Inverse dynamic analysis type of MPPT control strategy in a thermoelectric-solar hybrid energy harvesting system

This study presents the development of a novel inverse dynamic analysis-maximum power point tracking (IDA-MPPT) scheme in a hybrid energy harvesting system between thermoelectric module (TEM) and solar array (SA). The proposed method initially changes the harvested voltage response from both sources to be the third-order exponential function. This input function selection is based on the capability of this function to stabilize the initial response system and maintain its final position despite a prolonged response time. The mV voltage value from TEM is easily boosted up to nearly 5 V using this method. With this hybridization, the total obtained voltage is doubled to become 9.7 V, which results in a total power of 0.722 W. Furthermore, the method also allows for a fast tracking system, which enables faster voltage boosting and supercapacitor charging. The supercapacitor only requires less than 5 min to complete charging and boost the voltage to almost 5 V. Thus, a satisfactory value is obtained as compared with that of the TEM system with a chosen MPPC board.     

Optimal design of measurement network for neutronic activity field reconstruction by data assimilation

An optimal reconstruction of the neutronic activity field can be determined for a nuclear reactor core using a data assimilation framework to merge information from models and measurements. In this paper, we focus on solving the inverse problem of determining an optimal repartition of the measuring instruments within the core, to get the best possible results from the data assimilation reconstruction procedure. The position optimisation is realised using a Simulated Annealing algorithm, based on the Metropolis-Hastings proposition. Moreover, algebraic improvements of data assimilation have been developed, optimising their computational performance, and are presented here.     

A numerical reconstruction method in inverse elastic scattering

In this paper a new numerical method for the shape reconstruction of obstacles in elastic scattering is proposed. Initially, the direct scattering problem for a rigid body and the mathematical setting for the corresponding inverse one are presented. Inverse uniqueness issues for the general case of mixed boundary conditions on the boundary of our obstacle, which are valid for a rigid body as well are established. The inversion algorithm based on the factorization method is presented into a suitable form and a new numerical scheme for the reconstruction of the shape of the scatterer, using far-field measurements, is given. In particular, an efficient Tikhonov parameter choice technique, called Improved Maximum Product Criterion (IMPC) and its linchpin within the framework of the factorization method is exploited. Our regularization parameter is computed via a fast iterative algorithm which requires no a priori knowledge of the noise level in the far-field data. Finally, the effectiveness of IMPC is illustrated with various numerical examples involving a kite, an acorn, and a peanut-shaped object.     

Analysis of the sensor placement for optimal temperature distribution reconstruction

The temperature distribution as a function of time and space is reconstructed over a non-homogeneous media having an arbitrary three-dimensional geometry. This is done by applying an inverse problem to the collected data from optimally placed sensors on the boundary surface of the object under study. Sensor positioning and the choice of the number of sensors are optimized in terms of the resolution of the reconstructed temperature field and the error propagation of the method in case of uncertain measurements. The method can be performed in real time since the major computation burden is performed off-line.     

Novel approach for attapulgite/poly(acrylic acid) (ATP/PAA) nanocomposite microgels as selective adsorbent for Pb(II) Ion

A novel approach was developed for the preparation of the attapulgite/poly(acrylic acid) (ATP/PAA) nanocomposite microgels via the “one-pot” inverse suspension radical polymerization of acrylic acid (AA) with the multi-functionalized attapulgite nanorods (org-ATP) as the sole crosslinker. The parameters of the feeding ratio of the functional attapulgite (org-ATP) nanorods and AA (org-ATP/AA), oil (liquid paraffin)–water ratio, and feeding ratios of dispersing agent (sodium dodecyl benzene sulfonate (SDBS)) and initiator (ammonium persulfate (APS)) were optimized via 4-Variable 3-Level Orthogonal experiments. Under the optimized preparation condition, more than 85% of the monomer AA had been grafted onto the org-ATP nanorods to form the 3-dimensional network of the ATP/PAA nanocomposite microgel. The ATP/PAA nanocomposite microgel exhibited better mechanical stabilities (resistance to pressure and resistance to agitation) and selective adsorption to heavy metal ions, especially to Pb²⁺. The adsorbed Pb²⁺ ion could be completely eluted with HCl solution. The better mechanical stability and regeneration make it potential adsorbent for the heavy metal contaminated water.     

Sparse optimization for inverse problems in atmospheric modelling

We consider inverse problems in atmospheric modelling represented by a linear system which is based on a source-receptor sensitivity matrix and measurements. Instead of using the ordinary least squares, we add a weighting matrix based on the topology of measurement points and show the connection with Bayesian modelling. Since the source-receptor sensitivity matrix is usually ill-conditioned, the problem is often regularized, either by perturbing the objective function or by modifying the sensitivity matrix. However, both these approaches may be heavily dependent on specified parameters. To ease this burden, we propose to use techniques looking for a sparse solution with a small number of positive elements. Finally, we compare all these methods on the European Tracer Experiment (ETEX) data where there is no apriori information apart from the release position and some measurements.     

Towards fully automatic reliable 3D acquisition: From designing imaging network to a complete and accurate point cloud

This paper describes a novel system for accurate 3D digitization of complex objects. Its main novelties can be seen in the new approach, which brings together different systems and tools in a unique platform capable of automatically generating an accurate and complete model for an object of interest. This is performed through generating an approximate model of the object, designing a stereo imaging network for the object with this model and capturing the images at the designed postures through exploiting an inverse kinematics method for a non-standard six degree of freedom robot. The images are then used for accurate and dense 3D reconstruction using photogrammetric multi-view stereo method in two modes, including resolving scale with baseline and with control points. The results confirm the feasibility of using Particle Swarm Optimization in solving inverse kinematics for this non-standard robot. The system provides this opportunity to test the effect of incidence angle on imaging network design and shows that the matching algorithms work effectively for incidence angle of 10°. The accuracy of the final point cloud generated with the system was tested in two modes through a comparison with a dataset generated with a close range 3D colour laser scanner.     

Determination of concrete rebars characteristics by enhanced post-processing of GPR scan raw data

A method for the estimation of rebar radius by post-processing the raw data acquired by the ground penetrating radar as a B-scan radargram is proposed. Considering the hyperbola trace and the diffracted amplitudes an inverse problem consisting of two steps was stated. Using at first hyperbola fitting, the wave velocity and the coordinates of the hyperbola apex are identified. Then the rebar radius is retrieved as solution of a further optimization problem for which the cost function measures the misfit between the actual value of the maximum diffracted amplitudes and their theoretical predictions. The procedure was implemented in Matlab and tested in realistic situations. The obtained results showed improved accuracy.     

On capacity allocation for operating rooms

This paper studies capacity planning decisions that allocate surgical specialties to operating-room (OR) days with the objective of minimizing total expected costs due to penalties for any patients who are not accommodated and for under- (i.e., idleness) and over- (i.e., overtime) usage of OR capacity. It presents a prototypical non-linear, stochastic programming model to structure relevant and practical features of the problem and four adaptations, along with associated solution approaches, with the goal of facilitating solution by overcoming the computational disadvantages of the prototype. Each of these adaptations offers advantages but is also attended by disadvantages. Computational tests compare the four adaptations and solution approaches with respect to solution quality and run time.