Adaptive Space Research Articles

Investigation on a novel reliability analysis approach integrating adaptive space division and direction sampling

Multitudes of algorithms have been proposed to evaluate the failure probability of components and systems. Among all these algorithms, direction sampling is a promising one. However, a major computational effort involved in direction sampling is that for each direction sample a root-searching process needs to be conducted to obtain the distance between the origin and the failure surface, which can be computationally expensive. In addition, the failure probability along a direction is hard to obtain when the direction vector intersects the failure surface multiple times. This paper proposes a novel approach to obtain densely populated points locating on the failure surface and utilizing these points to conduct reliability analysis with direction sampling. The process of obtaining the population consists in dividing the initial hypercube space into multiple lattice grids, identifying the grids crossing the failure surface, and dividing these grids into smaller ones. The iterative division process stops when the grids cut across by the failure surface are small enough so that the centers of these grids can be considered located on the failure surface. Several numerical tests are performed to validate the applicability of the proposed approach. Through these tests, it can be concluded that for the situation where the intersection between the direction vector and the failure surface is unique, the proposed approach will lead, with high accuracy, to a failure probability. For the situation where the intersection is not unique, satisfactory estimation of failure probabilities can also be achieved using approximation methods. For high-dimensional reliability problems, clustering techniques can be utilized to reduce the computational cost.

Adaptive Indexing in High-Dimensional Metric Spaces

Similarity search in high-dimensional metric spaces is routinely used in many applications including content-based image retrieval, bioinformatics, data mining, and recommender systems. Search can be accelerated by the use of an index. However, constructing a high-dimensional index can be quite expensive and may not pay off if the number of queries against the data is not large. In these circumstances, it is beneficial to construct an index adaptively , while responding to a query workload. Existing work on multidimensional adaptive indexing partitions space into orthotopes (i.e., hyperrectangular units). This approach, however, is highly ineffective in high-dimensional spaces. In this paper, we propose AV-tree: an alternative method for adaptive high-dimensional indexing that exploits previously computed distances, using query centers as vantage points. Our experimental study shows that AV-tree yields cumulative cost for the first several hundred or even thousand queries much lower than that of pre-built indices. After thousands of queries, the per-query performance of the AV-tree converges or even surpasses that of the state-of-the-art MVP-tree. Arguably, our approach is commendable in environments where the expected number of queries is not large while there is a need to start answering queries as soon as possible, such as applications where data are updated frequently and past data soon become obsolete.

Energy-Efficient Collaborative Multi-Access Edge Computing via Deep Reinforcement Learning

The joint problem of task offloading, collaborative computing and resource allocation for Multi-access Edge Computing (MEC) is a challenging issue. In this paper, splitting computing tasks at MEC servers through collaboration among MEC servers and a cloud server, we investigate the joint problem of collaborative task offloading and resource allocation. A collaborative task offloading, computing resource allocation, and subcarrier and power allocation problem in MEC is formulated. The goal is to minimize the total energy consumption of the MEC system while satisfying a delay constraint. The formulated problem is a non-convex mixed-integer optimization problem. In order to solve the problem, we propose a Deep Reinforcement Learning (DRL) based bi-level optimization framework. The task offloading decision, computing collaboration decision, and power and subcarriers allocation subproblems are solved at the upper-level, while the computing resource allocation subproblem is solved at the lower-level. We combine Dueling-Deep-Q-Network (DQN), Double-DQN and add adaptive parameter space Noise (D <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> N-DQN) to improve DRL performance in MEC. Simulation results demonstrate that the proposed algorithm achieves near-optimal performance in Energy Efficiency (EE) and task completion rate compared to other DRL-based approaches and other benchmark schemes under various network parameter settings.

Adaptive window space direction laser speckle contrast imaging to improve vascular visualization.

Vascular visualization is crucial in monitoring, diagnosing, and treating vascular diseases. Laser speckle contrast imaging (LSCI) is widely used for imaging blood flow in shallow or exposed vessels. However, traditional contrast computation using a fixed-sized sliding window introduces noise. In this paper, we propose dividing the laser speckle contrast image into regions and using the variance criterion to extract pixels more suitable for the corresponding regions for calculation, and changing the shape and size of the analysis window at the vascular boundary regions. Our results show that this method has a higher noise reduction and better image quality in deeper vessel imaging, revealing more microvascular structure information.

