially in water-scarce regions.Therefore,sustainable approaches to water management are needed to cope with the e...Growth in urban population,urbanisation,and economic development has increased the demand for water,especially in water-scarce regions.Therefore,sustainable approaches to water management are needed to cope with the effects of the urbanisation on the water environment.This study aimed to design novel configurations of tidal-flow vertical subsurface flow constructed wetlands(VFCWs)for treating urban stormwater.A series of laboratory experiments were conducted with semi-synthetic influent stormwater to examine the effects of the design and operation variables on the performance of the VFCWs and to identify optimal design and operational strategies,as well as maintenance requirements.The results show that the VFCWs can significantly reduce pollutants in urban stormwater,and that pollutant removal was related to specific VFCW designs.Models based on the artificial neural network(ANN)method were built using inputs derived from data exploratory techniques,such as analysis of variance(ANOVA)and principal component analysis(PCA).It was found that PCA reduced the dimensionality of input variables obtained from different experimental design conditions.The results show a satisfactory generalisation for predicting nitrogen and phosphorus removal with fewer variable inputs,indicating that monitoring costs and time can be reduced.展开更多
In agent-based automated negotiation research area,a key problem is how to make software agent more adaptable to represent user preferences or suggestions,so that agent can take further proposals that reflect user req...In agent-based automated negotiation research area,a key problem is how to make software agent more adaptable to represent user preferences or suggestions,so that agent can take further proposals that reflect user requirements to implement ecommerce activities like automated transactions.The difficulty lies in the uncertainty of user preferences that include uncertain description and contents,non-linear and dynamic variability.In this paper,fuzzy language was used to describe the uncertainty and combine with multiple classified artificial neural networks(ANNs) for self-adaptive learning of user preferences.The refinement learning results of various negotiation contracts' satisfaction degrees in the extent of fuzzy classification can be achieved.Compared to unclassified computation,the experimental results illustrate that the learning ability and effectiveness of agents have been improved.展开更多
The main purpose of this study was to develop and apply an adaptive neuro-fuzzy inference system(ANFIS)and Artificial Neural Networks(ANNs)model for predicting the drying characteristics of potato,garlic and cantaloup...The main purpose of this study was to develop and apply an adaptive neuro-fuzzy inference system(ANFIS)and Artificial Neural Networks(ANNs)model for predicting the drying characteristics of potato,garlic and cantaloupe at convective hot air dryer.Drying experiments were conducted at the air temperatures of 40,50,60 and 70C and the air speeds of 0.5,1 and l.5 m/s.Drying properties were including kinetic drying,effective moisture diffusivity(Deff)and specific energy consumption(SEC).The highest value of Deff obtained 9.76×10^-9,0.13×10^-9 and 9.97×10^-10 m^2/s for potato,garlic,and cantaloupe,respectively.The lowest value of SEC for potato,garlic,and cantaloupe were calculated 1.94105,4.52105 and 2.12105 kJ/kg,respectively.Results revealed that the ANFIS model had the high ability to predict the Deff(R^2=0.9900),SEC(R^2=0.9917),moisture ratio(R^2=0.9974)and drying rate(R^2=0.9901)during drying.So ANFIS method had the high ability to evaluate all output as compared to ANNs method.展开更多
Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,a...Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,and can be addressed using clustering and routing techniques.Information is sent from the source to the BS via routing procedures.However,these routing protocols must ensure that packets are delivered securely,guaranteeing that neither adversaries nor unauthentic individuals have access to the sent information.Secure data transfer is intended to protect the data from illegal access,damage,or disruption.Thus,in the proposed model,secure data transmission is developed in an energy-effective manner.A low-energy adaptive clustering hierarchy(LEACH)is developed to efficiently transfer the data.For the intrusion detection systems(IDS),Fuzzy logic and artificial neural networks(ANNs)are proposed.Initially,the nodes were randomly placed in the network and initialized to gather information.To ensure fair energy dissipation between the nodes,LEACH randomly chooses cluster heads(CHs)and allocates this role to the various nodes based on a round-robin management mechanism.The intrusion-detection procedure was then utilized to determine whether intruders were present in the network.Within the WSN,a Fuzzy interference rule was utilized to distinguish the malicious nodes from legal nodes.Subsequently,an ANN was employed to distinguish the harmful nodes from suspicious nodes.The effectiveness of the proposed approach was validated using metrics that attained 97%accuracy,97%specificity,and 97%sensitivity of 95%.Thus,it was proved that the LEACH and Fuzzy-based IDS approaches are the best choices for securing data transmission in an energy-efficient manner.展开更多
