Marine cross-medium acoustic–radio collaborative networks must route traffic across heterogeneous underwater acoustic and radio links while coping with energy imbalance, congestion, and mobility-induced link instability. This paper proposes Q-L...
Human–robot collaborative assembly line balancing and scheduling constitutes an NP-hard combinatorial optimization problem involving the simultaneous optimization of task assignment, resource allocation, processing mode selection, station-level...
With the advancement of the new-type power system, the exponentially growing high-frequency distribution and consumption data imposes heavy transmission and processing pressure on resource-constrained edge devices. Existing compression methods face t...
To reduce the economic losses caused by debt evasion amongst lost-link borrowers (LBs) and improve the efficiency of finding information on LBs, this paper focuses on the cross-platform information collaborative search optimization problem for LBs. G...
In our modern society, there has been a noticeable increase in pollution due to the trend of post-use handling of items. This necessitates the adoption of recycling and remanufacturing processes, advocating for sustainable resource management. This p...
The entry of JD.com into the food delivery sector and the ensuing subsidy competition have resulted in irrational competition, merchant profit squeezes, and food safety risks in China. This study therefore investigates the collaborative governance me...
This paper applies federated reinforcement learning (FRL) in cellular vehicle-to-everything (C-V2X) communication to enable vehicles to learn communication parameters in collaboration with a parameter server that is embedded in an unmanned aerial veh...
With the rapid development of smart devices and the Internet of Things (IoT), the explosive growth of data has placed increasingly higher demands on real-time processing and intelligent decision making. Cloud-edge collaborative computing has emerged...
To improve the traffic efficiency of connected and automated vehicles (CAVs) in on-ramp merging areas, this study proposes a novel region-level multi-agent reinforcement learning framework, Regionally Coordinated Nash-Advantage Decomposition Deep Q-N...
Numerous studies have confirmed that microRNAs play a crucial role in the research of complex human diseases. Identifying the relationship between miRNAs and diseases is important for improving the treatment of complex diseases. However, traditional...
Unmanned aerial vehicle (UAV) clusters usually face problems such as complex environments, heterogeneous combat subjects, and realistic interference factors in the course of mission assignment. In order to reduce resource consumption and improve the...
Collaborative intelligent sensing in the Social Internet of Things (Social IoT) relies on distributed AI-enabled sensors to support complementary information sharing, multimodal perception, and real-time autonomous decision-making. Under high-load co...
Human-Robot Collaboration (HRC) is pivotal for flexible, worker-centric manufacturing in Industry 5.0, yet dynamic task allocation remains difficult because operator states—fatigue and skill—fluctuate abruptly. I address this gap with a h...
The flexible job shop scheduling problem (FJSP) with transportation resources such as automated guided vehicles (AGVs) is prevalent in manufacturing enterprises. Multi-type AGVs are widely adopted to transfer jobs and realize the collaboration of dif...
Public technology service platforms (PTSPs) increasingly rely on coalition structures to deliver complex services, yet achieving equitable benefit allocation remains a critical challenge for sustained collaboration. To address the allocation challeng...
Existing Q-Learning-based path planning methods face significant bottlenecks in large-scale collaboration, dynamic interference adaptation, and regional value differentiation, failing to meet the practical needs of mountain search and rescue. This st...
Addressing the imperative for energy transition amid depleting fossil fuels, distributed generation (DG) is increasingly integrated into distribution networks (DNs). This integration necessitates low-carbon dispatching solutions that reconcile econom...
This paper proposes a reinforcement learning-based power allocation for underwater acoustic communication networks (UACNs). The objective function is formulated as maximizing channel capacity under constraints of maximum power and minimum channel cap...
Effective grouping in collaborative learning is crucial for enhancing the efficiency of collaborative learning. A well-structured collaborative learning group can significantly enhance the learning effectiveness of both individuals and group members....
Catastrophic forgetting is a stability–plasticity imbalance that causes a machine learner to lose previously gained knowledge that is critical for performing a task. The imbalance occurs in transfer learning, negatively affecting the learner&rs...
Challenges related to effective supply and demand planning and inventory management impose critical planning issues for many small and medium-sized enterprises (SMEs). In recent years, data-driven methods in machine learning (ML) algorithms have prov...
The integration of unmanned aerial vehicles (UAVs) and the Internet of Things (IoT) has opened up new possibilities in various industries. However, with the increasing number of Internet of Drones (IoD) networks, the risk of network attacks is also r...
Recent advancements in lightweight electroencephalogram(EEG) signal classification have enabled real-time human–robot interaction, yet challenges persist in balancing computational efficiency and safety in dynamic path planning. This study prop...
