Hierarchical Algorithm Research Articles

Object Detection in Satellite Images with Hierarchical Collective Hunter Algorithm Optimized Ensemble Classifier

Object detection in remote sensing images plays an essential role in computer vision task, which significantly identifies the position and class of the located objects with arbitrary orientation information. Recently, object detection plays an important role in various applications such as Surveillance and security in airports, organizations, and so on. However, target tracking is a very challenging task in natural images to attain appropriate detection results together with they obtain certain issues such as limited availability of high-resolute images, background complexity, and so on. To avoid these limitations, developed a Hierarchical collective Hunter algorithm with a Deep convolutional Neural network and a Bidirectional long short-term classifier (2HC-DBTM) model, which improved the interaction range of feature extraction by reducing the background complexity. Additional attributes mechanism such as the Hierarchical Collective Hunter (HCH) algorithm and hybrid attention mechanism are integrated with the proposed model for improve the ability and performance with minimum computational complexity and achieves better detection accuracy. The experimental results obtained in the evaluation of the model are achieved with high robustness and efficiency when compared with other conventional methods. The achieved performance evaluation of the model for evaluation metric F1-score is 97.71%, precision is 97.16%, recall is 98.27% and accuracy is 98.05%.

A fast Lanczos-based hierarchical algorithm for tensor ring decomposition

Tensor ring (TR) decomposition has made remarkable achievements in numerous high-order data processing tasks. However, the current alternating least squares (ALS)- and singular value decomposition (SVD)-based algorithms for TR decomposition, i.e., TR-ALS and TR-SVD, especially the former, are computationally expensive, making them unfriendly for large-scale data processing. This paper adopts three strategies to propose a novel fast TR decomposition algorithm: (1) Use a more efficient Lanczos bidiagonalization algorithm than SVD to generate the TR core tensors. (2) Exploit the hierarchical strategy to generate the TR core tensors in parallel. (3) Employ new reshaping and unfolding operations to reduce the dimensionality of the data used to generate TR core tensors. By incorporating these three strategies, we propose the TR-HLanczos algorithm for fast TR decomposition. This algorithm seamlessly produces the TR core tensors through the Lanczos bidiagonalization algorithm in a hierarchical manner. The effectiveness of the proposed TR-HLanczos algorithm is demonstrated through experimental results on both highly oscillatory functions and real-world datasets. For instance, when dealing with data of size 505, TR-HLanczos is nearly 561 times and 18 times faster than algorithms based on ALS and SVD, respectively.

Fault-Tolerant Decentralized Control for Large-Scale Inverter-Based Resources for Active Power Tracking

Abstract Integration of Inverter Based Resources (IBRs) which lack the intrinsic characteristics such as the inertial response of the traditional synchronous generator (SG) based sources presents a new challenge in the form of analyzing the grid stability under their presence. While the dynamic composition of IBRs differs from that of the SGs, the control objective remains similar in terms of tracking the desired active power. This letter presents a decentralized primal-dual-based fault-tolerant control framework for the power allocation in IBRs. Overall, a hierarchical control algorithm is developed with a lower level addressing the current control and the parameter estimation for the IBRs and the higher level acting as the reference power generator to the low level based on the desired active power profile. The decentralized network-based algorithm adaptively splits the desired power between the IBRs taking into consideration the health of the IBRs transmission lines. The proposed framework is tested through a simulation on the network of IBRs and the high-level controller performance is compared against the existing framework in the literature. The proposed algorithm shows significant performance improvement in the magnitude of power deviation and settling time to the nominal value under faulty conditions as compared to the algorithm in the literature. Better active power tracking performance compared to the existing method in literature.

A novel capsule network based on Multi-Order Descartes Extension Transformation

In recent years, the capsule network has significantly impacted deep learning with its unique structure that robustly handles spatial relationships and image deformations like rotation and scaling. While previous research has primarily focused on enhancing the structural network of capsule networks to process complex images, little attention has been given to the rich semantic information contained within the capsules themselves. We recognize this gap and propose the Multi-Order Descartes Expansion Capsule Network (MODE-CapsNet). By introducing the Multi-Order Descartes Expansion Transformation (MODET), this innovative architecture enhances the expressiveness of a single capsule by enabling its projection into a higher-dimensional space. As far as we know, this is the first significant enhancement at the single-capsule granularity level, providing a new perspective for improving capsule networks. Additionally, we proposed a hierarchical routing algorithm designed explicitly for the MODE capsules, significantly optimizing computational efficiency and performance. Experimental results on datasets (MNIST, Fashion-MNIST, SVHN, CIFAR-10, tiny-ImageNet) showed that MODE capsules exhibited improved separability and expressiveness, contributing to overall network accuracy, robustness, and computational efficiency.

