Valid Configurations Research Articles

Feature-Oriented Modelling and Analysis of a Self-Adaptive Robotic System

Improved autonomy in robotic systems is needed for innovation in, e.g., the marine sector. Autonomous robots that are let loose in hazardous environments, such as underwater, need to handle uncertainties that stem from both their environment and internal state. While self-adaptation is crucial to cope with these uncertainties, bad decisions may cause the robot to get lost or even to cause severe environmental damage. Autonomous, self-adaptive robots that operate in uncontrolled environments full of uncertainties need to be reliable! Since these uncertainties are hard to replicate in test deployments, we need methods to formally analyse self-adaptive robots operating in uncontrolled environments. In this paper, we show how feature-oriented techniques can be used to formally model and analyse self-adaptive robotic systems in the presence of such uncertainties. Self-adaptive systems can be organised as two-layered systems with a managed subsystem handling the domain concerns and a managing subsystem implementing the adaptation logic. We consider a case study of an autonomous underwater vehicle (AUV) for pipeline inspection, in which the managed subsystem of the AUV is modelled as a family of systems, where each family member corresponds to a valid configuration of the AUV which can be seen as an operating mode of the AUV’s behaviour. The managing subsystem of the AUV is modelled as a control layer that is capable of dynamically switching between such valid configurations, depending on both environmental and internal uncertainties. These uncertainties are captured in a probabilistic and highly configurable model. Our modelling approach allows us to exploit powerful formal methods for feature-oriented systems, which we illustrate by analysing safety properties, energy consumption, and multi-objective properties, as well as performing parameter synthesis to analyse to what extent environmental conditions affect the AUV. The case study is realised in the probabilistic feature-oriented modelling language and verification tool ProFeat, and in particular exploits family-based probabilistic and parametric model checking.

Reusing d-DNNFs for Efficient Feature-Model Counting

Feature models are commonly used to specify valid configurations of a product line. In industry, feature models are often complex due to numerous features and constraints. Thus, a multitude of automated analyses have been proposed. Many of those rely on computing the number of valid configurations, which typically depends on solving a # SAT problem, a computationally expensive operation. Even worse, most counting-based analyses require evaluation for multiple features or partial configurations resulting in numerous # SAT computations on the same feature model. Instead of repetitive computations on highly similar formulas, we aim to improve the performance by reusing knowledge between these computations. In this work, we are the first to propose reusing d-DNNFs for performing repetitive counting queries on features and partial configurations. In our experiments, reusing d-DNNFs saved up-to \(\sim\) 99.98% compared to repetitive invocations of # SAT solvers even when including compilation times. Overall, our tool ddnnife combined with the d-DNNF compiler d4 appears to be the most promising option when dealing with many repetitive feature-model counting queries.

Hybrid Sampling-Based Path Planning for Mobile Manipulators Performing Pick and Place Tasks in Narrow Spaces

A mobile manipulator is capable of traversing a vast area while performing manipulation tasks in confined spaces. However, the high degree of freedom presents a challenge for path planning. In this paper, a hybrid sampling-based path planning method is proposed for mobile manipulators performing pick and place tasks in confined spaces. This method employs a random sampling approach, yet differs from the traditional RRT method. Firstly, a sampling-based configuration generation method for mobile manipulators is proposed, with the objective of generating a valid, collision-free configuration with the end-effector at the desired pose. A path for the end-effector corresponding to the goal configuration is then planned using the RRT method. Secondly, an area-restricted approach that samples in the vicinity of the previous configuration is introduced to generate the next valid configuration. Subsequently, a cost computation rule is devised to identify the optimal subsequent configuration utilizing the trajectory of the end-effector as a guiding principle. Finally, the obtained path is smoothed. Simulations demonstrate that the proposed hybrid sample-based method is an effective solution to the path planning problem for mobile manipulators performing pick and place tasks in narrow spaces.

