In this paper our studies about the sequential testing program for predicting and identificating carcinogens, sequential discriminant method and cost- effectiveness analysis are summarized. The analysis of our databas...In this paper our studies about the sequential testing program for predicting and identificating carcinogens, sequential discriminant method and cost- effectiveness analysis are summarized. The analysis of our database of carcinogeniclty and genotoxicity of chemicals demonstrates the uncertainty . of short- term tests ( STTs ) to predict carcinogens and the results of most routine STTs are statistically dependent. We recommend the sequential testing program combining STTs and carclnogenicity assay, the optimal STT batteries, the rules of the sequential discrimination and the preferal choices of STTs tor specific chemical class. For illustrative pmposes the carclnogenicity prediction of several sample chamicals is presented. The results of cost-effectiveness analysis suggest that this program has vast social-economic effectiveness.展开更多
Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models...Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.展开更多
Building heating,ventilation,and air conditioning(HVAC)systems consume large amounts of energy,and precise energy prediction is necessary for developing various energy-efficiency strategies.Energy prediction using dat...Building heating,ventilation,and air conditioning(HVAC)systems consume large amounts of energy,and precise energy prediction is necessary for developing various energy-efficiency strategies.Energy prediction using data-driven models has received increasing attention in recent years.Typically,two types of driven models are used for building energy prediction:sequential and parallel predictive models.The latter uses the historical energy of the target building as training data to predict future energy consumption.However,for newly built buildings or buildings without historical data records,the energy can be estimated using the parallel model,which employs the energy data of similar buildings as training data.The second predictive model is seldom studied because the model input feature is difficult to identify and collect.Herein,we propose a novel key-variable-based parallel HVAC energy predictive model.This model has informative input features(including meteorological data,occupancy activity,and key variables representing building and system characteristics)and a simple architecture.A general key-variable screening toolkit which was more versatile and flexible than present parametric analysis tools was developed to facilitate the selection of key variables for the parallel HVAC energy predictive model.A case study is conducted to screen the key variables of hotel buildings in eastern China,based on which a parallel chiller energy predictive model is trained and tested.The average cross-test error measured in terms of the coefficient of variation of the root mean square error(CV-RMSE)and normalized mean bias error(NMBE)of the parallel chiller energy predictive model is approximately 16%and 8.3%,which is acceptable for energy prediction without using historical energy data of the target building.展开更多
文摘In this paper our studies about the sequential testing program for predicting and identificating carcinogens, sequential discriminant method and cost- effectiveness analysis are summarized. The analysis of our database of carcinogeniclty and genotoxicity of chemicals demonstrates the uncertainty . of short- term tests ( STTs ) to predict carcinogens and the results of most routine STTs are statistically dependent. We recommend the sequential testing program combining STTs and carclnogenicity assay, the optimal STT batteries, the rules of the sequential discrimination and the preferal choices of STTs tor specific chemical class. For illustrative pmposes the carclnogenicity prediction of several sample chamicals is presented. The results of cost-effectiveness analysis suggest that this program has vast social-economic effectiveness.
文摘Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.
基金This research is sponsored by China Southern Power Grid Technology Co.LTD(No.GDKJXM20200569).
文摘Building heating,ventilation,and air conditioning(HVAC)systems consume large amounts of energy,and precise energy prediction is necessary for developing various energy-efficiency strategies.Energy prediction using data-driven models has received increasing attention in recent years.Typically,two types of driven models are used for building energy prediction:sequential and parallel predictive models.The latter uses the historical energy of the target building as training data to predict future energy consumption.However,for newly built buildings or buildings without historical data records,the energy can be estimated using the parallel model,which employs the energy data of similar buildings as training data.The second predictive model is seldom studied because the model input feature is difficult to identify and collect.Herein,we propose a novel key-variable-based parallel HVAC energy predictive model.This model has informative input features(including meteorological data,occupancy activity,and key variables representing building and system characteristics)and a simple architecture.A general key-variable screening toolkit which was more versatile and flexible than present parametric analysis tools was developed to facilitate the selection of key variables for the parallel HVAC energy predictive model.A case study is conducted to screen the key variables of hotel buildings in eastern China,based on which a parallel chiller energy predictive model is trained and tested.The average cross-test error measured in terms of the coefficient of variation of the root mean square error(CV-RMSE)and normalized mean bias error(NMBE)of the parallel chiller energy predictive model is approximately 16%and 8.3%,which is acceptable for energy prediction without using historical energy data of the target building.
