Hierarchical Method Research Articles

A Modified Particle Swarm Algorithm for the Multi-Objective Optimization of Wind/Photovoltaic/Diesel/Storage Microgrids

Microgrids have been widely used due to their advantages, such as flexibility and cleanliness. This study adopts the hierarchical control method for microgrids containing multiple energy sources, i.e., photovoltaic (PV), wind, diesel, and storage, and carries out multi-objective optimization in the tertiary control, i.e., optimizing the economic cost, environmental cost, and the degree of energy utilization of microgrids. As the traditional multi-objective particle swarm algorithm is prone to local convergence, this study introduces variable inertia weight and learning factors to obtain a modified particle swarm algorithm, which is more advantageous in multi-objective optimization. Compared to the traditional particle swarm algorithm, the modified particle swarm algorithm increased the photovoltaic absorbed rate from 0.7724 to 0.8683 and the wind energy absorbed rate from 0.6064 to 0.7158 in one day, which resulted in an increase in energy utilization by 14.89%, and a reduction in financial environmental costs from RMB 135,870 to RMB 132,230. The simulation of the optimization effect of the conventional particle swarm algorithm and the modified particle swarm algorithm on the microgrid were carried out, respectively, in MATLAB, which verifies the advantage of the modified particle swarm algorithm on the optimization of microgrids. Then, the optimization results, i.e., the data of the power scheduling process of the four power sources, were made into a table and imported into the microgrid model in Simulink. The simulation results indicated that the microgrid was able to output stable voltage, current, and frequency. Finally, the changes in microgrids affected by the external environment were further investigated from the aspects of the market environment and natural environment. Moreover, we verified the presence of a contradiction between the optimization of the microgrid economy and environmental protection. Thus, microgrids need to adjust their optimization focus according to the natural conditions in which they are located.

Retrofit building energy performance evaluation using an energy signature-based symbolic hierarchical clustering method

Retrofitting existing buildings is crucial for significantly reducing energy consumption in the building sector. The continuous monitoring and evaluation of retrofit building energy efficiency is necessary to maintain optimal energy performance. This study proposes a novel method for evaluating building energy performance that combines energy signature analysis and hierarchical clustering. Symbolic data were defined by k-means using differences in gradients and y-intercepts before and after retrofitting extracted from energy signatures. Hierarchical clustering was then performed using the symbolic datasets. This symbolic hierarchical clustering method enhances the utility of open data and facilitates rapid decision-making. Additionally, it allows for a simple assessment of energy performance at the city scale. Through implementing this approach in 49 retrofitted buildings in Gangwon-do, South Korea, five types of symbolic data were identified (Types 0–4). Using hierarchical clustering, these buildings were clustered into six groups (Clusters 1–6). Type 3, representing ideal retrofitting outcomes, was observed in Clusters 2, 3, and 4 (73.47 % of all buildings). Conversely, Type 4 symbols, indicating a rebound effect, were observed in Cluster 1 and 2 (6.12 %). These findings provide meaningful information after retrofitting at the regional level, contributing to effective building management during retrofitting.

Graph Neural Network Based Multi-Label Hierarchical Classification for Disease Predictions in General Practice.

General practitioners are supposed to be better diagnostics to detect patients with serious diseases earlier, and conduct early interventions and appropriate referrals of patients. However, in the current general practice, primary general practitioners lack sufficient clinical experiences, and the correct rate of general disease diagnosis is low. To assist general practitioners in diagnosis, this paper proposes a multi-label hierarchical classification method based on graph neural network, which integrates medical knowledge and electronic health record (EHR) data to build a disease prediction model. The experimental results based on data consist of 231,783 visits from EHR show that the proposed model outperforms all baseline models in the general disease prediction task with a top-3 recall of 0.865. The interpretable results of the model can effectively help clinicians understand the basis of the model's decision-making.

