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OFDM系统中一种A-MMSE信道估计算法
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作者 叶文伟 《半导体光电》 CAS 北大核心 2024年第2期308-312,共5页
针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统中最小均方误差(Minimum Mean Squared Error,MMSE)信道估计算法误码率(BER)高的问题,提出一种平均最小均方误差(Averaged-Minimum Mean Squared Error,A-MMSE)... 针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统中最小均方误差(Minimum Mean Squared Error,MMSE)信道估计算法误码率(BER)高的问题,提出一种平均最小均方误差(Averaged-Minimum Mean Squared Error,A-MMSE)信道估计算法。该算法首先基于802.11n标准而构造了一种新的导频结构,收发两端分别进行降采样和过采样处理,利用已知训练序列和导频获得信道频域响应。仿真结果表明,所提出的A-MMSE信道估计算法与传统的MMSE算法相比,在BER为10^(-3)时,信噪比改善了约8dB。因而所提出的信道估计算法能明显改善系统的BER性能。 展开更多
关键词 正交频分复用系统 导频 最小均方误差 误码率
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A Novel Fractional Dengue Transmission Model in the Presence of Wolbachia Using Stochastic Based Artificial Neural Network
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作者 Zeshan Faiz Iftikhar Ahmed +1 位作者 Dumitru Baleanu Shumaila Javeed 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第5期1217-1238,共22页
The purpose of this research work is to investigate the numerical solutions of the fractional dengue transmission model(FDTM)in the presence of Wolbachia using the stochastic-based Levenberg-Marquardt neural network(L... The purpose of this research work is to investigate the numerical solutions of the fractional dengue transmission model(FDTM)in the presence of Wolbachia using the stochastic-based Levenberg-Marquardt neural network(LM-NN)technique.The fractional dengue transmission model(FDTM)consists of 12 compartments.The human population is divided into four compartments;susceptible humans(S_(h)),exposed humans(E_(h)),infectious humans(I_(h)),and recovered humans(R_(h)).Wolbachia-infected and Wolbachia-uninfected mosquito population is also divided into four compartments:aquatic(eggs,larvae,pupae),susceptible,exposed,and infectious.We investigated three different cases of vertical transmission probability(η),namely when Wolbachia-free mosquitoes persist only(η=0.6),when both types of mosquitoes persist(η=0.8),and when Wolbachia-carrying mosquitoes persist only(η=1).The objective of this study is to investigate the effectiveness of Wolbachia in reducing dengue and presenting the numerical results by using the stochastic structure LM-NN approach with 10 hidden layers of neurons for three different cases of the fractional order derivatives(α=0.4,0.6,0.8).LM-NN approach includes a training,validation,and testing procedure to minimize the mean square error(MSE)values using the reference dataset(obtained by solving the model using the Adams-Bashforth-Moulton method(ABM).The distribution of data is 80% data for training,10% for validation,and,10% for testing purpose)results.A comprehensive investigation is accessible to observe the competence,precision,capacity,and efficiency of the suggested LM-NN approach by executing the MSE,state transitions findings,and regression analysis.The effectiveness of the LM-NN approach for solving the FDTM is demonstrated by the overlap of the findings with trustworthy measures,which achieves a precision of up to 10^(-4). 展开更多
关键词 WOLBaCHIa DENGUE neural network vertical transmission mean square error LEVENBERG-MaRQUaRDT
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Performance Analysis of ZF and RZF in Low-Resolution ADC/DAC Massive MIMO Systems
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作者 Talha Younas Shen Jin +4 位作者 Muluneh Mekonnen Gao Mingliang Saqib Saleem Sohaib Tahir Mahrukh Liaqat 《China Communications》 SCIE CSCD 2024年第8期115-126,共12页
