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 the present paper,the hydrodynamic performance of stepped planing craft is investigated by computational fluid dynamics(CFD)analysis.For this purpose,the hydrodynamic resistances of without step,one-step,and two-st...In the present paper,the hydrodynamic performance of stepped planing craft is investigated by computational fluid dynamics(CFD)analysis.For this purpose,the hydrodynamic resistances of without step,one-step,and two-step hulls of Cougar planing craft are evaluated under different distances of the second step and LCG from aft,weight loadings,and Froude numbers(Fr).Our CFD results are appropriately validated against our conducted experimental test in National Iranians Marine Laboratory(NIMALA),Tehran,Iran.Then,the hydrodynamic resistance of intended planing crafts under various geometrical and physical conditions is predicted using artificial neural networks(ANNs).CFD analysis shows two different trends in the growth rate of resistance to weight ratio.So that,using steps for planing craft increases the resistance to weight ratio at lower Fr and decreases it at higher Fr.Additionally,by the increase of the distance between two steps,the resistance to weight ratio is decreased and the porpoising phenomenon is delayed.Furthermore,we obtained the maximum mean square error of ANNs output in the prediction of resistance to weight ratio equal to 0.0027.Finally,the predictive equation is suggested for the resistance to weight ratio of stepped planing craft according to weights and bias of designed ANNs.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 paper describes the application of SDSM （statistical downscaling model） and ANNs （artificial neural networks） models for prediction of the hydrological trend due to the climate-change. The SDSM has been calibr...The paper describes the application of SDSM （statistical downscaling model） and ANNs （artificial neural networks） models for prediction of the hydrological trend due to the climate-change. The SDSM has been calibrated and generated for the possible future scenarios of meteorological variables, which are temperature and rainfall by using GCMs （global climate models）. The GCM used is SRES A2. The downscaled meteorological variables corresponding to SDSM were then used as input to the ANNs model calibrated with observed station data to simulate the corresponding future streamflow changes in the sub-catchment of Kurau River. This study has discovered the hydrological trend over the catchment. The projected monthly streamflow has shown a decreasing trend due to the increase in the, mean of temperature for overall months, except the month of August and November.展开更多
In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Senso...In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable operation.WSN data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network traversal.The mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring RPs.The unique determination of this study is the shortest path to reach RPs.As the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static sinks.In this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the MS.Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage.In addition,a method of using MS scheduling for efficient data collection is provided.Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.展开更多
A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductiv...A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.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
The stability of strip footings subjected to eccentrically inclined loads is critical for reliable foundation design.This study investigates the effect of a circular unlined tunnel in a rock mass on the ultimate beari...The stability of strip footings subjected to eccentrically inclined loads is critical for reliable foundation design.This study investigates the effect of a circular unlined tunnel in a rock mass on the ultimate bearing capacity(UBC)of a foundation with width B under inclined and eccentric loads.Adaptive finite element limit analysis was employed to evaluate the reduction in UBC of the footing resting above a tunnel.The examined critical parameters include normalized load eccentricity(e/B),load inclination(β),and horizontal and vertical distances of the tunnel from the foundation(P/B and Q/B,respectively),along with rock mass properties.The results reveal that for e/B≥0.25 and β≤60°,the reduction coefficient,R_(c)≥0.90,suggesting that the presence of a tunnel has a minimal impact on the load-bearing capacity of the footing,with failure primarily governed by load eccentricity and inclination.Additionally,potential failure mechanisms are explored,showing that at lower e/B,higher β,and lower Q/B,the tunnel significantly affects footing's failure envelope.Conversely,at higher e/B and lower β,failure is due to rotational effects of footing,regardless of the tunnel's position.To predict the Rc more accurately,due to the time-consuming nature of direct calculations,both MLR and ANN models were developed.The MLR model provided a baseline for comparison,while the ANN model,with a coefficient of determination(R2)of 0.98,demonstrated superior accuracy compared to the R2=0.96 of MLR.Using both approaches ensured robust and efficient predictions of Rc.Since Rc does not directly provide the reduced UBC of footing due to presence of tunnel,the study introduced bearing capacity factor(Nc)to enable direct calculation of the reduced UBC of footing.These findings offer theoretical guidelines for preliminary design and provide practitioners with an effective tool for evaluating UBC reduction in complex loading scenarios involving tunnels.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.
