Comparative study of RPSALG algorithm for convex semi-infinite programming

The Remez penalty and smoothing algorithm (RPSALG) is a unified framework for penalty and smoothing methods for solving min-max convex semi-infinite programing problems, whose convergence was analyzed in a previous paper of three of the authors. In this paper we consider a partial implementation of RPSALG for solving ordinary convex semi-infinite programming problems. Each iteration of RPSALG involves two types of auxiliary optimization problems: the first one consists of obtaining an approximate solution of some discretized convex problem, while the second one requires to solve a non-convex optimization problem involving the parametric constraints as objective function with the parameter as variable. In this paper we tackle the latter problem with a variant of the cutting angle method called ECAM, a global optimization procedure for solving Lipschitz programming problems. We implement different variants of RPSALG which are compared with the unique publicly available SIP solver, NSIPS, on a battery of test problems.

Analysis and Optimization of Synthetic Aperture Ultrasound Imaging Using the Effective Aperture Approach

An effective aperture approach is used as a tool for analysis and parameter optimization of mostly known ultrasound imaging systems - phased array systems, compounding systems and synthetic aperture imaging systems. Both characteristics of an imaging system, the effective aperture function and the corresponding two-way radiation pattern, provide information about two of the most important parameters of images produced by an ultrasound system - lateral resolution and contrast. Therefore, in the design, optimization of the effective aperture function leads to optimal choice of such parameters of an imaging systems that influence on lateral resolution and contrast of images produced by this imaging system. It is shown that the effective aperture approach can be used for optimization of a sparse synthetic transmit aperture (STA) imaging system. A new two-stage algorithm is proposed for optimization of both the positions of the transmitted elements and the weights of the receive elements. The proposed system employs a 64-element array with only four active elements used during transmit. The numerical results show that Hamming apodization gives the best compromise between the contrast of images and the lateral resolution.

Improved stochastic optimization of railway timetables

We present a general model to find the best allocation of a limited amount of supplements (extra minutes added to a timetable in order to reduce delays) on a set of interfering railway lines. By the best allocation, we mean the solution under which the weighted sum of expected delays is minimal. Our aim is to finely adjust an already existing and well-functioning timetable. We model this inherently stochastic optimization problem by using two-stage recourse models from stochastic programming, building upon earlier research from the literature. We present an improved formulation, allowing for an efficient solution using a standard algorithm for recourse models. We show that our model may be solved using any of the following theoretical frameworks: linear programming, stochastic programming and convex non-linear programming, and present a comparison of these approaches based on a real-life case study. Finally, we introduce stochastic dependency into the model, and present a statistical technique to estimate the model parameters from empirical data.

Implementing a structural continuity constraint and a halting method for the topology optimization of energy absorbers

This study investigates topology optimization of energy absorbing structures in which material damage is accounted for in the optimization process. The optimization objective is to design the lightest structures that are able to absorb the required mechanical energy. A structural continuity constraint check is introduced that is able to detect when no feasible load path remains in the finite element model, usually as a result of large scale fracture. This assures that designs do not fail when loaded under the conditions prescribed in the design requirements. This continuity constraint check is automated and requires no intervention from the analyst once the optimization process is initiated. Consequently, the optimization algorithm proceeds towards evolving an energy absorbing structure with the minimum structural mass that is not susceptible to global structural failure. A method is also introduced to determine when the optimization process should halt. The method identifies when the optimization method has plateaued and is no longer likely to provide improved designs if continued for further iterations. This provides the designer with a rational method to determine the necessary time to run the optimization and avoid wasting computational resources on unnecessary iterations. A case study is presented to demonstrate the use of this method.

