Robustness Of Systems Research Articles

Music Emotion Classification System Based on Quantum Feature Extraction and Its Effectiveness Evaluation

Strong emotions can be expressed and evoked via music. However, accurately recognizing the emotions in music using computational models is extremely challenging. When the music portions convey various complex emotions, the problem’s difficulty can rise significantly. In this work, a systematic strategy that blends multiple cutting-edge techniques is used to focus on the emotional classification of music. The Kaggle-sourced music emotion dataset provides a wide range of audio samples illustrating various emotional expressions in music. Spectral subtraction as a noise reduction approach is used to improve the quality of the analysis. This efficiently reduced unwanted background noise and improved the audio signals’ clarity. A Quantum Convolutional Neural Network (QCNN) is used for feature extraction, taking advantage of its special capacity to extract complex patterns and characteristics from the music data that are essential for emotion recognition. To provide accurate predictions, the training procedure is optimized and finally utilized Flexible Runge Kutta Optimized Multilayer Perceptrons (FRKO-MLP) for emotion classification. The goal of this combination of advanced classification approaches, quantum feature extraction and noise reduction is to increase the robustness and accuracy of music emotion identification systems. Based on the chosen datasets, the results of the experimental study demonstrate that the suggested model greatly increases the efficiency and accuracy of emotion classification in music. The proposed FRKO-MLP attains 93.21% accuracy, 90.52% precision, 87.34% sensitivity and 89.72% F1-score. These findings show that the model is far better at identifying emotions than traditional methods.

Evaluating the robustness of attributed dynamic bus-metro networks based on community reconstruction

Despite significant advances in applying complex network theory to analyze the robustness of public transport systems, one still cannot seamlessly incorporate complex topological structures into existing algorithms, network modelling remains inaccurate, and high-level features of public transport systems cannot be captured effectively. Moreover, the identification of community structures with dynamic transfer relationships between bus and metro systems requires well-designed formulas and sophisticated computer codes. This study aims to evaluate the robustness of bus-metro networks based on community reconstruction. Firstly, we constructed an attributed dynamic bus-metro network, considering the socio-economic environment of the public transport system and the dynamic transfer relationship between bus stops and metro stations. Secondly, we introduced the community reconstruction approach, proposed a robustness estimation model, and designed community-based strategies for node and edge attacks, which are ComNA and ComEA, respectively. Finally, taking a real-world bus-metro system in Xi'an as a case study, this research evaluates the system's robustness under various attack strategies and analyzes the impact of dynamic transfer relationships on robustness. The results reveal that: (1) The performance of public transport networks exhibits varying trends under different attack strategies. Notably, the bus-metro network demonstrates enhanced robustness to random attacks compared to targeted attacks. (2) Community-based attack strategies are more effective than traditional methods in dismantling public transport networks. (3) Enhancing the transfer capacity at metro stations significantly improves the overall robustness of public transport systems. These results are of critical importance for improving the robustness of public transport systems.

Real-Time PPG-Based Biometric Identification: Advancing Security with 2D Gram Matrices and Deep Learning Models.

The integration of liveness detection into biometric systems is crucial for countering spoofing attacks and enhancing security. This study investigates the efficacy of photoplethysmography (PPG) signals, which offer distinct advantages over traditional biometric techniques. PPG signals are non-invasive, inherently contain liveness information that is highly resistant to spoofing, and are cost-efficient, making them a superior alternative for biometric authentication. A comprehensive protocol was established to collect PPG signals from 40 subjects using a custom-built acquisition system. These signals were then transformed into two-dimensional representations through the Gram matrix conversion technique. To analyze and authenticate users, we employed an EfficientNetV2 B0 model integrated with a Long Short-Term Memory (LSTM) network, achieving a remarkable 99% accuracy on the test set. Additionally, the model demonstrated outstanding precision, recall, and F1 scores. The refined model was further validated in real-time identification scenarios, underscoring its effectiveness and robustness for next-generation biometric recognition systems.

