Hierarchical Method Research Articles

A generalized framework for agricultural field delineation from high-resolution satellite imageries

ABSTRACT Accurate digital data on agricultural fields are crucial for various agriculture applications. While deep learning methods have shown promise in delineating fields from high-resolution imagery, there is a lack of research evaluating key techniques for field boundary detection. Challenges also exist in converting detection results into high-quality fields. This study addresses these issues and proposes a generalized framework for agricultural field delineation (GF-AFD). First, we identify three key techniques for field boundary detection and apply them to MPSPNet model. Ablation and comparison experiments demonstrate significant performance enhancements due to techniques. They also prove effective for DeeplabV3+ model, which shares a similar architecture. The modified models outperform U-Net-based models and approach the state-of-the-art models. Second, we show that performing region segmentation on boundary results yields improved field shapes. To address weak boundary loss and unstable parameters issues in existing segmentation-based methods, we introduce the OWT method to enhance weak boundaries directionally before segmentation. We also develop a hierarchical merging method, leveraging the observational hierarchy of fields, resulting in stable parameters across regions and models. The proposed GF-AFD framework was validated cross three diverse Chinese counties. The results demonstrate the framework's robust performance, providing a valuable solution for delineating agricultural fields.

Hierarchical Control for Head-to-Head Autonomous Racing

We develop a hierarchical controller for head-to-head autonomous racing. We first introduce a formulation of a racing game with realistic safety and fairness rules. A high-level planner approximates the original formulation as a discrete game with simplified state, control, and dynamics to easily encode the complex safety and fairness rules and calculates a series of target waypoints. The low-level controller takes the resulting waypoints as a reference trajectory and computes high-resolution control inputs by solving an alternative approximation formulation with simplified objectives and constraints. We consider two approaches for the low-level planner, constructing two hierarchical controllers. One approach uses multi-agent reinforcement learning (MARL), and the other solves a linear-quadratic Nash game (LQNG) to produce control inputs. The controllers are compared against three baselines: an end-to-end MARL controller, a MARL controller tracking a fixed racing line, and an LQNG controller tracking a fixed racing line. Quantitative results show that the proposed hierarchical methods outperform their respective baseline methods in terms of head-tohead race wins and abiding by the rules. The hierarchical controller using MARL for low-level control consistently outperformed all other methods by winning over 90% of head-to-head races and more consistently adhered to the complex racing rules. Qualitatively, we observe the proposed controllers mimicking actions performed by expert human drivers such as shielding/blocking, overtaking, and long-term planning for delayed advantages. We show that hierarchical planning for game-theoretic reasoning produces competitive behavior even when challenged with complex rules and constraints.

Real-time energy management strategy for flexible traction power supply system

Energy management strategies (EMS) for the flexible traction power supply system (FTPSS) typically depend on the accurate predictive model or probabilistic estimation of traction load and renewable energy generation. However, it can be challenging due to the ultra-short-term dramatic fluctuation in traction load profile. To address the issue, a model-free EMS is proposed to achieve the power dispatch in real time based on the current state. Firstly, a sequential stochastic optimization model is built to optimize the long-term operating cost and load fluctuation. Through the relaxation of time-coupling constraints and incorporation of the Lyapunov optimization technique, the problem is decomposed into individual optimization problems at each time slot. Considering the complementary characteristic of hybrid energy storage system, a bi-level optimization framework is constructed to separately control powers of the supercapacitor and battery with the hierarchical sequence method. More importantly, a fully informative control strategy with the known future load profile and renewable energy generation is adopted as the evaluation benchmark. The gap between the proposed method and the benchmark can be demonstrated to fall within a certain range of below 10% by mathematical derivation and extensive cases.