文摘In the present paper,the hydrodynamic performance of stepped planing craft is investigated by computational fluid dynamics(CFD)analysis.For this purpose,the hydrodynamic resistances of without step,one-step,and two-step hulls of Cougar planing craft are evaluated under different distances of the second step and LCG from aft,weight loadings,and Froude numbers(Fr).Our CFD results are appropriately validated against our conducted experimental test in National Iranians Marine Laboratory(NIMALA),Tehran,Iran.Then,the hydrodynamic resistance of intended planing crafts under various geometrical and physical conditions is predicted using artificial neural networks(ANNs).CFD analysis shows two different trends in the growth rate of resistance to weight ratio.So that,using steps for planing craft increases the resistance to weight ratio at lower Fr and decreases it at higher Fr.Additionally,by the increase of the distance between two steps,the resistance to weight ratio is decreased and the porpoising phenomenon is delayed.Furthermore,we obtained the maximum mean square error of ANNs output in the prediction of resistance to weight ratio equal to 0.0027.Finally,the predictive equation is suggested for the resistance to weight ratio of stepped planing craft according to weights and bias of designed ANNs.
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.
文摘The paper describes the application of SDSM （statistical downscaling model） and ANNs （artificial neural networks） models for prediction of the hydrological trend due to the climate-change. The SDSM has been calibrated and generated for the possible future scenarios of meteorological variables, which are temperature and rainfall by using GCMs （global climate models）. The GCM used is SRES A2. The downscaled meteorological variables corresponding to SDSM were then used as input to the ANNs model calibrated with observed station data to simulate the corresponding future streamflow changes in the sub-catchment of Kurau River. This study has discovered the hydrological trend over the catchment. The projected monthly streamflow has shown a decreasing trend due to the increase in the, mean of temperature for overall months, except the month of August and November.
基金Research Supporting Project Number(RSP2024R421),King Saud University,Riyadh,Saudi Arabia.
文摘In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable operation.WSN data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network traversal.The mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring RPs.The unique determination of this study is the shortest path to reach RPs.As the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static sinks.In this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the MS.Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage.In addition,a method of using MS scheduling for efficient data collection is provided.Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.
基金supported by the Fundamental Research Funds for the Central Universities (No.3122020072)the Multi-investment Project of Tianjin Applied Basic Research(No.23JCQNJC00250)。
文摘A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.
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.
基金supported by the Civil Engineering Department, Madan Mohan Malaviya University of Technology, Gorakhpur, India
文摘The stability of strip footings subjected to eccentrically inclined loads is critical for reliable foundation design.This study investigates the effect of a circular unlined tunnel in a rock mass on the ultimate bearing capacity(UBC)of a foundation with width B under inclined and eccentric loads.Adaptive finite element limit analysis was employed to evaluate the reduction in UBC of the footing resting above a tunnel.The examined critical parameters include normalized load eccentricity(e/B),load inclination(β),and horizontal and vertical distances of the tunnel from the foundation(P/B and Q/B,respectively),along with rock mass properties.The results reveal that for e/B≥0.25 and β≤60°,the reduction coefficient,R_(c)≥0.90,suggesting that the presence of a tunnel has a minimal impact on the load-bearing capacity of the footing,with failure primarily governed by load eccentricity and inclination.Additionally,potential failure mechanisms are explored,showing that at lower e/B,higher β,and lower Q/B,the tunnel significantly affects footing's failure envelope.Conversely,at higher e/B and lower β,failure is due to rotational effects of footing,regardless of the tunnel's position.To predict the Rc more accurately,due to the time-consuming nature of direct calculations,both MLR and ANN models were developed.The MLR model provided a baseline for comparison,while the ANN model,with a coefficient of determination(R2)of 0.98,demonstrated superior accuracy compared to the R2=0.96 of MLR.Using both approaches ensured robust and efficient predictions of Rc.Since Rc does not directly provide the reduced UBC of footing due to presence of tunnel,the study introduced bearing capacity factor(Nc)to enable direct calculation of the reduced UBC of footing.These findings offer theoretical guidelines for preliminary design and provide practitioners with an effective tool for evaluating UBC reduction in complex loading scenarios involving tunnels.
