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检索条件"主题词=Detection Algorithms"

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Research on Autonomous detection algorithms for Spoofed Interference in Civil Aircraft Airborne Navigation Systems

Research on Autonomous Detection Algorithms for Spoofed Inte...

引用

Power, Electrical Engineering, Electronics and Control (PEEEC), International Conference on

作者： Jia Chen Commercial Aircraft Corporation of China Ltd Beijing Aeronautical Science &Technology Research Institute of COMAC Beijing China

ISBN: (数字)9798350378917

ISBN: (纸本)9798350378924

With the wide application of civil aircraft on-board navigation system, the demand for its safety and stability is increasing. In this paper, the aircraft navigation state data is acquired by GNSS and INS sensors, and the navigation state model is constructed. Through Kalman filtering algorithm, any abnormal or inconsistent navigation state is detected to monitor the real-time performance of the navigation system. When potential deceptive interference is detected, the algorithm is able to take appropriate decisions autonomously, such as switching to an alternate navigation system, sending warning notifications to the flight crew, and so on. In the simulated spoofed interference scenario, the algorithm successfully detected more than 95% of the interference events with an average response time of less than 5 seconds. When the on-board GNSS spoofing causes a position deviation of 250 m or more, the designed algorithm is able to realize a more accurate acquisition of the spoofing duration. The reliability and effectiveness of the proposed algorithm in spoofed jamming scenarios are verified, and the anti-jamming capability of the civil aircraft airborne navigation system is improved.

关键词： Global navigation satellite system Accuracy Interference Filtering algorithms Aerospace electronics Aircraft navigation Aircraft manufacture Time factors Aircraft detection algorithms

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Comparison of Atom detection algorithms for Neutral Atom Quantum Computing

Comparison of Atom Detection Algorithms for Neutral Atom Qua...

引用

Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Jonas Winklmann Andrea Alberti Martin Schulz Chair of Computer Architecture and Parallel Systems Technical University of Munich Munich Germany Quantum Many-Body Systems Division Max Planck Institute of Quantum Optics Garching Germany

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

In neutral atom quantum computers, readout and preparation of the atomic qubits are usually based on fluorescence imaging and subsequent analysis of the acquired image. For each atom site, the brightness or some comparable metric is estimated and used to predict the presence or absence of an atom. Across different setups, we can see a vast number of different approaches used to analyze these images. Often, the choice of detection algorithm is either not mentioned at all or it is not justified. We investigate several different algorithms and compare their performance in terms of both precision and execution run time. To do so, we rely on a set of synthetic images across different simulated exposure times with known occupancy states, which we generated using a previously validated imaging simulation. Since the use of simulation provides us with the ground truth of atom site occupancy, we can easily state precise error rates and variances of the reconstructed property. However, knowing the relative performance of these algorithms is not sufficient to justify their use, since better ones can exist that were not compared. To investigate this possibility, we calculated the Cramer-Rao bound in order to establish an upper limit that even a perfect estimator cannot outperform. As the metric of choice, we used the number of photonelectrons that can be contributed to a specific atom site. Every estimator that reconstructs a different property can simply be scaled accordingly. Since the bound depends on the occupancy of neighboring sites, we provide the best and worst cases, as well as a half filled one, which should represent an averaged bound best. Our comparison shows that of our tested algorithms, a global nonlinear least-squares solver that uses the optical system's point spread function (PSF) to return a global bias and each sites' number of photoelectrons performed the best, on average crossing the worst-case bound for longer exposure times. Its main drawback is its huge

关键词： Measurement Computers Runtime Deconvolution Error analysis Qubit Quantum mechanics Prediction algorithms detection algorithms Image reconstruction

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Automated Analysis of Teaching Models Based on Artificial Intelligence detection algorithms

Automated Analysis of Teaching Models Based on Artificial In...

引用

International Conference on Educational and Information Technology (ICEIT)

作者： Nie Shuai Wang Chongwen Jin Zening School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798331540883

ISBN: (纸本)9798331540890

This paper proposes an automated analysis of teaching models based on artificial intelligence detection algorithms. It aims to efficiently analyze actual classroom teaching models, providing insights that enable appropriate adjustments to teaching strategies, thereby improving the quality of education. We analyze classroom audio data streams using an improved Emphasized Channel Attention, Propagation and Aggregation-Time Delay Neural Network (ECAPA-TDNN) model and analyze video data streams using an enhanced You Only Look Once-v3 (YOLO-v3) model. Subsequently, the obtained information, such as head-raising rates and speaker identification, is automatically processed using an improved S-T analysis method to derive teaching models. Experimental results show that our analytical method, while ensuring that the results closely reflect reality, achieves an analysis speed 2.81 times faster than traditional methods. This demonstrates the advantages of applying automated teaching model analysis in the educational field.

关键词： Analytical models Accuracy Education Educational technology Streaming media Data models Reliability Artificial intelligence detection algorithms Streams

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Application of Time Series Anomaly detection algorithms in Risk Identification

Application of Time Series Anomaly Detection Algorithms in R...

