MDA-TOEPGA: A novel method to identify miRNA-disease association based on two-objective evolutionary programming genetic algorithm

The association between miRNA and disease has attracted more and more attention. Until now, existing methods for identifying miRNA related disease mainly rely on top-ranked association model, which may not provide a full landscape of association between miRNA and disease. Hence there is strong need of new computational method to identify the associations from miRNA group view. In this paper, we proposed a framework, MDA-TOEPGA, to identify miRNAdisease association based on two-objective evolutionary programming genetic algorithm, which identifies latent miRNAdisease associations from the view of functional module. To understand the miRNA functional module in diseases, the case study is presented. We have been compared MDA-TOEPGA with several state-of-the-art functional module algorithm. Experimental results showed that our method cannot only outperform classical algorithms, such as K-means, IK-means, MCODE, HC-PIN, and ClusterONE, but can also achieve an ideal overall performance in terms of a composite score consisting of f1, Sensitivity, and Accuracy. Altogether, our study showed that MDA-TOEPGA is a promising method to investigate miRNA-disease association from the landscapes of functional module.     

Methodology for robust multi-parametric control in linear continuous-time systems

This paper presents an extension of the recent multi-parametric (mp-)NCO-tracking methodology by Sun et al. [Comput. Chem. Eng. 92 (2016) 64–77] for the design of robust multi-parametric controllers for constrained continuous-time linear systems in the presence of uncertainty. We propose a robust-counterpart formulation and solution of multi-parametric dynamic optimization (mp-DO), whereby the constraints are backed-off based on a worst-case propagation of the uncertainty using either interval analysis or ellipsoidal calculus and an ancillary linear state feedback. We address the case of additive uncertainty, and we discuss approaches to dealing with multiplicative uncertainty that retain tractability of the mp-NCO-tracking design problem, subject to extra conservativeness. In order to assist with the implementation of these controllers, we also investigate the use of data classifiers based on deep learning for approximating the critical regions in continuous-time mp-DO problems, and subsequently searching for a critical region during on-line execution. We illustrate these developments with the case studies of a fluid catalytic cracking (FCC) unit and a chemical reactor cascade.     

考虑关节角加速度约束的仿人机器人偏摆力矩控制方法

针对仿人机器人步行过程中存在的机器人关节角加速度约束影响控制性能的问题，提出一种考虑关节角加速度约束的仿人机器人偏摆力矩控制方法.该方法充分考虑了双臂在摆动过程中对偏摆力矩的影响，根据力矩平衡条件得到需要抵消的偏摆力矩的大小与方向，将偏摆力矩的控制问题转化为带约束条件的二次规划问题，并设计了一种在线变步长迭代算法计算得到优化后的双臂摆动轨迹.实验表明，该方法能有效抵消机器人步行中产生的偏摆力矩，避免控制过程中的"削峰"现象，有效提高机器人的步行稳定性.     

Robust model for optimization of forming process for metallic bipolar plates of cleaner energy production system

Energy production systems such as proton-exchange membrane fuel cell (PEMFC) has a promising future in the cleaner energy market due to zero emissions. Rubber pad forming (RPF) process of metallic bipolar plates of PEMFCs is gaining attention among the researchers. Studies based on design of experiments have been conducted to find the crucial parameters of the forming process. These methods are based on the assumptions of the model structure, correlated residuals, etc., which can cause uncertainty in estimation ability of the model on unseen data. Therefore, the present study focuses on the design of robust models of these parameters for PEMFCs using an optimization approach of genetic programming (GP). The inputs from the experiments considered in GP are radius, the friction coefficient, the filling factor and the minimum thickness. Experiments on PEMFCs validates the performance of the GP models. Further, the relationships between the two inputs and the three outputs for PEMFCs are generated as well as the contributions of each input to each of the output. Optimization of the models generated by GP can further determine the forming quality of metallic bipolar plates of PEMFCs by an appropriate setting of the two inputs.     

