Degree of Freedom Analysis for Holographic MIMO Based on a Mutual-Coupling-Compliant Channel Model

Degree of freedom(DOF)is a key indicator for spatial multiplexing layers of a wireless channel.Traditionally,the channel of a multiple-input multiple-output(MIMO)half-wavelength dipole array has a DOF that equals the antenna number.However,recent studies suggest that the DOF could be less than the antenna number when strong mutual coupling is considered.We utilize a mutual-coupling-compliant channel model to investigate the DOF of the holographic MIMO(HMIMO)channel and give a upper bound of the DOF with strong mutual coupling.Our numerical simulations demonstrate that a dense array can support more DOF per unit aperture as compared with a half-wavelength MIMO system.

Virtual Plant Modeling Based on Mutual Feedback of Function-structure

[Objective] To study virtual plant modeling based on mutual feedback of function-structure.[Method] With the analysis of the shortcomings of current virtual plant modeling method,the modeling with the idea of function-structure mutual feedback was put forward,and the steps of this modeling were elaborated,including the determination of morphological structure model,biomass production model,biomass allocation model,organ reconstruction model,and the integration method of function model and morphological structure model.[Results] The breakthrough of function-structure mutual feedback based mechanism from the boundaries of physiological ecology model and morphological structure model can solve the difficulty of data transmission between the two models and build an integrated model from the two,which can effectively reflect the incidence relation between plant morphology and function,and more suitable for the growth mechanisms of plants.This modeling approach has significant advantages in the dynamic simulation of plant growth.[Conclusion] The virtual plant modeling based on function-structure mutual feedback provides basis for the simulation of plant growth status in the next stage,and has important significance for the accurate simulation of the dynamic growth process of plant.

Fault monitoring based on mutual information feature engineering modeling in chemical process

A large amount of information is frequently encountered when characterizing the sample model in chemical process.A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively remove the nonlinear correlation redundancy of chemical process in this paper.From the whole process point of view,the method makes use of the characteristic of mutual information to select the optimal variable subset.It extracts the correlation among variables in the whitening process without limiting to only linear correlations.Further,PCA(Principal Component Analysis)dimension reduction is used to extract feature subset before fault diagnosis.The application results of the TE(Tennessee Eastman)simulation process show that the dynamic modeling process of MIFE(Mutual Information Feature Engineering)can accurately extract the nonlinear correlation relationship among process variables and can effectively reduce the dimension of feature detection in process monitoring.

MTBAC： A Mutual Trust Based Access Control Model in Cloud Computing

As a new computing mode,cloud computing can provide users with virtualized and scalable web services,which faced with serious security challenges,however.Access control is one of the most important measures to ensure the security of cloud computing.But applying traditional access control model into the Cloud directly could not solve the uncertainty and vulnerability caused by the open conditions of cloud computing.In cloud computing environment,only when the security and reliability of both interaction parties are ensured,data security can be effectively guaranteed during interactions between users and the Cloud.Therefore,building a mutual trust relationship between users and cloud platform is the key to implement new kinds of access control method in cloud computing environment.Combining with Trust Management(TM),a mutual trust based access control(MTBAC) model is proposed in this paper.MTBAC model take both user's behavior trust and cloud services node's credibility into consideration.Trust relationships between users and cloud service nodes are established by mutual trust mechanism.Security problems of access control are solved by implementing MTBAC model into cloud computing environment.Simulation experiments show that MTBAC model can guarantee the interaction between users and cloud service nodes.

Stability Analysis and Hopf Bifurcation for ODE System of Predator-Prey Model with Mutual Interference

In this paper, the temporal and spatial patterns of a diffusive predator-prey model with mutual interference under homogeneous Neumann boundary conditions were studied. Specifically, first, taking the intrinsic growth rate of the predator as the parameter, we give a computational and theoretical analysis of Hopf bifurcation on the positive equilibrium for the ODE system. As well, we have discussed the conditions for determining the bifurcation direction and the stability of the bifurcating periodic solutions.

