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Flight Testing Instrumentation Development and Integration for a Subscale Integrated High Lift Propulsor Testbed

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Flight Testing Instrumentation Development and Integration f...

AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Dantsker, D. Mancuso, Renato Ward, Byron Miller, Christian Shea, Donovan Collins, Kyle Department of Intelligent Systems Engineering Indiana University BloomingtonIN47408 United States Department of Computer Science Boston University BostonMA02215 United States Wayfarer Aircraft Research and Development La MesaCA91941 United States Eagle Flight Research Center Embry Riddle Aeronautical University Dayonta BeachFL32114 United States Embry Riddle Aeronautical University Dayonta BeachFL32114 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Recent advancements in electric and hybrid-electric aircraft have sparked interest in Advanced Aerial Mobility (AAM), enabling innovative air transport solutions. Distributed Electric Propulsion (DEP), with its favorable aero-propulsive interactions, is a key focus for advanced aircraft designs, and thus an area of significant interest for academia and industry. Wayfarer Aircraft has developed and patented a novel new form of of blown wing lift augmentation called the Integrated High Lift Propulsor (IHLP), which uses DEP integrated with a Krueger slat or flap. This paper presents the instrumented, reconfigurable subscale IHLP distributed electric propulsion testbed that will be used to parameterize the performance and handling qualities (P&HQ) of IHLP. The subscale aircraft, which was developed from a 35%-scale Cessna 182 R/C scale model aircraft, was instrumented with a custom-integrated flight control and data acquisition system developed to enable this flight research — specifically a Holybro Pixhawk 6X autopilot running custom flight control software coupled with a custom version of the Al Volo FDAQ data acquisition system. The paper presents IHLP including its advantages and performance improvements in high-lift and cruise conditions compared to conventional distributed electric propulsion (DEP) systems. The detailed development of the IHLP subscale testbed are presented, including modifications, instrumentation design, and the integration of the data acquisition and flight control systems. Preliminary results from ground testing are presented to demonstrate the capabilities of the instrumented IHLP testbed. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Testbeds

Suicide ideation detection based on documents dimensionality expansion

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computers in Biology and Medicine 2025年第Pt B期192卷 110266-110266页

作者： Esmi, Nima Shahbahrami, Asadollah Gaydadjiev, Georgi de Jonge, Peter Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands Intelligent Systems Research Center Khazar University Baku Azerbaijan Department of Computer Engineering University of Guilan Rasht Iran Computer Engineering Laboratory Delft University of Technology Delft Netherlands Faculty of Behavioural and Social Sciences University of Groningen Groningen Netherlands

Accurate and secure classifying informal documents related to mental disorders is challenging due to factors such as informal language, noisy data, cultural differences, personal information and mixed emotions. Conventional deep learning models often struggle to capture patterns in informal text, as they miss long-range dependencies, explain words and phrases literally, and have difficulty processing non-standard inputs like emojis. To address these limitations, we expand data dimensionality, transforming and fusing textual data and signs from a 1D to a 2D space. This enables the use of pre-trained 2D CNN models, such as AlexNet, Restnet-50, and VGG-16 removing the need to design and train new models from scratch. We apply this approach to a dataset of social media posts to classify informal documents as either related to suicide or non-suicide content. Our results demonstrate high classification accuracy, exceeding 99%. In addition, our 2D visual data representation conceals individual private information and helps explainability. © 2025 The Authors

关键词： Convolutional neural networks

Advancing Systematic Literature Reviews Methodology Through Topic Modeling 4th

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Advancing Systematic Literature Reviews Methodology Through ...

4th International Conference on intelligent systems and Pattern Recognition, ISPR 2024

作者： Mekaoui, Salma Chaker, Ilham Zarghili, Arsalane Nikolov, Nikola S. Department of Computer Science and Information Systems University of Limerick LimerickV94 T9PX Ireland Laboratory of Intelligent Systems and Applications Faculty of Sciences and Technology University Sidi Mohamed Ben Abdellah Route Immouzer Fez Morocco

ISBN: (纸本)9783031821493

The benefits of conducting a systematic review (SR) within a research project are well recognised. Nonetheless, nowadays an SR demands significant time and effort as it typically requires manually sifting through a large corpus of papers. In this work, we propose an application that simplifies the SR process for researchers by performing topic modeling. Our approach involves more than just conducting topic modeling;it entails selecting the optimal method tailored to our system’s needs. We explore two widely recognized methods in topic modeling, namely Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF), alongside two prominent libraries, Scikit-learn and Gensim, which provide implementations for these methods. The evaluation of these methods and libraries is done with the help of the cosine similarity metric. Our findings indicate that, for our research context, LDA implemented in Scikit-learn stands out as the most effective method for topic modeling. Additionally, our study suggests that employing 10 topics, along with stemming and lemmatization, yields better results. Our findings culminate in the development of an application to facilitate SRs for researchers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Non-negative matrix factorization

Optimal Sensor Placement for Full-Set TDOA Localization Accounting for Sensor Location Errors

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Zhang, Chengjie Han, Xinyang Kong, He Ho, K.C. Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Shenzhen518055 China University of Missouri Electrical Engineering and Computer Science Department ColumbiaMO65211 United States

The positioning accuracy of a signal source is influenced by where the sensors are deployed. Studies in the literature for the optimal sensor placement (OSP) of localization often ignore the presence of sensor position errors. In practice, however, it is impossible to place the sensors precisely at the desired locations. This work designs OSP by accounting for the presence of Gaussian sensor position errors that are independent and identically distributed (IID), with the full set of Time Difference of Arrival measurements corrupted by Gaussian IID noise. The criterion is the A-optimality equal to the trace of the source position Cramer-Rao Bound (CRB). We show that the presence of sensor position errors introduces a multiplication factor to the same criterion for the case without sensor position errors. The latter factor is dependent on the ratio of the sensor position error power to the range difference noise power but independent of the sensor positions, implying that the A-optimality OSP in the presence of sensor position errors is the same as in the absence. To our knowledge, this is the first time such results have been derived. Simulation studies validate the derived OSP, show the minimal OSP performance gap with sensor positioning error and without, and illustrate the better source localization accuracy by OSP. © 1965-2011 IEEE.

