Scale-space effect and scale hybridization in image intelligent recognition of geological discontinuities on rock slopes

Geological discontinuity(GD)plays a pivotal role in determining the catastrophic mechanical failure of jointed rock masses.Accurate and efficient acquisition of GD networks is essential for characterizing and understanding the progressive damage mechanisms of slopes based on monitoring image data.Inspired by recent advances in computer vision,deep learning(DL)models have been widely utilized for image-based fracture identification.The multi-scale characteristics,image resolution and annotation quality of images will cause a scale-space effect(SSE)that makes features indistinguishable from noise,directly affecting the accuracy.However,this effect has not received adequate attention.Herein,we try to address this gap by collecting slope images at various proportional scales and constructing multi-scale datasets using image processing techniques.Next,we quantify the intensity of feature signals using metrics such as peak signal-to-noise ratio(PSNR)and structural similarity(SSIM).Combining these metrics with the scale-space theory,we investigate the influence of the SSE on the differentiation of multi-scale features and the accuracy of recognition.It is found that augmenting the image's detail capacity does not always yield benefits for vision-based recognition models.In light of these observations,we propose a scale hybridization approach based on the diffusion mechanism of scale-space representation.The results show that scale hybridization strengthens the tolerance of multi-scale feature recognition under complex environmental noise interference and significantly enhances the recognition accuracy of GD.It also facilitates the objective understanding,description and analysis of the rock behavior and stability of slopes from the perspective of image data.

Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.

On the Translation of Hybridity in the Chinese Version of Saving Fish from Drowning in the Light of Bakhtin's Theory of Dialogue

In translation, hybridity often poses a big challenge to the translator, especially when the hybrid text is a novel. The au-thor of this paper hopes to contribute to the study of the translation of hybridity in English novels by drawing on Bakhtin's theory ofdialogue. By analyzing the translation of linguistic hybridity in Saving Fish from Drowning, the author of this paper finds that thetranslation by Caiju is not good enough and tries to provide a new direction by virtue of which translators can choose right transla-tion strategies.

Formulation of thermo-hydro-mechanical coupling behavior of unsaturated soils based on hybrid mixture theory

Thermo-Hydro-Mechanical （THM） coupling pro- cesses in unsaturated soils are very important in both theoretical researches and engineering applications. A coupled formulation based on hybrid mixture theory is derived to model the THM coupling behavior of unsaturated soils. The free-energy and dissipative functions for different phases are derived from Taylor＇s series expansions. Constitutive relations for THM coupled behaviors of unsaturated soils, which include deformation, entropy change, fluid flow, heat conduction, and dynamic compatibility conditions on the interfaces, are then established. The number of field equations is shown to be equal to the number of unknown variables; thus, a closure of this coupling problem is established. In addition to modifications of the physical conservation equations with coupling effect terms, the constitutive equations, which consider the coupling between elastoplastic deformation of the soil skeleton, fluid flow, and heat transfer, are also derived.

Generalized Kinematics Analysis of Hybrid Mechanisms Based on Screw Theory and Lie Groups Lie Algebras

Advanced mathematical tools are used to conduct research on the kinematics analysis of hybrid mechanisms,and the generalized analysis method and concise kinematics transfer matrix are obtained.In this study,first,according to the kinematics analysis of serial mechanisms,the basic principles of Lie groups and Lie algebras are briefly explained in dealing with the spatial switching and differential operations of screw vectors.Then,based on the standard ideas of Lie operations,the method for kinematics analysis of parallel mechanisms is derived,and Jacobian matrix and Hessian matrix are formulated recursively and in a closed form.Then,according to the mapping relationship between the parallel joints and corresponding equivalent series joints,a forward kinematics analysis method and two inverse kinematics analysis methods of hybrid mechanisms are examined.A case study is performed to verify the calculated matrices wherein a humanoid hybrid robotic arm with a parallel-series-parallel configuration is considered as an example.The results of a simulation experiment indicate that the obtained formulas are exact and the proposed method for kinematics analysis of hybrid mechanisms is practically feasible.