引用

Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Dawei Li Jian Zhang Shuai Yang Jian Zhao Bin Zhou Lei Xu Competition Policy and Assessment Center of State Administration for Market Regulation Beijing China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Traditional risk identification methods often rely on manual inspections and post-event processing, which struggle to cope with the vast amounts of data and the real-time changes in the internet environment. Time series anomaly detection algorithms, as an effective data analysis tool, can identify abnormal changes in time series data. By utilizing spectral residual anomaly detection algorithms and sub-sequence anomaly detection algorithms, models were constructed to conduct an in-depth anomaly detection analysis of price data on the JD platform, providing a new solution for risk identification. The experimental results show that the precision, recall, and F1 scores of these two time series anomaly detection algorithms are significantly better than those of other comparative algorithms, demonstrating significant application value in risk identification.

关键词： Sensitivity Image processing Time series analysis Manuals Inspection Real-time systems Object recognition detection algorithms Anomaly detection Monitoring

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A comprehensive Evaluation of Community detection algorithms

A comprehensive Evaluation of Community Detection algorithms

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Complexity in Engineering (COMPENG)

作者： Ndeye Khady Aidara Issa Moussa Diop Cherif Diallo Hocine Cherifi LACCA LAB Gaston Berger University Saint-Louis Senegal DS4H I3S Université Côte d’Azur Nice France ICB UMR 6303 CNRS Université de Bourgogne Dijon France

ISBN: (数字)9798350382792

ISBN: (纸本)9798350382808

In many domains, including social network analysis, biology, and beyond, community structure discovery in networks is a basic topic. To find highly connected node groups—often referred to as communities—in these networks, researchers develop a wide range of techniques. While the field evolves rapidly, many comparative studies are carried out to assess the strengths and weaknesses of different algorithms on various types of networks and scenarios. This study attempts to provide a thorough comparative analysis involving both established techniques like Louvain, Label Propagation, Infomap, Girvan-Newman, and Walktrap, as well as newly proposed methods like PercoMVC, Walkscan, and Paris. This is due to the constantly changing landscape of community detection algorithms and the critical importance of understanding their strengths and weaknesses. We carry out a thorough empirical assessment of these eight community recognition methods on a variety of real-world networks, such as biological, social, and collaborative networks, among others with different topologies. A wide range of internal and external quality criteria, including conductance, density-based measurements, modularity, cut ratio, and the Friedman rank test, are incorporated into our analysis. Louvain consistently performs well on dense networks and is the best at optimizing modularity. Large, sparse networks work best with label propagation, effectively reducing the number of intercommunity links. PercoMVC shows high overall resilience and adaptability across a variety of network configurations. Infomap is good at capturing complex community structures and works well on various network types. Walktrap provides a solid balance between accuracy and efficiency and demonstrated high performance on congested networks. Our study provides insights to guide practitioners in selecting the most suitable community detection technique based on the network characteristics at hand, highlighting the complementary strengths

关键词： Social networking (online) Network topology Federated learning Solids Market research Biology Robustness Topology detection algorithms Resilience

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Dynamic Network Analysis of Cognitive Attacks Using Meta-Network Modeling and Community detection algorithms

Dynamic Network Analysis of Cognitive Attacks Using Meta-Net...

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IEEE International Conference on Cognitive Machine Intelligence (CogMI)

作者： Abdullah Melhem Zain Halloush Ahmed Aleroud School of Computer and Cyber Sciences Augusta University Augusta USA

ISBN: (数字)9798350386721

ISBN: (纸本)9798350386738

Cognitive attacks on Social media such as propaganda activities are a sophisticated form of psychological manipulation that targets the mental processes of individuals or groups to influence their perceptions, decisions, and behaviors. This paper utilizes dynamic network analysis to study propaganda and non-propaganda social media content in the Middle East. We transformed textual features of propaganda and non-propaganda news headlines and the corresponding comments on social media into meta-networks and applied various centrality measures and community detection algorithms to them. We then compared the structural characteristics of each network. Our findings reveal significant differences in the centrality and community structures. Our network analysis of propaganda and non-propaganda content has revealed distinct structural differences, with propaganda networks exhibiting lower topic centrality and more fragmented topic structures. This indicates a strategic focus on targeted messaging rather than broad-based engagement. The higher centrality values in non-propaganda networks reveal a more interconnected and diverse information flow. The findings of our research are significant for both practitioners and policymakers. Our results inform the development of more effective counter-propaganda strategies. Efforts can be made to disrupt echo chambers and introduce alternative narratives that challenge propaganda content.

关键词： Measurement Social networking (online) Heuristic algorithms Psychology Network analyzers Feature extraction detection algorithms Fake news Machine intelligence Context modeling

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Poster: IoTSimSecure: Towards an IoT Simulator Supporting Cyber-Threat detection algorithms

Poster: IoTSimSecure: Towards an IoT Simulator Supporting Cy...

