AI (Switzerland)

Situational awareness (SA) is defined as the perception of entities in the environment, comprehension of their meaning, and projection of their status in near future. From an Air Force perspective, SA refers to the capability to comprehend and project the current and future disposition of red and blue aircraft and surface threats within an airspace. In this article, we propose a model for SA and dynamic decision-making that incorporates artificial intelligence and dynamic data-driven application systems to adapt measurements and resources in accordance with changing situations. We discuss measurement of SA and the challenges associated with quantification of SA. We then elaborate a plethora of techniques and technologies that help improve SA ranging from different modes of intelligence gathering to artificial intelligence to automated vision systems. We then present different application domains of SA including battlefield, gray zone warfare, military- and air-base, homeland security and defense, and critical infrastructure. Finally, we conclude the article with insights into the future of SA.

Arslan Munir

Alexander Aved

Erik Blasch

Situational Awareness: Techniques, Challenges, and Prospects

The motivation for using artificial neural networks in this study stems from their computational efficiency and ability to model complex, high-level abstractions. Deep learning models were utilized to predict the structural responses of reinforced concrete (RC) buildings subjected to earthquakes. For this aim, the dataset for training and evaluation was derived from complex computational dynamic analyses, which involved scaling real ground motion records at different intensity levels (spectral acceleration Sa(T1) and the recently proposed INp). The results, specifically the maximum interstory drifts, were characterized for the output neurons in terms of their corresponding statistical parameters: mean, median, and standard deviation; while two input variables (fundamental period and earthquake intensity) were used in the neural networks to represent buildings and seismic risk. To validate deep learning as a robust tool for seismic predesign and rapid estimation, a prediction model was developed to assess the seismic performance of a complex RC building with buckling restrained braces (RC-BRBs). Additionally, other deep learning models were explored to predict ductility and hysteretic energy in nonlinear single degree of freedom (SDOF) systems. The findings demonstrated that increasing the number of hidden layers generally reduces prediction error, although an excessive number can lead to overfitting.

Omar Payán-Serrano

Edén Bojórquez

Julián Carrillo

Juan Bojórquez

Herian Leyva

Ali Rodríguez-Castellanos

Joel Carvajal

José Torres

Seismic Performance Prediction of RC, BRB and SDOF Structures Using Deep Learning and the Intensity Measure INp

使用深度学习和强度测度INp预测RC、BRB和SDOF结构的地震性能

The development of self-driving or autonomous vehicles has led to significant advancements in 3D object detection technologies, which are critical for the safety and efficiency of autonomous driving. Despite recent advances, several challenges remain in sensor integration, handling sparse and noisy data, and ensuring reliable performance across diverse environmental conditions. This paper comprehensively surveys state-of-the-art 3D object detection techniques for autonomous vehicles, emphasizing the importance of multi-sensor fusion techniques and advanced deep learning models. Furthermore, we present key areas for future research, including enhancing sensor fusion algorithms, improving computational efficiency, and addressing ethical, security, and privacy concerns. The integration of these technologies into real-world applications for autonomous driving is presented by highlighting potential benefits and limitations. We also present a side-by-side comparison of different techniques in a tabular form. Through a comprehensive review, this paper aims to provide insights into the future directions of 3D object detection and its impact on the evolution of autonomous driving.

Oluwajuwon A. Fawole

Danda B. Rawat

Recent Advances in 3D Object Detection for Self-Driving Vehicles: A Survey

自动驾驶车辆三维目标检测的最新进展：综述

Machine learning (ML) techniques have permeated various domains, offering intelligent solutions to complex problems. ML has been increasingly explored for applications in active power filters (APFs) due to its potential to enhance harmonic compensation, reference signal generation, filter control optimization, and fault detection and diagnosis. This paper reviews the most recent applications of ML in APFs, highlighting their abilities to adapt to nonlinear load conditions, improve fault detection and classification accuracy, and optimize system performance in real time. However, this paper also highlights several limitations of these methods, such as the high computational complexity, the need for extensive training data, and challenges with real-time deployment in distributed power systems. For example, the marginal improvements in total harmonic distortion (THD) achieved by ML-based methods often do not justify the increased computational overhead compared to traditional control methods. This review then suggests future research directions to overcome these limitations, including lightweight ML models for faster and more efficient control, federated learning for decentralized optimization, and digital twins for real-time system monitoring. While traditional methods remain effective, ML-based solutions have the potential to significantly enhance APF performance in future power systems.

