On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Mal...On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian-River basin. The results by calculating show that the solution based on BP algorithms are consis- tent with those based multiple - variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.展开更多
This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weat...This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.展开更多
In order to know the ventilating capacity of imperial smelt furnace(ISF), and increase the output of plumbum, an intelligent modeling method based on gray theory and artificial neural networks(ANN) is proposed, in whi...In order to know the ventilating capacity of imperial smelt furnace(ISF), and increase the output of plumbum, an intelligent modeling method based on gray theory and artificial neural networks(ANN) is proposed, in which the weight values in the integrated model can be adjusted automatically. An intelligent predictive model of the ventilating capacity of the ISF is established and analyzed by the method. The simulation results and industrial applications demonstrate that the predictive model is close to the real plant, the relative predictive error is 0.72%, which is 50% less than the single model, leading to a notable increase of the output of plumbum.展开更多
Virtual manufacturing is fast becoming an affordable technology with wide-ranging applications in modern manufacturing. Its advantages over existing technology are primarily that users can visualize, feel involvement ...Virtual manufacturing is fast becoming an affordable technology with wide-ranging applications in modern manufacturing. Its advantages over existing technology are primarily that users can visualize, feel involvement and interact with virtual representations of real world activities in real time. In this paper, a virtual cutting system is built which can simulate turning process, estimate tool wear and cutting force using artificial neural network etc. Using the simulated machining environment in virtual reality (VR), the user can practise and preview the operations for possible problems that might occur during implementation. This approach enables designers to evaluate and design feasible machining processes in a consistent manner as early as possible during the development process.展开更多
目的基于Logistic回归和人工神经网络构建老年糖尿病足(diabetic foot,DF)患者衰弱风险预测模型,并比较两种模型预测效能,为早期识别并预防老年DF患者衰弱的发生提供依据。方法2023年5-10月,采用便利抽样法选取天津市某两所三级甲等医院...目的基于Logistic回归和人工神经网络构建老年糖尿病足(diabetic foot,DF)患者衰弱风险预测模型,并比较两种模型预测效能,为早期识别并预防老年DF患者衰弱的发生提供依据。方法2023年5-10月,采用便利抽样法选取天津市某两所三级甲等医院内491例老年DF患者为研究对象。通过问卷调查及病历记录收集资料,绘制列线图模型及人工神经网络模型;受试者工作特征曲线和曲线下面积评估模型预测能力,敏感度和特异度评估模型预测价值。结果建模组列线图和人工神经网络模型的曲线下面积(area under curve,AUC)分别为0.973、0.742,敏感度分别为92.90%、95.50%,特异度分别为91.10%、50.50%。结论构建的老年DF患者衰弱风险预测的列线图模型预测性能较好,对有效识别高衰弱风险的老年DF患者有临床价值。展开更多
基金Supported by Brilliant Youth Fund in Hebei Province
文摘On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian-River basin. The results by calculating show that the solution based on BP algorithms are consis- tent with those based multiple - variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.
文摘This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.
文摘In order to know the ventilating capacity of imperial smelt furnace(ISF), and increase the output of plumbum, an intelligent modeling method based on gray theory and artificial neural networks(ANN) is proposed, in which the weight values in the integrated model can be adjusted automatically. An intelligent predictive model of the ventilating capacity of the ISF is established and analyzed by the method. The simulation results and industrial applications demonstrate that the predictive model is close to the real plant, the relative predictive error is 0.72%, which is 50% less than the single model, leading to a notable increase of the output of plumbum.
文摘Virtual manufacturing is fast becoming an affordable technology with wide-ranging applications in modern manufacturing. Its advantages over existing technology are primarily that users can visualize, feel involvement and interact with virtual representations of real world activities in real time. In this paper, a virtual cutting system is built which can simulate turning process, estimate tool wear and cutting force using artificial neural network etc. Using the simulated machining environment in virtual reality (VR), the user can practise and preview the operations for possible problems that might occur during implementation. This approach enables designers to evaluate and design feasible machining processes in a consistent manner as early as possible during the development process.
文摘目的基于Logistic回归和人工神经网络构建老年糖尿病足(diabetic foot,DF)患者衰弱风险预测模型,并比较两种模型预测效能,为早期识别并预防老年DF患者衰弱的发生提供依据。方法2023年5-10月,采用便利抽样法选取天津市某两所三级甲等医院内491例老年DF患者为研究对象。通过问卷调查及病历记录收集资料,绘制列线图模型及人工神经网络模型;受试者工作特征曲线和曲线下面积评估模型预测能力,敏感度和特异度评估模型预测价值。结果建模组列线图和人工神经网络模型的曲线下面积(area under curve,AUC)分别为0.973、0.742,敏感度分别为92.90%、95.50%,特异度分别为91.10%、50.50%。结论构建的老年DF患者衰弱风险预测的列线图模型预测性能较好,对有效识别高衰弱风险的老年DF患者有临床价值。
