Analysis and optimization of inventory variance and bullwhip in a manufacturing/remanufacturing system

This is the author accepted manuscript.Quantitative analysis of closed-loop supply chains is often based on a specified cost function; dynamical performance of the system is rarely considered. This paper adopted a control theory approach to build a simple dynamic model of a hybrid manufacturing/remanufacturing system. It highlights the effect of remanufacturing (and return) lead-time and the return rate on the inventory variance and bull whip produced by the ordering policy. The results show that a larger return rate leads to less bullwhip and less inventory variance. Thus returns can be used to improve dynamic performance by absorbing some of the demand fluctuations. Longer remanufacturing (and return) lead-times have less impact on reducing inventory variance and bullwhip than shorter lead-times. It is concluded that within our specified system the inventory variance and bullwhip is always less in supply chain with returns than that without returns

Multi-class Heterogeneous Domain Adaptation

© 2019 Joey Tianyi Zhou, Ivor W. Tsang, Sinno Jialin Pan, Mingkui Tan. A crucial issue in heterogeneous domain adaptation (HDA) is the ability to learn a feature mapping between different types of features across domains. Inspired by language translation, a word translated from one language corresponds to only a few words in another language, we present an efficient method named Sparse Heterogeneous Feature Representation (SHFR) in this paper for multi-class HDA to learn a sparse feature transformation between domains with multiple classes. Specifically, we formulate the problem of learning the feature transformation as a compressed sensing problem by building multiple binary classifiers in the target domain as various measurement sensors, which are decomposed from the target multi-class classification problem. We show that the estimation error of the learned transformation decreases with the increasing number of binary classifiers. In other words, for adaptation across heterogeneous domains to be successful, it is necessary to construct a sufficient number of incoherent binary classifiers from the original multi-class classification problem. To achieve this, we propose to apply the error correcting output correcting (ECOC) scheme to generate incoherent classifiers. To speed up the learning of the feature transformation across domains, we apply an efficient batch-mode algorithm to solve the resultant nonnegative sparse recovery problem. Theoretically, we present a generalization error bound of our proposed HDA method under a multi-class setting. Lastly, we conduct extensive experiments on both synthetic and real-world datasets to demonstrate the superiority of our proposed method over existing state-of-the-art HDA methods in terms of prediction accuracy and training efficiency

Hybrid heterogeneous transfer learning through deep learning

Copyright © 2014, Association for the Advancement of Artificial Intelligence. Most previous heterogeneous transfer learning methods learn a cross-domain feature mapping between heterogeneous feature spaces based on a few cross-domain instance-correspondences, and these corresponding instances are assumed to be representative in the source and target domains respectively. However, in many realworld scenarios, this assumption may not hold. As a result, the constructed feature mapping may not be precise due to the bias issue of the correspondences in the target or (and) source domain(s). In this case, a classifier trained on the labeled transformed-sourcedomain data may not be useful for the target domain. In this paper, we present a new transfer learning framework called Hybrid Heterogeneous Transfer Learning (HHTL), which allows the corresponding instances across domains to be biased in either the source or target domain. Specifically, we propose a deep learning approach to learn a feature mapping between crossdomain heterogeneous features as well as a better feature representation for mapped data to reduce the bias issue caused by the cross-domain correspondences. Extensive experiments on several multilingual sentiment classification tasks verify the effectiveness of our proposed approach compared with some baseline methods

Preparation and characterization of polycaprolactone microspheres by electrospraying

