Hierarchical Method Research Articles

Time series clustering based on polynomial fitting and multi-order trend features

Time series clustering serves as a potent data mining method, facilitating the analysis of an extensive array of time series data without the prerequisite of any prior knowledge. It finds wide-ranging use across various sectors, including but not limited to, financial and medical data analysis, and sensor data processing. Given the high dimensionality, non-linearity, and redundancy characteristics associated with time series, conventional clustering algorithms frequently fall short in yielding satisfactory results when directly applied to this kind of data. As such, there is a critical need to judiciously select suitable feature extraction methods and dimension reduction techniques. This paper introduces a time series clustering algorithm, drawing primarily from polynomial fitting derivative features as a wellspring for feature extraction to achieve effective clustering results. Initially, Hodrick Prescott (HP) filtering comes into play for the processing of raw time series data, thereby eliminating noise and redundancy. Subsequently, polynomial curve fitting (PCF) is applied to the data to derive a globally continuous function fitting this time series. Next, by securing multi-order derivative values via this function, the time series is transformed into a multi-order derivative feature sequence. Lastly, we designed a polynomial function derivative features-based dynamic time warping (PFD_DTW) algorithm for determining the distance between two equal or unequal granular length time series, and subsequently a hierarchical clustering method anchored on the PFD_DTW distances for time series clustering after computing interspecies distances. The effectiveness of this method is corroborated by experimental results obtained from several practical datasets.

Hierarchical fuzzy inference based on Bandler-Kohout subproduct

Fuzzy inference with the Bandler-Kohout subproduct (BKS) has been successfully applied in many fields such as fuzzy control, artificial intelligence, image processing, data mining, decision-making, prediction, classification and so on. However, one has to face with the rule explosion in these applications. To deal with this problem, hierarchical fuzzy systems with the compositional rule of inference (CRI) method have been constructed by a series of low-dimensional sub fuzzy systems. And it has been proved that hierarchical fuzzy inference method can efficiently restrain the explosion of fuzzy rules. Therefore, in order to increase the computational efficiency of the fuzzy inference based on the BKS when multi-input-single-output (MISO) fuzzy rules are involved, this paper mainly constructs two hierarchical fuzzy inference methods based on the BKS in which the if-then rules are respectively interpreted by fuzzy implications and ML-implications. Moreover, the validity of the two BKS hierarchical fuzzy inferences is studied with the GMP rules. Finally, two examples are employed to illustrate the computational efficiency of our proposed BKS hierarchical inference methods.

Evaluation of mine ecological environment based on fuzzy hierarchical analysis and grey relational degree

In order to improve the level of mine ecological environment management and restoration, and to improve and enhance the overall environmental quality of mines. This study takes coal mine as the perspective, and constructs evaluation indexes in two steps, i.e., social network analysis method and relevant policy documents are combined to construct evaluation indexes. The indicator system contains 5 first-level indicators and 23 s-level indicators, and the triangular fuzzy number hierarchical analysis method is introduced to determine the comprehensive weight of each evaluation indicator, which overcomes the inadequacy of the objective empowerment method or the subjective empowerment method of single empowerment. The grey correlation analysis theory is used to establish a grey correlation evaluation model of mine ecological environment, which is applied to the evaluation of ecological environmental protection level of four coal mines in a province of China, making full use of the information of each index for quantitative evaluation, and finally obtaining the evaluated value of ecological environmental protection level of each coal mine. It is demonstrated that the evaluation model can make scientific and effective evaluation of the level of ecological environmental protection in mines. The study concludes that coal mines should pay attention to improving the comprehensive utilization rate of coal gangue in the next mine ecological environment management and restoration, and at the same time, reduce the ton of coal power consumption, ton of coal water consumption, and Increase in vegetation cover. This study provides a useful evaluation method for ecological environment management in mining areas, which is helpful to improve the quality of ecological management in mining areas.

