This paper proposed a cooperative trajectory planning method with Dynamic Programming (DP) to address the real-time generation of safe trajectories for Multi-UAV in complex environments. The DP comprises three key components: (1) adaptive batch sampling of the state space to efficiently reduce computational complexity; (2) application of DP principles to compute feasible trajectories from the sampled states; (3) formulation of the trajectory optimization problem as a quadratic programming (QP) problem through spline curve parameterization, yielding optimal smooth trajectories. Extending the single-UAV planning paradigm, we introduce a distance-constrained information exchange for multi-UAV coordination. Through collaborative environmental sampling, each UAV gains access to extended spatial awareness beyond its individual sensor range, thereby expanding the solution space and enhancing the reliability and robustness of the planning outcomes. Experimental results demonstrate that our framework achieves real-time performance with computation times in the order of milliseconds. Comparative evaluations against state-of-the-art methods reveal superior trajectory quality, making it suitable for multi-UAV applications in complex environments.

Process discovery aims to derive a process model that accurately represents the observed behavior in an event log. As a state-of-the-art process discovery technique, Inductive Miner (IM) generates sound process models (i.e., free of deadlocks) while ensuring optimal replay fitness. However, IM may sometimes produce over-generalized process models with locally imprecise structures, often resulting in the creation of so-called flower structures. To address this limitation, this paper presents a novel technique that refines the process model generated by IM by optimizing its imprecise sub-processes. Specifically, the technique begins by identifying and extracting sub-logs corresponding to imprecise sub-processes in the initial IM-generated process model. Then, these imprecise sub-processes are iteratively optimized using a frequency-based filtering mechanism applied to the sub-logs. Once optimized, the imprecise sub-processes in the initial process model are replaced by the optimized ones, generating a set of candidates process models. Finally, the candidate with the best quality, in terms of fitness and precision, is selected as the final optimized process models. The proposed technique has been implemented as a plugin for the open-source process mining platform ProM. Through comparisons with state-of-the-art process discovery techniques using 10 publicly available real-life event logs, the experimental results demonstrate that the proposed method achieves an average absolute improvement of 0.173 in F-measure over its IMi variant, while also exhibiting competitive performance relative to other state-of-the-art approaches.

Manufacturing process discovery extracts insights from event logs recorded by Manufacturing Information Systems (MISs) to optimize operational processes. However existing process discovery techniques struggle with complex concurrency relations, resulting in imprecise sub-processes that compromise model accuracy. This paper proposes a novel enhancement to Inductive Miner (IM)-generated models by optimizing local imprecise structures in manufacturing process models. The method first identifies imprecise sub-processes and extracts their corresponding sub-logs. Then imprecise sub-processes are incrementally optimized using a frequency-based filter mechanism, generating multiple candidate models. Finally, the best-quality candidate model based on evaluation metrics is selected as the final output. The proposed technique has been implemented as an open source process mining toolkit ProM plugin and evaluated on six real-life manufacturing event logs. Experimental results demonstrate that it outperforms state-of-the-art techniques, producing higher quality process models, making it particularly suited for manufacturing process discovery.

In this paper, a WPT system that can switch between constant current (CC) and constant voltage (CV) in a single circuit is proposed. In the proposed WPT system, the CC and CV modes are achieved by varying the on-duty ratio of the buck converter. In addition, the proposed system also keeps the zero-voltage switching against coil misalignment and load variations without any information feedback from the rectifier to the inverter. This is because the load-independent behavior is built into the proposed system. The experimental results are consistent with the analytical predictions, which show the validity of the proposed circuit design strategy and the derived analytical expressions. Under rated conditions, the output power was 36.9 W and the power conversion efficiency was 86 % at the operating frequency of 6.78 MHz.