An adaptive continuous sliding mode feedback linearization task space control for robot manipulators

Two main domains have been followed; joint space control and operational (task) space control for robot manipulators. Task space control is simpler than joint space control as it avoids tackling the robot inverse kinematics which is very complex and challenging. However, in order to design robust and accurate controllers in operational space, both kinematics and dynamics uncertainties must be considered. Additionally, singularities that may occur while mapping between joint space and task space should be addressed. To tackle these issues, a new adaptive task space control approach is developed in this paper. The developed approach is able to overcome uncertainties and mitigate the chattering that commonly occur during the control process. The introduced control system has been proven to be globally stable using a designed Lyapunov function. The simulation results show that the suggested model is able to control the robot manipulators efficiently even in the existence of external disturbances.

Stand spatial structure outcomes of forest adaptation treatments in northern hardwood forests in North America

Spatial arrangement of trees is determined by a complex suite of factors, including disturbance history, competition, and resource availability. These spatial patterns drive adaptive capacity by influencing arrangement of growing space, neighborhood competitive relationships, and disturbance response, with irregular patterns supporting higher adaptive capacity. While spatial structure in relation to disturbance and climate change resilience has been studied in dry conifer forests and old-growth temperate forests, it has never been explored in the context of climate adaptive management in mesic, second-growth forests. To address this gap, we analyzed tree spatial patterns in second-growth northern hardwood forests under four different climate adaptation management approaches: no action; resistance or resilience to impacts of climate change; and transition to future-adapted forest types. We used spatial point statistics approaches to describe how patterns differed among the four treatments. We found that the treatments focused on future adaptation led to patterns with variable tree spacing and clumping, while those focused on perpetuating current conditions resulted in less pattern variation. This indicates that adaptation strategies that include uneven-aged regeneration methods that restore and maintain tree spatial patterns historically generated by gap dynamics can be successful in altering resource availability patterns and adaptation space in forest stands.

A global-local hybrid strategy with adaptive space reduction search method for structural health monitoring

The difficulty in computational convergence poses challenges of application for traditional heuristic optimization algorithms to solve the optimization-based structural identification problem, especially for the large-scale and complex structural systems where considerable number of unknown parameters and degrees of freedom involved. Unlike the classic identification methods, in this paper, a novel hybrid strategy, coarsely exploring the relatively large search limits with the improved Jaya algorithm and adaptive search space reduction method in the global stage, and then fine-tuning the identified best solution with local optimization methods to the optimum in the local stage, is proposed and evaluated. The improved Jaya algorithm includes three improvements compared to its original version, fuzzy clustering competitive learning, experience learning and Cauchy mutation mechanisms. Gradient based Levenberg-Marquardt method, sequential quadratic programming method and non-gradient based Nelder-Mead simplex method are inserted as local mathematical optimizers to further enhance identification accuracy and efficiency. The superiority of proposed improved Jaya algorithm is validated in optimizing classical and CEC05 benchmark functions by comparing with several state-of-the-art algorithms. Furthermore, the effectiveness of proposed global-local hybrid method is verified by a numerical example of truss structure and an experimental test of the steel grid benchmark structure with incomplete set of noise-polluted measurements. The statistical results show that the improved Jaya algorithm and adaptive search space reduction method combined with sequential quadratic programming can achieve better performance in structural damage identification than other methods.

Adaptive autoencoder latent space tuning for more robust machine learning beyond the training set for six-dimensional phase space diagnostics of a time-varying ultrafast electron-diffraction compact accelerator.

We present a general adaptive latent space tuning approach for improving the robustness of machine learning tools with respect to time variation and distribution shift. We demonstrate our approach by developing an encoder-decoder convolutional neural network-based virtual 6D phase space diagnostic of charged particle beams in the HiRES ultrafast electron diffraction (UED) compact particle accelerator with uncertainty quantification. Our method utilizes model-independent adaptive feedback to tune a low-dimensional 2D latent space representation of ∼1 million dimensional objects which are the 15 unique 2D projections (x,y),...,(z,p_{z}) of the 6D phase space (x,y,z,p_{x},p_{y},p_{z}) of the charged particle beams. We demonstrate our method with numerical studies of short electron bunches utilizing experimentally measured UED input beam distributions.

