Recursive Algorithm Research Articles

Examining nonlinear effects of socioecological drivers on urban solar energy development in China using machine learning and high-dimensional data

In the context of carbon neutrality target, renewable energy sources have been transforming from “supplementary energy” to “main energy”, which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the “resource-environment-demand” comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry.

Precise prediction of CO2 separation performance of metal–organic framework mixed matrix membranes based on feature selection and machine learning

Nowadays, the price of fossil fuels keeps setting new records, escalating continuing concerns about global warming from CO2 production from fuel combustion. As a promising membrane separation technique dealing with carbon capture, metal–organic framework (MOF) mixed matrix membranes (MMMs) have been extensively studied. Herein, a genetic algorithm (GA) optimized artificial neural network (ANN) was developed to form prediction model of MOF MMMs performances towards CO2/N2 separation. The MOF properties, polymer properties, and the operating conditions were used as the characteristic variables. To overcome the limitation, molecular descriptors were incorporated to reflect the physicochemical properties of polymers and target encoding was applied to digitalize the MOF and polymer types. In addition, recursive feature elimination algorithm was used to filter the optimal feature subset and Shapley additive explanations was utilized to analyze the feature importance. The results demonstrated that the model has a dramatically improved prediction performance than other machine learning methods.

Soil moisture retrieval at high spatial resolution over alpine ecosystems on Nagqu-Tibetan plateau: A comparative study on semiempirical and machine learning approaches

Soil moisture (SM) is an essential climate variable that directly and indirectly affects vegetation growth and survival through land‒atmosphere interactions. Alpine vegetation on the Tibetan Plateau is part of a unique ecosystem that is vulnerable to changes in environmental factors such as SM; consequently, this makes this ecosystem extremely sensitive to climate change. This study investigated the potential of synthetic aperture radar (SAR) vegetation indices based on Sentinel-1 data for retrieving SM at high spatial resolution (10 m) over an alpine grassland ecosystem in the Nagqu region. Several SAR vegetation indices, including the dual polarization SAR vegetation index (DPSVI), modified dual polarization SAR vegetation index (mDPSVI), dual polarimetric radar vegetation index (DpRVI), polarimetric radar vegetation index (PRVI), and radar vegetation index (RVI), were used in the semiempirical water cloud model (WCM) to determine which indices provide better SM retrievals in this alpine ecosystem. In addition, the potential of the distributed random forest (DRF) machine learning algorithm was explored using the same variables as the WCM together with several ecohydrological parameters from different data sources. The recursive feature elimination algorithm was used to establish the optimized DRF model. Among the vegetation indices based on SAR data, DPSVI, DpRVI, and PRVI showed similar results, with DPSVI performing slightly better than the other SAR indices, with a correlation coefficient (R2) of 0.70 and root mean squared error (RMSE) of 0.04 m3m-3. A comparison of the optimized DRF with the best fitted WCM reveals that the DRF algorithm outperformed the WCM, including having more predictors (10 variables) in the model. The results show that the overall accuracies in terms of the R2 values and the RMSEs of both the WCMs and the DRF models were 0.52–0.75 and 0.08 m3 m−3 to 0.04 m3 m−3, respectively, which was validated over in situ SM measurements in the Nagqu region.

Typicality- and instance-dependent label noise-combating: a novel framework for simulating and combating real-world noisy labels for endoscopic polyp classification

Learning with noisy labels aims to train neural networks with noisy labels. Current models handle instance-independent label noise (IIN) well; however, they fall short with real-world noise. In medical image classification, atypical samples frequently receive incorrect labels, rendering instance-dependent label noise (IDN) an accurate representation of real-world scenarios. However, the current IDN approaches fail to consider the typicality of samples, which hampers their ability to address real-world label noise effectively. To alleviate the issues, we introduce typicality- and instance-dependent label noise (TIDN) to simulate real-world noise and establish a TIDN-combating framework to combat label noise. Specifically, we use the sample’s distance to decision boundaries in the feature space to represent typicality. The TIDN is then generated according to typicality. We establish a TIDN-attention module to combat label noise and learn the transition matrix from latent ground truth to the observed noisy labels. A recursive algorithm that enables the network to make correct predictions with corrections from the learned transition matrix is proposed. Our experiments demonstrate that the TIDN simulates real-world noise more closely than the existing IIN and IDN. Furthermore, the TIDN-combating framework demonstrates superior classification performance when training with simulated TIDN and actual real-world noise.

