Hierarchical Method Research Articles

Automatic knee osteoarthritis severity grading based on X-ray images using a hierarchical classification method

Automatic knee osteoarthritis severity grading based on X-ray images using a hierarchical classification method

Evaluation of Logistics Development Under the Visual Field of Low-Carbon Environmental Protection Based on Hierarchical Methods

The development of the modern logistics industry in China is relatively backward, and the logistics industry has always shown characteristics of high emissions, high energy consumption, and high pollution. The low-carbon transformation and development of the logistics industry has become a prominent issue. At present, research on low-carbon logistics mainly focuses on its definition and characteristics. The research on its influencing factors is mostly qualitative analysis, and there is relatively little research on performance evaluation. This article establishes a performance evaluation index system from the perspective of low-carbon environmental protection, including information technology level, warehousing, transportation, packaging, etc., and constructs a comprehensive evaluation model that combines an analytic hierarchy process with data envelopment analysis for low-carbon logistics performance evaluation. By reviewing the collected data and taking five logistics enterprises in China as examples, the feasibility of low-carbon logistics performance evaluation indicators and models was verified.

Path Planning for Robots Combined with Zero-Shot and Hierarchical Reinforcement Learning in Novel Environments

Path planning for robots based on reinforcement learning encounters challenges in integrating semantic information about environments into the training process. In unseen or complex environmental information, agents often perform sub-optimally and require more training time. In response to these challenges, this manuscript pioneers a framework integrating zero-shot learning combined with hierarchical reinforcement learning to enhance agent decision-making in complex environments. Zero-shot learning enables agents to infer correct actions for previously unseen objects or situations based on learned semantic associations. Subsequently, the path planning component utilizes hierarchical reinforcement learning with adaptive replay buffer, directed by the insights gained from zero-shot learning, to make decisions effectively. Two parts are trained separately, so zero-shot learning is available in different and unseen environments. Through simulation experiments, we compare the traditional hierarchical reinforcement learning method with the proposed method. The results prove that this structure can make full use of environmental information to generalize across unseen environments and plan collision-free paths.

HCA-RFLA: A SAR Remote Sensing Ship Detection Based on Hierarchical Collaborative Attention Method and Gaussian Receptive Field-Driven Label Assignment Strategy

Ensuring safety at sea has become a primary focus of marine monitoring, driving the increasing adoption of ship detection technology in the maritime industry. Detecting small ship targets in SAR images presents challenges, as they occupy only a small portion of the image and exhibit subtle features, reducing detection efficiency. To address these challenges, we propose the HCA-RFLA algorithm for ship detection in SAR remote sensing. To better capture small targets, we design a hierarchical collaborative attention (HCA) mechanism that enhances feature representation by integrating multi-level features with contextual information. Additionally, due to the scarcity of positive samples for small targets under IoU and center sampling strategies, we propose a label assignment strategy based on Gaussian receptive fields, known as RFLA. RFLA assigns positive samples to small targets based on the Gaussian distribution between feature points and ground truth, increasing the model’s sensitivity to small samples. The HCA-RFLA was experimentally validated using the SSDD, HRSID, and SSD datasets. Compared to other state-of-the-art methods, HCA-RFLA improves detection accuracy by 6.2%, 4.4%, and 3.6%, respectively. These results demonstrate that HCA-RFLA outperforms existing algorithms in SAR remote sensing ship detection.

