An energy spectrum smoothing algorithm based on TCC-DEE

A smoothing algorithm for energy spectrum based on differential nonlinearity(DNL) error elimination with total counts conservation for high-energy particle detector systems is presented. It is physics based and is only determined by the DNL error of analog-to-digital converter device itself. From the experimental results, this algorithm slightly improves both noise performance and energy resolution, while greatly reduces the testing errors by almost a half compared to their original values. In addition, the reduced-x^2 statistic for evaluating the Gaussian fitting goodness is significantly reduced by almost two orders after smoothing. As a typical verification example,this algorithm is successfully applied in the ground calibration of the Low Energy X-ray Instrument onboard the Hard X-ray Modulation Telescope(HXMT-LE) satellite,lending it a powerful, nondestructive and low-cost tool for both calibration and data processing for high-energy particle detector systems.

Optimized Parallel Cooperative Spectrum Sensing Strategy Based on Iterative KuhnMunkres Algorithm

Spectrum sensing is the key and premise of cognitive radio( CR). Current parallel cooperative spectrum sensing strategies have some problems,such as large number of cooperative secondary users and lack of consideration for the sensing overhead and the transmission gain. To solve those problems,an optimized parallel cooperative spectrum sensing strategy based on iterative KuhnMunkres( KM) algorithm was proposed. To maximize the total system profit,it considers the tradeoff between the sensing overhead and the transmission gain. Iterative KM algorithm was applied to obtaining the optimal assignment,which indicated when and which channels secondary users should sense. Furthermore,the required detection probability was introduced to avoid unnecessary waste when the accuracy met the system requirement. Monte Carlo simulations show that the proposed strategy can obtain higher total system profit with fewer cooperative secondary users.

Membrane-inspired quantum shuffled frog leaping algorithm for spectrum allocation

To solve discrete optimization difficulty of the spectrum allocation problem,a membrane-inspired quantum shuffled frog leaping（MQSFL） algorithm is proposed.The proposed MQSFL algorithm applies the theory of membrane computing and quantum computing to the shuffled frog leaping algorithm,which is an effective discrete optimization algorithm.Then the proposed MQSFL algorithm is used to solve the spectrum allocation problem of cognitive radio systems.By hybridizing the quantum frog colony optimization and membrane computing,the quantum state and observation state of the quantum frogs can be well evolved within the membrane structure.The novel spectrum allocation algorithm can search the global optimal solution within a reasonable computation time.Simulation results for three utility functions of a cognitive radio system are provided to show that the MQSFL spectrum allocation method is superior to some previous spectrum allocation algorithms based on intelligence computing.

The extraction and smoothing algorithms for γ-ray spectrum of a CdZnTe detector system

The extraction algorithms for pulse amplitude and smoothing of energy spectrum have a great influence on energy spectrum of γ-rays during the digital detection and analysis procedure. For a CdZnTe digital γ detector system, different extraction algorithms for pulse amplitude and smoothing of energy spectrum are discussed in this paper. The results show that extraction of pulse amplitude using the first-order derivative method and smoothing of energy spectrum using the wavelet transformation method may obtain energy spectrum with good performance.

Cognitive Radio Spectrum Allocation Strategy Based on Improved Genetic Algorithm

With the rapid development of wireless communication industry, shortage situation of spectrum resource is increasingly significant. It has become an important topic to study cognitive radio spectrum allocation algorithm that is of higher spectrum utilization ratio, less system power consumption and better algorithm efficiency. Analyzes spectrum allocation models based on genetic algorithm, and then puts forward new improved genetic algorithm. The algorithm adopts niche crowding operation to avoid individual inbreeding. It adaptively adjusts crossover and mutation probability to keep them always in the appropriate state. It provides more equal individual competition opportunity by hierarchical measures, which can effectively avert premature convergence to local optimal solution. It obviously improves the district's total transfer rate on the premise that it has met the requirements of minimum user transfer rate and limitations of maximum total power and maximum bit error rate. Simulation results prove the effectiveness of the proposed algorithm.

