Matrix-vector multiplication is the key operation for many computationally intensive algorithms. The emerging metal oxide resistive switching random access memory （RRAM） device and RRAM crossbar array have demonstra...Matrix-vector multiplication is the key operation for many computationally intensive algorithms. The emerging metal oxide resistive switching random access memory （RRAM） device and RRAM crossbar array have demonstrated a promising hardware realization of the analog matrix-vector multiplication with ultra-high energy efficiency. In this paper, we analyze the impact of both device level and circuit level non-ideal factors, including the nonlinear current-voltage relationship of RRAM devices, the variation of device fabrication and write operation, and the interconnect resistance as well as other crossbar array parameters. On top of that, we propose a technological exploration flow for device parameter configuration to overcome the impact of non-ideal factors and achieve a better trade-off among performance, energy, and reliability for each specific application. Our simulation results of a support vector machine （SVM） and Mixed National Institute of Standards and Technology （MNIST） pattern recognition dataset show that RRAM crossbar array based SVM is robust to input signal fluctuation but sensitive to tunneling gap deviation. A further resistance resolution test presents that a 6-bit RRAM device is able to realize a recognition accuracy around 90%, indicating the physical feasibility of RRAM crossbar array based SVM. In addition, the proposed technological exploration flow is able to achieve 10.98% improvement of recognition accuracy on the MNIST dataset and 26.4% energy savings compared with previous work. Experimental results also show that more than 84.4% power saving can be achieved at the cost of little accuracy reduction.展开更多
By dividing the rewinding process of a three-roll rewinder into three zones, mathematical equations were formulated and diagrams were drawn to determine the structure factors of the rewinder, such as the diameters and...By dividing the rewinding process of a three-roll rewinder into three zones, mathematical equations were formulated and diagrams were drawn to determine the structure factors of the rewinder, such as the diameters and spatial distribution of the rewinding rolls. The qualitative and quantitative analysis results show that the roll gap of the first two rewinding rolls and the angle between the line that connects the center points of the first two rewinding rolls and the vertical line play an important role in determining the ideal log diameter, which is related to the production capacity of the rewinder. Additionally, the ideal roll gap has a significant effect on the spatial distribution of the three rewinding rolls, the rewinding dead zone, and the rewinding quality of the log, which should be taken into full consideration when designing and improving rewinders to enhance production efficiency and satisfy ultra-high quality requirements.展开更多
为了给人机界面设计提供有效的评价手段,围绕用户对人机界面的感性需求,提出了基于感性工学的人机界面多意象评价方法。首先运用感性工学方法建立感性指标评价体系,在指标权重分配上,通过将灰色关联分析法引入群层次分析(analytic hiera...为了给人机界面设计提供有效的评价手段,围绕用户对人机界面的感性需求,提出了基于感性工学的人机界面多意象评价方法。首先运用感性工学方法建立感性指标评价体系,在指标权重分配上,通过将灰色关联分析法引入群层次分析(analytic hierarchy process,AHP)法中,利用改进的群AHP法求取综合权重,解决了传统AHP法主观性过强的缺点;同时根据感性工学评价的模糊性,建立了一种直觉模糊集理论和TOPSIS(technique for order preference by similarity to an ideal solution)法相结合的综合评价模型。结合数控机床人机界面设计方案验证了该评价方法的有效性和可行性。该方法对人机界面感性设计与设计评价具有一定的参考意义。展开更多
为研究L3自动驾驶事故场景下人工接管后换道轨迹的评价和分类问题,通过驾驶模拟实验采集换道轨迹数据;从舒适性、高效性、生态性、安全性4个方面选取9个评价指标;采用熵权TOPSIS(technique for order preference by similarity to an id...为研究L3自动驾驶事故场景下人工接管后换道轨迹的评价和分类问题,通过驾驶模拟实验采集换道轨迹数据;从舒适性、高效性、生态性、安全性4个方面选取9个评价指标;采用熵权TOPSIS(technique for order preference by similarity to an ideal solution)模型对换道轨迹进行评价并完成标签标定;用标定后的数据训练得到支持向量机(support vector machine, SVM)分类器模型,并将其应用于换道轨迹的分类中,该模型在测试集的平均准确率为79.55%,平均精确率为79.52%,平均召回率为79.51%,平均F_(1)值为77.43%。结果表明:应用熵权TOPSIS模型得到的评分最高的换道轨迹在舒适性、高效性、生态性和安全性上综合表现优秀;SVM分类器能以较为稳定的准确率完成换道轨迹的分类。得到的最优换道轨迹可为驾驶员的换道提供指导,也可为自动驾驶车辆的轨迹遵循提供参考。展开更多
基金This work was supported by the National Basic Research 973 Program of China under Grant No. 2013CB329000, the National Natural Science Foundation of China under Grant Nos. 61373026, 61261160501, the Brain Inspired Computing Research of Tsinghua University under Grant No. 20141080934, Tsinghua University Initiative Scientific Research Program, and the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions.