On integrated lateral and longitudinal control of brain-controlled vehicles

A brain-controlled vehicle (BCV) is a vehicle that uses the Brain–Computer interface (BCI) to analyze the driver’s electroencephalogram (EEG) to obtain human control commands, which can help disabled people extend movement range and improve their self-independence. At this stage, research on the control of BCVs usually focuses only on the lateral control or longitudinal control, and few studies have focused on the problem of integrated lateral and longitudinal control. Given this situation, this paper investigates the integrated control of brain-controlled vehicles (BCV) in both lateral and longitudinal directions, and proposes a control method that utilizes speed as the coupling point. Firstly, the vehicle’s speed is determined by the provided road information. To control the vehicle’s longitudinal speed, a hierarchical control method based on model predictive control (MPC) is implemented. This method introduces a threshold σ to speed up the calculation process and reduce resource consumption. Then, a global controller is developed to conduct the motion control. This controller utilizes three local ANFIS intelligent controllers, employing soft-switching combinations, to generate the steering wheel angle signal. The generated steering wheel angle signal is combined with the cornering command issued by the driver. This fusion process produces the final command, which is responsible for the vehicle’s motion control. Finally, a simulation model is developed to test the integrated control of the BCV in terms of both lateral and longitudinal movements. Multiple experiments are conducted under varying conditions, and the results prove that the proposed method is able to complete the integrated control of the BCV well, which fully illustrates its effectiveness.

USING CLUSTER ANALYSIS TO CLASSIFY SOME ARAB COUNTRIES INTO HOMOGENEOUS GROUPS ACCORDING TO INSTITUTIONAL QUALITY CRITERIA

Objective: The objective of this study is to explore the application of cluster analysis in categorizing select Arab countries into cohesive groups based on institutional quality criteria, which consist of six indicators measuring good governance in country. These indicators cover various dimensions and allow describing the quality of services provided by the state in a specific area comprehensively. These dimensions include the scope of state intervention, these dimensions are The Political Dimension, The Economic Dimension and The Legal Dimension. Method: This study adopts a research methodology that combines both descriptive and quantitative approaches. The descriptive approach is based on presenting concepts and describing relationships among institutional quality criteria. The quantitative approach involves using cluster analysis methods: K-Means clustering and Hierarchical Clustering to classify 11 Arab countries: UAE, Bahrain, Algeria, Egypt, Jordan, Morocco, Qatar, Saudi Arabia, Sudan, Tunisia, and Yemen. This was done to conduct cluster analysis to test the homogeneity of these countries regarding institutional quality determinants, represented by the following variables: Participation and Accountability, Political Stability and Absence of Violence, Government Effectiveness, Legislative Quality, Rule of Law, Control of Corruption. Results and Discussion: The study reached the following results: - the K-Means Cluster Analysis Method is evident that The total number of cases classified in each cluster: Eleven countries were classified into seven countries: United Arab Emirates, Bahrain, Jordan, Morocco, Qatar, Saudi Arabia, and Tunisia in the first cluster and four countries in the second cluster: Algeria, Egypt, Sudan, and Yemen; -the variable Participation and Accountability does not significantly affect the classification of the Arab countries under study into homogeneous groups according to institutional quality indicators. However, variables such as Political Stability and Absence of Violence, Government Effectiveness, Regulatory Quality, Legislative Quality, Rule of Law, and Control of Corruption play a significant role in this classification; - the Hierarchical Clustering Analysis Method is evident that the Arab countries under study are distributed into two homogeneous groups. The first group forms Cluster 1, consisting of Jordan, Morocco, Tunisia, Bahrain, Saudi Arabia, Algeria, Egypt, the UAE, and Qatar. The second group belongs to Cluster 2, comprising Sudan and Yemen. This classification is based on institutional quality criteria. Originality/Value: This study sheds light on the importance of cluster analysis methods: the K-Means Cluster Analysis Method and the hierarchical cluster analysis method in classifying Arab countries according to the determinants of institutional quality, which are considered a determining factor for the economic growth of countries, and its consequent impact on various indicators reflecting economic, social, and political conditions. Consequently, institutional quality has emerged as a pivotal measure in highlighting developmental disparities among different countries. Many governments are endeavoring to create or adopt models to enhance the quality of their institutions, notwithstanding the similarities among Arab countries.

