Search results

Traditional robot arm trajectory planning methods have problems such as insufficient generalization performance and low adaptability. This paper aims to propose a method to plan the robot arm’s trajectory using the trajectory learning and generalization characteristics of dynamic motion primitives (DMPs).

This study aligns multiple demonstration motion primitives using dynamic time warping; use the Gaussian mixture model and Gaussian mixture regression methods to obtain the ideal primitive trajectory actions. By establishing a system model that improves DMPs, the parameters of the nonlinear function are learned based on the ideal primitive trajectory actions of the robotic arm, and the robotic arm motion trajectory is reproduced and generalized.

Experiments have proven that the robot arm motion trajectory learned by the method proposed in this article can not only learn to generalize and demonstrate the movement trend of the primitive trajectory, but also can better generate ideal motion trajectories and avoid obstacles when there are obstacles. The maximum Euclidean distance between the generated trajectory and the demonstration primitive trajectory is reduced by 29.9%, and the average Euclidean distance is reduced by 54.2%. This illustrates the feasibility of this method for robot arm trajectory planning.

It provides a new method for the trajectory planning of robotic arms in unstructured environments while improving the adaptability and generalization performance of robotic arms in trajectory planning.

The purpose of this paper is to argue for a transformative shift towards an inclusive and socially responsible framework in accounting education. Integrating the United Nations Sustainable Development Goals (SDGs) into accounting curricula can help accountants contribute positively towards the goals’ aim. This represents not merely an educational reform but a call to action to forge a path that empowers accounting students to be technically proficient and socially conscious graduates who act as change agents working towards the public interest.

This study challenges the technical focus of accounting, conceptualising it as a multidimensional technical, social and moral practice, transcending traditional boundaries to address complex societal issues. This paper is primarily discursive, using autoethnography through presenting vignettes written by four female accounting educators across three geographical regions. These first-person narratives foster a sense of interconnectedness and shared responsibility within the accounting community, reflecting a collective commitment to integrating SDGs into accounting education. By sharing personal experiences, the authors invite readers to engage in reflective pedagogy and contribute to shaping a better world through accounting education.

The transformative potential of purposefully incorporating SDGs into accounting education is not just a theoretical concept. The vignettes in this study provide concrete evidence of how this integration can shape future accountants into socially conscious professionals driven by ethics, equity and environmental responsibility. Our collective reflection underscores the importance of collaboration and continuous learning in aligning accounting education with the SDGs, offering a hopeful vision for the future of this field.

This study builds on existing literature to encourage communication, curriculum development, collaborative teaching approaches, experiential learning opportunities, ongoing evaluation and community dialogue on reshaping accounting education by giving a rare insight into what and how people teach and from what broader motivations. It offers a practical roadmap for educators to integrate SDGs into their teaching.

Thin cracks on the surface, such as those found in nuclear power plant concrete structures, are difficult to identify because they tend to be thin. This paper aims to design a novel segmentation network, called U-shaped contextual aggregation network (UCAN), for better recognition of weak cracks.

UCAN uses dilated convolutional layers with exponentially changing dilation rates to extract additional contextual features of thin cracks while preserving resolution. Furthermore, this paper has developed a topology-based loss function, called ℓ_cl Dice, which enhances the crack segmentation’s connectivity.

This paper generated five data sets with varying crack widths to evaluate the performance of multiple algorithms. The results show that the UCAN network proposed in this study achieves the highest F1-Score on thinner cracks. Additionally, training the UCAN network with the ℓ_cl Dice improves the F1-Scores compared to using the cross-entropy function alone. These findings demonstrate the effectiveness of the UCAN network and the value of incorporating the ℓ_cl Dice in crack segmentation tasks.

In this paper, an exponentially dilated convolutional layer is constructed to replace the commonly used pooling layer to improve the model receptive field. To address the challenge of preserving fracture connectivity segmentation, this paper introduces ℓ_cl Dice. This design enables UCAN to extract more contextual features while maintaining resolution, thus improving the crack segmentation performance. The proposed method is evaluated using extensive experiments where the results demonstrate the effectiveness of the algorithm.

