An analysis of adaptive planning capacity: The case of chilean water utilities

New long-term planning approaches capable of coping with uncertainties such as climate change, rapid urbanization, and changing societal values, have been put forward as a way of producing more robust and sustainable plans for the future. But is the planning practice ready for their adoption? This paper takes four key propositions from the adaptive planning literature and tests the existing capacity for adopting those propositions in the context of Chilean water utilities. We will then propose how existing capacities could be enhanced, and propose alternatives for current planning practices, highlighting the importance of implementation through experimentation.     

A novel long short-term memory networks-based data-driven prognostic strategy for proton exchange membrane fuel cells

Proton exchange membrane fuel cell (PEMFC) long-term prognostic facilitates reducing the time/cost of the durability tests and is a critical starting point for control/maintenance suggestions. Long short-term memory (LSTM) recurrent neural networks have excellent time series processing capabilities and are proved to be useful for the short-term prognostic of PEMFC. However, LSTM prognostic models usually suffer from accumulated errors and model recognition uncertainties, which make it difficult to break the historical degradation data limitations, resulting in unsatisfactory long-term prediction performance. To tackle the problem, this paper proposes a novel model named navigation sequence driven LSTM (NSD-LSTM) for long-term prognostic. In the strategy, a navigation sequence is firstly generated by using an autoregressive integrated moving average model with exogenous variables. The sequence is then fed iteratively into LSTM in the implementation stage to achieve long-term perdition. The proposed strategy is evaluated using the aging experimental data of two types of PEMFC under different operating conditions. The long-term prognostic performance of the proposed model and other two state-of-the-art prognostic models, namely, nonlinear autoregressive exogenous and echo state network, are evaluated through comparison experiments. The simulation and experimental results show that the proposed prognostic strategy has better long-term degradation trend prediction consistency and remaining useful life estimation robustness.     

Robustness of hollow clay masonry units and seismic behaviour of masonry walls

As the recent experimental investigations indicated, the robustness of masonry units is one among the most decisive parameters which define the behaviour of masonry walls when subjected to seismic loads. If local brittle failure of units occurs, the mechanism of behaviour and known relationships between the strength and ductility properties of masonry walls change. In such a case, the design values of load bearing and energy dissipation capacity of masonry walls are overestimated, although the equations and requirements specified in the codes have been taken into consideration. If brittle units are used for the construction of structural walls, the actual structure will be exposed to an increased level of seismic risk although it has been designed for earthquake loads according to codes. This is especially the case of reinforced masonry, where brittle local failure prevents the transfer of predicted forces from reinforcing steel to masonry units. In order to provide limitations for the use of hollow units with large hole volume ratio and thin shells and webs in seismic areas, a measure to define the qualitative term “sufficient robustness”, specified in Eurocode 8, should be found.     

Resilience analysis of shield tunnel lining under extreme surcharge: Characterization and field application

A resilient system should have the ability to mitigate the disruption caused by unfavorable environment and to rapidly recover to an acceptable performance level. In this paper, a detailed model to assess resilience of shield tunnel is presented. The performance robustness under disruption and the subsequent recovery rapidity are emphasized in this model. The tunnel horizontal convergence is selected as the performance indicator. The resilience index (Re) is defined by the ratio of the integral of the performance transition function over the integral of the normal performance function. The rationality and applicability of the model is validated by a real case of extreme surcharge on Shanghai metro tunnel. In this case, the performance transition and the normal performance degradation are characterized by the measured data. 70–80% of the normal performance is disrupted due to the surcharge, but only 1% is recovered by unloading of the surcharge in 9 days and 12.4% is recovered after 4 years by the soil grouting in 38 days. It results in a resilience index (Re) between 0.28 and 0.45. The lesson learned from the case indicates that the high vulnerability of lining convergence due to the severe surcharge and the long time duration between recovery measures could result in weak resilient abilities for shield tunnels. The value of resilience index Re could be significantly increased by 73% on average if the recovery duration were shortened.     