It is especially significant for a manufacturing company to select a proper maintenance policy because maintenance impacts not only on economy, reliability and availability but also on personnel safety. This article r...It is especially significant for a manufacturing company to select a proper maintenance policy because maintenance impacts not only on economy, reliability and availability but also on personnel safety. This article re- ports on research in the backlash error data interpretation and compensation for intelligent predictive maintenance in machine centers based on artificial neural networks （ANNs）. The backlash error, measurement system and prediction methods are analyzed in detail. The result indicates that it is possible to predict and compensate for the backlash error in both forward and backward directions in machine centers.展开更多
The present study was aimed to model the hydration characteristics of green chickpea(GC)using mathematical modelling and examine predictive ability of artificial neural network(ANN)modelling.Hydration of GC was perfor...The present study was aimed to model the hydration characteristics of green chickpea(GC)using mathematical modelling and examine predictive ability of artificial neural network(ANN)modelling.Hydration of GC was performed at different temperatures 25,35,45,55 and 65℃.Different mathematical models were tested for the hydration at different temperatures.In ANN modelling,the hydration time and hydration temperature were used as input variables and moisture ratio,moisture content and hydration ratio were taken as output variables.Peleg model best described the hydration behavior at 25℃;while hydration at high-temperature was better described by Page model and Ibarz et al.model.The optimum temperature obtained for hydration was 35℃.Effective mass diffusion coefficient(D_(e))increased from 1.5510^(-11)-1.7910^(-9) m^(2)/s with the increase in the hydration temperature.The low activation energy(39.66 kJ/moL)shows the low-temperature sensitiveness of GC.Low temperature hydration(25℃)required higher time(>200 min)to achieve the equilibrium moisture content(EMC),however high temperature hydration(35–65℃)reduced the EMC time(150 min).ANN was used to predict the hydration behavior and K fold cross validation was performed to check the over fitting of ANN model.Results show that the LOGSIGMOID transfer function showed better performance when used at the hidden layer input node in conjunction to both PURELIN and TANSIGMOID.TANSIGMOID was found suitable for moisture ratio(MR)and hydration ratio(HR)prediction,as opposed to PURELIN for moisture content(MC)data.Satisfactory model prediction was obtained when the number of neurons in the hidden layer for MC,MR and HR was 12,8 and 15,respectively.Mathematical and ANN modelling results are useful to improve/predict the MC,MR and HR during hydration process of GC at different temperature and other similar process.展开更多
基金This research was partly supported by the UK Engineering and Physical Sciences Research Council(EPSRC)Studentship and Asset International,who provided the HDPE materials used to build bespoke constructed wetlands.
文摘Growth in urban population,urbanisation,and economic development has increased the demand for water,especially in water-scarce regions.Therefore,sustainable approaches to water management are needed to cope with the effects of the urbanisation on the water environment.This study aimed to design novel configurations of tidal-flow vertical subsurface flow constructed wetlands(VFCWs)for treating urban stormwater.A series of laboratory experiments were conducted with semi-synthetic influent stormwater to examine the effects of the design and operation variables on the performance of the VFCWs and to identify optimal design and operational strategies,as well as maintenance requirements.The results show that the VFCWs can significantly reduce pollutants in urban stormwater,and that pollutant removal was related to specific VFCW designs.Models based on the artificial neural network(ANN)method were built using inputs derived from data exploratory techniques,such as analysis of variance(ANOVA)and principal component analysis(PCA).It was found that PCA reduced the dimensionality of input variables obtained from different experimental design conditions.The results show a satisfactory generalisation for predicting nitrogen and phosphorus removal with fewer variable inputs,indicating that monitoring costs and time can be reduced.
基金National Natural Science Foundation of China (No. 70631003)
文摘In agent-based automated negotiation research area,a key problem is how to make software agent more adaptable to represent user preferences or suggestions,so that agent can take further proposals that reflect user requirements to implement ecommerce activities like automated transactions.The difficulty lies in the uncertainty of user preferences that include uncertain description and contents,non-linear and dynamic variability.In this paper,fuzzy language was used to describe the uncertainty and combine with multiple classified artificial neural networks(ANNs) for self-adaptive learning of user preferences.The refinement learning results of various negotiation contracts' satisfaction degrees in the extent of fuzzy classification can be achieved.Compared to unclassified computation,the experimental results illustrate that the learning ability and effectiveness of agents have been improved.
