144 resultados para PC-algorithm


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东海陆架是世界上最宽阔的陆架之一,面积达770 000 km2左右。在末次冰盛期(LGM),东海海平面下降120~130 m左右,绝大部分陆架暴露出海面。而冲绳海槽是第四纪以来一直保持海洋环境的东海深海区。本文以冲绳海槽北部PC-1岩芯为材料,通过分析该孔的孢粉,加上详细的AMS 14C测年,恢复了周边地区24 cal.kaBP以来的古植被,并推测古环境和古气候变化,重点讨论了LGM时期出露大陆架上发育的植被。 PC-1孔(31°27.5′N,128°24.8′E)位于黑潮支流对马暖流东侧,水深590 m,柱长812 cm。孢粉分析按间隔8 cm取样,个别为4~6 cm,共分析了103个孢粉样品。利用9个AMS 14C数据建立年龄标尺,用Calib5.1.0软件进行年龄校正。通过相邻样品深度的线性内插获得每个样品的年龄,采用外延法得到顶部和底部的校正年龄分别为351cal aBP、24 280 cal aBP,孢粉样品的时间分辨率平均为230 a。 根据孢粉百分比和沉积率的变化,可划分出四个带:Ⅰ带(812~715 cm,24.2~21.1 cal. kaBP)、Ⅱ带(715~451 cm,21.1~15.2 cal. kaBP)、Ⅲ带(451~251 cm,15.2~10.8 cal. kaBP)、Ⅳ带(251~0 cm,10.8~0.3 cal. kaBP),分别对应MIS 3末期、末次冰盛期、冰消期和全新世。末次冰盛期草本植物花粉占优势,孢粉沉积率较高,此时草本花粉主要来源于出露的大陆架,其上发育了以蒿属为主的草地植被,气候比较寒冷干燥;冰消期海平面开始回升,松属花粉含量升高,草本植物花粉含量下降;全新世以木本植物花粉占绝对优势,栗属-栲属花粉迅速增加,蕨类孢子含量升高,草本植物花粉含量锐减,孢粉沉积率降低,由于海平面回升,大陆架被淹没,此时孢粉主要来源于日本岛,九州地区生长了以栲属、栎属为主的常绿阔叶与落叶阔叶林,气候温暖湿润。 叶枝杉属花粉在整个岩芯中零星出现。叶枝杉属植物分布于菲律宾吕宋北部至塔斯马尼亚和新西兰气候潮湿的山地林中,该属花粉在岩芯中的出现,可能暗示了黑潮的影响或者是较强的夏季风。 草本植物与松属花粉百分比变化很好的反映了海平面的升降,松属花粉含量较高指示海平面较高。对岩芯中主要类型的花粉百分比进行了频谱分析,显示存在千年尺度的准周期变化,有明显的6.8,3.8,2.2,1.6 ka的周期。 孢粉样品中的炭屑统计表明,末次冰消期炭屑含量最高,可能因为末次冰消期降雨量增加,炭屑可被降水带到沉积地点沉积下来;全新世的炭屑浓度较高,尤其在晚全新世,出现了一个峰值,究其原因可能与气候变化和人类活动有关。

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The alternate combinational approach of genetic algorithm and neural network (AGANN) has been presented to correct the systematic error of the density functional theory (DFT) calculation. It treats the DFT as a black box and models the error through external statistical information. As a demonstration, the AGANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides. Through the AGANN correction, the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set. For comparison, the neural network approach reduces the mean value to 2.56%. And for the common combinational approach of genetic algorithm and neural network, the value drops to 2.15%. The multiple linear regression method almost has no correction effect here.

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A novel approach for multi-dimension signals processing, that is multi-weight neural network based on high dimensional geometry theory, is proposed. With this theory, the geometry algorithm for building the multi-weight neuron is mentioned. To illustrate the advantage of the novel approach, a Chinese speech emotion recognition experiment has been done. From this experiment, the human emotions are classified into 6 archetypal classes: fear, anger, happiness, sadness, surprise and disgust. And the amplitude, pitch frequency and formant are used as the feature parameters for speech emotion recognition. Compared with traditional GSVM model, the new method has its superiority. It is noted that this method has significant values for researches and applications henceforth.

