UAV-assisted data collection for wireless sensor networks with dynamic working modes

Wireless Sensor Network(WSN)is a cornerstone of Internet of Things(IoT)and has rich application scenarios.In this work,we consider a heterogeneous WSN whose sensor nodes have a diversity in their Residual Energy(RE).In this work,to protect the sensor nodes with low RE,we investigate dynamic working modes for sensor nodes which are determined by their RE and an introduced energy threshold.Besides,we employ an Unmanned Aerial Vehicle(UAV)to collect the stored data from the heterogeneous WSN.We aim to jointly optimize the cluster head selection,energy threshold and sensor nodes’working mode to minimize the weighted sum of energy con-sumption from the WSN and UAV,subject to the data collection rate constraint.To this end,we propose an efficient search method to search for an optimal energy threshold,and develop a penalty-based successive convex approximation algorithm to select the cluster heads.Then we present a low-complexity iterative approach to solve the joint optimization problem and discuss the implementation procedure.Numerical results justify that our proposed approach is able to reduce the energy consumption of the sensor nodes with low RE significantly and also saves energy for the whole WSN.

借助外力 让感觉升华为思维——基于“Working Model”对2023年新课标卷第6题的拓展分析

“Working Model”是一个物理仿真模拟平台,可直观展示动力学中的物理过程和运动图像,形成真实的感觉。该平台可用于辅助教学,帮助学生通过感觉、知觉、记忆、想象、思维等认知过程来理解和掌握物理概念和规律。以2023年新课标卷第6题为切入点,结合近三年全国高考真题为例,探讨借助外力将感觉升华为思维的可能性,通过对“Working Model”的拓展分析提出一种新的思维模式,旨在帮助学生通过直观刺激和辅助工具将感觉体验转化为深度思考和创新思维。并通过理论分析和实证研究,探讨这种思维模式的实施方法、可行性和效果,为教育教学和认知科学领域的研究提供新的思路和启示。

利用Working Model模拟探究普适性的杠杆平衡条件

学生在学习杠杆平衡条件后,通常会对真实情境中的杠杆平衡条件的普适性产生疑问.通过对Working Model软件的学习,建立杠杆模型、科学推理、提出猜想、设计实验、收集与分析数据等,深度经历科学探究的过程,学生不仅能收获知识和实验探究的能力,还能够促进其质疑创新等核心素养的发展.

ANALYSIS OF WORKING MODES OF HYBRID EXCITATION DOUBLY SALIENT MOTOR

A more accurate analysis method on working modes is proposed by considering the winding terminal voltage and the eondueting power device as state parameters. For the three-phase hybrid excitation doubly salient machine （HEDSM） motor and its three-phase full-bridge inverter, in the proposed analytical method, all possible working modes are generally listed. Then, with the H_PWM-L_ON control strategy, the working modes are detailed with eorresponding equivalent circuits. Experimental results verify the robustness of the analysis.

基于Working Model 2D运动仿真的机构设计

介绍了基于Working Model 2D运动仿真的机构设计。该文利用该软件对四杆机构进行运动仿真和运动分析,结合Excel实现凸轮机构的凸轮轮廓线的设计并作运动仿真。此软件简单易学,仿真过程形象直观,为机构设计提供了一个极好的工具。

基于Working model自卸汽车举升机构的动力学分析与研究

本文运用working model软件,以自卸汽车举升机构为一工程事例,建立其动力学仿真模型,并对此机构进行动力学仿真分析,分析所得结果对机械设计提供了依据。本文所采用的分析方法,具有很强的实践应用性,对设计工作有一定的指导意义。

基于Working model吊车机构的动力学分析与研究

运用Working model软件以吊车机构为工程事例,建立其动力学仿真模型,并对此机构进行动力学仿真分析,所得结果对机械设计提供了依据。

Working Model在高中物理教学中的应用——以力学模型为例

基于Working Model的仿真实验功能,结合几个常见的力学运动模型展示Working Model在中学物理教学中的用途.利用Working Model将运动过程直观呈现,同时以数值、矢量、动态图像等输出形式将位移、速度和加速度等物理量同步反馈,以此进一步探究物理规律.

Working model运动仿真在物理教学中的应用

简单介绍Working model软件的功能以及在物理教学中的应用,它既可以进行运动仿真,同时也可以对物体的运动进行分析。本文结合一些简单的实例做出说明。

Trajectory tracking guidance of interceptor via prescribed performance integral sliding mode with neural network disturbance observer

This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots.

