High-Order Soliton Solutions and Hybrid Behavior for the (2 + 1)-Dimensional Konopelchenko-Dubrovsky Equations

In this paper, the evolutionary behavior of N-solitons for a (2 + 1)-dimensional Konopelchenko-Dubrovsky equations is studied by using the Hirota bilinear method and the long wave limit method. Based on the N-soliton solution, we first study the evolution from N-soliton to T-order (T=1,2) breather wave solutions via the paired-complexification of parameters, and then we get the N-order rational solutions, M-order (M=1,2) lump solutions, and the hybrid behavior between a variety of different types of solitons combined with the parameter limit technique and the paired-complexification of parameters. Meanwhile, we also provide a large number of three-dimensional figures in order to better show the degeneration of the N-soliton and the interaction behavior between different N-solitons.

Performance and Complexity Trade-Off between Short-Length Regular and Irregular LDPC

In this paper, both the high-complexity near-ML list decoding and the low-complexity belief propagation decoding are tested for some well-known regular and irregular LDPC codes. The complexity and performance trade-off is shown clearly and demonstrated with the paradigm of hybrid decoding. For regular LDPC code, the SNR-threshold performance and error-floor performance could be improved to the optimal level of ML decoding if the decoding complexity is progressively increased, usually corresponding to the near-ML decoding with progressively increased size of list. For irregular LDPC code, the SNR-threshold performance and error-floor performance could only be improved to a bottle-neck even with unlimited decoding complexity. However, with the technique of CRC-aided hybrid decoding, the ML performance could be greatly improved and approached with reasonable complexity thanks to the improved code-weight distribution from the concatenation of CRC and irregular LDPC code. Finally, CRC-aided 5GNR-LDPC code is evaluated and the capacity-approaching capability is shown.

Oviposition Regularity of Hybrid Silkworm Stock Moth of Liangguang No. 2

To cure pebrine using dry - hot air treatment of silkworm eggs, and control egg age within 12 hours, oviposition regularity of hybrid silkworm stock moth of Liangguang No. 2--current production variety in Guangdong Province was investigated. The experiment showed that most hybrid stock female moths of Liangguang No. 2 at 25 -28℃ oviposited in the first 5 hours, and egg production declined sharply after 5 hours. 7 · Xiang × Fu · 9 achieved the oviposition peak within 1 hour after casting the moth, the oviposition amount within 5 horns accounted for 92.94% of the total oviposition amount of female moth, that within 9 hours accounted for 96.47%. Fu · 9 × 7 · Xiang achieved the oviposition peak within 2 -4 hours after casting the moth, the oviposition amount within 5 hours accounted for 85.21% of the total oviposition amount of female moth, that within 9 hours accounted for 92.78%. Oviposition regularity of the hybrid silkworm stock moth of Liangguang No. 2 meets the need of dry-hot air treatment of silkworm pebrine-control egg age within 12 hours.

Enhanced Deep Reinforcement Learning Strategy for Energy Management in Plug-in Hybrid Electric Vehicles with Entropy Regularization and Prioritized Experience Replay

Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced performance.Energy management strategies(EMSs)that coordinate and control different energy sources is a critical component of PHEV control technology,directly impacting overall vehicle performance.This study proposes an improved deep reinforcement learning(DRL)-based EMSthat optimizes realtime energy allocation and coordinates the operation of multiple power sources.Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces.They often fail to strike an optimal balance between exploration and exploitation,and their assumption of a static environment limits their ability to adapt to changing conditions.Moreover,these algorithms suffer from low sample efficiency.Collectively,these factors contribute to convergence difficulties,low learning efficiency,and instability.To address these challenges,the Deep Deterministic Policy Gradient(DDPG)algorithm is enhanced using entropy regularization and a summation tree-based Prioritized Experience Replay(PER)method,aiming to improve exploration performance and learning efficiency from experience samples.Additionally,the correspondingMarkovDecision Process(MDP)is established.Finally,an EMSbased on the improvedDRLmodel is presented.Comparative simulation experiments are conducted against rule-based,optimization-based,andDRL-based EMSs.The proposed strategy exhibitsminimal deviation fromthe optimal solution obtained by the dynamic programming(DP)strategy that requires global information.In the typical driving scenarios based onWorld Light Vehicle Test Cycle(WLTC)and New European Driving Cycle(NEDC),the proposed method achieved a fuel consumption of 2698.65 g and an Equivalent Fuel Consumption(EFC)of 2696.77 g.Compared to the DP strategy baseline,the proposed method improved the fuel efficiency variances(FEV)by 18.13%,15.1%,and 8.37%over the Deep QNetwork(DQN),Double DRL(DDRL),and original DDPG methods,respectively.The observational outcomes demonstrate that the proposed EMS based on improved DRL framework possesses good real-time performance,stability,and reliability,effectively optimizing vehicle economy and fuel consumption.

