Dynamic Partitioning Research Articles

A review of coordinated multi-robot exploration

Abstract A multiple robots system provides advantages over a single robot when it comes to exploring an unfamiliar area. Due to its applications in localization and mapping, search and rescue, etc. This technology has attracted a lot of study interest. The main challenge is properly distributing target points among several robots so that they can each investigate a separate section of the environment simultaneously and ensure the shortest possible total exploration time. However, more work still needs to be done to incorporate research issues in a real setting, such as how to eliminate duplicate exploration areas by giving robots multiple target locations or how to avoid unanticipated barriers in new situations. The allocation algorithms, motion planning techniques, and exploration strategies used in multi-robot collaboration (CME) are reviewed in this paper. Additionally, it provides an explanation of the methods research teams employ to optimize traditional whale optimization algorithm (WOA), how they enhance exploration performance and speed, and a comparative analysis of these methods. We also discussed a cooperative exploration method based on dynamic Voronoi partitioning. However, we also need some obstacle avoidance algorithms to help understand the gaps and issues that will need to be solved in the upcoming years, namely how to handle unexpected impediments in unfamiliar surroundings. Lastly, a summary of potential future lines of inquiry and uses is given.

Multirate time-integration based on dynamic ODE partitioning through adaptively refined meshes for compressible fluid dynamics

In this paper, we apply the Paired-Explicit Runge-Kutta (P-ERK) schemes by Vermeire et al. [1,2] to dynamically partitioned systems arising from adaptive mesh refinement. The P-ERK schemes enable multirate time-integration with no changes in the spatial discretization methodology, making them readily implementable in existing codes that employ a method-of-lines approach.We show that speedup compared to a range of state of the art Runge-Kutta methods can be realized, despite additional overhead due to the dynamic re-assignment of flagging variables and restricting nonlinear stability properties. The effectiveness of the approach is demonstrated for a range of simulation setups for viscous and inviscid convection-dominated compressible flows for which we provide a reproducibility repository.In addition, we perform a thorough investigation of the nonlinear stability properties of the Paired-Explicit Runge-Kutta schemes regarding limitations due to the violation of monotonicity properties of the underlying spatial discretization. Furthermore, we present a novel approach for estimating the relevant eigenvalues of large Jacobians required for the optimization of stability polynomials.

Research on day-ahead and intraday scheduling strategy of distribution network based on dynamic partitioning

With the massive participation of new energy in the operation of distribution network (DN), the uncertainties arising from both load and renewable generation have brought challenges to its secure and reliable operation. In this paper, a model of day-ahead and intraday optimal scheduling based on dynamic partitioning (DP) of DN is constructed to realize the refinement of operation in both temporal and spatial dimensions, as well as to improve the reliability of system operation. For the strong fluctuation characteristics of the source-load in DN, the DP is achieved under the premise that the partition structure in each period is tight and the internal power supply has the ability to balance the power fluctuations. In order to improve the regional autonomy of DN, a day-ahead multi-objective optimal scheduling model is constructed based on the DP with the optimization objectives of weak inter-partition power coupling, lowest deficiency rate of flexibility, and lowest operating cost. And a scheme for articulation is proposed to ensure the effective guidance of day-ahead for intraday. Then the intraday dispatch model is constructed by shifting the focus to the incorporate characteristics of DP and quickly smooth the net load fluctuations. Finally, the performance and reliability of the proposed models are verified via case studies.

Dynamic hierarchical partitioning method of flexible resource for virtual power plant based on event triggering

Abstract With the advantage of efficient aggregation of flexible resources on the demand side, virtual power plants have been widely considered as one of the effective solutions to solve the lack of large-scale access systems and regulation capacity of new energy. Aiming at the problem of how the flexible resources of the virtual power plant can effectively participate in the optimal operation of hierarchical zoning of the power grid, this paper takes the aggregation of flexible resources of the virtual power plant to alleviate multiple line overload as an example. By deducing the transfer factor matrix under the linearized AC power flow model, an event-triggered hierarchical zoning method of flexible resources of virtual power plants considering resource effectiveness is proposed. The effectiveness of the algorithm is verified in several network examples, which provides a new idea for the virtual power plant to participate in the optimal operation of various levels and regions of the power grid.

