Consideration of the influence of supports in modeling the electromagnetic fields of 25 kV traction networks under emergency conditions

Single-phase 25 kV traction networks of electrified alternating current(AC)railways create electromagnetic fields(EMFs)with significant levels of intensity.The most intense magnetic fields occur when short circuits exist between the contact wire and rails or ground.Despite the short duration of exposure,they can adversely affect electronic devices and induce significant voltages in adjacent power lines,which is dangerous for operating personnel.Although numerous investigations have focused on modeling the EMF of traction networks and power lines,the challenge of determining the three-dimensional electromagnetic fields near metal supports during the flow of a short-circuit current through them is yet to be resolved.In this case,the field has a complex spatial structure that significantly complicates the calculations of intensities.This study proposes a methodology,algorithms,software,and digital models for determining the EMF in the described emergency scenarios.During the modeling process,the objects being studied were represented by segments of thin wires to analyze the distribution of the electric charge and calculate the intensities of the electric and magnetic fields.This approach was implemented in the Fazonord software,and the modeling results show a substantial increase in EMF levels close to the support,with a noticeable decrease in the levels as the distance from it increases.The procedure implemented in the commercial software Fazonord is universal and can be used to determine electromagnetic fields at any electrical power facility that includes live parts of limited length.Based on the proposed procedure,the EMF near the supports of overhead power lines and traction networks of various designs could be determined,the EMF levels at substations can be calculated,and the influence of metal structures located near traction networks,such as pedestrian crossings at railway stations,can be considered.

Condition assessment of structures under unknown support excitation

A new method is proposed to assess the condition of structures under unknown support excitation by simultaneously detecting local damage and identifying the support excitation from several structural dynamic responses. The support excitation acting on a structure is modeled by orthogonal polynomial approximations, and the sensitivities of structural dynamic response with respect to its physical parameters and orthogonal coefficients are derived. The identification equation is based on Taylor＇s first order approximation, and is solved with the damped least-squares method in an iterative procedure. A fifteen-story shear building model and a five-story three-dimensional steel frame structure are studied to validate the proposed method. Numerical simulations with noisy measured accelerations show that the proposed method can accurately detect local damage and identify unknown support excitation from only several responses of the structure. This method provides a new approach for detecting structural damage and updating models with unknown input and incomplete measured output information.

Turbopump Condition Monitoring Using Incremental Clustering and One-class Support Vector Machine

Turbopump condition monitoring is a significant approach to ensure the safety of liquid rocket engine （LRE）.Because of lack of fault samples,a monitoring system cannot be trained on all possible condition patterns.Thus it is important to differentiate abnormal or unknown patterns from normal pattern with novelty detection methods.One-class support vector machine （OCSVM） that has been commonly used for novelty detection cannot deal well with large scale samples.In order to model the normal pattern of the turbopump with OCSVM and so as to monitor the condition of the turbopump,a monitoring method that integrates OCSVM with incremental clustering is presented.In this method,the incremental clustering is used for sample reduction by extracting representative vectors from a large training set.The representative vectors are supposed to distribute uniformly in the object region and fulfill the region.And training OCSVM on these representative vectors yields a novelty detector.By applying this method to the analysis of the turbopump＇s historical test data,it shows that the incremental clustering algorithm can extract 91 representative points from more than 36 000 training vectors,and the OCSVM detector trained on these 91 representative points can recognize spikes in vibration signals caused by different abnormal events such as vane shedding,rub-impact and sensor faults.This monitoring method does not need fault samples during training as classical recognition methods.The method resolves the learning problem of large samples and is an alternative method for condition monitoring of the LRE turbopump.

Research on characters of surrounding rock in complex geology conditions and supporting time

The methods combined by test, field monitoring and theoretical analysis were adopted to do the systemic research on the rock mass from micro-structure to macro-deformation, and rheological model of Jinchuan rock mass was established to discuss the reasonable supporting time. Results show that supporting after suitable stress and displacement release can benefit for the long-term stability of surrounding rock.