Group Theory and Solutions of the Rubik’s Cube

The paper explores the application of group theory to solving the Rubik’s Cube. Group theory, a branch of abstract algebra, studies sets equipped with an operation that satisfies specific properties: closure, associativity, identity, and inverses. The Rubik’s Cube is treated as a group because its set of moves forms a structure that aligns with these properties. By representing the cube’s various configurations and moves through the mathematical constructs of groups, the paper analyzes how the elements transform during cube manipulations. Key concepts such as symmetric groups, homomorphisms, alternating groups, and disjoint cycle decomposition are used to break down the cube’s complexity. Additionally, the study demonstrates how group actions, particularly the parity of permutations and orientation sums, govern valid cube configurations. The paper also references methodologies from other studies to apply and refine these mathematical approaches for systematically solving the Rubik’s Cube. Through these methods, the research illustrates how the cube can be navigated using logical algorithms grounded in group theory principles

Insights into the scale effects on ship motions and wave loads considering hydroelasticity

Insights into the scale effects on ship motions and wave loads considering hydroelasticity

Supervisory Control for Dynamic Feature Configuration in Product Lines

In this paper a framework for engineering supervisory controllers for product lines with dynamic feature configuration is proposed. The variability in valid configurations is described by a feature model. Behavior of system components is achieved using (extended) finite automata and both behavioral and dynamic configuration constraints are expressed by means of requirements as is common in supervisory control theory. Supervisory controller synthesis is applied to compute a behavioral model in which the requirements are adhered to. For the challenges that arise in this setting, multiple solutions are discussed. The solutions are exemplified in the CIF toolset using a model of a coffee machine. A use case of the much larger Body Comfort System product line is performed to showcase feasibility for industrial-sized systems.

A novel framework for set-based steel connection design automation

Existing computational design tools for steel connection design predominantly employ a point-based design (PBD) approach, which requires iterative re-evaluation whenever there are changes in design specifications. This paper introduces a new framework that adopts a set-based design (SBD) approach, aiming to substantially reduce the iteration and time cost associated with steel connection design and rework. The framework integrates a component-set connection design model with a database storage and query-based data retrieval method. The first method enables the flexible and efficient generation of a large connection design space from all possible component combinations, and automated identification of valid connection configurations within it. The latter method allows for automated design space refinement from preference-based evaluation of connection design efficiency and high-speed comparison and selection of optimal connection designs. To evaluate the relative performance of SBD and PBD approaches, the framework is applied to a steel floor system design case study with 62 connections. Results showed that the SBD approach achieved near-instantaneous connection design automation, with a total execution time of fewer than 55 milliseconds, making it over 10 times faster than the corresponding PBD approach.

Differentially Private Ad Conversion Measurement

In this work, we study ad conversion measurement, a central functionality in digital advertising, where an advertiser seeks to estimate advertiser website (or mobile app) conversions attributed to ad impressions that users have interacted with on various publisher websites (or mobile apps). Using differential privacy (DP), a notion that has gained in popularity due to its strong mathematical guarantees, we develop a formal framework for private ad conversion measurement. In particular, we define the notion of an operationally valid configuration of the attribution rule, DP adjacency relation, contribution bounding scope and enforcement point. We then provide, for the set of configurations that most commonly arises in practice, a complete characterization, which uncovers a delicate interplay between attribution and privacy.

IxPopDyMod: an R package to write, run, and analyze tick population and infection dynamics models

Given the increasing prevalence of tick-borne diseases, such as Lyme disease, modeling the population and infection dynamics of tick vectors is an important public health tool. These models have applications for testing the effects of control methods or climate change on tick populations. There is an established history of tick population models, but code for them is rarely shared, especially not in a convenient format for others to modify and use. We present an R package, called IxPopDyMod, intended to function as a flexible and consistent framework for reproducible Ixodidae (hard-bodied ticks) population dynamics models. Here we focus on two key parts of the package: a function to create valid model configurations and a function to run a configured model and return the daily population over time. We provide three examples in appendices: one reproducing an existing Ixodes scapularis population model, one providing a novel Dermacentor albipictus model, and one showing Borrelia burgdorferi infection in ticks. Together these examples show the flexibility of the package to model scenarios of interest to tick researches.Graphical

Group theory behind Rubiks Cube

The Rubiks Cube is a widely recognized puzzle. The mathematics behind the Rubiks Cube is group theory. Group theory studies algebraic structures in mathematics such as groups, rings, and fields. The operation of the Rubiks Cube is rotation, which can be considered an operation of a group. The combination of two rotations of the Rubiks Cube can be considered the association of two operations of a group. The rotations and the combination operation of two rotations form a group called the Rubiks Cube group, and this paper presents the order of this group which is also the quantity of possible valid configurations of the Rubiks Cube. The valid configurations are the configurations that can be reached by a series of rotations from the starting configuration. This paper presents a method to illustrate the configurations of the Rubiks Cube, the requirements for making the configurations valid, and calculate the quantity of possible valid configurations.

