Non-probabilistic robust continuum topology optimization with stress constraints

Structural and Multidisciplinary Optimization - This paper proposes a non-probabilistic robust design approach, based on optimization with anti-optimization, to handle unknown-but-bounded loading...     

Evolutionary-computer-assisted design of image operators that detect interest points using genetic programming

This work describes a way of designing interest point detectors using an evolutionary-computer-assisted design approach. Nowadays, feature extraction is performed through the paradigm of interest point detection due to its simplicity and robustness for practical applications such as: image matching and view-based object recognition. Genetic programming is used as the core functionality of the proposed human-computer framework that significantly augments the scope of interest point design through a computer assisted learning process. Indeed, genetic programming has produced numerous interest point operators, many with unique or unorthodox designs. The analysis of those best detectors gives us an advantage to achieve a new level of creative design that improves the perspective for human-machine innovation. In particular, we present two novel interest point detectors produced through the analysis of multiple solutions that were obtained through single and multi-objective searches. Experimental results using a well-known testbed are provided to illustrate the performance of the operators and hence the effectiveness of the proposal.     

Long-term evaluation of three satellite ocean color algorithms for identifying harmful algal blooms (Karenia brevis) along the west coast of Florida: A matchup assessment

We present a simple algorithm to identify Karenia brevis blooms in the Gulf of Mexico along the west coast of Florida in satellite imagery. It is based on an empirical analysis of collocated matchups of satellite and in situ measurements. The results of this Empirical Approach is compared to those of a Bio-optical Technique - taken from the published literature - and the Operational Method currently implemented by the NOAA Harmful Algal Bloom Forecasting System for K. brevis blooms. These three algorithms are evaluated using a multi-year MODIS data set (from July, 2002 to October, 2006) and a long-term in situ database. Matchup pairs, consisting of remotely-sensed ocean color parameters and near-coincident field measurements of K. brevis concentration, are used to assess the accuracy of the algorithms. Fair evaluation of the algorithms was only possible in the central west Florida shelf (i.e. between 25.75°N and 28.25°N) during the boreal Summer and Fall months (i.e. July to December) due to the availability of valid cloud-free matchups. Even though the predictive values of the three algorithms are similar, the statistical measure of success in red tide identification (defined as cell counts in excess of 1.5 × 10⁴ cells L⁻¹) varied considerably (sensitivity—Empirical: 86%; Bio-optical: 77%; Operational: 26%), as did their effectiveness in identifying non-bloom cases (specificity—Empirical: 53%; Bio-optical: 65%; Operational: 84%). As the Operational Method had an elevated frequency of false-negative cases (i.e. presented low accuracy in detecting known red tides), and because of the considerable overlap between the optical characteristics of the red tide and non-bloom population, only the other two algorithms underwent a procedure for further inspecting possible detection improvements. Both optimized versions of the Empirical and Bio-optical algorithms performed similarly, being equally specific and sensitive (~ 70% for both) and showing low levels of uncertainties (i.e. few cases of false-negatives and false-positives: ~ 30%)—improved positive predictive values (~ 60%) were also observed along with good negative predictive values (~ 80%).     

Large‐signal distributed millimeter‐wave multifinger pHEMT modeling using time‐domain technique

The finite‐difference time‐domain (FDTD) method is used for the large‐signal modeling of a multifinger pHEMT, which is considered as five nonlinear coupled distributed transmission lines. The developed model, which is based on the exact physical layout of multifinger pHEMT, not only accurately describes the propagation effects along the electrodes at higher frequencies but it also includes major nonlinearities of the I–V and Q–V characteristics. Using the transmission line theory, a proper nonlinear equivalent lumped circuit model is allocated for the differential length of the quintuple‐line transistor and the nonlinear active multiconductor transmission line (NAMCTL) equations are derived. These nonlinear, coupled differential equations are numerically solved using the FDTD method. The proposed model is applied to a 100 nm GaAs pHEMT and the simulation results are compared with the results of conventional sliced model in Keysight ADS simulator. The developed transient nonlinear model accurately predicts both the S‐parameters (1–150 GHz) and large‐signal power performances especially at millimeter wave frequency range. The proposed model can be useful in design and analysis of various types of high‐frequency nonlinear integrated circuits.     

Virtual reality training system for maintenance and operation of high-voltage overhead power lines

The maintenance of high-voltage overhead power lines involves high-risk procedures; the accidents involving live lines maintenance can be lethal. This paper presents the architecture and main features of a novel non-immersive virtual reality training system for maintenance of high-voltage overhead power lines. The general aim of this work was to provide electric utilities a suitable workforce training system to train and to certify operators working in complex and unsafe environments. The developed system has three components: the virtual warehouse, interactive 3D environments, and a learning management system. The workforce training system consists of thirty-one maintenance maneuvers, including the application of different techniques and equipment designed for various structures. Additionally, the system, using 3D animations, illustrates the safety conditions required before starting the maintenance procedures. To fit the worker’s different skill levels, the system has three operation modes: learning, practice, and evaluation, which can be accessed according to the trainee’s level of knowledge. The system is currently used to train thousands of overhead power lines operators of an electric utility in Mexico. The system has demonstrated to be a cost-effective tool for transferring skills and knowledge to new workers while reducing the time and money invested in their training.     

