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整数上鲁棒分布式乘法计算方案 被引量:2
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作者 王宏 冯登国 肖国镇 《软件学报》 EI CSCD 北大核心 2002年第8期1412-1416,共5页
分布式乘法计算是安全多方计算中的重要部分,也是设计门限密码体制的基本协议.应用可验证秘密共享的方法,设计了两种不同情况下的整数环上多项相乘的鲁棒分布式乘法计算方案.其中并行不交互的鲁棒多项相乘的分布式乘法计算方案效率较高... 分布式乘法计算是安全多方计算中的重要部分,也是设计门限密码体制的基本协议.应用可验证秘密共享的方法,设计了两种不同情况下的整数环上多项相乘的鲁棒分布式乘法计算方案.其中并行不交互的鲁棒多项相乘的分布式乘法计算方案效率较高,且保持了不交互特性,而另一种方案却能达到最优弹性. 展开更多
关键词 整数 分布式乘法计算 秘密共享 门限密码学 安全多方计算 信息安全
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鲁棒化理论线损计算技术研究与系统构建(英文)
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作者 李文博 麻常辉 《山东电力技术》 2015年第4期26-32,37,共8页
理论线损计算中涉及电网数据量庞大,传统方法完全依赖人工经验进行数据操作和潮流调整,数据正确性和潮流断面合理性难以保证,尤其在大型输电网理论线损计算中经常遇到的潮流不收敛问题,给线损计算带来极大困难。针对线损理论计算中出现... 理论线损计算中涉及电网数据量庞大,传统方法完全依赖人工经验进行数据操作和潮流调整,数据正确性和潮流断面合理性难以保证,尤其在大型输电网理论线损计算中经常遇到的潮流不收敛问题,给线损计算带来极大困难。针对线损理论计算中出现的上述问题,总结理论线损计算经验,借助潮流理论和优化潮流理论,通过数据预处理、可疑数据分区定位、错误数据辨识及边界失配量归零模块,构建了鲁棒化理论线损计算系统,该系统贯穿理论线损计算全过程,能有力保障理论线损计算的高效性和合理性。最后,通过算例阐述冗余数据筛选方法以及潮流不收敛情况下可疑数据定位方法的原理并验证其有效性。 展开更多
关键词 理论线损 鲁棒计算系统 潮流计算 数据辨识 数据调整
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经典轨迹的鲁棒相似度量算法 被引量:5
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作者 王前东 《电子与信息学报》 EI CSCD 北大核心 2020年第8期1999-2005,共7页
针对经典轨迹与实时轨迹之间的大差异性,该文利用最长公共子序列理论,提出一种鲁棒的轨迹相似度量方法。该方法首先利用点到线段之间的距离判断经典轨迹的点与实时轨迹的线段是否一致;然后利用改进的多对1最长公共子序列算法,计算经典... 针对经典轨迹与实时轨迹之间的大差异性,该文利用最长公共子序列理论,提出一种鲁棒的轨迹相似度量方法。该方法首先利用点到线段之间的距离判断经典轨迹的点与实时轨迹的线段是否一致;然后利用改进的多对1最长公共子序列算法,计算经典轨迹与实时轨迹之间的最长公共子序列长度;最后将最长公共子序列长度与经典轨迹的点数的比值作为经典轨迹与实时轨迹之间的相似度。实验说明该算法的鲁棒性,该算法能够有效解决经典轨迹与实时轨迹之间的大差异轨迹相似度量问题。 展开更多
关键词 轨迹相似度量 大差异轨迹 多对1最长公共子序列 鲁棒计算 经典轨迹
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A Novel Robust Nonlinear Dynamic Data Reconciliation 被引量:4
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作者 高倩 阎威武 邵惠鹤 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2007年第5期698-702,共5页
Outlier in one variable will smear the estimation of other measurements in data reconciliation (DR). In this article, a novel robust method is proposed for nonlinear dynamic data reconciliation, to reduce the influe... Outlier in one variable will smear the estimation of other measurements in data reconciliation (DR). In this article, a novel robust method is proposed for nonlinear dynamic data reconciliation, to reduce the influence of outliers on the result of DR. This method introduces a penalty function matrix in a conventional least-square objective function, to assign small weights for outliers and large weights for normal measurements. To avoid the loss of data information, element-wise Mahalanobis distance is proposed, as an improvement on vector-wise distance, to construct a penalty function matrix. The correlation of measurement error is also considered in this article. The method introduces the robust statistical theory into conventional least square estimator by constructing the penalty weight matrix and gets not only good robustness but also simple calculation. Simulation of a continuous stirred tank reactor, verifies the effectiveness of the proposed algorithm. 展开更多
关键词 nonlinear dynamic data reconciliation ROBUST M-ESTIMATOR OUTLIER OPTIMIZATION
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IMPROVING VOICE ACTIVITY DETECTION VIA WEIGHTING LIKELIHOOD AND DIMENSION REDUCTION
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作者 Wang Huanliang Han Jiqing Li Haifeng Zheng Tieran 《Journal of Electronics(China)》 2008年第3期330-336,共7页
The performance of the traditional Voice Activity Detection (VAD) algorithms declines sharply in lower Signal-to-Noise Ratio (SNR) environments. In this paper, a feature weighting likelihood method is proposed for... The performance of the traditional Voice Activity Detection (VAD) algorithms declines sharply in lower Signal-to-Noise Ratio (SNR) environments. In this paper, a feature weighting likelihood method is proposed for noise-robust VAD. The contribution of dynamic features to likelihood score can be increased via the method, which improves consequently the noise robustness of VAD. Divergence based dimension reduction method is proposed for saving computation, which reduces these feature dimensions with smaller divergence value at the cost of degrading the performance a little. Experimental results on Aurora Ⅱ database show that the detection performance in noise environments can remarkably be improved by the proposed method when the model trained in clean data is used to detect speech endpoints. Using weighting likelihood on the dimension-reduced features obtains comparable, even better, performance compared to original full-dimensional feature. 展开更多
关键词 Voice Activity Detection (VAD) Weighting likelihood DIVERGENCE Dimension reduction Noise robustness
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Maximum Likelihood Blind Separation of Convolutively Mixed Discrete Sources
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作者 辜方林 张杭 朱德生 《China Communications》 SCIE CSCD 2013年第6期60-67,共8页