Due to the complexity and diversity of megaprojects, the architectural programming process often involves multiple stakeholders, making decision-making difficult and susceptible to subjective factors. This study aims to propose an architectural programming methodology system (APMS) for megaprojects based on group decision-making model to enhance the accuracy and transparency of decision-making, and to facilitate participation and integration among stakeholders. This method allows multiple interest groups to participate in decision-making, gathers various perspectives and opinions, thereby improving the quality and efficiency of architectural programming and promoting the smooth implementation of projects.

This study first clarifies the decision-making subjects, decision objects, and decision methods of APMS based on group decision-making theory and value-based architectural programming methods. Furthermore, the entropy weight method and fuzzy TOPSIS method are employed as calculation methods to comprehensively evaluate decision alternatives and derive optimal decision conclusions. The workflow of APMS consists of four stages: preparation, information, decision, and evaluation, ensuring the scientific and systematic of the decision-making process.

This study conducted field research and empirical analysis on a practical megaproject of a comprehensive transport hub to verify the effectiveness of APMS. The results show that, in terms of both short-distance and long-distance transportation modes, the decision-making results of APMS are largely consistent with the preliminary programming outcomes of the project. However, regarding transfer modes, the APMS decision-making results revealed certain discrepancies between the project's current status and the preliminary programming.

APMS addresses the shortcomings in decision accuracy and stakeholder participation and integration in the current field of architectural programming. It not only enhances stakeholder participation and interaction but also considers various opinions and interests comprehensively. Additionally, APMS has significant potential in optimizing project performance, accelerating project processes, and reducing resource waste.

The evolution of digital construction within Industrial Revolution 5.0 emphasises the harmonious integration of humans and technology, value-driven technology implementation, adaptive leadership and user-centric design solutions. This shift necessitates architects to enhance their soft skills to navigate the transition effectively. Failure to demonstrate these skills may confine architects to passive roles as technology consumers rather than innovative leaders. While existing research predominantly focuses on the technical aspects of digital construction, this study investigates how architects effectively manifest their soft skills within such dynamic contexts, addressing a critical gap in the current literature.

Employing an interpretive phenomenological approach, the research engaged 14 individual architects practicing in architectural consultancy practices (ACPs) in Malaysia through semi-structured interviews and written exercises to unveil their experiences.

Thematic analysis revealed six primary themes: “analytical and critical thinking,” “digital communication,” “diversity in leadership styles,” “emotional intelligence,” “ethics and professionalism,” and “curiosity and agility.”

Furthermore, the developed thematic map serves as a foundational resource for human resource practices aiming to design upskilling and reskilling programs that focus on nurturing soft skills. Emphasising soft skills, these programs are designed to thrive in the digital construction era and beyond.

These shared experiences significantly contribute to the theoretical understanding of soft skills within the digital construction context, providing novel insights into their practical application when addressing real-world implications.

This work conducts a comprehensive analysis of how to incorporate resilience and sustainability into capacity expansion strategies for business-to-business (B2B) chemical supply chains. This study aims to guide both researchers and managers on ensuring profitability in B2B chemical supply chains while minimizing environmental impacts, complying with regulations and mitigating disruptions and risks.

A systematic literature review is conducted to analyze the interplay between sustainability and resilience in chemical B2B supply chains, specify the quantitative and qualitative methods used to tackle this challenge and identify the drivers and barriers concerning capacity expansion. In addition, a comprehensive conceptual framework is suggested to outline a compelling research agenda.

The findings emphasize the increasing importance of modeling and resolving decision-making challenges related to sustainable and resilient supply chains, particularly in capital-intensive chemical industries. Yet, there is no standardized strategy for addressing these challenges. The predominant solution methods are heuristic and metaheuristic, and the selection of performance metrics tends to be empirical and tailored to specific cases. The main barriers to achieving sustainability and resilience arise from resource limitations within the supply chain. Conversely, the key drivers of performance focus on enhancing efficiency, competitiveness, cost effectiveness and risk management.

This work offers practitioners a conceptual framework that synthesizes the knowledge and tackles the challenges of designing sustainable and resilient supply chains as well as managing their operations in the context of B2B chemical supply chains. Results provide a practical guide for navigating the complex interplay of sustainability, resilience and chemical supply chain expansion.

The key concepts and dimensions associated with capacity expansion planning for a resilient and sustainable chemical supply chain are identified through structured and comprehensive analyses of existing literature. A conceptual framework is proposed for delineating the intersections among sustainability, resilience and chemical supply chain expansions. This mapping endeavor aims to facilitate a future characterized by the deployment of a nexus of resilience and sustainability in chemical supply chains. To this end, a promising future research agenda is accordingly outlined.

