Hierarchical Method Research Articles

A benchmarking framework for eye-tracking-based vigilance prediction of vessel traffic controllers

Vessel Traffic Controllers (VTCs) play a crucial role in ensuring safe navigation by maintaining a high level of vigilance. Eye-tracking has been identified as one of the most popular bio-signals for vigilance prediction. However, the existing studies on eye-tracking-based vigilance prediction usually utilized a limited set of features for analysis. A comprehensive analysis of various eye-tracking features and a unified model with general high performance remains a gap in current research. To address this issue, this study introduces a benchmarking framework for eye-tracking-based vigilance prediction and feature analysis. In the framework, a hierarchical analysis method is proposed, which explores a diverse set of eye-tracking features at both individual and group levels. Additionally, a vigilance ensemble model is proposed. Model interpretation is carried out by using the Shapley additive explanation (SHAP) method. The results highlight the superior performance of the proposed ensemble model. Moreover, a comparative analysis between professional VTCs and novices is performed. High-importance features and feature groups are identified separately. Upon comparison, it is also found that professionals demonstrate efficient attention allocation, while novices exhibit unique patterns influenced by their exploration strategies and scattered attention. The conclusions can serve as a valuable reference for maritime practitioners and provide insights into vigilance prediction across various domains.

Comparison of Hierarchical, K-Means and DBSCAN Clustering Methods for Credit Card Customer Segmentation Analysis Based on Expenditure Level

The amount of data from credit card users is increasing from year to year. Credit cards are an important need for people to make payments. The increasing number of credit card users is because it is considered more effective and efficient. The third method used today has a function to determine the effective outcome of credit card user scenarios. In this study, a comparison was made using the Hierarchical Clustering, K-Means and DBSCAN methods to determine the results of credit card customer segmentation analysis to be used as a market strategy. The results obtained based on the best silhouette coefficient score method is two cluster hierarchical clustering with 0.82322 score. Based on the best mean value customers are divided into two segments, and it is suggested to develop strategies for both segments.

A region-based hierarchical image compression method with simulated visual perception

Most learned image compression methods focus on reducing the average code length of the image, which is unreasonable for the human visual system. Thus, appropriate bit allocation gains significance. This paper presents a novel end-to-end method for perceptual image compression, emphasizing content prioritization and a region-based hierarchical strategy. Firstly, our modified class activation mapping (CAM) can serve as the visual perception network for simulating human visual perception. On this basis, we develop an efficient compression system that leverages a discretized hybrid entropy model to allocate bits automatically according to the perceptual prioritization of image content. In our compression system, an image is compressed jointly and hierarchically with different standards, guided by the importance map extracted by the visual perception network. We introduce content-weighted perceptual metrics to evaluate visual quality more reasonably and objectively. Experiments on publicly available datasets demonstrate that our method outperforms traditional codecs and recent content-oriented learned methods in overall performance. Compared with other methods, the proposed method reconstructs images that are more friendly to the human visual system.

The Construction of the Measurement Model of Balanced Development of Compulsory Education Resources based on the Hierarchical Analysis Method

The balanced development of compulsory education is an important measure to fully implement the equalization of basic education public services, promote education equity, and maintain social justice. Among the paths to the realization of balanced development of compulsory education, the most crucial is to ensure the balanced development of compulsory education resources, so it should attract high attention. The purpose of this paper is to construct a measurement model of the balanced development of compulsory education resources, to explore the key factors affecting the balanced development of compulsory education resources, and to find a scientific path for the balanced development of compulsory education resources. Based on this, this paper uses the method of hierarchical analysis to construct a measurement model of the balanced development of compulsory education resources, analyzes the measurement model of the balanced development of compulsory education resources in a certain region, and concludes that the main factors include school conditions, teacher quality, and education funding. Accordingly, this paper further proposes effective strategies for the balanced development of compulsory education resources from the aspects of increasing education funding, refining the assessment indicators of relevant staff, improving the personnel system of teachers, implementing the mobility policy of teachers, and strengthening the construction of basic resources. The research of this paper, on the one hand, can provide a guarantee for the development of balanced compulsory education resources in a certain region, and on the other hand, it can also provide certain references for the solution of the problem of balanced development of compulsory education resources in other regions with similar situations, and it has strong practical significance for the effective realization of balanced development of compulsory education.