Dynamic stream partitioning for time-triggered traffic in Time-Sensitive Networking

Time-Sensitive Networking (TSN) is a promising network technology that can ensure bounded latency and jitter for industrial real-time scenarios. It establishes the IEEE 802.1 Qbv standard to precisely control the periodic transmission time of the time-triggered (TT) traffic. However, it is still a challenge to efficiently schedule the TT traffic in large-scale networks with high transmission performance. In this paper, we propose a novel method called dynamic stream partitioning (DSP) to solve this problem. It satisfies the IEEE 802.1 Qbv standard and can significantly reduce the scheduling time by reducing the number of constraints. Based on the DSP method, we analyze the impact of stream partitioning on the transmission performance of the TT traffic, and propose an indicator called Normalized Degradation of Performance (NDOP) to quantify it dynamically. Furthermore, we design a partitioning-aware dynamic routing (PADR) to expand the scheduling space. Integrated with NDOP and PADR, we propose a joint routing and dynamic stream partitioning (JR/DSP) algorithm. Extensive simulation experiments verify that the proposed JR/DSP algorithm has higher scalability, transmission performance, and schedulability compared to the static stream partitioning algorithms.

Configuration and Operation Optimization for Industrial Steam Power System Sustainable Retrofit Considering Renewable Energy Integration

AbstractRenewable energy integration and operational optimization are crucial in energy sustainability and decarbonization, especially for industrial steam power systems (SPS). This study establishes an SPS superstructure that integrates wind, solar, and biomass energy. A mixed‐integer nonlinear programming (MINLP) model is developed to determine an optimal retrofit strategy that minimizes the life cycle cost, which includes carbon emission cost and energy cost. To solve this high‐dimensional complex optimization problem, a multistage strategy fusion differential evolution algorithm with dynamic partitioning of the SVM feasible domain (SVM‐MS‐DE) is developed. The results from the optimal strategy demonstrate a 9.02% reduction in the system's total cost and a 9.64% decrease in carbon emission through the incorporation of wind and solar energy. Additionally, the sensitivity analysis on biomass ratio, carbon emission price, energy demand, and carbon emission limits reveal that the region can contribute significantly to renewable energy initiatives. Several recommended policies are provided to encourage enterprises to move towards sustainable development.

Temperature Field Characteristics of Limited Slip Clutch under Various Condition Parameters

The temperature field of limited slip clutch (LSC) seriously interferes with torque transmission. In this paper, the attribute of dynamic partition of slip heat flux is considered. A calculation model of temperature field is also established by combining the proposed new method of fluid-solid equivalent convective heat transfer. The simulation and experiment of different relative speed, lubricant flow, and temperature are carried out. Furthermore, the comprehensive evaluation index of average temperature growth ratio and maximum radial temperature difference is proposed. The results show that the deviations of temperature rising rate and average temperature are not more than 0.2 °C/s and 6 °C, respectively. Heat flux partition value of separator plate increases approximately proportionally with relative speed. Changing relative speed from 40 r/min to 80 r/min, average temperature growth rate and maximum radial temperature difference of separator plate increase by 0.35 °C/s and 2.08 °C, respectively. On the contrary, increasing lubricant flow or reducing lubricant temperature weakens actual heat flux input of clutch. From 3 L/min to 10.5 L/min about lubricant flow, the average temperature growth rate and maximum radial temperature difference caused by higher relative speed are reduced by 0.32 and 2.71 °C, respectively. However, when lubricant temperature is adjusted from 65 °C to 30 °C, average temperature growth rate is reduced by 0.51 and the variation of maximum radial temperature difference does not exceed 0.32 °C. The research can accurately simulate actual energy flow at friction pair interface and assist in exploring the influence of condition parameters on temperature field.

Global pixel-value-ordering framework with dynamic sequence partition for reversible data hiding

Recently, pixel-value-ordering (PVO) based reversible data hiding (RDH) methods have become hotspots in spatial domain RDH research. In PVO-based methods, the pixel value correlation within a local region is exploited for data embedding. To embed secret data, the cover images are partitioned into non-overlapped rectangle blocks to be sorted locally, and a lower embedding distortion is thus obtained. However, their embedding capacities are often limited by the fixed local blocks from which pixels are sorted. To maintain the advantages and overcome disadvantages of the PVO-based methods, a new global pixel-value-ordering (GPVO) framework is proposed in this paper. By applying our GPVO framework, a PVO-based method can utilize pixels from any position in the cover image rather than from local pixel blocks. Then, through the dynamic sequence partition realized by the GPVO framework for the first time, the embedding capacity of PVO-based methods can be greatly improved while reducing embedding distortion further reduced. Finally, we propose a two-stage pairwise embedding scheme to be applied to the sequences, aiming to achieve an advanced embedding performance. Experimental results illustrate that the proposed GPVO works better than other related state-of-the-art RDH methods.

Incremental Fuzzy Clustering-Based Neural Networks Driven With the Aid of Dynamic Input Space Partition and Quasi-Fuzzy Local Models.

