基于CSSA-BPNN模型的胶结充填体动态抗压强度预测

充填采矿法二步骤回采时胶结充填体稳定性受爆破扰动而降低。为快速准确地获得充填体动态抗压强度,利用分离式霍普金森压杆(SHPB)进行了40组不同应变率的单轴冲击实验,以灰砂比、充填体密度、养护龄期和平均应变率作为输入参数,充填体动态抗压强度作为输出参数,建立了一种基于Logistic混沌麻雀搜索算法(CSSA)优化BP神经网络(BPNN)的预测模型,并与传统BPNN和麻雀搜索算法优化的BPNN进行了对比分析。结果表明:CSSA-BPNN模型的平均相对误差为4.11%,预测值与实测值之间拟合的相关系数均在0.96以上,模型预测精度高。CSSA-BPNN模型的均方根误差为0.395 0 MPa,平均绝对误差为0.359 2 MPa,决定系数为0.995 2,均优于另外两种预测模型。实现了对充填体动态抗压强度的准确预测,可大幅减小物理实验量,为矿山胶结充填体的强度设计提供了一种新方法。

Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

Deep neural network based on multi-level wavelet and attention for structured illumination microscopy

Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

An Expert System to Detect Political Arabic Articles Orientation Using CatBoost Classifier Boosted by Multi-Level Features

The number of blogs and other forms of opinionated online content has increased dramatically in recent years.Many fields,including academia and national security,place an emphasis on automated political article orientation detection.Political articles(especially in the Arab world)are different from other articles due to their subjectivity,in which the author’s beliefs and political affiliation might have a significant influence on a political article.With categories representing the main political ideologies,this problem may be thought of as a subset of the text categorization(classification).In general,the performance of machine learning models for text classification is sensitive to hyperparameter settings.Furthermore,the feature vector used to represent a document must capture,to some extent,the complex semantics of natural language.To this end,this paper presents an intelligent system to detect political Arabic article orientation that adapts the categorical boosting(CatBoost)method combined with a multi-level feature concept.Extracting features at multiple levels can enhance the model’s ability to discriminate between different classes or patterns.Each level may capture different aspects of the input data,contributing to a more comprehensive representation.CatBoost,a robust and efficient gradient-boosting algorithm,is utilized to effectively learn and predict the complex relationships between these features and the political orientation labels associated with the articles.A dataset of political Arabic texts collected from diverse sources,including postings and articles,is used to assess the suggested technique.Conservative,reform,and revolutionary are the three subcategories of these opinions.The results of this study demonstrate that compared to other frequently used machine learning models for text classification,the CatBoost method using multi-level features performs better with an accuracy of 98.14%.

Construction of a Multi-Level Strategic System for Cultivating Cultural Industry Management Talents in Colleges and Universities

Through SWOT(strengths,weaknesses,opportunities,and threats)and PEST(political,economic,social,and technological)analysis,this study discusses the construction of a multi-level strategic system for the cultivation of cultural industry management talents in colleges and universities.First of all,based on SWOT analysis,it is found that colleges and universities have rich educational resources and policy support,but they face challenges such as insufficient practical teaching and intensified international competition.External opportunities come from the rapid development of the cultivation of cultural industry management talents and policy promotion,while threats come from global market competition and talent flow.Secondly,PEST analysis reveals the key factors in the macro-environment:at the political level,the state vigorously supports the cultivation of cultural industry management talents;at the economic level,the market demand for cultural industries is strong;at the social level,the public cultural consumption is upgraded;at the technological level,digital transformation promotes industry innovation.On this basis,this paper puts forward a multi-level strategic system covering theoretical education,practical skill improvement,interdisciplinary integration,and international vision training.The system aims to solve the problems existing in talent training in colleges and universities and cultivate high-quality cultural industry management talents with theoretical knowledge,practical skills,and global vision,so as to adapt to the increasingly complex and diversified cultural industry management talents market demand and promote the long-term development of the industry.

CSSA算法在空间3R机械手逆运动学计算中应用

基于混沌麻雀搜索算法(Chaos Sparrow Search Algorithm)具有精度高,搜索能力强,收敛速度快等优点,提出一种将CSSA应用于多自由度机械臂逆运动学解的新方法。以空间3R机械手为例:首先用螺旋理论进行正向运动学建模并通过3R机械手空间几何关系,将关节空间划分为4个只有唯一逆解的子空间;其次通过雅可比矩阵行列式为0的方法求出3R机械手奇异位置;最后建立仿真,以各子空间边界作为约束条件,将子空间内的逆运动学求解转换为CSSA算法有约束寻优;验证CSSA算法求逆运动学解中的可行性;仿真结果表明,CSSA算法可精确求出3R机械手的所有逆运动学解,算法具有稳定性,收敛速度快且不受奇异性的影响。