THE IMPACT OF MATHEMATICAL MODELING ON AIR TRAFFIC CONTROL EFFICIENCY: A COMPREHENSIVE STRUCTURED REVIEW

Air travel is crucial for global connectivity, yet it faces increasing challenges due to rising air traffic volumes and complex operational demands. Mathematical modeling has emerged as a transformative solution in air traffic control (ATC), enhancing efficiency and safety across the aviation sector. This review delves into the application and outcomes of mathematical models in ATC by analyzing recent literature that incorporates various computational techniques for optimizing air traffic flow, predicting and mitigating conflicts, and improving decision-making processes. In order to attain this, we thoroughly searched scholarly articles from well-known databases, for instance, Scopus as well as IEEE articles published in the last decade, focusing on studies that demonstrated clear numerical results regarding the effectiveness of mathematical modeling in ATC operations. The flow of the study is based on the PRISMA framework. By analyzing a comprehensive selection using an advanced searching approach on Scopus and IEEE database found (n=22), the final primary data was analyzed. The review highlights significant advancements, such as the development of algorithms capable of optimizing flight paths, reducing airspace congestion, and enhancing fuel efficiency, which collectively contributes to more sustainable aviation practices. The findings are divided into four themes, which are (1) optimization of ATC operations, (2) route and flight scheduling optimization, (3) resilience and robustness in atm systems, and (4) technological advances and their integration in air traffic management (ATM). Conclusively, mathematical models have proven indispensable for maximizing ATC efficiency and are pivotal in guiding strategic planning and policy-making to accommodate future air traffic growth while maintaining high safety standards.

UNIFIED MULTIMODAL BIOMETRICS FUSION USING DEEP LEARNING FOR SECURING IOT

Advancements in multimodal biometrics, which amalgamate multiple biometric traits, hold promise for augmenting the accuracy and robustness of biometric identification systems. The focal point of this innovative study is the enhancement of multimodal biometrics identification, using face and iris images as the key biometric traits. This work taps into the expansive collection of face and iris images present in the WVU-Multimodal dataset for evaluation purposes. Our proposed approach employs “Convolutional Neural Network (CNN)” architectures, notable for their efficacy in computer vision tasks, to extract potent discriminative features from the input images. This work specifically incorporates three popular CNN architectures: ResNet-50, InceptionNet, XceptionNet, and fine-tuned CNN. To amalgamate the extracted features, investigate various fusion techniques in the security-centric industry: early fusion, and score-level fusion. Early fusion is an approach that merges the raw images of both face and iris at the input level to a single CNN model. Use the Gabor approach to enhance the image's quality and make the face and iris information more visible. This technique modifies the histogram equalization process for local regions, thus enabling better visibility and subsequent feature extraction. Our experimental evaluation employs performance metrics like accuracy, “Equal Error Rate”, and “Receiver Operating Characteristic” curves. In this work undertakes a comparative analysis to appraise the performance of the different CNN architectures and fusion techniques under scrutiny.

Enhanced Modeling and Control of Organic Rankine Cycle Systems via AM‐LSTM Networks Based Nonlinear MPC

ABSTRACTThe organic Rankine cycle (ORC) serves as an effective means of converting low‐grade heat sources into power, playing a pivotal role in environmentally friendly production and energy recovery. However, the inherent complexity, strong and unidentified nonlinearity, and control constraints pose significant challenges to designing an optimal controller for ORC systems. To address these issues, this research introduces a novel modeling and control framework for ORC systems. Leveraging an attention mechanism‐based long short‐term memory (AM‐LSTM) network, the dynamic characteristics of ORC systems, which are subject to non‐Gaussian disturbances, are accurately modeled. A performance metric based on survival information potential (SIP) is developed to optimize the network parameters. Furthermore, a multi‐objective optimization approach that integrates nonlinear model predictive control (NMPC) with the multiverse optimizer (MVO) algorithm is implemented to ensure effective control under varying operating conditions and constraints. Through extensive simulations, the proposed framework demonstrates superior accuracy, robustness, and control performance for ORC systems.