Regulating and Unraveling Electrochemical Behavior of Hierarchically‐Densifying Mesoporous Apocynum Carbon for High performance Supercapacitor

AbstractThe commercial carbon‐based supercapacitor with high power ability (~5 kW kg−1) is still unable to fulfill the superhigh power requirement of specific power‐type equipments (>20 kW kg−1), such as rail transit facilities, electromagnetic and laser equipment. To unravel the structure‐activity relationship and electrochemical behavior of power‐type densifying carbon is a key to overcome the contradiction of the suitable mesoporous ratio and highly‐densifying features toward the superhigh power requirement. Here, we built the hierarchically‐densifying mesoporous apocynum carbon (HDMC) with optimized mesoporous ratio by hierarchical activation method. More importantly, both the isothermal desorption/adsorption and high‐pressure mercury intrusion porosimetry methods were employed to synergistically uncover the microscopic surface carbon network stacking mechanism and the macroscopic carbon skeleton densification assembly mechanism. The highly‐densifying skeleton features and high mesoporous ratio properties were proved to be co‐existed in HDMC, which is in favour of rapidly ion/electron transferring toward electrochemically‐improving power behavior of HDMC. A combination of high tap density (0.387 g cm−3) and ideal microporous‐mesoporous system (23.1 % proportion of mesoporous) have taken this HDMC to provide a super‐high power density (33.5 kW kg−1) and a high volume power density (9.37 kW L−1) for HDMC‐based supercapacitor, more than those of commercial YP‐50F (14.9 kW kg−1 @ 4.63 kW L−1). Therefore, this work provides a synergistic strategy to incorporate the properties of mesoporous and densifying, and reveals its electrochemical behavior toward the further application of power‐type supercapacitors.

Human errors analysis for remotely controlled ships during collision avoidance

To address human errors in collision avoidance tasks of remotely controlled ships, this study aims to develop a comprehensive framework for human error analysis within the context of autonomous ships. Firstly, the Hierarchical Task Analysis method is utilized to identify crew collision avoidance tasks associated with the traditional ship, and these tasks are then dissected into different operational stages using the Information Decision Action in a Crew cognitive model. Secondly, a combination of the fault hypothesis method and expert opinions are used to identify potential human error that may occur during collision avoidance operations of remotely controlled ships. Thirdly, an integrated approach is proposed to build a quantitative risk assessment model, which combines Failure Mode and Effects Analysis, Evidential Reasoning, and Belief rules-based Bayesian Network. Then, axiomatic analysis is used to verify the robustness and applicability of the risk assessment model. Finally, based on the results of quantitative risk assessment, specific measures are proposed for enhancing the safety of collision avoidance process of remotely controlled ships. The findings show that uncoordinated interactions of human-computer systems during the decision-making stage are a pivotal factor in the collision avoidance process. Therefore, future design efforts for remote-control centre should prioritize improving the clarity of human-computer interaction interfaces.

A Comparative Analysis of the Sustainable Development of Urban Agriculture in Chengdu and Singapore

With the deepening global concern for sustainable development, urban agriculture has received more and more attention as an important combination of urban development and ecological environmental protection. This paper provides a comprehensive evaluation and comparison of two areas, Chengdu and Singapore, in terms of their capacity for sustainable development of urban agriculture through an empirical analysis method. To make a multidimensional evaluation index system, the study uses the hierarchical analysis method (AHP) and the fuzzy comprehensive evaluation method. The index covers important areas like the level of agricultural development, the intensity of applied inputs, the rate of energy use, the rate of resource utilization, and the level of agricultural factors. The results show that Chengdu performs significantly in terms of resource utilization rate and agricultural factor level, while Singapore performs better in terms of energy utilization rate. The study provides targeted recommendations, including strengthening scientific and technological innovation, policy support, and ecological and environmental protection, with the aim of providing references and lessons for the sustainable development of urban agriculture in the two places and other areas around the world.