This is the author accepted manuscript. Published online: 13 Sep 2016. The final version to be made available from the publisher via the DOI in this record.The ability to reproducibly produce and effectively collect electrosprayed polymeric microspheres with controlled morphology and size in bulk form is challenging. In this study, microparticles were produced by electrospraying polycaprolactone (PCL) of various molecular weights and solution concentrations in chloroform, and by collecting materials on different substrates. The resultant PCL microparticles were characterized by optical and electron microscopy to investigate the effect of molecular weight, solution concentration, applied voltage, working distance and flow rate on their morphology and size. The work demonstrates the key role of a moderate molecular weight and/or solution concentration in the formation of spherical PCL particles via an electrospraying process. Increasing the applied voltage was found to produce smaller and more uniform PCL microparticles. There was a relatively low increase in the particle average size with an increase in the working distance and flow rate. Four types of substrates were adopted to collect electrosprayed PCL particles: a glass slide, aluminium foil, liquid bath and copper wire. Unlike 2D bulk structures collected on the other substrates, a 3D tubular structure of microspheres was formed on the copper wire and could find application in the construction of 3D tumour mimics.The financial support received from the Cancer Research UK (CRUK) and Engineering and Physical Sciences Research Council (ESPRC) Cancer Imaging Centre in Cambridge and Manchester (C8742/A18097) is acknowledged

Effects of ceftiofur sodium liposomes on free radical formation in mice

To examine the effects of ceftiofur sodium liposomes on the free radical formation in liver of mice, 24 mice were assigned randomly into three groups, i.e., 1) ceftiofur sodium; 2) ceftiofur sodium liposomes and 3) physiological saline. Treatments were applied via intraperitoneal injections for 7 days. At the end of the treatment period, animals were euthanized and liver collected for analysis of superoxide dismutase (SOD) activity and malondialdehyde (MDA) contents and the ability of liver tissue to suppress hydroxyl radical formation. Ceftiofur sodium liposomes-treated mice had higher activity of SOD than ceftiofur sodium- and saline-treated mice; however, MDA content and the ability of liver tissue to suppress hydroxyl radical formation did not reach statistical significance among groups. It was concluded that ceftiofur sodium liposomes can improve the SOD activity compared to ceftiofur alone in mice

Ozone production in four major cities of China: sensitivity to ozone precursors and heterogeneous processes

Abstract. Despite a large volume of research over a number of years, our understandings of the key precursors that control tropospheric ozone production and the impacts of heterogeneous processes remain incomplete. In this study, we analyze measurements of ozone and its precursors made at rural/suburban sites downwind of four large Chinese cities – Beijing, Shanghai, Guangzhou and Lanzhou. At each site the same measurement techniques were utilized and a photochemical box model based on the Master Chemical Mechanism (v3.2) was applied, to minimize uncertainties in comparison of the results due to differences in methodology. All four cities suffered from severe ozone pollution. At the rural site of Beijing, export of the well-processed urban plumes contributed to the extremely high ozone levels (up to an hourly value of 286 ppbv), while the pollution observed at the suburban sites of Shanghai, Guangzhou and Lanzhou was characterized by intense in-situ ozone production. The major anthropogenic hydrocarbons were alkenes and aromatics in Beijing and Shanghai, aromatics in Guangzhou, and alkenes in Lanzhou. The ozone production was found to be in a VOCs-limited regime in both Shanghai and Guangzhou, and a mixed regime in Lanzhou. In Shanghai, the ozone formation was most sensitive to aromatics and alkenes, while in Guangzhou aromatics were the predominant ozone precursors. In Lanzhou, either controlling NOx or reducing emissions of olefins from the petrochemical industry would mitigate the local ozone production. The potential impacts of several heterogeneous processes on the ozone formation were assessed. The hydrolysis of dinitrogen pentoxide (N2O5), uptake of the hydroperoxyl radical (HO2) on particles, and surface reactions of NO2 forming nitrous acid (HONO) present considerable sources of uncertainty in the current studies of ozone chemistry. Further efforts are urgently required to better understand these processes and refine atmospheric models

Large eddy simulation of spray and combustion characteristics with realistic chemistry and high-order numerical scheme under diesel engine-like conditions