Deep learning‐accelerated multiscale approach for granular material modeling

AbstractThe hierarchical finite element method (FEM)–discrete element method (DEM) multiscale approach is a powerful tool for solving geotechnical boundary value problems. However, despite parallel computing can be resorted to, the high computational cost remains an insurmountable barrier to its practical application in engineering‐scale problems. As an alternative, a deep learning model (DLM) is employed to replace the representative volume element (RVE) in the DEM. The complex constitutive responses of sand under various loading paths can be reproduced by leveraging the powerful learning capacity of the DLM. During the numerical computations, the DLM is integrated into the Gauss integration points of the FEM mesh, where it receives strains as input and returns the prediction of stresses to advance the calculation. The applicability and accuracy of the approach were examined with three BVPs, in which the sources of error for both global and local performances were systematically analyzed. The feasibility of the approach in accounting for the inherent anisotropy of sand was also investigated. The results demonstrated that the incorporation of the DLM can achieve a significant computational acceleration of up to two orders of magnitude while maintaining a high degree of accuracy.

SHARDA–ARAS: A Methodology for Prioritising Project Managers in Sustainable Development

In sustainable economic development, top-level human capital, especially project management, is paramount. This article integrates the Systematic Hierarchical Attribute Ratio Delphic Rating (SHARDA) method and the Additive Ratio Rating (ARAS) method as a robust framework for identifying and training project managers. The research draws on a diverse panel of experts against the United Nations Sustainable Development Goals (SDGs) backdrop, emphasising stakeholder engagement and transparency in the decision-making processes. This study investigates the complexity of multi-criteria decision-making (MCDM) methods and focuses on SWARA and ARAS methods. These methodologies comprehensively improve the decision-making process, considering a range of subjective criteria. The extended and modified hierarchical SWARA method helps us understand each measure’s importance, while the ARAS method simplifies ranking and selection based on performance ratios. The research methodology seamlessly integrates these methods to form the SHARDA–ARAS methodology that addresses the challenging task of selecting project managers for sustainable development. This methodology guarantees a systematic and inclusive decision-making process, incorporating stakeholder perspectives seamlessly aligned with global sustainability goals. The studio’s innovation is wrapped in the synthesis of SWARA and ARAS into the SHARDA–ARAS methodology, presenting a nuanced and effective tool for project manager selection. Promoting an interconnected and holistic approach that contributes to sustainable development emphasises the methodology’s ability to balance economic, environmental, and social aspects. Thus, the article provides an invaluable method for organisations seeking global sustainable economic development.

Nonlinear flutter response and reliability of supersonic smart hybrid nanocomposite rupture trapezoidal plates subjected to yawed flow using DQHFEM

In the realm of aerospace engineering, the analysis of flutter under supersonic flight conditions is of paramount importance. Hybrid nanocomposite materials, known for their lightweight and high-strength properties, are increasingly being employed in aerospace engineering. This article examines the supersonic flutter behavior and reliability of smart hybrid nanocomposite trapezoidal plates, considering various practical factors. The structure is subjected to yawed flow conditions, a scenario often encountered during supersonic flight. The importance of the hybrid nanocomposite in various applications such as aerospace, public security tools, and so on, therefore the hybrid nanocomposite core layer reinforced by carbon nanotubes (CNTs) and carbon fibers incorporates real-world material complexities such as agglomeration, waviness, and random orientation of fibers into the analysis. The complexities and the accurate characterization of the hybrid nanocomposite properties are determined by using the Halpin-Tsai model. Going beyond the current field of application, the core layer is integrated with actuators and sensors, respectively at the top and bottom layers. The structure is controlled by a Proportional-Derivative (PD) controller, simulating real-world control mechanisms used in aerospace systems for flight safety. The structure is modeled by the nonlinear Refined Zigzag Plate (RZP) theory. The advanced numerical technique of the Differential Quadrature Hierarchical Finite Element Method (DQHFEM) for the solution of the motion equations provides a robust framework for studying the intricate flutter and aerodynamic behavior of the smart hybrid nanocomposite trapezoidal plates. The examination of the reliability of the structure employs the three-term conjugate finite-step length (TCFS) method. This technique is constructed through the utilization of the conjugate gradient method, incorporating a controlled scalar parameter to ensure numerical stability. The results of this article have been validated against existing experimental and numerical studies, demonstrating the validity of the presented model and the numerical technique used. The effects of various parameters, such as the smart controller, applied output voltage to the actuator layer, volume percentage of CNTs, agglomeration, and waviness of carbon fibers, geometric parameters, the skew angle of the plate, rupture, and yawed angle flow are examined on the flutter behavior, critical aerodynamic pressure, nonlinear frequency, and reliability index. The results indicate that the controlled system exhibits an approximately 18% increase in the flutter critical aerodynamic pressure compared to the uncontrolled system, along with a significant 50% rise in flutter frequency. Additionally, the structure with rupture displays approximately 20% lower flutter aerodynamic pressure and a 25% lower flutter frequency compared to a healthy plate. In the absence of carbon nanotubes and under positive external voltage, a damage parameter exceeding 0.35 leads to a reliability index below 0.931, indicating a risk of structural failure. Two solutions involve switching to negative voltage or increasing the volumetric percentage of carbon nanotubes, resulting in a 43% or 118% increase in reliability, respectively.

