IF 4 2区计算机科学

Journal of Optical Communications and Networking Pub Date : 2025-07-25 DOI: 10.1364/JOCN.547793

Maria Alvarez Roa;Catalina Stan;Sebastian Verschoor;Idelfonso Tafur Monroy;Simon Rommel

{"title":"Decentralized key distribution versus on-demand relaying for QKD networks","authors":"Maria Alvarez Roa;Catalina Stan;Sebastian Verschoor;Idelfonso Tafur Monroy;Simon Rommel","doi":"10.1364/JOCN.547793","DOIUrl":"https://doi.org/10.1364/JOCN.547793","url":null,"abstract":"Quantum key distribution (QKD) allows the distribution of secret keys for quantum-secure communication between two distant parties, vital in the quantum computing era in order to protect against quantum-enabled attackers. However, overcoming rate-distance limits in QKD and the establishment of quantum key distribution networks necessitate key relaying over trusted nodes. This process may be resource-intensive, consuming a substantial share of the scarce QKD key material to establish end-to-end secret keys. Hence, an efficient scheme for key relaying and the establishment of end-to-end key pools is essential for practical and extended quantum-secured networking. In this paper, we propose and compare two protocols for managing, storing, and distributing secret key material in QKD networks, addressing challenges such as the success rate of key requests, key consumption, and overhead resulting from relaying. We present an innovative, fully decentralized key distribution strategy as an alternative to the traditional hop-by-hop relaying via trusted nodes, where three experiments are considered to evaluate performance metrics under varying key demand. Our results show that the decentralized pre-flooding approach achieves higher success rates as application demands increase. This analysis highlights the strengths of each approach in enhancing QKD network performance, offering valuable insights for developing robust key distribution strategies in different scenarios.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"17 8","pages":"732-742"},"PeriodicalIF":4.0,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-07-25 DOI: 10.1111/coin.70110

引用次数: 0

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IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-07-25 DOI: 10.1111/coin.70109

引用次数: 0

A non-index mixed-asset portfolio optimization approach 一种非指数混合资产组合优化方法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-07-25 DOI: 10.1016/j.swevo.2025.102074

Yuyang Bai , Changsheng Zhang , Baiqing Sun , Bin Zhang

{"title":"A non-index mixed-asset portfolio optimization approach","authors":"Yuyang Bai , Changsheng Zhang , Baiqing Sun , Bin Zhang","doi":"10.1016/j.swevo.2025.102074","DOIUrl":"10.1016/j.swevo.2025.102074","url":null,"abstract":"<div><div>As the financial industry shifts from divided operations to mixed operations, mixed-asset portfolios have gradually gained ground in investment portfolios. Existing mixed-asset portfolio optimization approaches frequently introduce indices for representing asset classes to eliminate heterogeneity among asset classes. However, few introduced indices comprehensively and realistically represent asset classes, leading to a loss of feasible solutions and practical reliability. To address these limitations, this paper proposes a non-index mixed-asset portfolio optimization approach consisting of problem modeling and problem solving. For problem modeling, our approach models the mixed-asset portfolio optimization as a multi-objective bi-level optimization problem. In the inner-level optimization, optimal portfolios within each asset class are constructed to represent the corresponding asset class. These optimal portfolios contain more information and are constructed from realistically available products, thus representing the asset class more comprehensively and practically. In the outer-level optimization, the allocation among the asset classes is optimized to obtain an optimal mixed-asset portfolio. For problem solving, a multi-swarm dynamic cooperative optimization method is proposed to solve the modeled problem. Considering that obtaining the complete inner-level optimization of the modeled problem is challenging and time-consuming, a dynamic collaboration mechanism is designed to obtain the optimal subset of the inner-level optimization, thus solving the problem efficiently and effectively. To verify the effectiveness of our proposed non-index approach, an experiment is conducted to compare our proposed approach with four state-of-the-art approaches. Our proposed non-index approach problem outperforms competitors in 27 of 30 scenarios on both the Pareto optimality and the realistic performance.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102074"},"PeriodicalIF":8.2,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Collaborative strategy for hybrid actions of radar modes and maneuver decisions under observation errors 观测误差下雷达模式与机动决策混合行动的协同策略

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-07-25 DOI: 10.1016/j.engappai.2025.111774

