New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to 7G.Furthermore,it improves the array gain and directivity,increasing the detection range and angular resolution of radar systems.This study proposes two highly efficient SLL reduction techniques.These techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,respectively.The convolution process determines the element’s excitations while the GA optimizes the element spacing.For M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,respectively.When the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher SLL.Tomitigate the increased HPBWof the odd and even excitations,the element spacing is optimized using the GA.Thereby,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the SLL.Furthermore,for extreme SLL reduction,the DConv/GA is introduced.In this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation vectors.It provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.

基于SPA-GA-SVR模型的土壤水分及温度预测

土壤湿度和温度是影响水文循环和气候变化的重要参数,在农业实践活动和生态平衡中起着重要作用。为及时、准确地监测土壤含水量(Soil Moisture Content,SMC)及温度,提出了一种基于高光谱数据的预测方法。实验数据集来自为期5天的实地测量,所获得的高光谱数据包含大量的噪声及冗余信息,因此首先用Savitzky-Golay卷积平滑对光谱数据进行降噪处理,利用连续投影算法(Successive Projection Algorithm,SPA)提取数据特征波长,然后通过遗传算法(Genetic Algorithm,GA)对支持向量机回归(Support Vector Regression,SVR)的超参数权值和偏置进行优化,构建SPA-GASVR混合算法模型对土壤水分和温度进行预测,并与BP神经网络(Back Propagation Neural Network,BPNN)、SPA-BP、SVR、SPA-SVR、GA-SVR这5种模型的预测性能进行比较。实验结果表明:各模型在土壤湿度低于30%的情况下,表现出的预测能力差异并不显著。但整体上,复合模型相比于单一的神经网络或机器学习模型具有明显的优势,且经过连续投影算法优化的模型进一步的提高其预测能力,最终SPA-GA-SVR算法在各项指标上均优于其他模型,土壤水分预测模型的R^(2)=0.981、RMSE=0.473%,土壤温度预测模型R^(2)=0.963、RMSE=0.883℃。实验证明基于高光谱数据,经过SPA和GA优化的SVR模型能实现对土壤湿度和温度精准的预测。该方法具有一定的应用价值和现实意义,可应用于便携式高光谱仪和无人机上,实现对土壤水分和温度的实时监测,为今后的播种及灌溉提供理论参考。

基于GA的RBF神经网络气液两相流持液率预测模型优化

为了提高气液两相流持液率预测精度,针对传统径向基函数(RBF)神经网络预测气液两相流持液率网络拓扑结构困难和收敛速度慢等问题,提出一种基于遗传算法(GA)优化径向基函数神经网络的气液两相流持液率预测模型。通过系统聚类算法和灰色关联度分析(GRA)对收集的实验数据进行处理,优选出最优模型特征,同时结合遗传算法确定了RBF神经网络结构参数。基于室内实验数据进行训练,并与常用于持液率预测的反向传播(BP)神经网络、GA-BP神经网络及RBF神经网络进行对比,评估了模型的准确性及可行性。结果表明:GA-RBF神经网络模型均方误差为0.0017,均方根误差为0.0416,平均绝对误差为0.0281,拟合度为0.9483。相较于其他神经网络模型,该预测模型表现出更高的计算精度和更强的泛化能力。

基于GA-BP神经网络的大型客机气流角估计方法

为了解决硬件冗余难以克服的气流角传感器共因故障问题,进一步提高飞机气流角信号的可靠性,研究了基于GABP神经网络的气流角估计方法。通过BP神经网络融合姿态角、加速度、风速等参数来实现不依赖气流角传感器的气流角估计;引入遗传算法对神经网络权值和阈值进行全局优化,提高估计精度;对某大型客机的试飞数据预处理后用于模型的训练和测试。仿真结果表明,训练完成的GA-BP神经网络模型对气流角的估计值贴近实际值,稳定性和精度明显高于BP神经网络。上述方法给飞机增加一个余度的气流角信号,可用于传感器故障时为飞机提供可靠的气流角信号。