Enhanced Coordination in the PV–HESS Microgrids Cluster: Introducing a New Distributed Event Consensus Algorithm

To ensure reliable power delivery to customers under potential disturbances, the coordination of a microgrid cluster (MGC) is essential. Various control strategies—centralized, decentralized, distributed, and hierarchical—have been explored in the literature to achieve this goal. The hierarchical control method, with three distinct levels, has proven effective in fostering coordination among microgrids (MGs) within the cluster. The third control level, utilizing a time-triggering consensus protocol, relies on a continuous and reliable communication network for data exchange among MGs, leading to resource-intensive operations and potential data congestion. Moreover, uncertainties introduced by renewable energy sources (RESs) can adversely impact cluster performance. In response to these challenges, this paper introduces a new distributed event-triggered consensus algorithm (DETC) to enhance the efficiency in handling the aforementioned issues. The proposed algorithm significantly reduces communication burdens, addressing resource usage concerns. The performance of this approach is evaluated through simulations of a cluster comprising four DC MGs, in each of which were PV and a hybrid Battery-Super capacitor in the MATLAB environment. The key findings indicate that the proposed DETC algorithm achieves commendable results in terms of voltage regulation, precise power sharing among sources, and a reduction in triggering instants. Based on these results, this method can be deemed as a good development in MGC management, providing a more efficient and reliable means of coordination, particularly in scenarios with dynamic loads and renewable energy integration. It is also a viable option for current microgrid systems, due to its ability to decrease communication loads while retaining excellent performance.

Highly fluctuating short-term load forecasting based on improved secondary decomposition and optimized VMD

Short Term Load Forecasting (STLF) is a critical task in the power sector, enabling efficient resource allocation and grid management. However, the volatile and complex nature of short-term load series pose significant challenges to forecasting models. Traditional decomposition-prediction models are bottlenecked in that they often lack complexity-based clustering for efficiency and optimization of decomposition for optimal secondary decomposition. In this paper, we summarize the framework of the decomposition-prediction models, and propose the hybrid model to address these limitations. We propose a Sample Entropy-based hierarchical clustering method to cluster components according to complexity and improve the efficiency of secondary decomposition. Additionally, we propose the center frequency method to efficiently optimize the K parameter of VMD, ultimately achieving the optimal decomposition. In summary, firstly, to help minimize the difficulty of prediction, the load series is decomposed twice using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Optimized Variational Mode Decomposition (OVMD). Then, two separate Long Short-Term Memory (LSTM) frameworks are built to predict the components obtained from the two decompositions, thus leveraging the advantages of the previous basic framework. Finally, by superimposing the prediction results, we obtain the output of the proposed model. The Belgian power load dataset is divided into four groups by season for comparison experiments. The results reveal that our model outperforms the benchmark models, with the best average coefficient of determination and mean absolute error of 0.996 and 53.69. Additionally, the limitations of sample entropy in secondary decomposition were revealed through our findings. These insights emphasize the promising contribution that our study brings in enhancing the decomposition-prediction model.