Pure Membrane,Pseudo Membrane,and Semi Membrane Shell Theories of Hybrid Anisotropic Materials

Pure,pseudo and semi membrane theories may sound similar but totally different theories as well as its behavior.The differences are more significant when it comes to hybrid anisotropic materials,namely laminated shell wall thickness.The nomenclatures and classifications have been existed centuries for isotropic material shells since Donnell and Vlasov era.The methods of formulation of the theories are unique and never been used by others except by the authors.Governing differential equations are uniquely formulated for each theory by use of asymptotic expansion method which has never been used by others for isotropic or anisotropic materials.Longitudinal(L)and circumferential(Πor l)length scale were introduced during the course of asymptotic expansion method and the different theories among membrane theory are apparently classified.Characteristic behaviors of each theory are shown.

A density-functional-theory-based and machine-learning-accelerated hybrid method for intricate system catalysis

Being progressively applied in the design of highly active catalysts for energy devices,machine learning(ML)technology has shown attractive ability of dramatically reducing the computational cost of the traditional density functional theory(DFT)method,showing a particular advantage for the simulation of intricate system catalysis.Starting with a basic description of the whole workflow of the novel DFT-based and ML-accelerated(DFT-ML)scheme,and the common algorithms useable for machine learning,we presented in this paper our work on the development and performance test of a DFT-based ML method for catalysis program(DMCP)to implement the DFT-ML scheme.DMCP is an efficient and user-friendly program with the flexibility to accommodate the needs of performing ML calculations based on the data generated by DFT calculations or from materials database.We also employed an example of transition metal phthalocyanine double-atom catalysts as electrocatalysts for carbon reduction reaction to exhibit the general workflow of the DFT-ML hybrid scheme and our DMCP program.

Behavioral Decision-Making of Key Stakeholders in Public-Private Partnerships:A Hybrid Method and Benefit Distribution Study

Public-private partnerships(PPPs)have been used by governments around the world to procure and construct infrastructural amenities.It relies on private sector expertise and funding to achieve this lofty objective.However,given the uncertainties of project management,transparency,accountability,and expropriation,this phenomenon has gained tremendous attention in recent years due to the important role it plays in curbing infrastructural deficits globally.Interestingly,the reasonable benefit distribution scheme in a PPP project is related to the behavior decisionmaking of the government and social capital,aswell as the performance of the project.In this paper,the government and social capital which are the key stakeholders of PPP projects were selected as the research objects.Based on the fuzzy expected value model and game theory,a hybrid method was adopted in this research taking into account the different risk preferences of both public entities and private parties under the fuzzy demand environment.To alleviate the problem of insufficient utilization of social capital in a PPP project,this paper seeks to grasp the relationship that exists between the benefit distribution of stakeholders,their behavioral decision-making,and project performance,given that they impact the performance of both public entities and private parties,as well as assist in maximizing the overall utility of the project.Furthermore,four game models were constructed in this study,while the expected value and opportunity-constrained programming model for optimal decision-making were derived using alternate perspectives of both centralized decision-making and decentralized decision-making.Afterward,the optimal behavioral decision-making of public entities and private parties in four scenarios was discussed and thereafter compared,which led to an ensuing discussion on the benefit distribution system under centralized decision-making.Lastly,based on an example case,the influence of different confidence levels,price,and fuzzy uncertainties of PPP projects on the equilibrium strategy results of both parties were discussed,giving credence to the effectiveness of the hybrid method.The results indicate that adjusting different confidence levels yields different equilibriumpoints,and therefore signposts that social capital has a fair perception of opportunities,as well as identifies reciprocal preferences.Nevertheless,we find that an increase in the cost coefficient of the government and social capital does not inhibit the effort of both parties.Our results also indicate that a reasonable benefit distribution of PPP projects can assist them in realizing optimum Pareto improvements over time.The results provide us with very useful strategies and recommendations to improve the overall performance of PPP projects in China.