A data decomposition-based hierarchical classification method for multi-label classification of contractual obligations for the purpose of their governance

Contract governance ensures that the agreed outcomes between customers and vendors are fulfilled. Information Technology (IT) outsourcing organizations enter thousands of contractual relationships each month leading to a high volume of business-critical contractual text that must be reviewed and deciphered for effective governance. The key to effective governance of contracts is a model that facilitates assigning ownership of the obligations to the right departments in an organization and allocating their accountability to the right stakeholders. For this, the contractual obligations need to be identified and classified so that details such as actions to be taken by departments in an organization and their ownership as per a given clause are brought out for the purpose of governance. In this paper, we present our work on automated extraction and classification of obligations present in Software Engineering (SE) contracts for the purpose of contracts governance. We propose a novel data decomposition-based hierarchical classification method for a multi-label classification of contractual obligations. We conducted experiments for a fine-grained automated classification of more than 55,000 statements from 50 large real-life SE contract documents received from a large vendor organization into 152 governance-specific classes. The results indicate that the proposed method can bring about a 7–8% improvement in accuracies when compared to state-of-the-art classification baselines such as BERT, RoBERTa, and generative models such as GPT-2.

Evaluating the Development Levels of Green Urban Transportation Systems

To promote the green and resilient development of urban transportation and the sustainable development of cities, we developed a technique that can be used to evaluate the level of urban green transportation development. First, the toughness evaluation indexes were integrated according to the connotation requirements of green transportation development, and the evaluation index system was constructed using the hierarchical analysis method. Second, subjective and objective weighting methods, such as the G1, entropy weighting, and “addition” integration methods, which provide a simple and easy-to-use evaluation method that can improve evaluation accuracy, were adopted. Finally, Harbin, a typical city in the north, was chosen as a representative city to verify the method effectiveness. The study findings can be used to evaluate the current level of green development of a city as well as for future planning, providing a basis for formulating sustainable urban transportation development strategies. We will continue to conduct comparative studies on different evaluation methods and cities to improve the generalizability of the method.

Energy scheduling method for island micro-grid system based on prediction model

Abstract Renewable energy, due to its characteristics such as volatility and intermittency, brings difficulties to the energy scheduling of renewable energy power generation systems. The main way of island power generation is to use renewable energy power generation. This chapter proposes a hierarchical energy scheduling method based on the prediction model through the research on the energy scheduling method of the island microgrid system to realize the energy optimization between the microgrid sub-systems, to achieve the maximum consumption of renewable energy for power supply, and to reduce the power supply cost and improve user satisfaction. The effectiveness of this method in improving the energy supply-demand balance of the microgrid sub-systems is verified through experiments.

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

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

Research on cooperative control of vehicle path tracking and lateral stability

Abstract This study presents a novel trajectory tracking control approach integrating steering angle and wheel torque output. Unlike conventional hierarchical methods, it considers front wheel steering angle and direct swing torque control simultaneously. Utilizing Model Predictive Control for autonomous vehicles, the system employs path tracking deviation as the state variable, front wheel steering angle, and overall additional yaw moment as control inputs. The coordinated control strategy enhances vehicle stability and contributes to heading angle changes, improving trajectory tracking accuracy. Simulation results in Matlab/Simulink demonstrate the validity and superiority of the proposed method in trajectory tracking and vehicle stability.

Energy management for object tracking under the energy-harvesting: Hierarchical reinforcement learning method

This paper explores the target localization problem with signal transmitters which are powered by energy harvesting (EH) devices. Due to the remote transmission and random energy harvesting, the energy supplied to transmitters to transmit information is often insufficient, resulting in packet dropout. The rate of packet dropouts is influenced mainly by the distance from the target to the transmitter and transmission energy. Therefore, this paper aims to investigate the energy allocation policies to ensure the desired positioning performance using a two-level hierarchical framework. Initially, a lower-level policy works on a specified time scale and aims at minimize error covariances with prescribed distance to meet the step objective. Subsequently, a higher-level policy plans the step time scale and calls for the desired step objectives for the lower-level policy. Both policy levels are trained through deep reinforcement learning. Finally, an example is presented to illustrate the efficiency of the designed strategy.

