Real-time parameters monitoring system for underground equipment based on panoramic images

Complex terrain and working equipment in coal mine underground need a way to ensure coal mine safety. In this paper, the way to monitor the real-time status of underground equipment was put forward, and it was proved to be effective as commanding and dispatching system. Monitoring system for underground equipment based on panoramic images was effectively combined with real-time sensor data and static panoramic images of underground surrounding, which not only realizes real-time status monitoring for underground equipment, but also gets a direct scene for underground surrounding. B/S mode was applied in the monitoring system and this is convenient for users to monitor the equipment. Meantime, it can reduce the waste of the data resource.

An Optimal Double Inequality among the One-Parameter, Arithmetic and Geometric Means

In the present paper, we answer the question: for 0a what are the greatest value p(a) and the least value q(a) such that the double inequality Jp(a,b)aA(a,b)+ (1-a)G(a,b)Jq(a,b) holds for all a,b>0 with a is not equal to?b ?

Application of the asynchronous advantage actor–critic machine learning algorithm to real-time accelerator tuning

This paper describes a real-time beam tuning method with an improved asynchronous advantage actor–critic(A3C)algorithm for accelerator systems.The operating parameters of devices are usually inconsistent with the predictions of physical designs because of errors in mechanical matching and installation.Therefore,parameter optimization methods such as pointwise scanning,evolutionary algorithms(EAs),and robust conjugate direction search are widely used in beam tuning to compensate for this inconsistency.However,it is difficult for them to deal with a large number of discrete local optima.The A3C algorithm,which has been applied in the automated control field,provides an approach for improving multi-dimensional optimization.The A3C algorithm is introduced and improved for the real-time beam tuning code for accelerators.Experiments in which optimization is achieved by using pointwise scanning,the genetic algorithm(one kind of EAs),and the A3C-algorithm are conducted and compared to optimize the currents of four steering magnets and two solenoids in the low-energy beam transport section(LEBT)of the Xi’an Proton Application Facility.Optimal currents are determined when the highest transmission of a radio frequency quadrupole(RFQ)accelerator downstream of the LEBT is achieved.The optimal work points of the tuned accelerator were obtained with currents of 0 A,0 A,0 A,and 0.1 A,for the four steering magnets,and 107 A and 96 A for the two solenoids.Furthermore,the highest transmission of the RFQ was 91.2%.Meanwhile,the lower time required for the optimization with the A3C algorithm was successfully verified.Optimization with the A3C algorithm consumed 42%and 78%less time than pointwise scanning with random initialization and pre-trained initialization of weights,respectively.

Performance analysis model for real-time Ethernet-based computer numerical control system

In order to optimize the embedded system implementation for Ethernet-based computer numerical control （CNC） system, it is very necessary to establish the performance analysis model and further adopt the codesign method from the control, communication and computing perspectives. On the basis of analyzing real-time Ethemet, system architecture, time characteristic parameters of control-loop ere, a performance analysis model for real-time Ethemet-based CNC system was proposed, which is able to include the timing effects caused by the implementation platform in the simulation. The key for establishing the model is accomplished by designing the error analysis module and the controller nodes. Under the restraint of CPU resource and communication bandwidth, the experiment with a case study was conducted, and the results show that if the deadline miss ratio of data packets is 0.2%, then the percentage error is 1.105%. The proposed model can be used at several stages of CNC system development.

Restoring force correction based on online discrete tangent stiffness estimation method for real-time hybrid simulation

In real-time hybrid simulation（RTHS）, it is difficult if not impossible to completely erase the error in restoring force due to actuator response delay using existing displacement-based compensation methods. This paper proposes a new force correction method based on online discrete tangent stiffness estimation（online DTSE） to provide accurate online estimation of the instantaneous stiffness of the physical substructure. Following the discrete curve parameter recognition theory, the online DTSE method estimates the instantaneous stiffness mainly through adaptively building a fuzzy segment with the latest measurements, constructing several strict bounding lines of the segment and calculating the slope of the strict bounding lines, which significantly improves the calculation efficiency and accuracy for the instantaneous stiffness estimation. The results of both computational simulation and real-time hybrid simulation show that：（1） the online DTSE method has high calculation efficiency, of which the relatively short computation time will not interrupt RTHS; and（2） the online DTSE method provides better estimation for the instantaneous stiffness, compared with other existing estimation methods. Due to the quick and accurate estimation of instantaneous stiffness, the online DTSE method therefore provides a promising technique to correct restoring forces in RTHS.