Research on the impact of the socio-educational environment on the academic performance of college students: the mediating role of study motivation.

Enhancing the academic performance (AP) of college students can contribute to the overall scientific literacy among this population, thereby fostering societal progress. The study investigates the correlation between college students' AP and the socio-educational environment (SEE, including family, roommates, and teachers), study motivation (SM, including self-efficacy and study behaviors). Based on the research findings, recommendations are offered to students, educators, and school administrators. Utilizing a stratified sampling approach, data was collected by selecting a sample of 330 first-year computer science students from a specific local university in Hebei Province, China. Data will be collected through a hierarchical sampling method. Using correlation analysis, difference analysis, and structural equation modeling (SEM) as data analysis methods. The data passed reliability and validity analysis (Cronbach's Alpha = 0.88, KMO = 0.88, χ2/df = 1.49RMSEA = 0.04). The independent sample T-test results showed that female students had higher academic performance than male students (p < 0.001), and there was no significant difference in academic performance between students from single parent or orphan families and students from normal families (p = 0.14), from non urban areas and from urban areas (p = 0.67). The results of the mediating effect analysis indicate that SM exerts complete mediation in the association between SEE and AP, with a mediating effect value of 0.18. The educational disparity between urban and rural areas in China is gradually narrowing. Support policies for students from impoverished families in higher education institutions are showing initial effectiveness. The conducive learning environment and educational atmosphere for students can indirectly influence their psychological state, thus impacting their academic performance during their university years.

HiTaxon: a hierarchical ensemble framework for taxonomic classification of short reads.

Whole microbiome DNA and RNA sequencing (metagenomics and metatranscriptomics) are pivotal to determining the functional roles of microbial communities. A key challenge in analyzing these complex datasets, typically composed of tens of millions of short reads, is accurately classifying reads to their taxa of origin. While still performing worse relative to reference-based short-read tools in species classification, ML algorithms have shown promising results in taxonomic classification at higher ranks. A recent approach exploited to enhance the performance of ML tools, which can be translated to reference-dependent classifiers, has been to integrate the hierarchical structure of taxonomy within the tool's predictive algorithm. Here, we introduce HiTaxon, an end-to-end hierarchical ensemble framework for taxonomic classification. HiTaxon facilitates data collection and processing, reference database construction and optional training of ML models to streamline ensemble creation. We show that databases created by HiTaxon improve the species-level performance of reference-dependent classifiers, while reducing their computational overhead. In addition, through exploring hierarchical methods for HiTaxon, we highlight that our custom approach to hierarchical ensembling improves species-level classification relative to traditional strategies. Finally, we demonstrate the improved performance of our hierarchical ensembles over current state-of-the-art classifiers in species classification using datasets comprised of either simulated or experimentally derived reads. HiTaxon is available at: https://github.com/ParkinsonLab/HiTaxon.

A Framework for Assessing Hydrochars from Hydrothermal Carbonisation of Agrowaste with the Use of MCDA: Application with the Hierarchical SMAA-PROMETHEE Method

Large amounts of hydrochar have been produced during the last decade by various hydrothermal carbonisation (HTC) processes. While the products of HTC seem to have widespread acceptance as valuable and efficient materials with advantages in their energy and environmental applications, which include soil improvement, heavy metal recovery, and many more, a comprehensive framework for the assessment of the different hydrochars based on their characteristics is missing. In this study, a framework for the assessment of hydrochars is proposed with the utilisation of Multi-Criteria Decision-Aiding (MCDA) methodologies. A hierarchical structure of independent criteria is established on a comprehensive level including three lines of evidence (LoE), i.e., Environmental, Economic, and Social LoE, which further include the assessment criteria. Hierarchical-SMAA-PROMETHEE is proposed as the most suitable MCDA methodology to be applied for assessing hydrochars based on the proposed framework. A case study is performed to demonstrate the utility of the framework and the advantages it offers to analysts and decision-makers. Hierarchical-SMAA-PROMETHEE is a non-compensatory method that enables exploring the decision problem on more than one level (comprehensive vs. LoE) and includes robust recommendations on the preference model and the elicitation of weights.