Large number of antennas and higher bandwidth usage in massive multiple-input-multipleoutput(MIMO)systems create immense burden on receiver in terms of higher power consumption.The power consumption at the receiver ra... Large number of antennas and higher bandwidth usage in massive multiple-input-multipleoutput(MIMO)systems create immense burden on receiver in terms of higher power consumption.The power consumption at the receiver radio frequency(RF)circuits can be significantly reduced by the application of analog-to-digital converter(ADC)of low resolution.In this paper we investigate bandwidth efficiency(BE)of massive MIMO with perfect channel state information(CSI)by applying low resolution ADCs with Rician fadings.We start our analysis by deriving the additive quantization noise model,which helps to understand the effects of ADC resolution on BE by keeping the power constraint at the receiver in radar.We also investigate deeply the effects of using higher bit rates and the number of BS antennas on bandwidth efficiency(BE)of the system.We emphasize that good bandwidth efficiency can be achieved by even using low resolution ADC by using regularized zero-forcing(RZF)combining algorithm.We also provide a generic analysis of energy efficiency(EE)with different options of bits by calculating the energy efficiencies(EE)using the achievable rates.We emphasize that satisfactory BE can be achieved by even using low-resolution ADC/DAC in massive MIMO. 展开更多
关键词 low-bit analog-digital converter massive(multiple-input-multiple-output)MIMO minimum mean square error(MMSE) regularized zero forcing zero forcing
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Comparative Analysis of Machine Learning Models for Stock Price Prediction: Leveraging LSTM for Real-Time Forecasting
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作者 Bijay Gautam Sanif Kandel +1 位作者 Manoj Shrestha Shrawan Thakur 《Journal of Computer and Communications》 2024年第8期52-80,共29页
The research focuses on improving predictive accuracy in the financial sector through the exploration of machine learning algorithms for stock price prediction. The research follows an organized process combining Agil... The research focuses on improving predictive accuracy in the financial sector through the exploration of machine learning algorithms for stock price prediction. The research follows an organized process combining Agile Scrum and the Obtain, Scrub, Explore, Model, and iNterpret (OSEMN) methodology. Six machine learning models, namely Linear Forecast, Naive Forecast, Simple Moving Average with weekly window (SMA 5), Simple Moving Average with monthly window (SMA 20), Autoregressive Integrated Moving Average (ARIMA), and Long Short-Term Memory (LSTM), are compared and evaluated through Mean Absolute Error (MAE), with the LSTM model performing the best, showcasing its potential for practical financial applications. A Django web application “Predict It” is developed to implement the LSTM model. Ethical concerns related to predictive modeling in finance are addressed. Data quality, algorithm choice, feature engineering, and preprocessing techniques are emphasized for better model performance. The research acknowledges limitations and suggests future research directions, aiming to equip investors and financial professionals with reliable predictive models for dynamic markets. 展开更多
关键词 Stock Price Prediction Machine Learning LSTM aRIMa mean squared error
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Revisiting Akaike’s Final Prediction Error and the Generalized Cross Validation Criteria in Regression from the Same Perspective: From Least Squares to Ridge Regression and Smoothing Splines
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作者 Jean Raphael Ndzinga Mvondo Eugène-Patrice Ndong Nguéma 《Open Journal of Statistics》 2023年第5期694-716,共23页