Hierarchical node method for solving large-scale sparse linear equations in parallel

Hierarchical node method for solving large-scale sparse linear equations in parallel

An autonomous Unmanned Aerial Vehicle exploration platform with a hierarchical control method for post‐disaster infrastructures

AbstractCatastrophic natural disasters like earthquakes can cause infrastructure damage. Emergency response agencies need to assess damage precisely while repeating this process for infrastructures with different shapes and types. The authors aim for an autonomous Unmanned Aerial Vehicle (UAV) platform equipped with a 3D LiDAR sensor to comprehensively and accurately scan the infrastructure and map it with a predefined resolution r. During the inspection, the UAV needs to decide on the Next Best View (NBV) position to maximize the gathered information while avoiding collision at high speed. The authors propose solving this problem by implementing a hierarchical closed‐loop control system consisting of a global planner and a local planner. The global NBV planner decides the general UAV direction based on a history of measurements from the LiDAR sensor, and the local planner considers the UAV dynamics and enables the UAV to fly at high speed with the latest LiDAR measurements. The proposed system is validated through the Regional Scale Autonomous Swarm Damage Assessment simulator, which is built by the authors. Through extensive testing in three unique and highly constrained infrastructure environments, the autonomous UAV inspection system successfully explored and mapped the infrastructures, demonstrating its versatility and applicability across various shapes of infrastructure.

Learning Offset Probability Distribution for Accurate Object Detection

Object detection combines object classification and object localization problems. Current object detection methods heavily depend on regression networks to locate objects, which are optimized with various regression loss functions to predict offsets between candidate boxes and objects. However, these regression losses are difficult to assign the appropriate penalties for samples with large offset errors, resulting in suboptimal regression networks and inaccurate object offsets. In this article, we consider object location as offset bin classification problem, and propose a distance-aware offset bin classification network optimized with multiple binary cross entropy losses to learn various offset probability distribution, including single label distribution and distance-aware label distribution. On one hand, it provides gradient contributions for different samples based on the bounded probability instead of previous incalculable offset error. On the other hand, it explores the distance correlations between discrete offset bins to facilitate network learning. Specifically, we discretize the continuous offset into a number of bins, and predict the probability of each offset bin, in which the probability should be higher for the offset bin closer to the target offsets, and vice versa. Furthermore, we propose an expectation-based offset prediction and a hierarchical focusing method to improve the precision of prediction. We conduct extensive experiments to evaluate the effectiveness of our method. In addition, our method can be conveniently and flexibly inserted into existing object detection methods, which consistently achieves a large gain based on popular anchor-based and anchor-free methods on the PASCAL VOC, MS-COCO, KITTI, and CrowdHuman datasets. Code will be released at: https://github.com/QiuHeqian/DBC .

Enhancing logistics performance measurement: an effectiveness-based hierarchical data envelopment analysis approach

The logistics performance index (LPI) is a comprehensive and comparable composite index developed by the World Bank to assess a country’s logistics and trade facilitation environment. However, the existing LPI relies on externally assigned weights. To enhance LPI scores, this study adopts an effectiveness-based hierarchical data envelopment analysis method that internally allocates objective weights based on the dataset. Such endogenous weight information can provide additional valuable insights for countries to prioritize and strategize efforts to enhance their performance in the future. The results of this study indicate that focusing on improving the policy category yields greater benefits than improving the service category in terms of ranking national logistics performance. Furthermore, this study finds that logistics performance is influenced by income levels and geographical area. Income levels impact the regulatory and trade facilitation environment, with varying income levels leading to different priority policy areas. Geographical location also plays a crucial role in regional economic integration and trade facilitation. A favorable geographical location reduces costs and time while enhancing supply chain predictability and reliability. It is hoped that this study serves as a valuable resource for countries in identifying optimization strategies to improve their logistics performance.

Comparative Study of Multi-Objective and Single-Objective Optimisation of Colour Matching for Opaque Products

Opaque products usually refer to all types of products with opaque packaging. In order to improve the market competitiveness of opaque products, it is crucial to determine the optimal colour scheme for the products. In this paper, we study the colour matching scheme for opaque products, taking different concentrations of red, yellow and blue dye as the research variables, taking the minimum chromatic aberration, the lowest cost, and the type of dye as the objective function, establishing the mathematical model of the optimal colour matching scheme, adopting the single-objective particle swarm algorithm and multi-objective hierarchical sequential method respectively, and using specific examples to carry out a comparative analysis to check the two methods. The results show that the single-objective method can provide the colour matching scheme with the minimum chromatic aberration from the ideal colour, and the multi-objective method can consider the cost and price of dyes as well as the types of dyes to provide a more scientific colour matching scheme. The computational results also verify the validity of the model and show that the model can provide decision-making and support for colour matching schemes for opaque products.