Fusing feature and output space for unsupervised domain adaptation on medical image segmentation

AbstractImage segmentation requires large amounts of annotated data. However, collecting massive datasets with annotations is difficult since they are expensive and labor‐intensive. The unsupervised domain adaptation (UDA) for image segmentation is a promising approach to address the label‐scare problem on the target domain, which enables the trained model on the source labeled domain to be adaptive to the target domain. The adversarial‐based methods encourage extracting the domain‐invariant features by training a domain discriminator to mitigate the domain gap. Existing UDA segmentation methods fail to obtain satisfied segmentation results as they only consider the global knowledge of output space while neglecting the local information of feature space. In this paper, a fusing feature and output (FFO) space method is proposed for UDA, which in the context of medical image segmentation. The proposed model is learned by training a more powerful domain discriminator, which considers features extracted from both feature space and output space. Extensive experiments carried out on several medical image datasets show the adaptation effectiveness of our approach in improving the segmentation performance.

GNSS array receiver faced with overloaded interferences: anti-jamming performance and the incident directions of interferences

Anti-jamming solutions based on antenna arrays enhance the anti-jamming ability and the robustness of global navigation satellite system (GNSS) receiver remarkably. However, the performance of the receiver will deteriorate significantly in the overloaded interferences scenario. We define the overloaded interferences scenario as where the number of interferences is more than or equal to the number of antenna arrays elements. In this paper, the effect mechanism of interferences with different incident directions is found by studying the anti-jamming performance of the adaptive space filter. The theoretical analysis and conclusions, which are first validated through numerical examples, reveal the relationships between the optimal weight vector and the eigenvectors of the input signal autocorrelation matrix, the relationships between the interference cancellation ratio (ICR), the signal to interference and noise power ratio (SINR) of the adaptive space filter output and the number of interferences, the eigenvalues of the input signal autocorrelation matrix. In addition, two simulation experiments are utilized to further corroborate the theoretical findings through soft anti-jamming receiver. The simulation results match well with the theoretical analysis results, thus validating the effect mechanism of overloaded interferences. The simulation results show that, for a four elements circular array, the performance difference is up to 19 dB with different incident directions of interferences. Anti-jamming performance evaluation and jamming deployment optimization can obtain more accurate and efficient results by using the conclusions.

Architectural space as an open, adaptable system: A design experiment

Flexible and adaptable spaces have been a field of interest in western architecture since the 1930s and Modernism’s “Open plan.” Even though it started as a design problem, as cybernetics became a more active part of the design process, adaptable space became dependent on technology and mechanical systems. A lot of methods tend to diminish the role of architectural design and offer deterministic implementations that limit the user’s creative freedom and interaction with the adaptable system. This study explores how adaptable space can be approached through design and how adaptability can be implemented in existing spaces through a computational approach. The paper is structured in two parts, the analysis, and the concept method. The first part includes the literacy review and known architectural examples of adaptability. The second part focuses on proposing a design method for approaching adaptable spaces with a high degree of user creative freedom. The method is tested through a case study regarding the implementation of a spatial adaptable system in a 1970s apartment.

Direct Adaption Methods for Linear and Circular Antenna Arrays

Introduction. The mitigation of interferences that degrade the performance of navigation systems constitutes one of the most significant problems of contemporary satellite navigation. This problem is conventionally solved using digital adaptive space filters. Depending on a particular radio technical system, the mathematical description of digital signal processing methods may involve specific calculation structures implemented using specific calculation algorithms. For example, the use of centrosymmetric linear and circular antenna arrays in a radio navigation system allows the description of such systems in terms of Toeplitz and circulant sample covariance matrices, respectively, and the inversion of such matrices by means of special numerical methods in order to design a digital filter.Aim. A comparative analysis of the performance of space signal processing algorithms is carried out along with an estimation of Toeplitz and circulant sample covariance matrices and numerical methods of their inversion. The previously obtained results in this field are clarified.Materials and methods. An analysis of algorithm performance was carried out in the MATLAB environment using experimental recordings of satellite navigation signals and jammers obtained by an actual radio technical system.Results. A new expression was derived for estimating circulant sample covariance matrices. Formulae that describe a modification of the Bareiss numerical Toeplitz matrix inversion algorithm for the case of complex Hermitian matrix were introduced. An analysis of the results of computer simulation allowed the algorithms with the highest performance to be indicated. The amount of time taken by the algorithms based on Toeplitz and circulant matrices did not exceed 2.5 10⋅ −3 s and 0.04 s, respectively. The carrier-to-noise ratio in the processed signal was at least 46 dB. Conclusion. The formulae obtained and the algorithms analyzed can be used when implementing adaptive digital filtering of satellite navigation signals.

Adaptive space filtering scheme with allocated main channel in digital phased antenna array

Adaptive space filtering scheme with allocated main channel in digital phased antenna array

The Perceptions and Use of Urban Neighborhood Parks Since the Outbreak of COVID-19: A Case Study in South Korea.