A forecasting and forewarning model for methane hazard in working face of coal mine based on LS-SVM

To improve the precision and reliability in predicting methane hazard in working face of coal mine, we have proposed a forecasting and forewarning model for methane hazard based on the least square support vector (LS-SVM) multi-classifier and regression machine. For the forecasting model, the methane concentration can be considered as a nonlinear time series and the time series analysis method is adopted to predict the change in methane concentration using LS-SVM regression. For the forewarning model, which is based on the forecasting results, by the multi-classification method of LS-SVM, the methane hazard was identified to four grades: normal, attention, warning and danger. According to the forewarning results, corresponding measures are taken. The model was used to forecast and forewarn the K9 working face. The results obtained by LS-SVM regression show that the forecast- ing have a high precision and forewarning results based on a LS-SVM multi-classifier are credible. Therefore, it is an effective model building method for continuous prediction of methane concentration and hazard forewarning in working face.

An implementation of cellular automaton model for single-line train working diagram

According to the railway transportation system＇s characteristics, a new cellular automaton model for the single- line railway system is presented in this paper. Based on this model, several simulations were done to imitate the train operation under three working diagrams. From a different angle the results show how the organization of train operation impacts on the railway carrying capacity. By using the non-parallel train working diagram the influence of fast-train on slow-train is found to be the strongest. Many slow-trains have to wait in-between neighbouring stations to let the fast-train（s） pass through first. So the slow-train will advance like a wave propagating from the departure station to the arrival station. This also resembles the situation of a highway jammed traffic flow. Furthermore, the nonuniformity of travel times between the sections also greatly limits the railway carrying capacity. After converting the nonuniform sections into the sections with uniform travel times while the total travel time is kept unchanged, all three carrying capacities are improved greatly as shown by simulation. It also shows that the cellular automaton model is an effective and feasible way to investigate the railway transportation system.

Vapor-Liquid Equilibrium Prediction of Ammonia-Ionic Liquid Working Pairs of Absorption Cycle Using UNIFAC Model

On the basis of reported experimental vapor-liquid equilibrium （VLE） data of NH3-1-ethyl-3-methylimidazolium acetate （NH3-[Emim]Ac）, NH3-1-butyl-3-methylimidazolium tetrafluoroborate （NH3-[Bmim][BF4]）, NH3-1,3-dimethylimidazolium dimethyl phosphate （NH3-[Mmim]DMP） and NH3-1-ethyl-3-methylimidazolium ethylsulfate （NH3-[Emim]EtOSO3） binary systems, the interaction parameters of 14 new groups have been regressed by means of the UNIFAC model. To validate the reliability of the method, these parameters have been used to calculate the VLE data with the average relative deviation of pressures of less than 9.35%. The infinite dilution activity coefficient （ γ1∞ ） and the absorption potential （ φ1 ） are important evaluation criterions of the affinity between working pair species of the absorption cycle. The UNIFAC model is implemented to predict the values of and φ1 of t6 sets of NH3-ionic liquid （1L） systems. The work found that the φ1 gradually increases following the impact order： φ1（[Cnmim][BF4]）〈φ1（[Cnmim]EtOSO3）〈φ1（[Cnmim]DMP）〈φ1（[Cnmim]Ac） （n= 1, 2, 3, … ） at a given cation of IL species and constant temperature, and φ1（[Mmim]X）〈φ1（[Emim]X）〈φ1（[Pmim]X）〈 φ1（[Bmim]X）（X= Ac, [BF4], DMP or EtOSO3） at a given anion of IL species and constant temperature. Furthermore, the φ1 gradually increases with increasing temperature. Then, it could be concluded that the working pair NH3-[BmimlAc has the best potential research value relatively.

Effect of working parameters on performance characteristics of hydrostatic turntable by using FSI-thermal model

Effects of working parameters on performance characteristics of hydrostatic turntable are researched by applying the fluid-structure-thermal coupled model.Fluid-structure interaction(FSI)technique and computational fluid dynamics(CFD)method are both employed by this new model,and thermal effects are also considered.Hydrostatic turntable systems with a series of oil supply pressures,various oil recess depth and several surface roughness parameters are studied.Performance parameters,such as turntable displacement,system flow rate,temperature rise of lubrication,stiffness and damping coefficients,are derived from different working parameters(rotational speed of turntable and exerted external load)of the hydrostatic turntable.Numerical results obtained from this FSI-thermal model are presented and discussed,and theoretical predictions are in good agreement with the experimental data.Therefore,this developed model is a very useful tool for studying hydrostatic turntables.The calculation results show that in order to obtain better performance,a rational selection of the design parameters is essential.

Design and optimization of fluid lubricated bearings operated with extreme working performances——a comprehensive review

Fluid lubricated bearings have been widely adopted as support components for high-end equipment in metrology,semiconductor devices,aviation,strategic defense,ultraprecision manufacturing,medical treatment,and power generation.In all these applications,the equipment must deliver extreme working performances such as ultraprecise movement,ultrahigh rotation speed,ultraheavy bearing loads,ultrahigh environmental temperatures,strong radiation resistance,and high vacuum operation,which have challenged the design and optimization of reliable fluid lubricated bearings.Breakthrough of any related bottlenecks will promote the development course of high-end equipment.To promote the advancement of high-end equipment,this paper reviews the design and optimization of fluid lubricated bearings operated at typical extreme working performances,targeting the realization of extreme working performances,current challenges and solutions,underlying deficiencies,and promising developmental directions.This paper can guide the selection of suitable fluid lubricated bearings and optimize their structures to meet their required working performances.