Joint-optimized coverage path planning framework for USV-assisted offshore bathymetric mapping: From theory to practice

Designing effective coverage routes for unmanned surface vehicles (USVs) is crucial to improve the efficiency of offshore bathymetric surveys. However, existing coverage planning methods for practical use are limited, primarily due to the large-scale surveying areas and intricate region geometries caused by coastal features. This study aims to address these challenges by introducing a coverage path planning framework for USV-assisted bathymetric mapping, specifically aimed at the joint optimization of paths to cover numerous complex regions. Initially, we conceptualize the large-scale bathymetric survey mission as an integer programming model. The model uses four distinct decision variables to meticulously formulate length calculations, inter-regional connections, entry and exit point selections, and line sweep direction. Then, a novel hierarchical algorithm is devised to solve the problem. The method first incorporates a bisection-based convex decomposition method to achieve optimal partitioning of complex regions. Additionally, a hierarchical heuristic optimization algorithm that seamlessly integrates the optimization of all influencing factors is designed, which includes order generation, candidate pattern finding, tour finding, and final optimization. The reliability of the framework is validated through semi-physical simulations and lake trials using a real USV. Through comparative studies, our model demonstrates clear advantages in computational efficiency and optimization capability compared to state-of-the-arts, with its superiority becoming more pronounced as the problem scale increases. The results from lake trials further affirm the efficient and reliable performance of our model in practical bathymetric survey tasks.

Analysis of health related event detection in big data for physical education training movement detection.

Physical education and training are essential ways to improve the physical quality of the nation, and China has incorporated "building a healthy China" and "fitness for all" into its national development strategy, integrating a strong sports nation into the Chinese dream. The study of digital recording and automated training in sports is of profound value. Motion capture technology can digitally record the training process in a digital physical education training system. At the same time, accurate modeling and calculation can analyze the training effects and give appropriate guidance and feedback. This study develops a new and improved hierarchical K-means algorithm by combining the known classification algorithm K-means with a hierarchical algorithm. The performance of the old and new algorithms are compared and then applied to physical education training data to produce clustering results and analysis to reduce the model, which is used to reduce the number of parameters in the model and improve the recognition speed. The experimental results demonstrate that the relevant models proposed in this study achieve an average accuracy of 91.27% and 92.26%, respectively, which is better than a single network model and can effectively use big data for health event detection. The empirical results show that the improved model algorithm outperforms the single network model and can detect health events using big data.

Price-Matching-Based Regional Energy Market With Hierarchical Reinforcement Learning Algorithm

Price-Matching-Based Regional Energy Market With Hierarchical Reinforcement Learning Algorithm

Robust-EQA: Robust Learning for Embodied Question Answering With Noisy Labels.

Embodied question answering (EQA) is a recently emerged research field in which an agent is asked to answer the user's questions by exploring the environment and collecting visual information. Plenty of researchers turn their attention to the EQA field due to its broad potential application areas, such as in-home robots, self-driven mobile, and personal assistants. High-level visual tasks, such as EQA, are susceptible to noisy inputs, because they have complex reasoning processes. Before the profits of the EQA field can be applied to practical applications, good robustness against label noise needs to be equipped. To tackle this problem, we propose a novel label noise-robust learning algorithm for the EQA task. First, a joint training co-regularization noise-robust learning method is proposed for noisy filtering of the visual question answering (VQA) module, which trains two parallel network branches by one loss function. Then, a two-stage hierarchical robust learning algorithm is proposed to filter out noisy navigation labels in both trajectory level and action level. Finally, by taking purified labels as inputs, a joint robust learning mechanism is given to coordinate the work of the whole EQA system. Empirical results demonstrate that, under extremely noisy environments (45% of noisy labels) and low-level noisy environments (20% of noisy labels), the robustness of deep learning models trained by our algorithm is superior to the existing EQA models in noisy environments.