A Novel Nonlinear Adaptive Control Method for Longitudinal Speed Control for Four-Independent-Wheel Autonomous Vehicles

As autonomous driving technology and four-independent-wheel chassis systems advance, four-independent-wheel autonomous vehicles have increasingly become a focal area of modern research. The longitudinal control problem for four-independent-wheel autonomous vehicles presents challenges such as complex models, high nonlinearity, and strong system uncertainties. This paper proposes a novel hierarchical control algorithm to address these challenges, innovatively combining the advantages of adaptive backstepping and dynamic sliding mode control algorithms in the upper controller, allowing it to effectively overcome the impact of uncertain system parameters and suppress the common chattering phenomenon in the output of typical sliding mode control methods. Based on the design of the upper controller, an innovative optimized longitudinal force distribution strategy and the construction of a tire reverse longitudinal slip model are proposed, followed by the design of a fuzzy PID controller as the lower slip ratio controller to achieve precise whole-vehicle longitudinal speed tracking and improve overall control performance. This method not only improves the accuracy of speed tracking but also enhances the robustness and adaptability of the control system. Finally, the effectiveness and superiority of the proposed hierarchical control method are verified through CarSim simulations.

Towards Holistic Functional Task Analysis

ABSTRACTTask analysis (TA) can contribute to work systems design, accident investigation, risk assessment, human error identification, planning, and training. Despite the advantages of existing sequential and hierarchical methods, they decompose tasks into their structure and focus on the order in which tasks are accomplished. They do not trace all interactions among elements/subtasks/operations at different levels. As the complexity of tasks increases, not keeping track of all interactions may result in poor, unwanted outcomes. This research introduces a different approach to TA that decomposes tasks into their constituent functions, describes the functionality of the overall work system, traces (dynamic nonlinear) interactions among functions, and highlights the role of functional variability in forming emergent outcomes. This approach to TA is called functional task analysis (FTA). A case study on nursing work was used to demonstrate the suitability of the FTA approach. The findings of this study show that the FTA approach contributes to task modeling by building a nonsequential, nonhierarchical functional model of a complex task considering dynamic, nonlinear interactions among functions. The FTA also contributes to task description by explaining different ways a task can be accomplished. It also increases the understanding, interpretation, and analysis of how changes in work conditions shape good/acceptable and poor/unacceptable outcomes. The FTA can complement the TA by adding some aspects, including functionality, nonlinearity, dynamics, and emergence, that the TA does not normally consider. The findings highlight how the functional approach to TA can be deployed as an alternative (or complement) to other task analysis methods.

A Survey System for Artificial Intelligence-Based Painting Using Generative Adversarial Network Techniques

The purpose of this paper is to construct an evaluation system for AI painting software based on generative adversarial network (GAN) technology, which optimizes the performance of the related software in terms of functionality, ease of use, system performance, and safety. The results of the questionnaires are statistically analyzed. In addition, an exploratory factor analysis was conducted to extract the data of the study, which were ultimately used to calculate the weight and importance of each index using the fuzzy hierarchical analysis method. This study constructed an evaluation system for AI painting software based on GAN technology, including 16 indicators of functionality, 16 indicators of ease of use, 7 indicators of system performance, and 8 indicators of safety, respectively, whose alpha coefficients were 0.882, 0.962, 0.932, 0.932, and 0.932, respectively. In addition, the accumulated explanatory variances of their coefficients were 84.405%, 84.897%, 84.013%, 72.606%, 73.013%, and 72.606%, respectively. It is clear that the items included in each of the indicators are homogeneous, with a high degree of internal consistency. This paper suggests that the development of AI painting software focusing on functionality, ease of use, system performance, and safety can enhance the market competitiveness of the software.

HFS-CSR: A hierarchical feature selection method based on correlation and structural redundancy

Feature selection (FS) is a crucial step in data engineering, as it not only saves time and resources for model training but also improves the prediction performance. In many realistic data sets, the feature space is formed by binary features which are related via generalization-specialization relationships (a.k.a., hierarchical feature space). These interdependencies among features, which can be described via a directed acyclic graph, present new challenges for classical FS methods. In this paper, a Hierarchical Feature Selection method based on Correlation and Structural Redundancy (HFS-CSR) is proposed to address it. In HFS-CSR, the correlation between features and the class label is measured based on Fisher Score whereas the structural redundancy among features are assessed quantitatively via hierarchical redundancy and depth penalties. The importance of features are determined by combining them adaptively for feature filtering. Experiments are conducted based on two kinds of typical data sets with hierarchical feature space: aging gene data sets and NLP data sets. The results demonstrate that HFS-CSR can achieve better performance compared with seven existing FS methods.