The accuracy of large eddy simulation (LES) for turbulent combustion depends on suitably implemented numerical schemes and chemical mechanisms. In the original KIVA3V code, finite difference schemes such as QSOU (Quasi-second-order upwind) and PDC (Partial Donor Cell Differencing) cannot achieve good results or even computational stability when using coarse grids due to large numerical diffusion. In this paper, the MUSCL (Monotone Upstream-centered Schemes for Conservation Laws) differencing scheme is implemented into KIVA3V-LES code to calculate the convective term. In the meantime, Lu’s n-heptane reduced 58-species mechanisms (Lu, 2011) is used to calculate chemistry with a parallel algorithm. Finally, improved models for spray injection are also employed. With these improvements, the KIVA3V-LES code is renamed as KIVALES-CP (Chemistry with Parallel algorithm) in this study. The resulting code was used to study the gas–liquid two phase jet and combustion under various diesel engine-like conditions in a constant volume vessel. The results show that using the MUSCL scheme can accurately capture the spray shape and fuel vapor penetration using even a coarse grid, in comparison with the Sandia experimental data. Similarly good results are obtained for three single-component fuels, i-Octane (C8H18), n-Dodecanese (C12H26), and n-Hexadecane (C16H34) with very different physical properties. Meanwhile the improved methodology is able to accurately predict ignition delay and flame lift-off length (LOL) under different oxygen concentrations from 10% to 21% with ambient density increasing from 14.8 kg/m3 to 30.0 kg/m3 and ambient temperatures from 850 K to 1300 K in a constant volume combustion chamber. With increasing oxygen concentration, the ignition delay time and consequently the flame LOL decrease, as the flame moves upstream as expected. On the other hand, reduction in the ambient temperature from 1000 K to 900 K retards the auto-ignition time and moves the burning location downstream under different oxygen concentrations

Normalized Neural Network for Energy Efficient Bipedal Walking Using Nonlinear Inverted Pendulum Model

In this paper, we present a novel approach for bipedal walking pattern generation. The proposed method is designed based on 2D inverted pendulum model. All control variables are optimized for an energy efficient gait. To obviate the need of solving non-linear dynamics on-line, a deep neural network is adopted for fast non-linear mapping from desired states to control variables. Normalized dimensionless data is generated to train the neural network, therefore, the trained neural network can be applied to bipedal robots of any size, without any specific modification. The proposed method is later verified through numerical simulations. Simulation results demonstrated that the proposed approach can generate feasible walking motions, and regulate robot’s walking velocity successfully. Its disturbance rejection capability was also validated

Taurine change in visual cortex of neonatal monocular enucleated rat: a proton MRS study

Posters - MRS of Animal Brain: 4530Neonatal monocular enucleation has been used to study developmental mechanisms underlying visual perception and the cross-modal changes in the central nervous system caused by early loss of the visual input. In this study, we demonstrated that alteration in the metabolism of taurine in visual cortex accompanied with neonatal monocular enucleation could be monitored using 1H MRS at 7 T. The change in taurine signal with respect to creatine signal may possibly due to the increased taurine signal in the right control visual cortex, likely caused by the plasticity resulted from recruitment of resources to the remaining left eye for adaptation.postprin

Peacock Bundles: Bundle Coloring for Graphs with Globality-Locality Trade-off

Bundling of graph edges (node-to-node connections) is a common technique to enhance visibility of overall trends in the edge structure of a large graph layout, and a large variety of bundling algorithms have been proposed. However, with strong bundling, it becomes hard to identify origins and destinations of individual edges. We propose a solution: we optimize edge coloring to differentiate bundled edges. We quantify strength of bundling in a flexible pairwise fashion between edges, and among bundled edges, we quantify how dissimilar their colors should be by dissimilarity of their origins and destinations. We solve the resulting nonlinear optimization, which is also interpretable as a novel dimensionality reduction task. In large graphs the necessary compromise is whether to differentiate colors sharply between locally occurring strongly bundled edges ("local bundles"), or also between the weakly bundled edges occurring globally over the graph ("global bundles"); we allow a user-set global-local tradeoff. We call the technique "peacock bundles". Experiments show the coloring clearly enhances comprehensibility of graph layouts with edge bundling.Comment: Appears in the Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD 2016