In regression, despite being both aimed at estimating the Mean Squared Prediction Error (MSPE), Akaike’s Final Prediction Error (FPE) and the Generalized Cross Validation (GCV) selection criteria are usually derived ... In regression, despite being both aimed at estimating the Mean Squared Prediction Error (MSPE), Akaike’s Final Prediction Error (FPE) and the Generalized Cross Validation (GCV) selection criteria are usually derived from two quite different perspectives. Here, settling on the most commonly accepted definition of the MSPE as the expectation of the squared prediction error loss, we provide theoretical expressions for it, valid for any linear model (LM) fitter, be it under random or non random designs. Specializing these MSPE expressions for each of them, we are able to derive closed formulas of the MSPE for some of the most popular LM fitters: Ordinary Least Squares (OLS), with or without a full column rank design matrix;Ordinary and Generalized Ridge regression, the latter embedding smoothing splines fitting. For each of these LM fitters, we then deduce a computable estimate of the MSPE which turns out to coincide with Akaike’s FPE. Using a slight variation, we similarly get a class of MSPE estimates coinciding with the classical GCV formula for those same LM fitters. 展开更多
关键词 Linear Model mean squared Prediction error Final Prediction error Generalized Cross Validation Least squares Ridge Regression
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Low Complexity Minimum Mean Square Error Channel Estimation for Adaptive Coding and Modulation Systems 被引量:2
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作者 GUO Shuxia SONG Yang +1 位作者 GAO Ying HAN Qianjin 《China Communications》 SCIE CSCD 2014年第1期126-137,共12页
Performance of the Adaptive Coding and Modulation(ACM) strongly depends on the retrieved Channel State Information(CSI),which can be obtained using the channel estimation techniques relying on pilot symbol transmissio... Performance of the Adaptive Coding and Modulation(ACM) strongly depends on the retrieved Channel State Information(CSI),which can be obtained using the channel estimation techniques relying on pilot symbol transmission.Earlier analysis of methods of pilot-aided channel estimation for ACM systems were relatively little.In this paper,we investigate the performance of CSI prediction using the Minimum Mean Square Error(MMSE)channel estimator for an ACM system.To solve the two problems of MMSE:high computational operations and oversimplified assumption,we then propose the Low-Complexity schemes(LC-MMSE and Recursion LC-MMSE(R-LC-MMSE)).Computational complexity and Mean Square Error(MSE) are presented to evaluate the efficiency of the proposed algorithm.Both analysis and numerical results show that LC-MMSE performs close to the wellknown MMSE estimator with much lower complexity and R-LC-MMSE improves the application of MMSE estimation to specific circumstances. 展开更多
关键词 adaptive coding and modulation channel estimation minimum mean square error low-complexity minimum mean square error
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融合IMR-WGAN的时序数据修复方法 被引量:1
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作者 孟祥福 马荣国 《小型微型计算机系统》 CSCD 北大核心 2024年第3期641-650,共10页
工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小... 工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小迭代修复和改进WGAN混合模型的时序数据修复方法.首先,在预处理阶段,保留异常数据,进行信息标注等处理,从而充分挖掘异常值与真实值之间的特征约束.其次,在噪声模块提出了近邻参数裁剪规则,用于修正最小迭代修复公式生成的噪声向量.将其传递至模拟分布模块的生成器中,同时设计了一个动态时间注意力网络层,用于提取时序特征权重并与门控循环单元串联组合捕捉不同步长的特征依赖,并引入递归多步预测原理共同提升模型的表达能力;在判别器中设计了Abnormal and Truth奖励机制和Weighted Mean Square Error损失函数共同反向优化生成器修复数据的细节和质量.最后,在公开数据集和真实数据集上的实验结果表明,该方法的修复准确度与模型稳定性显著优于现有方法. 展开更多
关键词 数据修复 改进Wasserstein生成对抗网络 abnormal and Truth奖励机制 动态时间注意力机制 Weighted mean square error损失函数