Motion Planning and Controls with Safety and Temporal Constraints

Motion planning, or trajectory planning, commonly refers to a process of converting high-level task specifications into low-level control commands that can be executed on the system of interest. For different applications, the system will be different. It can be an autonomous vehicle, an Unmanned Aerial Vehicle(UAV), a humanoid robot, or an industrial robotic arm. As human machine interaction is essential in many of these systems, safety is fundamental and crucial. Many of the applications also involve performing a task in an optimal manner within a given time constraint. Therefore, in this thesis, we focus on two aspects of the motion planning problem. One is the verification and synthesis of the safe controls for autonomous ground and air vehicles in collision avoidance scenarios. The other part focuses on the high-level planning for the autonomous vehicles with the timed temporal constraints. In the first aspect of our work, we first propose a verification method to prove the safety and robustness of a path planner and the path following controls based on reachable sets. We demonstrate the method on quadrotor and automobile applications. Secondly, we propose a reachable set based collision avoidance algorithm for UAVs. Instead of the traditional approaches of collision avoidance between trajectories, we propose a collision avoidance scheme based on reachable sets and tubes. We then formulate the problem as a convex optimization problem seeking control set design for the aircraft to avoid collision. We apply our approach to collision avoidance scenarios of quadrotors and fixed-wing aircraft. In the second aspect of our work, we address the high level planning problems with timed temporal logic constraints. Firstly, we present an optimization based method for path planning of a mobile robot subject to timed temporal constraints, in a dynamic environment. Temporal logic (TL) can address very complex task specifications such as safety, coverage, motion sequencing etc. We use metric temporal logic (MTL) to encode the task specifications with timing constraints. We then translate the MTL formulae into mixed integer linear constraints and solve the associated optimization problem using a mixed integer linear program solver. We have applied our approach on several case studies in complex dynamical environments subjected to timed temporal specifications. Secondly, we also present a timed automaton based method for planning under the given timed temporal logic specifications. We use metric interval temporal logic (MITL), a member of the MTL family, to represent the task specification, and provide a constructive way to generate a timed automaton and methods to look for accepting runs on the automaton to find an optimal motion (or path) sequence for the robot to complete the task.

Efficient Hill Climber for Constrained Pseudo-Boolean Optimization Problems

Efficient hill climbers have been recently proposed for single- and multi-objective pseudo-Boolean optimization problems. For $k$-bounded pseudo-Boolean functions where each variable appears in at most a constant number of subfunctions, it has been theoretically proven that the neighborhood of a solution can be explored in constant time. These hill climbers, combined with a high-level exploration strategy, have shown to improve state of the art methods in experimental studies and open the door to the so-called Gray Box Optimization, where part, but not all, of the details of the objective functions are used to better explore the search space. One important limitation of all the previous proposals is that they can only be applied to unconstrained pseudo-Boolean optimization problems. In this work, we address the constrained case for multi-objective $k$-bounded pseudo-Boolean optimization problems. We find that adding constraints to the pseudo-Boolean problem has a linear computational cost in the hill climber.

Development and optimization of techniques and design parameters for the engineering of atmospheric pressure plasma devices for biomedical applications and plasma medicine

In the last decades, the possibility to generate plasma at atmospheric pressure gave rise to a new emerging field called plasma medicine; it deals with the application of cold atmospheric pressure plasmas (CAPs) or plasma-activated solutions on or in the human body for therapeutic effects. Thanks to a blend of synergic biologically active agents and biocompatible temperatures, different CAP sources were successfully employed in many different biomedical applications such as dentistry, dermatology, wound healing, cancer treatment, blood coagulation, etc.… Despite their effectiveness has been verified in the above-mentioned biomedical applications, over the years, researchers throughout the world described numerous CAP sources which are still laboratory devices not optimized for the specific application. In this perspective, the aim of this dissertation was the development and the optimization of techniques and design parameters for the engineering of CAP sources for different biomedical applications and plasma medicine among which cancer treatment, dentistry and bioaerosol decontamination. In the first section, the discharge electrical parameters, the behavior of the plasma streamers and the liquid and the gas phase chemistry of a multiwire device for the treatment of liquids were performed. Moreover, two different plasma-activated liquids were used for the treatment of Epithelial Ovarian Cancer cells and fibroblasts to assess their selectivity. In the second section, in accordance with the most important standard regulations for medical devices, were reported the realization steps of a Plasma Gun device easy to handle and expected to be mounted on a tabletop device that could be used for dental clinical applications. In the third section, in relation to the current COVID-19 pandemic, were reported the first steps for the design, realization, and optimization of a dielectric barrier discharge source suitable for the treatment of different types of bioaerosol.

Exergy and thermoeconomic analysis of a turbofan engine during a typical commercial flight

In an energy perspective of cost-reduction and configuration-optimization, it becomes necessary to develop and use advanced tools for the analysis, design and improvement of energy conversion systems. In the aeronautical industry, such trend is fundamental since this industry has evolved to design extremely complex aircrafts, with highly integrated systems, requiring more information in order to evaluate the whole system. The aim of this paper is to present an exergy-based analysis as to evaluate the global performance of a typical turbofan engine and its components. The study presents values for exergy efficiency over the whole flight cycle, critical equipment and flight phases considering exergy destruction and estimating internal and exhaust flow costs. (C) 2009 Elsevier Ltd. All rights reserved.