In this paper,a Maximum Likelihood(ML) approach,implemented by Expectation-Maximization(EM) algorithm,is proposed to blind separation of convolutively mixed discrete sources.In order to carry out the expectation proce... In this paper,a Maximum Likelihood(ML) approach,implemented by Expectation-Maximization(EM) algorithm,is proposed to blind separation of convolutively mixed discrete sources.In order to carry out the expectation procedure of the EM algorithm with a less computational load,the algorithm named Iterative Maximum Likelihood algorithm(IML) is proposed to calculate the likelihood and recover the source signals.An important feature of the ML approach is that it has robust performance in noise environments by treating the covariance matrix of the additive Gaussian noise as a parameter.Another striking feature of the ML approach is that it is possible to separate more sources than sensors by exploiting the finite alphabet property of the sources.Simulation results show that the proposed ML approach works well either in determined mixtures or underdetermined mixtures.Furthermore,the performance of the proposed ML algorithm is close to the performance with perfect knowledge of the channel filters. 展开更多
关键词 Blind Source Separation convolutive mixture EM Finite Alphabet
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GMCL: a robust global localization method for mobile robot
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作者 罗荣华 Hong Bingrong Min Huaqing 《High Technology Letters》 EI CAS 2006年第4期363-366,共4页
A large sample size is required for Monte Carlo localization (MCL) in multi-robot dynamic environ- ment, because of the "kidnapped robot" phenomenon, which will locate most of the samples in the regions with small... A large sample size is required for Monte Carlo localization (MCL) in multi-robot dynamic environ- ment, because of the "kidnapped robot" phenomenon, which will locate most of the samples in the regions with small value of desired posterior density. For this problem the crossover and mutation operators in evolutionary computation are introduced into MCL to make samples move towards the regions where the desired posterior density is large, so that the sample set can represent the density better. The proposed method is termed genetic Monte Carlo localization (GMCL). Application in robot soccer system shows that GMCL can considerably reduce the required number of samples, and is more precise and robust in dynamic environment. 展开更多
关键词 global localization Monte Carlo localization evolutionary computation robot soccer
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An Alternative Adiabatic Quantum Algorithm for the Hamiltonian Cycle Problem
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作者 张大剑 仝殿民 +1 位作者 陆遥 龙桂鲁 《Communications in Theoretical Physics》 SCIE CAS CSCD 2015年第5期554-558,共5页
We put forward an alternative quantum algorithm for finding ttamiltonian cycles in any N-vertex graph based on adiabatic quantum computing. With a yon Neumann measurement on the final state, one may determine whether ... We put forward an alternative quantum algorithm for finding ttamiltonian cycles in any N-vertex graph based on adiabatic quantum computing. With a yon Neumann measurement on the final state, one may determine whether there is a HamiRonian cycle in the graph and pick out a cycle if there is any. Although the proposed algorithm provides a quadratic speedup, it gives an alternative algorithm based on adiabatic quantum computation, which is of interest because of its inherent robustness. 展开更多
关键词 Iquantum algorithm Hamiltonian cycle problem adiabatic quantum computation
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A NEW STATISTICAL APPROACH FOR THE ANALYSIS OF UNCERTAIN SYSTEMS
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作者 Xinjia CHEN Kemin ZHOU Jorge ARAVENA 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2009年第1期1-34,共34页
This paper addresses the issues of conservativeness and computational complexity of probabilistie robustness analysis. The authors solve both issues by defining a new sampling strategy and robustness measure. The new ... This paper addresses the issues of conservativeness and computational complexity of probabilistie robustness analysis. The authors solve both issues by defining a new sampling strategy and robustness measure. The new measure is shown to be much less conservative than the existing one. The new sampling strategy enables the definition of efficient hierarchical sample reuse algorithms that reduce significantly the computational complexity and make it independent of the dimension of the uncertainty space. Moreover, the authors show that there exists a one to one correspondence between the new and the existing robustness measures and provide a computationally simple algorithm to derive one from the other. 展开更多
关键词 Computational complexity randomized algorithms risk analysis robustness analysis uncertain system.
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