Genetic algorithm and neural network hybrid approach for job-shop scheduling

Copyright @ 1998 ACTA PressThis paper proposes a genetic algorithm (GA) and constraint satisfaction adaptive neural network (CSANN) hybrid approach for job-shop scheduling problems. In the hybrid approach, GA is used to iterate for searching optimal solutions, CSANN is used to obtain feasible solutions during the iteration of genetic algorithm. Simulations have shown the valid performance of the proposed hybrid approach for job-shop scheduling with respect to the quality of solutions and the speed of calculation.This research is supported by the National Nature Science Foundation and National High -Tech Program of P. R. China

Evidence for very strong electron-phonon coupling in YBa_{2}Cu_{3}O_{6}

From the observed oxygen-isotope shift of the mid-infrared two-magnon absorption peak of YBa$_{2}$Cu$_{3}$O$_{6}$, we evaluate the oxygen-isotope effect on the in-plane antiferromagnetic exchange energy $J$. The exchange energy $J$ in YBa$_{2}$Cu$_{3}$O$_{6}$ is found to decrease by about 0.9% upon replacing $^{16}$O by $^{18}$O, which is slightly larger than that (0.6%) in La$_{2}$CuO$_{4}$. From the oxygen-isotope effects, we determine the lower limit of the polaron binding energy, which is about 1.7 eV for YBa$_{2}$Cu$_{3}$O$_{6}$ and 1.5 eV for La$_{2}$CuO$_{4}$, in quantitative agreement with angle-resolved photoemission data, optical conductivity data, and the parameter-free theoretical estimate. The large polaron binding energies in the insulating parent compounds suggest that electron-phonon coupling should also be strong in doped superconducting cuprates and may play an essential role in high-temperature superconductivity.Comment: 4 pages, 1 figur

Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter

This paper investigates the state estimation of a high-fidelity spatially resolved thermal- electrochemical lithium-ion battery model commonly referred to as the pseudo two-dimensional model. The partial-differential algebraic equations (PDAEs) constituting the model are spatially discretised using Chebyshev orthogonal collocation enabling fast and accurate simulations up to high C-rates. This implementation of the pseudo-2D model is then used in combination with an extended Kalman filter algorithm for differential-algebraic equations to estimate the states of the model. The state estimation algorithm is able to rapidly recover the model states from current, voltage and temperature measurements. Results show that the error on the state estimate falls below 1 % in less than 200 s despite a 30 % error on battery initial state-of-charge and additive measurement noise with 10 mV and 0.5 K standard deviations.Comment: Submitted to the Journal of Power Source

Mechanical performance of composite bonded joints in the presence of localised process-induced zero-thickness defects

Processing parameters and environmental conditions can introduce variation into the performance of adhesively bonded joints. The effect of such variation on the mechanical performance of the joints is not well understood. Moreover, there is no validated nondestructive inspection (NDI) available to ensure bond integrity post-process and in-service so as to guarantee initial and continued airworthiness in aerospace sector. This research studies polymer bond defects produced in the laboratory scale single-lap composite-to-composite joints that may represent the process-induced defects occurring in actual processing scenarios such as composite joining and repair in composite aircrafts. The effect of such defects on the degradation of a joint's mechanical performance is then investigated via quasi-static testing in conjunction with NDI ultrasonic C-scanning and pulsed thermography. This research is divided into three main sections: 1- manufacturing carbon fibre-reinforced composite joints containing representative nearly zero-thickness bond defects, 2- mechanical testing of the composite joints, and 3- assessment of the NDI capability for detection of the bond defects in such joints

Averages of shifted convolutions of d3(n)d_3(n)

We investigate the first and second moments of shifted convolutions of the generalised divisor function $d_3(n)$.Comment: 22 page

Argon protects against hypoxic-ischemic brain injury in neonatal rats through activation of Nuclear factor (erythroid-derived 2)-like 2

