Recursive Sampling Research Articles

Improved Compound Correction‐Electrical Equivalent Circuit Modeling and Double Transform‐Unscented Kalman Filtering for the High‐Accuracy Closed‐Circuit voltage and State‐of‐Charge Co‐Estimation of Whole‐Life‐Cycle Lithium‐Ion batteries

For complex energy storage conditions, it is necessary to monitor the state‐of‐charge (SOC) and closed‐circuit voltage (CCV) status accurately for the reliable power supply application of lithium‐ion batteries. Herein, an improved compound correction‐electrical equivalent circuit modeling (CC‐EECM) method is proposed by considering the influencing effects of ambient temperature and charge–discharge current rate variations to estimate the CCV. Then, an improved adaptive double transform‐unscented Kalman filtering (ADT‐UKF) method is constructed with recursive sampling data correction to estimate the nonlinear SOC. A dynamic window function filtering strategy is constructed to obtain the new sigma point set for the online weighting coefficient correction. For a temperature range of 5–45 °C, the CCV for the improved CC‐EECM responds well with a maximum error of 0.008608 V, and the maximum SOC estimation error is 6.317%. The proposed ADT‐UKF method improves the CCV and SOC estimation reliability and adaptability to the time‐varying current rate, temperature, and aging factors.

An effort saving method to establish global aerodynamic model using CFD

Purpose The typical approach of modeling the aerodynamics of an aircraft is to develop a complete database through testing or computational fluid dynamics (CFD). The database will be huge if it has a reasonable resolution and requires an unacceptable CFD effort during the conceptional design. Therefore, this paper aims to reduce the computing effort required via establishing a general aerodynamic model that needs minor parameters. Design/methodology/approach The model structure was a preconfigured polynomial model, and the parameters were estimated with a recursive method to further reduce the calculation effort. To uniformly disperse the sample points through each step, a unique recursive sampling method based on a Voronoi diagram was presented. In addition, a multivariate orthogonal function approach was used. Findings A case study of a flying wing aircraft demonstrated that generating a model with acceptable precision (0.01 absolute error or 5% relative error) costs only 1/54 of the cost of creating a database. A series of six degrees of freedom flight simulations shows that the model’s prediction was accurate. Originality/value This method proposed a new way to simplify the model and recursive sampling. It is a low-cost way of obtaining high-fidelity models during primary design, allowing for more precise flight dynamics analysis.

The hedge asset for BRICS stock markets: Bitcoin, gold or VIX

AbstractWe compare the weak/strong hedging abilities of three alternative assets, namely bitcoin, gold and US VIX futures, against the downside movements in BRICS stock market indices. Results from the cross‐quantilogram approach indicate that bitcoin and gold are weak hedges. Analysis from the recursive sampling shows that each of bitcoin, gold and VIX futures has a time‐varying hedging role in some BRICS countries, which has been shaped by the COVID‐19 outbreak. Results from the conditional diversification benefits show appealing roles for the three alternative assets for investors in BRICS stock markets. However, gold appears to have higher and more stable diversification benefits in China, especially during the COVID‐19 outbreak. Conversely, VIX futures offer higher diversification benefits in Brazil, Russia, India and South Africa during the abrupt of the COVID‐19 outbreak.

Hierarchical Density-Based Clustering Using MapReduce

Hierarchical density-based clustering is a powerful tool for exploratory data analysis, which can play an important role in the understanding and organization of datasets. However, its applicability to large datasets is limited because the computational complexity of hierarchical clustering methods has a quadratic lower bound in the number of objects to be clustered. MapReduce is a popular programming model to speed up data mining and machine learning algorithms operating on large, possibly distributed datasets. In the literature, there have been attempts to parallelize algorithms such as Single-Linkage, which in principle can also be extended to the broader scope of hierarchical density-based clustering, but hierarchical clustering algorithms are inherently difficult to parallelize with MapReduce. In this paper, we discuss why adapting previous approaches to parallelize Single-Linkage clustering using MapReduce leads to very inefficient solutions when one wants to compute density-based clustering hierarchies. Preliminarily, we discuss one such solution, which is based on an exact, yet very computationally demanding, random blocks parallelization scheme. To be able to efficiently apply hierarchical density-based clustering to large datasets using MapReduce, we then propose a different parallelization scheme that computes an approximate clustering hierarchy based on a much faster, recursive sampling approach. This approach is based on HDBSCAN*, the state-of-the-art hierarchical density-based clustering algorithm, combined with a data summarization technique called data bubbles. The proposed method is evaluated in terms of both runtime and quality of the approximation on a number of datasets, showing its effectiveness and scalability.

