On the detection of nearly optimal solutions in the context of single-objective space mission design problems

When making decisions, having multiple options available for a possible realization of the same project can be advantageous. One way to increase the number of interesting choices is to consider, in addition to the optimal solution x*, also nearly optimal or approximate solutions; these alternative solutions differ from x* and can be in different regions – in the design space – but fulfil certain proximity to its function value f(x*). The scope of this article is the efficient computation and discretization of the set E of e–approximate solutions for scalar optimization problems. To accomplish this task, two strategies to archive and update the data of the search procedure will be suggested and investigated. To make emphasis on data storage efficiency, a way to manage significant and insignificant parameters is also presented. Further on, differential evolution will be used together with the new archivers for the computation of E. Finally, the behaviour of the archiver, as well as the efficiency of the resulting search procedure, will be demonstrated on some academic functions as well as on three models related to space mission design

Separating Hate Speech and Offensive Language Classes via Adversarial Debiasing

Research to tackle hate speech plaguing online media has made strides in providing solutions, analyzing bias and curating data. A challenging problem is ambiguity between hate speech and offensive language, causing low performance both overall and specifically for the hate speech class. It can be argued that misclassifying actual hate speech content as merely offensive can lead to further harm against targeted groups. In our work, we mitigate this potentially harmful phenomenon by proposing an adversarial debiasing method to separate the two classes. We show that our method works for English, Arabic German and Hindi, plus in a multilingual setting, improving performance over baselines

Portrayal of fuzzy recharge areas for water balance modelling – a case study in northern Oman

The research project IWAS Oman aims at implementing integrated water resources management (IWRM) to a pilot area in Al Batinah, Oman. This requires – amongst others – a realistic assessment of groundwater recharge to the alluvial aquifer which obviously has to be based upon the extension of recharge areas. In this context, the subsequent investigation focuses on the role of vagueness as regards the portrayal of the areas that provide water for particular aquifers. For that purpose, concepts of fuzziness in spatial analysis are applied to describe possible extents of recharge areas. <br><br> In general, any water assessment is based on clearly delineated boundaries. However, in many cases, aquifer recharge areas are not clearly defined due to the nature of the study area. Hence, surfaces indicating a gradual membership to the recharge area of a particular aquifer are used in this investigation. These surfaces, which are based on available qualitative information, visualise a potential range of spatial extension. With regard to water balance calculations, functional relationships in tabular form are derived as well. Based on a regionalisation approach providing spatially distributed recharge rates, the corresponding recharge volume is calculated. Hence, this methodology provides fuzzy input data for water balance calculations. Beyond the portrayal of one singular aquifer recharge area, this approach also supports the complementary consideration of adjacent areas

Controlled Heterometallic Composition in Linear Trinuclear [LnCeLn] Lanthanide Molecular Assemblies.

The combination of two different β-diketone ligands facilitates the size-controlled assembly of pure heterometallic [LnLn'Ln] linear compounds thanks to two different coordination sites present in the molecular scaffold. [HoCeHo], [ErCeEr], and [YbCeYb] analogues are presented here and are characterized both in the solid state and in solution, demonstrating the selectivity of this unique method to produce heterometallic 4f molecular entities

Gas identification based on bias induced hysteresis of a gas-sensitive SiC field effect transistor

In this work dynamic variation of gate bias is used on a gas-sensitive SiC field effect transistor ("GasFET") to optimize its sensitivity and increase its selectivity. Gate bias ramps introduce strong hysteresis in the sensor signal. The shape of this hysteresis is shown to be an appropriate feature both for the discrimination of various gases (ammonia, carbon monoxide, nitrogen monoxide and methane) as well as for different gas concentrations (250 and 500 ppm). The shape is very sensitive to ambient conditions as well as to the bias sweep rate. Thus, the influences of oxygen concentration, relative humidity, sensor temperature and cycle duration, i.e., sweep rate, are investigated and reasons for the observed signal changes, most importantly the existence of at least two different and competing processes taking place simultaneously, are discussed. Furthermore, it is shown that even for very fast cycles, in the range of seconds, the gas-induced shape change in the signal is strong enough to achieve a reliable separation of gases using gate bias cycled operation and linear discriminant analysis (LDA) making this approach suitable for practical application