On the benefits of knowledge compilation for feature-model analyses

Feature models are commonly used to specify the valid configurations of product lines. As industrial feature models are typically complex, researchers and practitioners employ various automated analyses to study the configuration spaces. Many of these automated analyses require that numerous complex computations are executed on the same feature model, for example by querying a SAT or #SATsolver. With knowledge compilation, feature models can be compiled in a one-time effort to a target language that enables polynomial-time queries for otherwise more complex problems. In this work, we elaborate on the potential of employing knowledge compilation on feature models. First, we gather various feature-model analyses and study their computational complexity with regard to the underlying computational problem and the number of solver queries required for the respective analysis. Second, we collect knowledge-compilation target languages and map feature-model analyses to the languages that make the analysis tractable. Third, we empirically evaluate publicly available knowledge compilers to further inspect the potential benefits of knowledge-compilation target languages.

A novel reliability redundancy allocation problem formulation for complex systems

A novel reliability redundancy allocation problem formulation for complex systems

Revisiting the effects of configuration, predictability, and relevance on visual detection during interocular suppression

Revisiting the effects of configuration, predictability, and relevance on visual detection during interocular suppression

Can we predict long-duration running power output? A matter of selecting the appropriate predicting trials and empirical model.

When facing a long-distance race, athletes and practitioners could develop an efficient pacing strategy and training paces if an accurate performance estimate of the target distance is achieved. Therefore, this study aims to determine the validity of different empirical models (i.e. critical power [CP], Power law and Peronnet) to predict long-duration power output (i.e. 60min) when using two or three time trial configurations. In a 5-week training period, fifteen highly trained athletes performed nine-time trials (i.e. 1, 2, 3, 4, 5, 10, 20, 30, and 60min) in a randomized order. Their power-duration curves were defined through the work-time (CPwork), power-1/time (CP1/time), two-parameter hyperbolic (CP2hyp), three-parameter hyperbolic (CP3hyp) CP models using different two- and three-time trial configurations. The undisclosed proprietary CP models of the Stryd (CPstryd) and Golden Cheetah training software (CPcheetah)were also computed as well as the non-asymptotic Power law and Peronnet models. These were extrapolated to the 60-min power output and compared to the actual performance. The shortest valid configuration (95% confidence interval < 12 W) for CPwork and CP1/time was 3-30min (Bias: 8.3 [4.9 to 11.7] W), for CPstryd was 10-30min (Bias: 4.2 [-1.0 to 9.4] W), for CP2hyp, CP3hyp and CPcheetah was 3-5-30min (Bias < 5.7 W), for Power law was 1-3-10min (-1.0 [-11.9 to 9.9] W), and for Peronnet was 4-20min (-3.0 [-10.2 to 4.3] W). All the empirical models provided valid estimates when the two or three predicting trial configurations selected attended each model fitting needs.

On the Unreliability of Network Simulation Results FROM Mininet and iPerf

The networking research community and industry both use the Mininet network simulator to assess new network architectures and predict how new designs will perform. Mininet employs an emulation approach that uses concurrent processes running in a single computer instead of physical hardware. A user specifies a network consisting of network switches, routers, and host computers that have network interface connections and links connecting all the pieces. Mininet creates software artifacts to represent each of the network devices and allows application programs to send packets across the resulting network. Researchers often use the iPerf application to measure network performance. Many research papers report results from Mininet and iPerf and use the results to validate new designs for Software-Defined Networks (SDNs). However, there has been little investigation into the scenarios where these emulations can perform different than intended. The goal of this paper is to understand the edge cases of these emulation methods and understand the severity of these scenarios. This paper reports surprising anomalies in the results of Mininet and iPerf. We show that the choice of apparently valid configuration options can make the reported throughput completely invalid. Our initial discoveries focused on a complex simulation of a data center network. However, we were able to show that Mininet produces completely invalid results for a basic case: network traffic traveling across a single emulated link between two switches with no other network traffic. The paper makes recommendations for ways to configure Mininet to avoid some of the anomalies.