Principal causes of water damage in mining roofs under giant thick topsoil–lilou coal mine

The roof water inrush disaster induced by coal mining is becoming a vital bottleneck restricting mine safety. To accurately predict the water inrush position of the coal seam roof sandstone aquifer and accurately prevent and control it, this paper takes Lilou Coal Mine in Juye Coalfield as an example. Based on the comprehensive analysis of hydrogeological data, drilling data, and geophysiological data, this paper examines the water richness of the roof aquifer, the water insulation and geological structure, and the fracture development characteristics of the roof aquifer. Starting from the general side, nine factors, including fault strength index, tectonic intersection point, tip extinguishing point, development height of water conduction crack zone, roof sandstone aquifer thickness, aquifer drilling unit inrush, geophysical prospecting water-rich anomaly area, roof key layer thickness, roof aquifer thickness, brittle plastic rock thickness ratio, etc., form the roof plate roof. The main control factors for water inrush are deeply discussed. Through the spatial analysis function of GIS, unique drawings of different evaluation indicators are drawn, and the data are normalized. Combined with the AHP hierarchical analysis method, the corresponding weight is determined. Finally, a comprehensive water inrush risk assessment map of the roof of coal seam 3 in Lilou Coal Mine is obtained. Through the verification of the coal mine water inrush survey ledger over the years, it has been found that the evaluation results of the coal seam roof water inrush model are consistent with the actual situation. The evaluation results are reasonable and accurate, which can provide a reference basis for coal seam mining and water damage prevention and control in the future.

Micromechanical analysis of fiber-reinforced ceramic matrix composites by a geometrically nonlinear hierarchical quadrature element model

The stress fields of fiber-reinforced ceramic matrix composites (CMCs) under longitudinal tension are predicted using a non-linear hierarchical quadrature element method (HQEM) in this work. The HQEM combines the differential quadrature method (DQM) with the hierarchical finite element method (HFEM), which is a typical p-version finite element method (FEM). High-order interpolation functions of the HQEM allow accurate evaluation of stress distribution. Fiber, matrix, and interfaces of the CMCs are modeled as linear elastic materials that may have large displacements. The nonlinear HQEM estimation of CMCs stress distributions are validated by comparing with analytical results obtained using classical BHE shear-lag model. Stress distribution in three characteristic stages in the damage evolution process, namely, interface perfectly bonded, interface debonding, and fiber failure are investigated. It is shown that when fiber failure happens, geometric non-linearity must be considered to avoid excessive stress concentration. Furthermore, the stress distribution within three-dimensional cylindrical CMCs cell is studied, which sheds light on the future exploration of three-dimensional CMCs analyses.

Analytic Hierarchy Process-Fuzzy Comprehensive Evaluation Method-based Depletion Assessment Study of Xinshan Iron Ore Mine

Taking the Xinshan iron ore mine as an example, this paper, based on collecting and analyzing the actual production data and similar simulation test data of this iron ore mine, analyses various factors affecting ore depletion by bottomless column segmental chipping method by using hierarchical analysis method (AHP) and fuzzy comprehensive evaluation method (FCE), and establishes an evaluation system for comprehensively assessing the depletion of the ores. The results show that structural parameters, blasting parameters, loading parameters, and geological conditions are the main factors affecting ore depletion. The structural parameters are the most important factors, accounting for 35%. With the increase of the released amount, the released grade gradually decreases, the depletion rate gradually increases, and the comprehensive evaluation value gradually decreases. The released body is an approximate ellipsoidal block with a wide upper and narrower lower part. The end wall plays an obstructive role in the flow of the bulk body, which makes the end of the released grade higher and the middle of the released body higher. At the same time, due to the influence of blasting and shovel loading, the particles in the release body show some sorting phenomena. This paper provides a scientific basis and reference for predicting and controlling ore depletion in the bottomless column segmental chipping method.

