自主智能系统(英文)最新文献

AutoML systems seek to assist Artificial Intelligence users in finding the best configurations for machine learning models. Following this line, recently the area of Automated Reinforcement Learning (AutoRL) has become increasingly relevant, given the growing increase in applications for reinforcement learning algorithms. However, the literature still lacks specific AutoRL systems for combinatorial optimization, especially for the Sequential Ordering Problem (SOP). Therefore, this paper aims to present a new AutoRL approach for SOP. For this, two new methods are proposed using hyperparameter optimization and metalearning: AutoRL-SOP and AutoRL-SOP-MtL. The proposed AutoRL techniques enable the combined tuning of three SARSA hyperparameters, being ϵ-greedy policy, learning rate, and discount factor. Furthermore, the new metalearning approach enables the transfer of hyperparameters between two combinatorial optimization domains: TSP (source) and SOP (target). The results show that the application of metalearning generates a reduction in computational cost in hyperparameter optimization. Furthermore, the proposed AutoRL methods achieved the best solutions in 23 out of 28 simulated TSPLIB instances compared to recent literature studies.

Autonomous driving systems (ADS) are at the forefront of technological innovation, promising enhanced safety, efficiency, and convenience in transportation. This study investigates the potential of end-to-end reinforcement learning (RL) architectures for ADS, specifically focusing on a Go-To-Point task involving lane-keeping and navigation through basic urban environments. The study uses the Proximal Policy Optimization (PPO) algorithm within the CARLA simulation environment. Traditional modular systems, which separate driving tasks into perception, decision-making, and control, provide interpretability and reliability in controlled scenarios but struggle with adaptability to dynamic, real-world conditions. In contrast, end-to-end systems offer a more integrated approach, potentially enhancing flexibility and decision-making cohesion.

This research introduces CARLA-GymDrive, a novel framework integrating the CARLA simulator with the Gymnasium API, enabling seamless RL experimentation with both discrete and continuous action spaces. Through a two-phase training regimen, the study evaluates the efficacy of PPO in an end-to-end ADS focused on basic tasks like lane-keeping and waypoint navigation. A comparative analysis with modular architectures is also provided. The findings highlight the strengths of PPO in managing continuous control tasks, achieving smoother and more adaptable driving behaviors than value-based algorithms like Deep Q-Networks. However, challenges remain in generalization and computational demands, with end-to-end systems requiring extensive training time.

While the study underscores the potential of end-to-end architectures, it also identifies limitations in scalability and real-world applicability, suggesting that modular systems may currently be more feasible for practical ADS deployment. Nonetheless, the CARLA-GymDrive framework and the insights gained from PPO-based ADS contribute significantly to the field, laying a foundation for future advancements in AD.

Modern industries dependent on reliable asset operation under constrained resources employ intelligent maintenance methods to maximize efficiency. However, classical maintenance methods rely on assumed lifetime distributions and suffer from estimation errors and computational complexity. The advent of Industry 4.0 has increased the use of sensors for monitoring systems, while deep learning (DL) models have allowed for accurate system health predictions, enabling data-driven maintenance planning. Most intelligent maintenance literature has used DL models solely for remaining useful life (RUL) point predictions, and a substantial gap exists in further using predictions to inform maintenance plan optimization. The few existing studies that have attempted to bridge this gap suffer from having used simple system configurations and non-scalable models. Hence, this paper develops a hybrid DL model using Monte Carlo dropout to generate RUL predictions which are used to construct empirical system reliability functions used for the optimization of the selective maintenance problem (SMP). The proposed framework is used to plan maintenance for a mission-oriented series k-out-of-n:G system. Numerical experiments compare the framework’s performance against prior SMP methods and highlight its strengths. When minimizing cost, maintenance plans are frequently produced that result in mission survival while avoiding unnecessary repairs. The proposed method is usable in large-scale, complex scenarios and various industrial contexts. The method finds exact solutions while avoiding the need for computationally-intensive parametric reliability functions.

The field of human motion data capture and fusion has a broad range of potential applications and market opportunities. The capture of human motion data for wearable sensors is less costly and more convenient than other methods, but it also suffers from poor data capture accuracy and high latency. Consequently, in order to overcome the limitations of existing wearable sensors in data capture and fusion, the study initially constructed a model of the human joint and bone by combining the quaternion method and root bone human forward kinematics through mathematical modeling. Subsequently, the sensor data calibration was optimized, and the Madgwick algorithm was introduced to address the resulting issues. Finally, a novel human joint motion data capture and fusion model was proposed. The experimental results indicated that the maximum mean error and root mean square error of yaw angle of this new model were 1.21° and 1.17°, respectively. The mean error and root mean square error of pitch angle were maximum 1.24° and 1.19°, respectively. The maximum knee joint and elbow joint data capture errors were 3.8 and 6.1, respectively. The suggested approach, which offers a new path for technological advancement in this area, greatly enhances the precision and dependability of human motion capture, which has a broad variety of application possibilities.