基于GA-LSTM自适应卡尔曼滤波的路面不平度识别

准确、快速地识别出车辆当前行驶的路面激励信息,是实现智能底盘控制进而保证车辆平顺性的关键。针对传统路面不平度识别算法准确率低、自适应性差等问题,提出了基于遗传算法(genetic algorithm,GA)优化长短期记忆神经网络(long short-term memory networks,LSTM)自适应卡尔曼滤波的路面不平度识别算法。基于2自由度车辆悬架模型,通过灰色关联法选择LSTM神经网络的特征输入变量,并采用GA优化LSTM神经网络的模型参数以准确识别路面等级,并据此实时更新卡尔曼滤波器算法中的噪声矩阵,实现了在复杂路况下对路面不平度的自适应识别。仿真和试验研究表明,所提出的基于GA-LSTM自适应卡尔曼滤波算法能够快速准确的识别路面不平度与路面等级,与传统卡尔曼滤波算法相比,相关系数、均方根误差和最大绝对误差分别提高3.11%、37.5%和51.2%,表明所提算法对复杂工况具有很好的自适应能力。

基于GA-BP神经网络的新疆南疆核桃树生长模型研究

文章提出了一种利用遗传算法优化BP神经网络的核桃树生长模型来预测核桃树的树高、胸径的方法,通过优化BP神经网络的权值和阈值建立GA-BP模型,与多元线性回归模型的预测结果进行比较。结果表明:采用遗传算法优化后的模型具有更高的预测精度,对核桃树生长预测具有指导意义。

Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm

In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear sta- tistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two repre- sentative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method per- forms well in selecting genes and achieves high classification accuracies with these genes.

基于GRA-GASA-SVM的煤层瓦斯含量预测方法研究

为提升煤层瓦斯含量预测精度,提出一种采用遗传模拟退火算法混合优化支持向量机(SVM)参数的瓦斯含量预测模型(GRA-GASA-SVM模型)。该模型将GA和SA整合为遗传模拟退火算法协同优化SVM的参数,以解决传统网格寻优算法取值范围无法确定和单一智能算法优化程度有限等问题。利用灰色关联分析(GRA)压缩数据集维度,建立瓦斯含量预测参数体系并作为GASA-SVM的输入数据集。结果表明:SVM模型、GA-SVM模型和GASA-SVM模型10折交叉验证瓦斯含量预测总平均相对误差分别为15.98%、13.55%和10.58%。相比SVM模型和GA-SVM模型,GASA-SVM模型预测稳定性更优、预测精准度更高且对新样本泛化能力更强。

基于PSO-GA的分片区块链系统性能优化方法

在这篇文章中,针对分片区块链(Sharded Blockchain)系统性能优化问题,提出了一种结合粒子群和遗传算法的系统性能优化方法(PSO-GA),目的是为了在尽可能满足当前网络环境情况下,提升其系统吞吐量.该方法考虑分片区块链中节点的计算能力、恶意节点的概率以及节点之间的传输速率等不同网络环境下,找到响应网络状态的最佳分片区块链系统参数;为了避免传统粒子群优化算法陷入局部最优的问题,引入遗传算法中的交叉操作和变异操作,有效提高方法的准确性.通过大量仿真实验对方法的有效性进行验证分析.实验结果表明,相比于其他的方法,本文所提出的方法可以在更短的时间取得更高的系统吞吐量.

Geochemical Characteristics and Genetic Types of Natural Gas in the Xinchang Gas Field, Sichuan Basin, SW China

The molecular compositions and stable carbon and hydrogen isotopic compositions of natural gas from the Xinchang gas field in the Sichuan Basin were investigated to determine the genetic types. The natural gas is mainly composed of methane （88.99%-98.01%）, and the dryness coefficient varies between 0.908 and 0.997. The gas generally displays positive alkane carbon and hydrogen isotopic series. The geochemical characteristics and gas-source correlation indicate that the gases stored in the 5th member of the Upper Triassic Xujiahe Formation are coal-type gases which are derived from source rocks in the stratum itself. The gases reservoired in the 4th member of the Xujiahe Formation and Jurassic strata in the Xinchang gas field are also coal-type gases that are derived from source rocks in the 3rd and 4th members of the Xujiahe Formation. The gases reservoired in the 2nd member of the Upper Triassic Xujiahe Formation are mainly coal-type gases with small amounts of oil-type gas that is derived from source rocks in the stratum itself. This is accompanied by a small amount of contribution brought by source rocks in the Upper Triassic Ma＇antang and Xiaotangzi formations. The gases reservoired in the 4th member of the Middle Triassic Leikoupo Formation are oil-type gases and are believed to be derived from the secondary cracking of oil which is most likely to be generated from the Upper Permian source rocks.