Research on Urban Architecture Design under the Concept of Smart City

After the digital city and smart city concepts, the latest proposed smart city concept is a general summary of the advanced form of urban information development, which plays an important role in the economic transformation of cities, social governance, improvement of residents' quality of life and energy conservation and environmental protection. The smart city puts forward new requirements for the architectural design of the city, and how to reasonably use the smart city concept in the architectural design is still a major issue for architects and professionals to think about. To this end, this paper proposes a state tracking based full working condition modeling method for nonlinear industrial systems. For the problem that the amount of historical data is too large and it is difficult to screen the modeling data, a sliding window is designed to screen the steady-state data, and a fast recursive algorithm of standard deviation in the window is derived; the influence mechanism of unknown disturbances on the system is analyzed, and the data modeling by dynamic regression to the steady state is selected, and a data-driven modeling algorithm that can eliminate the influence of disturbances is proposed; the model information contained in the process industrial big data is used to apply the higher order function to fit each A linear variable parameter transfer function model based on the characteristic parameters is proposed. The identification of an industrial process is shown to be effective. The main points of urban architectural design should be noted under the concept of smart city from four aspects: safety, diversity, environmental protection and emotionality.

The Weighted Least-Squares Approach to State Estimation in Linear State Space Models: The Case of Correlated Noise Terms

In this article, a particular approach to deriving recursive state estimators for linear state space models is generalised, namely the weighted least-squares approach introduced by Duncan and Horn in 1972, for the case of the two noise processes arising in such models being cross-correlated; in this context, the fact that in the available literature two different non-equivalent recursive algorithms are presented for the task of state estimation in the aforementioned case is discussed. Although the origin of the difference between these two algorithms can easily be identified, the issue has only rarely been discussed so far. Then the situations in which each of the two algorithms apply are explored, and a generalised Kalman filter which represents a merger of the two original algorithms is proposed. While, strictly speaking, optimal state estimates can be obtained only through the non-recursive weighted least-squares approach, in examples of modelling simulated and real-world data, the recursive generalised Kalman filter shows almost as good performance as the optimal non-recursive filter.

Quantum-Informed Recursive Optimization Algorithms

Quantum-Informed Recursive Optimization Algorithms

Frame-wise speech extraction with recursive expectation maximization for partially deformable microphone arrays

Large-apertured wearable and deformable microphone arrays have shown their better performance than conventional small wearable and rigid microphone arrays for speech extraction tasks, especially when the desired speech is far away from the arrays or there exist a great number of directional noises in practical applications. Most existing beamforming methods that require the information of the desired sound direction often cannot be used directly on deformable arrays because their shape might continuously change over time. To tackle the problem of utilizing partially deformable microphone arrays for target speech extraction, this paper derives a frame-wise speech extraction method, which is referred to as the recursive multichannel Wiener filter with generalized Gamma priors. In this method, the complex spectrum of the desired speech is assumed to follow a zero-mean complex Gaussian distribution with time-varying variance, and the reciprocal of this time-varying variance known as the precision is further assumed to follow a Gamma distribution, while the complex spectrum of the noise is assumed to follow a zero-mean complex Gaussian distribution. Under these two assumptions, we derive the recursive expectation maximization algorithm to extract the desired speech in a frame-by-frame manner. Experimental results show the promising performance of the proposed method in terms of multiple objective metrics.

Quantization of stochastic volatility models: Numerical tests and an open source implementation

The aim of this paper is to discuss the implementation of the recursive marginal quantization algorithm of Fiorin et al., (2018) to several stochastic volatility models. After recalling the theoretical framework and the main features of the method, we evaluate the performance of the algorithm for the pricing of derivatives. We also discuss an open source implementation of the algorithm. For most models we consider, with the exception of the Stein and Stein model, recursive marginal quantization provides a viable alternative to Monte Carlo simulations.

Transient acceleration waveform replication of electro-hydraulic systems using convex combined adaptive controller with input shaping technique

Electro-hydraulic systems are extensively utilized to generate desired acceleration waveforms to provide a vibration environment for testing the performance and reliability of objects in various industrial applications. However, as electro-hydraulic systems are often affected by some inevitable drawbacks resulted from hydraulic nonlinearities, unwanted dynamic variations and disturbances, the generated acceleration waveform is generally far behind the expectation. In this paper, a convex combined adaptive controller with input shaping technique is proposed for enhancing the transient acceleration waveform replication accuracy of electro-hydraulic systems. The proposed controller is comprised of a three variable controller at the bottom level, an input shaping technique controller at the middle level, and a convex combined adaptive controller at the upper level. The three variable controller is firstly utilized for the establishment of a fundamental closed-loop acceleration control system, and then the input shaping technique controller is constructed by introducing an offline designed inverse prefilter utilizing the multi-innovation recursive least squares algorithm and the zero magnitude error tracking algorithm. The convex combined adaptive controller at the upper level is comprised of two individual adaptive filters with high and low step sizes, which provides the merits of fast convergence rate and high tracking accuracy, and it is further exploited to address for system’s dynamic variations, model uncertainties and unexpected perturbations. Comparative experiments of the proposed controller with a manually generated random waveform and a recorded earthquake waveform as the testing inputs are conducted on a typical electro-hydraulic test bench, and the corresponding results demonstrate the feasibility and superiority of the proposed controller in improving the transient acceleration waveform replication performance of electro-hydraulic systems.