Large-scale group-hierarchical DEMATEL method for complex systems.

Existing Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods are mostly suitable for simple systems with fewer factors, and lack effective integration of expert knowledge and experience from large-scale group populations, resulting in a potential compromise of the quality of the initial direct relation (IDR) matrix. To make DEMATEL better suited for the identification of critical factors in complex systems, this paper proposes a hierarchical DEMATEL method for large-scale group decision-making. Considering the limitations of expert knowledge and experience, a method based on expert consistency network for constructing the expert weight matrix is designed. The expert consistency network is constructed for different elements, and the weights of experts in different elements are determined using the clustering coefficient. Following the principles of the classic DEMATEL method, the steps for identifying key elements in complex systems using the large-scale group-hierarchical DEMATEL method are summarized. To objectively test the effectiveness and superiority of the decision algorithm, the robustness of the algorithm is analyzed in an interference environment. Finally, the superiority of the proposed method and algorithm is verified through a case study, which demonstrating that the proposed decision-making method is suitable for group decision-making in complex systems, with high algorithm stability and low algorithm deviation.

A hierarchical integration scheduling method for flexible job shop with green lot splitting

The integration of green scheduling and lot splitting scheduling is indispensable for ensuring the coordinated optimization of economic and environmental benefits in flexible job-shop scheduling (FJS). However, this integration involves not only the indicator of greenness and economy but also the process of production planning and scheduling, which is substantially complicated. To this end, a hierarchical integrated scheduling method is proposed by comprehensively considering the multilevel organizational structure and task configuration characteristics of flexible job-shop, as well as the differences in objectives on different scheduling levels: workshop level, process unit level, machine tool level. On the workshop level, a lot splitting model is presented to obtain the optimal processing task set for each production cycle with the minimum expected cost (startup cost, tardiness cost, and holding cost). On the process unit level, a task allocation model is given to allocate the optimal workload for each machine tool with the minimum processing energy consumption and maximum machine load. On the machine tool level, an operation sequencing model is established to obtain the optimal processing sequence for each machine tool with the minimum standby energy consumption and makespan. According to the solving characteristics of the hierarchical models, a multi-objective algorithm is applied. Finally, a case study is demonstrated to validate the proposed method.

Application of big data technology in traditional dance video movement recognition research

Abstract This paper proposes a multi-feature fusion approach for action recognition under big data technology with the goal of improving traditional dance video action recognition. By analyzing the basic method of dance action, the extraction process of dance action features is analyzed using both single-layer and hierarchical methods. Multi-feature fusion action recognition is chosen as the main method for action recognition. The image and audio features of the dance video are combined to improve the accuracy of recognizing dance actions. Use the optical flow algorithm to construct a histogram of the optical flow direction. The method’s feasibility is explored by applying the multi-feature fusion recognition method to traditional dance movement recognition. The results show that in traditional dance movement recognition performance, the performance of the method of multi-feature fusion recognition is improved by 7.6% compared to other traditional methods. The multi-feature fusion recognition method has more than 50% accuracy in recognizing different traditional dance movements and similar movements in terms of movement recognition accuracy. To a certain degree, this study enhances the efficiency of traditional dance movement recognition and conserves human and financial resources in dance movement recognition.

Hierarchical Error Compensation Method for Two-Degree-of-Freedom Manipulator Positioning Error in Hybrid Robots

In this paper, taking a two-degree-of-freedom parallelogram serial manipulator of a hybrid robot as an example, a hierarchical error compensation method that combines geometric and non-geometric error compensation is proposed. First, we establish the geometric kinematic modeling and error model and use the Levenberg-Marquardt (L-M) method to identify the geometric error parameters. Then, we use the grid interpolation method to compensate for non-geometric errors, thereby improving the overall positioning accuracy of the manipulator. Finally, a simulation is presented to verify the correctness and effectiveness of the proposed method. The results show that the positioning accuracy is improved from 4.8568 mm to 0.0036 mm, and the attitude error is improved from 0.9437° to 0.0105°.