Design and development of face recognition-based security system using expression game as liveness detection

Face recognition as a security system has undergone significant developments, but challenges in live detection are still a major issue in preventing fraud. Liveness detection is a method that helps face recognition security more resistant to fraud. This research aims to address this issue by developing an innovative security system that integrates face recognition with a facial expression game, enhancing live detection and user engagement. The primary objectives are to ensure seamless integration, maintain a fun and challenging user experience, and demonstrate practical applicability. We applied a Waterfall method in our research to ensure a straightforward approach. We successfully applied this system for the door lock-unlock mechanism, simulating a restricted area. YuNet, a face detection model runs in the web interface and controls the NodeMCU to either lock or unlock the door. The study concluded 95% success rate from the participants in making facial expressions: Smile, Normal, and Sad. However, expressing Sadness within the 3-second timeframe posed some difficulties. The average duration for completing the mini-game was approximately 16.31 seconds from the start. The integration of a facial expression game as a liveness detection required careful design to balance security and user engagement that is fun to experience. This research underscores the significance of addressing current challenges in biometric security by integrating an interactive element into the live detection process. The developed system contributes to the field by enhancing the robustness and user experience of face recognition security systems, demonstrating potential for broader application in restricted access scenarios.

A Weather-Adaptive Convolutional Neural Network Framework for Better License Plate Detection.

Automatic License Plate Recognition (ALPR) systems are essential for Intelligent Transport Systems (ITS), effective transportation management, security, law enforcement, etc. However, the performance of ALPR systems can be significantly affected by environmental conditions such as heavy rain, fog, and pollution. This paper introduces a weather-adaptive Convolutional Neural Network (CNN) framework that leverages the YOLOv10 model that is designed to enhance license plate detection in adverse weather conditions. By incorporating weather-specific data augmentation techniques, our framework improves the robustness of ALPR systems under diverse environmental scenarios. We evaluate the effectiveness of this approach using metrics such as precision, recall, F1, mAP50, and mAP50-95 score across various model configurations and augmentation strategies. The results demonstrate a significant improvement in overall detection performance, particularly in challenging weather conditions. This study provides a promising solution for deploying resilient ALPR systems in regions with similar environmental complexities.

From Binary to Multi-Class Classification: A Two-Step Hybrid CNN-ViT Model for Chest Disease Classification Based on X-Ray Images.

Chest disease identification for Tuberculosis and Pneumonia diseases presents diagnostic challenges due to overlapping radiographic features and the limited availability of expert radiologists, especially in developing countries. The present study aims to address these challenges by developing a Computer-Aided Diagnosis (CAD) system to provide consistent and objective analyses of chest X-ray images, thereby reducing potential human error. By leveraging the complementary strengths of convolutional neural networks (CNNs) and vision transformers (ViTs), we propose a hybrid model for the accurate detection of Tuberculosis and for distinguishing between Tuberculosis and Pneumonia. We designed a two-step hybrid model that integrates the ResNet-50 CNN with the ViT-b16 architecture. It uses the transfer learning on datasets from Guangzhou Women's and Children's Medical Center for Pneumonia cases and datasets from Qatar and Dhaka (Bangladesh) universities for Tuberculosis cases. CNNs capture hierarchical structures in images, while ViTs, with their self-attention mechanisms, excel at identifying relationships between features. Combining these approaches enhances the model's performance on binary and multi-class classification tasks. Our hybrid CNN-ViT model achieved a binary classification accuracy of 98.97% for Tuberculosis detection. For multi-class classification, distinguishing between Tuberculosis, viral Pneumonia, and bacterial Pneumonia, the model achieved an accuracy of 96.18%. These results underscore the model's potential in improving diagnostic accuracy and reliability for chest disease classification based on X-ray images. The proposed hybrid CNN-ViT model demonstrates substantial potential in advancing the accuracy and robustness of CAD systems for chest disease diagnosis. By integrating CNN and ViT architectures, our approach enhances the diagnostic precision, which may help to alleviate the burden on healthcare systems in resource-limited settings and improve patient outcomes in chest disease diagnosis.

