中重核素加速器质谱测量的实验谱模拟

加速器质谱测量中重核实验中存在多种离子的干扰,从而给实验谱的核素分析和在束确认带来困难。为了解决这一问题,我们建立了中重核素加速器质谱测量的实验谱模拟方法,用此方法的分析结果与实验测量结果进行了比较,结果显示此方法可以很好用于核素的分析。     

A study of ~(36)Cl age in Quaternary groundwater of Hebei plain, China

Chlorine-36 has various advantages as a dating tool for old groundwater. In this paper, 36CI of Quaternary groundwater in the Hebei plain of North China has been measured using accelerator mass spectrometry (AMS). Ages calculated from the 36CI/CI ratio show that ion filtration, which is responsible for the CI- concentration increasing with depth and along flow paths in Hebei plain groundwater. It is concluded that 36CI age of the groundwater in the 3rd Group of the Quaternary (Q2) in the Cangzhou area is 250ka, and that of the 4th Group(Q1) is 300ka.     

182Hf的AMS测量技术研究

为实现对¹⁸²Hf岩石样品的AMS精确测量,基于HI-13串列加速器对测量技术和方法等进行了系统的改进,主要包括：¹⁸²Hf空白样品的制备、偏置束流的同时监测技术、加速器系统稳定性检验、交替测量方案的实施、¹⁸²W扣除方法的改进、模拟样品测量检验、岩石样品测量检验及测量结果可信度检验等。实验结果表明,对于1×10^-10(¹⁸²Hf/¹⁸⁰Hf)水平的模拟样品,测量结果的相对不确定度约为12%,基本达到岩石样品的测量要求。岩石样品的测量不确定度较大,主要原因是岩石样品中W的含量较高。     

Taylor-AMS features and deep convolutional neural network for converting nonaudible murmur to normal speech

Communication becomes effective when the speech signal arrives with the profound characteristics. This insisted the researchers to develop an automatic system of recognizing the speech signals from the murmurs. Some of the traditional automatic recognition systems are unfit for the silent environments imposing a need for an effective recognition system. Also, the traditional automatic recognition methods, like Neural Networks, render poor performance in the presence of the murmurs. Thus, this article proposes a method for automatic whisper recognition using the Deep Convolutional Neural Network (DCNN). The training of the DCNN is performed using the proposed Stochastic-Whale Optimization Algorithm (Stochastic-WOA), which is designed by the integration of Stochastic Gradient Descent algorithm with WOA. The input to the classifier is the features that include pitch chroma, spectral centroid, spectral skewness, and Taylor-Amplitude Modulation Spectrogram (Taylor-AMS), which is obtained by combining Taylor series and Amplitude Modulation Spectrogram (AMS) features, of the preprocessed input speech signal. The experimentation of the method is performed using the real database and the analysis proves that the proposed method acquired a maximal accuracy of 0.9723, minimal False Positive Rate of 0.0257, and maximal True Positive Rate of 0.9981, respectively.     

Polynomial Approximation and Graph-Coloring

Consider a graph G=(V,E) of order n. In the minimum graph-coloring problem we try to color V with as few colors as possible so that no two adjacent vertices receive the same color. This problem is among the first ones proved to be intractable, and hence, it is very unlikely that an optimal polynomial-time algorithm could ever be devised for it. In this paper, we survey the main polynomial time approximation algorithms (the ones for which theoretical approximability bounds have been studied) for the minimum graph-coloring and we discuss their approximation performance and their complexity. Finally, we further improve the approximation ratio for graph-coloring. Received October 5, 2001; revised November 15, 2002 Published online: February 20, 2003     

一种准确快速定位的节点包标记算法*

目前对于拒绝服务攻击 (DoS)和分布式拒绝服务攻击(DDoS),概率包标记(PPM)和高级包标记方案(AMS)是有效的IP追踪技术,但其存在很大的误报率,不能快速追踪到攻击者。在AMS的基础上改进了包标记方法,合理假设在一个自治系统中,采用节点标记方法,经理论分析能降低误报率;经实验验证用较少的数据包就可以快速准确地定位到攻击者。     

Robust Deadlock Avoidance Policy for Automated Manufacturing System With Multiple Unreliable Resources

This work studies the robust deadlock control of automated manufacturing systems with multiple unreliable resources. Our goal is to ensure the continuous production of the jobs that only require reliable resources. To reach this goal, we propose a new modified Banker's algorithm(MBA) to ensure that all resources required by these jobs can be freed. Moreover,a Petri net based deadlock avoidance policy(DAP) is introduced to ensure that all jobs remaining in the system after executing the new MBA can complete their processing smoothly when their required unreliable resources are operational. The new MBA together with the DAP forms a new DAP that is robust to the failures of unreliable resources. Owing to the high permissiveness of the new MBA and the optimality of the DAP, it is tested to be more permissive than state-of-the-art control policies.     

Deadlock-free scheduling of an automated manufacturing system using an enhanced colored time resource Petri-net model-based evolutionary endosymbiotic learning automata approach

Deadlock-free scheduling of parts is vital for increasing the utilization of an Automated Manufacturing System (AMS). An existing literature survey has identified the role of an effective modeling methodology for AMS in ensuring the appropriate scheduling of the parts on the available resources. In this paper, a new modeling methodology termed as Extended Color Time Net of Set of Simple Sequential Process with Resources (ECTS³PR) has been presented that efficiently handles dynamic behavior of the manufacturing system. The model is subsequently utilized to obtain a deadlock-free schedule with minimized makespan using a new Evolutionary Endosymbiotic Learning Automata (EELA) algorithm. The ECTS³PR model, which can easily handle various relations and structural interactions, proves to be very helpful in measuring and managing system performances. The novel algorithm EELA has the merits of both endosymbiotic systems and learning automata. The proposed algorithm performs better than various benchmark strategies available in the literature. Extensive experiments have been performed to examine the effectiveness of the proposed methodology, and the results obtained over different data sets of varying dimensions authenticate the performance claim. Superiority of the proposed approach has been validated by defining a new performance index termed as the ‘makespan index’ (MI), whereas the ANOVA analysis reveals the robustness of the algorithm.     

FF智能仪表在核燃料后处理试验萃取装置中的应用

介绍了一种基于FF智能仪表的核燃料后处理萃取装置的控制系统结构。针对萃取试验台架的测控要求,给出了FF智能仪表的应用和在Delta V系统中的组态方法。     

Output-Sensitive Algorithm for Computing β-Skeletons

The β-skeleton is a measure of the internal shape of a planar set of points. We get an entire spectrum of shapes by varying the parameter β. For a fixed value of β, a β-skeleton is a geometric graph obtained by joining each pair of points whose β-neighborhood is empty. For β≥1, this neighborhood of a pair of points p _i ,p _j is the interior of the intersection of two circles of radius , centered at the points (1−β/2)p _i +(β/2)p _j and (β/2)p _i +(1−β/2)p _j , respectively. For β∈(0,1], it is the interior of the intersection of two circles of radius , passing through p _i and p _j . In this paper we present an output-sensitive algorithm for computing a β-skeleton in the metrics l ₁ and l _∞ for any β≥2. This algorithm is in O(nlogn+k), where k is size of the output graph. The complexity of the previous best known algorithm is in O(n ^5/2logn) [7]. Received April 26, 2000