Fast power flow calculation for distribution networks based on graph models and hierarchical forward-backward sweep parallel algorithm

IntroductionIn response to the issues of complexity and low efficiency in line loss calculations for actual distribution networks, this paper proposes a fast power flow calculation method for distribution networks based on Neo4j graph models and a hierarchical forward-backward sweep parallel algorithm.MethodsFirstly, Neo4j is used to describe the distribution network structure as a simple graph model composed of nodes and edges. Secondly, a hierarchical forward-backward sweep method is adopted to perform power flow calculations on the graph model network. Finally, during the computation of distribution network subgraphs, the method is combined with the Bulk Synchronous Parallel (BSP) computing model to quickly complete the line loss analysis.Results and DiscussionResults from the IEEE 33-node test system demonstrate that the proposed method can calculate network losses quickly and accurately, with a computation time of only 0.175s, which is lower than the MySQL and Neo4j graph methods that do not consider hierarchical parallel computing.

An improved hierarchical deep reinforcement learning algorithm for multi-intelligent vehicle lane change

Lane change is a pivotal technology in autonomous driving, playing a significant role in improving traffic conditions. The control task of lane change becomes exceptionally complex for coordination of multiple intelligent vehicles, especially in unpredictable situations. To address this issue, the paper proposes a novel multi-intelligent vehicle lane change method (MVLC) based on Deep Reinforcement Learning (DRL). The MVLC features a hierarchical framework that includes a lower-layer lane change controller and an upper-layer reinforcement learning decision-making method. In the lower layer, a lane change controller is designed to execute motion control for a single vehicle, consisting of a lateral controller utilizing dueling double deep Q-network to make lane change decision and a longitudinal controller that employs Intelligent Driver Model (IDM) to follow the preceding vehicle. In the upper layer, a model-free DRL method is used for simultaneous control of multiple intelligent vehicles. To learn the optimal policy, a comprehensive reward function is designed. Finally, experiments are conducted in mixed traffic to valid the effectiveness of the proposed method. Results show that MVLC achieves secure and timely lane changes for multiple vehicles. Therefore, the method is expected to have significant potential for improving overall traffic efficiency and mitigating traffic congestion.

Hierarchical Learning-Enhanced Chaotic Crayfish Optimization Algorithm: Improving Extreme Learning Machine Diagnostics in Breast Cancer

Extreme learning machines (ELMs), single hidden-layer feedforward neural networks, are renowned for their speed and efficiency in classification and regression tasks. However, their generalization ability is often undermined by the random generation of hidden layer weights and biases. To address this issue, this paper introduces a Hierarchical Learning-based Chaotic Crayfish Optimization Algorithm (HLCCOA) aimed at enhancing the generalization ability of ELMs. Initially, to resolve the problems of slow search speed and premature convergence typical of traditional crayfish optimization algorithms (COAs), the HLCCOA utilizes chaotic sequences for population position initialization. The ergodicity of chaos is leveraged to boost population diversity, laying the groundwork for effective global search efforts. Additionally, a hierarchical learning mechanism encourages under-performing individuals to engage in extensive cross-layer learning for enhanced global exploration, while top performers directly learn from elite individuals at the highest layer to improve their local exploitation abilities. Rigorous testing with CEC2019 and CEC2022 suites shows the HLCCOA’s superiority over both the original COA and nine renowned heuristic algorithms. Ultimately, the HLCCOA-optimized extreme learning machine model, the HLCCOA-ELM, exhibits superior performance over reported benchmark models in terms of accuracy, sensitivity, and specificity for UCI breast cancer diagnosis, underscoring the HLCCOA’s practicality and robustness, as well as the HLCCOA-ELM’s commendable generalization performance.

Distributed optimal scheduling for EVs charging and discharging: A penalty-based consensus approach

In preparing for the widespread adoption of electric vehicles (EVs), this paper investigates a two-stage optimization problem with priority for EV charging and discharging scheduling, while taking into account power load stability, economic objectives, and technical constraints of the distribution network. To this end, a distribution power system integrating energy source and EV owners is modeled. Considering the disturbance or even loss of the communication link, a periodical connected topology is adopted for information exchange among EV owners with the deployed smart meters, which is substantially different from the existing works. The main challenges of the problem are that the information flow among EV owners is time-varying and asymmetric, and each EV owner’s demand should be satisfied throughout the whole scheduling horizon. A novelty distributed hierarchical optimal algorithm, combined with iterative water-filling-based method and penalty-based consensus method, is proposed for the two-stage optimization problem with priority. The convergence and optimality of the algorithm are presented mathematically. In addition, the proposed method combined with receding horizon optimization is performed for EVs random arrival and departure scenarios in the system. Finally, we demonstrate advantages of the proposed algorithm in both theory and simulation.