Interference Mitigation in B5G Network Architecture for MIMO and CDMA: State of the Art, Issues, and Future Research Directions

The emergence of Beyond 5G (B5G) networks introduces novel challenges related to interference management, particularly within the context of Multiple-Input, Multiple-Output (MIMO) and Code Division Multiple Access (CDMA) technologies. In this comprehensive review paper, we delve into the intricacies of interference mitigation techniques within the B5G framework, with a specific focus on MIMO and CDMA systems. Firstly, we provide a brief overview of MIMO and CDMA principles, emphasizing their significance in B5G networks. MIMO leverages spatial diversity by employing multiple antennas in both the transmitter and the receiver, thereby enhancing capacity and reliability. CDMA, on the other hand, enables multiple users to share the same frequency band by assigning unique codes to each user. Next, we categorize the various types of interference encountered in MIMO and CDMA systems. These include co-channel interference, adjacent-channel interference, and multiuser interference. Understanding these interference sources is crucial for designing effective mitigation strategies. Our exploration of interference mitigation techniques covers state-of-the-art approaches tailored for MIMO and CDMA scenarios. Lastly, we discuss future research directions in interference mitigation for B5G networks. This review paper provides valuable insights for researchers, practitioners, and network designers seeking to enhance the robustness and efficiency of B5G communication systems by effectively mitigating interference in MIMO and CDMA contexts.

Robustness of internal insulation systems in practice – Role of installation, physical impact, paint types, and surface covering

To investigate the robustness of internal insulation throughout the service-life, this paper describes several practical issues connected to the installation and the performance of different systems in the operation phase. The study was conducted through laboratory and field tests, calibrated numerical simulations, and practical knowledge acquired on-site and from the users. Five issues were studied: 1) Practical problems in the installation were recorded through observations and interviews with craftsmen. This showed that the installation process was relatively easy for most systems and, therefore, robust towards installation. 2) A physical robustness experiment evaluated the robustness of the insulation systems against unintentional hitting. The results of standardized laboratory tests revealed that hemp with lime, and phenolic foam were the most robust systems, and aerogel was the weakest and needed the most repairs. However, no damage was observed on any systems after 5 - 8 years of daily use. 3) A wet cup test was conducted to test the vapor diffusion resistance of typical wall paint types that may be applied during the system's lifetime. The results were used in simulations to determine the paint's influence on the wall's diffusivity. Wet cup tests showed that both the silicate and acrylic paint are diffusion-open, with the acrylic ones being less open, and it is unlikely that a surface can be made too diffusion-tight by conventional silicate or acrylic paint. 4) Experiments were done regarding the microclimate between the external wall and furniture and paintings. The results indicate that it is a good idea to keep some distance to furniture and decorations from a wall insulated with capillary active insulation system to eliminate increased risk of mold growth. 5) Through inspections and questionnaires, residents were asked about the pros and cons of living with interior insulation. The walls showed no extraordinary signs of wear, and in general, the residents appreciated the better comfort and accepted the downside of having a more fragile wall. The process was made through observations and questionnaires to the craftsmen and the residents, and the results revealed that the installation process was relatively easy for most systems.

