AI (Switzerland)

Physics-informed neural networks (PINNs) represent a significant advancement at the intersection of machine learning and physical sciences, offering a powerful framework for solving complex problems governed by physical laws. This survey provides a comprehensive review of the current state of research on PINNs, highlighting their unique methodologies, applications, challenges, and future directions. We begin by introducing the fundamental concepts underlying neural networks and the motivation for integrating physics-based constraints. We then explore various PINN architectures and techniques for incorporating physical laws into neural network training, including approaches to solving partial differential equations (PDEs) and ordinary differential equations (ODEs). Additionally, we discuss the primary challenges faced in developing and applying PINNs, such as computational complexity, data scarcity, and the integration of complex physical laws. Finally, we identify promising future research directions. Overall, this survey seeks to provide a foundational understanding of PINNs within this rapidly evolving field.

Amer Farea

Olli Yli-Harja

Frank Emmert-Streib

Understanding Physics-Informed Neural Networks: Techniques, Applications, Trends, and Challenges

理解物理信息神经网络：技术、应用、趋势与挑战

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

情境意识：技术、挑战与前景

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

Soybean is a critical agricultural commodity, serving as a vital source of protein and vegetable oil, and contributing significantly to the economies of producing nations. However, soybean yields are frequently compromised by disease and pest infestations, which, if not identified early, can lead to substantial production losses. To address this challenge, we propose AgriNAS, a method that integrates a Neural Architecture Search (NAS) framework with an adaptive convolutional architecture specifically designed for plant pathology. AgriNAS employs a novel data augmentation strategy and a Spatial–Time Augmentation (STA) method, and it utilizes a multi-stage convolutional network that dynamically adapts to the complexity of the input data. The proposed AgriNAS leverages powerful GPU resources to handle the intensive computational tasks involved in NAS and model training. The framework incorporates a bi-level optimization strategy and entropy-based regularization to enhance model robustness and prevent overfitting. AgriNAS achieves classification accuracies superior to VGG-19 and a transfer learning method using convolutional neural networks.

Oluwatoyin Joy Omole

Renata Lopes Rosa

Muhammad Saadi

Demóstenes Zegarra Rodriguez

AgriNAS: Neural Architecture Search with Adaptive Convolution and Spatial–Time Augmentation Method for Soybean Diseases

AgriNAS：基于自适应卷积和时空增强方法的大豆病害神经网络架构搜索

Background: Integrating nonlinear behavior into the architecture of artificial neural networks is regarded as essential requirement to constitute their effectual learning capacity for solving complex tasks. This claim seems to be true for moderate-sized networks, i.e., with a lower double-digit number of layers. However, going deeper with neural networks regularly turns into destructive tendencies of gradual performance degeneration during training. To circumvent this degradation problem, the prominent neural architectures Residual Network and Highway Network establish skip connections with additive identity mappings between layers. Methods: In this work, we unify the mechanics of both architectures into Capsule Networks (CapsNet)s by showing their inherent ability to learn skip connections. As a necessary precondition, we introduce the concept of Adaptive Nonlinearity Gates (ANG)s which dynamically steer and limit the usage of nonlinear processing. We propose practical methods for the realization of ANGs including biased batch normalization, the Doubly-Parametric ReLU (D-PReLU) activation function, and Gated Routing (GR) dedicated to extremely deep CapsNets. Results: Our comprehensive empirical study using MNIST substantiates the effectiveness of our developed methods and delivers valuable insights for the training of very deep nets of any kind. The final experiments on Fashion-MNIST and SVHN demonstrate the potential of pure capsule-driven networks with GR.

Nikolai A. K. Steur

Friedhelm Schwenker

A Step Towards Neuroplasticity: Capsule Networks with Self-Building Skip Connections

迈向神经可塑性的一步：具有自建跳跃连接的胶囊网络

Among gynecological pathologies, cervical cancer has always represented a health problem with great social impact. The giant strides made as a result of both the screening programs perfected and implemented over the years and the use of new and accurate technological equipment have in fact significantly improved our clinical approach in the management and personalized diagnosis of precancerous lesions of the cervix. In this context, the advent of artificial intelligence and digital algorithms could represent new directions available to gynecologists and pathologists for the following: (i) the standardization of screening procedures, (ii) the identification of increasingly early lesions, and (iii) heightening the diagnostic accuracy of targeted biopsies and prognostic analysis of cervical cancer. The purpose of our review was to evaluate to what extent artificial intelligence can be integrated into current protocols, to identify the strengths and/or weaknesses of this method, and, above all, determine what we should expect in the future to develop increasingly safer solutions, as well as increasingly targeted and personalized screening programs for these patients. Furthermore, in an innovative way, and through a multidisciplinary vision (gynecologists, pathologists, and computer scientists), with this manuscript, we highlight a key role that AI could have in the management of HPV-positive patients. In our vision, AI will move from being a simple diagnostic device to being used as a tool for performing risk analyses of HPV-related disease progression. This is thanks to the ability of new software not only to analyze clinical and histopathological images but also to evaluate and integrate clinical elements such as vaccines, the composition of the microbiota, and the immune status of patients. In fact, the single-factor evaluation of high-risk HPV strains represents a limitation that must be overcome. Therefore, AI, through multifactorial analysis, will be able to generate a risk score that will better stratify patients and will support clinicians in choosing highly personalized treatments overall. Our study remains an innovative proposal and idea, as the literature to date presents a limitation in that this topic is considered niche, but we believe that the union of common efforts can overcome this limitation.

