Automobile companies that spend billions of dollars annually towards warranty cost, give high priority to warranty reduction programs. Forecasting of automobile warranty performance plays an important role towards the...Automobile companies that spend billions of dollars annually towards warranty cost, give high priority to warranty reduction programs. Forecasting of automobile warranty performance plays an important role towards these efforts. The forecasting process involves prediction of not only the specific months-in-service (MIS) warranty performance at certain future time, but also at future MIS values. However, 'maturing data' (also called warranty growth) phenomena that causes warranty performance at specific MIS values to change with time, makes such a forecasting task challenging. Although warranty forecasting methods such as log-log plots and dynamic linear models appear in literature, there is a need for applications addressing the well recognized issue of ‘maturing data’. In this paper we use an artificial neural network for the forecasting of warranty performance in presence of ‘maturing data’ phenomena. The network parameters are optimized by minimizing the training and testing errors using response surface methodology. This application shows the effectiveness of neural networks in the forecasting of automobile warranty performance in the presence of the ‘maturing data’ phenomena.展开更多
为了实现采用交织子载波分配方法的正交频分多址(orthogonal frequency division multiplexing access,OFDMA)上行链路多个用户频偏的联合估计,提出一种基于子空间的两阶段频偏搜索方法。由于多重信号分类法(multiple signal characteri...为了实现采用交织子载波分配方法的正交频分多址(orthogonal frequency division multiplexing access,OFDMA)上行链路多个用户频偏的联合估计,提出一种基于子空间的两阶段频偏搜索方法。由于多重信号分类法(multiple signal characteristic,MUSIC)的采用,使得该方法具有较高的精确度,并且该方法不需要知道接入的用户个数及用户所占用的子信道;因此适用于随机分配子信道的情况。仿真结果表明:当信噪比大于5 dB时,其标准均方根误差小于子载波间隔的1%,满足宽带无线接入系统关于载波频率偏差应小于子载波间隔2%的频率同步要求。展开更多
The Bertalanffy-Pütter (BP) five-parameter growth model provides a versatile framework for the modeling of growth. Using data from a growth experiment in literature about the average size-at-age of 24 species of ...The Bertalanffy-Pütter (BP) five-parameter growth model provides a versatile framework for the modeling of growth. Using data from a growth experiment in literature about the average size-at-age of 24 species of tropical trees over ten years in the same area, we identified their best-fit BP-model parameters. While different species had different best-fit exponent-pairs, there was a model with a good fit to 21 (87.5%) of the data </span><span style="font-family:Verdana;">(</span><span style="font-family:""><span style="font-family:Verdana;">“Good fit” means a </span><span style="font-family:Verdana;">normalized root-mean-squared-error <i></span><i><span style="font-family:Verdana;">NRMSE</span></i><span style="font-family:Verdana;"></i> below 2.5%. This threshold was the 95% quantile of the lognormal distribution that was fitted to the <i></span><i><span style="font-family:Verdana;">NRMSE</span></i><span style="font-family:Verdana;"></i> values for the best-fit models for the data)</span></span><span style="font-family:Verdana;">.</span><span style="font-family:Verdana;"> In view of the sigmoidal character of this model despite the early stand we discuss </span><span style="font-family:Verdana;">whether </span><span style="font-family:Verdana;">the setting of the growth experiment may have impeded growth.展开更多
文摘Automobile companies that spend billions of dollars annually towards warranty cost, give high priority to warranty reduction programs. Forecasting of automobile warranty performance plays an important role towards these efforts. The forecasting process involves prediction of not only the specific months-in-service (MIS) warranty performance at certain future time, but also at future MIS values. However, 'maturing data' (also called warranty growth) phenomena that causes warranty performance at specific MIS values to change with time, makes such a forecasting task challenging. Although warranty forecasting methods such as log-log plots and dynamic linear models appear in literature, there is a need for applications addressing the well recognized issue of ‘maturing data’. In this paper we use an artificial neural network for the forecasting of warranty performance in presence of ‘maturing data’ phenomena. The network parameters are optimized by minimizing the training and testing errors using response surface methodology. This application shows the effectiveness of neural networks in the forecasting of automobile warranty performance in the presence of the ‘maturing data’ phenomena.
文摘为了实现采用交织子载波分配方法的正交频分多址(orthogonal frequency division multiplexing access,OFDMA)上行链路多个用户频偏的联合估计,提出一种基于子空间的两阶段频偏搜索方法。由于多重信号分类法(multiple signal characteristic,MUSIC)的采用,使得该方法具有较高的精确度,并且该方法不需要知道接入的用户个数及用户所占用的子信道;因此适用于随机分配子信道的情况。仿真结果表明:当信噪比大于5 dB时,其标准均方根误差小于子载波间隔的1%,满足宽带无线接入系统关于载波频率偏差应小于子载波间隔2%的频率同步要求。
文摘The Bertalanffy-Pütter (BP) five-parameter growth model provides a versatile framework for the modeling of growth. Using data from a growth experiment in literature about the average size-at-age of 24 species of tropical trees over ten years in the same area, we identified their best-fit BP-model parameters. While different species had different best-fit exponent-pairs, there was a model with a good fit to 21 (87.5%) of the data </span><span style="font-family:Verdana;">(</span><span style="font-family:""><span style="font-family:Verdana;">“Good fit” means a </span><span style="font-family:Verdana;">normalized root-mean-squared-error <i></span><i><span style="font-family:Verdana;">NRMSE</span></i><span style="font-family:Verdana;"></i> below 2.5%. This threshold was the 95% quantile of the lognormal distribution that was fitted to the <i></span><i><span style="font-family:Verdana;">NRMSE</span></i><span style="font-family:Verdana;"></i> values for the best-fit models for the data)</span></span><span style="font-family:Verdana;">.</span><span style="font-family:Verdana;"> In view of the sigmoidal character of this model despite the early stand we discuss </span><span style="font-family:Verdana;">whether </span><span style="font-family:Verdana;">the setting of the growth experiment may have impeded growth.
