Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.

基于理性扩展热力学的L-S热声弹性理论框架

本文基于连续介质力学和理性扩展热力学分析流程,将L-S(Lord and Shulman)热弹性理论与声弹性理论相结合,建立L-S热声弹性理论的基本框架,包括运动学、力学与热力学、本构方程与演化方程、基本场方程四部分。在运动学部分,区分了Lagrange描述和Euler描述,以及3种不同的状态和构形,同时针对热声弹性情况定义了两类从自然状态到初始状态的转变过程;在力学与热力学部分,给出了质量守恒定律、动量守恒定律、角动量守恒定律、能量守恒定律以及熵产不等式,从而引出经典不可逆热力学的局限性;在本构方程与演化方程部分,介绍了扩展不可逆热力学原理,并基于理性扩展热力学流程,推导了从自然状态到初始状态、从初始状态到最终状态的热声弹性本构方程与演化方程,将热流作为本构自变量并考虑了热流与应变和温度的相关性;在最后一部分给出了基本场方程的运动方程形式和适用于数值模拟的一阶速度-应力-热流-温度微分方程。

Model-driven CT reconstruction algorithm for nano-resolution x-ray phase contrast imaging

The low-density imaging performance of a zone plate-based nano-resolution hard x-ray computed tomography(CT)system can be significantly improved by incorporating a grating-based Lau interferometer. Due to the diffraction, however,the acquired nano-resolution phase signal may suffer splitting problem, which impedes the direct reconstruction of phase contrast CT(nPCT) images. To overcome, a new model-driven nPCT image reconstruction algorithm is developed in this study. In it, the diffraction procedure is mathematically modeled into a matrix B, from which the projections without signal splitting can be generated invertedly. Furthermore, a penalized weighted least-square model with total variation(PWLSTV) is employed to denoise these projections, from which nPCT images with high accuracy are directly reconstructed.Numerical experiments demonstrate that this new algorithm is able to work with phase projections having any splitting distances. Moreover, results also reveal that nPCT images of higher signal-to-noise-ratio(SNR) could be reconstructed from projections having larger splitting distances. In summary, a novel model-driven nPCT image reconstruction algorithm with high accuracy and robustness is verified for the Lau interferometer-based hard x-ray nano-resolution phase contrast imaging.

A hybrid contact approach for modeling soil-structure interaction using the material point method

The grid-based multi-velocity field technique has become increasingly popular for simulating the Material Point Method(MPM)in contact problems.However,this traditional technique has some shortcomings,such as(1)early contact and contact penetration can occur when the contact conditions are unsuitable,and(2)the method is not available for contact problems involving rigid-nonrigid materials,which can cause numerical instability.This study presents a new hybrid contact approach for the MPM to address these limitations to simulate the soil and structure interactions.The approach combines the advantages of point-point and point-segment contacts to implement contact detection,satisfying the impenetrability condition and smoothing the corner contact problem.The proposed approach is first validated through a disk test on an inclined slope.Then,several typical cases,such as granular collapse,bearing capacity,and deformation of a flexible retaining wall,are simulated to demonstrate the robustness of the proposed approach compared with FEM or analytical solutions.Finally,the proposed method is used to simulate the impact of sand flow on a deformable structure.The results show that the proposed contact approach can well describe the phenomenon of soil-structure interaction problems.

True-temperature inversion algorithm for a multi-wavelength pyrometer based on fractional-order particle-swarm optimization

Herein,a method of true-temperature inversion for a multi-wavelength pyrometer based on fractional-order particle-swarm optimization is proposed for difficult inversion problems with unknown emissivity.Fractional-order calculus has the inherent advantage of easily jumping out of local extreme values;here,it is introduced into the particle-swarm algorithm to invert the true temperature.An improved adaptive-adjustment mechanism is applied to automatically adjust the current velocity order of the particles and update their velocity and position values,increasing the accuracy of the true temperature values.The results of simulations using the proposed algorithm were compared with three algorithms using typical emissivity models:the internal penalty function algorithm,the optimization function(fmincon)algorithm,and the conventional particle-swarm optimization algorithm.The results show that the proposed algorithm has good accuracy for true-temperature inversion.Actual experimental results from a rocket-motor plume were used to demonstrate that the true-temperature inversion results of this algorithm are in good agreement with the theoretical true-temperature values.