Miriam Dellino

Marco Cerbone

Antonio d’Amati

Mario Bochicchio

Antonio Simone Laganà

Andrea Etrusco

Antonio Malvasi

Amerigo Vitagliano

Vincenzo Pinto

Ettore Cicinelli

Gerardo Cazzato

Eliano Cascardi

Artificial Intelligence in Cervical Cancer Screening: Opportunities and Challenges

人工智能在宫颈癌筛查中的应用：机遇与挑战

Background: In the last decade, numerous methods have been proposed to define and detect outliers, particularly in complex environments like networks, where anomalies significantly deviate from normal patterns. Although defining a clear standard is challenging, anomaly detection systems have become essential for network administrators to efficiently identify and resolve irregularities. Methods: This study develops and evaluates a machine learning-based system for network anomaly detection, focusing on point anomalies within network traffic. It employs both unsupervised and supervised learning techniques, including change point detection, clustering, and classification models, to identify anomalies. SHAP values are utilized to enhance model interpretability. Results: Unsupervised models effectively captured temporal patterns, while supervised models, particularly Random Forest (94.3%), demonstrated high accuracy in classifying anomalies, closely approximating the actual anomaly rate. Conclusions: Experimental results indicate that the system can accurately predict network anomalies in advance. Congestion and packet loss were identified as key factors in anomaly detection. This study demonstrates the potential for real-world deployment of the anomaly detection system to validate its scalability.

Pilar Schummer

Alberto del Rio

Javier Serrano

David Jimenez

Guillermo Sánchez

Álvaro Llorente

Machine Learning-Based Network Anomaly Detection: Design, Implementation, and Evaluation

基于机器学习的网络异常检测：设计、实现与评估

The estimation of energy expenditure (EE) is often an integral part of algorithms for wearable electronics. In field practice, procedures based on an indirect estimation of EE from the heart rate (using empirically or statistically based relationships) work correctly only in a narrow range of physical loads, yet they are current considered state of the art. This pilot study aims to experimentally assess novel method using a wide range of input sensors and parameters (heart rate, RR intervals, and 3D motion activity in several places on the body) and neural network (NN) algorithms. Our proposed method consists of training an NN on a specific subject, with a specific set and placement of sensors during the so-called training run, using the golden standard method of indirect calorimetry as a reference. Then, the subject’s EE can be estimated using trained NN. The results of the experiments (carried out on a total of 12 subjects during various physical activities) show a statistically significant improvement in EE estimation with the new prospective method, and it outperforms the state-of-the-art method based on the heart rate and regression model.

Tomáš Veselý

Pavel Smrčka

Radim Kliment

Martin Vítězník

Zdeněk Hon

Karel Hána

Accuracy Improvement of Energy Expenditure Estimation Through Neural Networks: A Pilot Study

通过神经网络提高能量消耗估计的准确性：一项试点研究

Abbreviation
Journal Impact	3.15
Quartiles(Global)	COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS(Q2)

ISSN	2673-2688
h-index	19

Publication Information	Publisher: Multidisciplinary Digital Publishing Institute (MDPI)，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2020，Proportion of original research papers: 0.00%，Self Citation Rate:3.20%， Gold OA Rate: 99.34%
Journal Fees	$Details
Average review cycle	网友分享经验：11 Weeks

AI (Switzerland)

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Understanding Physics-Informed Neural Networks: Techniques, Applications, Trends, and Challenges

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

AgriNAS: Neural Architecture Search with Adaptive Convolution and Spatial–Time Augmentation Method for Soybean Diseases

A Step Towards Neuroplasticity: Capsule Networks with Self-Building Skip Connections

Artificial Intelligence in Cervical Cancer Screening: Opportunities and Challenges

Machine Learning-Based Network Anomaly Detection: Design, Implementation, and Evaluation

AgriNAS: Neural Architecture Search with Adaptive Convolution and Spatial–Time Augmentation Method for Soybean Diseases

Accuracy Improvement of Energy Expenditure Estimation Through Neural Networks: A Pilot Study