Bayesian hierarchical modeling in interim futility analysis for two parallel clinical trials.

Incorporating interim analysis into a trial design is gaining popularity in the field of confirmatory clinical trials, where two studies may be conducted in parallel (ie, twin studies) in order to provide substantial evidence per the requirement of FDA guidance. Interim futility analysis provides a chance to check for the "disaster" scenario when the treatment has a high probability to be not more efficacious than the control. Therefore, it is an efficient tool to mitigate risk of running a complete and expansive trial under such scenario. There is no agreement among trial designers that interim analysis should be based on individual study data or pooled data under the twin study scenario. In fact, it is a dilemma for most scientists when specifying the interim analysis strategy at the design stage as the true treatment effects of the twin studies are unknown no matter how similar they are intended to be. To address the issue, we developed a Bayesian hierarchical modeling method to allow dynamic data borrowing between twin studies and demonstrated a favorable characteristic of the new method over the separate and pooled analyses. We evaluated a wide spectrum of the heterogeneity hyperparameters and visualized its critical impact on the Bayesian model's characteristic. Based on the evaluation, we made a suggestion on the heterogeneity hyperparameter selection independent of any a priori knowledge. We also applied our method to a case study where predictive powers of different methods are compared.

Multiagent-Reinforcement-Learning-Based Stable Path Tracking Control for a Bionic Robotic Fish With Reaction Wheel

The path tracking of the robotic fish is a hotspot with its high maneuverability and environmental friendliness. However, the periodic oscillation generated by bionic fish-like propulsion mode may lead to unstable control. To this end, this paper proposes a novel framework involving a newly-designed platform and multi-agent reinforcement learning (MARL) method. Firstly, a bionic robotic fish equipped with a reaction wheel is developed to enhance the stability. Secondly, a MARL-based control framework is proposed for the cooperative control of tail-beating and reaction wheel. Correspondingly, a hierarchical training method including initial training and iterative training is designed to deal with the control coupling and frequency difference between two agents. Finally, extensive simulations and experiments indicate that the developed robotic fish and proposed MARL-based control framework can effectively improve the accuracy and stability of path tracking. Remarkably, the head-shaking is reduced about 40%. It provides a promising reference for the stability optimization and cooperative control of bionic swimming robots featuring oscillatory motions.

Predicting clinical decline over 36 months with a memory‐based digital biomarker

AbstractBackgroundWord‐list recall tests are routinely employed in clinical practice for dementia screening. Process scoring and latent modeling of these tests, where underlying neurocognitive mechanisms are exploited, have shown potential to enhance test accuracy without requiring test redesign. We examined Embic’s digital biomarker M, a measure of recall derived from multinomial processing trees and hierarchical Bayesian computational methods, as a predictor of conversion to mild cognitive impairment (MCI) and Alzheimer’s disease (AD), and of increase in Clinical Dementia Rating (CDR) score.MethodsSecondary data analyses were carried out from ADNI data. M was computed from Rey’s AVLT data, and compared to standard AVLT clinical metrics (total and delayed recall). We conducted logistic regression analyses with diagnosis at 36 months (normal vs. MCI + AD) as outcome; all 169 participants were classified as normal at baseline, and 13 of these progressed to a clinical diagnosis (see Table 1). We controlled for baseline age, gender and years of education; and ran separate analyses with other metrics to avoid multicollinearity. We then repeated the same analytical framework with CDR scores (stable at 0 vs. increased to 0.5 or 1) at 36 months as outcome: stable participants were 141, and 24 increased (see Table 2). Covariates and predictors were the same as in the previous analysis.Results M predicted conversion to MCI or AD after three years (FDR‐corrected p value = 0.048), while neither AVLT total nor delayed recall did. Performance metrics showed that M generated 92% accuracy, or probability of yielding a correct classification, and 100% specificity, or no false positives (Figure 1). Analogously, M, but not standard AVLT metrics, also predicted a CDR increase after three years (FDR‐corrected p value = 0.003), with high accuracy (84%), specificity (96%), and precision (80%), or positive predictive value (Figure 2). Sensitivity analyses with ordinal regression broadly confirmed these findings.ConclusionsThese results indicate Embic’s digital biomarker M outperforms some of AVLT’s traditional metrics in identifying individuals who will progress to MCI and AD after three years, from a healthy baseline.