Assessing the vulnerability of empirical infrastructure networks to natural catastrophes

Human-made infrastructures are complex systems continually exposed to events that threat their function, such as cascading failures, occurring when the flow of physical quantities is redistributed within the network as a consequence of localized disruptions. Nevertheless, the role played by exogenous catastrophic events and internal failures on the robustness of critical infrastructures is usually addressed independently, under simplifying assumptions and lacking a unified picture for realistic risk assessments. Here, we fill this gap by introducing the Operational-Affected-Dismantled (OAD) model that captures both local and nonlocal failure propagation mechanisms. The model combines reaction–diffusion processes for local spreading with a global field effect for long-range interactions, allowing us to quantitatively characterize the cascade dynamics in infrastructure networks. Moreover, we include information on external stressors to assess the robustness of empirical network infrastructures and build spatial risk maps. By using data from severe storms (2009–2016) and from earthquakes (2000–2023) as stressors of the North American power grid and the worldwide airline transportation system, respectively, we offer a quantitative way to rank events by their potential to trigger systemic effects. By analyzing the response of the European power grid to simulated severe storms, we find that it can show high levels of systemic risk. Uncertainty in global climate and the accelerating frequency of extreme events all over the globe call for novel strategies to quantify, adapt to and mitigate systemic risk. Our framework provides a suitable starting point to assess the robustness of empirical systems in realistic and what-if scenarios.

Performance Comparison of RANSAC and Other Model Estimation Methods in Panoramic Image Mosaic

As computer vision technology advances, the significance of outlier processing algorithms increases across various applications. Despite this, there is a lack of comprehensive comparisons to assess the performance of these algorithms. This paper focuses on evaluating the principles, advantages, and disadvantages of three key models: RANSAC, least squares, and lmed. Through experimental testing in diverse scenarios, the study identifies the most suitable model for different contexts. Results indicate that RANSAC exhibits the highest robustness against outliers, while lmed performs effectively with a moderate number of outliers. These findings are crucial for selecting appropriate models tailored to specific applications, ultimately enhancing the quality of panoramic mosaic images. The comparative analysis presented in this paper aims to guide practitioners in choosing the best outlier processing techniques based on their specific requirements and application environments, thereby improving the robustness and accuracy of computer vision systems.

Energy optimization of building-integrated photovoltaic for load shifting and grid robustness in high-rise buildings based on optimum planned grid output

This study proposes an energy management and optimization model of building-integrated photovoltaic (BIPV) systems integrating static battery storage and electric vehicles considering stochastic parking schedules. A novel energy management strategy of orienting grid robustness with optimum planned grid output is developed to effectively manage BIPV power for load shifting in a typical high-rise building under both low-energy case and zero-energy case. Innovative assessment indicators of load shifting factor and grid robustness factor are proposed to quantify the applicability of the orienting grid robustness strategy compared with the conventional maximizing PV utilization strategy. Multi-objective optimizations are conducted to explore optimum system capacity, planned grid output in cooling and non-cooling seasons balancing the load shifting factor, grid robustness factor and lifetime net present value. The research results indicate that the orienting grid robustness strategy is effective for load shifting in the high-rise building under both low-energy case and zero-energy case with the load shifting factor at about 76.17% and 74.62%, respectively. It achieves obvious grid integration performance reducing the grid robustness factor by 49.34% in the low-energy case and 71.65% in the zero-energy case. The net present value is decreased by about 27.87% and 13.59% in two optimum cases and the annual equivalent carbon emissions are decreased by 10.12% and 65.09%, respectively. The developed energy management and optimization framework with novel strategy and indicators can improve the grid robustness and energy economy of BIPV and storage systems for high-rise buildings towards low-energy and low-carbon operations.