Development of multilevel mesh adaptivity for global variance reduction in Monte Carlo code cosRMC

The mesh-based weight window method is a widely used global variance reduction technique for Monte Carlo particle transport calculation. In order to meet the fine voxel division required for high-quality weight window distribution, the uniform structured mesh division will lead to large memory footprint pressure. This work focused on the research of the multilevel adaptive mesh algorithm, using a recursive algorithm to adjust the voxel resolution adaptively according to the material of geometry. Then the adaptive mesh algorithm was applied to the weight window generator based on the response matrix method by establishing the neighbor relationship of adaptive mesh. And an optimization method for memory allocation of the response matrix solver was proposed, effectively reducing the memory footprint of the response matrix solver in parallel computing. In the calculation of the CFETR model, the calculation efficiency under the weight window based on adaptive mesh was increased by 53.3 % compared with the weight window based on uniform mesh, and the proportion of voxels with a statistical error less than 10 % reached 93.33 %, which verified the effectiveness of the multilevel adaptive mesh algorithm.

Construction of Logistics Information Management of Small and Medium-sized Enterprises Relying on Recursive Algorithm

With the rapid development of information technology, the application of information technology in logistics management is more and more, which makes traditional logistics gradually move towards modern logistics, and realizes the optimal allocation of logistics management and resources; at the same time, the logistics industry is also faced with huge data flow. , the low level of informatization and many other challenges, these problems restrict the development of enterprises. In order to solve many problems existing in the construction of logistics management informatization, this paper analyzes logistics management and its informatization application. By understanding the current situation of logistics management in small and medium-sized enterprises, it studies how to construct the informatization application of logistics management in small and medium-sized enterprises, and adds recursive algorithm to the informatization application, and finally constructs a logistics management informatization system. The system is realized through the architecture of B/S model, and the function and performance of the system are tested. The test results show that the system functions well, the maximum load capacity of the system is 600, and the longest response time does not exceed about 20s. The test results are in line with expectations, indicating that the recursive algorithm constructed in this paper is effective in the application of logistics management informatization.

GRAPH FRACTALS WITH THE VARIABILITY OF THE FORMATION PROCESS

Fractals, which are characterized by their self-similarity at different scales, are complex geometric entities created using recursive algorithms. They are widely used in computer graphics to create complex visual effects and to model natural phenomena such as river networks and mountain landscapes. Graph fractals combine the properties of fractals and graph structures and can be used for research in fields such as computer networks or medicine. This work presents the approach of constructive-production modeling, based on formal grammars, for the generation of graph fractals with the variability of the formation process.

Optimal trajectory tracking for uncertain linear discrete‐time systems using time‐varying Q‐learning

SummaryThis article introduces a novel optimal trajectory tracking control scheme designed for uncertain linear discrete‐time (DT) systems. In contrast to traditional tracking control methods, our approach removes the requirement for the reference trajectory to align with the generator dynamics of an autonomous dynamical system. Moreover, it does not demand the complete desired trajectory to be known in advance, whether through the generator model or any other means. Instead, our approach can dynamically incorporate segments (finite horizons) of reference trajectories and autonomously learn an optimal control policy to track them in real time. To achieve this, we address the tracking problem by learning a time‐varying ‐function through state feedback. This ‐function is then utilized to calculate the optimal feedback gain and explicitly time‐varying feedforward control input, all without the need for prior knowledge of the system dynamics or having the complete reference trajectory in advance. Additionally, we introduce an adaptive observer to extend the applicability of the tracking control scheme to situations where full state measurements are unavailable. We rigorously establish the closed‐loop stability of our optimal adaptive control approach, both with and without the adaptive observer, employing Lyapunov theory. Moreover, we characterize the optimality of the controller with respect to the finite horizon length of the known components of the desired trajectory. To further enhance the controller's adaptability and effectiveness in multitask environments, we employ the Efficient Lifelong Learning Algorithm, which leverages a shared knowledge base within the recursive least squares algorithm for multitask ‐learning. The efficacy of our approach is substantiated through a comprehensive set of simulation results by using a power system example.