Cross-subject emotion recognition using hierarchical feature optimization and support vector machine with multi-kernel collaboration

Objective. Due to individual differences, it is greatly challenging to realize the multiple types of emotion identification across subjects. Approach. In this research, a hierarchical feature optimization method is proposed in order to represent emotional states effectively based on peripheral physiological signals. Firstly, sparse learning combined with binary search is employed to achieve feature selection of single signals. Then an improved fast correlation-based filter is proposed to implement fusion optimization of multi-channel signal features. Aiming at overcoming the limitations of the support vector machine (SVM), which uses a single kernel function to make decisions, the multi-kernel function collaboration strategy is proposed to improve the classification performance of SVM. Main results. The effectiveness of the proposed method is verified on the DEAP dataset. Experimental results show that the proposed method presents a competitive performance for four cross-subject types of emotion identification with an accuracy of 84% (group 1) and 85.07% (group 2). Significance. The proposed model with hierarchical feature optimization and SVM with multi-kernel function collaboration demonstrates superior emotion recognition accuracy compared to state-of-the-art techniques. In addition, the analysis based on DEAP dataset composition characteristics presents a novel perspective to explore the emotion recognition issue more objectively and comprehensively.

Research on 3D Equipment Modeling Technology of 500 KV Substation

A three-dimensional simulation system for a flexible wire layout of a 500 KV substation is developed by using a hierarchical design method in this work. Firstly, the scenario model and device model for the 500 KV substation are established as the physical models. Then, the data model, flexible wire mathematical model, and connection model are established to form the rule models for the 500 KV substation. Finally, the three-dimensional simulation diagram of the 500 KV substation soft wire layout is generated by system integration. The system can quickly and accurately generate the three-dimensional simulation drawing of 500 KV substation soft wire by only seven structural parameter setting modules, achieving the purpose of programmable design. The simulation system software is proved to be feasible and correct by the data of several 500 KV substations.

Parametrization of subsegmental infarcts using high spatial resolution 2DSTE and synthetic ultrasonic data

Abstract Introduction: The purpose of this study was to assess the performance of a set of parameters in characterizing simulated infarcts in left ventricular (LV) models with variations in size and transmural extent. Material and methods: The deformation of the LV models with different infarct sizes was simulated using the Finite Element Method. These simulations provided meshes that were used to generate synthetic ultrasonic data within the FIELD II package. The strain components (longitudinal and circumferential) were then estimated over small subsegments of the of segments 7 and 12 (according to 17-segment left ventricle segmentation standard proposed by the American Heart Association - AHA17), using a hierarchical block matching method. The strain maps obtained were utilized to calculate the Strain Drop Factor (SDF) maps, which represent the percentage ratio of strain observed in the subsegments of the studied model to that observed in the healthy model. Infarct segmentation was performed using these maps, and various parameters were derived, including Infarct Cross-Section Area (ICSA), relative ICSA, Transmurality Ratio (TR), Mean Infarct Transmurality (MIT), strain drop factor in the infarcted region (SDFi), and Strain Contrast (SC). Results: The estimates of ICSA, SC, MIT, and SDFI showed good repeatability and demonstrated the ability to provide a quantitative assessment of the size and transmural extent of the infarcts. Conclusions: The study findings suggest that the evaluated parameters, including ICSA, SC, MIT, and SDFI, can be reliably used to assess the size and transmural extent of infarcts. These parameters offer a quantitative approach for characterizing infarcts based on strain analysis and have the potential to contribute to the diagnosis and evaluation of myocardial infarctions.