Youth not engaged in education, employment, or training: a discrete choice experiment of service preferences in Canada

BackgroundPrior research has showed the importance of providing integrated support services to prevent and reduce youth not in education, employment, or training (NEET) related challenges. There is limited evidence on NEET youth’s perspectives and preferences for employment, education, and training services. The objective of this study was to identify employment, education and training service preferences of NEET youth. We acknowledge the deficit-based lens associated with the term NEET and use ‘upcoming youth’ to refer to this population group.MethodsCanadian youth (14–29 years) who reported Upcoming status or at-risk of Upcoming status were recruited to the study. We used a discrete choice experiment (DCE) survey, which included ten attributes with three levels each indicating service characteristics. Sawtooth software was used to design and administer the DCE. Participants also provided demographic information and completed the Global Appraisal of Individual Needs–Short Screener. We analyzed the data using hierarchical Bayesian methods to determine service attribute importance and latent class analyses to identify groups of participants with similar service preferences.ResultsA total of n=503 youth participated in the study. 51% of participants were 24–29 years of age; 18.7% identified as having Upcoming status; 41.1% were from rural areas; and 36.0% of youth stated that they met basic needs with a little left. Participants strongly preferred services that promoted life skills, mentorship, basic income, and securing a work or educational placement. Three latent classes were identified and included: (i) job and educational services (38.9%), or services that include career counseling and securing a work or educational placement; (ii) mental health and wellness services (34.9%), or services that offer support for mental health and wellness in the workplace and free mental health and substance use services; and (iii) holistic skills building services (26.1%), or services that endorsed skills for school and job success, and life skills.ConclusionsThis study identified employment, education, and training service preferences among Upcoming youth. The findings indicate a need to create a service model that supports holistic skills building, mental health and wellness, and long-term school and job opportunities.

ANALYSIS OF RAINFALL IN INDONESIA USING A TIME SERIES-BASED CLUSTERING APPROACH

Indonesia has a tropical climate and has two seasons: dry and rainy. Prolonged drought can cause drought disasters, and rain can cause floods and landslides. According to information from the Meteorology, Climatology, and Geophysics Agency (BMKG), natural disasters such as floods and landslides due to heavy rains have been a severe problem in Indonesia for the past five years. Different regional characteristics can affect the intensity of rain that falls in every province in Indonesia. It can be grouped to determine which provinces have similar characteristics to natural disasters due to rainfall. Later, it can provide information to the government and the public so that they are more aware of natural disasters. So, it is necessary to research and classify provinces in Indonesia for rainfall with cluster analysis. The data used is secondary rainfall data taken from the official BMKG website. Cluster analysis of rainfall in 34 provinces in Indonesia used hierarchical and non-hierarchical methods in this study. The approach that is used in this research limits our clustering of the data. Further research with a machine learning approach is recommended. For the clustering method, the agglomerative hierarchical method includes single, average, and complete linkage. The non-hierarchical method includes k-medoids and fuzzy c-means. The cluster analysis results show that the dynamic time warping (DTW) distance measurement method with the average linkage method has the most optimal cluster results with a silhouette coefficient value of 0.813.

Multi-Criteria Analysis for Assessing Flood Susceptibility in the Municipality of Conselheiro Lafaiete (MG)

Objective: To evaluate the susceptibility to flooding in the municipality of Conselheiro Lafaiete – Minas Gerais, using the Hierarchical Analytical Process method. Theoretical Framework: Floods are events that affect several Brazilian cities, where the main related factors are denoted by the morphometric characteristics of the river basin(s) that comprise them, associated with slope, hypsometry, soil typology and use and land occupation. In this sense, multi-criteria analyzes are fundamental for mapping areas of susceptibility to flooding in river basins. Method: The study proceeded by characterizing the study area, integrating the diagnosis of the drainage system, morphometric analysis of the river basin in which Conselheiro Lafaiete is integrated, and the application of the Hierarchical Analysis Method (AHP) to map areas susceptibility to flooding of the river basin. Results and Discussion: The results obtained revealed [synthesize the main results of the research]. In the discussion section, these results are contextualized in light of the theoretical framework, highlighting the implications and relationships identified. Possible discrepancies and limitations of the study are also considered in this section. Research Implications: The study indicates gaps in Conselheiro Lafaiete PMSB with regard to urban drainage and points out, through mapping, the areas of the municipality most susceptible to the occurrence of flood events. Originality/Value: The mapping of areas susceptible to flooding carried out for Conselheiro Lafaiete considered a multi-criteria approach, integrating anthropogenic factors. The product can be applied as a basic tool for public management regarding urban drainage and flood risk management in the municipality.