Solving Dynamic Spectrum Management Problem Based on Cloud Computing Using Genetic Algorithm

With the rapid development of wireless sensor network （WSN）, the demands of limited radio frequency spectrum rise sharply, thereby dealing with the frequency assignment of WSN scientifically and efficiently becomes a popular topic. To improve the frequency utilization rate in WSN, a spectrum management system for WSN combined with cloud computing technology should be considered. From the optimization point of view, the study of dynamic spectrum management can be divided into three kinds of methods, including Nash equilibrium, social utility maximization, and competitive economy equilibrium. In this paper, we propose a genetic algorithm based approach to allocate the power spectrum dynamically. The objective is to maximize the sum of individual Shannon utilities with the background interference and crosstalk consideration. Compared to the approach in [1], the experimental result shows better balance between efficiency and effectiveness of our approach.

紫外吸收光谱结合SPA-ELM算法的水体磷酸根离子检测研究

在工业锅炉中随着水蒸气蒸发,大量的钙镁离子留在炉水中,如果不加处理,在水冷管中会形成水垢,造成爆管停炉。为了保障设备的安全运行,消除安全隐患,工业上通过维持水中一定含量的磷酸根离子来去除锅炉中的钙、镁水垢。传统的磷酸根离子检测技术主要有比色法、分光光度法、色谱法、电位法等,这些方法前期处理步骤较繁琐且耗时较长。光谱法是通过测定物质的吸收光谱并建立和浓度关系的数学模型,对物质浓度定量的一种分析方法。为了满足磷酸根离子快速、实时测量的要求,提出了一种基于紫外吸收光谱结合SPA-ELM算法快速测量磷酸根离子的方法。按照我国《工业锅炉水质GB/T 1576—2018》中所规定的进入热水锅炉前的水质参数要求,配置37份浓度范围在5~100 mg·L^(-1)磷酸根溶液,使用搭建的实验装置,采集紫外吸收光谱。使用SPXY将样品按照7∶3的比例随机划分训练集、测试集;使用Savitzky-Golay(S-G)滤波对数据预处理以提高光谱的信噪比;通过连续投影算法(SPA)压缩光谱数据,筛选出5个与磷酸根强相关的特征波长;使用极限学习机(ELM)将特征波长处的吸光度与样本浓度进行拟合,以决定系数R2和均方根误差RMSE作为模型评价指标,建立磷酸根离子的回归模型。采用所提出的建模方法所建立的模型训练集的R2与RMSE分别为0.9972和1.3015 mg·L^(-1),测试集的R^(2)与RMSE分别为0.9995和0.5174 mg·L^(-1)。为了验证所提出的SPA-ELM预测模型效果,另外建立了LASSO-ELM、PCA-ELM、SPA-PLS和SPA-SVR四种预测模型进行对比。实验结果表明,SPA-ELM建立的预测模型的R2和RMSE均优于其他四种预测模型,说明采用的特征选择方法和回归方法均为最优,能够对磷酸根浓度范围为5~100 mg·L^(-1)的水体进行准确预测,为水中磷酸根离子的检测提供了一种新的解决方法。

紫外光谱结合BP神经网络算法建立食用油掺伪煎炸油的快速鉴定模型

为建立一种快速食用油掺伪煎炸油检测方法,采用紫外光谱法鉴别其掺伪,本研究选取大豆油、玉米油和葵花籽油为代表分别煎炸,在纯油中掺入相应煎炸时间0~6 h及掺假梯度0%~90%的煎炸油制备掺伪油样,进行紫外光谱及二阶导数预处理,经处理后的光谱特征峰与BP(Backpropagation)神经网络算法结合建立食用油掺伪煎炸油模型,对掺入煎炸油类别、煎炸时间和煎炸油含量进行鉴别分析。结果表明二阶导数预处理后掺伪煎炸油的光谱特征峰中大豆油为446、462 nm、玉米油为268、274 nm、葵花籽油为280、288 nm,根据其特征峰位与峰值建立Levenberg–Marquardt算法(LMA)、动量梯度下降法(MGD)及弹性梯度下降法(EGD)掺伪模型识别率分别为98.15%、91.67%、95.52%。