Adaptive hierarchical positioning error compensation for long-term service of industrial robots based on incremental learning with fixed-length memory window and incremental model reconstruction

Industrial robots have been extensively used in industry, however, geometric errors mainly caused by connecting rod parameter error and non-geometric errors caused by deflection and friction, etc., limit its application in high-accuracy machining. Aiming at addressing these two types of errors, parametric methods for error compensation based on the kinematic model and non-parametric methods of directly establishing the mapping relationship between the actual and target poses of the robot end-effector are investigated and proposed. Currently both types of methods are mainly offline and will be no longer applicable when the pose of the end-effector in the workspace changes dramatically or the working performance of the robot degrades. Thus, to compensate the positioning error of an industrial robot during long-term operation, this research proposes an adaptive hierarchical compensation method based on fixed-length memory window incremental learning and incremental model reconstruction. Firstly, the correlation between positioning errors and robot poses is studied, a calibration sample library is created, and thus the actively evaluating mechanism of the pose mapping model is established to overcome the problem of the robot’ workspace having a differential distribution of error levels. Then, an incremental learning algorithm with fixed-length memory window and an incremental model reconstruction algorithm are designed to optimize the pose mapping model in terms of its parameters and architecture and overcome the problem that the performance degradation of the robot exacerbates the positioning error and affects the applicability of the pose mapping model, ensuring that the pose mapping model runs stably above the target accuracy level. Finally, the proposed method is applied to the long-term compensation case of a Stäubli industrial robot and a UR robot, and compared to state-of-art methods. Verification results show the proposed method reduces the position error of the Stäubli robot from 0.85mm to 0.13mm and orientation error from 0.68° to 0.07°, as well as reduces the position error of the UR robot from 2.11mm to 0.17mm, demonstrating that the proposed method works in real world scenarios and outperforms similar methods.

Publication Activity of Public Higher Education Institutions in Poland Corresponding to the Idea of Sustainable Energy Development – Results of a Cluster Analysis

Abstract Objective The aim of the article is to present the involvement of public Higher Education Institutions in Poland in popularizing the idea of sustainable development within the framework of their publishing activities. Methodology In this study, agglomerative clustering approach, which is a representative of the hierarchical method, was used. The distance between selected public Higher Education Institutions in Poland was determined based on the Squared Euclidean Distance. In turn, to estimate the distance between clusters, the Ward method was used. The study used three parameters, constructed from data taken from SciVal: (1) total number of publications, (2) citation count, and (3) field-weighted citation impact. Findings Three groups of Polish public HEIs were distinguished as a result of the cluster analysis that was performed (based on: the total number of publications, the citation count, and the field-weighted citation impact). The most publication-productive were seven technical universities, i.e., Warsaw University of Technology, Gdansk University of Technology, Silesian University of Technology, AGH University of Science and Technology, Lodz University of Technology, Poznan University of Technology, and Wroclaw University of Science and Technology. The publications prepared by the employees of the universities of technology were also characterized by the highest number of citations. Value Added This article is one of the first to present (in quantitative terms) the involvement of universities in popularizing sustainable energy development concept. The methodology used in it can be applied to the other Sustainable Development Goals. Recommendations Given the increasing importance of the concept of sustainability in the functioning of universities, it seems necessary to create tools to measure the degree of their involvement in popularizing this idea. Among other things, universities are obliged to share their knowledge in this area. Bibliometric data provided by the SciVal system may be helpful in this regard. It is necessary to conduct research taking into account both domestic and foreign universities.