Fuzzy clustering-based neural networks (FCNNs) based on information granulation techniques have been shown to be effective Takagi-Sugeno (TS)-type fuzzy models. However, the existing FCNNs could not cope well with sequential learning tasks. In this study, we introduce incremental FCNNs (IFCNNs), which could dynamically update themselves whenever new learning data (e.g., single datum or block data) are incorporated into the dataset. Specifically, we employ dynamic (incremental) fuzzy C-means (FCMs) clustering algorithms to reveal a structure in data and divide the entire input space into several subregions. In the aforementioned partition, the dynamic FCM adaptively adjusts the position of its prototypes by using sequential data. Due to the time-sharing arrival of training data, compared with batch learning models, incremental learning methods may lose classification (prediction) accuracy. In order to tackle this challenge, we utilize quasi-fuzzy local models (QFLMs) based on modified Schmidt neural networks to replace the popular linear functions in TS-type fuzzy models to refine and enhance the ability to represent the behavior of fuzzy subspaces. Meanwhile, the recursive least square error (LSE) estimation is utilized to update the weights of QFLMs from one-by-one or block-by-block (fixed or varying block size) learning data. In addition, the L2 regularization is considered to ameliorate the deterioration of generalization abilities caused by potential overfitting when carrying out weight estimation. The proposed method leads to the construction of FCNNs in a new way, which can effectively deal with incremental data as well as deliver sound generalization capability. Extensive machine-learning datasets and a real-world application are employed to show the validity and performance of the presented methods. From the experimental results, we show that the proposal can maintain sound classification accuracy when effectively processing sequential data.

GraphSER: Distance-Aware Stream-Based Edge Repartition for Many-Core Systems

With the explosive growth of graph data, distributed graph processing becomes popular and many graph hardware accelerators use distributed frameworks. Graph partitioning is foundation in distributed graph processing. However, dynamic changes in graph make existing partitioning shifted from its optimized points and cause system performance degraded. Therefore, more efficient dynamic graph partition methods are needed. In this work, we propose GraphSER, a dynamic graph partition method for many-core systems. In order to improve the cross-node spatial locality and reduce the overhead of repartition, we propose a stream-based edge repartition, in which each computing node sequentially traverses its local edge list in parallel, then migrating edges based on distance and replica degree. GraphSER does not need costly searching and prioritizes nodes so it can avoid poor cross-node spatial locality. Our evaluation shows that compared to state-of-the-art edge repartition software methods, GraphSER has an average speedup 1.52x, with the maximum up to 2x. Compared to the previous many-core hardware repartition method, GraphSER performance has an average of 40% improvement, with the maximum to 117%.

A geometric representative for the fundamental class in KK-duality of Smale spaces

A fundamental ingredient in the noncommutative geometry program is the notion of KK-duality, often called K-theoretic Poincaré duality, that generalises Spanier-Whitehead duality. In this paper we construct a θ-summable Fredholm module that represents the fundamental class in KK-duality between the stable and unstable Ruelle algebras of a Smale space. To find such a representative, we construct dynamical partitions of unity on the Smale space with highly controlled Lipschitz constants. This requires a generalisation of Bowen's Markov partitions. Along with an aperiodic point-sampling technique we produce a noncommutative analogue of Whitney's embedding theorem, leading to the Fredholm module.

An extended time-varying Budyko framework for quantifying the hydrological effect of vegetation restoration under climate variations at watershed scale

The Budyko framework, widely used to quantify the watershed hydrological response to the watershed characteristics and climate variabilities, is continuously refined to overcome the disadvantages of steady state assumption. However, dynamic variations in vegetations and climate variables are not fully integrated including coverages and precipitation regimes of intensity, frequency, and duration. To address this, we developed an innovative approach for determining the parameter ω in the Budyko framework to quantify the hydrological effects of vegetation restoration in a mesoscale watershed located in northern China. We found that fractional vegetation coverage (FVC), heavy precipitation amount (95pTOT), and the number of precipitation days (R01mm) are significant variables for estimating ω to improve the predictive capability of the watershed response. This extended time-varying Budyko framework can rigorously capture the temporal variations and underlying mechanisms of interactions between vegetation dynamic and precipitation regime partitioning precipitation (P) to R. Under the Budyko-Fu framework, compared to constant ω (ω‾) or ω that only considers FVC (ωP) or precipitation regimes (ωFVC) for simulating R, using ω that integrated FVC and precipitation regimes (ωP-FVC) can improve Nash-Sutcliffe efficiency coefficient (NSE) by 24.81%, while reduced the root mean squared error (RMSE) and relative error (RE) by 64.08% and 65.77%, respectively. Although the increase in climatic dryness (PET/P) resulted in decreased R, the increase in FVC has also a significant contribution to this decrease due to vegetation restoration. We highlight that decrease precipitation intensity (95pTOT) and frequency (R01mm) amplified the hydrological effects of vegetation restoration, causing a 79.09∼100.31% increase in R compared to the independent impact of changes in FVC. We conclude that the extended time-varying Budyko framework by precipitation regime is more rigorous for quantifying the hydrological effects of ecological restoration under climate change and providing more reliable approach for adaptive watershed management.