Improving the Reliability of Underwater Gravity Matching Navigation Based on a Priori Recursive Iterative Least Squares Mismatching Correction Method

This study focuses on the reliability of underwater gravity matching navigation. Firstly, the research started with the post-processing of mismatching. Under the guidance of priori recursive multiple matching and iterative least squares, a new priori recursive iterative least squares mismatching correction (PRILSMC) method was proposed based on statistics and the fitting principle. Secondly, according to factors such as matching algorithm probability, error of INS, and the actual situation, we comprehensively considered the effects of recursive sampling points, priori matching points, etc. The new mismatching judgment and dynamic correction (MJDC) model was constructed based on the PRILSMC method. Finally, under the same conditions, the MJDC model was used to verify a new matching point in three matching regions. The results showed that in the excellent-suitability region, the matching probability increased from about 96% to 100%, i.e., all mismatching points could essentially be eliminated. In the general-suitability region, the matching probability increased from about 64% to 92%, indicating that the probability of mismatching point was greatly reduced, and the reliability of the matching navigation was improved.

Prediction of Structure and Molecular Interaction with DNA of BvrR, a Virulence-Associated Regulatory Protein of Brucella.

Brucellosis, also known as “undulant fever” is a zoonotic disease caused by Brucella, which is a facultative intracellular bacterium. Despite efforts to eradicate this disease, infection in uncontrolled domestic animals persists in several countries and therefore transmission to humans is common. Brucella evasion of the innate immune system depends on its ability to evade the mechanisms of intracellular death in phagocytic cells. The BvrR-BvrS two-component system allows the bacterium to detect adverse conditions in the environment. The BvrS protein has been associated with genes of virulence factors, metabolism, and membrane transport. In this study, we predicted the DNA sequence recognized by BvrR with Gibbs Recursive Sampling and identified the three-dimensional structure of BvrR using I-TASSER suite, and the interaction mechanism between BvrR and DNA with Protein-DNA docking and molecular dynamics (MD) simulation. Based on the Gibbs recursive Sampling analysis, we found the motif AAHTGC (H represents A, C, and T nucleotides) as a possible sequence recognized by BvrR. The docking and EMD simulation results showed that C-terminal effector domain of BvrR protein is likely to interact with AAHTGC sequence. In conclusion, we predicted the structure, recognition motif, and interaction of BvrR with DNA.

A Recursive Subdivision Technique for Sampling Multi-class Scatterplots.

We present a non-uniform recursive sampling technique for multi-class scatterplots, with the specific goal of faithfully presenting relative data and class densities, while preserving major outliers in the plots. Our technique is based on a customized binary kd-tree, in which leaf nodes are created by recursively subdividing the underlying multi-class density map. By backtracking, we merge leaf nodes until they encompass points of all classes for our subsequently applied outlier-aware multi-class sampling strategy. A quantitative evaluation shows that our approach can better preserve outliers and at the same time relative densities in multi-class scatterplots compared to the previous approaches, several case studies demonstrate the effectiveness of our approach in exploring complex and real world data.