Robust Fuel Cell Electrodes: Pt Thin Film Catalysts for Anode Reversal Tolerance

In recent years, polymer electrolyte membrane fuel cells (PEMFCs) have garnered significant attention due to their remarkable attributes including high power density, energy efficiency, and zero-emission characteristics. This surge in interest has primarily been driven by the potential of PEMFC-powered fuel cell electric vehicles (FCEVs) to substantially reduce carbon dioxide emissions in the transportation sector, thereby propelling the advancement of the hydrogen economy on a global scale. However, the widespread commercialization of FCEVs hinges upon addressing key challenges surrounding the durability and resilience of fuel cell systems.One critical issue that has emerged is the phenomenon of hydrogen starvation in the anode, particularly during start-up/shutdown or cold start scenarios, which can lead to rapid and severe degradation of PEMFC performance and durability through cell reversal. This degradation manifests quickly, often within minutes, resulting in abrupt cell failure. Extensive prior research has been devoted to unraveling the mechanisms underlying degradation induced by hydrogen starvation and cell reversal in PEMFCs, with the goal of identifying effective mitigation strategies to minimize damage to membrane electrode assembly (MEA) components.In this study, we introduce an innovative materials approach aimed at mitigating cell damage by employing a thin film Pt anode catalyst supported on Nafion nanowires in a co-axial configuration, referred to as coaxial nanowire electrode (CANE). Leveraging the high roughness of Nafion nanowires, CANE eliminates the need for conventional carbon support and exhibits exceptional tolerance to reversal-induced stress. We present a comprehensive evaluation of CANE as a reversal-tolerant anode, comparing its performance to that of conventional supported catalysts (Pt/C). Furthermore, we explore the potential for further enhancing durability by integrating iridium with the CANE anode. Finally, we will delve into our efforts to evaluate the feasibility of implementing this concept in structures that can be manufactured at scale. This research contributes to the ongoing efforts to enhance the robustness and longevity of PEMFC systems, thereby advancing the prospects of FCEVs as a sustainable and viable solution for future transportation needs.AcknowledgementThis research was supported by the Hydrogen and Fuel Cell Technologies Office (HFTO), Office of Energy Efficiency and Renewable Energy, US Department of Energy (DOE) through the Million Mile Fuel Cell Truck (M2FCT) consortia, technology managers G. Kleen and D. Papageorgopoulos. Authors would also like to acknowledge support for this work from the Laboratory Directed Research and Development (LDRD) program at Los Alamos National Laboratory (LANL) (2020200DR).

An Ensemble Approach for Speaker Identification from Audio Files in Noisy Environments

Automatic noise-robust speaker identification is essential in various applications, including forensic analysis, e-commerce, smartphones, and security systems. Audio files containing suspect speech often include background noise, as they are typically not recorded in soundproof environments. To this end, we address the challenges of noise robustness and accuracy in speaker identification systems. An ensemble approach is proposed combining two different neural network architectures including an RNN and DNN using softmax. This approach enhances the system’s ability to identify speakers even in noisy environments accurately. Using softmax, we combine voice activity detection (VAD) with a multilayer perceptron (MLP). The VAD component aims to remove noisy frames from the recording. The softmax function addresses these residual traces by assigning a higher probability to the speaker’s voice compared to the noise. We tested our proposed solution on the Kaggle speaker recognition dataset and compared it to two baseline systems. Experimental results show that our approach outperforms the baseline systems, achieving a 3.6% and 5.8% increase in test accuracy. Additionally, we compared the proposed MLP system with Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) classifiers. The results demonstrate that the MLP with VAD and softmax outperforms the LSTM by 23.2% and the BiLSTM by 6.6% in test accuracy.

Enhancing performance of end-to-end communication system using Attention Mechanism-based Sparse Autoencoder over Rayleigh fading channel