Association between dietary fiber intake and diabetic nephropathy among adult diabetes mellitus in the United States: A cross-sectional study

ObjectiveThere has been some evidence that dietary fiber may be associated with diabetic nephropathy (DN), but the relationship is still unclear. The purpose of this study was to examine the association between dietary fiber intake and DN. MethodsThis cross-sectional study used National Health and Nutrition Examination Survey (NHANES) data collected between 2007 and 2020. Weighted multivariate logistic regression was used to examine the relation between dietary fiber intake and DN. In addition, fitted smoothed curves were used to explore potential non-linear relationships. If non-linearity was observed, inflection points were further calculated by a recursive algorithm. ResultsThe study finally included 5964 subjects ≥20 years of age. The mean age was 60.8 ± 13.4 years with males (52.4 %), and non-Hispanic Whites (62.4 %), and the weighted prevalence of DN was 36.7 %. Dietary fiber was negatively associated with the risk of DN after controlling for all confounding variables (OR = 0.89, 95%CI: 0.80, 0.99). Smoothed curve fit plots of the dose relationship showed that dietary fiber intake was linearly related to DN, whereas males (inflection point of 8.0 g/d) and non-Hispanic Blacks (inflection point of 14.9 g/d) followed a non-linear inverted U-shaped curve relationship. In United States adults aged 20 and older, dietary fiber intake may be associated with a reduced risk of DN. ConclusionAppropriate increases in dietary fiber intake may offer potential benefits for DN. In conclusion, it appears that increasing dietary fiber intake may be one of the most effective strategies for the prevention and management of DN.

Wind Turbine Tower State Reconstruction Method Based on the Corner Cut Recursion Algorithm

This study introduces an innovative approach for the reconstruction of wind turbine tower states using a tangential recursion algorithm. The primary objective is to enable real-time monitoring of the operational condition of wind turbine towers. The proposed method is rooted in strain–load theory, which enables the accurate identification of tower load states. The tangential recursion algorithm is utilized to translate the strain data acquired from strategically placed sensors into reconstructed point positions. The subsequent refinement of these positions incorporates considerations of torsional loads and geometric deformations, culminating in the comprehensive and precise reconstruction of the tower’s deformation behavior. Through the use of the OpenFAST V8 simulation software, a thorough analysis is conducted to investigate the load and deformation characteristics of the NREL 5 MW wind turbine tower across diverse operational scenarios. Furthermore, the load conditions corresponding to rated operating circumstances are applied to a finite element model constructed with the lumped mass method. The identification of tower load states and the comprehensive reconstruction of deformation patterns are realized through the extraction of strain data from critical points in the finite element model. The credibility and accuracy of the proposed method are rigorously evaluated by juxtaposing the identification and reconstruction outcomes with the results derived from the OpenFAST simulations and finite element analyses. Notably, the proposed method circumvents the requirement for external auxiliary calibration equipment for the tower, rendering it adaptable to a broader spectrum of operational contexts and making it consistent with unfolding trajectories in wind power advancement.

ZZPolyCalc: An open-source code with fragment caching for determination of Zhang-Zhang polynomials of carbon nanostructures

Determination of topological invariants of graphene flakes, nanotubes, and fullerenes constitutes a challenging task due to its time-intensive nature and exponential scaling. The invariants can be organized in a form of a combinatorial polynomial commonly known as the Zhang-Zhang (ZZ) polynomial or the Clar covering polynomial. We report here a computer program, ZZPolyCalc, specifically designed to compute ZZ polynomials of large carbon nanostructures. The curse of the exponential scaling is avoided for a broad class of nanostructures by employing a sophisticated bookkeeping algorithm, in which each fragment appearing in the recursive decomposition is stored in the cache repository of molecular fragments indexed by a hash of the corresponding adjacency matrix. Although exponential scaling persists for the remaining nanostructures, the computational time is reduced by a few orders of magnitude owing to efficient use of hash-based fragment bookkeeping. The provided benchmark timings show that ZZPolyCalc allows for treating much larger carbon nanostructures than previously envisioned. Program summaryProgram Title: ZZPolyCalcCPC Library link to program files:https://doi.org/10.17632/vmnr5xfb6t.1Developer's repository link:https://github.com/quantumint/zzpolycalcLicensing provisions: GPLv3Programming language: Fortran 2008, CNature of problem: Calculations of Zhang-Zhang (ZZ) polynomials for carbon nanostructures have traditionally been performed by the application of a recursive algorithm, which scales exponentially with the size of the system. For large nanostructures, the problem often becomes intractable due to prohibitive computational cost. Consequently, ZZ polynomials were reported so far only for small or model systems, for which analytical formulas could be determined; larger systems, such as graphene flakes, fullerenes, or nanotubes, have been intractable with the existing implementations.Solution method: The process of determination of ZZ polynomials, based on recursive decomposition of a given carbon nanostructure, produces many identical fragments. In ZZPolyCalc, we avoid redundant calculations for such fragments by storing all the intermediate data in a hash table and reusing it efficiently whenever necessary. This new algorithm is particularly effective for elongated graphene flakes or carbon nanotubes, where the scaling becomes polynomial, but it also greatly reduces the computational time for other, less regular nanostructures.