基于POE和CSSA的冗余机械臂逆运动学求解方法

为提高7自由度冗余机械臂逆运动学求解精度,提出一种基于旋量指数积和混沌麻雀搜索算法的逆运动学求解方法。对一种7自由度仿人机械手臂建模,采用旋量指数积公式法建立机械臂正运动学方程并在MATLAB中进行验证。根据机械臂位置矢量和接近矢量构建适应度函数,利用混沌麻雀搜索算法求解冗余机械臂的逆解。在MATLAB软件中编写逆解算法程序,将求得的关节值代入所建立的正运动学模型,计算冗余机械臂末端期望位姿与实际位姿的误差。

Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture

With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

FPGA based hardware platform for trapped-ion-based multi-level quantum systems

We report a design and implementation of a field-programmable-gate-arrays(FPGA)based hardware platform,which is used to realize control and signal readout of trapped-ion-based multi-level quantum systems.This platform integrates a four-channel 2.8 Gsps@14 bits arbitrary waveform generator,a 16-channel 1 Gsps@14 bits direct-digital-synthesisbased radio-frequency generator,a 16-channel 8 ns resolution pulse generator,a 10-channel 16 bits digital-to-analogconverter module,and a 2-channel proportion integration differentiation controller.The hardware platform can be applied in the trapped-ion-based multi-level quantum systems,enabling quantum control of multi-level quantum system and highdimensional quantum simulation.The platform is scalable and more channels for control and signal readout can be implemented by utilizing more parallel duplications of the hardware.The hardware platform also has a bright future to be applied in scaled trapped-ion-based quantum systems.

Neighborhood Effects and Political Trust: A Multi-level Analysis of Chinese Rural-to-Urban Migrants’ Trust in County Government

Massive rural-to-urban migration in China is consequential for political trust: rural-to-urban migrants have been found to hold lower levels of trust in local government than their rural peers who choose to stay in the countryside (mean 4.92 and 6.34 out of 10, respectively, p < 0.001). This article explores why migrants have a certain level of political trust in their county-level government. Using data of rural-to-urban migrants from the China Family Panel Survey, this study performs a hierarchical linear modeling (HLM) to unpack the multi-level explanatory factors of rural-to-urban migrants’ political trust. Findings show that the individual-level socio-economic characteristics and perceptions of government performance (Level-1), the neighborhood-level characteristics-the physical and social status and environment of neighborhoods (Level-2), and the objective macroeconomic performance of county-level government (Level-3), work together to explain migrants’ trust levels. These results suggest that considering the effects of neighborhood-level factors on rural-to-urban migrants’ political trust merits policy and public management attention in rapidly urbanizing countries.

结合CSSA-BP神经网络的砂带磨抛表面粗糙度预测研究

镍基合金在航空涡轮机、压气机的叶片制造中广泛应用,提升其加工效率与质量是行业热点。为实现航发叶片恒力磨抛的表面粗糙度准确预测,提出一种基于改进麻雀搜索算法(CSSA)优化BP神经网络的预测模型,开展机器人砂带磨抛工艺参数预测研究。选取4个主要影响因素为变量进行正交试验和极差分析;利用BP神经网络建立表面粗糙度预测模型,引入Tent混沌映射和种群多样性变异,进行麻雀搜索算法的改进,再通过CSSA对预测模型进行优化。结果表明:优化后的粗糙度预测误差降低了85.9%,相比优化前提升明显,可准确预测所输入工艺参数对应的粗糙度值。

基于改进混沌麻雀搜索算法S盒与神经元电路的图像加密技术

鉴于目前的图像加密算法所采用的S盒结构过于简单,易被破解。选用由改进的脉冲耦合尖峰神经元电路得到的混沌映射(MPSNC),并使用该映射对混沌麻雀搜索算法(CSSA)进行优化。随后,结合MPSNC映射和改进后的CSSA,设计新型的混沌S盒。最后,基于MPSNC映射和新型混沌S盒构造一种图像加密算法,为增加S盒的变化,对其进行S8变换。通过迭代混沌映射生成混沌序列,使用混沌序列和S8S盒对图像进行扩散和置乱操作,得到加密后的图像。经实验验证,该加密算法参数丰富,复杂度高,并由于S盒的应用,提高了算法的非线性度,具有良好的性能,适用于图像加密。

新时代中国特色社会主义主要矛盾变化研究——以CSSA理论为分析基础

本文以中国资本积累的社会结构理论(CSSA理论)为分析工具,详细梳理了1949年新中国成立以来党对主要矛盾的论述与各阶段社会结构变化的矛盾关系,阐明了十九大报告中"新时代"论断与"新矛盾"变化的历史依据和理论逻辑。指出"新矛盾"的形成是基于"转型CSSA"崩溃期中国劳资、资资、政府经济角色、国际竞争关系、主流意识形态和生态矛盾等六大制度特征的必然结果,"新矛盾"的具体表现也由上述六大制度特征所决定。同时本文总结了十九大报告中的"新方略""新任务"和"新举措"与CSSA六大制度的关系,认为它们指明了有助于解决"新时代""新矛盾"的CSSA未来变革的方向。