Design of artificial neural networks using a genetic algorithm to predict saturates of vacuum gas oil

Accurate prediction of chemical composition of vacuum gas oil （VGO） is essential for the routine operation of refineries. In this work, a new approach for auto-design of artificial neural networks （ANN） based on a genetic algorithm （GA） is developed for predicting VGO saturates. The number of neurons in the hidden layer, the momentum and the learning rates are determined by using the genetic algorithm. The inputs for the artificial neural networks model are five physical properties, namely, average boiling point, density, molecular weight, viscosity and refractive index. It is verified that the genetic algorithm could find the optimal structural parameters and training parameters of ANN. In addition, an artificial neural networks model based on a genetic algorithm was tested and the results indicated that the VGO saturates can be efficiently predicted. Compared with conventional artificial neural networks models, this approach can improve the prediction accuracy.

An Improved Hybrid Genetic Algorithm for Chemical Plant Layout Optimization with Novel Non-overlapping and Toxic Gas Dispersion Constraints

New approaches for facility distribution in chemical plants are proposed including an improved non-overlapping constraint based on projection relationships of facilities and a novel toxic gas dispersion constraint. In consideration of the large number of variables in the plant layout model, our new method can significantly reduce the number of variables with their own projection relationships. Also, as toxic gas dispersion is a usual incident in a chemical plant, a simple approach to describe the gas leakage is proposed, which can clearly represent the constraints of potential emission source and sitting facilities. For solving the plant layout model, an improved genetic algorithm (GA) based on infeasible solution fix technique is proposed, which improves the globe search ability of GA. The case study and experiment show that a better layout plan can be obtained with our method, and the safety factors such as gas dispersion and minimum distances can be well handled in the solution.

Genetic types and distribution of shallow-buried natural gases

Great volumes of shallow-buried （〈2,000 m） natural gases which are mainly composed of biogases and low-mature gases have been found in the Mesozoic-Cenozoic sedimentary basins in China. Many shallow gas reservoirs in China are characterized by coexistence of biogas and low-mature gas, so identifying the genetic types of shallow gases is important for exploration and development in sedimentary basins. In this paper, we study the gas geochemistry characteristics and distribution in different basins, and classify the shallow gas into two genetic types, biogas and low-mature gas. The biogases are subdivided further into two subtypes by their sources, the source rock-derived biogas and hydrocarbon-derived biogas. Based on the burial history of the source rocks, the source rock-derived biogases are divided into primary and secondary biogas. The former is generated from the source rocks in the primary burial stage, and the latter is from uplifted source rocks or those in a secondary burial stage. In addition, the identifying parameters of each type of shallow gas are given. Based on the analysis above, the distributions of each type of shallow gas are studied. The primary biogases generated from source rocks are mostly distributed in Quaternary basins or modem deltas. Most of them migrate in watersoluble or diffused mode, and their migration distance is short. Reservoir and caprock assemblages play an important role in primary biogas accumulation. The secondary biogases are distributed in a basin with secondary burial history. The oil-degraded biogases are distributed near heavy oil pools. The low-mature gases are widely distributed in shallow-buried reservoirs in the Meso-Cenozoic basins.

A New Image Watermarking Scheme Using Genetic Algorithm and Residual Numbers with Discrete Wavelet Transform

Transmission of data over the internet has become a critical issue as a result of the advancement in technology, since it is possible for pirates to steal the intellectual property of content owners. This paper presents a new digital watermarking scheme that combines some operators of the Genetic Algorithm (GA) and the Residue Number (RN) System (RNS) to perform encryption on an image, which is embedded into a cover image for the purposes of watermarking. Thus, an image watermarking scheme uses an encrypted image. The secret image is embedded in decomposed frames of the cover image achieved by applying a three-level Discrete Wavelet Transform (DWT). This is to ensure that the secret information is not exposed even when there is a successful attack on the cover information. Content creators can prove ownership of the multimedia content by unveiling the secret information in a court of law. The proposed scheme was tested with sample data using MATLAB2022 and the results of the simulation show a great deal of imperceptibility and robustness as compared to similar existing schemes.

FPGA PLACEMENT OPTIMIZATION BY TWO-STEP UNIFIED GENETIC ALGORITHM AND SIMULATED ANNEALING ALGORITHM

Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool.