This study aims to address the issue of random movement and non coordination between docking mechanisms and locking mechanisms, and proposes a comprehensive dynamic docking control architecture that integrates perception, planning, and motion control.

Firstly, the proposed dynamic docking control architecture uses laser sensors and a charge-coupled device camera to perceive the pose of the target. The sensor data are mapped to a high-dimensional potential field space and fused to reduce interference caused by detection noise. Next, a new potential function based on multi-dimensional space is developed for docking path planning, which enables the docking mechanism based on Stewart platform to rapidly converge to the target axis of the locking mechanism, which improves the adaptability and terminal docking accuracy of the docking state. Finally, to achieve precise tracking and flexible docking in the final stage, the system combines a self-impedance controller and an impedance control algorithm based on the planned trajectory.

Extensive simulations and experiments have been conducted to validate the effectiveness of the dynamic docking system and its control architecture. The results indicate that even if the target moves randomly, the system can successfully achieve accurate, stable and flexible dynamic docking.

This research can provide technical guidance and reference for docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

This study aims to develop and apply a process model for prioritizing and selecting basic research projects in developing countries.

Basic research is mainly funded by governments and since, unlike technological research, it does not have clear business goals, its prioritization is one of the complicated issues in formulating science and technology policy. Adopting a design science research methodology, the authors chose a general framework for project portfolio selection as an appropriate artifact for solving this problem. By customizing it for two specific features of this study, i.e. national scale of the problem and the basic nature of research proposals, the authors developed the proposed framework for solving the problem of priority setting.

The process for selecting basic research proposals consists of several steps, which can be categorized into eight steps including strategic decisions, preparation, pre-screening, evaluating individual proposals, screening, portfolio selection and monitoring. This study emphasizes the necessity of defining goals that can be evaluated for the national basic research portfolio, as a key strategic decision. Evaluating individual proposals is a peer-review-based process. In contrast, portfolio selection is done through a zero-one linear programming model. The validity of the proposed framework has been confirmed based on the data obtained from the Iran National Science Foundation.

To the best of the authors’ knowledge, in this research, for the first time, a mathematical model for prioritizing basic research at the national level has been presented, which effectively contributes to policymaking regarding the development of an optimum national research portfolio.

The dynamic nature of the built environment and trending smart construction project complexities demand proactive needs tailored towards architecture, engineering and construction (AEC) education. It is a task for the built environment professionals (BEP) to prepare for the future, including the quantity surveying (QS) profession. Studies are scarce in preparing QS education from Nigeria’s stakeholders’ perspective regarding digital technology. Therefore, this paper aims to investigate how to improve QS education by continually updating curriculum digitalisation to meet the construction industry requirements.

Data were sourced from elite virtual interviews across Nigeria. A total of 40 key stakeholders knowledgeable in QS education, advocating a future template for the advancement of QS education in higher institutions, were engaged, and saturation was achieved.

Findings show that improving QS education through continually updating curriculum digitalisation to meet industry requirements cannot be over-emphasised in the 21st-century-built environment industry. The outcomes of the results led to the conclusion that the current QS education curriculum was not meeting the expectations of other BEP stakeholders. Thus, for competitiveness in the future, the QS education curriculum needs to infuse more related-digital technology modules/courses to assist in the sustainability and relevance of the profession within the BEP.

This paper focussed on improving Nigeria’s QS education using digital technologies via a qualitative approach. Future study is needed via a quantitative approach for broader coverage and validation.

The research revealed the need for designing QS programmes to provide for industry demands with emphasis on digital technologies modules/courses. Nigeria’s QS education stakeholders have been stirred up to embrace the curriculum review and make the profession digitalised and relevant within the BEP. The built environment sector is trending towards digitalisation, and the QS programmes cannot afford to be behind.

This research identified the current gap regarding digitalisation of the curriculum. This study will stir QS educational providers and regulators to improve future programmes via digital technologies. It would encourage the use of digital technologies with the right enabling environment. The outcome would mitigate the gap and improve Nigeria’s QS education in the future.

Dynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.

To mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.

To validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.

This paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.