Rolling force for strip casting of 1Cr17 ferritic stainless steel was predicted using theoretical model and artificial intelligence.Solution zone was classified into two parts by kiss point position during casting str...Rolling force for strip casting of 1Cr17 ferritic stainless steel was predicted using theoretical model and artificial intelligence.Solution zone was classified into two parts by kiss point position during casting strip.Navier-Stokes equation in fluid mechanics and stream function were introduced to analyze the rheological property of liquid zone and mushy zone,and deduce the analytic equation of unit compression stress distribution.The traditional hot rolling model was still used in the solid zone.Neural networks based on feedforward training algorithm in Bayesian regularization were introduced to build model for kiss point position.The results show that calculation accuracy for verification data of 94.67% is in the range of ±7.0%,which indicates that the predicting accuracy of this model is very high.展开更多
In the current study, the efficiency of Wavelet-based Least Square Support Vector Machine （WLSSVM） model was examined for prediction of daily and monthly Suspended Sediment Load （SSL） of the Mississippi River. For...In the current study, the efficiency of Wavelet-based Least Square Support Vector Machine （WLSSVM） model was examined for prediction of daily and monthly Suspended Sediment Load （SSL） of the Mississippi River. For this purpose, in the first step, SSL was predicted via ad hoc LSSVM and Artificial Neural Network （ANN） models; then, streamflow and SSL data were decomposed into sub- signals via wavelet, and these decomposed sub-time series were imposed to LSSVM and ANN to simulate discharge-SSL relationship. Finally, the ability of WLSSVM was compared with other models in multi- step-ahead SSL predictions. The results showed that in daily SSL prediction, LSSVM has better outcomes with Determination Coefficient （DC）=o.92 than ad hoc ANN with DC=o.88. However unlike daily SSL, in monthly modeling, ANN has a bit accurate upshot. WLSSVM and wavelet-based ANN （WANN） models showed same consequences in daily and different in monthly SSL predictions, and adding wavelet led to more accuracy of LSSVM and ANN. Furthermore, conjunction of wavelet to LSSVM and ANN evaluated via multi-step-ahead SSL predictions and, e.g., DCLssVM=0.4 was increased to the DCwLsSVM=0.71 in 7- day ahead SSL prediction. In addition, WLSSVM outperformed WANN by increment of time horizon prediction.展开更多
This study aimed to contribute in establishing an international journalism model of professionalism in the production of the news. The main purpose is to explore the degree to which this model predicts the professiona...This study aimed to contribute in establishing an international journalism model of professionalism in the production of the news. The main purpose is to explore the degree to which this model predicts the professional values in the media content. In particular, this model was tested on the content of a leading news organization in the Middle East, AI Jazeera, to identify whether or not AI Jazeera reflected professional values in news production or other non-professional values. A total of 592 news stories--234 from AJE and 358 from AJA--published from January I, 2014, to April 30, 2014, were analyzed. The findings of this study indicate that AI Jazeera reflects professional values to a substantial degree. The professional values were reflected highly and nearly two thirds of the stories had professional values in the content. The chi square tests shows there are frequency/percentage differences, but overall the patterns are similar, with no statistically significant differences in the AJA and AlE. Scholarly implications, future studies and limitations were presented in this study.展开更多
An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accur...An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation(BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand(gross domestic product(GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand(population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.展开更多
Leakage power is the dominant source of power dissipation for Sub-100 nm VLSI （very large scale integration） circuits. Various techniques were proposed to reduce the leakage power at nano-scale; one of these techniq...Leakage power is the dominant source of power dissipation for Sub-100 nm VLSI （very large scale integration） circuits. Various techniques were proposed to reduce the leakage power at nano-scale; one of these techniques is MTV （multi-threshold voltage） In this paper, the exact and optimal value of threshold voltage （Vth） for each transistor in any sequential circuit in the design is found, so that the value of the total leakage current in the design is at the minimum. This could be achieved by applying AI （artificial intelligence） search algorithm. The proposed algorithm is called LOAIS （leakage optimization using AI search）. LOAIS exploits the total slack time of each transistor＇s location and their contributions in the leakage current. It is introduced by AI heuristic search algorithms under 22 nm BSIM4 predictive technology model. The proposed approach saves around 80% of the sub-threshold leakage current without degrading the performance of the circuit.展开更多
