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21.
《矿业科学技术学报(英文版)》2016,26(2):327-332
Combined with a digital bored photography system and in-situ statistics concerning the joints and fissures of both ore-body and surrounding rock,a 2D discrete model was constructed using UDEC.The stress field and displacement field changes of different sublevel stoping systems were also studied.Changes in the overlying rock strata settlement pattern has been analyzed and validated by in-situ monitoring data.The results show that:in the caving process,there exists an obvious delay and jump for the overlying rock strata displacement over time,and a stable arch can be formed in the process of caving,which leads to hidden goafs.Disturbed by the mining activity,a stress increase occurred in both the hanging wall and the foot wall,demonstrating a hump-shaped distribution pattern.From the comparison between simulation results and in-situ monitoring results,land subsidence shows a slow-development,suddenfailure,slow-development cycle pattern,which leads eventually to a stable state.This pattern validates the existence of balanced arch and hidden goafs. 相似文献
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In a basic related-key attack against a block cipher, the adversary has access to encryptions under keys that differ from the target key by bit-flips. In this short note we show that for a quantum adversary such attacks are quite powerful: if the secret key is (i) uniquely determined by a small number of plaintext–ciphertext pairs, (ii) the block cipher can be evaluated efficiently, and (iii) a superposition of related keys can be queried, then the key can be extracted efficiently. 相似文献
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郝昕 《计算机光盘软件与应用》2011,(16)
计算机网络信息安全与人们的生产生活息息相关,分析计算机网络存在的安全问题,并找出防护网络信息安全的对策,期望通过对这个问题的研究,为保护网络信息安全提供帮助与借鉴意义。 相似文献
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基于FMFCC和HMM的说话人识别 总被引:2,自引:0,他引:2
美尔频率倒谱系数(MFCC)是说话人识别中常用的特征参数,而语音信号是非平稳信号,MFCC并不能很好的反映语音的时频特性。针对这一缺陷,为了提高说话人的识别率,结合新的时频分析工具分数傅立叶变换(FRFT)。将MFCC推广到分数形式,得到分数美尔频率倒谱系数(FMFCC),用以表征语音信号的特征;并利用可分性测度验证了特征参数的有效性;通过建立20个不同说话人的FMFCC特征库,采用隐马尔可夫模型(HMM)对说话人进行仿真识别。仿真结果表明,在合适的变换阶次下,说话人的平均识别率可达93%以上。 相似文献
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In this paper we illustrate the optimal filtering of log returns of commodity prices in which both the mean and volatility are modulated by a hidden Markov chain with finite state space. The optimal estimate of the Markov chain and the parameters of the price model are given in terms of discrete-time recursive filters. We provide an application on a set of high frequency gold price data for the period 1973-2006 and analyse the h-step ahead price predictions against the Diebold-Kilian metric. Within the modelling framework where the mean and volatility are switching regimes, our findings suggest that a two-state hidden Markov model is sufficient to describe the dynamics of the data and the gold price is predictable up to a certain extent in the short term but almost impossible to predict in the long term. The proposed model is also benchmarked with ARCH and GARCH models with respect to price predictability and forecasting errors. 相似文献
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This paper presents a new extension of Gaussian mixture models (GMMs) based on type-2 fuzzy sets (T2 FSs) referred to as T2 FGMMs. The estimated parameters of the GMM may not accurately reflect the underlying distributions of the observations because of insufficient and noisy data in real-world problems. By three-dimensional membership functions of T2 FSs, T2 FGMMs use footprint of uncertainty (FOU) as well as interval secondary membership functions to handle GMMs uncertain mean vector or uncertain covariance matrix, and thus GMMs parameters vary anywhere in an interval with uniform possibilities. As a result, the likelihood of the T2 FGMM becomes an interval rather than a precise real number to account for GMMs uncertainty. These interval likelihoods are then processed by the generalized linear model (GLM) for classification decision-making. In this paper we focus on the role of the FOU in pattern classification. Multi-category classification on different data sets from UCI repository shows that T2 FGMMs are consistently as good as or better than GMMs in case of insufficient training data, and are also insensitive to different areas of the FOU. Based on T2 FGMMs, we extend hidden Markov models (HMMs) to type-2 fuzzy HMMs (T2 FHMMs). Phoneme classification in the babble noise shows that T2 FHMMs outperform classical HMMs in terms of the robustness and classification rate. We also find that the larger area of the FOU in T2 FHMMs with uncertain mean vectors performs better in classification when the signal-to-noise ratio is lower. 相似文献
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作为一种基于正定核的学习方法,传统支持向量机(Support Vector Machine,SVM)能较好地解决小样本、非线性、过学习、维数灾和局部极小等问题,从而广泛应用于模式识别、回归估计等领域。当前,核方法及其在故障诊断中的应用引起了人们的广泛重视并成为研究热点。为解决传统支持向量对核函数正定性的限制及求解速度不高的缺陷,通过引入最小二乘支持向量机分类算法提高学习速度,采用隐核特征映射技术实现核函数的进一步扩展,提出了一种新的隐核最小二乘分类器(HKLSC)算法。将其应用于实际工业过程的故障诊断中并根据采集的滚动轴承数据进行了仿真。结果表明,该隐核分类器具有很好的故障诊断性能,为故障诊断提供了一种新的有效途径。 相似文献