Perinatal hypoxic ischaemic encephalopathy (HIE) has a high mortality rate with neuropsychological impairment. This study investigated the neuroprotective effects of argon against neonatal hypoxic-ischaemic brain injury. In vitro cortical neuronal cell cultures derived from rat foetuses were subjected to an oxygen and glucose deprivation (OGD) challenge for 90 minutes and then exposed to 70% argon or nitrogen with 5% carbon dioxide and balanced with oxygen for 2 hours. In vivo, seven-day-old rats were subjected to unilateral common carotid artery ligation followed by hypoxic (8% oxygen balanced with nitrogen) insult for 90 minutes. They were exposed to 70% argon or nitrogen balanced with oxygen for 2 hours. In vitro, argon treatment of cortical neuronal cultures resulted in a significant increase of p-mTOR and Nuclear factor (erythroid-derived 2)-like 2(Nrf2) and protection against OGD challenge. Inhibition of m-TOR through Rapamycin or Nrf2 through siRNA abolished argon-mediated cyto-protection. In vivo, argon exposure significantly enhanced Nrf2 and its down-stream effector NAD(P)H Dehydrogenase, Quinone 1(NQO1) and superoxide dismutase 1(SOD1). Oxidative stress, neuroinflammation and neuronal cell death were significantly decreased and brain infarction was markedly reduced. Blocking PI-3K through wortmannin or ERK1/2 through U0126 attenuated argon-mediated neuroprotection. These data provide a new molecular mechanism for the potential application of Argon as a neuroprotectant in HIE

Modelling human behaviours and reactions under dangerous environment

This paper describes the framework of a real-time simulation system to model human behavior and reactions in dangerous environments. The system utilizes the latest 3D computer animation techniques, combined with artificial intelligence, robotics and psychology, to model human behavior, reactions and decision making under expected/unexpected dangers in real-time in virtual environments. The development of the system includes: classification on the conscious/subconscious behaviors and reactions of different people; capturing different motion postures by the Eagle Digital System; establishing 3D character animation models; establishing 3D models for the scene; planning the scenario and the contents; and programming within Virtools (TM) Dev. Programming within Virtools (TM) Dev is subdivided into modeling dangerous events, modeling character's perceptions, modeling character's decision making, modeling character's movements, modeling character's interaction with environment and setting up the virtual cameras. The real-time simulation of human reactions in hazardous environments is invaluable in military defense, fire escape, rescue operation planning, traffic safety studies, and safety planning in chemical factories, the design of buildings, airplanes, ships and trains. Currently, human motion modeling can be realized through established technology, whereas to integrate perception and intelligence into virtual human's motion is still a huge undertaking. The challenges here are the synchronization of motion and intelligence, the accurate modeling of human's vision, smell, touch and hearing, the diversity and effects of emotion and personality in decision making. There are three types of software platforms which could be employed to realize the motion and intelligence within one system, and their advantages and disadvantages are discussed

Learning-aided Stochastic Network Optimization with Imperfect State Prediction

We investigate the problem of stochastic network optimization in the presence of imperfect state prediction and non-stationarity. Based on a novel distribution-accuracy curve prediction model, we develop the predictive learning-aided control (PLC) algorithm, which jointly utilizes historic and predicted network state information for decision making. PLC is an online algorithm that requires zero a-prior system statistical information, and consists of three key components, namely sequential distribution estimation and change detection, dual learning, and online queue-based control. Specifically, we show that PLC simultaneously achieves good long-term performance, short-term queue size reduction, accurate change detection, and fast algorithm convergence. In particular, for stationary networks, PLC achieves a near-optimal $[O(\epsilon)$, $O(\log(1/\epsilon)^2)]$ utility-delay tradeoff. For non-stationary networks, \plc{} obtains an $[O(\epsilon), O(\log^2(1/\epsilon)$ $+ \min(\epsilon^{c/2-1}, e_w/\epsilon))]$ utility-backlog tradeoff for distributions that last $\Theta(\frac{\max(\epsilon^{-c}, e_w^{-2})}{\epsilon^{1+a}})$ time, where $e_w$ is the prediction accuracy and $a=\Theta(1)>0$ is a constant (the Backpressue algorithm \cite{neelynowbook} requires an $O(\epsilon^{-2})$ length for the same utility performance with a larger backlog). Moreover, PLC detects distribution change $O(w)$ slots faster with high probability ($w$ is the prediction size) and achieves an $O(\min(\epsilon^{-1+c/2}, e_w/\epsilon)+\log^2(1/\epsilon))$ convergence time. Our results demonstrate that state prediction (even imperfect) can help (i) achieve faster detection and convergence, and (ii) obtain better utility-delay tradeoffs