文摘局部晚期直肠癌患者无法直接切除病灶,在实施新辅助放化疗(neoadjuvant chemoradiotherapy,nCRT)后,部分人群反应较敏感,会出现完全的肿瘤反应,因此,对于此类患者局部切除或“观察等待”疗法有望取代手术切除,从而可以保留患者肛门和避免不必要的手术并发症。因而需要一种无创且可靠的评估方法判断nCRT后的肿瘤反应。MRI在直肠癌的初次分期和重新评估肿瘤对nCRT的反应方面起着至关重要的作用。目前评估手段主要有常规MRI、功能MRI(functional magnetic resonance imaging,fMRI)以及基于MRI的人工智能预测模型。本文就以上三种评估方式在预测局部晚期直肠癌nCRT后肿瘤反应的研究进展进行综合阐述。
基金Project(2004CB619108) supported by National Basic Research Program of China
文摘Rolling force for strip casting of 1Cr17 ferritic stainless steel was predicted using theoretical model and artificial intelligence.Solution zone was classified into two parts by kiss point position during casting strip.Navier-Stokes equation in fluid mechanics and stream function were introduced to analyze the rheological property of liquid zone and mushy zone,and deduce the analytic equation of unit compression stress distribution.The traditional hot rolling model was still used in the solid zone.Neural networks based on feedforward training algorithm in Bayesian regularization were introduced to build model for kiss point position.The results show that calculation accuracy for verification data of 94.67% is in the range of ±7.0%,which indicates that the predicting accuracy of this model is very high.
基金supported by the University of Tabriz under grant No. 1117394325
文摘In the current study, the efficiency of Wavelet-based Least Square Support Vector Machine （WLSSVM） model was examined for prediction of daily and monthly Suspended Sediment Load （SSL） of the Mississippi River. For this purpose, in the first step, SSL was predicted via ad hoc LSSVM and Artificial Neural Network （ANN） models; then, streamflow and SSL data were decomposed into sub- signals via wavelet, and these decomposed sub-time series were imposed to LSSVM and ANN to simulate discharge-SSL relationship. Finally, the ability of WLSSVM was compared with other models in multi- step-ahead SSL predictions. The results showed that in daily SSL prediction, LSSVM has better outcomes with Determination Coefficient （DC）=o.92 than ad hoc ANN with DC=o.88. However unlike daily SSL, in monthly modeling, ANN has a bit accurate upshot. WLSSVM and wavelet-based ANN （WANN） models showed same consequences in daily and different in monthly SSL predictions, and adding wavelet led to more accuracy of LSSVM and ANN. Furthermore, conjunction of wavelet to LSSVM and ANN evaluated via multi-step-ahead SSL predictions and, e.g., DCLssVM=0.4 was increased to the DCwLsSVM=0.71 in 7- day ahead SSL prediction. In addition, WLSSVM outperformed WANN by increment of time horizon prediction.
文摘This study aimed to contribute in establishing an international journalism model of professionalism in the production of the news. The main purpose is to explore the degree to which this model predicts the professional values in the media content. In particular, this model was tested on the content of a leading news organization in the Middle East, AI Jazeera, to identify whether or not AI Jazeera reflected professional values in news production or other non-professional values. A total of 592 news stories--234 from AJE and 358 from AJA--published from January I, 2014, to April 30, 2014, were analyzed. The findings of this study indicate that AI Jazeera reflects professional values to a substantial degree. The professional values were reflected highly and nearly two thirds of the stories had professional values in the content. The chi square tests shows there are frequency/percentage differences, but overall the patterns are similar, with no statistically significant differences in the AJA and AlE. Scholarly implications, future studies and limitations were presented in this study.
文摘An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation(BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand(gross domestic product(GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand(population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.
文摘Leakage power is the dominant source of power dissipation for Sub-100 nm VLSI （very large scale integration） circuits. Various techniques were proposed to reduce the leakage power at nano-scale; one of these techniques is MTV （multi-threshold voltage） In this paper, the exact and optimal value of threshold voltage （Vth） for each transistor in any sequential circuit in the design is found, so that the value of the total leakage current in the design is at the minimum. This could be achieved by applying AI （artificial intelligence） search algorithm. The proposed algorithm is called LOAIS （leakage optimization using AI search）. LOAIS exploits the total slack time of each transistor＇s location and their contributions in the leakage current. It is introduced by AI heuristic search algorithms under 22 nm BSIM4 predictive technology model. The proposed approach saves around 80% of the sub-threshold leakage current without degrading the performance of the circuit.