A hierarchical clustering algorithm for addressing multi-modal multi-objective optimization problems

Many evolutionary multi-modal multi-objective algorithms (MMEAs) have been proposed to solve multi-modal multi-objective optimization problems (MMOPs). Unfortunately, the environmental selection process causes many algorithms to place too much emphasis on solution variety in the decision space, which leads to solutions with low convergence quality. As a result, not only are all local Pareto fronts reversed, but objective values are also far lower than the global Pareto Fronts. To tackle these tasks, this paper proposes a hierarchical clustering-based MMOEA_DC_HR model that uses decision space clustering methods to group neighborhood solutions into several local clusters, preserving local Pareto Sets. And secondary clustering is performed in the objective space to select temporary populations from these local clusters to maintain the diversity of the objective space. Additionally, a hierarchical ranking method is introduced to update the convergence archive aiding in maintaining the convergence of the algorithm and controlling the quality of the Pareto Front. The test results show that this novel algorithm exhibits competitive performance in solving selected benchmark problems when compared to other cutting-edge MMEAs.

Prioritizing conservation sites for multi-pond systems to maintain protection of water quality in a fragmented agricultural catchment

Precise targeting of conservation practices to the most effective sites in multi-pond systems (MPS) is critical for resource optimization and water quality improvement. Previous studies generally prioritized ponds for conservation practices considering nutrient removal efficiency. However, they have frequently overlooked the role of ponds in sediment interception and the impact of human activities and environmental factors around the pond. Herein, the present study developed and applied a novel framework for pond prioritization by integrating the Pressure-State-Response (PSR) model, graph theory, and K-mean clustering. The framework consists of three components. An indicator system is developed to represent the nutrient removal performance of any MPS, impacts on catchment sediment connectivity, external threats, and human-initiated conservation. A flow path network considering natural and artificial elements was constructed to calculate indicator values. A cluster analysis was conducted on the index values of different ponds, and a hierarchical sorting method were used to prioritize ponds. The framework was applied to the Guilinqiao Catchment, a typical fragmented agricultural catchment in the Yangtze River Basin, China. The study has quantified the Pressure, State, and Response indices of different ponds in this catchment, prioritized the ponds, and drawn recommendations for conserving MPSs based on field surveys and remote sensing data. Ponds with higher Pressure index, higher State index, and lower Response index scores should be targeted as conservation priorities. This framework provides an effective method for ensuring management of MPSs to sustainably maximize water cleanup capacity with limited resources.

Profit-driven distributed trading mechanism for IoT data

Data trading is a crucial means of unlocking the value of Internet of Things (IoT) data. However, IoT data differs from traditional material goods due to its intangible and replicable nature. This difference leads to ambiguous data rights, confusing pricing, and challenges in matching. Additionally, centralized IoT data trading platforms pose risks such as privacy leakage. To address these issues, we propose a profit-driven distributed trading mechanism for IoT data. First, a blockchain-based trading architecture for IoT data, leveraging the transparent and tamper-proof features of blockchain technology, is proposed to establish trust between data owners and data requesters. Second, an IoT data registration method that encompasses both rights confirmation and pricing is designed. The data right confirmation method uses non-fungible token to record ownership and authenticate IoT data. For pricing, we develop an IoT data value assessment index system and introduce a pricing model based on a combination of the sparrow search algorithm and the back propagation neural network. Finally, an IoT data matching method is designed based on the Stackelberg game. This establishes a Stackelberg game model involving multiple data owners and requesters, employing a hierarchical optimization method to determine the optimal purchase strategy. The security of the mechanism is analyzed and the performance of both the pricing method and matching method is evaluated. Experiments demonstrate that both methods outperform traditional approaches in terms of error rates and profit maximization.

