A multi-type target collaborative allocation method with temporal constraints

This paper explores the reconstruction of teaching paradigms for art and design majors in colleges and universities driven by AIGC technology. It proposes and practices a new “teacher-machine-student” ternary collaborative teaching paradigm, reconstructing the teaching process through the integration of AIGC tools: revolutionizing teaching content by incorporating AIGC artistic language, computational thinking, and human-machine collaborative creation methods; innovating teaching methods through project-driven and reverse engineering approaches; and constructing a diversified evaluation system that emphasizes process assessment and creative integration capabilities. This paradigm aims to enhance creative efficiency and diversity, stimulate innovative thinking, facilitate personalized learning, transcend spatial and temporal constraints, ultimately improving teaching quality and cultivating interdisciplinary design talents capable of adapting to industry transformations. Taking the practice of animation creation courses as an example, this paper provides a referenceable path for AIGC to empower the reform of art and design education.

In this study, a novel collaborative method is developed to optimize hybrid sensor networks (HSN) for environmental monitoring and anomaly search tasks. A weighted Gaussian coverage method hs been designed for static sensor allocation, and the Active Monitoring and Anomaly Search System method is adapted to mobile sensor path planning. To validate the network performance, a simulation environment has been developed for fire search and detection with dynamic temperature field and non-uniform fire probability distribution. The performance metrics adopted are the detection time lag, source localization uncertainty, and state estimation error. Computational experiments are conducted to evaluate the performance of HSNs. The results demonstrate that the optimal collaborative deployment strategy allocates static sensors at high-risk locations and directs mobile sensors to patrol the remaining low-risk areas. The results also identify the conditions under which HSNs significantly outperform either only static or only mobile sensor networks in terms of the monitoring performance metrics.

Human–machine collaborative optimization method for dynamic worker allocation in aircraft final assembly lines

The integrated terrestrial-satellite network (ITSN) combines cellular networks and satellite networks to provide services for the ever-increasing users, where the coexistence of the two systems can not only highlight their advantages but also bring serious interference. Confronted with the target of maximizing the network capacity and ensuring the quality-of-service (QoS) for large number of users, we propose a framework-involved joint optimization of the multigroup precoding and resource allocation in the ITSN. In the multigroup precoding, we ameliorate the interference by optimizing the user access scheme, where the users with weak correlation on channel state information are distributed into the same group, such that all the users are divided into multiple groups and served at different time slots. To realize this multigroup precoding, first, we propose the multilevel clustering based grouping approach to obtain the groups in each cell or satellite. Second, we match those groups at different time slots to minimize intra-system interference and inter-system interference according to the proposed shortest path pairing algorithm and satellite users precoding method, respectively. In the resource allocation, we first transform the complicated non-convex problem into multiple convex subproblems, and then propose the collaborative power allocation method to optimize the power control for the whole network. Our simulation results show that the joint multigroup precoding and the resource allocation method is superior to the existing algorithms in terms of system capacity and QoS, which can significantly improve the performance of the ITSN.

In Industry 4.0, smart factories are essential to next-generation manufacturing, driven by advanced technologies like Cyber-Physical Systems (CPS) and the Internet of Things (IoT), rely on efficient material handling systems such as automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) for flexible. However, current multi-robot systems face scalability and efficiency problems in raw material supply. This paper introduces a multi-robot task allocation approach to optimize raw material supply in smart factory supply chains. A task optimization model is proposed, integrating task requirements and travel distances, reinterpreting the supply challenge as a multi-robot task allocation problem. To enhance efficiency and reduce energy consumption, a collaborative allocation method is used, combining greedy task distribution with Nash equilibrium principles. Experimental results confirm that the proposed algorithm effectively manages supply tasks, and has the potential to lighten the energy burden in smart factories.

In many practical scenarios, decision-making by an AI model alone is undesirable or even impossible, and the use of an AI model is only part of a complex decision-making process that includes a human expert. Nevertheless, this fact is often overlooked when creating and training AI models – the model is trained to make decisions independently, which is not always optimal. The paper presents a review of methods that allow taking into account the joint work of AI and a human expert in the process of designing (in particular, training) AI systems, which more accurately corresponds to the practical application of the model, allows to increase the accuracy of decisions made by the system “human – AI model”, as well as to explicitly control other important parameters of the system (e.g., human workload). The review includes an analysis of the current literature on a given topic in the following main areas: 1) scenarios of interaction between a human and an AI model and formal problem statements for improving the efficiency of the “human – AI model” system; 2) methods for ensuring the efficient operation of the “human – AI model” system; 3) ways to assess the quality of human-model AI collaboration. Conclusions are drawn regarding the advantages, disadvantages, and conditions of applicability of the methods, as well as the main problems of existing approaches are identified. The review can be useful for a wide range of researchers and specialists involved in the application of AI for decision support.

This paper investigates the collaborative security control and communication resource optimization problem in multi-region communication systems under multiple cyber attacks, based on the Stackelberg game theory. Attackers weaken information interaction between subsystems by disrupting wireless transmission channels among regions, thereby altering the synchronization coefficients of regional tie-lines and threatening the overall system stability. Under energy budget constraints for both the system and attackers, this study establishes a Stackelberg game model between the system and multiple attackers with incomplete information, focusing on energy allocation efficiency of transmission channels as the core performance metric. By solving the equilibrium solution of this game model, the optimal energy allocation schemes for both attack strategies and system transmission channels are derived. These solutions determine the system coefficient matrices of subsystems and further compute the control gains that ensure asymptotic stability of the system. Finally, simulation experiments on a practical power system validate the correctness and effectiveness of the proposed method.

With the continuous improvement of e-government platforms and the rapid development of artificial intelligence technology, an innovative public fund allocation model has emerged, which achieves precise allocation of public funds through automatic filling of application forms and intelligent verification of enterprise qualifications. However, existing studies still lacks systematic modeling of the implementation mechanism of this public fund allocation model. In response to the inherent complexity of the operating logic of this model and the unstructured characteristics of policy texts and enterprise data, this paper innovatively proposes a large language models-based multi-agent collaborative intelligent allocation method for public funds. This method consists of three core intelligent agents: policy agent, enterprise agent, and match agent. Among them, the policy agent is responsible for parsing policy texts and simulating government decision-making processes. The enterprise agent constructs the enterprise profile by integrating structured and unstructured data, and automatically generate funding application forms. The match agent is responsible for outputting the matching results and their decision basis. Through experimental research on a real-world dataset, this paper verifies the effectiveness of the proposed method.

In this chapter, three bio-inspired collaborative controls are proposed for multi-level systems. Firstly, a novel bio-inspired multi-layered intelligent cooperative controller (BMLICC) is proposed. Secondly, a novel cytokine-network-based stretching network (CNSN) is proposed to achieve the desired control performance in large production lines. Finally, a novel immune-based self–adaptive collaborative control allocation (ICCA) method is proposed for multi-level stretching processes in large industrial production lines.