METrust: A Mutual Evaluation-based Trust Model for P2P Networks

It is necessary to construct an effective trust model to build trust relationship between peers in peer-to-peer （P2P） network and enhance the security and reliability of P2P systems. The current trust models only focus on the consumers＇ evaluation to a transaction, which may be abused by malicious peers to exaggerate or slander the provider deliberately. In this paper, we propose a novel trust model based on mutual evaluation, called METrust, to suppress the peers＇ malicious behavior, such as dishonest evaluation and strategic attack. METrust considers the factors including mutual evaluation, similarity risk, time window, incentive, and punishment mechanism. The trust value is composed of the direct trust value and the recommendation trust value. In order to inhibit dishonest evaluation, both participants should give evaluation information based on peers＇ own experiences about the transaction while computing the direct trust value. In view of this, the mutual evaluation consistency factor and its time decay function are proposed. Besides, to reduce the risk of computing the recommendation trust based on the recommendations of friend peers, the similarity risk is introduced to measure the uncertainty of the similarity computing, while similarity is used to measure credibility. The experimental results show that METrust is effective, and it has advantages in the inhibition of the various malicious behaviors.

Accurate Fault Location Modeling for Parallel Transmission Lines Considering Mutual Effect

:A new accurate algorithms based on mathematical modeling of two parallel transmissions lines system(TPTLS)as influenced by the mutual effect to determine the fault location is discussed in this work.The distance relay measures the impedance to the fault location which is the positive-sequence.The principle of summation the positive-,negative-,and zero-sequence voltages which equal zero is used to determine the fault location on the TPTLS.Also,the impedance of the transmission line to the fault location is determined.These algorithms are applied to single-line-to-ground(SLG)and double-line-to-ground(DLG)faults.To detect the fault location along the transmission line,its impedance as seen by the distance relay is determined to indicate if the fault is within the relay’s reach area.TPTLS under study are fed from one-and both-ends.A schematic diagrams are obtained for the impedance relays to determine the fault location with high accuracy.

A Theory of Ratio Selection—Lattice Model for Obligate Mutualism

Mutualisms are cooperative interactions between members of different species. We focus on obligate mutualism, where each species cannot survive without the other. From a theoretical aspect, obligate mutualism is similar to the relationship between male and female. Empirical data indicate a sex-ratio selection: male and female have a specific ratio in their population sizes. In the present paper, we apply lattice model to obligate mutualism between two species, and present a theory of “ratio selection” which is a generalization of sex-ratio selection. Computer simulations are carried out by two methods: local and global interactions. In the former, interactions occur between neighbouring cells, while in the latter they occur between any pair of cells. Simulations in both interactions show the so-called Allee effect: both species can survive, when both densities are large in some extent. However, we find a large difference between local and global simulations. In the case of local interaction, restriction for survival is found to be extremely severe compared to global interaction. Both species require a proper ratio for their sustainability. This result leads to the theory of ratio selection: when interaction occurs locally, the ratio of both species is uniquely determined. We discuss that the ratio selection explains not only the evolution of endosymbionts from free-living ancestors but also the evolution from endosymbionts to organelles.

Mutual Learning Model for Skin Lesion Classification

Skin lesion classification in the dermoscopy images exerts an enormous function on the improvement of diagnostic performance and reduction of melanoma deaths. This skin lesion classification task remains a challenge. Deep learning requires a lot of training data, and the classification algorithms of skin lesions have certain limitations. These two points make the accuracy of the skin lesion classification needs to be further improved. In this paper, a mutual learning model was presented to separate malignant from benign skin lesions using the skin dataset. This model enabled dual deep convolutional neural networks to mutually learn from each other. Experimental results on the ISIC 2016 Skin Lesion Classification dataset indicate that the mutual learning model obtains the most advanced performance.

半市场化社区互助养老:农村老年人照料中的集体统筹与实践定位

互助养老实践打破了纯福利型和纯市场化养老模式存在的弊端,但在服务对象和服务内容上仍然存在一定不足。半市场化社区互助养老把市场和社区要素同时纳入,依托村社集体发挥公共角色功能,为高龄老人推荐低龄老人资源,提供低于市场价的半市场化养老服务,成为当前人口老龄化背景下的合适选择。在半市场化社区养老实践中,村社集体不但能够对内外部养老资源进行整合和社会动员,还可以介入养老服务过程,进行居间协调公证、监督养老服务质量、保护照料双方合法权益,强化互助养老的公益属性。半市场化社区互助养老以集体统筹为核心,通过统合政府、市场和家庭等多元主体的角色,为村庄老年人搭建起低成本、高福利的互助养老公共平台。