Plasmonic hybridization properties in polyenes octatetraene molecules based on theoretical computation

Relationship of plasmonic properties of multiple clusters to molecular interactions and properties of a single cluster or molecule have become increasingly important due to the continuous emergence of molecular and cluster devices or systems.A hybrid phenomenon similar to plasmonic nanoparticle hybridization exists between two molecules with plasmon excitation modes.We use linear-response time-dependent density functional theory,real-time propagation time-dependent density functional theory,the plasmonicity index,and transition contribution maps(TCMs)to identify the plasmon excitation modes for the linear polyenes octatetraene with–OH and–NH_(2)groups and analyze the hybridization characteristics using charge transitions.The results show that molecular plasmon hybridization exists when the two molecules are coupled.The TCM analysis shows that the plasmon modes and hybridization result from collective and single-particle excitation.The plasmon mode is stronger,and the individual properties of the molecules are maintained after coupling when there is extra charge depose in the molecules because the electrons are moving in the molecules.This study provides new insights into the molecular plasmon hybridization of coupled molecules.

基于虚拟仿真平台的线上线下混合式教学研究

伴随着大数据时代的来临,高等教育模式正面临着重大改革。为提高相似理论与相似方法课程的教学效果与质量,该文构建基于虚拟仿真平台的线上线下混合式教学模式,从课前学习、课上教学、课堂讨论和课后辅导等方面对混合式教学进行综合的论述,通过教学反思,对教学模式提出相应建议与心得,以期更好地提高学生学习的效率,为混合式教育模式的改革分享经验。

基于布鲁姆教育理论的船舶辅机混合式实验教学

为了满足工程教育专业认证对航海类专业人才培养的新要求,优化虚实结合的实验教学模式,以“液压起货机的操作与管理”实践环节为例,提出基于布鲁姆教育理论的船舶辅机混合式实验教学。在构建知识和能力目标的基础上,进一步增设价值目标,引导学生培养安全责任意识,树立正确的工程价值观念。根据虚拟系统和实物设备的教学优势开展教学活动,结合传统的实验考核方法与动态变化的实操题目实施教学评价,显著提高了学生的专业实践技能,对工程教育专业认证背景下轮机专业实践类课程的改革起到了推动作用。

改进巴氏距离算法下混合级联直流输电纵联阻抗保护优化

为提高纵联保护在LCC-MMC混合级联高压直流输电系统中的鲁棒性,提出一种基于纵联阻抗和改进巴氏距离算法的高压直流输电纵联保护方案。该方案将巴氏距离算法与纵联阻抗保护相结合,对被保护线路首末两端电气量的差异进行量化,并通过量化后的数据,进行区内外故障的判定,然后基于能量函数构建了故障选极判据,保证了保护的可靠性;在此基础上,通过分形维数构建了改进的自适应滑窗,提高了保护的速动性。最后,基于Matlab与PSCAD/EMTDC平台建立了“白江工程”±800 kV LCC-MMC混合级联高压直流输电系统模型,并对所提改进后的保护方案进行验证。仿真结果表明所提保护方案能够快速可靠地判别区内外故障并正确完成故障选极,且有着较好的耐受过渡电阻能力。

基于二元堆积理论高致密CuW复合材料制备及耐电弧侵蚀性能研究

目的提高CuW复合材料的致密度及耐电弧侵蚀性能。方法基于二元堆积理论设计并制备了双粒径W颗粒混杂CuW75复合材料,通过微观组织表征、硬度测试、导电率测试及密度测试,重点对比研究了单粒径CuW75复合材料和双粒径混杂CuW75复合材料致密性和微观组织的内在关联,并通过电接触实验研究了双粒径混杂CuW75复合材料耐电弧的侵蚀性能。结果双粒径混杂CuW75复合材料微观组织均匀,W相及Cu相网络连通程度高,致密度高达99.79%,较CuW75复合材料国家标准致密度提升了2.9%,硬度较CuW75复合材料国家标准提升了6.7%。电接触实验结果表明,双粒径混杂CuW75复合材料熔焊力波动幅度小,平均熔焊力(35.06cN)较1μm单粒径CuW75复合材料的降低了11%;平均燃弧能量(147.29mJ)及平均燃弧时间(2.53ms)较1μm单粒径CuW75复合材料的分别下降了38%和36%。双粒径混杂CuW75复合材料受电弧侵蚀面积小,侵蚀坑较浅,耐电弧侵蚀性能优异。结论双粒径混杂W颗粒制备CuW复合材料可以明显提升材料的致密度及微观结构连通性。双粒径混杂CuW复合材料可以更好地分散电弧,具有优异的抗熔焊性能及耐电弧侵蚀性能。

