Nonholonomic Theory of Principal-direction Orthonormal Basis for a Layer of Surfaces

In order to carry out tensor analysis in a neighborhood of a reference surface,the principal-direction orthogonal basis accompanying with Lame s coefficients or general curvilinear coordinate systems are widely used.A novel kind of field theory termed as the nonholonomic theory of the Principal-Direction Orthonormal Basis(PDOB)is presented systematically in the present paper,in which the formal Christoffel symbols are related directly to the principal and geodesic curvatures with respect to the principal directions of the surface.Furthermore,a systematic and simple way to determine the curvatures of the surface are presented with some examples.It provides a way to recognize qualitatively the bending property of a surface.

Principal Equatorial Null Geodesic Congruences in the Kerr Metric, and Their Quantum Propagators

Using the Raychaudhuri equation, we associate quantum probability amplitudes (propagators) to equatorial principal ingoing and outgoing null geodesic congruences in the Kerr metric. The expansion scalars diverge at the ring singularity;however, the propagators remain finite, which is an indication that at the quantum level singularities might disappear or, at least, become softened.

Mechanism of principal stress rotation and deformation failure behavior induced by excavation in roadways

The failure modes of rock after roadway excavation are diverse and complex.A comprehensive investigation of the internal stress field and the rotation behavior of the stress axis in roadways is essential for elucidating the mechanism of roadway failure.This study aimed to examine the spatial relationship between roadways and stress fields.The law of stress axis rotation under three-dimensional(3D)stress has been extensively studied.A stress model of roadways in the spatial stress field was established,and the far-field stress state at different spatial positions of the roadways was analyzed.A mechanical model of roadways under a 3D stress state was established using far-field stress solutions as boundary conditions.The distribution of principal stressesσ1,σ2 andσ3 around the roadways and the variation of the stress principal axis were solved.It was found that the stability boundary of the stress principal axis exhibits hysteresis when compared with that of the principal stress magnitudes.A numerical analysis model for spatial roadways was established to validate the distribution of principal stress and the mechanism of principal axis rotation.Research has demonstrated that the stress axis undergoes varying degrees of spatial rotation in different orientations and radial depths.Based on the distribution of principal stress and the rotation law of the stress principal axis,the entire evolution mechanism of the two stress adjustments to form the final failure form after roadway excavation has been revealed.The on-site detection results also corroborate the findings presented in this paper.The results provide a basis for the analysis of the failure mechanism under a 3D stress state.

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.

A Hybrid Optimization Approach of Single Point Incremental Sheet Forming of AISI 316L Stainless Steel Using Grey Relation Analysis Coupled with Principal Component Analysiss

We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.

Rock strength weakening subject to principal stress rotation:Experimental and numerical investigations

During the construction and operation of gas storage reservoirs,changes in the principal stress direction can induce fracture propagation under conditions of lower differential stress,potentially leading to failure in the surrounding rock.However,the weakening of strength due to pure stress rotation has not yet been investigated.Based on fracture mechanics,an enhanced Mohr-Coulomb strength criterion considering stress rotation is proposed and verified with experimental and numerical simulations.The micro-damage state and the evolution of the rock under the pure stress-rotation condition are analyzed.The findings indicate that differential stress exceeding the crack initiation stress is a prerequisite for stress rotation to promote the development of rock damage.As the differential stress increases,stress rotation is more likely to induce rock damage,leading to a transition from brittle to plastic failure,characterized by wider fractures and a more complex fracture network.Overall,a negative exponential relationship exists between the stress rotation angle required for rock failure and the differential stress.The feasibility of applying the enhanced criterion to practical engineering is discussed using monitoring data obtained from a mine-by tunnel.This study introduces new concepts for understanding the damage evolution of the surrounding rock under complex stress paths and offers a new theoretical basis for predicting the damage of gas storage reservoirs.

Robust Principal Component Analysis Integrating Sparse and Low-Rank Priors

Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Analysis (RPCA) addresses these limitations by decomposing data into a low-rank matrix capturing the underlying structure and a sparse matrix identifying outliers, enhancing robustness against noise and outliers. This paper introduces a novel RPCA variant, Robust PCA Integrating Sparse and Low-rank Priors (RPCA-SL). Each prior targets a specific aspect of the data’s underlying structure and their combination allows for a more nuanced and accurate separation of the main data components from outliers and noise. Then RPCA-SL is solved by employing a proximal gradient algorithm for improved anomaly detection and data decomposition. Experimental results on simulation and real data demonstrate significant advancements.

A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.

Principals’and Teachers’Awareness,Knowledge,and Differentiation of Privatization-A Secondary Publication

Based on the keynote report by Professor Martin Thrupp,this paper discusses the hollowing out of education provision by the state and the permeation of managerialism.It was pointed out that principals and boards of trustees in socioeconomically advantaged areas may not be willing to share their benefits with schools in less advantaged areas.The new liberal policies have hollowed out state provision of education,so the education system has come to rely heavily on private actors.This paper also presents the current stage of privatization in Japan and the principals’and teachers’perceptions of privatization.