时变滑模变结构与智能模糊结合的非线性系统控制

时变滑模变结构与智能模糊结合的非线性系统控制,先采用时变滑模变结构控制,通过设计动态滑模面,系统的任意初始状态一开始就处于系统的滑模面上,使系统对内部参数变化和外部干扰均具有全局鲁棒性.然后设计智能模糊控制器,使整个系统的鲁棒性在线调整,不仅增强了全局抗干扰能力,而且有效消除系统的抖振现象.     

基于RBF最小参数学习法的正流量变量泵滑模自适应控制

为了提高正流量变量泵的性能,提出基于RBF最小参数学习法的正流量变量泵滑模自适应控制方法。分析正流量变量泵电液伺服系统的动力学特性,并进行系统辨识实验获得较为精确的系统数学函数模型;基于RBF最小参数学习法设计滑模控制器,在系统参数不确定性、摩擦力干扰和系统泄漏等非线性因素的情况下实现对目标流量的跟踪响应和自适应控制;最后利用MATLAB/Simulink对正流量变量泵的控制系统性能进行仿真实验,并和传统的PID控制器和模糊PID控制器进行比较。仿真实验结果验证了所设计控制方法的可行性和有效性。     

Battery state of health monitoring by estimation of the number of cyclable Li-ions

This paper introduces a method to monitor battery state of health (SOH) by estimating the number of cyclable Li-ions, a health-relevant electrochemical variable. SOH monitoring is critical to battery management in balancing the trade-off between maximizing system performance and minimizing battery degradation. The decrease of cyclable Li-ions indicates the effect on the SOH of degradation mechanisms that consume cyclable Li-ions. The unavailability of the number of cyclable Li-ions through non-invasive measurements makes its estimation necessary for in-situ SOH monitoring. In this paper, the extended Kalman filter (EKF) is used to estimate the number of cyclable Li-ions as an unknown battery parameter. The single particle model (SPM), a simplified battery electrochemical model, is used as the model in the EKF to achieve a computational complexity suitable for on-line estimation. Simulations are performed under typical electric vehicle current trajectories using an example parameter set for a hybrid-electric-vehicle battery. In the simulations, the battery is represented by the Doyle–Fuller–Newman (DFN) model, an electrochemical model with higher fidelity than the SPM. To comply with the practice, instead of using the same parameters as the DFN model in the SPM, parameterization of the SPM is performed before estimation of the number of cyclable Li-ions. The simulations show high estimation accuracy of the number of cyclable Li-ions using the EKF, even with the structural and parametric differences between the DFN model and the SPM, under both the ideal conditions and various non-ideal conditions (i.e., SOC estimation error, additional modeling error, and measurement noise).     

Structural robustness of city road networks based on community

Road network robustness is the ability of a road network to operate correctly under a wide range of attacks. A structural robustness analysis can describe the survivability of a city road network that is under attack and can help improve functions such as urban planning and emergency response. In this paper, a novel approach is presented to quantitatively evaluate road network robustness based on the community structure derived from a city road network, in which communities refer to those densely connected subsets of nodes that are sparsely linked to the remaining network. First, a road network is reconstructed into a set of connected communities. Then, successive simulated attacks are conducted on the reconstructed road networks to test the performance of the networks under attack. The performance of the networks is represented by efficiency and the occurrence of fragmentation. Three attack strategies, including a random attack and two intentional attacks, are performed to evaluate the survivability of the road network under different situations. Contrary to the traditional road segment-based approach, the community-based robustness analysis on a city road network shows distinct structural diversity between communities, providing greater insight into network vulnerability under intentional attacks. Six typical city road networks on three different continents are used to demonstrate the proposed approach. The evaluation results reveal an important feature of the structure of city road networks from a community-based perspective, i.e., that the structure is robust under random failure but fragile under intentional attack. This result is highly consistent in different city road network forms.     

Note on a Method for Calculating Corrected Sums of Squares and Products

Two models of non-random initial misclassifications are studied. In these models, observations which are closer to the mean of the “wrong” population have a greater chance of being misclassified than others. Sampling studies show that (a) the actual error rates of the rules from samples with initial misclassification are only slightly affected; (b) the apparent error rates, obtained by resubstituting the observations into the calculated discriminant function, are drastically affected, and cannot be used; and (c) the Mahalanobis D ² is greatly inflated.