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基于概率优化的神经网络模型组合算法 被引量:3
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作者 李炎 李宪 +1 位作者 杨明业 孙国庆 《复杂系统与复杂性科学》 CAS CSCD 北大核心 2022年第3期104-110,共7页
高额的存储与计算成本限制了神经网络模型在低算力平台的应用,为提高神经网络模型的实用性,提出了两种组合优化算法,通过对多个轻型并行神经网络在连续时间窗口内的概率优化,在保证识别准确率的前提下显著降低了计算成本。为验证算法的... 高额的存储与计算成本限制了神经网络模型在低算力平台的应用,为提高神经网络模型的实用性,提出了两种组合优化算法,通过对多个轻型并行神经网络在连续时间窗口内的概率优化,在保证识别准确率的前提下显著降低了计算成本。为验证算法的可行性,以痛苦表情识别为对象,展开了系列对比实验。实验表明在保持相似准确率的前提下,其计算量相比传统深度学习算法极大降低,提高了神经网络的实用性,并极大降低了存储与计算成本。 展开更多
关键词 低计算成本 存储成本 神经网络 概率优化 组合优化算法
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Multi-line code: A low complexity revocable fingerprint template for cancelable biometrics 被引量:1
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作者 WONG Wei-jing WONG Mou-ling Dennis KHO Yau-hee 《Journal of Central South University》 SCIE EI CAS 2013年第5期1292-1297,共6页
A low computational cost cancelable fingerprint template, namely the multi-line codes was proposed. The formulation of a single-line code involves the inspection of minutiae distribution along a straight line construc... A low computational cost cancelable fingerprint template, namely the multi-line codes was proposed. The formulation of a single-line code involves the inspection of minutiae distribution along a straight line constructed based on the reference minutia. Multi-line code is introduced to elevate the performance by combining several single-line codes. Experiments were carried out on a few FVC databases. It has been proven that the proposed method yields relatively low computational complexity as compared to existing minutiae distribution-based methods, while preserving the performance. The equal error rate obtained for FVC2002 DB1 is 4.69% in stolen-key case, and the total arithmetic operations utilized are 14 520 additions and zero multiplication. 展开更多
关键词 cancelable fingerprint template multi-line code single-line code stolen-key case
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Low-cost cloud computing solution for geo-information processing 被引量:3
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作者 高培超 刘钊 +1 位作者 谢美慧 田琨 《Journal of Central South University》 SCIE EI CAS CSCD 2016年第12期3217-3224,共8页
Cloud computing has emerged as a leading computing paradigm,with an increasing number of geographic information(geo-information) processing tasks now running on clouds.For this reason,geographic information system/rem... Cloud computing has emerged as a leading computing paradigm,with an increasing number of geographic information(geo-information) processing tasks now running on clouds.For this reason,geographic information system/remote sensing(GIS/RS) researchers rent more public clouds or establish more private clouds.However,a large proportion of these clouds are found to be underutilized,since users do not deal with big data every day.The low usage of cloud resources violates the original intention of cloud computing,which is to save resources by improving usage.In this work,a low-cost cloud computing solution was proposed for geo-information processing,especially for temporary processing tasks.The proposed solution adopted a hosted architecture and can be realized based on ordinary computers in a common GIS/RS laboratory.The usefulness and effectiveness of the proposed solution was demonstrated by using big data simplification as a case study.Compared to commercial public clouds and dedicated private clouds,the proposed solution is more low-cost and resource-saving,and is more suitable for GIS/RS applications. 展开更多
关键词 cloud computing geo-information processing geo-processing
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