Khaled Chahine

Machine Learning in Active Power Filters: Advantages, Limitations, and Future Directions

有源电力滤波器中的机器学习：优势、局限性与未来方向

(1) Background: The mutual relationship between evolution and learning is a controversial argument among the artificial intelligence and neuro-evolution communities. After more than three decades, there is still no common agreement on the matter. (2) Methods: In this paper, the author investigates whether combining learning and evolution permits finding better solutions than those discovered by evolution alone. In further detail, the author presents a series of empirical studies that highlight some specific conditions determining the success of such combination. Results are obtained in five qualitatively different domains: (i) the 5-bit parity task, (ii) the double-pole balancing problem, (iii) the Rastrigin, Rosenbrock and Sphere optimization functions, (iv) a robot foraging task and (v) a social foraging problem. Moreover, the first three tasks represent benchmark problems in the field of evolutionary computation. (3) Results and discussion: The outcomes indicate that the effect of learning on evolution depends on the nature of the problem. Specifically, when the problem implies limited or absent agent–environment conditions, learning is beneficial for evolution, especially with the introduction of noise during the learning and selection processes. Conversely, when agents are embodied and actively interact with the environment, learning does not provide advantages, and the addition of noise is detrimental. Finally, the absence of stochasticity in the experienced conditions is paramount for the effectiveness of the combination. Furthermore, the length of the learning process must be fine-tuned based on the considered task.

Paolo Pagliuca

Learning and Evolution: Factors Influencing an Effective Combination

学习与进化：影响有效结合的因素

Accurately forecasting power consumption is crucial important for efficient energy management. Machine learning (ML) models are often employed for this purpose. However, tuning their hyperparameters is a complex and time-consuming task. The article presents a novel multi-objective (MO) hybrid evolutionary-based approach, GA-SHADE-MO, for tuning ML models aimed at solving the complex problem of forecasting power consumption. The proposed algorithm simultaneously optimizes both hyperparameters and feature sets across six different ML models, ensuring enhanced accuracy and efficiency. The study focuses on predicting household power consumption at hourly and daily levels. The hybrid MO evolutionary algorithm integrates elements of genetic algorithms and self-adapted differential evolution. By incorporating MO optimization, GA-SHADE-MO balances the trade-offs between model complexity (the number of used features) and prediction accuracy, ensuring robust performance across various forecasting scenarios. Experimental numerical results show the superiority of the proposed method compared to traditional tuning techniques, and random search, showcasing significant improvements in predictive accuracy and computational efficiency. The findings suggest that the proposed GA-SHADE-MO approach offers a powerful tool for optimizing ML models in the context of energy consumption forecasting, with potential applications in other domains requiring precise predictive modeling. The study contributes to the advancement of ML optimization techniques, providing a framework that can be adapted and extended for various predictive analytics tasks.

Aleksei Vakhnin

Ivan Ryzhikov

Harri Niska

Mikko Kolehmainen

A Novel Multi-Objective Hybrid Evolutionary-Based Approach for Tuning Machine Learning Models in Short-Term Power Consumption Forecasting

一种用于短期电力消耗预测中机器学习模型调优的新型多目标混合进化方法

The widespread use of machine and deep learning algorithms for anomaly detection has created a critical need for robust explanations that can identify the features contributing to anomalies. However, effective evaluation methodologies for anomaly explanations are currently lacking, especially those that compare the explanations against the true underlying causes, or ground truth. This paper aims to address this gap by introducing a rigorous, ground-truth-based framework for evaluating anomaly explanation methods, which enables the assessment of explanation correctness and robustness—key factors for actionable insights in anomaly detection. To achieve this, we present an innovative benchmark dataset of digital circuit truth tables with model-based anomalies, accompanied by local ground truth explanations. These explanations were generated using a novel algorithm designed to accurately identify influential features within each anomaly. Additionally, we propose an evaluation methodology based on correctness and robustness metrics, specifically tailored to quantify the reliability of anomaly explanations. This dataset and evaluation framework are publicly available to facilitate further research and standardize evaluation practices. Our experiments demonstrate the utility of this dataset and methodology by evaluating common model-agnostic explanation methods in an anomaly detection context. The results highlight the importance of ground-truth-based evaluation for reliable and interpretable anomaly explanations, advancing both theory and practical applications in explainable AI. This work establishes a foundation for rigorous, evidence-based assessments of anomaly explanations, fostering greater transparency and trust in AI-driven anomaly detection systems.