The typical AV accident scenarios in the urban area obtained by clustering and association rule mining of real-world accident reports

Automated Vehicles (AVs) based on a collection of advanced technologies such as big data and artificial intelligence have opened an opportunity to reduce traffic accidents caused by human drivers. Nevertheless, traffic accidents of AVs continue to occur, which raises safety and reliability concerns about AVs. AVs are particularly vulnerable to accidents on urban roads than on highways due to various dynamic objects and more complex infrastructure. Several studies proposed a scenario-based approach of experimenting with the response of AVs to specific situations as a way to test their safety. Reliable and concrete scenarios are necessary to test AV safety under critical conditions accurately. This study aims to derive a typical accident scenario for evaluating the safety of AVs, specifically in urban areas, by analysing collisions reported by the DMV of California, USA. We applied a hierarchical clustering method to find groups of similar reports and then executed association rule mining on each cluster to correlate between accident factors and collision types. We combined statistically significant association rules to constitute a total of 14 scenarios that are described according to an adapted PEGASUS framework. The newly obtained scenarios exhibit significantly different accident patterns than the typical Human-driven Vehicles (HVs) in urban areas reported by National Highway Traffic Safety Administration. Our discovery urges AV safety to be tested reliably under scenarios more relevant than the existing HV accident scenarios.

A multiscale FEM-MD coupling method for investigation into atomistic-scale deformation mechanisms of nanocrystalline metals under continuum-scale deformation

This study aims to develop a multiscale bridging method for investigating nanocrystalline metals based on macro-scale deformation. For this purpose, we propose a hierarchical multiscale computational method that can focus on some of the elements in a finite element model for scale bridging to atomistic-scale models. This method assumes that atomistic-scale nanocrystalline models are related to the integration points in a finite element and deform based on the macro-scale deformation. Nanocrystalline aluminum was chosen for the validation of the multiscale method. The finite element method (FEM) and the molecular dynamics (MD) method were used for continuum-scale and atomistic-scale simulations, respectively. We utilized the notion of the CauchyBorn rule (CBR) for communicating deformation information from the continuum scale to the atomistic scale. We studied three different cases with two nanocrystalline models and two loading cases to compare differences resulting from crystal structures and loading. Based on the crystal structure change during relaxation, nonequilibrium grain boundaries (NEGBs) were shown to play a role as deformation mechanisms in the plastic regime and induce the onset and migration of crystal defects, including deformation twins, as reported in the experiment. Furthermore, the crystal orientation dependence of the onset of crystal defects was confirmed by the comparison of the results from the two different nanocrystalline models. The qualitative agreement of the results with experimental observations is also confirmed. The proposed ‘FEM-MD’ method can bridge a large-scale gap, for example, from a nano-scale to a continuum-scale such that an MD model can be coupled to a millimeter or centimeter scale compared to other embedding methods. The present method is ideal for investigating the dislocation behavior of nanocrystalline materials, which contain multi-grained nanostructure at finite temperature, undergoing various loading scenarios at the macro-scale.

Analisis Perbandingan Metode Hierarchical dan Non-Hierarchical dalam Pembentukan Cluster Provinsi di Indonesia Berdasarkan Indikator Women Empowerment

<p>The focus on improving the quality of women’s live to lessen discrimination and gender inequality is set in the fifth’s goals of SDGs. In Indonesia, the RPJMN 2020-2024 contains measure to improve the contribution of women to equitable development. The Central Bureau of Statistics has developed several indicators related to gender, including Gender Development Index (GDI) an Gender Empowerment Index (GEI), which contain women’s improvement on education and health as well as their participation in economic and political fields. The Ministry of Women’s Empowerment and Child Protection did a quadrant analysis to split Indonesia’s 34 provinces into four categories based solely on GDI and GEI using the national average as a constraint. This study compares the Hierarchical, K-Means, and Fuzzy C-Means method to form number of clusters in Indonesia based on the gender development and empowerment in 2021 in order to complement the quadrant analysis. To choose the number of optimum cluster, Elbow method and Calinski-Harabasz Index were used and the best k value is five. From the validation with Silhoutte Index, K-Means was chosen as the best clustering model.</p><p><strong>Keywords: </strong>clustering; fuzzy; k-means; hierarchical; women empowerment</p>