引用

IEEE International Conference on Fog and Edge Computing (ICFEC)

作者： Reham Almutairi Giacomo Bergami Graham Morgan School of Computing Newcastle University United Kingdom College of Computer Science and Engineering University of Hafr Albatin Saudi Arabia

ISBN: (数字)9798350361353

ISBN: (纸本)9798350361360

We introduce IoTSimSecure, a novel IoT simulator addressing the gap of IoT simulators for dealing with cyber-security threats, specifically battery draining attacks occurring in real-time. This will serve as a valuable testbed for evaluating current AI-driven security strategies. IoTSimSecure aims to address these needs by offering an architecture that combines signature-based and anomaly-based detection methods.

关键词： Time series analysis Computer architecture Real-time systems Batteries Security detection algorithms Monitoring Edge computing

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Lightweight Fire detection algorithms for Complex Scenarios

Lightweight Fire Detection Algorithms for Complex Scenarios

引用

Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Yuefan Liu Xinni Wang Chen Guo School of Railway Intelligent Engineering Da Lian Jiao Tong University Da Lian China

ISBN: (数字)9798331517090

ISBN: (纸本)9798331517106

Given the challenges faced by fire detection models in complex contexts, such as low detection accuracy, excessive memory consumption and high error rate of tiny targets, introduces a lightweight algorithm for fire detection that is appropriate for complex situations. The introduction of the lightweight network model EMO and the MHSA attention mechanism, built on an enhanced Neck structure, leads to improved detection accuracy, fewer model parameters. To address the issue of limited intersection between predicted and ground-truth values in tiny target detection, a new loss function that combines CIoU and NWD interactively has been proposed to reinforce tiny target detection. Findings are as follows, compared with the primitive YOLOv8s model, the mAP@0.5:0.95 and mAP@0.5 are augmented by 3.6% and 4.4%. Additionally, parameters is reduced by 41%, and the improved model effectively realizes fire detection in complex backgrounds.

关键词： Accuracy Error analysis Memory management Fires Object detection Feature extraction Prediction algorithms Real-time systems Neck detection algorithms

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Exploring Community detection algorithms for Analyzing Dark Web Networks

Exploring Community Detection Algorithms for Analyzing Dark ...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Ashwini Dalvi Irfan Siddavatam Abhijeet Pasi K.J Somaiya College of Engineering Somaiya Vidyavihar University India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

The "dark web" is a term used to describe a network of websites not indexed by traditional search engines and can only be accessed through specific software, such as Tor (The Onion Router). The dark web has gained a reputation for hosting various activities, including illegal marketplaces, forums, and communication channels. In the context of the dark web, researchers have conducted studies to analyze its topology to understand its organization and characteristics of the dark *** study proposes an approach for community detection in the dark web using a combination of graph theory and machine learning techniques. A graph representation of the dark web is constructed using data collected from Tor dark web. Three popular community detection algorithms, namely Leiden, Louvain, and Label Propagation, are applied to crawled dark web data. The evaluation of these algorithms on a dataset of the dark web demonstrated its effectiveness in identifying meaningful communities. The results obtained from these approaches can provide valuable insights into the organization and structure of the dark web, which can benefit law enforcement agencies and cybersecurity researchers in their efforts to comprehend activities on the dark web.

关键词： Dark Web Machine learning algorithms Law enforcement Organizations Machine learning Search engines Graph theory Software Topology detection algorithms

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Comparative Analysis and Optimization of detection algorithms for Apples on Orchards with Complex Backgrounds

Comparative Analysis and Optimization of Detection Algorithm...

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IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

作者： Ermin Kevric Jiahua Wei Philipp Braun Hendrik Wilhem Rose Institute for Logistics Engineering Hamburg University of Technology Hamburg Germany

ISBN: (数字)9798350355444

ISBN: (纸本)9798350355451

Developing systems for accurate apple detection plays a significant role in precision agriculture tasks and orchard management practices, hence having a direct impact on the economics of orchard fields. Thus, this research compares between different detection algorithms as it plays the baseline for subsequent tasks of counting, apple grasping and growth monitoring. In this work, four different state-of-the-art (SOTA) object detection models have been compared for apple detection: Faster R-CNN, YOLOv5, YOLOv8 and RT-DETR. The models were examined for their accuracy in apple detection and their invariance to the background apples. As Faster R-CNN and YOLOv5 are commonly used algorithms in this domain, they were chosen alongside YOLOv8. Furthermore, as the transformer-based detectors like RT-DETR outperform YOLO on large datasets, their comparison for apple detection is also warranted. The models are evaluated on the MinneApple dataset and a manually collected dataset. In addition, the effect of attention mechanisms in YOLOv8 architecture, hyperparameter optimization and data augmentation were explored. The evaluation showed that RT-DETR outperformed all models for $\mathbf{mAP}_{\mathbf{0.50}:\mathbf{0.95}}$ on the MinneApple and custom dataset for the trained image size. Furthermore, given the smaller background interference at a larger mAP, the RT-DETR model not only outperformed YOLO and Faster R-CNN models, but was also more background invariant.

关键词： YOLO Training Accuracy Biological system modeling Computer architecture Data augmentation Transformers Data models Real-time systems detection algorithms

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