A hierarchical multi-UAV cooperative framework for infrastructure inspection and reconstruction

Unmanned aerial vehicles (UAVs) are emerging as a powerful tool for inspections and repair works in large-scale and unstructured 3D infrastructures, but current approaches take a long time to cover the entire area. Planning using UAVs for inspections and repair works puts forward a requirement of improving time efficiency in large-scale and cluster environments. This paper presents a hierarchical multi-UAV cooperative framework for infrastructure inspection and reconstruction to balance the workload and reduce the overall task completion time. The proposed framework consists of two stages, the exploration stage and the exploitation stage, resolving the task in a sequential manner. At the exploration stage, the density map is developed to update global and local information for dynamic load-balanced area partition based on reconstructability and relative positions of UAVs, and the Voronoi-based planner is used to enable the UAVs to reach their best region. After obtaining the global map, viewpoints are generated and divided while taking into account the battery capacity of each UAV. Finally, a shortest path planning method is used to minimize the total traveling cost of these viewpoints for obtaining a high-quality reconstruction. Several experiments are conducted in both a simulated and real environment to show the time efficiency, robustness, and effectiveness of the proposed method. Furthermore, the whole system is implemented in real applications.

Eutrophication hotspots, nitrogen fluxes and climate impacts in estuarine ecosystems: A model study of the Odra estuary system

The Odra estuary in the southern Baltic Sea comprises the Odra (Szczecin) Lagoon, the Pomeranian Bay and a number of other shallow water areas and channels. Known for its abundance of fish, eutrophication in the Odra Lagoon is a pressing issue for science and environmental management representing a global problem: What determines the seasonal variability of nitrogen and nitrogen turnover in shallow water areas, and how does seasonal variability change due to climatic changes such as warming and sea level rise? How do such changes affect nutrient exports to the regional ocean? This study employs a high-resolution unstructured model system to investigate physical-biogeochemical interactions, nitrogen turnover, and conditions leading to nitrogen export to the Baltic Sea within the Odra estuary. The research comprises hindcast and a climatic experiment with modified water level and temperature inputs. The model reproduces the thermohaline dynamics of brackish shallow water areas, phytoplankton blooms and the variability of inorganic nitrogen. The simulations identify the dynamic partitioning of the Odra Lagoon into the highly eutrophic, lake-like Small Lagoon and more frequently flushed, zooplankton-rich Great Lagoon. Although the two years of the hindcast simulation feature very different boundary conditions in terms of river forcing, comparable patterns of seasonal nitrogen export emerge. In a climate change experiment with increased sea levels and global temperatures, the system appears sensitive, but remains stable with regard to nutrient transport and is therefore predictable. The climate change experiment reveals enhanced primary producer biomass concentrations, suggesting heightened eutrophication. While in the shallow waters of Odra Lagoon oxygen concentration remains relatively stable, oxygen depletion intensifies as the lagoon outflows enter the Pomeranian Bay. This phenomenon is linked to increased denitrification within the stratified Odra plume. Deeper, meandering channels, such as Swina, demonstrate resilience to oxygen reduction, influenced by sea level rise and enhanced currents. Based on the temporal-spatial high-resolution coupled, validated simulations, it is possible to develop tailor-made management solutions without having to run expensive and complicated observation campaigns in the shallow waters with complex topography.