Pixelated non-volatile programmable photonic integrated circuits with 20-level intermediate states

Multi-level programmable photonic integrated circuits(PICs)and optical metasurfaces have gained widespread attention in many fields,such as neuromorphic photonics,opticalcommunications,and quantum information.In this paper,we propose pixelated programmable Si_(3)N_(4)PICs with record-high 20-level intermediate states at 785 nm wavelength.Such flexibility in phase or amplitude modulation is achieved by a programmable Sb_(2)S_(3)matrix,the footprint of whose elements can be as small as 1.2μm,limited only by the optical diffraction limit of anin-house developed pulsed laser writing system.We believe our work lays the foundation for laser-writing ultra-high-level(20 levels and even more)programmable photonic systems and metasurfaces based on phase change materials,which could catalyze diverse applications such as programmable neuromorphic photonics,biosensing,optical computing,photonic quantum computing,and reconfigurable metasurfaces.

一种基于树的蛋白质功能预测算法:KDE–CSSA

针对在每个标签类上直接学习分类模型计算代价高和树层次中低层结点训练数据扭曲的问题,提出了一种基于树层次的蛋白质功能预测算法:核依赖估计–压缩排序选择算法(KDE–CSSA)。该算法先将标签向量投影到标签核的主成分上,仅仅学习少量的回归模型,然后将预测的数值向量投影回原来标签向量空间,利用压缩排序和选择算法获取满足树属性的0,1标签向量。在12个基因组数据集上使用精确率和召回率作为评测标准的实验结果表明,KDE–CSSA算法性能优于目前优秀的CLUS–HMC算法。

Multi-level access control model for tree-like hierarchical organizations

An access control model is proposed based on the famous Bell-LaPadula （BLP） model.In the proposed model,hierarchical relationships among departments are built,a new concept named post is proposed,and assigning security tags to subjects and objects is greatly simplified.The interoperation among different departments is implemented through assigning multiple security tags to one post, and the more departments are closed on the organization tree,the more secret objects can be exchanged by the staff of the departments.The access control matrices of the department,post and staff are defined.By using the three access control matrices,a multi granularity and flexible discretionary access control policy is implemented.The outstanding merit of the BLP model is inherited,and the new model can guarantee that all the information flow is under control.Finally,our study shows that compared to the BLP model,the proposed model is more flexible.

Parameter-driven Level of Detail Derivation Method for Semantic Building Facade Model

The relentless progress in the research of geographic spatial data models and their application scenarios is propelling an unprecedented rich Level of Detail(LoD)in realistic 3D representation and smart cities.This pursuit of rich details not only adds complexity to entity models but also poses significant computational challenges for model visualization and 3D GIS.This paper introduces a novel method for deriving multi-LOD models,which can enhance the efficiency of spatial computing in complex 3D building models.Firstly,we extract multiple facades from a 3D building model(LoD3)and convert them into individual semantic facade models.Through the utilization of the developed facade layout graph,each semantic facade model is then transformed into a parametric model.Furthermore,we explore the specification of geometric and semantic details in building facades and define three different LODs for facades,offering a unique expression.Finally,an innovative heuristic method is introduced to simplify the parameterized facade.Through rigorous experimentation and evaluation,the effectiveness of the proposed parameterization methodology in capturing complex geometric details,semantic richness,and topological relationships of 3D building models is demonstrated.

露天矿机械设备故障诊断与维修决策研究

通过深入分析了露天矿机械设备的故障特点与参数特征,构建了基于CSSA优化的故障诊断模型,以此提高诊断的准确性和可靠性。同时,通过引入选择比例风险模型来计算露天矿机械设备故障率,使故障率的计算更加精确。最后,综合考虑到故障概率、后果严重性和可探测度等因素,通过引入风险优先数进行评估来进行维修决策,为露天矿机械设备的故障维修提供科学、有效的指导。

Shape retrieval using multi-level included angle functions-based Fourier descriptor

An effective shape signature namely multi-level included angle functions MIAFs is proposed to describe the hierarchy information ranging from global information to local variations of shape.Invariance to rotation translation and scaling are the intrinsic properties of the MIAFs.For each contour point the multi-level included angles are obtained based on the paired line segments derived from unequal-arc-length partitions of contour.And a Fourier descriptor derived from multi-level included angle functions MIAFD is presented for efficient shape retrieval.The proposed descriptor is evaluated with the standard performance evaluation method on three shape image databases the MPEG-7 database the Kimia-99 database and the Swedish leaf database. The experimental results of shape retrieval indicate that the MIAFD outperforms the existing Fourier descriptors and has low computational complexity.And the comparison of the MIAFD with other shape description methods also shows that the proposed descriptor has the highest precision at the same recall value which verifies its effectiveness.

应用软件的集成工具——CSSA 软件集成系统

本文开发了一组软件工具-CSSA 软件集成系统,通过它可将相互独立的、多学科的应用程序集成为单一的评估分析系统。为进行程序之间的数据通信,本文还在定义、转换数据方面提出了一个实用的方法。