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非均匀网络中半径可调的ARDV-Hop定位算法
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作者 马千里 钱惠梦 +1 位作者 张琦 齐鑫 《传感技术学报》 CAS CSCD 北大核心 2024年第9期1613-1621,共9页
针对无线传感网络中传统DV-Hop(Distance Vector Hop)定位算法节点分布不均匀导致定位误差较大的问题,提出了非均匀网络中半径可调的ARDV-Hop(Adjustable Radius DV-Hop in Non-uniform Networks)定位算法。该算法通过半径可调的方式对... 针对无线传感网络中传统DV-Hop(Distance Vector Hop)定位算法节点分布不均匀导致定位误差较大的问题,提出了非均匀网络中半径可调的ARDV-Hop(Adjustable Radius DV-Hop in Non-uniform Networks)定位算法。该算法通过半径可调的方式对节点间的跳数进行细化,用细化后呈小数级的跳数代替传统的整数级跳数,并建立了数据能量消耗模型,优化了网络传输性能。ARDV-Hop算法还针对节点分布不均匀的区域提出跳距优化算法:在节点密度大的区域,采用余弦定理优化跳距;密度小的区域,采用最小均方误差(Least Mean Square,LMS)来修正跳距。仿真实验表明,在同等网络环境下,与传统DV-Hop算法、GDV-Hop算法和WOA-DV-Hop算法相比,ARDV-Hop算法能更有效地降低定位误差. 展开更多
关键词 无线传感网络 DV-HOP 半径可调 非均匀网络 最小均方误差
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应答器上行链路信号自适应解调方法的FPGA实现
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作者 李建国 薛千树 陈明福 《科学技术与工程》 北大核心 2024年第20期8715-8722,共8页
为降低电磁干扰对信号传输的影响,分析了应答器上行链路信号传输过程及其易遭受干扰信号的特点,设计了基于符号最小均方误差(least mean square,LMS)算法的自适应解调方法。为在硬件平台中实现该解调方法,通过仿真计算,确定LMS算法的自... 为降低电磁干扰对信号传输的影响,分析了应答器上行链路信号传输过程及其易遭受干扰信号的特点,设计了基于符号最小均方误差(least mean square,LMS)算法的自适应解调方法。为在硬件平台中实现该解调方法,通过仿真计算,确定LMS算法的自适应算法中间变量变化范围,使用截位操作完成权值系数的更新,设置均衡器长度、步长因子、中值滤波系数分别为1、1/64、16,可在不占用过多硬件资源情况下获得良好的解调性能。解调算法在现场可编程门阵列(field programmable gata array,FPGA)上予以验证,实验表明,当信噪比为6 dB时,FPGA中自适应解调误码率为0.000001,在信噪比大于等于6 dB时,实测误码率与仿真分析误码率基本一致;FPGA自适应解调方法在列车不同速度等级下误码率均小于10^(-6)。 展开更多
关键词 应答器 自适应解调 最小均方误差(LMS)算法 现场可编程门阵列(FPGa) 信噪比 误码率
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自适应分数阶偏微分方程修正模型的能量泛函及Euler-Lagrange方程研究
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作者 王晓霞 《佳木斯大学学报(自然科学版)》 CAS 2024年第9期172-176,共5页
首先对分数阶微分方程进行构建,结合全变分项,提出了修正的自适应分数阶偏微分方程模型。研究首先确定出分数阶偏分去噪模型的最优分数阶数,当分数阶次为1.8时,峰值信噪比和结构相似度达到33.12和0.874,均方根误差降低至5.62。然后将研... 首先对分数阶微分方程进行构建,结合全变分项,提出了修正的自适应分数阶偏微分方程模型。研究首先确定出分数阶偏分去噪模型的最优分数阶数,当分数阶次为1.8时,峰值信噪比和结构相似度达到33.12和0.874,均方根误差降低至5.62。然后将研究提出的模型与全变分模型、分数阶偏分去噪模型等在图像上进行对比实验,研究提出的模型在峰值信噪比、结构相似度上达到最高,分别为29.045与0.839,均方根误差为9.427,表明模型能够抑制阶梯效应,具有优越的去噪性能。 展开更多
关键词 自适应 分数阶 能量泛函 均方根误差 偏微分方程
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SC-FDMA系统的MMSE-FSE算法分析
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作者 孙亮亮 任颖 《计算机与网络》 2024年第1期89-94,共6页
单载波频分多址(Single-Carrier Frequency Division Multiple Access,SC-FDMA)系统均衡器的输入信号通常是按符号间隔进行采样的,其对抽样时间十分敏感。在短波波段,由于多径反射显著,当多径延时接近符号周期长度时,对抽样时间敏感的... 单载波频分多址(Single-Carrier Frequency Division Multiple Access,SC-FDMA)系统均衡器的输入信号通常是按符号间隔进行采样的,其对抽样时间十分敏感。在短波波段,由于多径反射显著,当多径延时接近符号周期长度时,对抽样时间敏感的缺点会被放大。针对短波信道的特征,研究了SC-FDMA系统的分数间隔均衡器(Fractional Spaced Equalizer,FSE)模型,通过与符号间隔均衡器对比发现,虽然符号间隔均衡器可以补偿接收信号的频率响应,但其对短时延衰落信道的补偿效果较差;FSE对于抽样时间的选择不敏感,在多径信道下能够获得更好的性能。链路仿真结果表明,在短时衰落信道环境下,FSE的译码性能比符号间隔均衡器有最大1.5 dB的增益。 展开更多
关键词 无线通信 多径信道 单载波频分多址 分数间隔均衡器 最小均方误差
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Performance of the CMA-GD Model in Predicting Wind Speed at Wind Farms in Hubei, China 被引量:1
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作者 许沛华 成驰 +3 位作者 王文 陈正洪 钟水新 张艳霞 《Journal of Tropical Meteorology》 SCIE 2023年第4期473-481,共9页
This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,China.The observed wind speeds at the height of 70m in wind turbines of two win... This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,China.The observed wind speeds at the height of 70m in wind turbines of two wind farms in Suizhou serve as the actual observation data for comparison and testing.At the same time,the wind speed predicted by the EC model is also included for comparative analysis.The results indicate that the CMA-GD model performs better than the EC model in Wind Farm A.The CMA-GD model exhibits a monthly average correlation coefficient of 0.56,root mean square error of 2.72 m s^(-1),and average absolute