A Stackelberg Game Based Optimization Method for Heterogeneous Building Aggregations in Local Energy Markets

This paper proposes an optimal operational method and a customized pricing strategy for a Microgrid (MG) incorporating heterogeneous buildings from different communities in a local energy market (LEM). A customized pricing strategy in the LEM for the heterogeneous building aggregations (BAs) is proposed considering their heterogeneous insulation performances. In this regard, a hierarchical framework with two levels for optimal decision-making of the MG as the price maker and BAs as the price takers in the LEM are presented. At the upper level, the MG aims to maximize its profit by optimizing the customized prices and its energy schedules. At the lower level, a detailed physical model of the building with adjustable heating, ventilation, and air conditioning (HVAC) systems is developed while considering the building's thermal dynamics. The objective of each BA in each community is to minimize its operating cost while ensuring the users' thermal comfort according to MG's prices. The Stackelberg game is used to model the interactions between the MG (leader) as the price maker and multiple heterogeneous BAs (followers) as the price takers in the LEM. Then, the proposed optimization model is converted into a mixed integer linear programming using the Karush-Kuhn-Tucker conditions and strong duality theorem. Numerical results show that the hierarchical method benefits both the MG and heterogeneous BAs with customized pricing schemes. The HVAC of the building with good thermal insulation performance can make more adjustments to indoor temperature to reduce its operating costs. Moreover, it has a more significant impact on MG's electricity prices.

Phenotypic Diversity and Genetic Parameters of Coffea canephora Clones

The simultaneous analysis of the maximum number of chemical elements present in plant tissues provides more comprehensive information about their chemical constitution and increases the number of characteristics for the selection process in various plant breeding programs. The objective of this study was to analyze productivity, grain yield, and concentration of chemical elements in tissues of Coffea canephora clones to study phenotypic diversity and estimate genetic parameters for use in breeding. This experiment was carried out in Manaus, Amazonas, Brazil, in randomized blocks with four replications. The concentrations of elements in various organs were quantified using total reflection X-ray fluorescence (TXRF). Genetic parameters and genetic divergence were estimated, and genotypes were clustered using the UPGMA hierarchical method and non-metric multidimensional scaling analysis. The study allowed us to differentiate the performance of the clones in terms of the absorption of essential and non-essential chemical elements for plant development and to analyze the correlation of the characteristics in the selection process. TXRF efficiently characterizes the presence and concentration of multiple elements, aiding genotype discrimination for C. canephora improvement.

Correlation-based hierarchical clustering of time series with spatial constraints

Correlation-based hierarchical clustering methods for time series typically are based on a suitable dissimilarity matrix derived from pairwise measures of association. Here, this dissimilarity is modified in order to take into account the presence of spatial constraints. This modification exploits the geometric structure of the space of correlation matrices, i.e. their Riemannian manifold. Specifically, the temporal correlation matrix (based on van der Waerden coefficient) is aggregated to the spatial correlation matrix (obtained from a suitable Matérn correlation function) via a geodesic in the Riemannian manifold. Our approach is presented and discussed using simulated and real data, highlighting its main advantages and computational aspects.

Constructing a Teaching-Ability Evaluation System for Chinese Student Teachers using Delphi and Analytical Hierarchical Process Methods

With the deepening of teacher educational reforms, increasing focus has been placed on the importance of evaluating student teachers’ teaching abilities in Chinese universities. The Delphi method and an Analytic Hierarchical Process were used to construct an evaluation system for student teachers’ teaching abilities. Delphi specialists included administrators, teachers and teacher educators working in primary, secondary and tertiary education. The evaluation items consisted of four dimensions, namely teacher ethics, teaching skills, educational ability, and developmental ability and a total of 31 indicators. Teaching skills were ranked highest, followed by teacher ethics, educational ability and developmental ability. The overall mean of the dimensions and components was rated as very important to important after three rounds of the Delphi process. Overall, the evaluation system demonstrated high reliability and validity; its’ entries were reasonably distributed indicating its evaluation potential and its’ weighting system denotes priorities for student teachers’ competencies’ evaluation.The evaluation system could help teachers identify key capacities in teacher training ,guide the development of talent nurturing programs, and provide developmental feedback for student teachers.