Artificial Intelligence Review最新文献

The Kepler Optimisation Algorithm (KOA) is a recently proposed algorithm that is inspired by Kepler’s laws to predict the positions and velocities of planets at a given time. However, although promising, KOA can encounter challenges such as convergence to sub-optimal solutions or slow convergence speed. This paper proposes an improvement to KOA by integrating chaotic maps to solve complex engineering problems. The improved algorithm, named Chaotic Kepler Optimization Algorithm (CKOA), is characterized by a better ability to avoid local minima and to reach globally optimal solutions thanks to a dynamic diversification strategy based on chaotic maps. To confirm the effectiveness of the suggested approach, in-depth statistical analyses were carried out using the CEC2020 and CEC2022 benchmarks. These analyses included mean and standard deviation of fitness, convergence curves, Wilcoxon tests, as well as population diversity assessments. The experimental results, which compare CKOA not only to the original KOA but also to eight other recent optimizers, show that the proposed algorithm performs better in terms of convergence speed and solution quality. In addition, CKOA has been successfully tested on three complex engineering problems, confirming its robustness and practical effectiveness. These results make CKOA a powerful optimisation tool in a variety of complex real-world contexts. After final acceptance, the source code will be uploaded to the Github account: nawal.elghouate@usmba.ac.ma.

The continuous improvement in the connectivity, automation and autonomy levels of Intelligent Connected Vehicles (ICVs) significantly increases the probability of potential security threats. Over-the-Air (OTA) is a promising technique for upgrading features of ICVs and enhancing their reliability and security against environmental disturbances as well as malicious attacks. To better understand the potential security risks and possible countermeasures, we survey research works in ICV security during OTA from cloud upgrade, terminal upgrade, and object upgrade. We also summarize existing methods in OTA upgrading techniques and systematically investigate the overall framework of OTA upgrading methods from the perspectives of Software-Over-the-Air (SOTA) and Firmware-Over-the-Air (FOTA).We further discuss possible mitigation strategies and open issues yet to be resolved in this research direction. This survey shows that OTA provides a powerful technique for upgrading the ICV features and improving ICV security.

The edge computing paradigm is becoming increasingly commercialized due to the widespread adoption of wireless communication technologies and the growing demand for compute-intensive mobile applications. Edge computing complements the cloud computing model by deploying computation, storage, and network resources to the edge locations of wireless access networks, empowering end devices to run resource-intensive applications. In order to promote the commercialization of edge computing, it is important to explore effective ways to reduce the cost of edge computing networks. This paper provides a comprehensive review of the research findings in recent years, offering a clear perspective on the research dynamics. This paper first recalls the architectural framework of edge computing. Then, the main optimization objectives and optimization methods are comprehensively described. Mainstream mathematical models for cost reduction are then shown in depth. The paper also discusses the methods used to evaluate the effectiveness. Then, typical examples of typical application scenarios for edge computing networks are examined in depth. Finally, the paper identifies some unresolved issues. We expect future research to make more attempts in these directions.

In the realm of electrical distribution, managing power quality is critical due to its significant impact on infrastructure and customer satisfaction. Addressing issues such as voltage sags and swells, along with current and voltage harmonics, is imperative. The innovative approach proposed in this paper centers on a dual-phase control strategy using a Universal Power Quality Conditioner that integrates series and parallel compensations to rectify these disturbances simultaneously. Our methodology introduces a hybrid control scheme that employs adaptive dynamic neural networks (ADNN), a sinusoidal tracking filter (STF), and a proportional-integral (PI) controller optimized via an improved krill herd (IKH) algorithm. The first phase utilizes the ADNN-based adaptive integrated estimator for quick and accurate disturbance detection and estimation. Subsequently, the second phase employs the STF, omitting the Low Pass Filter and employing a Phase Locking Loop to generate precise reference voltages and currents for the series and parallel active filters based on dynamic load and source conditions. This advanced control mechanism not only enhances system efficacy but also reduces the need for extensive computational resources. Furthermore, the performance of both series and parallel inverters is finely tuned through a PI controller optimized with the IKH algorithm, improving the DC link voltage regulation. Our extensive testing under various conditions, including voltage imbalances and harmonic disturbances, demonstrates the robustness of the proposed solution in both transient and steady-state scenarios.

Underwater welding robots play a crucial role in addressing challenges such as low efficiency, suboptimal performance, and high risks associated with underwater welding operations. These robots face a dual challenge encompassing both hardware deployment and software algorithms. Recent years have seen significant interest in humanoid robots and artificial intelligence (AI) technologies, which hold promise as breakthrough solutions for advancing underwater welding capabilities. Firstly, this review delves into the hardware platforms envisioned for future underwater humanoid welding robots (UHWR), encompassing both underwater apparatus and terrestrial support equipment. Secondly, it provides an extensive overview of AI applications in underwater welding scenarios, particularly focusing on their implementation in UHWR. This includes detailed discussions on multi-sensor calibration, vision-based three-dimensional (3D) reconstruction, extraction of weld features, decision-making for weld repairs, robot trajectory planning, and motion planning for dual-arm robots. Through comparative analysis within the text, it becomes evident that AI significantly enhances capabilities such as underwater multi-sensor calibration, vision-based 3D reconstruction, and weld feature extraction. Moreover, AI shows substantial potential in tasks like underwater image enhancement, decision-making processes, robot trajectory planning, and dual-arm robot motion planning. Looking ahead, the development trajectory for AI in UHWR emphasizes multifunctional models, edge computing in compact models, and advanced decision-making technologies in expansive models.