Comparative Analysis of Differences among Northern,Jiangnan,and Lingnan Classical Private Gardens Using Principal Component Cluster Method

This paper investigates the design essence of Chinese classical private gardens,integrating their design elements and fundamental principles.It systematically analyzes the unique characteristics and differences among classical private gardens in the Northern,Jiangnan,and Lingnan regions.The study examines nine classical private gardens from Northern China,Jiangnan,and Lingnan by utilizing the advanced tool of principal component cluster analysis.Based on literature analysis and field research,273 variables were selected for principal component analysis,from which four components with higher contribution rates were chosen for further study.Subsequently,we employed clustering analysis techniques to compare the differences among the three types of gardens.The results reveal that the first principal component effectively highlights the differences between Jiangnan and Lingnan private gardens.The second principal component serves as the key to defining the types of Northern private gardens and distinguishing them from the other two types,and the third principal component indicates that Lingnan private gardens can be categorized into two distinct types as well.

Comparative assessment of the frying efficiency of standard and low linolenic rapeseed oils: Principal Component Analysis (PCA)

In this research,the performance of regular rapeseed oil(RSO)and modified low-linolenic rapeseed oil(LLRO)during frying was assessed using a frying procedure that commonly found in fast-food restaurants.Key physicochemical attributes of these oils were investigated.RSO and LLRO differed for initial linolenic acid(12.21%vs.2.59%),linoleic acid(19.15%vs.24.73%).After 6 successive days frying period of French fries,the ratio of linoleic acid to palmitic acid dropped by 54.49%in RSO,higher than that in LLRO(51.54%).The increment in total oxidation value for LLRO(40.46 unit)was observed to be significantly lower than those of RSO(42.58 unit).The changes in carbonyl group value and iodine value throughout the frying trial were also lower in LLRO compared to RSO.The formation rate in total polar compounds for LLRO was 1.08%per frying day,lower than that of RSO(1.31%).In addition,the formation in color component and degradation in tocopherols were proportional to the frying time for two frying oils.Besides,a longer induction period was also observed in LLRO(8.87 h)compared to RSO(7.68 h)after frying period.Overall,LLRO exhibited the better frying stability,which was confirmed by principal component analysis(PCA).

Optimizing data aggregation and clustering in Internet of things networks using principal component analysis and Q-learning

The Internet of things(IoT)is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring,surveillance,and healthcare.To address the limitations imposed by inadequate resources,energy,and network scalability,this type of network relies heavily on data aggregation and clustering algorithms.Although various conventional studies have aimed to enhance the lifespan of a network through robust systems,they do not always provide optimal efficiency for real-time applications.This paper presents an approach based on state-of-the-art machine-learning methods.In this study,we employed a novel approach that combines an extended version of principal component analysis(PCA)and a reinforcement learning algorithm to achieve efficient clustering and data reduction.The primary objectives of this study are to enhance the service life of a network,reduce energy usage,and improve data aggregation efficiency.We evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop monitoring.Our proposed approach(PQL)was compared to previous studies that utilized adaptive Q-learning(AQL)and regional energy-aware clustering(REAC).Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network.

基于Agent人工智能的异构网络多重覆盖节点入侵检测系统设计

异构网络具有结构复杂、多重覆盖面积大等特征,使得网络入侵检测较为隐蔽,威胁网络运行的安全性;为此,对基于Agent人工智能的异构网络多重覆盖节点入侵检测系统进行了研究;通过检测Agent和通信Agent装设主机Agent,以Cisco Stealthwatch流量传感器作为异构网络传感器检测攻击行为,采用STM32L151RDT664位微控制器传输批量数据,由MAX3232芯片实现系统电平转化,实现硬件系统设计;软件部分设计入侵检测标准,采用传感器设备捕获网络实时数据,通过Agent技术解析异构网络协议并提取数据运行特征,综合考虑协议解析结果及与检测标准匹配度,实现异构网络多重覆盖节点入侵检测;经实验测试表明,基于Agent人工智能的异构网络多重覆盖节点入侵检测系统入侵行为的漏检率和入侵类型误检率的平均值仅为6%和5%,能够有效提高检测精度,减小检测误差。

Incentive mechanism analysis of information security outsourcing based on principal-agent model

In order to solve principal-agent problems caused by interest inconformity and information asymmetry during information security outsourcing, it is necessary to design a reasonable incentive mechanism to promote client enterprises to complete outsourcing service actively. The incentive mechanism model of information security outsourcing is designed based on the principal-agent theory. Through analyzing the factors such as enterprise information assets value, invasion probability, information security environment, the agent cost coefficient and agency risk preference degree how to impact on the incentive mechanism, conclusions show that an enterprise information assets value and invasion probability have a positive influence on the fixed fee and the compensation coefficient; while information security environment, the agent cost coefficient and agency risk preference degree have a negative influence on the compensation coefficient. Therefore, the principal enterprises should reasonably design the fixed fee and the compensation coefficient to encourage information security outsourcing agency enterprises to the full extent.