Real-Time Monitoring of Climactic and Geotechnical Variables during Landslides on the Slopes of Serra do Mar and Serra da Mantiqueira (São Paulo State, Brazil)

The municipalities of Ubatuba, Campos do Jord?o, and S?o José dos Campos are located in the region of S?o Paulo State (Brazil). These municipalities are recognized nationally for having an elevated number of recorded landslides on slopes and embankments. In addition, these municipalities contain multiple areas that are at risk for landslides. Various soil landslides occurred in these municipalities in January 2013, when real-time climactic and geotechnical variables were monitored by automatic rain gauges, humidity sensors and soil temperature and suction devices. The resulting data were used to understand the functions of each variable in the occurrence of land- slides. Analyses of rainfall, humidity and soil temperature were used with field investigations to formulate a hypothesis regarding the predominant rupture mechanism and the role of each monitored variable in the deflagration of the soil landslides that occurred in the three studied municipalities. The geotechnical variable data revealed that both temperature and soil moisture contents played fundamental roles in the deflagration of shallow planar landslides in urban areas. The hourly rain intensity and/or rainfall accumulation for 24 and/or 72 h were responsible for the deflagration of the landslides that occurred in the studied areas, along with the existing anthropic constraints in the risk areas. Significant variations did not occur in the soil suction data during the landslides, principally due to the unsatisfactory sensor precision when reading field suction between –10 and?–100 kPA (±25%).

A real-time data smoothness method based on xPC-Target

A method of real-time data smoothness which is applied in a hardware-in-the-loop （HIL） simulation platform for a plug-in hybrid electric vehicle synthetical power device is described. The input signal of the platform comes from a AC/DC switch power with output containing noises. A linear slide average arithmetic is applied to smooth the noises. To average a certain number input sample signals, this method can decrease the noises voltage level, which meet the requirement of the simulation platform. The efficiency and signal delay time are presented to describe the result of this method, and a statistical index is used to judge the arithmetic＇ s efficiency. The tests results show that the arithmetic fit the requirement of the HIL simulation platform.

Investigate Weld Quality of Gas Metal Arc Welding Process Through Online Monitoring of Input Parameters

Gas metal arc welding(GMAW)is also referred as the metal inert gas(MIG)welding which is a process of welding done by the formation of an electric arc between the consumable wire electrode and the workpiece.Through the welding process,a continuous flow of inert gas is supplied,and it avoids the weld being subjected to react with atmospheric air.The process can be automatic or semi-automatic where the main input parameters like current and the voltage can be direct and constant,respectively.Not only the current and voltage the welding quality depends on some more input parameters such as arc gap,velocity,and temperature.In this paper,we explain about a setup which is capable of real-time monitoring of input parameters mentioned above and selecting the best MIG welding parameters for the mild steel.The setup is composed of several sensors and microcontrollers for the collection and the measurement of the input parameters.The samples were categorized according to the federate and the voltage adjustment of the selected welding machine.Then the final objective was to identify the samples of the weld with different parameter changes which are monitored through the system.For the analysis,the samples were subjected to tensile and hardness tests,and microstructure tests to find the dependence of the input parameters which effect for the weld quality.Finally,the experimental results verified the effectiveness of the system for the selection of the quality weld.

一组加权平均值参数不等式

采用变量替换,构建了一组加权平均值参数不等式,对Popovic不等式与Rado不等式进行了加权推广,加细了加权算术几何调和平均值不等式.

Real-time Operation Optimization in Active Distribution Networks Based on Multi-agent Deep Reinforcement Learning

The increasing integration of intermittent renewable energy sources(RESs)poses great challenges to active distribution networks(ADNs),such as frequent voltage fluctuations.This paper proposes a novel ADN strategy based on multiagent deep reinforcement learning(MADRL),which harnesses the regulating function of switch state transitions for the realtime voltage regulation and loss minimization.After deploying the calculated optimal switch topologies,the distribution network operator will dynamically adjust the distributed energy resources(DERs)to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm.Owing to the model-free characteristics and the generalization of deep reinforcement learning,the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments.Additionally,integrating parameter sharing(PS)and prioritized experience replay(PER)mechanisms substantially improves the strategic performance and scalability.This framework has been tested on modified IEEE 33-bus,IEEE 118-bus,and three-phase unbalanced 123-bus systems.The results demonstrate the significant real-time regulation capabilities of the proposed strategy.

Integrated Analysis of Water Quality in Artificial Fishponds Using WQI and GIS in South-East Sierra Leone

Artificial fishponds play a pivotal role in global aquaculture, serving as a source of livelihood and nourishment for many communities. Ensuring the sustained health and productivity of Fishes in these environments relies heavily on water quality management. This assessment was done to determine the water quality of ten artificial fishponds in the south-eastern part of Sierra Leone using twelve physicochemical factors (pH, BOD, EC, TDS, turbidity, COD, Fe2+, Mg2+, Ca2+, NH3, , and alkalinity) to find out the Water Quality Index (WQI) and spatial distribution of respective parameters. The assessment of artificial fishponds using WQI and Inverse Distant Weighting (IDW) integration represents a relatively underexplored area within the domain of environmental water resources. The WQI was determined using the “Weighted Arithmetic Water Quality Index’’ method. The results of WQI in the study area range from 65.05 to 147.26. Several locations have water quality deemed unsuitable for consumption, while others range from good to very poor. It is essential to address and improve water quality in locations categorized as unsuitable for consumption and very poor to ensure safe and healthy water sources. It was also clear from the calculation that the smaller the mean concentration value of the pH as compared to the ideal value (7), the smaller the WQI value and the better the water quality. To keep the artificial fishpond water in good condition, mass domestic use should be controlled, and draining of surrounding organic matter should be stopped in ponds Bo_001, Kenema_001, and Kenema_002.