error of 2.11 m s^(-1).In contrast,the EC model shows a monthly average correlation coefficient of 0.51,root mean square error of 2.83 m s^(-1),and average absolute error of 2.21 m s^(-1).Conversely,in Wind Farm B,the EC model outperforms the CMA-GD model.The CMA-GD model achieves a monthly average correlation coefficient of 0.55,root mean square error of 2.61 m s^(-1),and average absolute error of 2.13 m s^(-1).By contrast,the EC model displays a monthly average correlation coefficient of 0.63,root mean square error of 2.04 m s^(-1),and average absolute error of 1.67 m s^(-1). 展开更多
关键词 CMa-GD wind speed prediction wind farm root mean square error performance evaluation
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Integrated Approach of Brain Disorder Analysis by Using Deep Learning Based on DNA Sequence
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作者 Ahmed Zohair Ibrahim P.Prakash +1 位作者 V.Sakthivel P.Prabu 《Computer Systems Science & Engineering》 SCIE EI 2023年第6期2447-2460,共14页
In order to research brain problems using MRI,PET,and CT neuroimaging,a correct understanding of brain function is required.This has been considered in earlier times with the support of traditional algorithms.Deep lea... In order to research brain problems using MRI,PET,and CT neuroimaging,a correct understanding of brain function is required.This has been considered in earlier times with the support of traditional algorithms.Deep learning process has also been widely considered in these genomics data processing system.In this research,brain disorder illness incliding Alzheimer’s disease,Schizophrenia and Parkinson’s diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods.Moeover,deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks(DBN).Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm(DBNJZZ)approach.The suggested approach is executed and tested by using the performance metric measure such as accuracy,root mean square error,Mean absolute error and mean absolute percentage error.Proposed DBNJZZ gives better performance than previously available methods. 展开更多
关键词 Deep belief networks zig zag deep learning mean absolute percentage error mean absolute error root mean square error DNa GENOMICS
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Using self-location to calibrate the errors of observer positions for source localization 被引量:2
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作者 Wanchun Li Wanyi Zhang Liping Li 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2014年第2期194-202,共9页
The uncertainty of observers' positions can lead to significantly degrading in source localization accuracy. This pa-per proposes a method of using self-location for calibrating the positions of observer stations in ... The uncertainty of observers' positions can lead to significantly degrading in source localization accuracy. This pa-per proposes a method of using self-location for calibrating the positions of observer stations in source localization to reduce the errors of the observer positions and improve the accuracy of the source localization. The relative distance measurements of the two coordinative observers are used for the linear minimum mean square error (LMMSE) estimator. The results of computer si-mulations prove the feasibility and effectiveness of the proposed method. With the general estimation errors of observers' positions, the MSE of the source localization with self-location calibration, which is significantly lower than that without self-location calibra-tion, is approximating to the Cramer-Rao lower bound (CRLB). 展开更多
关键词 self-location errors of the observer positions linearminimum mean square error (LMMSE) estimator accuracy of thesource localization Cramer-Rao lower bound (CRLB).
