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.     

Robustness of strong stability of discrete semigroups

In this paper we study the robustness of strong stability of a discrete semigroup on a Hilbert space under bounded perturbations. As the main result we present classes of perturbations preserving the strong stability of the semigroup.     

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.     

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。     

基于模糊PID算法的挤出机机筒温度控制系统设计

针对传统塑料挤出机机筒温度控制系统存在的收敛时间长、超调量大、温度控制精度低的现象,对塑料挤出机机筒的温度控制采取模糊PID控制算法,可以使系统既具有PID控制的可靠性和快速性,又具有模糊控制的高鲁棒性和强适应性,并能消除传统温控系统中普遍存在的温度控制不确定时滞,从而确保系统具有良好的动、静态特性,保证精确控温。开发了基于西门子S7-300 PLC平台的模糊PID温度控制系统,经过现场测试,稳定时间较开关控制法缩短30%左右,超调量确保在2℃以内,达到良好的控制效果。     

Robust non-fragile power system stabilizer

This paper presents a step towards the design of robust non-fragile power system stabilizers (PSSs) for single-machine infinite-bus systems. To ensure resiliency of a robust PSS, the proposed approach presents a characterization of all stabilizers that can guarantee robust stability (RS) over wide range of operating conditions. A three-term controller (x₁ + x₂s)/(1 + x₃s) is considered to accomplish the design. Necessary and sufficient stability constraints for existing of such controller at certain operating point are derived via Routh–Hurwitz criterion. Continuous variation in the operating point is tackled by an interval plant model where RS problem is reduced to simultaneous stabilization of finite number of plants according to Kharitonov theorem. Controller triplets that can robustly stabilize vertex plants are characterized in a similar manner. The most resilient controller is computed at the center of maximum-area inscribed rectangle. Simulation results confirm robustness and resiliency of the proposed stabilizer.     

局部纠缠交换对量子随机网络鲁棒性的影响

利用纠缠渗流的方法，在量子随机网络中任意两个节点间建立远距离纠缠态，同时还进行了局部纠缠交换的操作。通过此操作改变了网络中局部节点间的连接情况，也使整个网络的拓扑结构发生变化。重点探讨了局部操作对量子随机网络鲁棒性的影响，这种影响通过纠缠渗流的阈值变化情况来体现。通过理论分析和数值模拟，发现有操作时度大的节点数目明显增多，遭受随机攻击后的阈值变化也较小。这说明进行局部纠缠交换操作能够大幅度提高量子网络受到攻击后的连接鲁棒性。     

Risk-aware intermediate dataset backup strategy in cloud-based data intensive workflows

Data-intensive workflows are generally computing- and data-intensive with large volume of data generated during their execution. Therefore, some of the data should be saved to avoid the expensive re-execution of tasks in case of exceptions. However, cloud-based data storage services come at some expense. In this paper, we introduce the risk evaluation model tailored for workflow structure to measure and achieve the trade-off between the overhead of backup storage and the cost of data regeneration in failure, making the service selection and execution more efficient and robust. The proposed method computes and compares the potential loss with and without data backup to achieve the trade-off between overhead of intermediate dataset backup and task re-execution after exceptions. We also design the utility function with the model and apply a genetic algorithm to find the optimized schedule. The results show that the robustness of the schedule is increased while the possible risk of failure is minimized, especially when the volume of generated data is not large in comparison with the input.