以银发经济推进农村互助养老发展的政策支持研究——以J省X市颐养之家为例

我国首部“银发经济”政策文件出台,为农村互助养老模式提供了“有为政府”与“有效市场”相结合的实践场域。通过对具有全国性影响的J省X市颐养之家的典型案例分析发现,当前农村互助养老模式的主要政策目标是为有需要的农村留守、独居及困难老年人提供基本生活保障的兜底型养老。在银发经济“产品化-商品化-产业化”阶段性发展的大趋势下,农村互助养老还可以是普惠式养老,更应该是高质量的积极养老。针对农村互助养老模式在政策执行中分配基础、分配内容、输送策略、筹资机制等方面存在的不足,建议将发挥有效市场作用与有为政府作用更好结合,进一步完善优化政策支持,纳入“一老一小”,扩展受益对象;引进商品化竞争机制,丰富服务供给;推动多主体跨部门合作,形成网状输送;划定红线并链接资源,发展团结经济,着力推动农村互助养老高质量发展。

基于沙地猫群优化–最小二乘支持向量机的动态NOx排放预测

针对火电机组频繁调峰导致机组燃烧状态不稳,进而导致锅炉出口NOx浓度波动范围大的问题,提出一种基于沙地猫群优化(sand cat sarm optimization,SCSO)的最小二乘支持向量机(leastsquaressupportvectormachine,LSSVM) NOx动态预测模型。首先利用k近邻互信息计算时间延迟的同时筛选辅助变量。然后,基于SCSO算法进行输入变量阶次的选择。使用包含辅助变量时间延迟和阶次的信息作为模型的输入,SCSO算法优化最小二乘支持向量机参数,建立动态NOx排放最小二乘支持向量机预测模型(SCSO-LSSVM动态软测量模型)。最后将模型与未加入迟延的LSSVM模型,加入迟延的LSSVM模型和粒子群优化算法(particle swarm optimization,PSO)优化最小二乘支持向量机参数的动态软测量模型进行对比验证。结果表明,相较于其他模型,该文建立SCSO-LSSVM动态软测量模型均方根误差、平均绝对误差、平均绝对误差最小,预测精度最高,而且在NOx浓度剧烈波动时也能够较好地预测NOx浓度,具有很好的动态特性。

实践创新能力驱动的化工人才培养模式构建与实施

以中北大学“化学工程与工艺”专业为例,构建“学训赛创”四元互促人才培养模式,在扎实的理论教学基础上,融入实验、实习、实训,并结合学科赛事,全面提升学生实践创新能力。实践结果表明,该模式强化了学生对复杂工程问题的处理能力,并显著提升了师资水平,为行业培养了工程创新人才,展现出良好的示范性和推广价值。

基于时域卷积网络与Transformer的茶园蒸散量预测模型

在茶园水资源管理中,蒸散量(Evapotranspiration,ET)是评估作物水分需求的关键指标,由于茶园蒸散量预测具有时序性、不稳定性以及非线性耦合等特点,目前的茶园蒸散量预测模型存在预测精度较低的问题,针对此问题本文提出了一种新型的茶园蒸散量预测模型。首先使用互信息算法(Mutual information,MI)与主成分分析算法(Principal component analysis,PCA)相融合的数据处理算法(MIPCA),筛选强相关的特征并提取主成分;其次将时域卷积网络(Temporal convolutional network,TCN)与Transformer融合,利用灰狼算法(Grey wolf optimization,GWO)优化超参数,捕捉茶园数据的全局依赖关系;最后整合2个网络构建了MIPCA-TCN-GWO-Transformer模型,通过消融试验和对比试验验证了模型性能,并对模型在不同时间步长下的性能进行测试。结果表明,该模型平均绝对百分比误差(Mean absolute percentage error,MAPE)、均方根误差(Root mean square error,RMSE)和决定系数(Coefficient of determination,R^(2))3个评价指标分别为0.015 mm/d、0.312 mm/d和0.962,优于长短期记忆模型(Long short term memory,LSTM)等传统预测模型。在小时尺度、日尺度和月尺度下的R^(2)分别为0.986、0.978和0.946,在不同时间步长下展现了良好的适应性和准确性。本文构建的MIPCA-TCN-GWO-Transformer模型具有较高的预测精度和稳定性,可为茶园水资源优化管理和灌溉制度制定提供科学参考。

基于多重互信息约束的高表现力语音转换

随着语音转换在人机交互领域的广泛应用,对于获取高表现力语音的需求日益显著.当前语音转换主要通过解耦声学特征实现,侧重对内容和音色特征的解耦,很少考虑语音中混合的情感特性,导致转换音频情感表现力不足.为解决上述问题,本文提出一种基于多重互信息约束的高表现力语音转换模型(MMIC-EVC).在对内容和音色特征进行解耦的基础上,引入表现力模块分别对话语级韵律和节奏特征进行建模,以实现情感特性的传递;随后通过最小化各特征之间的多重互信息变分对数上界,约束各编码器专注于解耦对应的声学嵌入.在CSTR-VCTK和ESD语音数据集上的实验表明,本模型的转换音频语音自然度评分(MOS)达到3.78,梅尔倒谱失真为5.39 dB,最佳最差占比测试结果大幅领先于基线模型,MMIC-EVC能够有效解耦韵律和节奏特征,并实现高表现力语音转换,为人机交互带来更加出色和自然的用户体验.