Analysis of Intelligent Risk Rating of Corporate Internal Control Compliance under Information Convergence Technology

Abstract The alarming problems related to illegal construction in individual enterprises have begun to raise questions about the effectiveness of internal control in these enterprises. In order to better achieve the internal control objectives, a set of scientific and complete rating index systems and evaluation models were constructed. This paper first proposes a fuzzy comprehensive evaluation method for enterprise internal control compliance risks, establishes a factor set, the weight allocation judged by the analytic hierarchy process, and builds the internal control evaluation model framework through the hierarchical decomposition method. Secondly, the core indicators in compliance internal control are determined by the form-filling method, the core indicators are filled into the framework determined by the hierarchical decomposition method, and finally, the weights of each indicator in each level are determined through weighting calculation, and the model is built. Through the specific application of an enterprise, it is found that the effectiveness of internal control has developed from “qualified” to “excellent” in the past five years, and the POOR value has shown a downward trend year by year, from a high of 0.2821 to 0.0717. The only thing that has not developed to excellent is that in 2022, the enterprise did not carry out construction under the provisions of the planning permit and was fined 675,900 yuan, and the grade was downgraded to “good”, but the overall degree of realization of the company’s internal control risk rating is improving year by year.

A Study on Data-Driven Cluster Analysis of Teaching Quality in Civic and Political Education in Colleges and Universities

Abstract The implementation of teaching quality evaluation has a certain guiding effect on a series of teaching works, such as teaching reform and teaching management, and the selection of appropriate evaluation methods is very important for the implementation of teaching quality evaluation. In this study, the hierarchical analysis method and K-means clustering algorithm were used to construct and assess a teaching quality evaluation model for college Civics education. Prior to evaluating the teaching quality of Civic and political education in colleges and universities, the hierarchical structure model and index system were established using the hierarchical analysis method. Then, the K-means clustering algorithm is utilized to execute the data mining work to assess the quality of data-driven teaching through clustering. The algorithm’s purity and F1 values are 0.895 and 0.834, respectively, and it performs well in clustering. Finally, the clustering analysis of the teaching quality of college civic education was conducted. The final scores of teaching quality, teaching method, teaching content, teaching effect, and teaching characteristics accounted for 98.28%, 87.76%, 88.03%, 71.4%, and 83.2% of the total scores of each, respectively. All the scores are in the middle of the range, indicating that the overall teaching situation is in the middle of the range with better results. The purpose of this study is to provide lessons and references for cluster analysis of civic education teaching quality in colleges and universities.

Exploring the role of traditional music elements in music art appreciation based on the hierarchical analysis method

Abstract In this paper, the hierarchical analysis method is improved based on the expert empowerment method, and the enhanced cluster AHP variable weight comprehensive evaluation model is constructed. Then, various elements and important features of music were evaluated perceptually. The correlation between musical elements and emotional appreciation of music art was analyzed through the indexes of harmonic concordance and sound area. The constructed model was used to make a comprehensive evaluation of the application effect of musical elements in the appreciation of music art. The results show that the proportion of people paying attention to the acoustic concordance of melody, tempo, and rhythm in music appreciation is 88.2%, 66.5%, and 75%, respectively. All VIP values for acoustic consonance and the range of tonal regions exceeded 1, which implies that harmonies with more unity and music in higher tonal regions can increase pleasantness perception. This study explores new research paths and provides new theoretical guidance for designing music appreciation teaching.