Inflammatory biotype of ADHD is linked to chronic stress: a data-driven analysis of the inflammatory proteome

The association between Attention Deficit Hyperactivity Disorder (ADHD) and low-grade inflammation has been explored in children but rarely in adults. Inflammation is characteristic of some, but not all, patients with ADHD and might be influenced by ADHD medication but also lifestyle factors including nutrition, smoking, and stress. It is also still unclear if any specific symptoms are related to inflammation. Therefore, we assessed 96 inflammatory proteins in a deeply phenotyped cohort of 126 adult ADHD participants with a stable medication status using OLINK technology. A data-based, unsupervised hierarchical clustering method could identify two distinct biotypes within the 126 ADHD participants based on their inflammatory profile: a higher inflammatory potential (HIP) and a lower inflammatory protein potential (LIP) group. Biological processes that differed strongest between groups were related to the NF-κB pathway, chemokine signaling, IL-17 signaling, metabolic alterations, and chemokine attraction. A comparison of sample characteristics revealed that the HIP group was more likely to have higher levels of chronic stress (p < 0.001), a higher clinical global impression scale score (p = 0.030), and a higher risk for suicide (p = 0.032). Medication status did not influence protein levels significantly (p ≥ 0.074), but psychotropic co-medication (p ≤ 0.009) did. In conclusion, our data suggest the presence of two distinct biotypes in adults with ADHD. Higher levels of inflammatory proteins in ADHD are linked to higher levels of chronic perceived stress in a linear fashion. Further research on inflammation in adults with ADHD should take stress levels into account.

Weakly supervised phase unwrapping for single-camera fringe projection profilometry

Phase unwrapping is essential in fringe projection profilometry (FPP). FPP's primary focus is to simultaneously realize high-speed, high-precision three-dimensional (3D) reconstruction. The dual-frequency hierarchical temporal phase unwrapping method (DF-TPU) technique is one prominent technique for accomplishing this goal. However, phase errors are inevitable and typically limit the DF-TPU approach's maximal period number, reducing reconstruction accuracy. The fully supervised approaches based on deep learning can unwrap dual-frequency phase maps of single-camera FPP systems but require accurate labels of high-frequency absolute phase. This study proposes a novel deep learning phase unwrapping scheme suited to single-camera FPP setups. Without the need for accurate labels corresponding to the high-frequency phase maps, supervisory signals are extracted from the high-frequency phase map, the one-period phase map, and the geometry constraint to guide the training of the phase unwrapping network. This training is a weakly supervised learning, which makes the time-consuming and expertise-demanding precise labeling unnecessary. Data collected in multiple real-world circumstances, such as motion blur, isolated objects, non-uniform reflectivity, and phase discontinuities, are used to validate the effectiveness of this weakly supervised method. Experimental results demonstrate that the proposed method outperforms the fully supervised and conventional DF-TPU methods in terms of depth accuracy and measurement efficiency in terms of fringe pattern number.

DTC: Deep Tracking Control.

Legged locomotion is a complex control problem that requires both accuracy and robustness to cope with real-world challenges. Legged systems have traditionally been controlled using trajectory optimization with inverse dynamics. Such hierarchical model-based methods are appealing because of intuitive cost function tuning, accurate planning, generalization, and, most importantly, the insightful understanding gained from more than one decade of extensive research. However, model mismatch and violation of assumptions are common sources of faulty operation. Simulation-based reinforcement learning, on the other hand, results in locomotion policies with unprecedented robustness and recovery skills. Yet, all learning algorithms struggle with sparse rewards emerging from environments where valid footholds are rare, such as gaps or stepping stones. In this work, we propose a hybrid control architecture that combines the advantages of both worlds to simultaneously achieve greater robustness, foot-placement accuracy, and terrain generalization. Our approach uses a model-based planner to roll out a reference motion during training. A deep neural network policy is trained in simulation, aiming to track the optimized footholds. We evaluated the accuracy of our locomotion pipeline on sparse terrains, where pure data-driven methods are prone to fail. Furthermore, we demonstrate superior robustness in the presence of slippery or deformable ground when compared with model-based counterparts. Last, we show that our proposed tracking controller generalizes across different trajectory optimization methods not seen during training. In conclusion, our work unites the predictive capabilities and optimality guarantees of online planning with the inherent robustness attributed to offline learning.