基于混合正则化方法的结构载荷识别与响应重构

针对结构载荷识别与响应重构问题中存在的不适定性,提出一种最小平方残差(least square minimal residual,LSMR)算法与Tikhonov正则化方法相结合的混合正则化方法,实现利用结构有限测点的加速度响应识别载荷并重构未知的各类型响应。首先,基于时域状态空间模型构建结构的传递矩阵,并建立载荷识别与响应重构方程;其次,采用混合正则化方法改善载荷识别问题的不适定性,得到载荷的正则化解,并结合响应重构方程的传递矩阵对结构的位移、速度和加速度响应进行重构;最后,通过简支梁数值仿真和试验分析验证所提方法的可行性。结果表明,所提方法能改善重构方程的不适定性,从而对结构未知载荷和各类型响应进行有效重构。

General M-lumps, T-breathers, and hybrid solutions to (2+1)-dimensional generalized KDKK equation

A special transformation is introduced and thereby leads to the N-soliton solution of the(2+1)-dimensional generalized Konopelchenko-Dubrovsky-Kaup-Kupershmidt(KDKK) equation.Then,by employing the long wave limit and imposing complex conjugate constraints to the related solitons,various localized interaction solutions are constructed,including the general M-lumps,T-breathers,and hybrid wave solutions.Dynamical behaviors of these solutions are investigated analytically and graphically.The solutions obtained are very helpful in studying the interaction phenomena of nonlinear localized waves.Therefore,we hope these results can provide some theoretical guidance to the experts in oceanography,atmospheric science,and weather forecasting.

Hybrid High‑Order Methods for the Acoustic Wave Equation in the Time Domain

We devise hybrid high-order(HHO)methods for the acoustic wave equation in the time domain.We frst consider the second-order formulation in time.Using the Newmark scheme for the temporal discretization,we show that the resulting HHO-Newmark scheme is energy-conservative,and this scheme is also amenable to static condensation at each time step.We then consider the formulation of the acoustic wave equation as a frst-order system together with singly-diagonally implicit and explicit Runge-Kutta(SDIRK and ERK)schemes.HHO-SDIRK schemes are amenable to static condensation at each time step.For HHO-ERK schemes,the use of the mixed-order formulation,where the polynomial degree of the cell unknowns is one order higher than that of the face unknowns,is key to beneft from the explicit structure of the scheme.Numerical results on test cases with analytical solutions show that the methods can deliver optimal convergence rates for smooth solutions of order O(hk+1)in the H1-norm and of order O(h^(k+2))in the L^(2)-norm.Moreover,test cases on wave propagation in heterogeneous media indicate the benefts of using high-order methods.

结合粒子群优化和最小二乘法的1-D正则化反演方法

对于地球物理反演问题来说,确定性反演方法容易陷入局部最优解,而随机优化方法理论上能够收敛至全局最优解,因此一直备受关注。在众多随机优化方法中,粒子群优化(PSO)的原理简单且需要调整的参数少,已经应用于求解地球物理反演问题.为了克服反演的非唯一性问题,可以将模型约束加入PSO优化过程中.然而,在PSO迭代中采用固定的正则化参数,相当于默认模型约束保持在一定水平,从而导致反演结果极大的受到模型约束的影响。本文提出了一种混合方法PSO-RLSM,它将正则化最小二乘法(RLSM)与PSO方法相结合。正则化最小二乘法用于改进全局最优粒子并加速收敛,自适应正则化策略用于更新正则化参数以避免模型约束对反演结果的影响。本文以音频大地电磁合成和大地电磁实测数据为例,对比分析了正则化最小二乘法和混合算法的反演结果。经测试表明,本文所提出的混合算法的反演结果要优于正则化最小二乘法。另外,与标准的PSO算法相比,混合算法仅需要一个较为宽泛的模型空间,更小的粒子群和迭代步数,减少了反演所用的先验条件和运算量。

A GENERAL HIGH-ORDER MULTI-DOMAIN HYBRID DG/WENO-FD METHOD FOR HYPERBOLIC CONSERVATION LAWS

In this paper, a general high-order multi-domain hybrid DG/WENO-FD method, which couples a p^th-order （p ≥ 3） DG method and a q^th-order （q ≥ 3） WENO-FD scheme, is developed. There are two possible coupling approaches at the domain interface, one is non-conservative, the other is conservative. The non-conservative coupling approach can preserve optimal order of accuracy and the local conservative error is proved to be upmost third order. As for the conservative coupling approach, accuracy analysis shows the forced conservation strategy at the coupling interface deteriorates the accuracy locally to first- order accuracy at the ＇coupling cell＇. A numerical experiments of numerical stability is also presented for the non-conservative and conservative coupling approaches. Several numerical results are presented to verify the theoretical analysis results and demonstrate the performance of the hybrid DG/WENO-FD solver.