DeepSurNet-NSGA II:Deep Surrogate Model-Assisted Multi-Objective Evolutionary Algorithm for Enhancing Leg Linkage in Walking Robots

This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II(Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II)for solving complex multiobjective optimization problems,with a particular focus on robotic leg-linkage design.The study introduces an innovative approach that integrates deep learning-based surrogate models with the robust Non-dominated Sorting Genetic Algorithm II,aiming to enhance the efficiency and precision of the optimization process.Through a series of empirical experiments and algorithmic analyses,the paper demonstrates a high degree of correlation between solutions generated by the DeepSurNet-NSGA II and those obtained from direct experimental methods,underscoring the algorithm’s capability to accurately approximate the Pareto-optimal frontier while significantly reducing computational demands.The methodology encompasses a detailed exploration of the algorithm’s configuration,the experimental setup,and the criteria for performance evaluation,ensuring the reproducibility of results and facilitating future advancements in the field.The findings of this study not only confirm the practical applicability and theoretical soundness of the DeepSurNet-NSGA II in navigating the intricacies of multi-objective optimization but also highlight its potential as a transformative tool in engineering and design optimization.By bridging the gap between complex optimization challenges and achievable solutions,this research contributes valuable insights into the optimization domain,offering a promising direction for future inquiries and technological innovations.

A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm

Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.

A Comparative Study of Metaheuristic Optimization Algorithms for Solving Real-World Engineering Design Problems

Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions.

基于L-SHADE算法的AUV载体磁干扰参数辨识的数值模拟

采用自主水下航行器(Autonomous Underwater Vehicle,AUV)磁测平台可开展海洋地磁场测量、水下磁性目标探测和识别等工作,AUV磁测平台具有广阔的应用前景,但目前AUV载体磁干扰补偿技术研究尚不成熟,制约着水下航行器测磁精度。基于磁测平台抗磁干扰基本原理,提出一种基于线性种群规模缩减和成功历史的参数自适应差分进化(Success History-based Adaptive Differential Evolution with Linear Population Size Reduction,L-SHADE)算法的AUV载体磁干扰参数辨识的数值模拟方法。用磁偶极子和旋转椭球壳混合模型来等效模拟AUV载体磁干扰,通过模拟航行获得多组磁测数据,据此建立磁干扰参数辨识模型,并采用L-SHADE算法求解。通过数值模拟实验定量分析研究磁测平台测磁精度随磁传感器、平台姿态及航向等误差的传播规律。研究结果表明:当磁传感器测量精度为10 nT、姿态测量精度为0.01°、航向测量精度为0.1°时,测磁误差可小于100 nT。设计的AUV磁测平台抗干扰试验表明,地磁场总量最大相对误差为1.07%。

Short-Term Wind Power Prediction Based on WVMD and Spatio-Temporal Dual-Stream Network

As the penetration ratio of wind power in active distribution networks continues to increase,the system exhibits some characteristics such as randomness and volatility.Fast and accurate short-term wind power prediction is essential for algorithms like scheduling and optimization control.Based on the spatio-temporal features of Numerical Weather Prediction(NWP)data,it proposes the WVMD_DSN(Whale Optimization Algorithm,Variational Mode Decomposition,Dual Stream Network)model.The model first applies Pearson correlation coefficient(PCC)to choose some NWP features with strong correlation to wind power to form the feature set.Then,it decomposes the feature set using Variational Mode Decomposition(VMD)to eliminate the nonstationarity and obtains Intrinsic Mode Functions(IMFs).Here Whale Optimization Algorithm(WOA)is applied to optimise the key parameters of VMD,namely the number of mode components K and penalty factor a.Finally,incorporating attention mechanism(AM),Squeeze-Excitation Network(SENet),and Bidirectional Gated Recurrent Unit(BiGRU),it constructs the dual-stream network(DSN)for short-term wind power prediction.Comparative experiments demonstrate that the WVMD_DSN model outperforms existing baseline algorithms and exhibits good generalization performance.The relevant code is available at https://github.com/ruanyuyuan/Wind-power-forecast.git(accessed on 20 August 2024).

Horizontal-to-vertical spectral ratio inversion method based on multimodal forest optimization algorithm

The exploration of urban underground spaces is of great significance to urban planning,geological disaster prevention,resource exploration and environmental monitoring.However,due to the existing of severe interferences,conventional seismic methods cannot adapt to the complex urban environment well.Since adopting the single-node data acquisition method and taking the seismic ambient noise as the signal,the microtremor horizontal-to-vertical spectral ratio(HVSR)method can effectively avoid the strong interference problems caused by the complex urban environment,which could obtain information such as S-wave velocity and thickness of underground formations by fitting the microtremor HVSR curve.Nevertheless,HVSR curve inversion is a multi-parameter curve fitting process.And conventional inversion methods can easily converge to the local minimum,which will directly affect the reliability of the inversion results.Thus,the authors propose a HVSR inversion method based on the multimodal forest optimization algorithm,which uses the efficient clustering technique and locates the global optimum quickly.Tests on synthetic data show that the inversion results of the proposed method are consistent with the forward model.Both the adaption and stability to the abnormal layer velocity model are demonstrated.The results of the real field data are also verified by the drilling information.