基于遗传算法的炮击振动环境谱编制方法

为了提高炮击振动环境谱编制精度,提出了基于遗传算法(genetic algorithm,简称GA)的炮击振动环境谱特征点确定方法。首先,介绍了现有标准的预估炮击振动谱的编制方法;其次,以炮舱结构模拟件为对象,开展了炮击振动试验并获取了实测谱,基于飞机飞行振动环境测量数据的归纳方法对实测谱进行了归纳处理;然后,以特征点构成的环境谱与实测数据归纳谱的能量最接近为目标建立目标函数,采用遗传算法对目标函数进行优化以确定炮击振动谱特征点坐标,在对数坐标下通过折线将特征点连接以实现炮击振动环境谱的编制;最后,将基于遗传算法得到的环境谱与基于标准得到的预估谱进行对比,分析验证了所提方法的有效性。结果表明,所采用的方法获得的炮击振动环境谱在均方根值(rootmean square,简称RMS)和谱形特征上都更接近实测数据归纳谱。

基于X射线双光谱的物质识别方法研究

针对工业生产、材料科学和安检等领域对于物质的检测需求,开展了基于X射线双光谱的物质识别方法研究。提出了针对不同物质的双光谱识别算法模型,模型引入边界物质界定与伪彩色算法,实现了对物质种类的识别。搭建X射线双光谱透射实验系统,实测了复杂背景、多种物体的X射线双光谱图像,研究了物质界定算法与伪彩色算法对于物质种类识别的影响。基于此优化了算法模型,实现了灵活、快速地对物质种类的识别,对于工业生产物质分类和安检快速筛查具有重要意义。

基于级联碰撞缺陷数据库的源项对辐照微结构演化影响团簇动力学模拟研究

团簇动力学(CD)方法是模拟核材料在高能粒子辐照下微观结构演化的重要方法之一,源项是团簇动力学方法的关键输入。经典CD方法中源项通常采用经验拟合得到,未能充分利用原子尺度获得的初始缺陷信息。随着分子动力学等方法的发展,级联碰撞缺陷数据库大为丰富,结合初级离位原子(PKA)能谱足以得到更为合理的源项。由于级联碰撞缺陷数据库的能量值数量相对于准连续PKA能谱仍然偏少,本文提出了5种从准连续PKA能谱得到级联能量分立值的抽样算法,并基于团簇动力学方法模拟低剂量中子辐照纯钨实验对算法进行了验证和比较。

基于Stacking融合的LSTM-SA-RBF短期负荷预测

为了解决单个神经网络预测的局限性和时间序列的波动性,提出了一种奇异谱分析(singular spectrum analysis,SSA)和Stacking框架相结合的短期负荷预测方法。利用随机森林筛选出与历史负荷相关性强烈的特征因素,采用SSA为负荷数据降噪,简化模型计算过程;基于Stacking框架,结合长短期记忆(long and short-term memory,LSTM)-自注意力机制(self-attention mechanism,SA)、径向基(radial base functions,RBF)神经网络和线性回归方法集成新的组合模型,同时利用交叉验证方法避免模型过拟合;选取PJM和澳大利亚电力负荷数据集进行验证。仿真结果表明,与其他模型比较,所提模型预测精度高。

基于混沌序列的MIMO-OFDM雷达通信一体化波形设计

为了解决多输入多输出的正交频分复用(multiple input multiple output orthogonal frequency division multiplexing,MIMO-OFDM)雷达通信一体化信号波形由于通信信息的不确定性导致模糊函数旁瓣水平较高以及系统雷达探测能力较差的问题,提出利用混沌序列对通信信息进行直接扩频。但普通单级混沌序列具有保密性低,可用数量少,容易被破解的缺点。因此采用一种基于加权算法优化的混合型级联混沌序列对通信信息进行扩频调制,然后再通过MIMO-OFDM一体化系统进行传输。由仿真结果可知,加权后的级联混沌序列相关性和误码率性能较m序列和Gold序列更好,经此序列扩频后的一体化信号模糊函数旁瓣水平会更低,故该一体化波形雷达探测性能更佳。且混沌序列越长,信号波形雷达探测能力越好。

基于RepNet的自闭症健康监测方法研究

自闭症谱系障碍(ASD)患者在社交互动、语言交流和兴趣等方面表现出正常行为的偏差。随着自闭症患者数量的增加,迫切需要在早期进行自闭症筛查,以尽早提供专业治疗。目前自闭症筛查主要仰赖家长填写调查问卷和医疗专业人员进行人工观察和评估,这种方式效率较低且需要耗费较长的时间。机器算法在自闭症筛查领域的应用尚未得到广泛采纳。为提高筛查效率和准确性,探讨机器视觉算法RepNet在识别自闭症患者重复动作方面的有效性,使用RepNet算法分析包含重复性和非重复性动作的视频,评估Repnet的准确性;通过分析包含自闭症患儿重复动作的视频,评估RepNet在自闭症重复性动作筛查中的准确性和匹配性。结果表明:RepNet在检测自闭症患者的重复行为方面表现出极高的有效性和准确性。

