Real time eco-driving of high speed trains by simulation-based dynamic multi-objective optimization

Eco-driving is a traffic operation measure that may lead to important energy savings in high speed railway systems. Eco-driving optimization has been applied offline in the design of commercial services. However, the benefits of the efficient driving can also be applied on-line in the regulation stage to recover train delays or in general, to adapt the driving to the changing conditions in the line. In this paper the train regulation problem is stated as a dynamic multi-objective optimization model to take advantage in real time of accurate results provided by detailed train simulation. If the simulation model is realistic, the railway operator will be confident on the fulfillment of punctuality requirements. The aim of the optimization model is to find the Pareto front of the possible speed profiles and update it during the train travel. It continuously calculates a set of optimal speed profiles and, when necessary, one of them is used to substitute the nominal driving. The new speed profile is energy efficient under the changing conditions of the problem. The dynamic multi-objective optimization algorithms DNSGA-II and DMOPSO combined with a detailed simulation model are applied to solve this problem. The performance of the dynamic algorithms has been analyzed in a case study using real data from a Spanish high speed line. The results show that dynamic algorithms are faster tracking the Pareto front changes than their static versions. In addition, the chosen algorithms have been compared with the typical delay recovery strategy of drivers showing that DMOPSO provides 7.8% of energy savings.     

Contribution-based prioritization of LCI database improvements: Method design,demonstration, and evaluation

The largest Life Cycle Inventory (LCI) databases contain about 10,000 unit process datasets. Assuming that updating one dataset would, on average, require one working day, then updating the entire database would roughly require the continuous work of five workers for ten years. Methods are therefore needed to prioritize datasets to be updated, and such a method should be able to identify the unit processes that contribute the most across different Life Cycle Impact Assessment (LCIA) methods. To date, such prioritization methods are not available.This paper presents an operational prioritization method. We demonstrate and evaluate our method by applying it to the ecoinvent database. The case study shows that prioritization of improvement efforts is highly useful because a robust nucleus of unit processes proves to be consistently important across all product systems and many LCIA methods. Focusing research efforts on these processes allows the effective improvement of the LCI database.     

Blind image quality assessment with improved natural scene statistics model

No-reference (NR)/blind image quality assessment (IQA) metrics play an important role in the area of image processing. Natural scene statistics (NSS) model assumes that natural images possess certain regular statistical properties and is widely used in NR IQA metrics. Most existing NSS-based NR algorithms are achieved by measuring the variation of image statistics, which are characterized by the fitting parameters of NSS model, across different distortions. However, distortions not only change the image statistics, but also disturb the statistical regularity held by natural images. As a result, the distribution of distorted images can not well follow the NSS model. There exists fitting error between the real distribution of the distorted image and the fitted one under certain NSS model. In this paper, the statistical distributions of the distorted images are discussed in detail. We suggest to take the fitting errors into account as well as the fitting parameters for feature extraction, and propose a novel NR IQA algorithm. Experimental results on several image databases demonstrate that the proposed metric performs highly consistent with human visual perception.     

A class of low-pass FIR input shaping filters achieving exact residual vibration cancelation

This paper describes the construction of low-pass FIR filters for application as command input shapers in motion control systems. The filters are designed to operate on an arbitrary command input signal to ensure a finite settling time for system modes with known natural frequency and damping ratio. In addition, the required roll-off rate of the filter frequency response may be prescribed in the design. Excitation of unmodeled high-frequency modes can thereby be reduced. The filters also produce an input-smoothing effect that is useful in situations where discontinuities in the input signal or its derivatives would be detrimental to system performance or function. Numerical case studies are presented to clarify these effects.     

FeCo nanocrystals encapsulated in N-doped carbon nanospheres/thermal reduced graphene oxide hybrids: Facile synthesis,magnetic and catalytic properties

The hybrids of FeCo nanocrystals encapsulated in N-doped carbon nanospheres (FeCo–NCNS) supported on thermal reduced graphene oxide (FeCo–NCNS/TRGO) are synthesized by in situ growth of Co₂Fe(CN)₆ onto graphene oxide (GO), followed with a subsequent pyrolysis process. The as-synthesized products are characterized by transmission electron microscopy, powder X-ray diffraction, Raman spectroscopy, elements analysis and inductively coupled plasma optical emission spectrometer. The hybrids exhibit excellent ferromagnetic behavior, superior electrocatalytic activity toward oxygen reduction reaction (ORR), and markedly enhanced catalytic activity toward the reduction of 4-nitrophenol (4-NP) by NaBH₄, which make the hybrids be attractive materials for various potential applications. Moreover, the performance of the hybrids can be optimized through adjusting the content of FeCo–NCNS on thermal reduced graphene oxide. This novel and elegant method presented here could be extended to synthesize other metal/alloy-NCNS/TRGO hybrids with various functions.     