A Study of EM Algorithm as an Imputation Method: A Model-Based Simulation Study with Application to a Synthetic Compositional Data

Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.

基于CQL-SAC的自动驾驶防撞决策方法

针对深度强化学习在自动驾驶任务中存在价值函数过估计、学习效率低、安全性差等问题,提出了一种自动驾驶防撞决策方法。首先,将保守Q学习(conservative Q-learning, CQL)算法与软行动评论(soft actor-critic, SAC)算法融合,提出CQL-SAC算法,以缓解价值过估计问题。然后,在算法训练过程中引入专家经验,实现算法快速收敛,以解决学习效率低的问题。最后,利用防撞模块对CQL-SAC算法输出的动作进行安全检查和矫正,避免车辆碰撞。在基于高速公路的仿真场景下对方法有效性进行验证。仿真结果表明,在训练阶段,CQL-SAC算法相比SAC算法和样本内行动评论(in-sample actor-critic, InAC)算法收敛速度分别提升12.5%、5.4%,引入专家经验后算法收敛速度进一步提升14.3%;在测试阶段,本文算法与SAC和InAC算法相比,成功率分别提升17、12百分点,平均回合奖励分别提升23.1%、10.7%。

基于OBL-SAMPSO算法的机器人零力控制摩擦力项辨识方法

针对机器人零力控制中摩擦模型参数辨识,提出一种基于反向学习(OBL)策略的改进模拟退火(SA)寻优算法(OBL-SAMPSO算法)进行LuGre摩擦模型参数辨识。仿真结果表明:相较于传统的PSO算法,OBL-SAMPSO算法使参数辨识的绝对误差平均下降了85.86%;基于单关节实验平台进行LuGre摩擦辨识,并应用到其关节零力控制策略中,实现了具有良好柔顺性的拖曳操作。

Research on Rice Panicle L-system Model Based on Substructure Algorithm

In order to decrease model complexity of rice panicle for its complicated morphological structure,an interactive L-system based on substructure algorithm was proposed to model rice panicle in this study.Through the analysis of panicle morphology,the geometrical structure models of panicle spikelet,axis and branch were constructed firstly.Based on that,an interactive panicle L-system model was developed by using substructure algorithm to optimize panicle geometrical models with the similar structure.Simulation results showed that the interactive L-system panicle model based on substructure algorithm could fast construct panicle morphological structure in reality.In addition,this method had the well reference value for other plants model research.

乳腺癌前哨淋巴结中Oct-4、VEGF-D、L-selectin水平与前哨淋巴结转移的关系及意义

目的探讨乳腺癌前哨淋巴结中八聚体结合转录因子-4(Oct-4)、血管内皮生长因子-D(VEGF-D)、L-选择素(L-selectin)水平与前哨淋巴结转移(SLNM)的关系及意义。方法选取2020年4月—2022年7月收治的147例乳腺癌,根据SLNM情况分为SLNM组67例、无SLNM组80例,比较2组及不同T分期、区域淋巴结分期患者前哨淋巴结中Oct-4、VEGF-D、L-selectin水平,采用多因素Logistic回归分析寻找乳腺癌SLNM的危险因素,采用决策曲线分析(DCA)和临床影响曲线(CIC)分析前哨淋巴结中Oct-4、VEGF-D、L-selectin评估SLNM的临床效用和与实际情况的符合度。结果SLNM组前哨淋巴结中Oct-4、VEGF-D、L-selectin水平高于无SLNM组(P<0.01)。随着T分期和区域淋巴结分期升高,前哨淋巴结中Oct-4、VEGF-D、L-selectin水平逐渐升高,差异有统计学意义(P<0.05)。前哨淋巴结中Oct-4、VEGF-D、L-selectin均是乳腺癌患者SLNM的独立危险因素(P<0.01);绘制DCA曲线显示,前哨淋巴结中Oct-4、VEGF-D、L-selectin评估乳腺癌患者SLNM均有正向的净获益,临床效用排序:Oct-4、L-selectin、VEGF-D,三者联合较单独指标评估的临床获益度均明显升高;绘制CIC显示,阈概率值>0.6时,三者联合评估SLNM与实际情况具有较高的符合率。结论乳腺癌前哨淋巴结中Oct-4、VEGF-D、L-selectin水平与SLNM及肿瘤分期有关,是患者发生SLNM的一个潜在机制,三者联合评估SLNM具有良好的临床获益度,可作为评估乳腺癌患者SLNM的可靠方法。

