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We study optimal input design and bias-compensating parameter estimation methods for continuous-time models applied on a mechanical laboratory experiment. Within this task we compare two online estimation methods that are based on Poisson moment functions with focus on quantized system outputs due to an angular encoder: The standard recursive least-squares (RLS) approach and a bias-compensating recursive...
A coprime array enables an increased number of degrees-of-freedom by deriving a non-uniform virtual array. However, existing work such as spatial smoothing fails to utilize all of the information provided by the coprime array, which results in performance loss. In this paper, we propose a novel coprime virtual array interpolation-based direction- of-arrival (DOA) estimation algorithm by Toeplitz matrix...
The article investigates spatial signal processing in antenna arrays. The main attention is paid to optimization of spatial structure of antenna array to improve interference immunity and information parameters accuracy estimation. On the basis of statistical approximation the optimal spatial structure is estimated and the variance of estimation error is defined. The analysis of estimation-correlation-compensation...
This paper presents an optimization approach based differential search algorithm (DSA) for amplitude and phase estimation of time varying power signal in electrical power systems. Proposed method has been tested for two well-known literature problems that include fundamental, sub- and inter-harmonic estimation cases. The performance of the suggested DSA method has been compared with those of the Genetic...
This paper introduces a proposal of optimized management strategy to control a benchmark grid-connected microgrid consisting of some residential loads, some renewable generators (wind and PV), a genset generator, and a battery storage system. The proposal is to manage the microgrid according to the solution an optimization problem aimed to minimize an economic objective function. Microgrid performance...
We present an improved model for MRF-based depth upsampling, guided by image-as well as 3D surface normal features. By exploiting the underlying camera model we define a novel regularization term that implicitly evaluates the planarity of arbitrary oriented surfaces. Our method improves upsampling quality in scenes composed of predominantly planar surfaces, such as urban areas. We use a synthetic...
This paper presents a fast RGB-D dense visual odometry estimating 12-DoF state information including 3D motion and 6-DoF spatial velocity of a camera-strapdown system. To reduce computational loads, we extract informative pixels through a zero-crossing difference of Gaussian (DoG) and non-maximum gradient pixel extraction. For extracted regions, the 3D motion is estimated through inverse compositional...
Many enterprises have a growing interest in service composition to construct their business applications. With the increase of alternative services, Quality of Service (QoS) becomes an important indicator of obtaining optimal composite services. Due to the dynamic nature of the service environment, a composite service may not guarantee to deliver an overall optimal QoS. Re-optimization approaches...
Despite its capability of measuring out of plane displacement gradients in the sub-micrometer range, and the possibility of successfully detecting defects (delaminations, debondings, impacts) without compromising the investigated structure (which is very important in the aerospace field), Shearography usually remains a qualitative technique. This is due to several uncertainty sources and the difficulty...
Precise radius estimation is of high interest for rebar and pipe characterization but very challenging. In this work, we present a novel 3D frequency-domain full-waveform inversion (FWI) approach with which the geometrical information of subsurface cylindrical objects and the dielectric properties of the penetrating medium are simultaneously extracted from ground penetrating radar (GPR) data. The...
The complexity of transportation systems often dictates detailed representation of time-dependent demand and supply interaction through Dynamic Traffic Assignment (DTA). These complex models involve a large number of global parameters (behavior and congestion features) and main inputs (demand and supply characteristics) that require to be calibrated off-line, while stream of data coming from the field...
In this paper, we consider a problem of sampling a Wiener process, with samples forwarded to a remote estimator via a channel that consists of a queue with random delay. The estimator reconstructs a real-time estimate of the signal from causally received samples. Motivated by recent research on age-of-information, we study the optimal sampling strategy that minimizes the mean square estimation error...
The estimation of Origin — Destination (OD) matrix is a methodologically and computationally challenging, yet essential step in setting up a transportation planning model. In this process, demand (and supply) parameters need to be calibrated to match the simulation output with real observations. In this paper, we investigate how information extracted from a Jacobian matrix can be applied to improve...
In this paper, the authors present a Two-Step approach that sequentially adjusts generation and distribution values of the (dynamic) OD matrix. While the proposed methodology already provided excellent results for updating demand flows on a motorway, the aim of this paper is to validate this conclusion on a real network: Luxembourg City. This network represents the typical middle-sized European city...
With the prevalence of the online to offline (O2O) commerce, the short-distance instant logistics service becomes increasingly popular in China, which is a new logistics model emerged recently and provides timely logistics for O2O local life service. Being different from traditional logistics, extra waiting time generates when order's pick-up location arrival time is early than its pick-up ready time,...
We consider the problem of parameter estimation under a sequential framework. Specifically we assume that an i.i.d. random process is observed sequentially with its common pdf having a random parameter that must be estimated. We are interested in designing a stopping time that will decide when is the best moment to stop sampling the process and an estimator that will use the acquired samples in order...
We propose a new method for optimizing fixed-time traffic control plans and timings at isolated intersections by minimizing the Conditional Value-at-Risk (CVaR) of the intersection's average delay over an extended peak period. The optimization algorithm was implemented in Matlab using a scatter search algorithm to minimize the CVaR of the average delay which is based on the HCM formula for multiple...
To enable an intelligent traffic light system (ITLS) to consider the interactions between the signal controls and the traffic flow distribution resulting from the selfish-routing behaviors of travelers, a dynamic origin-destination (O-D) demand estimation model and a dynamic combined traffic assignment and signal control (CTA-SC) model are needed. However, the ITLS may collect inaccurate and incomplete...
A dynamic real-time optimization (D-RTO) methodology has been developed and applied to a batch reactor where polymer grafting reactions take place. The objective is to determine the on-line reactor temperature profile that minimizes the batch time while meeting terminal constraints on the overall conversion rate and grafting efficiency. The methodology combines a constrained dynamic optimization method...
A good initial guess is critical for the convergence of the steepest descent method. This paper presents a new method to estimate the initial guess of the near field phase. Based on image theory, tangential near fields on two scanning planes are linked by a radiation matrix. The radiation matrix is then scaled by the near field amplitudes. It is shown that the phase of an eigenvector of the scaled...
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