Dynamics of coastal land-based aquaculture pond in China and Southeast Asia from 1990 to 2020

The extensive expansion of coastal land-based aquaculture ponds (CLAP) has resulted in substantial wetland loss. To effectively monitor the dynamics of CLAP on a large scale, critical spatial information derived from continuous datasets is essential. Here, we generate the inaugural map depicting the changes in aquaculture pond coverage across the coastal regions both of China and Southeast Asia from 1990 to 2020 by utilizing the long time-series Landsat archive with an object-oriented hierarchical classification method. We extracted coastal water bodies using the Otsu algorithm and established a water inundation frequency index to eliminate interference from temporary water facilities. This process allowed us to identify potential distribution areas of aquaculture ponds. To precisely map the spatial distribution of CLAP within these identified areas, we employed an object-oriented K-nearest neighbor approach with spectral, geometric, and water index features as classification features. The cumulative coverage of CLAP in China and Southeast Asia reached 23,121 km2 by 2020, marking a net increase of 12,700 km2 since 1990. Notably, China, Indonesia, and Vietnam emerged as the primary contributors to these changes within our study area. Regions exhibiting dense aquaculture activities are predominantly concentrated in the northern region above 10 degrees latitude and along the eastern coastlines of various countries. This aquaculture pond map reserves fine spatial detail by effectively distinguishing drainage channels, rice seedling fields, and embankments surrounding aquaculture ponds. It serves as a valuable resource for guiding coastal resource management and environmental protection initiatives.

Supervised Hierarchical Online Hashing for Cross-modal Retrieval

Online cross-modal hashing has gained attention for its adaptability in processing streaming data. However, existing methods only define the hard similarity between data using labels. This results in poor retrieval performance, as they fail to exploit the semantic structure information of labels and miss the high-quality hash codes guided by the hierarchical relevance between labels. In addition, they ignore the bit-flipping problem, which leads to sub-optimal cross-modal retrieval performance. To address these issues, we propose Supervised Hierarchical Online Hashing (SHOH) for cross-modal retrieval. Our approach acquires hierarchical similarity via cross-layer affiliation of labels and explores its application to online hashing. We design a hierarchical similarity learning method in the online learning framework, which includes virtual center learning and hierarchical similarity embedding. Labels with soft similarity bridge the label hierarchy and cross-modal hash embedding. Furthermore, we propose a Weighted Retrieval Strategy (WRS) to mitigate the impact caused by bit-flipping errors. Extensive experiments and verification on hierarchical and non-hierarchical datasets demonstrate that SHOH preserves accurate inter-class distances and achieves performance improvements compared to state-of-the-art methods. The source code is available at https://github.com/HUST-IDSM-AI/SHOH .

Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images

Timely and accurate rice spatial distribution maps play a vital role in food security and social stability. Early-season rice mapping is of great significance for yield estimation, crop insurance, and national food policymaking. Taking Tongjiang City in Heilongjiang Province with strong spatial heterogeneity as study area, a hierarchical K-Means binary automatic rice classification method based on phenological feature optimization (PFO-HKMAR) is proposed, using Google Earth Engine platform and Sentinel-1/2, and Landsat 7/8 data. First, a SAR backscattering intensity time series is reconstructed and used to construct and optimize polarization characteristics. A new SAR index named VH-sum is built, which is defined as the summation of VH backscattering intensity for specific time periods based on the temporal changes in VH polarization characteristics of different land cover types. Then comes feature selection, optimization, and reconstruction of optical data. Finally, the PFO-HKMAR classification method is established based on Simple Non-Iterative Clustering. PFO-HKMAR can achieve early-season rice mapping one month before harvest, with overall accuracy, Kappa, and F1 score reaching 0.9114, 0.8240 and 0.9120, respectively (F1 score is greater than 0.9). Compared with the two crop distribution datasets in Northeast China and ARM-SARFS, overall accuracy, Kappa, and F1 scores of PFO-HKMAR are improved by 0.0507–0.1957, 0.1029–0.3945, and 0.0611–0.1791, respectively. The results show that PFO-HKMAR can be promoted in Northeast China to enable early-season rice mapping, and provide valuable and timely information to different stakeholders and decision makers.