网联环境中基于免疫思想的公交通行策略

在V2X网联环境中,公交系统能够获取全局的动态信息,以相邻公交车站之间的道路为场景开展网联公交的通行策略研究。以绿信比之差为主要参数,构建公交快速通行的数学模型;遗传算法与免疫思想相结合,设计亲和度,选取优秀抗体,提出一种混合遗传算子;改进自适应交叉、变异概率,提出一种基于免疫思想的公交通行策略。仿真结果表明:对比固定相位时长,采用遗传算法和基于免疫思想的通行策略均可较大程度地减少公交车的运行时间、等待时间和停车次数等重要指标;对比遗传算法的通行策略,基于免疫思想的通行策略绿信比变化降低了约8.8%,收敛速度提升了约26.8%,改进的策略减少了陷入局部最优的风险,能实现网联公交的快速通行,提升公交系统的运行效率。

三维氮掺杂分级多孔碳纳米片的制备及储锌性能

以MgO@ZIF-8为双模板和辅助碳源,KHCO_(3)为活化剂,煤沥青(CTP)为碳源,制备了三维氮掺杂分级多孔碳纳米片(N-CNs),探究了KHCO_(3)与CTP的质量比对N-CNs的结构、化学组成和锌离子储存性能的影响.结果表明,当KHCO_(3)与CTP的质量比为3∶1时,制备的样品(N-CN_(3))具有三维互连的碳纳米片结构、高比表面积(2360m^(2)/g)和吡咯氮含量.将N-CN_(3)作为锌离子混合电容器(ZHCs)正极时,其在2mol/LZnSO_(4)电解液中展现了出色的比容量(当电流密度为0.2A/g时,比容量为157.6mA·h/g)、高的能量密度(在176.1W/kg功率密度下能量密度为126.5 W·h/kg)和优异的循环稳定性(当电流密度为5 A/g时,经过20000次循环后容量仅衰减6.4%,库仑效率为99.6%).密度泛函理论计算结果证实,吡咯氮掺杂增加了电极材料对锌离子的吸附能,提高了N-CNs对锌离子的吸附容量.

基于信息间隙决策理论-分布鲁棒优化的含电-氢-热混合储能综合能源系统需求响应策略

混合储能系统具有储能容量大、调节能力强等优点,有助于提高综合能源系统(integrated energy system,IES)的需求响应能力。首先,构建了一种电-氢-热混合储能系统(electric-hydrogen-thermal hybrid energy storage system,EHT-HESS),其中采用电解槽(electrolytic cell,EC)、蒸气重整反应(steam methane reforming,SMR)装置、储氢、热电联产氢燃料电池(hydrogen fuel cell,HFC)设备,实现电、气向氢能的转换,以及以氢能作为中间模态的“制氢-储氢-放氢/电/热”功能。其次,建立考虑EHT-HESS的IES需求响应策略优化模型,其中考虑IES响应电价和气价,同时根据富余风电量,进行购电、购气、用电、用热、用氢等策略决策的综合需求响应(integrated demand response,IDR)行为;并采用信息间隙决策理论(information gap decision theory,IGDT)计入概率分布未知的风电严重不确定性,采用基于综合范数的分布鲁棒优化(distributionally robust optimization,DRO)方法计入概率分布不完备的电价严重不确定性。最后,算例验证了模型和方法的合理性及有效性,并表明IES装设热电联产HFC构建EHT-HESS可实现氢能向电能与热能的转换,有助于增加风电消纳量,增加IDR决策的鲁棒性。

