Space Algorithm Research Articles

CORTO: The Celestial Object Rendering TOol at DART Lab

The Celestial Object Rendering TOol (CORTO) offers a powerful solution for generating synthetic images of celestial bodies, catering to the needs of space mission design, algorithm development, and validation. Through rendering, noise modeling, hardware-in-the-loop testing, and post-processing functionalities, CORTO creates realistic scenarios. It offers a versatile and comprehensive solution for generating synthetic images of celestial bodies, aiding the development and validation of image processing and navigation algorithms for space missions. This work illustrates its functionalities in detail for the first time. The importance of a robust validation pipeline to test the tool’s accuracy against real mission images using metrics like normalized cross-correlation and structural similarity is also illustrated. CORTO is a valuable asset for advancing space exploration and navigation algorithm development and has already proven effective in various projects, including CubeSat design, lunar missions, and deep learning applications. While the tool currently covers a range of celestial body simulations, mainly focused on minor bodies and the Moon, future enhancements could broaden its capabilities to encompass additional planetary phenomena and environments.

Speeding up k-means clustering in high dimensions by pruning unnecessary distance computations

Standard k-means clustering necessitates computing pairwise Euclidean distances between each instance x in a data set D and all cluster centers, resulting in inadequate efficiency when dealing with high-dimensional data sets. Given its widespread usage, it is imperative that k-means clustering should be performed quickly to ensure efficient solutions. This paper is dedicated to exploring ways to improve the efficiency of the k-means algorithm in high-dimensional space. Unlike approximated approaches, our proposed method LBKC can achieve acceleration while yielding clustering results that are the same as what standard k-means clustering generates. LBKC utilizes the lower bound of Euclidean distance to safely avoid a large number of unnecessary distance calculations, thus achieving the goal of accelerating k-means process. Three carefully designed lower bounds based on the block vector, segment mean, and nonlinear embedding are presented in this paper, and they are employed in the proposed method. Furthermore, our approach LBKC is orthogonal to state-of-the-art methods, and we show how LBKC can be naturally combined with them to further improve their performance. Comprehensive experiments are conducted on a variety of data sets to evaluate the performance of the proposed approaches and related competitors, and the experimental results verify the effectiveness of our proposals.

A nonlinear data-driven approach to bias correction of XCO2 for NASA's OCO-2 ACOS version 10

Abstract. Measurements of column-averaged dry air mole fraction of CO2 (termed XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) contain systematic errors and regional-scale biases, often induced by forward model error or nonlinearity in the retrieval. Operationally, these biases are corrected for by a multiple linear regression model fit to co-retrieved variables that are highly correlated with XCO2 error. The operational bias correction is fit in tandem with a hand-tuned quality filter which limits error variance and reduces the regime of interaction between state variables and error to one that is largely linear. While the operational correction and filter are successful in reducing biases in retrievals, they do not allow for throughput or correction of data in which biases become nonlinear in predictors or features. In this paper, we demonstrate a clear improvement in the reduction in error variance over the operational correction by using a set of nonlinear machine learning models, one for land and one for ocean soundings. We further illustrate how the operational quality filter can be relaxed when used in conjunction with a nonlinear bias correction, which allows for an increase in sounding throughput by 14 % while maintaining the residual error in the operational correction. The method can readily be applied to future Atmospheric CO2 Observations from Space (ACOS) algorithm updates, to OCO-2's companion instrument OCO-3, and to other retrieved atmospheric state variables of interest.

Pre-Cutoff Value Calculation Method for Accelerating Metric Space Outlier Detection

Outlier detection is an important data mining technique. In this article, the triangle inequality of distances is leveraged to design a pre-cutoff value (PCV) algorithm that calculates the outlier degree pre-threshold without additional distance computations. This algorithm is suitable for accelerating various metric space outlier detection algorithms. Experimental results on multiple real datasets demonstrate that the PCV algorithm reduces the runtime and number of distance computations for the iORCA algorithm by 14.59% and 15.73%, respectively. Even compared to the new high-performance algorithm ADPOD, the PCV algorithm achieves 1.41% and 0.45% reductions. Notably, the non-outlier exclusion for the first data block in the dataset is significantly improved, with an exclusion rate of up to 36.5%, leading to a 23.54% reduction in detection time for that data block. While demonstrating excellent results, the PCV algorithm maintains the data type generality of metric space algorithms.