L-SeMC对酒精致中枢兴奋小鼠学习记忆和自发活动的影响

目的探讨L-硒-甲基硒代半胱氨酸(L-selenium-methylselenocysteine,L-SeMC)对酒精致中枢兴奋小鼠学习记忆和自发活动的影响。方法将雄性昆明小鼠随机分为对照组、酒精组及L-SeMC低、中和高剂量组,分别灌胃给予L-SeMC 10.4、13.0、39.0μg·kg^(-1),30 min后给予酒精1.5 g·kg^(-1),进行自发活动检测、跳台实验和避暗实验,检测血清ALT和AST、肝脏组织MDA含量及SOD活力。结果10.4、13.0、39.0μg·kg^(-1)L-SeMC可显著减少酒精致中枢兴奋小鼠活动次数和站立次数;10.4μg·kg^(-1)L-SeMC可显著延长跳台实验潜伏期,13.0μg·kg^(-1)L-SeMC可显著延长潜伏期,减少错误次数;10.4μg·kg^(-1)L-SeMC可显著延长避暗实验潜伏期,减少错误次数。3个剂量L-SeMC可降低血清ALT、AST水平,降低肝脏组织MDA含量,增强SOD活性。结论10.4、13.0μg·kg^(-1)L-SeMC可改善酒精致中枢兴奋对小鼠学习记忆功能的降低,抑制酒精引起的中枢兴奋自发活动,其机制可能与改善酒精致中枢兴奋小鼠的肝功能和肝脏氧化损伤有关。

外源添加L-脯氨酸对棉花黄萎病发生及其根际土壤微生物群落的影响

【目的】根系分泌物是植物与土壤微生物进行互作的信号媒介,对于植物病害发生和植株生长均具有重要调控功能。论文旨在明确棉花根系分泌物L-脯氨酸抵御棉花黄萎病发生的微生态机制,揭示L-脯氨酸介导的根际微生物与棉花黄萎病发生的互作关系,为构建生物防治土传病害的有益菌群提供新视角。【方法】通过温室盆栽试验,以外源添加不同浓度的L-脯氨酸(0、50、100、200和400 mmol·L^(-1))为试验处理,采用实时荧光定量PCR(real-time qPCR)和宏基因组测序技术分别测定不同处理的土壤中大丽轮枝菌(Verticillium dahliae)DNA拷贝数量和土壤微生物群落结构及功能,采用主成分分析比较不同处理的根际土壤微生物群落结构,利用冗余分析研究土壤养分因子与微生物群落结构的相关性,利用斯皮尔曼相关分析研究微生物群落结构功能代谢途径的相关性。【结果】与空白对照相比,低浓度(50 mmol·L^(-1))L-脯氨酸处理不能够减轻棉花黄萎病的发生,高浓度(100、200和400 mmol·L^(-1))L-脯氨酸处理的病情指数分别下降22.51%、60.23%和64.23%。qPCR结果表明,L-脯氨酸处理不能够显著降低土壤中大丽轮枝菌拷贝数量。宏基因组测序分析表明,L-脯氨酸处理后细菌多样性Shannon指数显著增加,真菌多样性Shannon指数呈下降趋势。在属水平上,L-脯氨酸处理后类诺卡氏菌属(Nocardioides)、溶杆菌属(Lysobacter)、节杆菌属(Arthrobacter)、Phycicoccus、假单胞菌属(Pseudomonas)和毛霉菌属(Mucor)的相对丰度呈上升趋势。线性判别分析表明,除浓度为100 mmol·L^(-1)的L-脯氨酸外,外源添加L-脯氨酸处理后根际土壤微生物KEGG通路的富集情况发生改变。冗余分析表明,细菌群落组成受pH、电导率、硝态氮、铵态氮和有机质显著影响,而真菌群落组成与铵态氮存在显著相关性。Spearman相关分析表明,细菌的KEGG代谢通路与pH、有机质、铵态氮含量呈负相关关系,而与电导率和硝态氮呈正相关关系;真菌的大部分KEGG代谢通路与土壤养分的相关性较差。【结论】外源添加适量L-脯氨酸通过改变土壤细菌群落结构和功能,增加有益微生物的相对丰度,从而影响棉花黄萎病的发生,但其不能够改变病原菌数量。同时,根际细菌群落组成和功能均与土壤养分存在相关性。

Γ-半群的L-反模糊理想

给出了L-反模糊子半群和L-反模糊Γ-子半群的定义,并研究了Γ-半群的L-反模糊理想的基本性质.进一步得到Γ-半群的L-模糊子集是L-反模糊双Γ-理想的充分必要条件.

湿法热解法制备L-焦谷氨酸的工艺优化研究

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