Graph-based semi-supervised feature selection has aroused continuous attention in processing high-dimensional data with most unlabeled and fewer data samples. Many graph-based models perform on a pre-defined graph, which is separated from the procedure of feature selection, making the model hard to select the discriminative features. To address this issue, we exploit a self-adjusted graph for semi-supervised embedded feature selection method (SAGFS), which learns an optimal sparse similarity graph to replace the pre-defined graph to alleviate the effect of data noise. SAGFS allows the learned graph itself to be adjusted according to the local geometric structure of the data and the procedure of selecting features to select the most representative features. Besides that, we introduce (l_{2,p})-norm to constrain the projection matrix for efficient feature selection. An efficient alternating optimization algorithm is presented, together with analyses on its convergence. Systematical experiments on several publicly datasets are performed to analyze the proposed model from several aspects, and demonstrate that our approaches outperform other comparison methods.

In the rapidly evolving field of plant disease detection, the number and complexity of crop diseases are increasing, made worse by factors like climate change. Addressing these challenges requires robust and efficient methodologies capable of early and accurate disease identification. This paper explores the integration of advanced deep learning techniques, including pre-trained models, zero-shot learning, and semantic attributes to enhance the effectiveness of plant disease detection systems. High level features extracted from the images by these pretrained models capture crucial patterns, while domain-specific semantic attributes, such as leaf texture and color variations, enhance the understanding. Incorporating zero-shot learning enables adaptation to new and unseen diseases using semantic descriptions. Experimental validation across diverse plant species and disease types underscores the approach’s reliability in real-world agricultural scenarios. Our approach has demonstrated superior performance with plant village dataset, showing a significant improvement in accuracy and generalization. These results underscore the potential of our method to revolutionize plant disease detection and management in agricultural practices.

Attributed graph clustering, leveraging both structural and attribute information, is crucial in various real-world applications. However, current approaches face challenges stemming from the sparsity of graphs and sensitivity to noise in Graph Convolutional Networks (GCNs). Moreover, GCN-based methods are often designed based on the assumption of homophilic graph and ignore heterophilic graph. To address these, we propose a graph clustering method that consists of four phases: graph enhance, graph reconstruction, graph refine, and dual-guidance supervisor module. An enhanced graph module is defined by an auxiliary graph to consider distant relationships in the topology structure to alleviate the limitations of sparse graphs. The graph reconstruction phase includes the creation and integration of homophily and heterophily graphs to achieve graph-agnostic. In graph refine, the auxiliary graph is iteratively improved to enhance the generalization of the representation. In this phase, a subspace clustering module is applied to convert attribute-based embeddings into relationship-based representations. Finally, the extracted graphs are fed to a dual-guidance supervisor module to obtain the final clustering result. Experimental validation on several benchmark datasets demonstrates the efficiency of our model. Meanwhile, the findings offer significant advancements in attributed graph clustering, promising improved applicability in various domains.

This paper proposes a novel decision-support system (DSS) to assist decision-makers in the ULTIMO project with integrating Autonomous Vehicles (AVs) in Geneva, Switzerland. Specifically, it aids in selecting the best scenario for incorporating AVs into Geneva’s public transportation system. The proposed DSS is architected on a combined integrated framework that includes a machine learning (ML) algorithm, random forest (RF) algorithm, and three novel multi-criteria decision-making (MCDM) algorithms: (1) Modified E-ARWEN (ME-ARWEN) for selecting the best scenario with high sensitivity; (2) Compromiser—Positive, Neutral, Negative (Compromiser-PNN) for extracting weights from stakeholders, considering their preferences and potential conflicts; and (3) Collective Weight Processor (CWP) for deriving weights from expert opinions. Besides the main objective, this article also aims to: (1) Address the gap in practical DSS software within AV-related studies by providing Python codes of the DSS; (2) Develop a highly sensitive and comprehensive MCDM framework to address the project’s needs; and (3) Employ Artificial Intelligence within the DSS to optimize outputs. By the application of the proposed DSS, four scenarios were evaluated: (1) Full integration of AVs; (2) Partial integration; (3) Pilot project in limited areas; and (4) Delayed integration. The analysis identified partial integration as the best scenario for integrating AVs. Furthermore, comprehensive analyses conducted to validate the DSS outputs demonstrated the reliability of the results.

Agriculture is one of the most crucial sectors, meeting the fundamental food needs of humanity. Plant diseases increase food economic and food security concerns for countries and disrupt their agricultural planning. Traditional methods for detecting plant diseases require a lot of labor and time. Consequently, many researchers and institutions strive to address these issues using advanced technological methods. Deep learning-based plant disease detection offers considerable progress and hope compared to classical methods. When trained with large and high-quality datasets, these technologies robustly detect diseases on plant leaves in early stages. This study systematically reviews the application of deep learning techniques in plant disease detection by analyzing 160 research articles from 2020 to 2024. The studies are examined in three different areas: classification, detection, and segmentation of diseases on plant leaves, while also thoroughly reviewing publicly available datasets. This systematic review offers a comprehensive assessment of the current literature, detailing the most popular deep learning architectures, the most frequently studied plant diseases, datasets, encountered challenges, and various perspectives. It provides new insights for researchers working in the agricultural sector. Moreover, it addresses the major challenges in the field of disease detection in agriculture. Thus, this study offers valuable information and a suitable solution based on deep learning applications for agricultural sustainability.