Solving multimodal optimization problems by a knowledge-driven brain storm optimization algorithm

Multimodal optimization problem (MMOP) refers to the problem having more than one optima or satisfied solution in the decision space. The accuracy and diversity of solutions should be considered when solving MMOPs. In the brain storm optimization (BSO) algorithm, the information on current solutions is analyzed, but the information on previous solutions needs to be more effectively used to guide the search. A knowledge-driven BSO in objective space (KBSOOS) algorithm is proposed to enhance the search performance and to maintain the diversity of the solutions for solving MMOPs. In addition, a diversity indicator is proposed as a quantitative measurement to measure the performance of various algorithms for solving MMOPs. The 30 nonlinear equation system (NES) problems are modeled as MMOPs and solved by six swarm intelligence algorithms to validate the proposed algorithm’s performance. Based on the experimental results, the diversity indicator could give a good indication of the performance of algorithms, and the KBSOOS algorithm could enhance the performance of various BSO algorithms.

A criterion space algorithm for solving linear multiplicative programming problems

A criterion space algorithm for solving linear multiplicative programming problems

Massively parallel and streaming algorithms for balanced clustering

Clustering is a fundamental problem with a wide range of applications. In this paper, we study a balanced version of the well-known k-center clustering problem, where the objective is to select k centers from a given set of points, and assign to each center at most L of the input points, so as to minimize the maximum distance from a point to the center to which it is assigned. We assume soft capacities, where points are allowed to be selected as center more than once. Motivated by applications involving massive datasets, we study the problem in the massively parallel computation (MPC) and streaming models. In particular, we present a two-round MPC algorithm for the balanced k-center problem, achieving an approximation factor of 5α+2, where α is the approximation factor of the corresponding sequential algorithm. This substantially improves the currently best approximation factor of 32α, available for the problem. We show that the approximation factor of our algorithm can be further improved to 5+ε in all spaces with bounded doubling dimension, including the Euclidean space. We also consider the balanced k-center problem in the streaming model, and present a constant-factor streaming algorithm in any metric space using O(knε) memory, and a factor 5+ε streaming algorithm using O(kpolylogn) memory in doubling metrics.

Rhythmic temporal prediction enhances neural representations of movement intention for brain–computer interface

Objective. Detecting movement intention is a typical use of brain–computer interfaces (BCI). However, as an endogenous electroencephalography (EEG) feature, the neural representation of movement is insufficient for improving motor-based BCI. This study aimed to develop a new movement augmentation BCI encoding paradigm by incorporating the cognitive function of rhythmic temporal prediction, and test the feasibility of this new paradigm in optimizing detections of movement intention. Methods. A visual-motion synchronization task was designed with two movement intentions (left vs. right) and three rhythmic temporal prediction conditions (1000 ms vs. 1500 ms vs. no temporal prediction). Behavioural and EEG data of 24 healthy participants were recorded. Event-related potentials (ERPs), event-related spectral perturbation induced by left- and right-finger movements, the common spatial pattern (CSP) and support vector machine, Riemann tangent space algorithm and logistic regression were used and compared across the three temporal prediction conditions, aiming to test the impact of temporal prediction on movement detection. Results. Behavioural results showed significantly smaller deviation time for 1000 ms and 1500 ms conditions. ERP analyses revealed 1000 ms and 1500 ms conditions led to rhythmic oscillations with a time lag in contralateral and ipsilateral areas of movement. Compared with no temporal prediction, 1000 ms condition exhibited greater beta event-related desynchronization (ERD) lateralization in motor area (P< 0.001) and larger beta ERD in frontal area (P< 0.001). 1000 ms condition achieved an averaged left–right decoding accuracy of 89.71% using CSP and 97.30% using Riemann tangent space, both significantly higher than no temporal prediction. Moreover, movement and temporal information can be decoded simultaneously, achieving 88.51% four-classification accuracy. Significance. The results not only confirm the effectiveness of rhythmic temporal prediction in enhancing detection ability of motor-based BCI, but also highlight the dual encodings of movement and temporal information within a single BCI paradigm, which is promising to expand the range of intentions that can be decoded by the BCI.