English

Coffee breeding programs aim at developing superior genotypes, which entail exploring the genetic variability existing in conilon/robusta coffee. This study aimed at examining the variability and estimate genetic param of physical and physiological nature in Coffea canephora seeds in order to provide bases for the selection of genotypes for superior seeds quality. Forty-three C. canephora genotypes were evaluated for 11 physical and physiological seed traits. Estimates of genetic param, environmental variance, phenotypic variance, genotypic variance, broad-sense heritability, variation index and Pearson's correlation were obtained. The Dissimilarity between genotypes was obtained by generalized Mahalanobis distance. The dendrogram was constructed using unweighted pair group method with arithmetic mean (UPGMA). C. canephora genotypes presented high germination potential and exhibited genetic diversity for all seed traits, with estimates of genetic variance greater than environmental variance. Heritability reached values above 96.0% and all genetic variation indices found were higher than unity. The UPGMA hierarchical method grouped the genotypes in three groups. The physical traits (circumference, area and roundness) were those that contributed the most to explaining the dissimilarity between genotypes. Genotypes Alecrim, AP, Bamburral, Z39 and Tardio C were the most divergent. The results revealed the presence of genetic variability between C. canephora genotypes and greater genetic than environmental influence on the characters of seeds, indicating that selection for the studied traits showed favorable conditions in terms of immediate gain.

Study on the damage characteristics of high-temperature superconducting cable insulation under air gap discharge

This study explores the impact of small air gaps in high-temperature superconducting cables on the insulating material polypropylene-laminated paper (PPLP), and the aging rules and mechanisms of the insulating material during practical uses. An air gap discharge test platform was built to simulate air gap fault defects of superconducting cables in the real operating environment. Hierarchical clustering method was used to divide the gap discharge process of defect model into four stages. Insulation damage assessment was conducted on the intermediate layer PP of the superconducting insulation material PPLP at different discharge stages, revealing surface changes and periodic alterations in dielectric properties. The morphological features, roughness, infrared spectra, dielectric loss, surface resistivity, and other phase characteristics of the superconducting insulation layer material were analyzed at different stages of air gap defects. Molecular group cracking in PP was attributed to the bond breakage on the main chain. These findings provide insights into high-temperature superconducting cable insulation under air gap discharge and provide a guideline for practical applications in semi-conductive industries.

Cluster analysis of travel behaviour threats in chosen European functional urban areas during the economic crisis

Motives: Urban mobility models are constantly changing due to economic, social, infrastructural, and geographical factors. Preferences for transport modes vary by country, region, and city, and transport infrastructure and policy have a significant impact on residents’ choices. Additionally, economic factors such as economic crises, public policy decisions, and events such as armed conflicts have a significant impact on the dynamics of mobility in functional urban areas (FUAs).Aim: The main goal of the research was to create groups of countries (clusters) that have similar priorities regarding travel behaviour threats revealed as a result of the economic crisis resulting from the conflict in Ukraine.Results: The study shows that the main threats during the economic crisis (resulting from the war in Ukraine) are economic factors (e.g., ticket prices, an increase in fuel costs), legal factors (e.g., loss of a driving license or passenger transport license), and infrastructural factors (e.g., poor condition of infrastructure). The analysed countries were grouped based on the hierarchical prioritization of threats, using Ward’s hierarchical clustering method. Interestingly, the distance from the conflict epicentre did not significantly influence the prioritization of threat factors influencing transportation behaviour in FUAs. Instead, factors such as membership in international organizations, energy dependence on Russia, the level of economic growth, and transport infrastructure played a more key role in the priority-setting process.