A Hierarchical Classification Method for High-accuracy Instruction Disassembly with Near-field EM Measurements

Electromagnetic (EM) fields have been extensively studied as potent side-channel tools for testing the security of hardware implementations. In this work, a low-cost side-channel disassembler that uses fine-grained EM signals to predict a program's execution trace with high accuracy is proposed. Unlike conventional side-channel disassemblers, the proposed disassembler does not require extensive randomized instantiations of instructions to profile them, instead relying on leakage-model-informed sub-sampling of potential architectural states resulting from instruction execution, which is further augmented by using a structured hierarchical approach. The proposed disassembler consists of two phases: (i) In the feature-selection phase, signals are collected with a relatively small EM probe, performing high-resolution scans near the chip surface, as profiling codes are executed. The measured signals from the numerous probe configurations are compiled into a hierarchical database by storing the min-max envelopes of the probed EM fields and differential signals derived from them, a novel dimension that increases the potency of the analysis. The envelope-to-envelope distances are evaluated throughout the hierarchy to identify optimal measurement configurations that maximize the distance between each pair of instruction classes. (ii) In the classification phase, signals measured for unknown instructions using optimal measurement configurations identified in the first phase are compared to the envelopes stored in the database to perform binary classification with majority voting, identifying candidate instruction classes at each hierarchical stage. Both phases of the disassembler rely on a four-stage hierarchical grouping of instructions by their length, size, operands, and functions. The proposed disassembler is shown to recover ∼97–99% of instructions from several test and application benchmark programs executed on the AT89S51 microcontroller.

A comparative study on walking accessibility of high-speed railway station areas in China and Japan based on analytic hierarchy process -- Beijing station and Osaka station as examples

ABSTRACT To capture the factors influencing and measurements of pedestrian accessibility to high-speed rail station areas, ensure the balance between the transportation function and place performance of high-speed railway station areas, the correlation factors and laws of pedestrian space characteristics of high-speed railway station areas under different development modes are studied through the comparison of typical high-speed railway station areas in China and Japan. Combining the circle law and hierarchical analysis method, we establish a walking accessibility index system, construct a walking accessibility evaluation model for the high-speed rail station area, measure the degree of walking accessibility in the high-speed rail station area, and analyze the influence mechanism of different walking space components on the walking accessibility of the high-speed rail station area circle. The results indicate that: 1) Osaka Station space is much more walkable than Beijing Station in all circles; 2) The spatial development within the Beijing Station Station Circle is uneven, and the degree of pedestrian accessibility decreases as the circle’s extent increases; 3)Different station spaces have their own advantages and disadvantages in terms of the distribution of public facilities and amenities, roadway attributes, pedestrian path characteristics, depending on their respective national conditions and station development patterns.

Perceived risk-based sport spectator segmentation during the pandemic situation

PurposeAlthough many attempts to discover key segments of sport spectators have been extant, little segmentation effort has been made to reflect pandemic situations such as the COVID-19 pandemic. The purpose of this research is twofold: (1) to classify sport spectators into key segments based on perceived risks associated with a mass-gathered sporting event during the COVID-19 pandemic and (2) to identify each segment’s profiles.Design/methodology/approachQuestionnaire surveys of spectators attending a Japanese rugby game during the COVID-19 pandemic (January–June 2021) were conducted (n = 1,410). A combination of hierarchical and non-hierarchical clustering methods was executed.FindingsThe results revealed the five-cluster solution as the optimal number of clusters representing the samples (i.e. spectators with extremely low-risk perception, those with low-risk perception, those with moderate-risk perception, those with high-risk perception and those with higher social risk perception). This five-cluster solution showed sufficient stability and validity. Moreover, each segment had different profiles regarding three background aspects – demographics, psychographics and behavioral variables.Originality/valueThis study is the first effort to segment sport spectators based on perceived risks associated with a mass-gathered sporting event in the pandemic situation. Despite extensive segmentation studies to explore sport fans, contribution reflecting the post-crisis situations is scant. Therefore, the findings provide insight into this realm by providing a new viewpoint for understanding sport spectators during a possible future pandemic era.