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基于SimAM注意力机制的轴承故障迁移诊断模型 被引量:1
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作者 包从望 朱广勇 +1 位作者 邹旺 郭灏 《机电工程》 CAS 北大核心 2024年第5期862-869,893,共9页
针对轴承故障在跨工况迁移诊断时,其域不变特征难以提取,易出现模型过拟合这一问题,提出了一种基于无参数注意力模块(SimAM)的轴承故障迁移诊断方法。首先,以一维卷积神经网络作为基本框架,利用自适应批量归一化(AdaBN)对各输出层进行... 针对轴承故障在跨工况迁移诊断时,其域不变特征难以提取,易出现模型过拟合这一问题,提出了一种基于无参数注意力模块(SimAM)的轴承故障迁移诊断方法。首先,以一维卷积神经网络作为基本框架,利用自适应批量归一化(AdaBN)对各输出层进行了归一化处理,经两层卷积层和两层池化层后,对输出特征进行了随机节点失活操作;然后,利用改进后的参数化修正线性单元(PReLU)激活函数自适应提取负值输入权值系数,分别以交叉熵损失函数监督训练有标签的源域数据,以均方对数误差(MSLE)作为损失函数训练无标签的目标数据;最后,利用自制实验台数据和凯斯西储轴承公开数据对模型进行了验证,分别以不同的单一工况作为源域,其余工况作为目标域进行了迁移诊断任务研究。研究结果表明:基于SimAM的轴承故障迁移诊断方具有较好的域不变特征提取的性能,且所提特征具有较好的聚类效果;自制实验台中的平均迁移精度在89.1%以上,最高均值可达97.85%,CWRU数据集中的平均迁移精度达98.68%。该成果可为后续轴承故障由实验向工业现场的迁移诊断奠定基础。 展开更多
关键词 轴承故障诊断 迁移学习 无参数注意力机制 自适应批量归一化 参数化修正线性单元 均方对数误差 卷积神经网络
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一种基于ASLC的数字波束抗干扰改进算法研究
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作者 赵楠 韩国栋 张建超 《现代雷达》 CSCD 北大核心 2024年第6期74-78,共5页
针对阵列雷达系统在自适应抗干扰处理过程中设备复杂度高、期望信号信噪比下降等问题,在自适应旁瓣对消(ASLC)理论基础上,研究了一种改进的数字抗干扰处理算法。该算法分析了辅助天线单元独立设计引起的空间资源占用等问题,通过在天线... 针对阵列雷达系统在自适应抗干扰处理过程中设备复杂度高、期望信号信噪比下降等问题,在自适应旁瓣对消(ASLC)理论基础上,研究了一种改进的数字抗干扰处理算法。该算法分析了辅助天线单元独立设计引起的空间资源占用等问题,通过在天线阵列中灵活选取数字单元形成辅助信号的方式,优化阵列资源的同时增加了主阵列与辅助单元收到的干扰信号相关性,提高了系统的干扰抑制比;根据最小均方误差准则对辅助信号进行预加权处理,削减辅助波束接收的期望信号能量,改善了由于辅助波束接收信号的自相关矩阵中含有期望信号引起的期望信号相消问题。通过系统测试,验证了该技术的有效性,实测结果表明,该算法在简化天线阵列设计的同时,干扰调零深度达到了51.6 dB,而期望信号信噪比仅损失0.65 dB,解决了传统ASLC算法效率下降的难题,具有广泛的工程应用前景。 展开更多
关键词 最小均方误差 自适应旁瓣对消 数字波束抗干扰
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Asymptotic Consistency of the James-Stein Shrinkage Estimator
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作者 Alex Samuel Mungo Victor Mooto Nawa 《Open Journal of Statistics》 2023年第6期872-892,共21页
The study explores the asymptotic consistency of the James-Stein shrinkage estimator obtained by shrinking a maximum likelihood estimator. We use Hansen’s approach to show that the James-Stein shrinkage estimator con... The study explores the asymptotic consistency of the James-Stein shrinkage estimator obtained by shrinking a maximum likelihood estimator. We use Hansen’s approach to show that the James-Stein shrinkage estimator converges asymptotically to some multivariate normal distribution with shrinkage effect values. We establish that the rate of convergence is of order  and rate , hence the James-Stein shrinkage estimator is -consistent. Then visualise its consistency by studying the asymptotic behaviour using simulating plots in R for the mean squared error of the maximum likelihood estimator and the shrinkage estimator. The latter graphically shows lower mean squared error as compared to that of the maximum likelihood estimator. 展开更多
关键词 aSYMPTOTIC CONSISTENCY CONVERGENCE EFFICIENCY mean squared error SHRINKaGE
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New Efficient Estimators of Population Mean Using Non-Traditional Measures of Dispersion 被引量:1
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作者 Rajesh Kumar Gupta Subhash Kumar Yadav 《Open Journal of Statistics》 2017年第3期394-404,共11页
One of the aims in survey sampling is to search for the estimators with highest efficiency. In the present paper, three improved estimators of population mean have been proposed using some non-traditional measures of ... One of the aims in survey sampling is to search for the estimators with highest efficiency. In the present paper, three improved estimators of population mean have been proposed using some non-traditional measures of dispersion of auxiliary variable such as Gini’s mean difference, Downton’s method and probability weighted moments early given by Abid [1] with a special population parameter of auxiliary variable. The large sample properties that are biased and mean squared errors of the proposed estimators have been derived up to the first order of approximation. A theoretical comparison of the proposed estimators has been made with the other existing estimators of population mean using auxiliary information. The conditions under which the proposed estimators perform better than the other existing estimators of population mean have been given. A numerical study is also carried out to see the performances of the proposed and existing estimators of population mean and verify the conditions under which proposed estimators are better than other estimators. It has been shown that the proposed estimators perform better than the existing estimators as they are having lesser mean squared error. 展开更多
关键词 Study VaRIaBLE aUXILIaRY VaRIaBLE BIaS mean squared error Efficiency
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Okumura Hata Propagation Model Optimization in 400 MHz Band Based on Differential Evolution Algorithm: Application to the City of Bertoua