Topology optimization for designing strain-gauge load cells

Load cells are used extensively in engineering fields. This paper describes a novel structural optimization method for single- and multi-axis load cell structures. First, we briefly explain the topology optimization method that uses the solid isotropic material with penalization (SIMP) method. Next, we clarify the mechanical requirements and design specifications of the single- and multi-axis load cell structures, which are formulated as an objective function. In the case of multi-axis load cell structures, a methodology based on singular value decomposition is used. The sensitivities of the objective function with respect to the design variables are then formulated. On the basis of these formulations, an optimization algorithm is constructed using finite element methods and the method of moving asymptotes (MMA). Finally, we examine the characteristics of the optimization formulations and the resultant optimal configurations. We confirm the usefulness of our proposed methodology for the optimization of single- and multi-axis load cell structures.

Simulated annealing applied to the irregular rotational placement of shapes over containers with fixed dimensions

This work deals with the problem of minimizing the waste of space that occurs on a rotational placement of a set of irregular two dimensional polygons inside a two dimensional container. This problem is approached with an heuristic based on simulated annealing. Traditional 14 external penalization"" techniques are avoided through the application of the no-fit polygon, that determinates the collision free area for each polygon before its placement. The simulated annealing controls: the rotation applied, the placement and the sequence of placement of the polygons. For each non placed polygon, a limited depth binary search is performed to find a scale factor that when applied to the polygon, would allow it to be fitted in the container. It is proposed a crystallization heuristic, in order to increase the number of accepted solutions. The bottom left and larger first deterministic heuristics were also studied. The proposed process is suited for non convex polygons and containers, the containers can have holes inside. (C) 2009 Elsevier Ltd. All rights reserved.

Piezoresistive sensor design using topology optimization

Piezoresistive materials, materials whose resistivity properties change when subjected to mechanical stresses, are widely utilized in many industries as sensors, including pressure sensors, accelerometers, inclinometers, and load cells. Basic piezoresistive sensors consist of piezoresistive devices bonded to a flexible structure, such as a cantilever or a membrane, where the flexible structure transmits pressure, force, or inertial force due to acceleration, thereby causing a stress that changes the resistivity of the piezoresistive devices. By applying a voltage to a piezoresistive device, its resistivity can be measured and correlated with the amplitude of an applied pressure or force. The performance of a piezoresistive sensor is closely related to the design of its flexible structure. In this research, we propose a generic topology optimization formulation for the design of piezoresistive sensors where the primary aim is high response. First, the concept of topology optimization is briefly discussed. Next, design requirements are clarified, and corresponding objective functions and the optimization problem are formulated. An optimization algorithm is constructed based on these formulations. Finally, several design examples of piezoresistive sensors are presented to confirm the usefulness of the proposed method.

Influence of Phase Inversion on the Formation and Stability of One-Step Multiple Emulsions

A novel method of preparation of water-in-oil-in-micelle-containing water (W/O/W(m)) Multiple emulsions using the one-step emulsification method is reported. These multiple emulsions were normal (not temporary) and stable over a 60 day test period. Previously, reported multiple emulsion by the one-step method were abnormal systems that formed at the inversion point of simple emulsion (where there is an incompatibility in the Ostwald and Bancroft theories, and typically these are O/W/O systems). Pseudoternary phase diagrams and bidimensional process-composition (phase inversion) maps were constructed to assist in process and composition optimization. The surfactants used were PEG40 hydrogenated castor oil and sorbitan oleate, and mineral and vegetables oils were investigated. Physicochemical characterization studies showed experimentally, for the First time, the significance of the ultralow surface tension point oil multiple emulsion formation by one-step via phase inversion processes. Although the significance of ultralow surface tension has been speculated previously, to the best of our knowledge, this is the first experimental confirmation. The multiple emulsion system reported here was dependent not only upon the emulsification temperature, but also upon the component ratios, therefore both the emulsion phase inversion and the phase inversion temperature were considered to fully explain their formation. Accordingly, it is hypothesized that the formation of these normal multiple emulsions is not a result of a temporary incompatibility (at the inversion point) during simple emulsion preparation, as previously reported. Rather, these normal W/O/W(m) emulsions are a result of the simultaneous occurrence of catastrophic and transitional phase inversion processes. The formation of the primary emulsions (W/O) is in accordance with the Ostwald theory and the formation of the multiple emulsions (W/O/W(m)) is in agreement with the Bancroft theory.