Three‐year fade and inter‐fade duration statistics from the Q‐band Alphasat propagation experiment in Madrid

Propagation campaigns are carried out at different frequencies and geographical areas to characterize the slant‐path propagation channel. One of the objectives of the Alphasat Propagation Experiment is to evaluate the performance of satellite links that operate in the Q/V band. Since March 2014, the copolar level of the Alphasat Q‐band beacon signal has been measured at Universidad Politécnica de Madrid, Spain. The fade dynamics—fade and inter‐fade durations—results for three complete years (March 2014 to February 2017) of measurements are presented in this paper. Moreover, the experimental setup and receiver characteristics are described in detail. The collected data (with a mean availability of 97%) can be used to evaluate the atmospheric propagation impairments with a very good degree of accuracy. The probability of occurrence and the fraction of time of fade duration for an average‐year have been compared with the ITU‐R and CRC models with moderate agreement. For this reason, a modeling effort has been made leading to the conclusion that there is room for improvement in the models.     

Yet Another Intelligent Code-Generating System: A Flexible and Low-Cost Solution

Modern compilers apply various code transformation algorithms to improve the quality of the target code. However, a complex problem is to determine which transformation algorithms must be utilized. This is difficult because of three reasons: a number of transformation algorithms, various combination possibilities, and several configuration possibilities. Over the last few years, various intelligent systems were presented in the literature. The goal of these systems is to search for transformation algorithms and thus apply them to a certain program. This paper proposes a flexible, low-cost and intelligent system capable of identifying transformation algorithms for an input program, considering the program’s specific features. This system is flexible for parameterization selection and has a low-computational cost. In addition, it has the capability to maximize the exploration of available computational resources. The system was implemented under the Low Level Virtual Machine infrastructure and the results indicate that it is capable of exceeding, up to 21.36%, performance reached by other systems. In addition, it achieved an average improvement of up to 17.72% over the most aggressive compiler optimization level of the Low Level Virtual Machine infrastructure.     

Functional process zones and their fish communities in temperate Andean river networks

Fluvial geomorphology provides an initial starting point for characterizing and understanding the ecogeomorphology of river networks. Knowledge of the spatial organization of morphological features and the way they influence processes within river networks is important for identifying potential links between the physical and ecological character of river systems. Here, a top‐down geographic information system‐based approach for determining the physical typology of river networks was used to determine and characterize the functional process zones (FPZs; large tracts of the river network with similar hydrogeomorphological character) of two rivers in central Chile. Seven distinct FPZs emerged from the analysis, and these had a nonuniform distribution along the river networks of the Biobío and Imperial Rivers. Some FPZs were frequent in number, with each FPZ segment being variable in length, whereas others were short and represented in limited number. A strong association between the physical character of FPZs and fish community was shown for the Biobío and Imperial Rivers. Specifically, dominant fish species varied among FPZs, and their habitat preferences were strongly related to the hydrogeomorphic character of the FPZ they dominated. These results are significant, as they can inform design of future ecological research and development of effective monitoring programmes.     

Swarm intelligence and the quest to solve a garbage and recycling collection problem

This work focuses on the application of Swarm Intelligence to a problem of garbage and recycling collection using a swarm of robots. Computational algorithms inspired by nature, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization, have been successfully applied to a range of optimization problems. Our idea is to train a number of robots to interact with each other, attempting to simulate the way a collective of animals behave, as a single cognitive entity. What we have achieved is a swarm of robots that interacts like a swarm of insects, cooperating with each other accurately and efficiently. We describe two different PSO topologies implemented, showing the obtained results, a comparative evaluation, and an explanation of the rationale behind the choices of topologies that enhanced the PSO algorithm. Moreover, we describe and implement an Ant Colony Optimization (ACO) approach that presents an unusual grid implementation of a robot physical simulation. Hence, generating new concepts and discussions regarding the necessary modifications for the algorithm towards an improved performance. The ACO is then compared to the PSO results in order to choose the best algorithm to solve the proposed problem.     

Reliability-based topology optimization of continuum structures subject to local stress constraints

Topology optimization of continuum structures is a challenging problem to solve, when stress constraints are considered for every finite element in the mesh. Difficulties are compounding in the reliability-based formulation, since a probabilistic problem needs to be solved for each stress constraint. This paper proposes a methodology to solve reliability-based topology optimization problems of continuum domains with stress constraints and uncertainties in magnitude of applied loads considering the whole set of local stress constrains, without using aggregation techniques. Probabilistic constraints are handled via a first-order approach, where the principle of superposition is used to alleviate the computational burden associated with inner optimization problems. Augmented Lagrangian method is used to solve the outer problem, where all stress constraints are included in the augmented Lagrangian function; hence sensitivity analysis may be performed only for the augmented Lagrangian function, instead of for each stress constraint. Two example problems are addressed, for which crisp black and white topologies are obtained. The proposed methodology is shown to be accurate by checking reliability indices of final topologies with Monte Carlo Simulation.