As the COVID-19 pandemic continues, the stress of city dwellers is increasing, and some adapt to the pandemic by pursuing physical and psychological well-being in neighborhood parks. To improve the resilience of the social-ecological system against COVID-19, it is important to understand the mechanism of adaptation by examining the perception and use of neighborhood parks. The purpose of this study is to investigate users' perceptions and use of urban neighborhood parks since the outbreak of COVID-19 in South Korea using systems thinking. To verify the hypotheses about the relationship between variables involved in COVID-19 adaptive feedback, two research objectives were set. First, this study determined the causal structure leading to park visits using systems thinking. Second, the relationship between stress, motivation, and the frequency of visits to neighborhood parks was empirically verified. To conduct the research, the system of use and perceptions of parks were analyzed through a causal loop diagram to determine the feedback between psychological variables. Then, a survey was conducted to verify the relationship between stress, motivation for visits, and visit frequency, which are the major variables derived from the causal structure. A total of three feedback loops were derived in the first step, including a loop in which COVID-19 stress was relieved by visits to parks and a loop in which COVID-19 stress worsened due to crowding in parks. Finally, the relationship of stress leading to park visits was confirmed, and the empirical analysis showed that anger about contagion and social disconnection were linked as motives for park visits, and that park visits were mainly motivated by the desire to go out. The neighborhood park functions as an adaptive space for the stress of COVID-19 and will maintain its role as social distancing becomes more important to various socio-ecological changes. The strategies driven by the pandemic can be adapted in park planning to recover from stress and improve resilience.

Bayesian asymmetric quantized neural networks

This paper develops a robust model compression for neural networks via parameter quantization. Traditionally, quantized neural networks (QNN) were constructed by binary or ternary weights where the weights were deterministic. This paper generalizes QNN in two directions. First, M-ary QNN is developed to adjust the balance between memory storage and model capacity. The representation values and the quantization partitions in M-ary quantization are mutually estimated to enhance the resolution of gradients in neural network training. A flexible quantization with asymmetric partitions is formulated. Second, the variational inference is incorporated to implement the Bayesian asymmetric QNN. The uncertainty of weights is faithfully represented to enhance the robustness of the trained model in presence of heterogeneous data. Importantly, the multiple spike-and-slab prior is proposed to represent the quantization levels in Bayesian asymmetric learning. M-ary quantization is then optimized by maximizing the evidence lower bound of classification network. An adaptive parameter space is built to implement Bayesian quantization and neural representation. The experiments on various image recognition tasks show that M-ary QNN achieves similar performance as the full-precision neural network (FPNN), but the memory cost and the test time are significantly reduced relative to FPNN. The merit of Bayesian M-ary QNN using multiple spike-and-slab prior is investigated.

Apollo: Adaptive Polar Lattice-Based Local Obstacle Avoidance and Motion Planning for Automated Vehicles.

The motion planning module is the core module of the automated vehicle software system, which plays a key role in connecting its preceding element, i.e., the sensing module, and its following element, i.e., the control module. The design of an adaptive polar lattice-based local obstacle avoidance (APOLLO) algorithm proposed in this paper takes full account of the characteristics of the vehicle's sensing and control systems. The core of our approach mainly consists of three phases, i.e., the adaptive polar lattice-based local search space design, the collision-free path generation and the path smoothing. By adjusting a few parameters, the algorithm can be adapted to different driving environments and different kinds of vehicle chassis. Simulations show that the proposed method owns strong environmental adaptability and low computation complexity.

Identifying opportunities for living shorelines using a multi-criteria suitability analysis

The need to develop more sustainable solutions for coastal hazard risk reduction and to protect and restore degraded coastal habitats has led to an increased interest in living shorelines. A barrier to the wider implementation of living shorelines is a lack of guidance on where it is suitable to implement these solutions. We developed a shoreline suitability model to select areas of a representative coastline that would be either suitable for a soft (natural habitats only) or hybrid (natural habitats in combination with hard structures) approaches. We created species distribution models in MaxEnt to predict the potential distribution of 14 coastal species including four seagrasses, one mangrove, three saltmarsh, three shellfish and three dune species. These were combined in a multi-criteria analysis that also accounted for the accommodation (current space in the intertidal) and adaptation space (amount of space between the intertidal and nearest infrastructure) available to implement living shorelines at a 250 m resolution. This was done for the state of Victoria, Australia as a case study location where there is a high percentage of coastal infrastructure reaching the end of its design life. For the Victorian coastline 74% was suitable for hybrid approaches, while 65% was suitable for soft approaches and 4% of the coastline was not suitable for either approach. For the coastline already protected with hard defence structures, 67 and 69% would be suitable for at least one taxa, using a soft or hybrid approach, respectively. The percentage of coastline suitable for soft or hybrid approaches was similar in rural areas, however, suitability for hybrid was greater than soft approaches in urban and built-up areas, which could be due to a combination of habitat suitability and space available on the foreshore. This study has demonstrated how spatial multi-criteria analysis can be adapted to a complex coastal environment and inform more diverse coastal hazard mitigation actions to risk reduction and climate adaptation.

