There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops quickly.The high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices...There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops quickly.The high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices make it challenging to guarantee the service quality of these devices.To maximise the data rate fairness of Narrow Band IoT devices,a multi‐dimensional indoor localisation model is devised,consisting of transmission power,data scheduling,and time slot scheduling,based on a network model that employs non‐orthogonal multiple access via a relay.Based on this network model,the optimisation goal of Narrow Band IoT device data rate ratio fairness is first established by the authors,while taking into account the Narrow Band IoT network:The multidimensional indoor localisation optimisation model of equipment tends to minimize data rate,energy constraints and EH relay energy and data buffer constraints,data scheduling and time slot scheduling.As a result,each Narrow Band IoT device's data rate needs are met while the network's overall performance is optimised.We investigate the model's potential for convex optimisation and offer an algorithm for optimising the distribution of multiple resources using the KKT criterion.The current work primarily considers the NOMA Narrow Band IoT network under a single EH relay.However,the growth of Narrow Band IoT devices also leads to a rise in co‐channel interference,which impacts NOMA's performance enhancement.Through simulation,the proposed approach is successfully shown.These improvements have boosted the network's energy efficiency by 44.1%,data rate proportional fairness by 11.9%,and spectrum efficiency by 55.4%.展开更多
With the exponential growth of the computing power,machine learning techniques have been successully used in various applications.This paper intended to predict and optimize the shear strength of single lap cassava st...With the exponential growth of the computing power,machine learning techniques have been successully used in various applications.This paper intended to predict and optimize the shear strength of single lap cassava starch-based adhesive joints for comparison with the application of artificial intelligence(AI)methods.The shear strength was firstly determined by the experiment with three independent experimental variables(starch content,NaOH concentration and reaction temperature).The analysis of range(ANORA)and analysis of variance(ANOVA)were applied to investigate the optimal combination and the significance of each factor for the shear strength based on the orthogonal experiment.The performance of all AI models was char acterized by mean absolute error(MAE),root mean square error(RMSE)and regression coefficient(R^(2))compared with the experi-mental ones.The GA optimized ANN model was combined with the genetic algorithm(GA)to find the optimal combination of factors for the finalized optimized cassava starch adhesives(CSA-OP).The physicochemical prop-erties of the cassava starch and CSA-OP were determined by the FTIR,TGA and SEM EDS,respectively.The results showed that the numerical optimized condition of the GA optimized ANN model was superior to the orthogonal experimental optimized condition.The sensitivity analysis revealed that the relative importance of variables was consistent with the results from ANOVA.FTIR results showed that there were high hydroxyl groups in cassava starch.TGA results showed that the residue of CSA OP was higher than the assava starch.SEM EDS results showed that both the cassava starch and CSA OP had abundant carbon and oxygen functional groups.Consequently,the obtained results revealed that the use of AI methods was an adequate approach to model and optimize the experimental variables of the shear strength of single lap cassava starch-based adhesive joints.展开更多
文摘There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops quickly.The high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices make it challenging to guarantee the service quality of these devices.To maximise the data rate fairness of Narrow Band IoT devices,a multi‐dimensional indoor localisation model is devised,consisting of transmission power,data scheduling,and time slot scheduling,based on a network model that employs non‐orthogonal multiple access via a relay.Based on this network model,the optimisation goal of Narrow Band IoT device data rate ratio fairness is first established by the authors,while taking into account the Narrow Band IoT network:The multidimensional indoor localisation optimisation model of equipment tends to minimize data rate,energy constraints and EH relay energy and data buffer constraints,data scheduling and time slot scheduling.As a result,each Narrow Band IoT device's data rate needs are met while the network's overall performance is optimised.We investigate the model's potential for convex optimisation and offer an algorithm for optimising the distribution of multiple resources using the KKT criterion.The current work primarily considers the NOMA Narrow Band IoT network under a single EH relay.However,the growth of Narrow Band IoT devices also leads to a rise in co‐channel interference,which impacts NOMA's performance enhancement.Through simulation,the proposed approach is successfully shown.These improvements have boosted the network's energy efficiency by 44.1%,data rate proportional fairness by 11.9%,and spectrum efficiency by 55.4%.
基金This work was supported by the Fundamental Research Funds for the Central Universities(Y0201800586)the Regional Cooperative Innovation in Autonomous Region(2019E0241).
文摘With the exponential growth of the computing power,machine learning techniques have been successully used in various applications.This paper intended to predict and optimize the shear strength of single lap cassava starch-based adhesive joints for comparison with the application of artificial intelligence(AI)methods.The shear strength was firstly determined by the experiment with three independent experimental variables(starch content,NaOH concentration and reaction temperature).The analysis of range(ANORA)and analysis of variance(ANOVA)were applied to investigate the optimal combination and the significance of each factor for the shear strength based on the orthogonal experiment.The performance of all AI models was char acterized by mean absolute error(MAE),root mean square error(RMSE)and regression coefficient(R^(2))compared with the experi-mental ones.The GA optimized ANN model was combined with the genetic algorithm(GA)to find the optimal combination of factors for the finalized optimized cassava starch adhesives(CSA-OP).The physicochemical prop-erties of the cassava starch and CSA-OP were determined by the FTIR,TGA and SEM EDS,respectively.The results showed that the numerical optimized condition of the GA optimized ANN model was superior to the orthogonal experimental optimized condition.The sensitivity analysis revealed that the relative importance of variables was consistent with the results from ANOVA.FTIR results showed that there were high hydroxyl groups in cassava starch.TGA results showed that the residue of CSA OP was higher than the assava starch.SEM EDS results showed that both the cassava starch and CSA OP had abundant carbon and oxygen functional groups.Consequently,the obtained results revealed that the use of AI methods was an adequate approach to model and optimize the experimental variables of the shear strength of single lap cassava starch-based adhesive joints.
