The alpha–beta filter algorithm has been widely researched for various applications,for example,navigation and target tracking systems.To improve the dynamic performance of the alpha–beta filter algorithm,a new pred...The alpha–beta filter algorithm has been widely researched for various applications,for example,navigation and target tracking systems.To improve the dynamic performance of the alpha–beta filter algorithm,a new prediction learning model is proposed in this study.The proposed model has two main components:(1)the alpha–beta filter algorithm is the main prediction module,and(2)the learning module is a feedforward artificial neural network(FF‐ANN).Furthermore,the model uses two inputs,temperature sensor and humidity sensor data,and a prediction algorithm is used to predict actual sensor readings from noisy sensor readings.Using the novel proposed technique,prediction accuracy is significantly improved while adding the feed‐forward backpropagation neural network,and also reduces the root mean square error(RMSE)and mean absolute error(MAE).We carried out different experiments with different experimental setups.The proposed model performance was evaluated with the traditional alpha–beta filter algorithm and other algorithms such as the Kalman filter.A higher prediction accuracy was achieved,and the MAE and RMSE were 35.1%–38.2%respectively.The final proposed model results show increased performance when compared to traditional methods.展开更多
The growing interest in the anaerobic ammonium oxidizing(AMX)process in treating high nitrogen containing wastewaters and a comprehensive study into the granulation mechanism of these bacteria under diverse environmen...The growing interest in the anaerobic ammonium oxidizing(AMX)process in treating high nitrogen containing wastewaters and a comprehensive study into the granulation mechanism of these bacteria under diverse environmental conditions over the years have been unequal.To this effect,the distinctive differences in saline adapted AMX(S_AMX)and nonsaline adapted AMX(NS_AMX)granules are presented in this study.It was observed that substrate utilisation profiles,granule formation mechanism,and pace towards granulation differed marginally for the two adaptation conditions.The different microbial dominant aggregation types aided in splitting the 471 days operated lab-scale SBRs into three distinct phases.In both reactors,phase III(granules dominant phase)showed the highest average nitrogen removal efficiency of 87.9%±4.8%and 85.6%±3.6%for the S_AMX and NS_AMX processes,respectively.The extracellular polymeric substances(EPS)quantity and major composition determined its role either as a binding agent in granulation or a survival mechanism in saline adaptation.It was also observed that granules of the S_AMX reactor were mostly loosely and less condensed aggregates of smaller sub-units and flocs while those of the NS_AMX reactor were compact agglomerates.The ionic gradient in saline enrichment led to an increased activity of the Na^(+)/K^(+)–ATPase,hence enriched granules produced higher cellular adenosine triphosphate molecules which finally improved the granules active biomass ratio by 32.96%.Microbial community showed that about three to four major known AMX species made up the granules consortia in both reactors.Proteins and expression of functional genes differed for these different species.展开更多
基金supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2020‐0‐01441,Artificial Intelligence Convergence Research Center(Chungnam National University))“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS‐004).
文摘The alpha–beta filter algorithm has been widely researched for various applications,for example,navigation and target tracking systems.To improve the dynamic performance of the alpha–beta filter algorithm,a new prediction learning model is proposed in this study.The proposed model has two main components:(1)the alpha–beta filter algorithm is the main prediction module,and(2)the learning module is a feedforward artificial neural network(FF‐ANN).Furthermore,the model uses two inputs,temperature sensor and humidity sensor data,and a prediction algorithm is used to predict actual sensor readings from noisy sensor readings.Using the novel proposed technique,prediction accuracy is significantly improved while adding the feed‐forward backpropagation neural network,and also reduces the root mean square error(RMSE)and mean absolute error(MAE).We carried out different experiments with different experimental setups.The proposed model performance was evaluated with the traditional alpha–beta filter algorithm and other algorithms such as the Kalman filter.A higher prediction accuracy was achieved,and the MAE and RMSE were 35.1%–38.2%respectively.The final proposed model results show increased performance when compared to traditional methods.
基金supported by the National Research Foundation(NRF)of Korea(project#:NRF-2020R1A2C2006769)。
文摘The growing interest in the anaerobic ammonium oxidizing(AMX)process in treating high nitrogen containing wastewaters and a comprehensive study into the granulation mechanism of these bacteria under diverse environmental conditions over the years have been unequal.To this effect,the distinctive differences in saline adapted AMX(S_AMX)and nonsaline adapted AMX(NS_AMX)granules are presented in this study.It was observed that substrate utilisation profiles,granule formation mechanism,and pace towards granulation differed marginally for the two adaptation conditions.The different microbial dominant aggregation types aided in splitting the 471 days operated lab-scale SBRs into three distinct phases.In both reactors,phase III(granules dominant phase)showed the highest average nitrogen removal efficiency of 87.9%±4.8%and 85.6%±3.6%for the S_AMX and NS_AMX processes,respectively.The extracellular polymeric substances(EPS)quantity and major composition determined its role either as a binding agent in granulation or a survival mechanism in saline adaptation.It was also observed that granules of the S_AMX reactor were mostly loosely and less condensed aggregates of smaller sub-units and flocs while those of the NS_AMX reactor were compact agglomerates.The ionic gradient in saline enrichment led to an increased activity of the Na^(+)/K^(+)–ATPase,hence enriched granules produced higher cellular adenosine triphosphate molecules which finally improved the granules active biomass ratio by 32.96%.Microbial community showed that about three to four major known AMX species made up the granules consortia in both reactors.Proteins and expression of functional genes differed for these different species.
