Method for reconstructing safety and arming motion process by integrating Kalman filter and KCF

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 11, 2025

This paper addresses the challenge of reconstructing motion process safety and arming (S&A) mechanism in fuze by transforming problem into a target detection tracking problem. A novel method, which fuses an improved Kalman filter with temporal scale-adaptive KCF (AKF-CF), is proposed. The methodology introduces key innovations: (1) Extraction grayscale images directional gradient histogram (HOG) features target, followed use Adaptive Wave PCA-Autoencoder (AWPA) method to accurately capture multi-modal multi-scale target; (2) Application bilinear interpolation hybrid filtering techniques generate spatial bounding box for filtered enabling dynamic adjustment size; (3) Integration occlusion-aware using average peak correlation energy (APCE) trigger Kalman-based position prediction when occluded, thus mitigating drift. Finally, curve plotted, facilitating reconstruction S&A mechanism's trajectory. Experimental results from five datasets indicate effectiveness proposed method. Compared ACSRCF algorithm on OTB50 dataset, achieves accuracy success rate improvements 0.8 0.6%, respectively. On OTB100 it attains 92.50% 68.10% rate, outperforming other related algorithms. These highlight significant demonstrating algorithm's robustness handling challenging scenarios. Additionally, reconstructed curves effectively replicate mechanical trajectories, showcasing strong performance complex occlusion environments.

Language: Английский

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Enhancing neurological disease diagnostics: fusion of deep transfer learning with optimization algorithm for acute brain stroke prediction using facial images

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 10, 2025

Language: Английский

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Artificial intelligence-driven cybersecurity system for internet of things using self-attention deep learning and metaheuristic algorithms

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 16, 2025

Language: Английский

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Enhanced anomaly network intrusion detection using an improved snow ablation optimizer with dimensionality reduction and hybrid deep learning model

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 17, 2025

With the enlarged utilization of computer networks, security has become one critical issues. A network intrusion by malicious or unauthorized consumers may cause severe interruption to networks. So, progress a strong and dependable detection system (IDS) is gradually significant. Intrusion relates suite models employed recognize attacks against infrastructures computers. There are dual main models, such as misuse anomaly detection. Anomaly central part in which disruptions normal behaviour propose presence unintentionally intentionally induced attacks, defects, faults, etc. arrival anomaly-based IDS, many have progressed tracking new threats systems. Machine learning (ML) deep (DL) currently leveraged for cybersecurity. This manuscript proposes an Enhanced Detection using Optimization Algorithm with Dimensionality Reduction Hybrid Model (EAID-OADRHM) technique. The proposed EAID-OADRHM technique presents approach perceiving migrating Min-max scaling normalization primarily at data pre-processing level clean transform input into consistent range. Furthermore, utilizes equilibrium optimizer (EO) model dimensionality reduction process. Additionally, classification performed employing long short-term memory autoencoder (LSTM-AE) model. Finally, improved Snow Ablation Optimizer (ISAO) optimally tunes hyperparameters LSTM-AE model, leading enhanced performance. simulation validation examined under CIC-IDS2017 dataset, outcomes computed numerous measures. experimental assessment portrayed superior accuracy value 99.46% over existing methods

Language: Английский

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Blockchain-Enabled Supply Chain Management: A Review of Security, Traceability, and Data Integrity Amid the Evolving Systemic Demand

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(9), P. 5168 - 5168

Published: May 6, 2025

As supply chains become increasingly digitized and decentralized, ensuring security, traceability, data integrity has emerged as a critical concern. Blockchain technology shown significant potential to address these challenges by providing immutable records, transparent flows, tamper-resistant transaction logs. However, the effective application of blockchain in real-world requires careful evaluation both architectural design technical limitations, including scalability, interoperability, privacy. This review systematically examines existing blockchain-based chain solutions, classifying them based on their structural models, cryptographic foundations, storage strategies. Special attention is also given underexplored humanitarian logistics scenarios. It introduces three-dimensional framework assess across different approaches. In doing so, it explores key technological enablers, advanced mechanisms such zero-knowledge proofs (ZKPs) cross-chain architectures, meet evolving privacy interoperability demands. Furthermore, this study outlines conceptual interaction scenario involving permissioned permissionless networks, connected through bridge mechanism supported representative smart contract logic. The model illustrates how decentralized stakeholders can interact securely heterogeneous platforms. By integrating quantitative metrics, simulations, qualitative analyses, paper contributes deeper understanding blockchain’s role next-generation chains, offering guidance for researchers practitioners aiming resilient trustworthy management (SCM) systems.

Language: Английский

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