A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems

In this article we propose a descent method for equality and inequality constrained multiobjective optimization problems (MOPs) which generalizes the steepest descent method for unconstrained MOPs by Fliege and Svaiter to constrained problems by using two active set strategies. Under some regularity assumptions on the problem, we show that accumulation points of our descent method satisfy a necessary condition for local Pareto optimality. Finally, we show the typical behavior of our method in a numerical example

Computing the set of Epsilon-efficient solutions in multiobjective space mission design

In this work, we consider multiobjective space mission design problems. We will start from the need, from a practical point of view, to consider in addition to the (Pareto) optimal solutions also nearly optimal ones. In fact, extending the set of solutions for a given mission to those nearly optimal significantly increases the number of options for the decision maker and gives a measure of the size of the launch windows corresponding to each optimal solution, i.e., a measure of its robustness. Whereas the possible loss of such approximate solutions compared to optimal—and possibly even ‘better’—ones is dispensable. For this, we will examine several typical problems in space trajectory design—a biimpulsive transfer from the Earth to the asteroid Apophis and two low-thrust multigravity assist transfers—and demonstrate the possible benefit of the novel approach. Further, we will present a multiobjective evolutionary algorithm which is designed for this purpose

Perturbation theory for ac-driven interfaces in random media

We study $D$-dimensional elastic manifolds driven by ac-forces in a disordered environment using a perturbation expansion in the disorder strength and the mean-field approximation. We find, that for $D\le 4$ perturbation theory produces non-regular terms that grow unboundedly in time. The origin of these non-regular terms is explained. By using a graphical representation we argue that the perturbation expansion is regular to all orders for $D>4$. Moreover, for the corresponding mean-field problem we prove that ill-behaved diagrams can be resummed in a way, that their unbounded parts mutually cancel. Our analytical results are supported by numerical investigations. Furthermore, we conjecture the scaling of the Fourier coefficients of the mean velocity with the amplitude of the driving force $h$.Comment: 23 pages, substantial changes, replaced with the published versio

Influence of measurement uncertainty on machine learning results demonstrated for a smart gas sensor

Humans spend most of their lives indoors, so indoor air quality (IAQ) plays a key role in human health. Thus, human health is seriously threatened by indoor air pollution, which leads to 3.8 ×106 deaths annually, according to the World Health Organization (WHO). With the ongoing improvement in life quality, IAQ monitoring has become an important concern for researchers. However, in machine learning (ML), measurement uncertainty, which is critical in hazardous gas detection, is usually only estimated using cross-validation and is not directly addressed, and this will be the main focus of this paper. Gas concentration can be determined by using gas sensors in temperature-cycled operation (TCO) and ML on the measured logarithmic resistance of the sensor. This contribution focuses on formaldehyde as one of the most relevant carcinogenic gases indoors and on the sum of volatile organic compounds (VOCs), i.e., acetone, ethanol, formaldehyde, and toluene, measured in the data set as an indicator for IAQ. As gas concentrations are continuous quantities, regression must be used. Thus, a previously published uncertainty-aware automated ML toolbox (UA-AMLT) for classification is extended for regression by introducing an uncertainty-aware partial least squares regression (PLSR) algorithm. The uncertainty propagation of the UA-AMLT is based on the principles described in the Guide to the Expression of Uncertainty in Measurement (GUM) and its supplements. Two different use cases are considered for investigating the influence on ML results in this contribution, namely model training with raw data and with data that are manipulated by adding artificially generated white Gaussian or uniform noise to simulate increased data uncertainty, respectively. One of the benefits of this approach is to obtain a better understanding of where the overall system should be improved. This can be achieved by either improving the trained ML model or using a sensor with higher precision. Finally, an increase in robustness against random noise by training a model with noisy data is demonstrated.</p