战场频率指配问题研究综述及展望

战场频率指配能够在复杂电磁环境下将战场中有限的频谱资源指配至用频装备,对用频装备作战的效能发挥与电磁频谱作战筹划具有重要意义。本文从数学模型、求解算法两个方面分别总结归纳了静态频率指配问题(static frequency assignment problem,S-FAP)与动态频率指配问题(dynamic frequency assignment problem,D-FAP)的研究现状,分析评述了模型的适用性及算法优缺点,最后对战场频率指配未来的发展趋势进行了展望。

无人驾驶单钢轮碾压机最优换道路径规划研究

为了确保单钢轮碾压机在换道碾压过程中转向稳定、保证压实质量,提出了一种换道路径规划方法,该方法将单钢轮碾压机换道路径曲线平滑度、转向稳定性和换道作业能耗作为优化目标,采用五阶贝塞尔曲线作为换道路径曲线,建立铰接式运动学模型与换道路径点状态的对应关系,采用光谱优化器(Light Spectrum Optimizer,LSO)算法对五阶贝塞尔曲线中未知关键控制点坐标进行优化,得到近似最优换道路径。以某型单钢轮碾压机为例,仿真与实验结果均表明,该方法规划的最优换道路径曲线在起点和终点处的航向角和曲率均为0,且全程曲率连续,符合单钢轮碾压机作业要求,与文献[10]方法相比,该方法规划的最优换道路径最大横向误差和平均横向误差分别降低了3.8%和16.7%,最大曲率和平均曲率分别降低了18.0%和9.3%,平均整车油耗减少了3.8%。研究结果可为单钢轮碾压机无人驾驶作业提供实际应用参考价值。

基于LM-LSO算法的磷石膏堆场边坡形态与稳定性智能优化方法

大量欠稳定边坡阻碍了人类工程活动,亟需采用合理的优化方案来确保工程安全。为此,提出了基于列文伯格—马夸尔特(Levenberg-Marquardt,LM)算法和光谱优化算法(Light Spectrum Optimizer,LSO)的边坡形态智能优化方法(LM-LSO)。首先,采用LM算法对样本数据进行非线性拟合;其次,采用拒绝不可行解方法处理稳定性系数的约束问题;最后,利用LSO搜索最优方案,并与另外4种算法进行比较分析。该方法应用于四川省某磷石膏堆场边坡优化,得到了最小挖方量的优化设计方案。该方法综合考虑了边坡优化的经济性和稳定性,保障了堆场的堆填方量和安全。

基于加密模式的协同式激光敌我识别技术

为了解决普通的激光敌我识别中,信号容易被侦收后破解的问题,提出了一种基于加密模式的协同式激光敌我识别技术,对激光敌我识别系统工作原理进行了研究与分析,采用通过时间信息产生一组伪随机数,利用一组高强度的改进加密算法对这组伪随机数进行变换产生加密数据,并经过扩频函数对变换后的加密数据进行扩频,结合RS纠错码产生最终编码的方法,增强数据的抗干扰能力,经实验测试实现了提高信号保密性和安全性的目的,为激光敌我识别的发展提供了新的思路。

基于S变换时频谱和KHA-CNN的换流变故障声纹识别

为准确进行换流变压器故障诊断,保证直流输电可靠性,提出一种基于S变换时频谱和KHA-CNN模式的换流变故障声纹识别方法。利用自适应补充集合经验模态分解算法(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN),实现换流变本体声纹信号与噪声的分离;通过S变换得到时频谱图,实现声纹信号的特征提取;通过磷虾群优化算法(krill herd algorithm,KHA)对卷积神经网络进行超参数寻优,将S时频谱图作为特征输入到KHA-CNN,实现故障诊断。研究结果表明:该方法对于换流变故障具有很好的识别效果,能为换流变故障诊断提供有效参考。