混合学习情境下直播教学平台体验设计研究

探索疫情背景下,高校混合学习直播教学平台的用户体验框架与构成要素,为混合学习直播教学情境搭建与受众深度体验设计提供交互设计策略。基于扎根理论研究方法,首先,从高校直播教学软件用户的深度访谈中获取初步文本数据;其次对数据进行开放性编码、主轴性编码、选择性编码,形成框架并进行策略阐释。混合学习直播教学体验要素由拟真性体验、自如性体验、效益性体验、共创性体验4个宏观维度以及信息直观、功能适切等8个微观层面组成。并围绕发掘的维度进一步提出相应设计优化策略。混合学习背景下直播教学平台体验需设计适于在线教学的互动功能和布局,搭建沉浸的互动环境,赋能知识共创,综合提升学习者的深度学习体验。

基于灰数描述的不确定工时作业车间E/T调度优化

针对不确定加工时间的作业车间调度问题,考虑每个工件的交货期窗口,旨在最小化工件提前/拖期成本和机器空闲成本。分别采用基本遗传算法和混合遗传算法进行求解,并比较两种算法的求解质量。通过灰色理论和模糊数理论对不确定加工时间分别建立调度模型,分析两种模型的优化程度和稳定性。结果表明,与基本遗传算法相比,引入按指定邻域结构进行局部搜索的混合遗传算法具有更好收敛能力;与模糊数方式相比,采用灰色理论能够更好地描述不确定加工时间,且在求解过程中也具有更好的适应性和稳定性。

电动汽车锂电池组双层环形均衡器研究

传统Buck-Boost均衡电路传输路径长且均衡效率低,现有的环形均衡器无法从根本上解决该问题。针对该问题,文中提出了一种基于Buck-Boost及开关电容的锂电池双层环形均衡器。该均衡电路采用模块化均衡的方法在底层模块和顶层模块采用Buck-Boost和开关电容的混合电路实现均衡,并采用图论分析的方法,从均衡速度、均衡效率两方面分析电路结构。结果表明,该均衡电路具有能量传输路径少、能量传输效率高的优点。文中在MATLAB/Simulink电力仿真平台上搭建了均衡电路的仿真模型,仿真结果表明基于Buck-Boost及开关电容的双层环形均衡器比单层环形均衡器均衡速度提高了46%。文中搭建了4节电池的实验样机对环形均衡电路工作原理及系统均衡效率进行实验验证,结果表明双层环形均衡器比单层环形均衡器均衡效率可提高26.78%。仿真与实验结果与理论分析一致,说明电路结构合理有效。

考虑心理潜变量的城市轨道站点接驳方式选择行为研究

本文以计划行为理论为基础框架,从出行者使用接驳工具的经验和对接驳工具属性评价两个维度分别引入出行习惯、感知态度两类潜变量,构建城市轨道站点接驳方式选择意向的多指标多因果模型,探究城市轨道站点接驳方式选择意向的影响因素及其作用路径,并在此基础上结合巢式Logit模型构建混合选择模型,探究出发端与到达端接驳方式选择行为的影响因素及其影响机理。结果表明:行为习惯潜变量直接影响接驳方式选择意向,感知态度对步行、共享单车接驳选择意向具有直接影响;显著影响接驳方式选择意向的可观测变量有学历、车辆保有情况、收入情况、接驳距离及出行人数;相较于慢行交通与道路公共交通,私人小汽车、电动车的有无对私人交通方式接驳选择行为影响较大;学历和职业等个人社会经济属性,出行目的和出行人数等出行属性,以及主观规范和行为意向等心理属性对出行者城市轨道出发端和到达端接驳方式选择行为均有显著影响;相较于到达端,出行费用、主观规范及行为态度对出发端接驳方式选择行为的影响更大,且出发端接驳方式选择对到达端的接驳决策行为具有影响。