Analysis and Controlling of Uncertainty in BLDC Motor Using Optimal Hybrid Algorithm in State Space

In this article, we present a novel approach for enhancing the performance of Brushless DC Motors (BLDC) by utilizing a Hybrid Controller that combines Proportional – Integral – Derivative (PID) and Artificial Neural Network (ANN) elements. We employ this innovative controller to assess the motor’s behavior under steady-state conditions and internal faults within a state space model. To evaluate the controller’s effectiveness, we compare it with traditional controllers in scenarios involving rapid load changes, and variations in speed, as well as open circuit faults and short circuit faults, which are known to induce disruptive states in the motor. Our study involves a comprehensive examination of the proposed controller’s steady-state stability using state-space representation and the Lyapunov Technique. We construct a prototype model to capture the Steady State and dynamic characteristic variables of a 300 W BLDC motor system. The suggested Hybrid PID-ANN Controller clearly outperforms the traditional PID controller, providing greater control capabilities for BLDC motor applications depending on the results from both simulations and data from experiments.

The STATiX pipeline for the detection of X-ray transients in three dimensions

ABSTRACT The recent serendipitous discovery of a new population of short duration X-ray transients, thought to be associated with collisions of compact objects or stellar explosions in distant galaxies, has motivated efforts to build up statistical samples by mining X-ray telescope archives. Most searches to date, however, do not fully exploit recent developments in the signal and imaging processing research domains to optimize searches for short X-ray flashes. This paper addresses this issue by presenting a new source detection pipeline, STATiX (Space and Time Algorithm for Transients in X-rays), which directly operates on three-dimensional X-ray data cubes consisting of two spatial and one temporal dimension. The algorithm leverages wavelet transforms and the principles of sparsity to denoise X-ray observations and then detect source candidates on the denoised data cubes. The light curves of the detected sources are then characterized using the Bayesian blocks algorithm to identify flaring periods. We describe the implementation of STATiX in the case of XMM–Newton data, present extensive validation and performance verification tests based on simulations and also apply the pipeline to a small subset of seven XMM–Newton observations, which are known to contain transients sources. In addition to known flares in the selected fields, we report a previously unknown short duration transient found by our algorithm that is likely associated with a flaring Galactic star. This discovery demonstrates the potential of applying STATiX to the full XMM–Newton archive.

An Automatic Pipe-Routing Algorithm Based on Improved Sine Cosine Algorithm for Complex Space

An Automatic Pipe-Routing Algorithm Based on Improved Sine Cosine Algorithm for Complex Space

Five-Level Inverter Fed Sensorless PMSM Drive Using Modified MPTC with Novel Reduced Search Space Algorithm

Five-Level Inverter Fed Sensorless PMSM Drive Using Modified MPTC with Novel Reduced Search Space Algorithm

Development of a mixed reality assisted escape system for underground mine- based on the mine water-inrush accident background