The number of applications in which industrial robots share their working environment with people is increasing. Robots appropriate for such applications are equipped with safety systems according to ISO/TS 15066:2016 and are often referred to as col...
This paper addresses the problem of side lobe interference in 5G networks by proposing a unique collaborative beamforming strategy based on Deep Q-Network (DQN) reinforcement learning. Our method, which operates in the sub-6 GHz band, maximizes beam...
The operational and safety performance of intersections is the key to ensuring the efficient operation of urban traffic. With the development of automated driving technologies, the ability of adaptive traffic signal control has been improved accordin...
Currently, Maritime Autonomous Surface Ships (MASS) have become one of the most attractive research areas in shipping and academic communities. Based on the ship-to-shore and ship-to-ship communication network, they can exploit diversified and distri...
This study investigates the integrated multi-objective scheduling problems of job shops and material handling robots (MHR) with minimising the maximum completion time (makespan), earliness or tardiness, and total energy consumption. The collaborative...
With the rapid development of science and technology and the improvement of people’s living standards, vehicles have gradually become the main means of travel. The increase in vehicles has also brought about an increasing incidence of car accid...
The increasing volume of traffic has led to severe challenges, including traffic congestion, heightened energy consumption, increased air pollution, and prolonged travel times. Addressing these issues requires innovative approaches for optimizing roa...
With the rapid development of artificial intelligence and Internet of Things technologies, smart libraries increasingly require low-latency and energy-efficient computing support for heterogeneous services such as access control, intelligent recommen...
Although tracking research has achieved excellent performance in mathematical angles, it is still meaningful to analyze tracking problems from multiple perspectives. This motivation not only promotes the independence of tracking research but also inc...
With the exponential growth of wireless devices and the demand for real-time processing, traditional server architectures face challenges in meeting the ever-increasing computational requirements. This paper proposes a collaborative edge computing fr...
The advent of a wide variety of affordable communication devices and cameras has enabled IoT systems to provide effective solutions for a wide range of civil and military applications. One of the potential applications is a surveillance system in whi...
Precast component production is an important part of industrialization in construction. However, in actual production, it often faces problems such as high resource occupation, long production cycles, and high delivery costs caused by unreasonable sc...
Efficient congestion management in Software-Defined Networks (SDNs) remains a significant challenge due to dynamic traffic patterns and complex topologies. Conventional congestion control techniques based on static or heuristic rules often fail to ad...
Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main res...
The engine room is the core area of a ship, critical to its operation, safety, and efficiency. Currently, many researchers merely address the ship engine room layout design (SERLD) problem using optimization algorithms and independent layout strategi...
Symmetry plays a crucial role in balancing production efficiency and energy consumption within distributed manufacturing systems. This study leverages symmetric decision-making structures in resource allocation and maintenance scheduling to achieve a...
Airport surface scheduling at multi-runway airports is a complex system engineering task that balances efficiency, safety, and sustainability, making it a key research focus in the field of air traffic management. This study proposes a cosine curve-b...
In intelligent cluster systems, the spatio-temporal complexity of agent data collection and resource allocation, as well as the problems in collaborative organizations, present substantial challenges to efficient resource distribution. To address thi...
The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during t...
This paper proposes a noble multi-robot path planning algorithm using Deep q learning combined with CNN (Convolution Neural Network) algorithm. In conventional path planning algorithms, robots need to search a comparatively wide area for navigation a...
Post-disaster rescue scenarios often involve complex and variable terrains, imposing heterogeneous mobility requirements on different transport modes. Single-type vehicles face challenges in independently completing comprehensive rescue tasks. This s...
Multi-agent swarm collaboration, inspired by the collective behaviors of biological swarms in nature, has wide applications in dynamic open environments. However, hybrid action spaces in multi-agent reinforcement learning (MARL) present a critical ch...
Devices used in Internet of Things (IoT) networks continue to perform sensing, gathering, modifying, and forwarding data. Since IoT networks have a lot of participants, mitigating and reducing collisions among the participants becomes an essential re...
Efficient spectrum sharing is essential for maximizing data communication performance in Vehicular Networks (VNs). In this article, we propose a novel hybrid framework that leverages Multi-Agent Reinforcement Learning (MARL), thereby combining both c...
The increasing frequency and intensity of wildfires has created an urgent demand for scalable and autonomous wildfire response systems. While recent advances in multi-agent reinforcement learning (MARL) have demonstrated promise for collaborative unc...
The existing research on digital–intelligent innovation collaboration networks has not effectively connected the evolution of real collaborative relationships with governance methods, and governance models often struggle to accommodate both emp...
With the rapid advancement of quantum computing technology, traditional encryption methods are encountering unprecedented challenges in the Internet of Things (IoT), blockchain systems, and digital learning (e-learning) platforms. Therefore, we syste...
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