It is obviously advantageous to use single-pattern cell ternary tree (T-gate)network to obtain ternary logic function. Many scholars at home and abroad have done much in minimization of T-gate realization of multiple-...It is obviously advantageous to use single-pattern cell ternary tree (T-gate)network to obtain ternary logic function. Many scholars at home and abroad have done much in minimization of T-gate realization of multiple-valued logic. It is generally acknowledged that it is necessary to try N! times in order to get an optimal result. However, using the Input Vector Map presented here, which is as simple and convenient as Binary Karnaugh Map, we can get an optimal result by trying only N times.展开更多
A method that applies clustering technique to reduce the number of samples of large data sets using input-output clustering is proposed.The proposed method clusters the output data into groups and clusters the input d...A method that applies clustering technique to reduce the number of samples of large data sets using input-output clustering is proposed.The proposed method clusters the output data into groups and clusters the input data in accordance with the groups of output data.Then,a set of prototypes are selected from the clustered input data.The inessential data can be ultimately discarded from the data set.The proposed method can reduce the effect from outliers because only the prototypes are used.This method is applied to reduce the data set in regression problems.Two standard synthetic data sets and three standard real-world data sets are used for evaluation.The root-mean-square errors are compared from support vector regression models trained with the original data sets and the corresponding instance-reduced data sets.From the experiments,the proposed method provides good results on the reduction and the reconstruction of the standard synthetic and real-world data sets.The numbers of instances of the synthetic data sets are decreased by 25%-69%.The reduction rates for the real-world data sets of the automobile miles per gallon and the 1990 census in CA are 46% and 57%,respectively.The reduction rate of 96% is very good for the electrocardiogram(ECG) data set because of the redundant and periodic nature of ECG signals.For all of the data sets,the regression results are similar to those from the corresponding original data sets.Therefore,the regression performance of the proposed method is good while only a fraction of the data is needed in the training process.展开更多
为缓解负偏置温度不稳定性(negative bias temperature instability,NBTI)效应引起的电路老化,提高电路可靠性,提出一种在电路待机状态下应用输入向量约束的门替换方法.运用动态和静态的NBTI模型进行感知NBTI的静态时序分析,确定潜在关...为缓解负偏置温度不稳定性(negative bias temperature instability,NBTI)效应引起的电路老化,提高电路可靠性,提出一种在电路待机状态下应用输入向量约束的门替换方法.运用动态和静态的NBTI模型进行感知NBTI的静态时序分析,确定潜在关键路径,考虑路径相关性的关键门算法以确定关键门,并生成能使关键门最大限度处于恢复阶段的输入向量.对输入向量无法控制的关键门采用门替换方法进行内部控制.对ISCAS标准电路的实验结果表明,电路时序余量为5%时,该方法的平均门替换率降低到9.68%,时延改善率提高到39.65%.展开更多
文摘It is obviously advantageous to use single-pattern cell ternary tree (T-gate)network to obtain ternary logic function. Many scholars at home and abroad have done much in minimization of T-gate realization of multiple-valued logic. It is generally acknowledged that it is necessary to try N! times in order to get an optimal result. However, using the Input Vector Map presented here, which is as simple and convenient as Binary Karnaugh Map, we can get an optimal result by trying only N times.
基金supported by Chiang Mai University Research Fund under the contract number T-M5744
文摘A method that applies clustering technique to reduce the number of samples of large data sets using input-output clustering is proposed.The proposed method clusters the output data into groups and clusters the input data in accordance with the groups of output data.Then,a set of prototypes are selected from the clustered input data.The inessential data can be ultimately discarded from the data set.The proposed method can reduce the effect from outliers because only the prototypes are used.This method is applied to reduce the data set in regression problems.Two standard synthetic data sets and three standard real-world data sets are used for evaluation.The root-mean-square errors are compared from support vector regression models trained with the original data sets and the corresponding instance-reduced data sets.From the experiments,the proposed method provides good results on the reduction and the reconstruction of the standard synthetic and real-world data sets.The numbers of instances of the synthetic data sets are decreased by 25%-69%.The reduction rates for the real-world data sets of the automobile miles per gallon and the 1990 census in CA are 46% and 57%,respectively.The reduction rate of 96% is very good for the electrocardiogram(ECG) data set because of the redundant and periodic nature of ECG signals.For all of the data sets,the regression results are similar to those from the corresponding original data sets.Therefore,the regression performance of the proposed method is good while only a fraction of the data is needed in the training process.
文摘为缓解负偏置温度不稳定性(negative bias temperature instability,NBTI)效应引起的电路老化,提高电路可靠性,提出一种在电路待机状态下应用输入向量约束的门替换方法.运用动态和静态的NBTI模型进行感知NBTI的静态时序分析,确定潜在关键路径,考虑路径相关性的关键门算法以确定关键门,并生成能使关键门最大限度处于恢复阶段的输入向量.对输入向量无法控制的关键门采用门替换方法进行内部控制.对ISCAS标准电路的实验结果表明,电路时序余量为5%时,该方法的平均门替换率降低到9.68%,时延改善率提高到39.65%.