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In this paper, we constructed a Iris recognition algorithm based on point covering of high-dimensional space and Multi-weighted neuron of point covering of high-dimensional space, and proposed a new method for iris recognition based on point covering theory of high-dimensional space. In this method, irises are trained as "cognition" one class by one class, and it doesn't influence the original recognition knowledge for samples of the new added class. The results of experiments show the rejection rate is 98.9%, the correct cognition rate and the error rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the rejection rate of test samples excluded in the training samples class is very high. It proves the proposed method for iris recognition is effective.

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A design algorithm of an associative memory neural network is proposed. The benefit of this design algorithm is to make the designed associative memory model can implement the hoped situation. On the one hand, the designed model has realized the nonlinear association of infinite value pattern from n dimension space to m dimension space. The result has improved the ones of some old associative memory neural network. On the other hand, the memory samples are in the centers of the fault-tolerant. In average significance the radius of the memory sample fault-tolerant field is maximum.

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In this paper, a novel algorithm for removing facial makeup disturbances as a face detection preprocess based on high dimensional imaginal geometry is proposed. After simulation and practical application experiments, the algorithm is theoretically analyzed. Its apparent effect of removing facial makeup and the advantages of face detection with this pre-process over face detection without it are discussed. Furthermore, in our experiments with color images, the proposed algorithm even gives some surprises.

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The quantum coherence control of a solid-state charge qubit is studied by using a suboptimal continuous feedback algorithm within the Bayesian feedback scheme. For the coherent Rabi oscillation, the present algorithm suggests a simple bang-bang control protocol, in which the control parameter is modulated between two values. For the coherence protection of the idle state, the present approach is applicable to arbitrary states, including those lying on the equator of the Bloch sphere which are out of control in the previous Markovian feedback scheme.

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A novel image restoration approach based on high-dimensional space geometry is proposed, which is quite different from the existing traditional image restoration techniques. It is based on the homeomorphisms and "Principle of Homology Continuity" (PHC), an image is mapped to a point in high-dimensional space. Begin with the original blurred image, we get two further blurred images, then the restored image can be obtained through the regressive curve derived from the three points which are mapped form the images. Experiments have proved the availability of this "blurred-blurred-restored" algorithm, and the comparison with the classical Wiener Filter approach is presented in final.

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Compared with the ordinary adaptive filter, the variable-length adaptive filter is more efficient (including smaller., lower power consumption and higher computational complexity output SNR) because of its tap-length learning algorithm, which is able to dynamically adapt its tap-length to the optimal tap-length that best balances the complexity and the performance of the adaptive filter. Among existing tap-length algorithms, the LMS-style Variable Tap-Length Algorithm (also called Fractional Tap-Length Algorithm or FT Algorithm) proposed by Y.Gong has the best performance because it has the fastest convergence rates and best stability. However, in some cases its performance deteriorates dramatically. To solve this problem, we first analyze the FT algorithm and point out some of its defects. Second, we propose a new FT algorithm called 'VSLMS' (Variable Step-size LMS) Style Tap-Length Learning Algorithm, which not only uses the concept of FT but also introduces a new concept of adaptive convergence slope. With this improvement the new FT algorithm has even faster convergence rates and better stability. Finally, we offer computer simulations to verify this improvement.

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In this paper, a face detection algorithm which is based on high dimensional space geometry has been proposed. Then after the simulation experiment of Euclidean Distance and the introduced algorithm, it was theoretically analyzed and discussed that the proposed algorithm has apparently advantage over the Euclidean Distance. Furthermore, in our experiments in color images, the proposed algorithm even gives more surprises.

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This paper discusses the algorithm on the distance from a point and an infinite sub-space in high dimensional space With the development of Information Geometry([1]), the analysis tools of points distribution in high dimension space, as a measure of calculability, draw more attention of experts of pattern recognition. By the assistance of these tools, Geometrical properties of sets of samples in high-dimensional structures are studied, under guidance of the established properties and theorems in high-dimensional geometry.