Prevalence of the Double Burden of Malnutrition among Adolescents: Associations with Lifestyle Behaviors and Clusters of Social Determinants.

The double burden of malnutrition (DBM) is a condition in which malnutrition coexists with overweight, reflecting a new layer of malnutrition. Our objectives were to assess prevalence; test associations between DBM and 24-hour movement behaviors; and investigate whether DBM is associated with clusters of social determinants. Methods: This multicenter cross-sectional study included 1152 adolescents (12 to 17 years old) from four Brazilian cities. Body mass index (BMI, kg/m2) was used to estimate overweight, and the adopted cutoff points took into account the curves established for age and sex: Z-score > 1 and ≤2 (overweight) and Z-score > 2 (obesity). The serum concentration of 25-hydroxyvitamin D [25(OH)D] was stratified into three levels: vitamin D deficiency ≤ 20 ng/mL; vitamin D insufficiency = 21-29 ng/mL; optimal vitamin D ≥ 30 ng/mL. We used multilevel Poisson regression models to estimate prevalence ratios (PRs) and their respective 95% confidence intervals (95%CI) and to analyze the association between DBM and covariates. A significance level of p < 0.05 was considered. Cluster analyses were performed by applying a combination of hierarchical and non-hierarchical methods. Results: A population prevalence of DBM of 7.3% (95% CI: 5.9-8.9) was revealed. A percentage of 19.2% (95% CI: 17.0-21.6) of adolescents were overweight, and 8.3% (95% CI: 6.8-10.1) were obese. A total of 41.5% (95% CI: 38.7-44.4) had vitamin D deficiency, and 25.8% (95% CI: 23.4-28.4) had vitamin D insufficiency. However, 24-hour movement behaviors were not associated with DBM. Adolescents living in the southern region of the country, from public schools whose mothers have higher education, have a 1.94 [PR = 2.94 (95% CI: 1.20-7.23)] times greater chance of developing DBM. These results highlight the importance of specific factors to improve the nutritional health of adolescents, considering the specific social determinants identified in this study.

The Construction of Financial Information Security Assessment Indicators based on Hierarchical Analysis Methods and Legal Regulation

The Construction of Financial Information Security Assessment Indicators based on Hierarchical Analysis Methods and Legal Regulation

A Novel UNet 3+ Change Detection Method Considering Scale Uncertainty in High-Resolution Imagery

The challenge of detecting changes in high-resolution remote sensing imagery often stems from the difficulties in effectively extracting features and constructing appropriate change detection models considering the scale characteristics of ground objects. To solve these issues, we propose a novel UNet 3+ change detection method that considers the scale characteristics inherent in various land-cover change types. Our method includes three key steps: a multi-scale segmentation method, a class-specific UNet 3+ method, and an object-oriented change detection method based on UNet 3+. To verify the effectiveness of this method, we select two datasets for experiments and compare our proposed method with the UNet 3+ single-scale sampling method, the class-specific UNet 3+ single-scale sampling method, and the UNet 3+ multi-scale hierarchical sampling method. Our experimental results show that our proposed method has higher overall accuracy and F1, lower missed detection rate and false detection rate, and can detect more changes in ground features than other methods. To verify the scalability of this method, we compare this method with traditional change detection methods such as PCA-k-means, OCVA, a single-scale sampling method based on random forest, and a class-specific object-based method. Experimental results and accuracy indexes show that our proposed method better considers the scale characteristics of ground objects and achieves higher accuracy. Additionally, we compared our proposed method with other DLCD methods including LamboiseNet, BIT, CDNet, FCSiamConc, and FCSiamDiff. Our results show that our proposed method effectively considers edge information and has an acceptable time consumption. Our approach not only considers the full-scale characteristics of the feature extraction but also the scale characteristics of the change detection model. In addition, it considers a more practical feature extraction unit (object), making it more accurate.