Electroencephalogram Signal Correlations between Default Mode Network and Attentional Functioning

Attentional issues may affect acquiring new information, task performance, and learning. Cortical network activities change during different functional brain states, including the default mode network (DMN) and attention network. We investigated the neural mechanisms underlying attentional functions and correlations between DMN connectivity and attentional function using the Trail-Making Test (TMT)-A and -B. Electroencephalography recordings were performed by placing 19 scalp electrodes per the 10 - 20 system. The mean power level was calculated for each rest and task condition. Non-parametric Spearman’s rank correlation was used to examine the correlation in power levels between the rest and TMT conditions. The most significant correlations during TMT-A were observed in the high gamma wave, followed by theta and beta waves, indicating that most correlations were in the parietal lobe, followed by the frontal, central, and temporal lobes. The most significant correlations during TMT-B were observed in the beta wave, followed by the high and low gamma waves, indicating that most correlations were in the temporal lobe, followed by the parietal, frontal, and central lobes. Frontoparietal beta and gamma waves in the DMN may represent attentional functions.

Relationship between physical activity and specific working memory indicators of depressive symptoms in university students

BACKGROUND The detection rate of depression among university students has been increasing in recent years,becoming one of the main psychological diseases that endangers their physical and mental health.According to statistics,self-harm and suicide,for which there is no effective intervention,are the second leading causes of death.AIM To explore the relationship between different elements and levels of physical activity and college students’depression-symptom-specific working memory indicators.METHODS Of 143 college students were analyzed using the Beck Depression Self-Rating Scale,the Physical Activity Rating Scale,and the Working Memory Task.RESULTS There was a significant difference between college students with depressive symptoms and healthy college students in completing verbal and spatial working memory(SWM)tasks correctly(all P<0.01).Physical Activity Scale-3 scores were significantly and positively correlated with the correct rate of the verbal working memory task(r=0.166)and the correct rate of the SWM task(r=0.210)(all P<0.05).There were significant differences in the correct rates of verbal and SWM tasks according to different exercise intensities(all P<0.05)and different exercise durations(all P<0.05),and no significant differences in the correct rates of verbal and SWM tasks by exercise frequency(all P>0.05).CONCLUSION An increase in physical exercise among college students,particularly medium-and high-intensity exercise and exercise of 30 min or more,can improve the correct rate of completing working memory tasks.

Sound Quality Prediction of Vehicle Interior Noise under Multiple Working Conditions Using Back-Propagation Neural Network Model

This paper presents a back-propagation neural network model for sound quality prediction (BPNN-SQP) of multiple working conditions’ vehicle interior noise. According to the standards and regulations, four kinds of vehicle interior noises under operating conditions, including idle, constant speed, accelerating and braking, are acquired. The objective psychoacoustic parameters and subjective annoyance results are respectively used as the input and output of the BPNN-SQP model. With correlation analysis and significance test, some psychoacoustic parameters, such as loudness, A-weighted sound pressure level, roughness, articulation index and sharpness, are selected for modeling. The annoyance values of unknown noise samples estimated by the BPNN-SQP model are highly correlated with the subjective annoyances. Conclusion can be drawn that the proposed BPNN-SQP model has good generalization ability and can be applied in sound quality prediction of vehicle interior noise under multiple working conditions.

A Review on Technologies for the Use of CO2 as a Working Fluid in Refrigeration and Power Cycles

The use of carbon dioxide as a working fluid has been the subject of extensive studies in recent years, particularly in the field of refrigeration where it is at the heart of research to replace CFC and HCFC. Its thermodynamic properties make it a fluid of choice in the efficient use of energy at low and medium temperatures in engine cycles. However, the performance of transcritical CO2 cycles weakens under high temperature and pressure conditions, especially in refrigeration systems;On the other hand, this disadvantage becomes rather interesting in engine cycles where CO2 can be used as an alternative to the organic working fluid in small and medium-sized electrical systems for low quality or waste heat sources. In order to improve the performance of systems operating with CO2 in the field of refrigeration and electricity production, research has made it possible to develop several concepts, of which this article deals with a review of the state of the art, followed by analyzes in-depth and critical of the various developments to the most recent modifications in these fields. Detailed discussions on the performance and technical characteristics of the different evolutions are also highlighted as well as the factors affecting the overall performance of the systems studied. Finally, perspectives on the future development of the use of CO2 in these different cycles are presented.

Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.