Water pipeline leaks pose significant risks to urban infrastructure, leading to water wastage and potential structural damage. Existing leak detection methods often face challenges, such as heavily relying on the manual selection of frequency bands or complex feature extraction, which can be both labour-intensive and less effective. To address these limitations, this paper introduces a Frequency-Informed Transformer model, which integrates the Fast Fourier Transform and self-attention mechanisms to enhance water pipe leak detection accuracy. Experimental results show that FiT achieves 99.9% accuracy in leak detection and 98.7% in leak type classification, surpassing other models in both accuracy and processing speed, with an efficient response time of 0.25 seconds. By significantly simplifying key features and frequency band selection and improving accuracy and response time, the proposed method offers a potential solution for real-time water leak detection, enabling timely interventions and more effective pipeline safety management.

Transportation of heavy loads is often performed by multi-axle multi-steered heavy duty vehicles In this article a novel nonlinear optimal control method is applied to the kinematic model of the five-axle and three-steering coupled vehicle system. First, it is proven that the dynamic model of this articulated multi-vehicle system is differentially flat. Next. the state-space model of the five-axle and three-steering vehicle system undergoes approximate linearization around a temporary operating point that is recomputed at each time-step of the control method. The linearization is based on Taylor series expansion and on the associated Jacobian matrices. For the linearized state-space model of the five-axle and three-steering vehicle system a stabilizing optimal (H-infinity) feedback controller is designed. This controller stands for the solution of the nonlinear optimal control problem under model uncertainty and external perturbations. To compute the controller’s feedback gains an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. The proposed nonlinear optimal control approach achieves fast and accurate tracking of setpoints under moderate variations of the control inputs and minimal dispersion of energy by the propulsion and steering system of the five-axle and three-steering vehicle system.

Federated learning (FL) is a technology that allows multiple devices to collaboratively train a global model without sharing original data, which is a hot topic in distributed intelligent systems. Combined with satellite network, FL can overcome the geographical limitation and achieve broader applications. However, it also faces the issues such as straggler effect, unreliable network environments and non-independent and identically distributed (Non-IID) samples. To address these problems, we propose an intelligent hierarchical FL system based on semi-asynchronous and scheduled synchronous control strategies in cloud-edge-client structure for satellite network. Our intelligent system effectively handles multiple client requests by distributing the communication load of the central cloud to various edge clouds. Additionally, the cloud server selection algorithm and the edge-client semi-asynchronous control strategy minimize clients’ waiting time, improving the overall efficiency of the FL process. Furthermore, the center-edge scheduled synchronous control strategy ensures the timeliness of partial models. Based on the experiment results, our proposed intelligent hierarchical FL system demonstrates a distinct advantage in global accuracy over traditional FedAvg, achieving 2% higher global accuracy within the same time frame and reducing 52% training time to achieve the target accuracy.

Road scene parsing is a crucial capability for self-driving vehicles and intelligent road inspection systems. Recent research has increasingly focused on enhancing driving safety and comfort by improving the detection of both drivable areas and road defects. This article reviews state-of-the-art networks developed over the past decade for both general-purpose semantic segmentation and specialized road scene parsing tasks. It also includes extensive experimental comparisons of these networks across five public datasets. Additionally, we explore the key challenges and emerging trends in the field, aiming to guide researchers toward developing next-generation models for more effective and reliable road scene parsing.

Service Composition and Optimization Selection (SCOS) is crucial in Cloud Manufacturing (CMfg), but the uncertainties in service states and working environments pose challenges for existing QoS-based methods. Recently, digital twins have gained prominence in CMfg due to their predictive capabilities, enhancing the reliability of service composition. Heuristic algorithms are widely used in this field for their flexibility and compatibility with uncertain environments. This paper proposes the Whale-Goshawk Optimization Algorithm (WGO), which combines the Whale Optimization Algorithm (WOA) and Northern Goshawk Optimization Algorithm (NGO). A novel similar integer coding method, incorporating spatial feature information, addresses the limitations of traditional integer coding, while a whale-optimized prey generation strategy improves NGO’s global optimization efficiency. Additionally, a local search method based on similar integer coding enhances WGO’s local search ability. Experimental results demonstrate the effectiveness of the proposed approach.

The explainability of recommendation systems refers to the ability to explain the logic that guides the system’s decision to endorse or exclude an item. In industrial-grade recommendation systems, the high complexity of features, the presence of embedding layers, the existence of adversarial samples and the requirements for explanation accuracy and efficiency pose significant challenges to current explanation methods. This paper proposes a novel framework AdvLIME (Adversarial Local Interpretable Model-agnostic Explanation) that leverages Generative Adversarial Networks (GANs) with Embedding Constraints to enhance explainability. This method utilizes adversarial samples as references to explain recommendation decisions, generating these samples in accordance with realistic distributions and ensuring they meet the structural constraints of the embedding module. AdvLIME requires no modifications to the existing model architecture and needs only a single training session for global explanation, making it ideal for industrial applications. This work contributes two significant advancements. First, it develops a model-independent global explanation method via adversarial generation. Second, it introduces a model discrimination method to guarantee that the generated samples adhere to the embedding constraints. We evaluate the AdvLIME framework on the Behavior Sequence Transformer (BST) model using the MovieLens 20 M dataset. The experimental results show that AdvLIME outperforms traditional methods such as LIME and DLIME, reducing the approximation error of real samples by 50% and demonstrating improved stability and accuracy.