A hierarchical reinforcement learning approach for energy‐aware service function chain dynamic deployment in IoT

AbstractTraffic volume is increasing dramatically due to the quick development of technologies like online gaming, on‐demand video streaming, and the Internet of Things (IoT). The telecommunications industry's large‐scale expansion is increasing its energy usage and carbon footprint. Given the desire to minimize energy consumption and carbon emissions, one of the most essential concerns of future communication networks is ensuring rigorous performance restrictions of IoT services while improving energy efficiency. In this regard, a convolutional neural network‐based hierarchical reinforcement learning approach is provided to lower total energy consumption and carbon emissions in the dynamic service function chaining situations. This method can more effectively lower energy consumption and carbon emissions when compared to other hierarchical algorithms based on conventional deep neural networks and non‐hierarchical algorithms. The suggested method is tested in three typical complicated networks with different network parameters to show its suitability in different network scenarios.

A hierarchical algorithm with randomized learning for robust tissue segmentation and classification in digital pathology

Highly detailed and accurate segmentation and classification of images constitutes an important class of tasks in computer vision. Typical “universal” domain-agnostic methods are known to suffer from instabilities and are prone to adversarial perturbations. Natural heterogeneity inherent in biological tissue structures complicates the interpretation of images even by trained physicians. Yet, algorithms in the medical domain require a high level of stability and interpretability to ensure their adoption by clinical experts and acceptance in clinical decision-making. In this work, we propose a novel method for segmentation and classification to address these challenges. The method is based on a hierarchical approach and biologically-informed feature extraction. The method's technical pipeline includes the automatic extraction of key biologically-informed features typically considered by physicians. This is followed by image classification using these features. Both stages rely on randomized ML techniques. The proposed hierarchical biomedically-informed approach significantly improved the image classification quality compared to the baseline solution of image classification in the task of colorectal cancer (CRC) analysis. The average F1-score for the four tissue types increased from 0.737 to 0.956. Using tumor tissue classification task as an example, we showed that the proposed algorithm offers an effective and practical avenue to solve these challenging issues.

Hierarchical estimator-based fixed-time and prescribed-time bipartite consensus of multiple Euler-Lagrange systems with a directed signed graph

This article addresses the fixed-time and prescribed-time bipartite consensus problem of Euler-Lagrange systems (ELSs) under a directed sign graph, taking into consideration both the parametric uncertainties and external disturbances. Novel hierarchical control algorithms employing estimator approaches are proposed to tackle the aforementioned challenges. More specifically, the control protocol is first designed to accomplish fixed-time bipartite consensus (Fix-TBC). The settling time obtained by using fixed-time control algorithm is restricted to its upper bound, but it may result in a conservative estimation of the bound. Consequently, it indicates the need for designing another control protocol to address the prescribed-time bipartite consensus (Pre-TBC) problem. By invoking Lyapunov stability theory, some sufficient conditions for achieving fixed-time and prescribed-time bipartite consensus of the ELSs are derived. Numerical examples are ultimately provided to demonstrate results.

Generation and deployment of honeytokens in relational databases for cyber deception

Despite considerable investments in database security, global statistics indicate an exponential increase in data breaches. Organizations are often unaware of data breaches for weeks, months, or even years. Sufficient for adversaries to compromise and ex-filtrate business or mission-critical data. Recent research suggests using honeytokens for early detection of data breaches in organizations. Existing honeytoken generation methods rely on regular expressions, rule mining, constraint satisfaction, or representation learning, which are complex and limited to a few attributes. We created a framework for generating and deploying honeytokens in relational databases that actively monitor sensitive records and quickly detect data breaches and their misuse. To generate the honeytoken we have used the hierarchical machine learning algorithm which uses a recursive technique to model the parent–child relationships of multi-table databases. The proposed method enables the organization to take remedial action to reduce the impact of data breaches and complement existing database security solutions.