Liat Antwarg Friedman

Chen Galed

Lior Rokach

Bracha Shapira

Evaluating Anomaly Explanations Using Ground Truth

使用基准事实评估异常解释

As machine learning (ML) transforms industries, the need for efficient model development tools using high-performance computing (HPC) and ensuring interpretability is crucial. This paper presents SIBILA, an AutoML approach designed for HPC environments, focusing on the interpretation of ML models. SIBILA simplifies model development by allowing users to set objectives and preferences before automating the search for optimal ML pipelines. Unlike traditional AutoML frameworks, SIBILA is specifically designed to exploit the computational capabilities of HPC platforms, thereby accelerating the model search and evaluation phases. The emphasis on interpretability is particularly crucial when model transparency is mandated by regulations or desired for stakeholder understanding. SIBILA has been validated in different tasks with public datasets. The results demonstrate that SIBILA consistently produces models with competitive accuracy while significantly reducing computational overhead. This makes it an ideal choice for practitioners seeking efficient and transparent ML solutions on HPC infrastructures. SIBILA is a major advancement in AutoML, addressing the rising demand for explainable ML models on HPC platforms. Its integration of interpretability constraints alongside automated model development processes marks a substantial step forward in bridging the gap between computational efficiency and model transparency in ML applications. The tool is available as a web service at no charge.

Antonio Jesús Banegas-Luna

Horacio Pérez-Sánchez

SIBILA: Automated Machine-Learning-Based Development of Interpretable Machine-Learning Models on High-Performance Computing Platforms

SIBILA：基于高性能计算平台的自动化机器学习解释性模型开发

短名
Journal Impact	3.15
国际分区	COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS(Q2)

ISSN	2673-2688
h-index	19

涉及主题	计算机科学人工智能机器学习数学工程类心理学生物哲学程序设计语言物理统计语言学医学认知心理学计算机视觉地理模式识别（心理学）操作系统量子力学经济人工神经网络地质学认识论
出版信息	出版商: Multidisciplinary Digital Publishing Institute (MDPI)，出版周期: ，期刊类型: journal，开源期刊: 是
基本数据	创刊年份: 2020，原创研究文献占比: 0.00%，自引率:3.20%， Gold OA占比: 99.34%
期刊费用	$详情
平均审稿周期	网友分享经验：11 Weeks

AI (Switzerland)

期刊引文格式

有 1 位以上作者的期刊

有 2 位作者的期刊

有 3 位作者的期刊

有 5 位以上作者的期刊

书籍引用格式

学位论文引用格式

网页引用格式

专利引用格式

手工熬夜修改参考文献？研飞自动匹配期刊，一键轻松成稿，支持 Word/WPS

投稿经验分享

Situational Awareness: Techniques, Challenges, and Prospects

Seismic Performance Prediction of RC, BRB and SDOF Structures Using Deep Learning and the Intensity Measure INp

Recent Advances in 3D Object Detection for Self-Driving Vehicles: A Survey

Machine Learning in Active Power Filters: Advantages, Limitations, and Future Directions

Learning and Evolution: Factors Influencing an Effective Combination

A Novel Multi-Objective Hybrid Evolutionary-Based Approach for Tuning Machine Learning Models in Short-Term Power Consumption Forecasting

Machine Learning in Active Power Filters: Advantages, Limitations, and Future Directions

Learning and Evolution: Factors Influencing an Effective Combination

Evaluating Anomaly Explanations Using Ground Truth

SIBILA: Automated Machine-Learning-Based Development of Interpretable Machine-Learning Models on High-Performance Computing Platforms