Optimal Configuration of Additional Heat Source for CHP System considering Demand Response Based on Comprehensive Benefits

An optimal configuration method for the combined heat and power (CHP) system considering demand response is proposed to scientifically and reasonably configure the parameters of the additional heat source and reduce unnecessary investment and construction costs. First, additional heat sources and demand response are utilized to decouple power generation and heating supply, enhancing the flexibility of the CHP system. Second, a multiobjective optimization configuration model of the CHP system is established, taking the system’s comprehensive satisfaction, economic cost, and wind power consumption capacity as the objectives, the unit output, and the capacity parameters of the additional heat source as the decision variables. Furthermore, an improved memetic algorithm (IMA) combined with a hierarchical sequence method is designed to solve the optimization model characterized by multiple objectives, hierarchical levels, and nonlinearity. The hierarchical sequence method solves problems sequentially based on the importance of optimization goals, ensuring the satisfaction of the configuration scheme. The IMA employs adaptive crossover and mutation probabilities, enhancing the algorithm’s convergence and quality. Finally, case analysis demonstrates that the CHP system achieves the best benefits when heat storage tanks and electric boilers are configured simultaneously. Moreover, compared to the MA, IPSO, and IABC algorithms, the IMA algorithm reduces the average economic cost by approximately 5.11%, 2.70%, and 8.43%, respectively.

Hamlet: Hierarchical Multi-Objective Reinforcement Learning Method for Charging Power Control of Electric Vehicles With Dynamic Quantity

Electric vehicle (EV) is emerging as an effective choice to reduce carbon emissions in the modern transportation system. However, the large access of EVs brings tremendous passive influence on the stability and performance of power grid operation. Thus, it is valuable to investigate the charging power management method to improve charging experience of EV drivers while maintaining normal bus voltage of the power system. This paper builds the charging power control problem of multiple EVs as a hierarchical Markov decision process (MDP) model and propose a hierarchical multi-objective reinforcement learning method (Hamlet) to obtain the real-time charging power control decisions. Specifically, by treating EVs with the same remaining charging time as the same agent, this paper addresses the dynamic state and action spaces issue and significantly reduce the input dimension of both state and action spaces. Besides, this paper quantifies drivers’ anxiety about the battery volume and distributes the comfort reward over the entire charging interval, which overcomes the delayed comfort reward issue. In order to alleviate the influence of multiple objectives on training stability, this paper develops a multi-objective learning method to dynamically adjust the optimization direction. Simulation results on IEEE 33 bus and 69 bus test feeders prove the validity of the proposed method in mitigating voltage fluctuation, minimizing bill payment and maximizing battery comfort.

A Study of Information Ethics Education Strategies and Practice Models in the Face of Cyber Violence in Colleges and Universities

Abstract This paper uses SPSS statistical software to factor analyze the data, combines the hierarchical analysis method to measure the weights of college students’ network morality, and finally constructs a complete network morality evaluation index system for college students that contains one first-level index, two second-level indexes, and eight third-level indexes. Based on the data on students’ network morality evaluations in five colleges and universities, a questionnaire has been prepared to assess the current situation of network morality among college students. Based on the above survey results, this paper gives the education model of cyber violence intervention, and with the help of structural equation modeling, it explores the influence of the teaching content, methods, and objectives of the education model of cyber violence intervention on the teaching effect. After analyzing, it can be seen that a total of 8 public factors were obtained, and these 8 public factors are set as the third-level indicators of the evaluation system. One of the secondary indicators has the largest weight value of cyber ethical behavior (0.642), and the weight value of fabrication of false news in cyber ethical behavior is 0.187. Some students can’t restrain their behaviors well online. In the online violence intervention education model, teaching objectives have a positive influence effect on teaching content and teaching methods, teaching content has a positive influence effect on teaching methods, and teaching methods have a significant effect on teaching effectiveness(P&lt;0.05).

