It is feasible and safe to use unmanned aerial vehicle (UAV) as the data collection platform of the Internet of things (IoT). In order to save the energy loss of the platform and make the UAV perform the collection work effectively, it is necessary to optimize the deployment of UAV. The objective problem is to minimize the sum of the lost energy of UAV and the loss of data transmission of Internet of things devices. The key to solving the problem is to calculate the location of the docking points and the number of docking points when the UAV is working to collect data. This paper proposes a coding scheme based on swarm intelligence optimization, which encapsulates the docking position of UAV into a dimension, so the number of docking points to be calculated is the dimension number of optimization objective. This problem is considered as a dynamic dimension optimization problem. Each individual in swarm intelligence algorithm is a solution. When adjusting the dimension, the best individual is added or deleted to achieve dynamic search in the evolutionary process. Collaborative search among multiple individuals can improve the local optimal limit of search to a certain extent. Finally, the validity of the swarm intelligence-based coding approach is verified by simulation experiments under seven different IoT device distribution scenarios.

Mobile edge computing (MEC) significantly boosts the computing power and reduces the energy consumption of Internet of Things (IoT) devices, serving as a valuable complement to cloud computing. The application of unmanned aerial vehicle (UAV) for MEC systems can effectively alleviate the issue of insufficient or damaged communication facilities in remote areas, further expanding the scope of MEC applications. In this article, we present a system model for UAV-assisted wireless-powered MEC systems in a dynamic environment with the objective of maximizing the computation rate of user devices. Due to the complexity of the optimization objective in dynamic environments, we propose a swarm intelligence-based optimization framework with a mechanism for responding to environmental changes, which is intended to enhance population diversity in both static and dynamic environments with the aim of overcoming premature convergence. We integrate particle swarm optimization and harmony search into the proposed framework, naming them DOPSO and DOHS, respectively. Simulation results for two offloading modes in UAV-assisted MEC systems indicate that the proposed framework significantly outperforms other dynamic optimization algorithms.

Due to the low detection accuracy in steel surface defect detection and the constraints of limited hardware resources, we propose an improved model for steel surface defect detection, named CBiF-FC-EFC-YOLOv8s (CFE-YOLOv8s), including CBS-BiFormer (CBiF) modules, Faster-C2f (FC) modules, and EMA-Faster-C2f (EFC) modules. Firstly, because of the potential information loss that convolutional neural networks (CNN) may encounter when dealing with miniature targets, the CBiF combines CNN with Transformer to optimize local and global features. Secondly, to address the increased computational complexity caused by the extensive use of convolutional layers, the FC uses the FasterNet block to reduce redundant computations and memory access. Lastly, the EMA is incorporated into the FC to design the EFC module and enhance feature fusion capability while ensuring the light weight of the model. CFE-YOLOv8s achieves mAP@0.5 values of 77.8% and 69.5% on the NEU-DET and GC10-DET datasets, respectively, representing enhancements of 3.1% and 2.8% over YOLOv8s, with reductions of 22% and 18% in model parameters and FLOPS. The CFE-YOLOv8s demonstrates superior overall performance and balance compared to other advanced models.

Syntactical rule based approaches for aspect extraction, which are free from expensive manual annotation, are promising in practice. These approaches extract aspects mainly through the dependency relations in the surface sentence structures. However, deep and rich semantic information hidden in sentences which can help improve aspect extraction, is difficult for them to capture. In order to address the problem, this paper first proposes to employ Logic Programming to explore the feasibility of deep semantic representation, then proposes Deep2S, a hybrid rule-based method to improve the performance of aspect extraction. Deep2S integrates deep semantic representation such as Abstract Meaning Representation (AMR) with syntactic structure. It can take advantage of the syntactical rules to obtain dependency relations in the surface structure as well as the semantic rules to capture deep semantic information. Our experiments are conducted on eight popular review datasets using two evaluation metrics. Experimental results demonstrate the usefulness of deep semantic representation and the ability of Deep2S to improve the performance of aspect extraction in opinion mining.