Chebyshev Partition-Based Modulation Technique Applied to Power Converters

In this paper, a new modulation technique is proposed for three-phase voltage-source inverters (VSIs). It is based on time partition obtained by Chebyshev polynomials’ zeros of the first kind which are used to build up nonuniform recursive sampling. A detailed mathematical description of the method and its implementation are given. Obtained results show that the proposed technique gives a total harmonic distortion factor lesser than conventional sinusoidal pulsewidth modulation under the same loading conditions. By comparing results obtained by proposed technique with results carried out by the wavelet modulation method, it is shown that they are very close. The proposed modulation returns lesser switching losses in comparison to sinusoidal pulsewidth modulation and space vector modulation. A MATLAB code that generates switching pulses to activate a Simulink model of the three-phase VSIs is developed to implement the proposed Chebyshev modulation technique. Experiments verify the correctness of the proposed method.

Resilience-Constrained Hourly Unit Commitment in Electricity Grids

This paper proposes a resilience-constrained unit commitment (RCUC) framework in which the system operation constraints, heterogeneity of power flow distribution and lines forced outages are simultaneously addressed. The proportional hazard model (PHM) is adopted to shape line forced outage rates with the cumulative effect of weather conditions and line loading rates. A sequential and Monte Carlo-based framework is established for RCUC using the recursive sampling process. The RCUC solution in each period affects the previously accumulated outage probabilities, thus it would have an impact on the outage process. In turn, the sampled line outages affect the RCUC solution in the next period and the loading of remaining lines. This is a sequential process in which the RCUC and the outage sampling of power system components are computed alternatively until the scheduling horizon is culminated. During each period, two penalty terms are introduced into the RCUC model to seek a tradeoff between operation cost and the homogeneity of flow distribution in power network and the loading rates of local lines affected by extreme weather. Since the penalty terms are modeled by absolute value functions, a general linearization method for absolute value functions in the mix-integer programming is presented. The overall convergence of the Monte Carlo technique is quantified by the coefficient of variation of total costs over multiple simulations. If the convergence criterion is not satisfied, the Monte Carlo solution approach will be continued. The case studies illustrate the effectiveness of the proposed RCUC model.

Critical Components for Maintenance Outage Scheduling Considering Weather Conditions and Common Mode Outages in Reconfigurable Distribution Systems

This paper proposes a model for identifying critical components that affect the maintenance outage scheduling in reconfigurable distribution systems. The model considers critical factors such as prevailing severe weather conditions and component aging conditions, common mode outages, and repair rates in distribution systems. The effects of critical factors on forced outage rates of distribution system components are quantified by the proportional hazard model. A recursive sampling method is proposed to generate Monte Carlo scenarios with the given forced outage rate. In each scenario, the distribution system reconfiguration is optimized efficiently using a standard mixed-integer second-order cone program. Then absolute and relative criticality indices are calculated to describe the criticality of components. A new scenario is generated subsequently and the process is continued in the following iteration until the convergence condition is satisfied. The overall convergence of multiple scenarios is quantified by the coefficient of variation of criticality index. The final absolute and relative criticality indices are the average over multiple scenarios. The influence of component aging condition and forced outage characteristics, weather condition, and the system reconfiguration on component criticality for the maintenance outage scheduling is distinguished and exhibited in case studies. The case studies illustrate the effectiveness of proposed model and the solution method for identifying the critical components.

Stochastic Co-Optimization of Midterm and Short-Term Maintenance Outage Scheduling Considering Covariates in Power Systems

This paper proposes an integrated framework based on covariates, which coordinates short-term generation and transmission maintenance scheduling with midterm maintenance decisions by considering the effects of short-term security-constrained unit commitment (SCUC). A recursive sampling method is introduced in the proposed Monte Carlo-based framework for generating scenarios, in which the effects of component aging and covariates on the outage process are quantified by the proportional hazard model (PHM). For each sampled scenario, an iterative dynamic scenario updating approach is introduced to consider interactions among covariate conditions, random component outages, and maintenance outage scheduling. The co-optimization problem is decoupled into three separate optimization subproblems by Lagrangian relaxation (LR), which include generation maintenance scheduling, transmission maintenance scheduling, and short-term SCUC problems. Each scenario is dynamically updated based on the optimal maintenance outage and SCUC solutions, and maintenance and SCUC solutions are re-optimized using the updated scenario. The iterative procedure stops when neither the optimal schedule nor the dynamic scenario changes any further. The overall convergence of the proposed Monte Carlo-based framework is checked by the coefficient of variation (CV) of costs over multiple scenarios. Case studies on the 6-bus system and the IEEE 118-bus system are used to exhibit the effectiveness of proposed framework.