A network condition classification scheme for supporting video delivery over wireless Internet*

Real-time video transport over wireless Internet faces many challenges due to the heterogeneous environment including wireline and wireless networks. A robust network condition classification algorithm using multiple end-to-end metrics and Support Vector Machine (SVM) is proposed to classify different network events and model the transition pattern of network conditions. End-to-end Quality-of-Service (QoS) mechanisms like congestion control, error control, and power control can benefit from the network condition information and react to different network situations appropriately. The proposed network condition classifica- tion algorithm uses SVM as a classifier to cluster different end-to-end metrics such as end-to-end delay, delay jitter, throughput and packet loss-rate for the UDP traffic with TCP-friendly Rate Control (TFRC), which is used for video transport. The algorithm is also flexible for classifying different numbers of states representing different levels of network events such as wireline congestion and wireless channel loss. Simulation results using network simulator 2 (ns2) showed the effectiveness of the proposed scheme.

White Paper:China’s Policies toward Ethnic Minorities Well Fit National Conditions and Win Support from All Nationalities

The State Council Information Office published on September 27, 2009, a white paper entitled China＇ s Policies toward Minorities and Common Prosperity and Development of All Nationalities. The white paper says that the policies well fit the national conditions and are correct. They are instrumental to promoting harmonious coexistence of all nationalities that work with one heart and one mind and in mutual cooperation; they have helped open up a good national situation characterized by economic development, political stability,

Using the curve moment and the PSO-SVM method to diagnose downhole conditions of a sucker rod pumping unit

Downhole working conditions of sucker rod pumping wells are automatically identified on a computer from the analysis of dynamometer cards. In this process, extraction of feature parameters and pattern classification are two key steps. The dynamometer card is firstly divided into four parts which include different production information according to the ＂four point method＂ used in actual oilfield production, and then the moment invariants for pattern recognition are extracted. An improved support vector machine （SVM） method is used for pattern classification whose error penalty parameter C and kernel function parameter g are optimally chosen by the particle swarm optimization （PSO） algorithm. The simulation results show the method proposed in this paper has good classification results.

A new diagnostic method for identifying working conditions of submersible reciprocating pumping systems

The submersible pumping unit is a new type of pumping system for lifting formation fluids from onshore oil wells, and the identification of its working condition has an important influence on oil production. In this paper we proposed a diagnostic method for identifying the working condition of the submersible pumping system. Based on analyzing the working principle of the pumping unit and the pump structure, different characteristics in loading and unloading processes of the submersible linear motor were obtained at different working conditions. The characteristic quantities were extracted from operation data of the submersible linear motor. A diagnostic model based on the support vector machine （SVM） method was proposed for identifying the working condition of the submersible pumping unit, where the inputs of the SVM classifier were the characteristic quantities. The performance and the misjudgment rate of this method were analyzed and validated by the data acquired from an experimental simulation platform. The model proposed had an excellent performance in failure diagnosis of the submersible pumping system. The SVM classifier had higher diagnostic accuracy than the learning vector quantization （LVQ） classifier.

Model Parameter Transfer for Gear Fault Diagnosis under Varying Working Conditions

Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications.However,the various working conditions would degrade the diagnostic performance and make gear fault diagnosis(GFD)more and more challenging.In this paper,a novel model parameter transfer(NMPT)is proposed to boost the performance of GFD under varying working conditions.Based on the previous transfer strategy that controls empirical risk of source domain,this method further integrates the superiorities of multi-task learning with the idea of transfer learning(TL)to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition(target domain)and another(source domain),and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are unavailable.For NMPT implementation,insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD task.Related experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working conditions.The proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.

A Method of Shield Attitude Working Condition Classification

Aiming at solving shield attitude rectification failure problem,a method of shield working condition classification based on support vector data description( SVDD) was introduced. Shield attitude mechanics model containing priori knowledge was helpful to feature selection. SVDD handled the one class classification problem and a decision function for attitude rectification was proposed. Experimental results indicate that the method is able to accomplish the shield attitude working condition classification.