A New Hybrid Hierarchical Roadside Unit Deployment Scheme Combined with Parking Cars

This paper proposes a new hybrid hierarchical roadside unit (RSU) deployment algorithm that combines traditional RSU deployment strategies with temporary RSUs (t-RSUs) selected from many parked vehicles. The driving vehicles in the corresponding RSU coverage section are divided into clusters and the demand is thus summarized. We first solve the problem of how to choose the appropriate cars serving as t-RSUs and then optimize the RSU deployment with the dynamic existing t-RSUs. We simulate the deployment application in a downtown area of Shanghai, China, using the simulation of urban mobility (SUMO), version 1.11.0, an open-source traffic simulation package, and its traffic-control interface (TraCI) with Python. The simulation results show that, within a given economic cost, the proposed hybrid hierarchical RSU deployment algorithm outperforms the maximum vehicle coverage deployment algorithm (MVCD) and the random deployment algorithm (RandDeploy) in terms of overall vehicle coverage and point-to-point vehicle connectivity ratio. Our scheme provides new solutions and ideas for the deployment of RSUs in future urban traffic environments.

Hierarchical learning multi-objective firefly algorithm for high-dimensional feature selection

Feature selection is a crucial data preprocessing technique extensively employed in machine learning and image processing. However, feature selection encounters significant challenges when addressing high-dimensional data due to the huge and discrete decision space. This paper proposes a hierarchical learning multi-objective firefly algorithm (HMOFA) for solving the feature selection task in high-dimensional data. The main contributions are as follows: 1) Features are clustered based on the evaluation of multiple metrics, which are used to initialize the population and improve the quality of the initial population; 2) A hierarchy-guided learning model is proposed, where individuals move toward superior solutions while moving away from inferior solutions, avoiding the oscillation phenomenon that occurs under the full attraction model, and reducing the likelihood of the population being trapped in a local optimum; 3) Use duplicate solution modification mechanism to reduce the number of duplicate individuals in the population. The proposed method is compared with 8 competitive feature selection methods using 15 datasets, and the results demonstrate that HMOFA can achieve higher classification accuracy while selecting fewer features.

Creating spatially complete zoning maps using machine learning

Zoning regulates land use and intensity of urban development at the county and municipal level in the United States, promoting economic growth, community health, and environmental preservation. However, limited availability of zoning data at scale hinders regional assessments of regulations and coordinated resilience planning efforts. In this study, we developed an open-source, replicable, and transferable framework to predict spatially complete zoning in areas where zoning information is publicly unavailable. We applied a Hierarchical Random Forest algorithm to predict multilevel zoning districts, including three core districts (residential, non-residential, mixed use) and 13 sub-districts. To mimic real-world data accessibility challenges, we evaluated two models: one filling gaps within a county (within-county) and the other extrapolating for counties with no available data (between-county). We tested our models statewide in North Carolina (NC), USA, and developed the State's first comprehensive zoning map. We found strong predictive performance for our within-county model (∼99% accuracy; macro averaged F1 score of ∼0.97) irrespective of district breakdown (i.e., core and sub). However, our between-county model performance was lower and varied depending on the training counties sampled and the district breakdown considered (19–90% accuracy; macro averaged F1 score of 0.105–0.451). Our framework provides spatially complete zoning maps for previously inaccessible locations, enabling researchers and planners to conduct large-scale comprehensive zoning assessments.

Streamflow prediction model for agriculture dominated tropical watershed using machine learning and hierarchical predictor selection algorithms

Study regionRana watershed, located in the mid-Mahanadi River basin in the state of Odisha, India. Study focusThis study attempted to develop a generalizable machine learning (ML)-based streamflow prediction model implementing prediction selection algorithms to the physiographic characteristics, and hydro-meteorological data collected for Rana Watershed. New hydrological insightsThe pertinent predictors identified were land use/ land cover (LULC), one and two-day lagged rainfall, one-day lagged PET, and one-day lagged streamflow and its categorized flow regime. The random forest algorithm, which outperformed the other five algorithms evaluated, was trained using identified predictors to develop a streamflow prediction model called “stRFlow”. The mean absolute error, root mean squared error, coefficient of determination, and Nash-Sutcliffe efficiency during training were 0.753 m3/s, 3.584 m3/s, 0.973, and 0.972 and testing were 2.829 m3/s, 10.503 m3/s, 0.855, and 0.851, respectively. The Kling-Gupta efficiency was found to be 0.96 and 0.92 during training and testing, respectively. There was an enhancement to model proficiency with the addition of LULC to temporal predictors. Moreover, the partial auto-correlation factor for the streamflow and examining the categorization of specific lagged flow regimes enhanced the predictive capacities of “stRFlow”. Results depict the potential of stRFlow and the framework in streamflow modeling in similar hydroclimatic regions with applicability for practical and reliable streamflow predictions globally.