Intermittent target inhibition with dasatinib 100 mg once daily preserves efficacy and improves tolerability in imatinib-resistant and -intolerant chronic-phase chronic myeloid leukemia

Purpose Dasatinib is a BCR-ABL inhibitor, 325-fold more potent than imatinib against unmutated BCR-ABL in vitro. Phase II studies have demonstrated efficacy and safety with dasatinib 70 mg twice daily in chronic-phase (CP) chronic myelogenous leukemia (CML) after imatinib treatment failure. In phase I, responses occurred with once-daily administration despite only intermittent BCR-ABL inhibition. Once-daily treatment resulted in less toxicity, suggesting that toxicity results from continuous inhibition of unintended targets. Here, a dose-and schedule-optimization study is reported. Patients and Methods In this open-label phase III trial, 670 patients with imatinib-resistant or -intolerant CP-CML were randomly assigned 1: 1: 1: 1 between four dasatinib treatment groups: 100 mg once daily, 50 mg twice daily, 140 mg once daily, or 70 mg twice daily. Results With minimum follow-up of 6 months (median treatment duration, 8 months; range, = 1 to 15 months), marked and comparable hematologic (complete, 86% to 92%) and cytogenetic (major, 54% to 59%; complete, 41% to 45%) response rates were observed across the four groups. Time to and duration of cytogenetic response were similar, as was progression-free survival (8% to 11% of patients experienced disease progression or died). Compared with the approved 70-mg twice-daily regimen, dasatinib 100 mg once daily resulted in significantly lower rates of pleural effusion (all grades, 7% v 16%; P = .024) and grade 3 to 4 thrombocytopenia (22% v 37%; P = .004), and fewer patients required dose interruption (51% v 68%), reduction (30% v 55%), or discontinuation (16% v 23%). Conclusion Dasatinib 100 mg once daily retains the efficacy of 70 mg twice daily with less toxicity. Intermittent target inhibition with tyrosine kinase inhibitors may preserve efficacy and reduce adverse events.

Implicit vector equilibrium problems with applications

Let X and Y be Hausdorff topological vector spaces, K a nonempty, closed, and convex subset of X, C: K--> 2(Y) a point-to-set mapping such that for any x is an element of K, C(x) is a pointed, closed, and convex cone in Y and int C(x) not equal 0. Given a mapping g : K --> K and a vector valued bifunction f : K x K - Y, we consider the implicit vector equilibrium problem (IVEP) of finding x* is an element of K such that f (g(x*), y) is not an element of - int C(x) for all y is an element of K. This problem generalizes the (scalar) implicit equilibrium problem and implicit variational inequality problem. We propose the dual of the implicit vector equilibrium problem (DIVEP) and establish the equivalence between (IVEP) and (DIVEP) under certain assumptions. Also, we give characterizations of the set of solutions for (IVP) in case of nonmonotonicity, weak C-pseudomonotonicity, C-pseudomonotonicity, and strict C-pseudomonotonicity, respectively. Under these assumptions, we conclude that the sets of solutions are nonempty, closed, and convex. Finally, we give some applications of (IVEP) to vector variational inequality problems and vector optimization problems. (C) 2003 Elsevier Science Ltd. All rights reserved.

Self-optimization module for scheduling using case-based reasoning

Metaheuristics performance is highly dependent of the respective parameters which need to be tuned. Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The process of defining which parameters setting should be used is not obvious. The values for parameters depend mainly on the problem, the instance to be solved, the search time available to spend in solving the problem, and the required quality of solution. This paper presents a learning module proposal for an autonomous parameterization of Metaheuristics, integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems. The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining that systems must continuously and proactively improve their performance. For the learning implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In the use of Case-based Reasoning it is assumed that similar cases have similar solutions. After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are described. Finally, a computational study is presented where the proposed module is evaluated, obtained results are compared with previous ones, some conclusions are reached, and some future work is referred. It is expected that this proposal can be a great contribution for the self-parameterization of Metaheuristics and for the resolution of scheduling problems on dynamic environments.