Real-time measurement of atmospheric parameters for the 127-element adaptive optics system of 1.8-m telescope

A real-time method for measuring atmospheric parameters based on co-processor field-programmable gate array （FPGA） and main processor digital signal processing （DSP） is proposed for ground-based telescopes with adaptive optics （AO） systems. Coherence length, outer scale, average wind speed, and coherence time are estimated according to closed-loop data on the residual slopes and the corrected voltages of AO systems. This letter introduces the principle and architecture design of the proposed method, which is successfully applied in the 127-element AO system of the 1.8-m telescope of Yunnan Astronomical Observatory. The method enables real-time atmospheric observations with the same object and path of the AO system. This method is also applicable to extended objects.

Optimal parameter estimation of open quantum systems

In quantum information technologies,quantum weak measurement is beneficial for protecting coherence of systems.In order to further improve the protection effect of quantum weak measurement on coherence,we propose an optimization scheme of quantum Fisher information(QFI)protection in an open quantum system by combing no-knowledge quantum feedback control with quantum weak measurement.On the basis of solving the dynamic equations of a stochastic two-level quantum system under feedback control,we compare the effects of different feedback Hamiltonians on QFI and find that via no-knowledge quantum feedback,the observation operatorσx(orσx andσz)can protect QFI for a long time.Namely,no-knowledge quantum feedback can improve the estimation precision of feedback coefficient as well as that of detection coefficient.

An Optimal Inequality for One-Parameter Mean

In the present paper, we answer the question: for 0 what are the greatest value p(a) and the least value q(a) such that the inequality. For more information about abstract,please download the PDF file.

加权幂-算术平均值不等式的参数推广

基于加权平均值不等式,采用求函数极值的方法,建立了一个加权幂-算术平均值不等式的参数推广。

液力变矩器叶栅角度参数优化及算法

为了在提高液力变矩器经济性的同时不降低动力性,提出以各轮进出口角为优化参数的优化策略.提出一种参数网格化,不依赖于惩罚函数而对约束进行处理的新的算法.进行了实例分析,得到较好的结果,证明了该策略以及算法的可靠性.

风电场恒速发电机动态等值参数聚合的研究

风电场发电机数量众多,在研究含有大规模风电机组的电力系统稳定性时,为减少仿真计算量和时间,可采取动态等值的方法对风电场进行简化,为此提出了基于改进加权法的恒速风力发电机参数聚合实用化方法。在MATLAB/SIMULINK工具箱中对一实例系统进行建模和仿真,结果表明了该等值方法的正确性、精确性、简便性和有效性,为大规模并网型风电场的进一步研究提供了有利条件。

BP网络在优化机械加工参数中的应用

机械加工中的误差复映现象使加工参数的选择主要依靠工人的经验,为此,利用BP网的任意非线性映射能力,通过学习人的经验,实现机械加工参数的优化选择。分析了机械加工中误差复映问题的特点,利用改进后的BP网络算法的非线性映射能力,逼近误差复映系数与工件材料硬度、进给量等因素之间的非线性关系,对训练成熟的网络输入加工前毛坯误差、工件材料硬度等,可以输出满足加工要求的加工次数和各次的加工量。通过实例说明了采用附加动量法和自适应学习率改进后的BP算法收敛快,且不易限入局部极小值。在分析误差复映问题模型和比较不同网络结构的训练结果的基础上,确定了BP网络结构。通过在MATLAB中对网络的测试结果,验证了用BP网络实现优化机械加工参数的可行性。

水平裂缝油藏整体压裂改造数值模拟研究

根据低渗透油藏注水开发的生产特点和水平压裂裂缝的渗流特征,考虑到油藏非均质和各向异性、裂缝导流能力随生产时间的失效性以及不同井网类型等因素的影响,建立了三维油水两相的油藏和水平裂缝渗流数学模型,并构造了模型的高效数值算法。在此基础上研制了水平裂缝参数优化软件,通过计算不同裂缝半径和导流能力对油井产量、含水率、采出程度和最终采收率等指标的影响,优化合理的裂缝参数。用于指导现场的评价和设计工作。

模糊桁架结构在模糊激励下的动力响应分析

研究了模糊参数桁架结构在模糊荷载激励下的动力响应分析问题.不仅模糊桁架结构的物理参数、几何尺寸具有模糊性,而且外载荷幅值也具有模糊性时,从Duhamel积分式出发,利用模糊因子法、区间运算和振型叠加法求出了结构动力响应模糊变量的表达式.最后通过算例考察了结构参数和作用荷载的模糊性对结构动力响应的影响,表明所提出的计算模型和分析方法是正确与可行的.