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作者 Eric Michel Deussom Djomadji Ivan Basile Kabiena +2 位作者 Joel Thibaut Mandengue Felix Watching Emmanuel Tonye 《Journal of Computer and Communications》 2023年第5期52-69,共18页
Propagation models are the foundation for radio planning in mobile networks. They are widely used during feasibility studies and initial network deployment, or during network extensions, particularly in new cities. Th... Propagation models are the foundation for radio planning in mobile networks. They are widely used during feasibility studies and initial network deployment, or during network extensions, particularly in new cities. They can be used to calculate the power of the signal received by a mobile terminal, evaluate the coverage radius, and calculate the number of cells required to cover a given area. This paper takes into account the standard k factors model and then uses the differential evolution algorithm to set up a propagation model adapted to the physical environment of the Cameroonian cities of Bertoua. Drive tests were made on the LTE TDD network in the city of Bertoua. Differential evolution algorithm is used as the optimization algorithm to deduct a propagation model which fits the environment of the considered town. The calculation of the root mean square error between the actual data from the drive tests and the prediction data from the implemented model allows the validation of the obtained results. A comparative study made between the RMSE value obtained by the new model and those obtained by the Okumura Hata and free space models, allowed us to conclude that the new model obtained is better and more representative of our local environment than the Okumura Hata currently used. The implementation shows that Differential evolution can perform well and solve this kind of optimization problem;the newly obtained models can be used for radio planning in the city of Bertoua in Cameroon. 展开更多
关键词 Radio Measurements Root mean square error Differential Evolution algorithm
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COST 231-Hata Propagation Model Optimization in 1800 MHz Band Based on Magnetic Optimization Algorithm: Application to the City of Limbé
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作者 Eric Michel Deussom Djomadji Kabiena Ivan Basile +1 位作者 Fobasso Segnou Thierry Tonye Emanuel 《Journal of Computer and Communications》 2023年第2期57-74,共18页
Network planning is essential for the construction and the development of wireless networks. The network planning cannot be possible without an appropriate propagation model which in fact is its foundation. Initially ... Network planning is essential for the construction and the development of wireless networks. The network planning cannot be possible without an appropriate propagation model which in fact is its foundation. Initially used mainly for mobile radio networks, the optimization of propagation model is becoming essential for efficient deployment of the network in different types of environment, namely rural, suburban and urban especially with the emergence of concepts such as digital terrestrial television, smart cities, Internet of Things (IoT) with wide deployment for different use cases such as smart grid, smart metering of electricity, gas and water. In this paper we use an optimization algorithm that is inspired by the principles of magnetic field theory namely Magnetic Optimization Algorithm (MOA) to tune COST231-Hata propagation model. The dataset used is the result of drive tests carry out on field in the town of Limbe in Cameroon. We take into account the standard K-factor model and then use the MOA algorithm in order to set up a propagation model adapted to the physical environment of a town. The town of Limbe is used as an implementation case, but the proposed method can be used everywhere. The calculation of the root mean square error (RMSE) between the real data from the radio measurements and the prediction data obtained after the implementation of MOA allows the validation of the results. A comparative study between the value of the RMSE obtained by the new model and those obtained by the optimization using linear regression, by the standard COST231-Hata models, and the free space model is also done, this allows us to conclude that the new model obtained using MOA for the city of Limbe is better and more representative of this local environment than the standard COST231-Hata model. The new model obtained can be used for radio planning in the city of Limbé in Cameroon. 展开更多
关键词 Radio Measurements Root mean square error Magnetic Optimization algorithm
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