The underground mine is an essential infrastructure for national resources and economy, and its safety system is an important issue, especially the miners’ safety. They should not only enhance their preventive awareness of disasters but also enhance their response capability. When miners are in danger with invalid escape lighting, it is difficult for them to overcome panic. Therefore, the automatic assisted escape system has a particular engineering significance in underground space. This study attempts to develop a reference technical framework for emergency evacuation of underground space workers. A human-environment-computer interaction system is developed, which combines the A*-3D Time algorithm with mixed reality (MR) technology. It is constrained by the spatial geometric information of the mine and the escape time. Firstly, the target mine’s geographic information and spatial structure are surveyed on the field, and a 3D digital model is built by the 3DMine; the BIM is used to integrate 3DMine information (data and material) and design the spatial structure. Secondly, based on the A* algorithm in 3D space, the escape time is introduced to evaluate the escape capability and construct the A*-3D Time model. Thirdly, the timeliness of mine water-inrush scenario is simulated by the Comsol; the interaction method and navigation map are developed by the OpenXR and MR Toolkit (MRTK) in Game Engine; the A*-3D Time model is introduced into the C# script and assigned to the digital objects (Mine and Miners). Finally, the performance of this system is analyzed based on the underground metal mine. Based on game thinking to solve safety problems can be used as a reference for underground space engineering.

Innovative research on the design of aging-friendly interior space in the context of computer simulation

Abstract This paper firstly takes the demand of older people for indoor space environment as a breakthrough and analyzes the characteristics of the elderly group, the spatial environment demand generated by the physiological and psychological characteristics of the elderly and the application of computer simulation in the design of aging-friendly indoor space. Secondly, we use a color space algorithm and feature extraction algorithm to construct an aging-friendly design model based on a human-computer interaction algorithm. Finally, several elderly individuals’ preferences are examined and analyzed. The results show that more older adults request the design of three aspects, namely anti-slip measures, intelligent door lock and handrail, in the space design, accounting for 86%, 83% and 87%, respectively. These data were used to generate the final interface form suitable for most older adults.

Revisiting maximum satisfiability and related problems in data streams

We revisit the maximum satisfiability problem (Max-SAT) in the data stream model. In this problem, the stream consists of m clauses that are disjunctions of literals drawn from n Boolean variables. The objective is to find an assignment to the variables that maximizes the number of satisfied clauses. Chou et al. (FOCS 2020) showed that Ω(n) space is necessary to yield a 2/2+ε approximation of the optimum value; they also presented an algorithm that yields a 2/2−ε approximation of the optimum value using O(ε−2log⁡n) space.In this paper, we not only focus on approximating the optimum value, but also on obtaining the corresponding Boolean assignment using sublinear o(mn) space. We present randomized single-pass algorithms that w.h.p.1 yield:•A 1−ε approximation using O˜(n/ε3) space and exponential post-processing time•A 3/4−ε approximation using O˜(n/ε) space and polynomial post-processing time. Our ideas also extend to dynamic streams. However, we show that the streaming k-SAT problem, which asks whether one can satisfy all size-k input clauses, must use Ω(nk) space.We also consider the related minimum satisfiability problem (Min-SAT), introduced by Kohli et al. (SIAM J. Discrete Math. 1994), that asks to find an assignment that minimizes the number of satisfied clauses. For this problem, we give a O˜(n2/ε2) space algorithm, which is sublinear when m=ω(n), that yields an α+ε approximation where α is the approximation guarantee of the offline algorithm. If each variable appears in at most f clauses, we show that a 2fn approximation using O˜(n) space is possible.Finally, for the Max-AND-SAT problem where clauses are conjunctions of literals, we show that any single-pass algorithm that approximates the optimal value up to a factor better than 1/2 with success probability at least 2/3 must use Ω(mn) space.

Phase space visibility graph

We introduce a topological approach for quantifying the dynamical complexity of time series. A novel complex network of visibility graph family is proposed based on defining visibility algorithm in phase space. The statistical properties of the constructed network show powerful potentiality for distinguishing stochastic and chaotic systems. And for some remarkable chaotic systems, it allows for quantitative correspondence with Lyapunov exponents. The potential practical application of this approach is demonstrated on the multiphase flow system and bearing fault identification. This work paves a natural way constructing complex network in phase space, and provides a complexity quantitative estimate for chaotic time series.