Experimental and optimization of material synthesis process parameters for improving capacity of lithium‐ion battery

New methods for synthesis of active materials have been developed to improve capacity and cycle life performance of lithium‐ion batteries. Past studies have focused on routes of development of materials and new processes, which might not be economical for large‐scale production. In this regard, this study examines a widely employed carbothermal reduction technology for the synthesis of lithium‐iron phosphate (LiFePO₄/C) and investigates effects of process conditions during this synthesis on final battery performance. An experimental combined genetic programming approach is used to model the effects of crucial process conditions (sintering time, the carbon content, and the sintering temperature) on the discharge capacity of the assembled battery. Experiments are conducted to collect the discharge capacity data based on varying LiFePO₄/C synthesis conditions, and genetic programming is employed to develop a suitable functional relationship between them. The results show that the battery discharge capacity is controlled significantly by adjusting sintering temperature and carbon content, while the effect of sintering time is found to be insignificant. Further, the interaction effect of the sintering time and carbon content is much more obvious than that of the sintering time and the sintering temperature. The findings from the study pave the way for the optimum design of the synthesis process of LiFePO₄/C for a higher battery performance.     

Shale gas pad development planning under price uncertainty

In this article, we study shale gas pad development under natural gas price uncertainty. We optimize the sequence of operations, gas curtailment, and storage on a single pad to maximize the net present value. The optimization problem is formulated as an mixed-integer linear programming model, which is similar to the one proposed by Ondeck et al. We investigate how natural gas price uncertainty affects the operation strategy in the pad development. Both two-stage and multistage stochastic programming are used as the mathematical framework to hedge against uncertainty. Our case study shows that there is value of using stochastic programming when the price variance is high. However, when the variance of the price is low, solving the stochastic programming problems does not create additional value compared with solving the deterministic problem.     

基于四端E-SOP的有源配电网运行效率优化

柔性软开关SOPs(soft open points)通过准确控制换流器有功功率交换,提供了现有配电自动化体系所不具备的实时精细潮流调节优化能力。为了解决SOP对有源配电网运行效率的优化成效问题,本文首先建立了带有储能装置的四端SOP即E-SOP(SOP with energy storage)的接入拓扑和数学模型,考虑了四端E-SOP换流器和直流侧储能装置的运行约束。其次,建立了基于四端E-SOP的有源配电网运行效率优化模型。然后,利用半定规划方法对模型进行了线性化求解。最后,通过算例验证了所提方法的有效性和实用性。     

Design and analysis of an aging‐aware energy management system for islanded grids using mixed‐integer quadratic programming

The rapid increase of renewable energy sources made coordinated control of the distributed and intermittent generation units a more demanded task. Matching demand and supply is particularly challenging in islanded microgrids. In this study, we have demonstrated a mixed‐integer quadratic programming (MIQP) method to achieve efficient use of sources within an islanded microgrid. A unique objective function involving fuel consumption of diesel generator, degradation in a lithium‐ion battery energy storage system, carbon emissions, load shifting, and curtailment of the renewable sources is constructed, and an optimal operating point is pursued using the MIQP approach. A systematic and extensive methodology for building the objective function is given in a sequential and explicit manner with an emphasis on a novel model‐based battery aging formulation. Performance of the designed system and a sensitivity analysis of resulting battery dispatch, diesel generator usage, and storage aging against a range of optimization parameters are presented by considering real‐world specifications of the Semakau Island, an island in the vicinity of Singapore.     

Development of new energy management strategy for a household fuel cell/battery hybrid system

This work aims to construct an efficient and robust fuel cell/battery hybrid operating system for a household application. The ability to dispatch the power demands, sustain the state of charge (SOC) of battery, optimize the power consumption, and more importantly, ensure the durability as well as extend the lifetime of a fuel cell system is the basic requirements of the hybrid operating system. New power management strategy based on fuzzy logical combined state machine control is developed, and its effectiveness is compared with various strategies such as dynamic programming (DP), state machine control, and fuzzy logical control with simulation. Experimental results are also presented, except for DP because of difficulties in achieving real‐time implementation and much faster response to load variation. The given current from the energy management system (EMS) as a reference of the fuel cell output current is determined by filtering out various harmful signals. The new power management strategy is applied to a 1‐kW stationary fuel cell/battery hybrid system. Results show that the fuel cell hybrid system can run much smoothly with prolonged lifetime.     

双层过道布置问题的混合整数非线性规划模型及两阶段改进模拟退火算法

结合实际布局活动中，设施在多层空间布置的实际情况，提出了双层过道布置问题，并构建了该问题的混合整数非线性规划（MINLP）模型。针对问题特征，提出了一种改进模拟退火算法，通过采用两阶段改进策略，对退火过程及抽样过程进行改进。该算法以自适应搜索策略替代马氏链搜索长度，并引入记忆功能、回火操作以提高求解质量，通过设置双阈值来提高求解效率。应用所提算法对24个基准算例进行测试，并将其与原模拟退火算法和GUROBI精确方法作对比，验证了所提算法的有效性。