We’re Not Gonna Break It! Consistency-Preserving Operators for Efficient Product Line Configuration

When configuring a software product line, finding a good trade-off between multiple orthogonal quality concerns is a challenging multi-objective optimisation problem. State-of-the-art solutions based on search-based techniques create invalid configurations in intermediate steps, requiring additional repair actions that reduce the efficiency of the search. In this work, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">consistency-preserving configuration operators</i> (CPCOs)—genetic operators that maintain valid configurations throughout the entire search. CPCOs bundle coherent sets of changes: the activation or deactivation of a particular feature together with other (de)activations that are needed to preserve validity. In our evaluation, our instantiation of the IBEA algorithm with CPCOs outperforms two state-of-the-art tools for optimal product line configuration in terms of both speed and solution quality. The improvements are especially pronounced in large product lines with thousands of features.

Optimization of Steel Portal Frames under a Parametric Structural Design Framework

This paper presents a framework for the structural design of single-story industrial steel portal frames. The structural mechanical utilization, stability, and serviceability design requirements employing a parametric structural tridimensional computational model are included. The model parameterization consists of adopting the column, rafter, purlins, and cladding span lengths as parameters, as well as the cross-section sizes of these members, the latter in accordance with the standard commercial cross section’s tables. The main outcome is the possibility to scan the range of parameters and store the structural performance ratios, generating a solution space for this structural system. This space of valid solutions attending structural code requirements defines a set of valid structural system topological configuration and dimensions, making it possible to obtain relationships between the parameters and material consumption rate. Concave-hull 2D plots are used to visualize the solution space and identify the best choice of parameters in the design process. It is shown that the solution space generation and the concave-hull 2D plots are powerful design aids that help to identify the best combination of structural system topological dimensions and cross-section types and dimensions.

Evaluating state-of-the-art # SAT solvers on industrial configuration spaces

Product lines are widely used to manage families of products that share a common base of features. Typically, not every combination (configuration) of features is valid. Feature models are a de facto standard to specify valid configurations and allow standardized analyses on the variability of the underlying system. A large variety of such analyses depends on computing the number of valid configurations. To analyze feature models, they are typically translated to propositional logic. This allows to employ # SAT solvers that compute the number of satisfying assignments of the propositional formula translated from a feature model. However, the # SAT problem is generally assumed to be even harder than SAT and its scalability when applied to feature models has only been explored sparsely. Our main contribution is an investigation of the performance of off-the-shelf # SAT solvers on computing the number of valid configurations for industrial feature models. We empirically evaluate 21 publicly available # SAT solvers on 130 feature models from 15 subject systems. Our results indicate that current solvers master a majority of the evaluated systems (13/15) with the fastest solvers requiring less than one second for each successfully evaluated feature model. However, there are two complex systems for which none of the evaluated solvers scales. For the given experiment design, the solvers that consumed the least runtime are sharpSAT (2.5 seconds in sum for the 13 systems) and Ganak (3.5 seconds).

A matrix-based method for searching configurations of planetary gear trains

A matrix-based method for searching configurations of planetary gear trains

Semi-supervised learning for topographic map analysis over time: a study of bridge segmentation

Geographical research using historical maps has progressed considerably as the digitalization of topological maps across years provides valuable data and the advancement of AI machine learning models provides powerful analytic tools. Nevertheless, analysis of historical maps based on supervised learning can be limited by the laborious manual map annotations. In this work, we propose a semi-supervised learning method that can transfer the annotation of maps across years and allow map comparison and anthropogenic studies across time. Our novel two-stage framework first performs style transfer of topographic map across years and versions, and then supervised learning can be applied on the synthesized maps with annotations. We investigate the proposed semi-supervised training with the style-transferred maps and annotations on four widely-used deep neural networks (DNN), namely U-Net, fully-convolutional network (FCN), DeepLabV3, and MobileNetV3. The best performing network of U-Net achieves F1_{inst:0.1} = 0.725 and F1_{inst:0.01} = 0.743 trained on style-transfer synthesized maps, which indicates that the proposed framework is capable of detecting target features (bridges) on historical maps without annotations. In a comprehensive comparison, the F1_{inst:0.1} of U-Net trained on Contrastive Unpaired Translation (CUT) generated dataset (0.662 pm 0.008) achieves 57.3 % than the comparative score (0.089 pm 0.065) of the least valid configuration (MobileNetV3 trained on CycleGAN synthesized dataset). We also discuss the remaining challenges and future research directions.