Deep learning has revolutionized communication systems by introducing innovative approaches to address channel impairments through end-to-end models. Autoencoders, a type of deep learning architecture, are adept at learning compact data representations. However, conventional autoencoders in end-to-end models can suffer from overfitting, which limits their effectiveness in noisy communication environments. To address this issue, we propose a Sparse Autoencoder-based (SAE) model that enforces sparsity and promotes the extraction of robust features. Despite its effectiveness, the SAE model may still lack the ability to focus on the most relevant features of the input data. To overcome this limitation, we further introduce an Attention Mechanism-based Sparse Autoencoder (ASA) model. This model integrates the feature extraction capabilities of a sparse autoencoder with an attention mechanism that selectively highlights informative features of the signal. Through simulations, we demonstrate that both proposed models significantly improve M-PSK and M-QAM communication system performance. When trained at 7 dB, both proposed models exhibit significant performance improvements at higher testing average SNRs. Our results show that the SAE model outperforms the conventional Maximum Likelihood Detection (MLD) model and baseline autoencoder systems but suffers from error floor issues. The SAE model suffers from an error floor at average SNRs beyond 16 dB for BPSK and 14 dB for higher-order modulation schemes. As the value of M increases, the performance gap between the MLD and the proposed SAE model narrows. The ASA model, however, effectively mitigates the error floor observed in the SAE model for all values of M and across all modulation schemes. This research highlights the benefits of integrating an attention mechanism with SAE, resulting in enhanced robustness and reliability in communication systems characterized by improved accuracy and reduced error rates.

6G Technology for Indoor Localization by Deep Learning with Attention Mechanism

This paper explores 6G technology for indoor positioning, focusing on accuracy and reliability using convolutional neural networks (CNN) with channel state information (CSI). Indoor positioning is critical for smart applications and the Internet of Things (IoT). 6G is expected to significantly enhance positioning performance through the use of higher frequency bands, such as terahertz frequencies with wider bandwidth. Preliminary results show that 6G-based systems are expected to achieve centimeter-level positioning accuracy due to the integration of advanced artificial intelligence algorithms and terahertz frequencies. In addition, this paper also investigates the impact of self-attention (SA) and channel attention (CA) mechanisms on indoor positioning systems. The combination of these attention mechanisms with conventional CNNs has been proposed to further improve the accuracy and robustness of localization systems. CNN with SA demonstrates a 50% reduction in RMSE compared to CNN by capturing spatial dependencies more effectively.

S-RAF: A Simulation-Based Robustness Assessment Framework for Responsible Autonomous Driving

As artificial intelligence (AI) technology advances, ensuring the robustness and safety of AI-driven systems has become paramount. However, varying perceptions of robustness among AI developers create misaligned evaluation metrics, complicating the assessment and certification of safety-critical and complex AI systems such as autonomous driving (AD) agents. To address this challenge, we introduce Simulation-Based Robustness Assessment Framework (S-RAF) for autonomous driving. S-RAF leverages the CARLA Driving simulator to rigorously assess AD agents across diverse conditions, including faulty sensors, environmental changes, and complex traffic situations. By quantifying robustness and its relationship with other safety-critical factors, such as carbon emissions, S-RAF aids developers and stakeholders in building safe and responsible driving agents, and streamlining safety certification processes. Furthermore, S-RAF offers significant advantages, such as reduced testing costs, and the ability to explore edge cases that may be unsafe to test in the real world.

Research on gesture recognition technology based on machine learning

Abstract. Hand Gesture Recognition (HGR), as a significant technological advancement in the field of Human-Computer Interaction (HCI), aims to develop systems capable of accurately recognizing and interpreting human gestures for a diverse range of applications, including device control, virtual reality, gesture passwords, and gesture interaction. With the continuous advancement of machine learning algorithms, especially deep learning techniques, machine learning-based gesture recognition has garnered widespread attention. This paper presents a review of the development of gesture recognition techniques from traditional approaches to the current mainstream deep learning-based methods, and outlines the challenges and technical difficulties encountered. It analyzes several of the most popular classification techniques, including Naive Bayes, K-Nearest Neighbors (KNN), Random Forests, XGBoost, Support Vector Classifiers (SVCs), and Convolutional Neural Networks (CNNs). Furthermore, this paper examines the application of these algorithms in both dynamic and static gesture recognition and compares their performance and suitability in different scenarios. The results demonstrate that the accuracy and robustness of gesture recognition systems can be markedly enhanced through the prudent selection and optimization of the algorithms, which serves as a valuable reference for future research and applications.