Reconstructing media frames in multimodal discourse: The John/Ivan Demjanjuk trial

This article explores a way to reconstruct the verbally and visually constituted frames used in the coverage of the trial of John/Ivan Demjanjuk, a Ukraine-born U.S. citizen accused of holocaustrelated war crimes. The study looks at an exemplary case of current multimodal discourse, in which written messages and images from broadcasts and press, as well as the comments and visuals that spread through social media, can be seen to relate to each other in framing public issues. To establish a viable perspective that takes into account both the communicative organisation and the semiotic constitution of such discourses, this analysis combines approaches from frame semantics and social semiotics together with recursive sampling and coding. The article then explains the analytical procedures used to reconstruct the framing of the accused as either a responsible culprit or a victim of circumstances.

Monocular 3-D Gait Tracking in Surveillance Scenes

Gait recognition can potentially provide a noninvasive and effective biometric authentication from a distance. However, the performance of gait recognition systems will suffer in real surveillance scenarios with multiple interacting individuals and where the camera is usually placed at a significant angle and distance from the floor. We present a methodology for view-invariant monocular 3-D human pose tracking in man-made environments in which we assume that observed people move on a known ground plane. First, we model 3-D body poses and camera viewpoints with a low dimensional manifold and learn a generative model of the silhouette from this manifold to a reduced set of training views. During the online stage, 3-D body poses are tracked using recursive Bayesian sampling conducted jointly over the scene's ground plane and the pose-viewpoint manifold. For each sample, the homography that relates the corresponding training plane to the image points is calculated using the dominant 3-D directions of the scene, the sampled location on the ground plane and the sampled camera view. Each regressed silhouette shape is projected using this homographic transformation and is matched in the image to estimate its likelihood. Our framework is able to track 3-D human walking poses in a 3-D environment exploring only a 4-D state space with success. In our experimental evaluation, we demonstrate the significant improvements of the homographic alignment over a commonly used similarity transformation and provide quantitative pose tracking results for the monocular sequences with a high perspective effect from the CAVIAR dataset.

A Two-Pass Exact Algorithm for Selection on Parallel Disk Systems.

Numerous OLAP queries process selection operations of "top N", median, "top 5%", in data warehousing applications. Selection is a well-studied problem that has numerous applications in the management of data and databases since, typically, any complex data query can be reduced to a series of basic operations such as sorting and selection. The parallel selection has also become an important fundamental operation, especially after parallel databases were introduced. In this paper, we present a deterministic algorithm Recursive Sampling Selection (RSS) to solve the exact out-of-core selection problem, which we show needs no more than (2 + ε) passes (ε being a very small fraction). We have compared our RSS algorithm with two other algorithms in the literature, namely, the Deterministic Sampling Selection and QuickSelect on the Parallel Disks Systems. Our analysis shows that DSS is a (2 + ε)-pass algorithm when the total number of input elements N is a polynomial in the memory size M (i.e., N = Mc for some constant c). While, our proposed algorithm RSS runs in (2 + ε) passes without any assumptions. Experimental results indicate that both RSS and DSS outperform QuickSelect on the Parallel Disks Systems. Especially, the proposed algorithm RSS is more scalable and robust to handle big data when the input size is far greater than the core memory size, including the case of N ≫ Mc .

A scenario generation-based lower bounding approach for stochastic scheduling problems

In this paper, we investigate scenario generation methods to establish lower bounds on the optimal objective value for stochastic scheduling problems that contain random parameters with continuous distributions. In contrast to the Sample Average Approximation (SAA) approach, which yields probabilistic bound values, we use an alternative bounding method that relies on the ideas of discrete bounding and recursive stratified sampling. Theoretical support is provided for deriving exact lower bounds for both expectation and conditional value-at-risk objectives. We illustrate the use of our method on the single machine total weighted tardiness problem. The results of our numerical investigation demonstrate good properties of our bounding method, compared with the SAA method and an earlier discrete bounding method.