文摘Matrix-vector multiplication is the key operation for many computationally intensive algorithms. The emerging metal oxide resistive switching random access memory （RRAM） device and RRAM crossbar array have demonstrated a promising hardware realization of the analog matrix-vector multiplication with ultra-high energy efficiency. In this paper, we analyze the impact of both device level and circuit level non-ideal factors, including the nonlinear current-voltage relationship of RRAM devices, the variation of device fabrication and write operation, and the interconnect resistance as well as other crossbar array parameters. On top of that, we propose a technological exploration flow for device parameter configuration to overcome the impact of non-ideal factors and achieve a better trade-off among performance, energy, and reliability for each specific application. Our simulation results of a support vector machine （SVM） and Mixed National Institute of Standards and Technology （MNIST） pattern recognition dataset show that RRAM crossbar array based SVM is robust to input signal fluctuation but sensitive to tunneling gap deviation. A further resistance resolution test presents that a 6-bit RRAM device is able to realize a recognition accuracy around 90%, indicating the physical feasibility of RRAM crossbar array based SVM. In addition, the proposed technological exploration flow is able to achieve 10.98% improvement of recognition accuracy on the MNIST dataset and 26.4% energy savings compared with previous work. Experimental results also show that more than 84.4% power saving can be achieved at the cost of little accuracy reduction.
文摘By dividing the rewinding process of a three-roll rewinder into three zones, mathematical equations were formulated and diagrams were drawn to determine the structure factors of the rewinder, such as the diameters and spatial distribution of the rewinding rolls. The qualitative and quantitative analysis results show that the roll gap of the first two rewinding rolls and the angle between the line that connects the center points of the first two rewinding rolls and the vertical line play an important role in determining the ideal log diameter, which is related to the production capacity of the rewinder. Additionally, the ideal roll gap has a significant effect on the spatial distribution of the three rewinding rolls, the rewinding dead zone, and the rewinding quality of the log, which should be taken into full consideration when designing and improving rewinders to enhance production efficiency and satisfy ultra-high quality requirements.
文摘为了给人机界面设计提供有效的评价手段,围绕用户对人机界面的感性需求,提出了基于感性工学的人机界面多意象评价方法。首先运用感性工学方法建立感性指标评价体系,在指标权重分配上,通过将灰色关联分析法引入群层次分析(analytic hierarchy process,AHP)法中,利用改进的群AHP法求取综合权重,解决了传统AHP法主观性过强的缺点;同时根据感性工学评价的模糊性,建立了一种直觉模糊集理论和TOPSIS(technique for order preference by similarity to an ideal solution)法相结合的综合评价模型。结合数控机床人机界面设计方案验证了该评价方法的有效性和可行性。该方法对人机界面感性设计与设计评价具有一定的参考意义。
文摘为研究L3自动驾驶事故场景下人工接管后换道轨迹的评价和分类问题,通过驾驶模拟实验采集换道轨迹数据;从舒适性、高效性、生态性、安全性4个方面选取9个评价指标;采用熵权TOPSIS(technique for order preference by similarity to an ideal solution)模型对换道轨迹进行评价并完成标签标定;用标定后的数据训练得到支持向量机(support vector machine, SVM)分类器模型,并将其应用于换道轨迹的分类中,该模型在测试集的平均准确率为79.55%,平均精确率为79.52%,平均召回率为79.51%,平均F_(1)值为77.43%。结果表明:应用熵权TOPSIS模型得到的评分最高的换道轨迹在舒适性、高效性、生态性和安全性上综合表现优秀;SVM分类器能以较为稳定的准确率完成换道轨迹的分类。得到的最优换道轨迹可为驾驶员的换道提供指导,也可为自动驾驶车辆的轨迹遵循提供参考。
