Current Issue : October - December Volume : 2019 Issue Number : 4 Articles : 5 Articles
An interest is often present in knowing evolving variables that are not directly observable; this is the case in aerospace, engineering\ncontrol, medical imaging, or data assimilation.What is at hand, though, are time-varying measured data, a model connecting them\nto variables of interest, and a model of how to evolve the variables over time.However, both models are only approximation and the\nobserved data are tainted with noise. This is an ill-posed inverse problem.Methods, such as Kalman filter (KF), have been devised to\nextract the time-varying quantities of interest.These methods applied to this inverse problem, nonetheless, are slow, computation\nwise, since they require large matrices multiplications and even matrix inversion. Furthermore, these methods are not usually\nsuitable to impose some constraints. This article introduces a new iterative filtering algorithm based on alternating projections.\nExperiments were run with simulated moving projectiles and were compared with results using KF. The new optimization algorithm\nproves to be slightly more accurate than KF, but, more to the point, it is much faster in terms of CPU time....
In this paper, a limited-memory adaptive extended Kalman Filter (LM-AEKF) to estimate tire-road friction coefficient is proposed.\nBy combining extended Kalman filter (EKF) with the limited-memory filter, this algorithm can reduce the effects of old\nmeasurement data on filtering and improve the estimation accuracy. Self-adaptive regulatory factors were introduced to weigh\ncovariance matrix of evaluated error. Meanwhile, measured noise covariance matrix was adjusted dynamically by fuzzy inference\nto accurately track the breaking status of system.Therefore, problems, including large filter error and divergence caused by incorrect\nmodel, can be solved. Joint simulation was conducted for the proposed algorithm with Carsim and Matlab/Simulink. Under the\ndifferent road conditions, real-vehicle road tests were conducted in various working conditions for contrast with traditional EKF\nresults. Simulation and real-vehicle road tests show that this algorithm can enhance the filter stability, improve the estimation\naccuracy of algorithm, and increase algorithm robustness....
It is extensively acknowledged that excessive on-site electricity power load often causes power failure across a construction site and\nsurrounding residential zones and can result in unforeseen schedule delay, construction quality problems, life inconvenience, and\neven property loss. However, energy management, such as power load optimization, has long been ignored in construction\nscheduling. )is study aims to develop a modified shuffled frog-leaping algorithm (SFLA) approach in project scheduling to aid\ndecision-makers in identifying the best Pareto solution for time-cost-resource trade-off (TCRTO) problems under the constraint of\nprecedence, resource availability, and on-site peak electricity power load. A mathematical model including three objective functions\nand five constraints was established followed by the application of the modified SLFA on real-case multiobjective optimization\nproblems in construction scheduling. )e performance of SLFA was compared with that of the nondominated sorting genetic\nalgorithm (NSGA II). )e results showed that the developed new approach was superior in identifying optimal project planning\nsolutions, which could essentially assist on-site power load-oriented schedule decision-making for construction teams....
In construction project management, there are several factors influencing the final project cost. Among various approaches, estimate\nat completion (EAC) is an essential approach utilized for final project estimation. ,The main merit of EAC is including the probability\nof the project performance and risk. In addition, EAC is extremely helpful for project managers to define and determine the critical\nthroughout the project progress and determine the appropriate solutions to these problems. In this research, a relatively new\nintelligent model called deep neural network (DNN) is proposed to calculate the EAC. ,The proposed DNN model is authenticated\nagainst one of the predominated intelligent models conducted on the EAC prediction, namely, support vector regression model\n(SVR). In order to demonstrate the capability of the model in the engineering applications, historical project information obtained\nfrom fifteen projects in Iraq region is inspected in this research.,The second phase of this research is about the integration of two input\nalgorithms hybridized with the proposed and the comparable predictive intelligent models. ,These input optimization algorithms are\ngenetic algorithm (GA) and brute force algorithm (BF). ,The aim of integrating these input optimization algorithms is to approximate\nthe input attributes and investigate the highly influenced factors on the calculation of EAC. Overall, the enthusiasm of this study is to\nprovide a robust intelligent model that estimates the project cost accurately over the traditional methods. Also, the second aim is to\nintroduce a reliable methodology that can provide efficient and effective project cost control. ,e proposed GA-DNN is demonstrated\nas a reliable and robust intelligence model for EAC calculation....
We emphasized explicitly on the derivation and implementation of a new\nnumerical algorithm scheme which gave stable results that show the applicability\nof the method. In this paper, we aimed to solve some second order initial\nvalue problems of ordinary differential equations and compare the results\nwith the theoretical solution. Using this method to solve some initial value\nproblems of second order ordinary differential equations, we discovered that\nthe results compared favorably with the theoretical solution which led to the\nconclusion that the new numerical algorithm scheme derived in the research\nis approximately correct and can be prescribed for any related ordinary differential\nequations....
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