Study on Direct Synthesis of Diphenyl Carbonate with Heterogeneous Catalytic Reaction (V) Screening Catalysts and Optimizing Synthesis Conditions

Pd/LaxPbyMnOz, Pd/C, Pd/molecular sieve and Pd-heteropoly acid catalysts for direct synthesis of diphenyl carbonate (DPC) by heterogeneous catalytic reaction were compared and the results of DPC synthesis indicated that the catalyst Pd/LaxPbyMnOz had higher activity. The Pd/LaxPbyMnOz catalyst and the support was characterized by XRD, SEM and TEM, the main phase was La0.62Pb0.38MnO3 and the average diameter could be about 25.4 nm. The optimum conditions for synthesis of DPC with Pd/LaxPbyMnOz were determined by orthogonal experiments and the experimental results showed that reaction temperature was the first factor of effect on the selectivity and yield of DPC, and the concentration of O2 in gas phase also had significant effect on selectivity of DPC. The optimum reaction conditions were catMyst/phenol mass ratio 1 to 50, pressure 4.5 MPa,volume concentration of O2 25%, reaction temperature 60° and reaction time 4 h. The maximum yield and average selectivity could reach 13% and 97% respectively in the batch operation.

Adaptive Signal Conditioning Technology in Fighter Weapon System Test

In order to realize the real-time and precise test for a weapon system of a certain type of fighter,a signal classification method according to attributes is proposed,common input channels for multiple signals are configured optimally,and a test adapter and an adaptive signal conditioning module is designed. The hardware of conditioning module can be configured flexibly and the programmable test range can be adjusted owing to programmable multiplexer. An FPGA adaptive filter is designed by the calculated filter coefficient vectors with LMS method to solve the problem of parallel test of fighter weapon system in electromagnetic interference environment. The adaptive signal conditioning technology is characterized by high efficiency,precision and integration. Its application makes the test system successful to conduct real-time and parallel test for a weapon system,which is developed based on VXI bus and virtual-instrument technology.

Informal Caregiving for Persons with Chronic Conditions: Trends and Considerations

Aging adults with chronic conditions rely heavily on an informal network of caregivers to remain within their communities of choice. This reliance can take a significant toll on caregivers through the lens of physical and psychological problems, financial issues, and social isolation. These variables may then lead to less desirable outcomes for care recipients. This review highlights existing support services in their many forms, including: psychosocial interventions, environmental interventions, respite care, and health information technology as a method of delivery. Given the current trend with informal caregivers assuming increased responsibility in healthcare, programs and services supporting these caregivers must be understood and trialed to ensure that their needs are not overlooked.

Fault diagnosis of diesel engine valve clearance under variable operating condition based on soft interval SVM

The fault detection and diagnosis of diesel engine valve clearance can effectively improve the availability and safety of diesel engine and have extremely important value and significance.Diesel engines generally operate in various stable operating conditions,which have important influence on the fault diagnosis.However,many fault diagnosis methods have been put forward under specific stable operating condition based on vibration signal.As the result of great impact caused by operating conditions,corresponding diagnosis models cannot deal with the fault diagnosis under different operating conditions with required accuracy.In this paper,a fault diagnosis of diesel engine valve clearance under variable operating condition based on soft interval support vector machine(SVM)is proposed.Firstly,the fault features with weak condition sensitivity have been extracted according to the influence analysis of fault on vibration signal.Moreover,soft interval constraint has been applied to SVM algorithm to reduce the random influence of vibration signal on fault features.In addition,different machine learning algorithms based on different feature sets are adopted to conduct the fault diagnosis under different operating conditions for comparison.Experimental results show that the proposed method is applicable for fault diagnosis under variable operating condition with good accuracy.

四边不同支承条件下矩形板的结构计算

采用带补充项的傅立叶级数作为挠度函数,针对四边不同支承矩形薄板,推导了确定待定系数的方程组,给出可处理简支边、固支边和自由边任意组合条件下统一的结构计算公式.探讨了集中荷载作用处弯矩级数解不收敛的处理办法,以及双向板简化为单向板需要达到的长宽比问题.结果表明,集中荷载作用处的弯矩,可采用挠度值按中心差分公式进行计算,差分步长可取10 mm.对边支承对边自由板及一边固支三边自由板,可视作单向板.当四边支承板的长宽比达到2∶1、2.5∶1及4.5∶1时,可分别简化为两端固支、一端简支一端固支及两端简支单向板.三边支承一边自由板长宽比达到1∶1及2∶1时,可分别简化为两端固支(及一端简支一端固支)及两端简支单向板;长宽比达到6∶1时,可简化为悬臂单向板.两邻边支承两邻边自由板若要简化为悬臂单向板,在两支承边为固支时,长宽比需要达到2∶1;在支承边为一边简支一边固支时,长宽比要达到1.5∶1.