Xingyu Wang , Zhen Yang , Jichuan Huang , Bao Zhang , Yuhe Zhang , Deyun Zhou

{"title":"Collaborative strategy for hybrid actions of radar modes and maneuver decisions under observation errors","authors":"Xingyu Wang , Zhen Yang , Jichuan Huang , Bao Zhang , Yuhe Zhang , Deyun Zhou","doi":"10.1016/j.engappai.2025.111774","DOIUrl":"10.1016/j.engappai.2025.111774","url":null,"abstract":"<div><div>The rapid advancement of airborne avionics has driven modern air combat to rely heavily on information-centric operations, with radar serving as a primary tool for information acquisition and playing a critical role in air combat. However, existing research on air combat strategies often overlooks the impact of different radar operating modes on maneuvering strategies, as well as the challenges posed by learning strategies under observational disturbances. To address these gaps, this study investigates the problem of hybrid actions decision-making for radar modes and maneuver decisions in the presence of observational errors. Specifically, the characteristics of various radar operating modes are analyzed and modeled, followed by an exploration of the convergence process of reinforcement learning strategies under observational disturbances. To mitigate the instability and volatility in strategy learning caused by observation errors, Entropy-Decoupling-Noisy-net Proximal Policy Optimization-Advanced (EDN-PPOA) algorithm is proposed, which significantly enhances the robustness and exploratory capability of the model. Simulation results demonstrate that the proposed algorithm effectively achieves coordinated tactical integration of radar modes and maneuvers in complex hybrid action spaces, producing flexible tactical strategies that outperform expert-designed heuristics. Furthermore, compared to the existing algorithms, the proposed method exhibits superior stability and robustness in noisy observational environments, providing a reliable technical foundation for intelligent decision-making in complex adversarial scenarios.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111774"},"PeriodicalIF":7.5,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computationally efficient multi-objective optimization of an interior permanent magnet synchronous machine using neural networks 基于神经网络的内置永磁同步电机的高效多目标优化

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-07-25 DOI: 10.1016/j.engappai.2025.111753

Mitja Garmut , Simon Steentjes , Martin Petrun

{"title":"Computationally efficient multi-objective optimization of an interior permanent magnet synchronous machine using neural networks","authors":"Mitja Garmut , Simon Steentjes , Martin Petrun","doi":"10.1016/j.engappai.2025.111753","DOIUrl":"10.1016/j.engappai.2025.111753","url":null,"abstract":"<div><div>Improving the power density of an interior permanent magnet synchronous machine requires a complex and comprehensive approach that includes electromagnetic and thermal aspects. To achieve that, a multi-objective optimization of the machine’s geometry was performed according to selected key performance indicators by using numerical and analytical models. The primary objective of this research was to create a computationally efficient and accurate alternative to a direct finite element method-based optimization. By integrating artificial neural networks as meta-models, we aimed to demonstrate their performance in comparison to existing State-of-the-Art approaches. The artificial neural network approach achieved a nearly 20-fold reduction compared with the finite element method-based approach in computation time while maintaining accuracy, demonstrating its effectiveness as a computationally efficient alternative. The obtained artificial neural network can also be reused for different optimization scenarios and for iterative fine-tuning, further reducing the computation time. To highlight the advantages and limitations of the proposed approach, a multi-objective optimization scenario was performed, which increased the power-to-mass ratio by 16.5%.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111753"},"PeriodicalIF":7.5,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Myocardial ischemic classification using a knowledge-guided polar transformer in two-dimensional echocardiography 在二维超声心动图中使用知识引导的极性变压器进行心肌缺血分类

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-07-25 DOI: 10.1016/j.engappai.2025.111871