Lessons from a Space Lab: An Image Acquisition Perspective

The use of deep learning (DL) algorithms has improved the performance of vision-based space applications in recent years. However, generating large amounts of annotated data for training these DL algorithms has proven challenging. While synthetically generated images can be used, the DL models trained on synthetic data are often susceptible to performance degradation when tested in real-world environments. In this context, the Interdisciplinary Center of Security, Reliability and Trust (SnT) at the University of Luxembourg has developed the “SnT Zero-G Lab,” for training and validating vision-based space algorithms in conditions emulating real-world space environments. An important aspect of the SnT Zero-G Lab development was the equipment selection. From the lessons learned during the lab development, this article presents a systematic approach combining market survey and experimental analyses for equipment selection. In particular, the article focuses on the image acquisition equipment in a space lab: background materials, cameras, and illumination lamps. The results from the experiment analyses show that the market survey complimented by experimental analyses is required for effective equipment selection in a space lab development project.

Deep reinforcement learning based energymanagement strategy considering running costs and energy source aging for fuel cell hybrid electric vehicle

The main contribution of this study is to integrate energy source aging and running costs into the deep reinforcement learning (DRL) based EMS of fuel cell hybrid electric vehicles (FCHEV). For the FCHEV, a multi-objective energy management strategy (EMS) based on twin delayed deep deterministic policy gradient (TD3) is proposed, which aims to simultaneously reduce energy source degradation and lower running costs. To achieve this, the paper innovatively designs the reward function and it's comparative approach. Additionally, it verifies the superiority of the proposed EMS over other EMS based on continuous action space algorithm, including previous action guided deep deterministic policy gradient (PA-DDPG) and soft actor-critic (SAC). Lastly, the agent's action output is changed from fuel cell (FC) current to FC power ratio, and a comparative analysis on results generated by different action outputs is conducted. Simulation results show that the proposed EMS can reduce the running costs while extending the lifespan of battery and FC efficiently. This work holds significant practical significance in the energy distribution of automobiles.

Differential evolution with nearest better clustering for multimodal multiobjective optimization

The main aim of multimodal multiobjective optimization algorithms is to find multiple optimal solutions for multiple objectives. The problem with multiple objectives is to maintain the diversity and convergence balance in both decision and objective space. Better performance in one space can lead to deterioration of performance in another space. To effectively perform exploration and exploitation in the decision space, we have proposed a niching technique in our proposed algorithm based on differential evolution to form groups of the population. The niching technique also incorporates a balancing strategy to average the size of niches and a generation strategy to add new elements by finding promising areas. We have also proposed an archive strategy to store the possible optimal solutions that help to explore the decision space and ensure diversity as well as the convergence of the solutions. To show the efficiency of our proposed algorithm, we have performed exhaustive experimentation on the benchmark test suite and compared the results with state-of-the-art algorithms. The experimental results obtained using the proposed algorithm highlight the efficiency and effectiveness of our proposed algorithm in both decision and objective space.

A two-stage Bayesian model updating framework based on an iterative model reduction technique using modal responses

A two-stage Bayesian model updating framework based on an affine-invariance sampling in Transitional Markov Chain Monte Carlo (TMCMC) algorithm is proposed. In the first stage, unknown modal coordinates are identified with an improved iterative model reduction technique. Subsequently, a modified TMCMC algorithm is proposed where obtaining the statistical estimators in the usual TMCMC algorithm is not necessary. In detail, an affine-invariance sampling approach is proposed to estimate a multi-dimensional scaling (MDS) factor in the affine-transformed space that accounts for the frequencies and mode shapes which are of different natures and dimensions at the levels of likelihood, prior and posterior distributions. The TMCMC sampling adaptively utilizes the plausibility value from the proposed MDS-based tuning algorithm to improve its transition levels. The proposed algorithm in the affine-invariance sampling space accounting for the observables of different dimensions is expected to facilitate the estimation of the posterior distribution of model parameters. The effectiveness of the proposed algorithm is demonstrated by considering a simply-supported steel beam, a ten-storied reinforced concrete building model and an eight-degree-of-freedom spring–mass model for which experimental data is available. The second example demonstrates the applicability of the approach for updating large finite element models involving substructuring.