Approximating vertex cover in dense hypergraphs

We consider the Minimum Vertex Cover problem in hypergraphs in which every hyperedge has size k (also known as Minimum Hitting Set problem, or minimum set cover with element frequency k). Simple algorithms exist that provide k-approximations, and this is believed to be the best approximation achievable in polynomial time. We show how to exploit density and regularity properties of the input hypergraph to break this barrier. In particular, we provide a randomized polynomial-time algorithm with approximation factor k/(1+(k−1)d¯kΔ), where d¯ and Δ are the average and maximum degree, and Δ must be Ω(nk−1/logn). The proposed algorithm generalizes the recursive sampling technique of Imamura and Iwama (SODAʼ05) for vertex cover in dense graphs. As a corollary, we obtain an approximation factor arbitrarily close to k/(2−1/k) for subdense regular hypergraphs, which is shown to be the best possible under the Unique Games conjecture.

Rational Social Learning by Random Sampling

This paper explores rational social learning in which everyone only sees unordered random samples from the action history. In this model, herds need not occur when the distant past can be sampled. If private signal strengths are unbounded and the past is not over-sampled -- not forever affected by any individual -- there is complete learning and a correct proportionate herd. With recursive sampling, welfare almost surely converges under the new proviso that the recent past is not over-sampled. In this case, there is almost surely complete learning with unbounded beliefs and unit sample sizes. The sampling noise in this Polya urn model induces a path-dependent structure, so that re-running the model with identical signals generally produces different outcomes.

Quantum lower bound for recursive Fourier sampling

Erratum:

Algorithms for Storing and Aggregating Historical Streaming Data

The current research work over data streams is mainly focused on dealing with the arrival of recent data in memory, neglecting the analysis and management of historical streaming data. An approach is proposed to store and query historical streaming data by using multi-layer recursive sampling method and HDS-Tree structure, which indexes the aggregation of historical streaming data and supports all kinds of aggregation queries over historical streaming data. The time-complexity and the error of aggregation algorithms are also analyzed based on HDS-Tree. The analytical and experimental results show that the approach can be effectively used to store and analyze the historical streaming data.

A statistical sampling algorithm for RNA secondary structure prediction.

An RNA molecule, particularly a long-chain mRNA, may exist as a population of structures. Further more, multiple structures have been demonstrated to play important functional roles. Thus, a representation of the ensemble of probable structures is of interest. We present a statistical algorithm to sample rigorously and exactly from the Boltzmann ensemble of secondary structures. The forward step of the algorithm computes the equilibrium partition functions of RNA secondary structures with recent thermodynamic parameters. Using conditional probabilities computed with the partition functions in a recursive sampling process, the backward step of the algorithm quickly generates a statistically representative sample of structures. With cubic run time for the forward step, quadratic run time in the worst case for the sampling step, and quadratic storage, the algorithm is efficient for broad applicability. We demonstrate that, by classifying sampled structures, the algorithm enables a statistical delineation and representation of the Boltzmann ensemble. Applications of the algorithm show that alternative biological structures are revealed through sampling. Statistical sampling provides a means to estimate the probability of any structural motif, with or without constraints. For example, the algorithm enables probability profiling of single-stranded regions in RNA secondary structure. Probability profiling for specific loop types is also illustrated. By overlaying probability profiles, a mutual accessibility plot can be displayed for predicting RNA:RNA interactions. Boltzmann probability-weighted density of states and free energy distributions of sampled structures can be readily computed. We show that a sample of moderate size from the ensemble of an enormous number of possible structures is sufficient to guarantee statistical reproducibility in the estimates of typical sampling statistics. Our applications suggest that the sampling algorithm may be well suited to prediction of mRNA structure and target accessibility. The algorithm is applicable to the rational design of small interfering RNAs (siRNAs), antisense oligonucleotides, and trans-cleaving ribozymes in gene knock-down studies.