The capacity to analyze the causes of poor decisions made by visual recognition models is becoming increasingly crucial as the security requirements of various real-world systems continue to escalate. However, the complex structure and blackbox nature within deep neural networks constrain the mining of their error causes. Based on this, we propose a concept-based (e.g. a group of pixel blocks that contain leaves represents the concept of leaves) automated strong localization interpretation framework, called hierarchically optimized concept-sensitive interpretation (HOCS), to provide quantitative analysis of the semantics of wrong decisions in the classification network is provided from two directions of internal and external information interference of samples. HOCS was applied to models with spurious correlation and well-distributed data in the training set. The results showed that it provided concrete explanations in a way that was understandable to humans and demonstrated the significant advantages of HOCS in terms of efficiency and accuracy.

In this work, we investigate an uplink unmanned aerial vehicles (UAVs)-enabled intelligent transportation system to collect data from traveling vehicles on a specific highway road. To ensure the freshness of information delivered from the traveling vehicles to UAV base stations, we use the new age of information (AoI) metric to characterize the information freshness and formulate the AoI minimization problem by optimizing the UAVs’ trajectories and the communication time of vehicles jointly. In order to handle the mixed-integer nonlinear problem, a multi-agent deep reinforcement learning scheme is proposed by applying independent flight direction and time slot action spaces, in which each UAV working as an independent agent adjusts to the dynamic environment quickly based on stored experience. The AoI-related reward function is proposed to select the beneficial action space to guarantee the information freshness. Numerical simulation results show the proposed scheme outperforms the benchmark schemes.

In this work, we investigate a trajectory design problem in uplink unmanned aerial vehicles (UAVs)-enabled data collection system for massive time-sensitive Internet of Things (IoT) services. Although UAV has the advantages of automatic maneuverability and flexible mobility, it is challenging to guarantee the information freshness of collected data under the limited flying energy constraint. Thus we employ Age of Information (AoI) as a new metric to characterize the information freshness and formulate a joint power control and trajectory design optimization problem to minimize average AoI. In order to solve this non-convex problem, we decompose it as a power control subtask and trajectory design subtask, and propose a multi-agent deep reinforcement learning (DRL)-based scheme to solve the subtasks with independent state space, action space and reward function. Simulation results show that the proposed scheme can obtain better performance gain compared to the benchmark scheme and has the superior stability under different settings.

Mobile edge computing (MEC) is a promising technique to meet the demand of computation resources in unmanned aerial vehicle (UAV)-assisted vehicle-to-everything (V2X) networks by offloading computation-intensive tasks to UAV base stations (UBSs). In this paper, we consider an uplink UAV-assisted V2X communication system and characterize the information freshness between UAV and vehicles by applying the new age of information (Aol) metric. To solve the non-convex Aol minimization problem in the high-dimensional action space, a deep Q-network (DQN)-based scheme is proposed to optimize the transmit power and offloading ratio based on the stored experience, in which the action space consists of independent transmit power and offloading ratio. Meanwhile, each UBS selects the beneficial action based on the reward function related to Aol to guarantee the information freshness. The simulation results show that the flight height of UBS and the number of vehicles have a negative impact on Aol. Compared with the benchmark schemes, the proposed scheme can reduce Aol by 40.6%. In addition, the simulation results show that the offloading has a significant effect on Aol compared with the transmit power.

Summary The transmission process of disaster backup with long distance and massive data causes huge energy consumption. Reducing the number of occupied intermediate forwarding devices and shortening the transmission completion time are two key factors for energy saving. Not jointly considering them, previous work can hardly realize optimal energy‐aware transmission only by single objective optimization. For the first time, we leverage multiobjective reinforcement learning to simultaneously minimize the number of occupied intermediate forwarding devices and the transmission completion time in software defined network. We propose two‐level reinforcement learning, consisting of search and selection operation. In the internal reinforcement learning, we aim to reduce hop number, improve node sharing degree, and give priority to the links with larger residual capacity. Then in the external level, we aim to reduce the total number of occupied devices and increase the total backup flow. We leverage Chebyshev scalarization function based on pseudo‐random proportional rule to simplify weight selection, and enforce exploration to avoid falling into local optimum. We design the vector of rewards for different objectives, and update Pareto approximate set by multiple state steps to approach the optimal solution. Our strategy solves the weight selection problem successfully and achieves lower energy consumption.