The Genetic Mechanism and Model of Deep-Basin Gas Accumulation and Methods for Predicting the Favorable Areas

As a kind of abnormal natural gas formed with special mechanism, the deep-basin gas, accumulated in the lower parts of a basin or syncline and trapped by a tight reservoir, has such characteristics as gas-water inversion, abnormal pressure, continuous distribution and tremendous reserves. Being a geological product of the evolution of petroliferous basins by the end of the middle-late stages, the formation of a deep-basin gas accumulation must meet four conditions, i.e., continuous and sufficient gas supply, tight reservoirs in continuous distribution, good sealing caps and stable structures. The areas, where the expansion force of natural gas is smaller than the sum of the capillary force and the hydrostatic pressure within tight reservoirs, are favorable for forming deep-basin gas pools. The range delineated by the above two forces corresponds to that of the deep-basin gas trap. Within the scope of the deep-basin gas trap, the balance relationship between the amounts of ingoing and overflowing gases determines the gas-bearing area of the deep-basin gas pool. The gas volume in regions with high porosity and high permeability is worth exploring under current technical conditions and it is equivalent to the practical resources (about 10%-20% of the deep-basin gas). Based on studies of deep-basin gas formation conditions, the theory of force balance and the equation of material balance, the favorable areas and gas-containing ranges, as well as possible gas-rich regions are preliminarily predicted in the deep-basin gas pools in the Upper Paleozoic He-8 segment of the Ordos basin.

基于GA-GRNN的AWJ强化3D打印AlSi10Mg表面性能实验研究

为提高磨料水射流(Abrasive Water Jet,AWJ)强化工艺对3D打印AlSi10Mg表面性能的强化效果预测的准确性及高效性,首先开展磨料水射流强化AlSi10Mg表面强化实验;然后分别以表面硬度和表面残余应力作为目标,基于遗传算法-广义回归神经网络(Genetic Algorithm-Generalized Ragression Neural Network,GA-GRNN)对实验数据样本进行训练,建立3D打印AlSi10Mg表面性能预测模型;最后,利用遗传算法对建立的神经网络预测模型中的AWJ强化主要参数进行优化。研究结果表明,经过磨料水射流强化后的AlSi10Mg表面硬度与表面残余应力均得到有效提高;建立的GA-GRNN预测模型与校验值误差在2.3%以内,具有较高的准确性;经遗传算法优化后,得到表面硬度最佳参数组合:射流压力为33 MPa,磨料粒径为0.15 mm,靶距为12.4 mm,此时表面硬度为159.25HV;表面残余应力最佳参数组合:射流压力为40 MPa,磨料粒径为0.13 mm,靶距为15 mm,此时表面残余应力为-137.4 MPa。为后续磨料水射流强化零件表面的参数选择提供数据支撑。

基于GA-BP神经网络战储仓库选址决策模型

针对各战略方向战储仓库选址决策问题,提出基于GA-BP神经网络的战储仓库选址决策模型。通过分析战储仓库选址的影响因素,构建战储库选址决策指标体系;基于BP神经网络建立选址决策模型,运用遗传算法(geneticalgorithm,GA)对反向传播(backpropagation,BP)神经网络的权值和阈值进行优化,并给出实例证明遗传算法优化BP神经网络,可提高选址决策的效率和精准度。结果表明,该研究可为科学开展战储仓库选址工作提供思路和方法。

纳入雷诺数修正的GA-Elman算法对EGTM的研究

为进一步减小排气温度裕度计算误差,对发动机起飞排气温度裕度基线观察值和雷诺数影响系数进行了多元非线性拟合,提出了利用雷诺数影响系数修正排气温度(Exhaust Gas Temperature,EGT)基线观察值的方法,将雷诺数影响系数加入神经网络的输入层,利用遗传算法(Genetic Algorithm,GA)优化Elman网络模型,建立排气温度裕度(Exhaust Gas Temperature Margin,EGTM)的预测模型。通过结合飞行数据计算,对比多元非线性拟合以及Elman网络模型和基于Elman网络优化的GA-Elman模型的计算误差效果,得出实验结果:GA-Elman对EGTM计算精度更高,鲁棒性更强。

基于肌音信号的KPCAGASVM步态模式识别

外骨骼机器人发展迅速,基于生理信号的运动意图识别在人机协同控制研究中得以重视。针对肌电信号易受肌肉疲劳影响和采集要求高的缺点,提出一种基于肌音信号的核主成分分析和改进支持向量机(KPCAGASVM)的模式识别方案,对平地行走、上楼下楼和上坡下坡5种步态进行模式识别研究。基于遗传算法进行参数调优,其识别方案KPCAGASVM的识别准确率为97.33%,优于PCAGASVM和其他分类器。实验验证,基于肌音信号的KPCAGASVM为一种高效的步态运动识别方案。