Rmcvit:一种融合卷积与自注意力的轻量级图像识别算法

为了解决目前基于Transformer的模型需要较大的参数量而无法有效应用在资源受限的移动端设备中,提出一种融合卷积与Transformer两者优势的轻量级混合模型.模型中Rmcvit Block将输入张量分成多个通道组,利用深度可分离卷积和跨通道维度的互协方差注意力来增加感受野并融合多尺度特征,并融合unflod模块在保证其空间顺序的前提下,降低每个向量序列(token)送入自注意力模块后的计算消耗.基于整理后的Imagenet数据集的对比实验,RmcVit-M以5.81M的参数量大小达到了85.2%的准确率,其模型相关变体性能超过了参数量相似的Transformer模型和卷积神经网络.结果表明,Rmcvit能够有效结合卷积神经网络与Transformer的优势,达到了以较少的模型参数获得更高准确率的目的.

再生水投入与长江和黄河流域经济发展的互馈效应研究

基于面板向量自回归模型,收集了2011—2020年长江和黄河流域18个省(自治区、直辖市)的面板数据,在验证了面板数据的有效性基础上,分析了模型的结果、脉冲响应函数及方差分解结果,考察了再生水投入与长江和黄河流域经济发展之间的相互作用及动态双向影响。结果表明:再生水投入与长江和黄河流域经济发展之间具有相互促进、共同发展的关系,并具有区域差异性特征;在流域经济发展对再生水投入的影响上,黄河流域的依赖性明显大于长江流域;在再生水投入对流域经济发展的影响上,黄河流域和长江流域整体上均呈现正向影响,但长江流域正向影响滞后性更强。

社会交换理论视角下的城市社区老年人互助养老意愿影响因素研究

互助养老服务是机构养老服务、社区居家养老服务的有益补充。如何保障互助养老服务健康、可持续发展是一个亟须解决的社会问题。社会交换理论认为,城市社区老年人参与互助养老服务的意愿取决于预期收益、预期成本、信任和参与风险的综合影响。通过问卷调查,利用Amos25软件对已构建的概念模型以及提出的假设进行实证检验和路径分析。结果表明:城市社区老年人参与互助养老服务的意愿,随着预期收益的增加和信任度的提高,呈现出正向增长的趋势,随着预期成本增加和参与风险增强,呈现出负向增长的趋势;互助养老服务的健康运行需要建立一系列的激励机制、保障机制并需要进行广泛宣传。

中国式现代化进程中的互助养老——内驱力、定位与发展走向

当前中国互助养老存在着内在驱动力不足、定位与发展方向不够明晰的问题。通过剖析几类主要的互助养老模式产生和发展的内在驱动力,发现在党建引领这一内在驱动力之外,各类互助养老模式有其独特的驱动。为了进一步深入挖掘互助养老的内在驱动力,持续激发老年人的“互助”动能,明确互助养老的定位与发展方向,提出坚持党建引领、重构社会文化支持体系、营造社区熟人环境、构建互助养老的分层次发展模式等对策建议。

基于特征选择与Transformer的涡扇发动机剩余使用寿命预测

针对传统剩余使用寿命预测模型难以解决长时依赖问题以及不同特征组合对模型预测精度影响大的问题,提出一种基于特征选择与Transformer的剩余使用寿命预测模型。首先利用以互信息为理论基础的最大相关最小冗余特征选择算法,捕获特征与标签、特征与特征的关系,得到最佳特征组合;然后以Transformer的编码器为主体并加入门控卷积单元组成预测模型,使模型可以充分捕捉全局信息且提高运算效率的基础上也更加注重局部信息;通过网格搜索与粒子群算法确定模型超参数。最后将最优特征组合的变量数据输入模型实现涡扇发动机剩余使用寿命预测。利用此方法在C-MAPSS数据集进行验证,并进行对比实验,结果表明预测误差与模型效率均有一定改进。