Priority-Elastic net for binary disease outcome prediction based on multi-omics data

BackgroundHigh-dimensional omics data integration has emerged as a prominent avenue within the healthcare industry, presenting substantial potential to improve predictive models. However, the data integration process faces several challenges, including data heterogeneity, priority sequence in which data blocks are prioritized for rendering predictive information contained in multiple blocks, assessing the flow of information from one omics level to the other and multicollinearity.MethodsWe propose the Priority-Elastic net algorithm, a hierarchical regression method extending Priority-Lasso for the binary logistic regression model by incorporating a priority order for blocks of variables while fitting Elastic-net models sequentially for each block. The fitted values from each step are then used as an offset in the subsequent step. Additionally, we considered the adaptive elastic-net penalty within our priority framework to compare the results.ResultsThe Priority-Elastic net and Priority-Adaptive Elastic net algorithms were evaluated on a brain tumor dataset available from The Cancer Genome Atlas (TCGA), accounting for transcriptomics, proteomics, and clinical information measured over two glioma types: Lower-grade glioma (LGG) and glioblastoma (GBM).ConclusionOur findings suggest that the Priority-Elastic net is a highly advantageous choice for a wide range of applications. It offers moderate computational complexity, flexibility in integrating prior knowledge while introducing a hierarchical modeling perspective, and, importantly, improved stability and accuracy in predictions, making it superior to the other methods discussed. This evolution marks a significant step forward in predictive modeling, offering a sophisticated tool for navigating the complexities of multi-omics datasets in pursuit of precision medicine’s ultimate goal: personalized treatment optimization based on a comprehensive array of patient-specific data. This framework can be generalized to time-to-event, Cox proportional hazards regression and multicategorical outcomes. A practical implementation of this method is available upon request in R script, complete with an example to facilitate its application.

Analysis of Online User Reviews for Popular Tourist Attractions: Almaty Case

Attractions in the tourism industry are one of the components that motivate tourists to visit destinations, such as entertainment, natural, cultural, and historical richness. For such reasons, people decide to visit unique destinations and spend time there. Almaty, the largest city of Kazakhstan, is one of the significant attraction centers of the Central Asia region, offering tourists unique and pleasant features with several tourist attractions. This study aims to analyze online user reviews of tourist attractions in Almaty, Kazakhstan, using machine learning and text mining methods. The primary focus is on identifying the main thematic clusters of reviews and their sentiment and comparing these themes with the types of attractions: historical, natural, and man-made. A total of 7,515 reviews were collected from the TripAdvisor website. The data was processed using sentiment analysis, topic modeling, and hierarchical clustering methods. The analysis revealed that 38% of the reviews were related to natural attractions, 34% to man-made, and 28% to historical ones. The most positive reviews were associated with natural attractions, while historical and man-made attractions received 79.38% and 81.40% positive reviews, respectively. In addition, the items that make up these attractions are identified, and their sentiment levels are pointed out. In addition to this situation, visitors have the most positive expressions for natural attractions, especially landscapes and lakes. The findings emphasize the importance of considering review themes to improve the quality of tourist services and to enhance the positive image of Almaty as a tourist destination.

An operational risk assessment method for petrochemical plants based on deep learning

The petrochemical industry is an important guarantee for the development of people’s lives, and the operational risk assessment method of personnel in the operation of petrochemical enterprises is the most important. Based on a deep learning algorithm, a new method based on the micro-Doppler effect and fuzzy analytic hierarchy process is proposed to evaluate the operational risk of personnel in petrochemical enterprises. The original monitoring image of petrochemical equipment is invoked, and micro-Doppler analysis is performed based on the original image to identify the activity target of the personnel on the job site and generate the activity map of the petrochemical plant operators. Based on the analysis data of the micro-Doppler effect, the fuzzy function and hierarchical analysis method are combined. The fuzzy theory is introduced into the analytic hierarchy process, and the inherent expert evaluation scores are transformed into fuzzy numbers. It makes the expert evaluation of petrochemical plants more accurate in the subsequent coupling and improves the objectivity of the traditional analytic hierarchy process. The scheme proposed in this paper can monitor real-time operation safety and provide a guarantee for the personal safety of field operators. This method plays an important role in improving the safety of petrochemical plants.