Electrochemical impedance spectroscopy analysis of V–I characteristics and a fast prediction model for PEM-based electrolytic air dehumidification

PEM-based electrolytic air dehumidification is innovative due to its high efficiency, compact size and cleanness. However, high polarization loss and severe performance degradation have been observed, especially at high applied voltages (>2.5 V). Understanding the V–I characteristics is critical to performance optimization. This study experimentally investigated the V–I characteristics and internal response of materials under various operating conditions, with in-situ Electrochemical Impedance Spectroscopy (EIS) methods. Real-time mass transfer, electrochemical polarization and reaction dynamics of PEM components during dehumidification were derived by EIS. Then, a fast prediction model was built to directly predict the dehumidification rate and attenuation without any iteration, suitable for online monitoring and adjustment. Compared to other models, this model can take a quick understanding of the impact of operating conditions on the material characteristics inside the PEM element. The deviations of current density, PEM proton conductivity and moisture removal were 3%, 11.2% and 15.3%, respectively, compared to experiment data. Results showed that when the applied voltage changed from 1.5 to 3.5 V, the high-frequency resistance of the PEM element increased from 1.69 to 2.69 Ω, and the PEM proton conductivity decreased by about 38 times. The sharp drop in PEM proton conductivity resulted in a current attenuation. With this model, requirements for key components of PEM dehumidification were also obtained. Analysis of the overpotential distribution showed that increasing the water retention and reducing the dependence of proton conductivity on water molecules of the PEM can effectively improve the performance. This research provides guidance for the performance optimization and material selection of PEM-based dehumidification.     

This month ‘Technik ist alles’

The objective of this prospective cohort study was to evaluate if peak or cumulative musculoskeletal discomfort may predict future low-back, neck or shoulder pain among symptom-free workers. At baseline, discomfort per body region was rated on a 10-point scale six times during a working day. Questionnaires on pain were sent out three times during follow-up. Peak discomfort was defined as a discomfort level of 2 at least once during a day; cumulative discomfort was defined as the sum of discomfort during the day. Reference workers reported a rating of zero at each measurement. Peak discomfort was a predictor of low-back pain (relative risk (RR) 1.79), neck pain (RR 2.56), right or left shoulder pain (RR 1.91 and 1.90). Cumulative discomfort predicted neck pain (RR 2.35), right or left shoulder pain (RR 2.45 and 1.64). These results suggest that both peak and cumulative discomfort could predict future musculoskeletal pain.     

Developing multiple regression models from the manufacturer's ground-source heat pump catalogue data

The performance of ground-source heat pumps (GSHP), often expressed as Power drawn and/or the COP, depends on several operating parameters. Manufacturers usually publish such data in tables for certain discrete values of the operating fluid temperatures and flow rates conditions. In actual applications, such as in dynamic simulations of heat pump system integrated to buildings, there is a need to determine equipment performance under operating conditions other than those listed. This paper describes a simplified methodology for predicting the performance of GSHPs using multiple regression (MR) models as applicable to manufacturer data. We find that fitting second-order MR models with eight statistically significant x-variables from 36 observations appropriately selected in the manufacturer catalogue can predict the system global behavior with good accuracy. For the three studied GSHPs, the external prediction error of the MR models identified following the methodology are 0.2%, 0.9% and 1% for heating capacity (HC) predictions and 2.6%, 4.9% and 3.2% for COP predictions. No correlation is found between residuals and the response, thus validating the models. The operational approach appears to be a reliable tool to be integrated in dynamic simulation codes, as the method is applicable to any GSHP catalogue data.     

基于灰色关联故障树的运营期隧道病害风险研究

隧道运营期的管理决定了铁路运行的安全,为保证运营安全,必须对隧道运营期病害的风险进行管理研究。用故障树方法对隧道病害进行分析,找出影响隧道运营安全作为底事件,分析出故障树系统任意一个底事件相关割集的重要度。利用灰色关联的思想得出任意最小割集的关联度,再依据关联度得出需要重视的底事件(既有排水设施年久失修阻塞,材料强度下降,隧道内照明不良),并提出具有针对性的管理改进建议。     

微震三维建模动态分析在冲击地压预测预报中的应用

在采掘工程平面图的基础上建立微震三维模型,从不同角度观察微震监测事件空间分布规律,达到预测工作面推进前方顶底板活动规律的目的,文章以3206工作面、W121集轨等区域微震三维建模动态分析为实例,说明微震三维动态分析在冲击地压预测预报中的应用。     

Development of PCA-based cluster quantile regression (PCA-CQR) framework for streamflow prediction: Application to the Xiangxi river watershed,China

In this study, a PCA-based cluster quantile regression (PCA-CQR) method was proposed through integrating principal component analysis and quantile regression approaches into a stepwise cluster analysis framework. In detail, the principal component analysis was adopted to overcome the multicollinearity among the explanatory variables, while the quantile regression approach was used to provide probabilistic information in prediction. The proposed PCA-CQR method can effectively capture discrete and nonlinear relationships between explanatory and response variables. The applicability of PCA-CQR was demonstrated by a case study of monthly streamflow prediction in the Xiangxi River, China. The nonlinearity between the hydro-meteorological variables and the streamflow measurements was characterized through the measure of maximal information coefficient (MIC), which demonstrated the need of the proposed PCA-CQR method. The results showed that the previous monthly streamflow and precipitation, as well as potential evapotranspiration in current month posed significant nonlinear impacts on the streamflow in current month. Three components could well reflect the total variance of the input variables. Comparison between traditional SCA and PCA-CQR showed that the proposed approach could provide more accurate predictions than traditional SCA methods. Moreover, probabilistic forecasts could be provided by PCA-CQR, and the 90% predictive intervals could well bracket the observations in both calibration and validation periods. Also, sensitivity analysis was performed to identify the impacts of the control parameters in PCA-CQR on the performance of the proposed model. The results showed the proposed PCA-CQR improved the robustness of traditional SCA. Finally, comparison among PCA-CQR, GRNN and MLR also showed the effectiveness of the proposed method.     

GMDH网络在进出口贸易预测中的应用

随着世界经济一体化的加快，对外进出口贸易在一国经济中发挥越来越重要的作用。进出口贸易影响因素较多，波动性大，一般方法难以准确预测。针对该问题，采用基于PSO优化的GMDH网络预测出口贸易总额，该方法首先根据输入数据特点自适应构造GMDH网络，然后用PSO算法优化GMDH网络权值，最终得到较为精确的预测器。通过仿真结果表明，该方法能够较为准确的预测中国年度进出口贸易总额。     

A two level real-time path planning method inspired by cognitive map and predictive optimization in human brain

A biologically inspired two level method is proposed for real-time path planning in a complex and dynamic environment, employable in ground vehicles. This method takes the advantage of both global and local path finding procedures. In the first level, i.e., global level, the planner utilizes a neural network architecture as a sensory-motor map, similar to the cognitive map used by humans, and an optimization algorithm to produce a coarse path. In the second level, i.e., local level, the global path is improved by employing a model-based prediction method with a finite prediction horizon in a way that future information about the environment is involved in the planner's decision making. In the suggested method, the prediction horizon is variable and is adjusted in each step of the planning in agreement with the kinematic features of the closest obstacle in the visual field of the planner. We considered four different path planning tasks in a virtual dynamic environment to evaluate the performance of the proposed method against the human path planning strategy. The results demonstrate the ability of the method to plan a strategy comparable to the driving scenarios chosen by most subjects and to generate a real-time collision-free path in a dynamic environment with obstacles.