新型装配式可调电缆支架的承载力特性试验研究

为解决圆形隧道内电缆支架安装困难的问题,提出一种新型的装配式可调电缆支架形式,并对该新型支架的承载力特性展开试验。研究不同角度工况下(0°、-45°和45°)支架的承载力特性分析,考虑支架长度、横担高度和宽度、斜撑直径和水平螺栓规格等对支架承载力特性的影响,研究其变形特征、荷载位移曲线特征和典型部位应变发展规律,同时开展新型支架承载力特性的非线性数值模拟分析,在此基础上提出该新型支架的承载力计算理论及设计方法。结果表明:支架的典型失效模式为斜撑整体失稳破坏,并伴随横担受压侧弯扭变形;支架的竖向刚度和极限承载力随调节角度的增加而下降,在45°工况下达到最小,而支架横向承载力特性受调节角度的影响较小,因此45°工况被视为最不利工况;新型电缆支架体系受力合理,具有足够的强度、刚度和稳定承载力,可应用于工程实践。

培养过程中的条件保障如何影响博士生就读满意度?——基于26所普通高校的调查研究

通过对26所普通高校学术学位博士生的问卷调查,探究培养过程中条件保障的现状以及各类条件保障对博士生就读经历满意度的影响。研究发现:第一,高校的管理制度和导师指导能够较好地满足博士生需求,国际交流和奖助机制的支持力度较弱,科研训练硬件条件充分但课题项目等软件资源匮乏。第二,“双一流”建设地方高校和非“双一流”建设高校在多项条件保障上优于“双一流”建设部属高校;理工科在科研训练条件上具有显著优势,但人文社科的导生互动更加深入,不同学科在不同的奖助来源上各有优势。第三,各类条件保障均能够有效地提升博士生的就读经历满意度,其中,管理制度的推动作用最强,奖助机制的作用最弱。对此,在博士生培养过程中应激励深度的科研参与,推进良性的导生互动,拓展对外交流渠道,健全落实管理制度,优化资助体系结构。

2023年《ESPEN指南:共病住院患者营养支持》解读

由于我国人口老龄化趋势加快和居民生活行为方式的转变,致使慢性病共病成为公众健康的一大挑战。共病现象使患者病情更加复杂、营养状况不佳,给患者健康和社会造成沉重负担。2018年,欧洲临床营养和代谢学会(ESPEN)发布了《共病住院患者营养支持指南》,从共病住院患者的营养筛查、评估、需求、监测和干预等方面提供了22项建议和4项声明。基于不断更新的研究证据,2023年6月ESPEN指南工作组对2018版指南进行了更新,即2023年《ESPEN指南:共病住院患者营养支持》,为共病住院患者提供了基于最新循证证据的营养支持建议。本文对该指南进行解读,并就营养筛查和评估、口服营养补充、肠内营养和肠外营养、能量需求量的估算、蛋白质目标量、微量营养素的补充、特定疾病的营养补充、早期营养支持、出院后营养支持、身体功能检测、能量和蛋白质达标量、营养支持的组织管理、基础疾病对营养支持的影响、药物或营养素相互作用、营养生物标志物共15个方面进行重点分析,旨在为我国临床共病住院患者营养管理实践提供指导。

基于非线性方法的核级双层容器及支承结构热棘轮效应研究

核级双层容器承担一回路压力边界和一回路冷却剂“纵深防御”包容的双重安全功能,是高温反应堆本体常用的一种结构形式。然而内层容器在瞬态工况下温度变化剧烈,内外层容器之间导热性能差,内外温差大,热棘轮效应是可能的重要失效机制。本文通过建立核级双层容器及支承结构轴对称有限元模型,使用非线性方法分析其在热-机循环载荷作用下温度、应力和应变的响应,研究棘轮效应分布的规律和特征。结果表明:热棘轮效应显著,是该类容器的主要失效模式;热棘轮敏感区域分布具有多点式的特点,在内外层容器不同部位均有涉及;危险区域的塑性应变累积行为主要出现在循环温度载荷的降温阶段,应变增量随循环次数的增加逐步稳定。结论指出热棘轮效应是核级双层容器结构设计必须关注的重点问题,所建立的非线性分析方法是解决该问题的有效手段。本研究可为核级双层容器热棘轮分析以及一回路工况参数优化设计和应变监测测点布置提供重要方法指导。