Analysis of the Utility of Chinese Traditional Culture in Contemporary Digital Media Communication Based on a Big Data Platform

Abstract Chinese traditional culture is profound and has important historical and cultural values, and in the digital information era, the dissemination of Chinese traditional culture is facing new challenges and opportunities. This paper begins with the utility mechanism of Chinese traditional culture dissemination and develops a path for Chinese traditional culture dissemination using digital media. On this basis, a digital platform for Chinese traditional culture is established through the modular division of digital platforms and joint visual and interaction design. Then, the hierarchical analysis method (AHP) is combined with (PCA) to complete the weighting. Then, the subjective and objective weights are balanced by the information entropy method (IE). Finally, the comprehensive evaluation model of the utility of digital communication in Chinese traditional culture is constructed by combining with the method of the distance between the superiority and inferiority solutions (TOPSIS), and the evaluation index system of the utility of cultural communication is established. Among them, the benefit value has the highest weight of 0.2007. The other indicators in order of weight are cultural, emotional experience (0.1735), cultural content experience (0.1687), interactive experience (0.1533), social impact (0.1531), and digital function experience (0.1507). Finally, the communication effect of traditional Chinese culture is analyzed using the digital platform of this paper. The average score is 8.75, with excellent communication effects. Based on the utility analysis of digital media communication, this paper provides a digital path for the communication of Chinese traditional culture and establishes a comprehensive evaluation method of communication utility, which provides an effective research method for the utility of digital media communication of traditional culture.

Self-Reconfigurable Hierarchical Frameworks for Formation Control of Robot Swarms.

Hierarchical frameworks-a special class of directed frameworks with a layer-by-layer architecture-can be an effective mechanism to coordinate robot swarms. Their effectiveness was recently demonstrated by the mergeable nervous systems paradigm (Mathews et al., 2017), in which a robot swarm can switch dynamically between distributed and centralized control depending on the task, using self-organized hierarchical frameworks. New theoretical foundations are required to use this paradigm for formation control of large swarms. In particular, the systematic and mathematically analyzable organization and reorganization of hierarchical frameworks in a robot swarm is still an open problem. Although methods for framework construction and formation maintenance via rigidity theory exist in the literature, they do not address cases of hierarchy in a robot swarm. In this article, we extend bearing rigidity to directed topologies and extend the Henneberg constructions to generate self-organized hierarchical frameworks with bearing rigidity. We investigate three-key self-reconfiguration problems: 1) framework merging; 2) robot departure; and 3) framework splitting. We also derive the mathematical conditions of these problems and then develop algorithms that preserve rigidity and hierarchy using only local information. Our approach can be used for formation control generally, as in principle it can be coupled with any control law that makes use of bearing rigidity. To demonstrate and validate our proposed hierarchical frameworks and methods, we apply them to four scenarios of reactive formation control using an example control law.

Research on Supply Chain Optimization Model Construction and Efficiency Improvement Strategy in Automobile Industry

Abstract This thesis combines the ABC classification method and hierarchical analysis method to propose the evaluation method of logistics spatial layout and applies it to actual automobile manufacturing enterprises. The spatial layout scheme of logistics nodes in the park has been adjusted and optimized to achieve dynamic inventory classification and improve inventory management. When the demand of the decision-making body decreases, the optimal production and transportation scheduling plan for the automobile industry supply chain is formulated using the dynamic scheduling decision-making model. The results show that the spatial layout optimization scheme of supply chain logistics nodes in the automobile industrial park is RDC-B3, VDC-B5, and PDC-B1. The total cost of supply chain logistics after optimization is reduced by 3.8 × 108 yuan. The inventory items in the storage center are divided into 3 categories: A category for inventory items numbered 1-6, B category for 7-9, and C category for 10-11, to improve the turnover rate of the whole inventory and reduce the impact of inventory risk. When the demand side of the supply chain is reduced, the total cost of scheduling the optimal solution is 138.01 million. The research results of this paper have some reference significance.