基金supported by the Humanities and Social Sciences Youth Foundation of the Ministry of Education of PR of China under Grant No.11YJC870028the Selfdetermined Research Funds of CCNU from the Colleges’Basic Research and Operation of MOE under Grant No.CCNU13F030+1 种基金China Postdoctoral Science Foundation under Grant No.2013M530753National Science Foundation of China under Grant No.71390335
文摘The main purpose of this study was to develop and apply an adaptive neuro-fuzzy inference system(ANFIS)and Artificial Neural Networks(ANNs)model for predicting the drying characteristics of potato,garlic and cantaloupe at convective hot air dryer.Drying experiments were conducted at the air temperatures of 40,50,60 and 70C and the air speeds of 0.5,1 and l.5 m/s.Drying properties were including kinetic drying,effective moisture diffusivity(Deff)and specific energy consumption(SEC).The highest value of Deff obtained 9.76×10^-9,0.13×10^-9 and 9.97×10^-10 m^2/s for potato,garlic,and cantaloupe,respectively.The lowest value of SEC for potato,garlic,and cantaloupe were calculated 1.94105,4.52105 and 2.12105 kJ/kg,respectively.Results revealed that the ANFIS model had the high ability to predict the Deff(R^2=0.9900),SEC(R^2=0.9917),moisture ratio(R^2=0.9974)and drying rate(R^2=0.9901)during drying.So ANFIS method had the high ability to evaluate all output as compared to ANNs method.
文摘Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,and can be addressed using clustering and routing techniques.Information is sent from the source to the BS via routing procedures.However,these routing protocols must ensure that packets are delivered securely,guaranteeing that neither adversaries nor unauthentic individuals have access to the sent information.Secure data transfer is intended to protect the data from illegal access,damage,or disruption.Thus,in the proposed model,secure data transmission is developed in an energy-effective manner.A low-energy adaptive clustering hierarchy(LEACH)is developed to efficiently transfer the data.For the intrusion detection systems(IDS),Fuzzy logic and artificial neural networks(ANNs)are proposed.Initially,the nodes were randomly placed in the network and initialized to gather information.To ensure fair energy dissipation between the nodes,LEACH randomly chooses cluster heads(CHs)and allocates this role to the various nodes based on a round-robin management mechanism.The intrusion-detection procedure was then utilized to determine whether intruders were present in the network.Within the WSN,a Fuzzy interference rule was utilized to distinguish the malicious nodes from legal nodes.Subsequently,an ANN was employed to distinguish the harmful nodes from suspicious nodes.The effectiveness of the proposed approach was validated using metrics that attained 97%accuracy,97%specificity,and 97%sensitivity of 95%.Thus,it was proved that the LEACH and Fuzzy-based IDS approaches are the best choices for securing data transmission in an energy-efficient manner.
文摘It is especially significant for a manufacturing company to select a proper maintenance policy because maintenance impacts not only on economy, reliability and availability but also on personnel safety. This article re- ports on research in the backlash error data interpretation and compensation for intelligent predictive maintenance in machine centers based on artificial neural networks （ANNs）. The backlash error, measurement system and prediction methods are analyzed in detail. The result indicates that it is possible to predict and compensate for the backlash error in both forward and backward directions in machine centers.
文摘The present study was aimed to model the hydration characteristics of green chickpea(GC)using mathematical modelling and examine predictive ability of artificial neural network(ANN)modelling.Hydration of GC was performed at different temperatures 25,35,45,55 and 65℃.Different mathematical models were tested for the hydration at different temperatures.In ANN modelling,the hydration time and hydration temperature were used as input variables and moisture ratio,moisture content and hydration ratio were taken as output variables.Peleg model best described the hydration behavior at 25℃;while hydration at high-temperature was better described by Page model and Ibarz et al.model.The optimum temperature obtained for hydration was 35℃.Effective mass diffusion coefficient(D_(e))increased from 1.5510^(-11)-1.7910^(-9) m^(2)/s with the increase in the hydration temperature.The low activation energy(39.66 kJ/moL)shows the low-temperature sensitiveness of GC.Low temperature hydration(25℃)required higher time(>200 min)to achieve the equilibrium moisture content(EMC),however high temperature hydration(35–65℃)reduced the EMC time(150 min).ANN was used to predict the hydration behavior and K fold cross validation was performed to check the over fitting of ANN model.Results show that the LOGSIGMOID transfer function showed better performance when used at the hidden layer input node in conjunction to both PURELIN and TANSIGMOID.TANSIGMOID was found suitable for moisture ratio(MR)and hydration ratio(HR)prediction,as opposed to PURELIN for moisture content(MC)data.Satisfactory model prediction was obtained when the number of neurons in the hidden layer for MC,MR and HR was 12,8 and 15,respectively.Mathematical and ANN modelling results are useful to improve/predict the MC,MR and HR during hydration process of GC at different temperature and other similar process.