Ziwei Pang , Yi Du , Yanhui Guo , Shuang Chen , Bo Yu , Siqi Guo , Guo-Qing Du

{"title":"Myocardial ischemic classification using a knowledge-guided polar transformer in two-dimensional echocardiography","authors":"Ziwei Pang , Yi Du , Yanhui Guo , Shuang Chen , Bo Yu , Siqi Guo , Guo-Qing Du","doi":"10.1016/j.engappai.2025.111871","DOIUrl":"10.1016/j.engappai.2025.111871","url":null,"abstract":"<div><div>Myocardial ischemia, characterized by inadequate blood supply to the heart muscles, is critical to cardiovascular diseases. Timely and accurate identification of ischemic segments is essential for prompt intervention and patient care. This study developed a Transformer-based model to identify myocardial ischemia in left ventricle short-axis (LVSA) two-dimensional echocardiography (2DE) images where a novel Knowledge-Guided Polar Transformer (KGPT) was proposed that integrated the unique characteristics of 2DE images with the prior clinical knowledge. 305 patients (aged 57.6 ± 8.8 years) were selected and underwent transthoracic echocardiography within 1–3 days prior to invasive coronary angiography (ICA). With ICA and quantitative flow ratio as the gold standard of myocardial ischemia, the KGPT model was trained to classify the LVSA 2DE images as ischemia or non-ischemia by capturing spatial features in a radial orientation. Its performance was evaluated with five-fold cross-validation and receiver operating characteristic curve (ROC) analysis. It achieved an area under ROC (AUC) of 0.8326 ± 0.0906, with an accuracy of 79.50 ± 5.40 %, precision of 79.07 ± 6.70 %, recall of 80.79 ± 7.87 %, and F1 score of 78.43 ± 6.56 %. In comparison, the original Swin-Transformer model produced an AUC of 0.7011 ± 0.0334, accuracy of 70.20 ± 1.04 %, precision of 68.58 ± 3.12 %, recall of 63.21 ± 3.60 %, and F1 score of 63.13 ± 3.78 %. The differences were statistically significant (P < 0.05). The KGPT also demonstrated significantly superior performance to radiologists. It effectively classifies ischemic regions in 2DE images, presenting a promising tool for diagnosing myocardial ischemia. The integration of clinical knowledge with Transformer enhances the accuracy and reliability of ischemia classification, potentially revolutionizing the diagnosis and monitoring of myocardial ischemic diseases.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111871"},"PeriodicalIF":7.5,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model identification of a cable-based mechanical transmission for robotics using a Best Linear Approximation approach 基于最佳线性逼近方法的机器人缆索机械传动模型辨识

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-07-25 DOI: 10.1016/j.conengprac.2025.106457

Bassem Boukhebouz , Guillaume Mercère , Mathieu Grossard , Édouard Laroche

引用次数: 0

Output feedback control of an underactuated flying inverted pendulum 欠驱动飞行倒立摆的输出反馈控制

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-07-25 DOI: 10.1016/j.conengprac.2025.106474

Weiming Yang , Joel Reis , Gan Yu , Carlos Silvestre

引用次数: 0

MSDNet: Multi-scale decoder for few-shot semantic segmentation via transformer-guided prototyping MSDNet：基于变压器引导原型的多尺度语义分割解码器

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2025-07-25 DOI: 10.1016/j.imavis.2025.105672

Amirreza Fateh, Mohammad Reza Mohammadi, Mohammad Reza Jahed-Motlagh

{"title":"MSDNet: Multi-scale decoder for few-shot semantic segmentation via transformer-guided prototyping","authors":"Amirreza Fateh, Mohammad Reza Mohammadi, Mohammad Reza Jahed-Motlagh","doi":"10.1016/j.imavis.2025.105672","DOIUrl":"10.1016/j.imavis.2025.105672","url":null,"abstract":"<div><div>Few-shot Semantic Segmentation addresses the challenge of segmenting objects in query images with only a handful of annotated examples. However, many previous state-of-the-art methods either have to discard intricate local semantic features or suffer from high computational complexity. To address these challenges, we propose a new Few-shot Semantic Segmentation framework based on the Transformer architecture. Our approach introduces the spatial transformer decoder and the contextual mask generation module to improve the relational understanding between support and query images. Moreover, we introduce a multi scale decoder to refine the segmentation mask by incorporating features from different resolutions in a hierarchical manner. Additionally, our approach integrates global features from intermediate encoder stages to improve contextual understanding, while maintaining a lightweight structure to reduce complexity. This balance between performance and efficiency enables our method to achieve competitive results on benchmark datasets such as <span><math><mrow><mi>P</mi><mi>A</mi><mi>S</mi><mi>C</mi><mi>A</mi><mi>L</mi><mtext>-</mtext><msup><mrow><mn>5</mn></mrow><mrow><mi>i</mi></mrow></msup></mrow></math></span> and <span><math><mrow><mi>C</mi><mi>O</mi><mi>C</mi><mi>O</mi><mtext>-</mtext><mn>2</mn><msup><mrow><mn>0</mn></mrow><mrow><mi>i</mi></mrow></msup></mrow></math></span> in both 1-shot and 5-shot settings. Notably, our model with only 1.5 million parameters demonstrates competitive performance while overcoming limitations of existing methodologies. <span><span>https://github.com/amirrezafateh/MSDNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"162 ","pages":"Article 105672"},"PeriodicalIF":4.2,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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