LS-14 test suite for long sequences

Random number sequences are used in many branches of science. Because of many techni- cal reasons and their practicality, pseudo random sequences are usually employed in place of true number sequences. Whether a sequence generated through a deterministic process is a pseudo random, in other words, random-looking sequence or it contains certain pat- terns, can be determined with the help of statistics and mathematics. Although, in the literature there are many statistical randomness tests for this purpose, there is no much work on test suites specialized for long sequences, that is sequences of length 1,000,000 bits or more. Most of the randomness tests for long sequences use some mathematical approximations to compute expected values of the random variables and hence their results contain some errors. Another approach to evaluate randomness criteria of long sequences is to partition the long sequence into a collection short sequences and evaluate the collection for the ran- domness using statistical goodness of fit tests. The main advantage of this approach is, as the individual sequences are short, there is no need to use mathematical approximations. On the other hand when the second approach is preferred, partition the long sequence into a collection of fixed length subsequences and this approach causes a loss of information in some cases. Hence the idea of dynamic partition should be included to perform a more reliable test suite. In this paper, we propose three new tests, namely the entire R2 run, dynamic saturation point, and dynamic run tests. Moreover, we introduce a new test suite, called LS-14, consisting of 14 tests to evaluate randomness of long sequences. As LS-14 employs all three approaches: testing the entire long sequence, testing the collection of fixed length partitions of it, and finally, testing the collection obtained by the dynamic partitions of it, the proposed LS-14 test suit differs from all existing suites. Mutual comparisons of all 14 tests in the LS-14 suite, with each other are computed. Moreover, results obtained from the proposed test suite and NIST SP800-22 suite are compared. Examples of sequences with certain patterns which are not observed by NIST SP800-22 suite but detected by the proposed test suite are given.

Learning nonequilibrium statistical mechanics and dynamical phase transitions

Nonequilibrium statistical mechanics exhibit a variety of complex phenomena far from equilibrium. It inherits challenges of equilibrium, including accurately describing the joint distribution of a large number of configurations, and also poses new challenges as the distribution evolves over time. Characterizing dynamical phase transitions as an emergent behavior further requires tracking nonequilibrium systems under a control parameter. While a number of methods have been proposed, such as tensor networks for one-dimensional lattices, we lack a method for arbitrary time beyond the steady state and for higher dimensions. Here, we develop a general computational framework to study the time evolution of nonequilibrium systems in statistical mechanics by leveraging variational autoregressive networks, which offer an efficient computation on the dynamical partition function, a central quantity for discovering the phase transition. We apply the approach to prototype models of nonequilibrium statistical mechanics, including the kinetically constrained models of structural glasses up to three dimensions. The approach uncovers the active-inactive phase transition of spin flips, the dynamical phase diagram, as well as new scaling relations. The result highlights the potential of machine learning dynamical phase transitions in nonequilibrium systems.

Pseudo-Second-Order Kinetic Equation for Describing the Effect of Sorbent and Sorbate Concentrations.

The sorbent concentration (Cs) effect and sorbate initial concentration (C0) effect are common phenomena observed in the study of adsorption kinetics at solid-liquid interfaces. That is, adsorption rate constants simulated with classical kinetic equations, such as the pseudo-second-order (PSO) model, for a given system vary with Cs and C0. The classical kinetic equations cannot predict or describe the "Cs-effect" and "C0-effect" (called "C-effects" here). In the current work, the dynamic partition coefficient of sorbate between solid and liquid phases (Kt) was used to describe the adsorption kinetic processes. Based on the surface component activity (SCA) model, which assumes the activity coefficients of the surface components (fs) are not equal to unity but rather a function of Cs and the adsorption capacity (or C0) and referring to the classical PSO model, a new kinetic equation was established, called the "SCA-PSO kinetic model", and its two parameters, the intrinsic equilibrium partition coefficient (Ke0) and the intrinsic rate constant (k20), are independent of Cs and C0. In addition, the new model relates Kt and the rate constant (k2) to Cs and C0 via fs, and can thus describe the C-effects. The fs can be estimated from the change of equilibrium partition coefficient (Ke) with Cs and C0. The new model predicts that with the increase of Cs and C0, Ke decreases while k2 increases. Its rationality was confirmed by the literature-reported adsorption kinetic data of heavy metals on inorganic and biomass sorbents with the C-effects.

A dynamic partitioning mechanism spatiotemporally polarizes membrane protein distributions during amoeboid migration and cortical patterning

A dynamic partitioning mechanism spatiotemporally polarizes membrane protein distributions during amoeboid migration and cortical patterning

Cutting-Edge Inference: Dynamic DNN Model Partitioning and Resource Scaling for Mobile AI

Cutting-Edge Inference: Dynamic DNN Model Partitioning and Resource Scaling for Mobile AI

Voltage Regulation of Electricity Distribution Systems with Penetration of Distributed Renewable Generation

This paper exploited the distributed reactive power compensation and optimization adjustments. Firstly, based on electrical distance, a dynamic partitioning strategy for the distribution network is formulated using the dynamic decomposition method. Then, in the modeling of reactive power dynamic adjustment within the sub-region, the expected value of active power loss obtained by the probabilistic power flow calculation method is used as the objective function, and an improved genetic algorithm is used for the solution. Through using MATLAB software programming and the IEEE 33-bus test distribution system as an example for experimental analysis, the effectiveness and feasibility of the proposed method were verified through the analysis and comparison of experimental results.