Inductive transfer learning for unlabeled target-domain via hybrid regularization

Recent years have witnessed an increasing interest in transfer learning. This paper deals with the classification problem that the target-domain with a different distribution from the source-domain is totally unlabeled, and aims to build an inductive model for unseen data. Firstly, we analyze the problem of class ratio drift in the previous work of transductive transfer learning, and propose to use a normalization method to move towards the desired class ratio. Furthermore, we develop a hybrid regularization framework for inductive transfer learning. It considers three factors, including the distribution geometry of the target-domain by manifold regularization, the entropy value of prediction probability by entropy regularization, and the class prior by expectation regularization. This framework is used to adapt the inductive model learnt from the source-domain to the target-domain. Finally, the experiments on the real-world text data show the effectiveness of our inductive method of transfer learning. Meanwhile, it can handle unseen test points.

电阻率成像的混合正则化反演算法

利用混合正则化方法对二维分片常值电阻率进行反演研究,该方法结合了全变差正则化方法(totalvariation regularization)和经典吉洪诺夫正则化方法(classical Tikhonov regularization)的优点.全变差正则化能够有效地重构分片常值电阻率和识别边界,而经典吉洪诺夫正则化方法则能有效地重构光滑的电阻率,从数值算例中可以看出,将这两种方法有效地结合到一起能够改进重构效果.

基于混合正则化的最小二乘三维电阻率反演成像

基于经典Tikhonov正则化(classical Tikhonov regularization)的最小二乘反演是三维电阻率反演的主要方法。对于电阻率分片连续的地质体,由于光滑反演解的光滑性使得目标区域与背景区域间边界模糊,不能很好地体现异常体的形态信息和位置信息,成像效果不好。将总变分正则化(total variation regularization)与经典Tikhonov正则化结合,提出混合正则化反演方法,通过模型分析比较经典Tikhonov正则化、TV正则化、混合正则化在反演结果上的不同,证明了引入混合正则化约束的最小二乘反演既保持了经典Tikhonov正则化方法解的稳定性,又具有TV正则化方法解的保边缘性,有效地改善了三维电阻率成像效果。最后将混合正则化的反演方法应用到实际工程,验证了该方法的有效性和实用性。

基于Tikhonov和变差正则化的磁感应断层成像重建算法

为了解决磁感应断层成像(MIT)逆问题的病态性和改善重建图像的质量,提出一种新的组合算法.该组合算法首先利用Tikhonov正则化算法对解的适定性产生初步的成像区域,之后再利用变差正则化算法对解的保边缘性和锐化作用进行图像重建.该组合算法与Tikhonov正则化算法及变差正则化算法相比,不仅有效地克服了磁感应断层成像(MIT)重建图像数值解的不稳定性,还加快了重建图像的收敛速度,提高了目标导体的分辨能力,有效改善了重构图像的质量.仿真结果表明了该组合算法的有效性.

基于混合差分进化算法的地球物理线性反演

地球物理反问题线性化处理之后,各种反演算法归结为对病态线性方程组的求解.为了快速准确地计算出地球物理参数,本文提出了一种全新的基于LSQR算法的混合差分进化算法(Hybrid Differential EvolutionAlgorithm,HDE).该算法利用LSQR算法给出DE算法的初始种群,提高DE算法的计算速度和稳定性.在不同噪声水平下,对四种正则化方法Tikhonov、TSVD、LSQR和HDE的反演结果进行详细比较.理论模型和实际数据反演的结果都表明:改进的HDE算法应用于地球物理反问题的求解是成功的:反演结果与原设定模型具有较高的相关性,在稳定性和准确性上较常规的反演算法都具有一定的优势;而且不需要给定正则化参数,具有更强的实用性.