基于双层规划的自动卡车运输专用网络设计优化

为保障交通系统安全性和卡车货运自动化的发展,有必要对自动卡车货物运输专用网络进行科学规划与布局.考虑到自动卡车专用道会减少普通车辆的路权,对普通车辆的出行路径选择行为造成影响,首先从路网整体出发,以系统出行时间最小为目标,充分考虑路网普通车辆的出行路径选择行为,构建一种新的自动卡车专用运输网络设计的双层规划模型;然后提出一种基于实数编码的改进差分进化算法求解建立的双层规划模型,不仅保证解的可行性,还可避免复杂的不可行解修复过程.通过Sioux\ Falls基准网络实例和大量随机算例对比测试验证了所提出模型和算法的有效性.     

Two new 2D organic–inorganic hybrids assembled by lanthanide-substituted polyoxotungstate dimers and copper–complex linkers

Two new organic–inorganic hybrid copper–lanthanide heterometallic polyoxometalates (POMs), [Cu(dap)₂]_3.5[Cu(dap)₂(H₂O)]₃[La(α-SiW₁₁O₃₉)₂]·5H₂O (1) and H[Cu(dap)₂(H₂O)][Cu(dap)₂]₄[Eu(α-PW₁₁O₃₉)₂]·13H₂O (2) (dap = 1,2-diaminopropane), have been made under hydrothermal conditions and characterized by elemental analyses, IR spectra, XRD, thermogravimetric (TG) analyses and X-ray single-crystal diffraction. They both display interesting 2D organic-inorganic hybrid POMs containing 3d-4f cations and organic dap ligands. To the best of knowledge, 1 represents the first 2D Cu–Ln heterometallic silicotungstate assembled by [La(α-SiW₁₁O₃₉)₂]^13 − dimers and [Cu(dap)₂]^2 + linkers, while 2 is a new 2D Cu-Eu heterometallic Keggin-type phosphotungstate with dap ligands. Furthermore, the variable-temperature magnetic susceptibility of 2 has been investigated.     

Outlier based literature exploration for cross-domain linking of Alzheimer’s disease and gut microbiota

In knowledge discovery, experts frequently need to combine knowledge from different domains to get new insights and derive new conclusions. Intelligent systems should support the experts in the search for relationships between concepts from different domains, where huge amounts of possible combinations require the systems to be efficient but also sufficiently general, open and interactive to enable the experts to creatively guide the discovery process. The paper proposes a cross-domain literature mining methodology that achieves this functionality by combining the functionality of two complementary text mining tools: clustering and topic ontology creation tool OntoGen and cross-domain bridging terms exploration tool CrossBee. Focusing on outlier documents identified by OntoGen contributes to the efficiency, while CrossBee allows for flexible and user-friendly bridging concepts exploration and identification. The proposed approach, which is domain independent and can support cross-domain knowledge discovery in any field of science, is illustrated on a biomedical case study dealing with Alzheimer’s disease, one of the most threatening age-related diseases, deteriorating lives of numerous individuals and challenging the ageing society as a whole. By applying the proposed methodology to Alzheimer’s disease and gut microbiota PubMed articles, we have identified Nitric oxide synthase (NOS) as a potentially valuable link between these two domains. The results support the hypothesis of neuroinflammatory nature of Alzheimer’s disease, and is indicative for the quest for identifying strategies to control nitric oxide-associated pathways in the periphery and in the brain. By addressing common mediators of inflammation using literature-based discovery, we have succeeded to uncover previously unidentified molecular links between Alzheimer’s disease and gut microbiota with a multi-target therapeutic potential.     

One-pot synthesis of PdBi/reduced graphene oxide catalyst under microwave irradiation used for formic acid electrooxidation

The Pd and PdBi nanoparticles dispersed on the reduced graphene oxide (Pd/rGO and PdBi/rGO) have been synthesized through one-pot reaction under the irradiation of microwave and the obtained composites have been characterized by transmission electron microscopy, X-ray diffraction and X-ray photoelectron spectroscopy, and their electrocatalytic activities have also been evaluated. It is found that the PdBi_0.05/rGO catalyst exhibits higher activity and better stability toward formic acid electrooxidation compared with Pd/C and Pd/rGO. The excellent electrocatalytic performance indicates that the addition of appropriate amount of Bi can greatly enhance the activity and stability of Pd catalysts for the formic acid oxidation.