HYDI-DSI revisited: Constrained non-parametric EAP imaging without q-space re-gridding.

Hybrid Diffusion Imaging (HYDI) was one of the first attempts to use multi-shell samplings of the q-space to infer diffusion properties beyond Diffusion Tensor Imaging (DTI) or High Angular Resolution Diffusion Imaging (HARDI). HYDI was intended as a flexible protocol embedding both DTI (for lower b-values) and HARDI (for higher b-values) processing, as well as Diffusion Spectrum Imaging (DSI) when the entire data set was exploited. In the latter case, the spherical sampling of the q-space is re-gridded by interpolation to a Cartesian lattice whose extent covers the range of acquired b-values, hence being acquisition-dependent. The Discrete Fourier Transform (DFT) is afterwards used to compute the corresponding Cartesian sampling of the Ensemble Average Propagator (EAP) in an entirely non-parametric way. From this lattice, diffusion markers such as the Return To Origin Probability (RTOP) or the Mean Squared Displacement (MSD) can be numerically estimated. We aim at re-formulating this scheme by means of a Fourier Transform encoding matrix that eliminates the need for q-space re-gridding at the same time it preserves the non-parametric nature of HYDI-DSI. The encoding matrix is adaptively designed at each voxel according to the underlying DTI approximation, so that an optimal sampling of the EAP can be pursued without being conditioned by the particular acquisition protocol. The estimation of the EAP is afterwards carried out as a regularized Quadratic Programming (QP) problem, which allows to impose positivity constraints that cannot be trivially embedded within the conventional HYDI-DSI. We demonstrate that the definition of the encoding matrix in the adaptive space allows to analytically (as opposed to numerically) compute several popular descriptors of diffusion with the unique source of error being the cropping of high frequency harmonics in the Fourier analysis of the attenuation signal. They include not only RTOP and MSD, but also Return to Axis/Plane Probabilities (RTAP/RTPP), which are defined in terms of specific spatial directions and are not available with the former HYDI-DSI. We report extensive experiments that suggest the benefits of our proposal in terms of accuracy, robustness and computational efficiency, especially when only standard, non-dedicated q-space samplings are available.

Residual‐based a posteriori error estimates for nonconforming finite element approximation to parabolic interface problems

AbstractIn this paper, we derive a residual‐based a posteriori error estimates for nonconforming finite element approximation to parabolic interface problems. The present approach does not involve the Helmholtz decomposition while analyzing the reliability of the estimator. The constants involved in the estimators are independent of the jump of the diffusion coefficient across the interface, and the quasi‐monotonocity assumption on the diffusion coefficient is relaxed. The reliability bound of the estimator consists of the element residual, the edge flux jump and the edge solution jump. The efficiency of the estimator is analyzed by employing a coarsening strategy introduced by Chen and Feng's study. We derive both global upper bound and a local lower bound for the error and an adaptive space–time algorithm is prescribed using the derived estimators. Numerical results illustrating the behavior of the estimators are provided.

Agile project management as a stage for creativity: a conceptual framework of five creativity-conducive spaces

PurposeThe purpose of this paper is to demonstrate how agile project management can foster creativity in project teams.Design/methodology/approachThe study is based on an extensive literature review of agile project management and team creativity and is matching these two to answer the following research questions: (1) how agile project management approach can foster creativity in project teams? and (2) which principles and practices promoted by the most popular agile methodologies enhance creativity in project teams?FindingsFive creativity-conducive spaces in agile project management were identified and integrated into a conceptual framework, namely, a space for generative social interactions, a space for learning, a space for change and adaptation, a space for exploration and a space promoting team members' well-being. In the next step, based on a thorough analysis of seven widespread agile project management methods, a large number of agile principles and practices were mapped into each of the five conceptual spaces.Originality/valueThis study provides new insights into how agile project management can foster creativity in project teams. The conceptual framework developed in this paper might be utilized to enhance creativity in agile teams, it can also serve as a starting point for future research.