多范数混合约束的正则化图像盲复原

针对传统的正则化盲复原方法中图像和点扩散函数(PSF)的保真项和正则项分别采用同范数约束致使复原图像的质量下降,估计出的PSF准确性降低的问题,提出了多范数混合约束的正则化图像盲复原方法。首先,复原图像的保真项和正则项分别采用L1范数和全变分(TV)范数来消除复原图像中的阶梯效应并较好地保护复原图像的边缘。其次,PSF的保真项和正则项分别采用L2范数和H1范数以降低估计PSF时参数调节的难度。最后,通过分裂布雷格曼迭代方法对提出的模型进行最优化求解。在人造模糊图像和真实模糊图像上进行了实验,结果表明:提出的方法能够对运动、散焦等多种模糊类型的图像进行有效复原,并准确地估计出相应的PSF。与近年来一些较好的模糊图像盲复原方法相比,不仅在主观视觉效果上有较为明显的改进,而且在客观的改善信噪比(ISNR)上也提高了0.36dB到14.66dB。

惠民凹陷孔店组—沙四段“膏盐岩”层沉积机理——以MS1井“膏盐岩”层分析为例

以MS1井现场实钻录井资料为依据,结合研究区构造、沉积发育史分析,引入“沉积分异作用”、“混合沉积作用”及物源区与沉积区“岩石倒序规律”,对惠民凹陷孔一段—沙四段的“膏盐岩”层的物源进行了反序追踪对比。孔一段—沙四段的“膏盐岩”层段并非传统意义上的“石膏”层,而是物理、化学共同作用形成的湖盆化学盐类与砂岩的混合沉积,是邻近凸起下古生界碳酸盐岩风化剥蚀后提供物源及气候干旱导致湖盆收缩共同作用的结果,其准确定名应该为“含膏质粉、细砂岩”。

高阶混合正则化图像盲复原方法

提出了一种高阶混合正则化图像盲复原方法,用于实现模糊噪声图像的清晰化盲复原。根据自然图像边缘的稀疏特性,对图像的边缘细节成分进行了全变差(total variation TV)正则化约束,根据自然图像同性质平滑区域内像素值的变化规律,将一种高阶的类Tikhonov正则化约束运用于图像的平滑区域中,提出了一种新的高阶混合正则化模型。最后,提出一种多变量分裂布雷格曼(Multi-variable Split Bregman MSB)最优化迭代策略对提出的模型进行最优化求解。实验结果表明,提出的方法能够很好地保护图像的边缘细节,同时有效地消除图像平滑区域内的阶梯和假边缘瑕疵。与近几年的一些较好的图像盲复原方法相比,本文方法的信噪比增量(increase of the signal to noise ratio ISNR)增加了0.03～2.5dB。

湿法冶金浸出过程金浓度的混合预报模型

金氰化浸出是湿法冶金提金法的重要步骤,是一个典型的化工过程,建立其准确、可靠的数学模型是对该生产过程实施优化与控制的前提。提出了一种金氰化浸出过程的动态串联型混合模型,利用物料守恒方程建立金氰化浸出过程的动态机理模型,并利用神经网络估计机理模型中的未知参数——金的溶解速度和氰根离子的消耗速度,最终得到金氰化浸出过程的动态串联型混合模型。由于动力学反应速度是不能测量的,利用Tikhonov正则化方法估计金氰化浸出过程的动力学反应速度值,可以有效减少浓度测量值中噪声对动力学反应速度估计的影响。最终通过仿真实验验证了该混合模型比纯机理模型和神经网络黑箱模型具有更好的预测性能,能够较准确地预测金氰化浸出过程的各组分浓度,为后续金氰化浸出过程的优化与控制奠定了重要的模型基础。

撑×绿杂交竹梢枯病病原及发生规律研究

通过分离、接种、再分离首次证实杂交竹梢枯病的病原为暗孢节菱孢菌Arthrinium phaeosper-mum(Corda)M.B.Ellis。该病菌以菌丝体和子实体在病组织、病组织残体或土壤中越冬,其中病组织中菌丝体和子实体中的分生孢子为越冬的重要形式,是病害发生主要的初侵染源;病菌主要以分生孢子或菌丝通过伤口从节叉处侵入;病原菌主要通过孢子借助风力和雨水传播,从4月初开始释放孢子,6月为病原孢子释放的高峰期,并以分生孢子进行再侵染。

基于二维混合模型和状态观测器的重复控制设计

针对一类正则线性系统,提出一种基于状态观测器和二维混合模型的重复控制系统设计方法.首先,通过构造一个状态观测器来重构系统的状态,建立基于重构状态的线性控制律.然后,通过独立地考虑重复控制系统的连续控制过程与离散学习行为,给出基于状态观测器和重构状态反馈的连续/离散二维混合模型.针对这个混合模型,运用二维Lyapunov泛函方法,以线性矩阵不等式(Linear matrix inequality,LMI)的形式给出重复控制系统存在重复控制器和状态观测器的充分条件,所给条件可用Matlab工具箱方便地求解.数值仿真验证了本文所提方法的有效性.