Deciphering peri-implantitis: Unraveling signature genes and immune cell associations through bioinformatics and machine learning.

Early diagnosis of peri-implantitis (PI) is crucial to understand its pathological progression and prevention. This study is committed to investigating the signature genes, relevant signaling pathways and their associations with immune cells in PI. We analyzed differentially expressed genes (DEGs) from a PI dataset in the gene expression omnibus database. Functional enrichment analysis was conducted for these DEGs. Weighted Gene Co-expression Network Analysis was used to identify specific modules. Least absolute shrinkage and selection operator and support vector machine recursive feature elimination were ultimately applied to identify the signature genes. These genes were subsequently validated in an external dataset. And the immune cells infiltration was classified using CIBERSORT. A total of 180 DEGs were screened from GSE33774. Weighted Gene Co-expression Network Analysis revealed a significant association between the MEturquoise module and PI (cor = 0.6, P < .0001). Least absolute shrinkage and selection operator and support vector machine recursive feature elimination algorithms were applied to select the signature genes, containing myeloid-epithelial-reproductive tyrosine kinase, microfibrillar-associated protein 5, membrane-spanning 4A 4A, tribbles homolog 1. In the validation on the external dataset GSE106090, all these genes achieved area under curve values exceeding 0.95. GSEA analysis showed that these genes were correlated with the NOD-like receptor signaling pathway, metabolism of xenobiotics by cytochrome P450, and arachidonic acid metabolism. CIBERSORT revealed elevated levels of macrophage M2 and activated mast cells in PI. This study provides novel insights into understanding the molecular mechanisms of PI and contributes to advancements in its early diagnosis and prevention.

On the Regularization of Recursive Least-Squares Adaptive Algorithms Using Line Search Methods

Stereophonic acoustic echo cancellation (SAEC) requires the identification of four unknown impulse responses corresponding to four loudspeaker-to-microphone pairs. Recent developments in the field of adaptive filters for SAEC setups have allowed for the handling of a single complex-valued adaptive impulse response, instead of the four classical real-valued adaptive filters. With the simplified framework provided by the widely linear (WL) model, more advanced versions of recursive least-squares (RLS) were employed in order to take advantage of their superior tracking speeds when working with highly correlated input signals (such as speech). Despite the performances and numerical stability provided by using exponentially weighted versions of the RLS family in combination with line search methods (LSMs), the SAEC configurations have limited capabilities in mitigating the negative effects caused by high-level disturbances affecting the two microphone signals. Such is the case of double-talk scenarios, which considerably reduce the update accuracy of the adaptive system. This paper analyzes a regularization technique for the named WL-RLS-LSM adaptive filters by adjusting the correlation matrix associated with the input signals and creating a reaction in the update process. The proposed method is designed to considerably slow (or even freeze) the adaptation process while the disturbance is manifested. Simulation results are discussed in order to validate the theoretical content.

A modular computational framework for the dynamic analyses of cable-driven parallel robots with different types of actuation including the effects of inertia, elasticity and damping of cables

Abstract Dynamic simulations of the cable-driven parallel robots (CDPRs) with cable models closer to reality can predict the motions of moving platforms more accurately than those with idealisations. Hence, the present work proposes an efficient and modular computational framework for this purpose. The primary focus is on the developments required in the context of CDPRs actuated by moving the exit points of cables while the lengths are held constant. Subsequently, the framework is extended to those cases where simultaneous changes in the lengths of cables are employed. Also, the effects due to the inertia, stiffness and damping properties of the cables undergoing 3D motions are included in their dynamic models. The efficient recursive forward dynamics algorithms from the prior works are utilised to minimise the computational effort. Finally, the efficacy of the proposed framework and the need for such an inclusive dynamic model are illustrated by applying it to different application scenarios using the spatial $4$ - $4$ CDPR as an example.