流程型生产安全数据流的多Agent节点协同分流优化方法

为实现流程型生产安全监测系统的及时、准确决策,结合数据流采集与隐患识别过程,分析非关键数据的冗余、关键数据的缺失和数据计算时延较大的问题,提出基于多Agent的流程型生产安全数据流网络分流调度规划方法和节点流量划分识别机制。研究结果表明:相较于分簇传输方法,数据流网络分流调度方法可以实现关键隐患数据更高的传输成功率;相较于常规的复杂事件处理方法,本文提出的流量划分识别机制在2种类型数据集上实现隐患事件识别均有更低的计算时延。研究结果可为流程行业安全生产数字化管控模式和数据高质量获取提供参考。

基于AI agent的6G内生智能技术框架及其应用

未来6G网络将内生支持通信和AI一体化服务,赋能丰富多彩的新业务,支撑社会高效可持续发展。为此,借鉴了IT行业AI Agent的应用范式,基于电信应用场景创新地提出了6G AI Agent技术框架的三大设计理念,包括多模型融合、定制化Agent和插件式环境交互,并基于该理念构建了6G AI Agent技术框架。通过环境交互层、Agent引擎层、模型调度层、模型基座层交互协同,实现了自主环境感知、自主任务生成和自主执行任务的能力。此外,以移动网络的智能感知任务为例,探索了AI Agent的使用场景及价值,为AI新技术在电信领域发展提供了新的思路和技术支撑。

基于免疫Agent的电力电缆线路故障检测系统

为区别故障信号、非故障信号,实现对电缆线路故障的准确检测,设计了基于免疫Agent的电力电缆线路故障检测系统。利用前端检测电路为故障分析模块、测距方式切换模块提供电量传输信号,完成系统前端检测装置的连接。按照免疫Agent检测原理提取电力电缆线路故障信号的特征,联合已获取信号对象,求解检测插值指标的具体数值。结合各级硬件设备结构,完成系统设计。实验结果表明,该系统可同时检测波频为10~20 Hz、40~50 Hz、60~70 Hz的故障信号与波频为20~30 Hz、30~40 Hz、50~60 Hz的非故障信号,可以在精准辨别故障与非故障信号的同时,实现对电力电缆线路故障的准确检测。

基于多Agent的服装可持续消费行为建模与仿真

“双碳”目标下,随着服装消费引起的气候变化和环境污染等生态问题日益严重,迫切需要重新思考和重塑服装消费模式,推动服装消费向质量型、低碳化转变,从而实现服装产业的绿色转型。文章运用多Agent建模方法,构建服装可持续消费行为仿真模型,采用Netlogo平台进行仿真实验,探求服装可持续消费行为的影响机理。结果表明,消费者可持续消费认知、产品可持续性和设施供应条件均显著促进可持续消费行为;但不同强度下,产品可持续性和设施供应条件的推动作用存在差异。基于此,从可持续消费认知和供应条件两方面提出了引导中国消费者服装可持续消费行为的建议和对策。

基于多Agent深度强化学习的无人机协作规划方法

人机协作控制是多无人机任务规划的重要方式。考虑多无人机任务环境协同解释和策略控制一致性需求,提出基于多Agent深度强化学习的无人机协作规划方法。依据任务知识和行为状态,构建基于任务分配Agent的任务规划器,生成人机交互的相互依赖关系;设计一种深度学习强化方法,解决群体行为最优策略和协同控制方法,并利用混合主动行为选择机制评估学习策略。实验结果表明:作为人机交互实例,所提方法通过深度强化学习使群体全局联合动作表现较好,学习速度和稳定性均能优于确定性策略梯度方法。同时,在跟随、自主和混合主动3种模式比较下,可以较好地控制无人机飞行路径和任务,为无人机集群任务执行提供了智能决策依据。

一种基于Agent的任务自生成方法

任务自生成是基于预先设定的业务工作流完成任务执行流程制定的一个过程,任务执行流程制定主要包括任务执行步骤选取与任务执行步骤相应的解决方案选取。提出一种基于Agent的任务自生成方法来解决基于传统的业务工作流的流程制定机制,在面对有差异性需求的不同任务时,因其不能做出任务执行流程制定的自适应性改变而在处理任务时出现处理速度慢、结果不准确等问题。利用Agent的环境感知能力实现对任务需求信息和任务自生成系统环境的感知,利用Agent的自主行为决策能力结合感知的信息实现对任务执行流程制定工作的自主管理,在真实应用场景测试中,通过一系列比较实验验证了这种方法在处理速度和准确度方面具有明显优势。