关键词： Cramer-Rao bounds

Fixed-Point RNNs: From Diagonal to Dense in a Few Iterations

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arXiv 2025年

作者： Movahedi, Sajad Sarnthein, Felix Cirone, Nicola Muça Orvieto, Antonio ELLIS Institute Tübingen Tübingen AI Center Germany Max Planck Institute for Intelligent Systems Tübingen Germany Department of Computer Science ETH Zurich Switzerland Department of Mathematics Imperial College London United Kingdom

Linear recurrent neural networks (RNNs) and state-space models (SSMs) such as Mamba have become promising alternatives to softmax-attention as sequence mixing layers in Transformer architectures. Current models, however, do not exhibit the full state-tracking expressivity of RNNs because they rely on channel-wise (i.e. diagonal) sequence mixing. In this paper, we propose to compute a dense linear RNN as the fixed-point of a parallelizable diagonal linear RNN in a single layer. We explore mechanisms to improve its memory and state-tracking abilities in practice, and achieve state-of-the-art results on the commonly used toy tasks A5, S5, copying, and modular arithmetics. We hope our results will open new avenues to more expressive and efficient sequence mixers. © 2025, CC BY.

关键词： Recurrent neural networks

Privacy-Aware Joint DNN Model Deployment and Partition Optimization for Delay-Efficient Collaborative Edge Inference

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arXiv 2025年

作者： Cheng, Zhipeng Xia, Xiaoyu Wang, Hong Liwang, Minghui Chen, Ning Fan, Xuwei Wang, Xianbin School of Future Science and Engineering Soochow University Suzhou215006 China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai201804 China Qingdao China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Department of Electrical and Computer Engineering Western University ON Canada

Edge inference (EI) is a key solution to address the growing challenges of delayed response times, limited scalability, and privacy concerns in cloud-based Deep Neural Network (DNN) inference. However, deploying DNN models on resource-constrained edge devices faces more severe challenges, such as model storage limitations, dynamic service requests, and privacy risks. This paper proposes a novel framework for privacy-aware joint DNN model deployment and partition optimization to minimize long-term average inference delay under resource and privacy constraints. Specifically, the problem is formulated as a complex optimization problem considering model deployment, user-server association, and model partition strategies. To handle the NP-hardness and future uncertainties, a Lyapunov-based approach is introduced to transform the long-term optimization into a single-time-slot problem, ensuring system performance. Additionally, a coalition formation game model is proposed for edge server association, and a greedy-based algorithm is developed for model deployment within each coalition to efficiently solve the problem. Extensive simulations show that the proposed algorithms effectively reduce inference delay while satisfying privacy constraints, outperforming baseline approaches in various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

Data leakage detection in machine learning code: transfer learning, active learning, or low-shot prompting?

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PeerJ computer science 2025年 11卷 e2730-e2730页

作者： Alturayeif, Nouf Hassine, Jameleddine Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Computing Department Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

With the increasing reliance on machine learning (ML) across diverse disciplines, ML code has been subject to a number of issues that impact its quality, such as lack of documentation, algorithmic biases, overfitting, lack of reproducibility, inadequate data preprocessing, and potential for data leakage, all of which can significantly affect the performance and reliability of ML models. Data leakage can affect the quality of ML models where sensitive information from the test set inadvertently influences the training process, leading to inflated performance metrics that do not generalize well to new, unseen data. Data leakage can occur at either the dataset-level (i.e., during dataset construction) or at the code-level. Existing studies introduced methods to detect code-level data leakage using manual and code analysis approaches. However, automated tools with advanced ML techniques are increasingly recognized as essential for efficiently identifying quality issues in large and complex codebases, enhancing the overall effectiveness of code review processes. In this article, we aim to explore ML-based approaches for limited annotated datasets to detect code-level data leakage in ML code. We proposed three approaches, namely, transfer learning, active learning, and low-shot prompting. Additionally, we introduced an automated approached to handle the imbalance issues of code data. Our results show that active learning outperformed the other approaches with an F-2 score of 0.72 and reduced the number of needed annotated samples from 1,523 to 698. We conclude that existing ML-based approaches can effectively mitigate the challenges associated with limited data availability. © 2025 Alturayeif and Hassine

关键词： Information leakage

Enhancing Mobile Crowdsensing Efficiency: A Coverage-aware Resource Allocation Approach

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arXiv 2025年

作者： Fu, Yaru Zhang, Yue Shi, Zheng Guo, Yongna Liu, Yalin School of Science and Technology Hong Kong Metropolitan University Hong Kong Department of Electronic and Information Engineering Shantou University China School of Intelligent Systems Science and Engineering Jinan University China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

In this study, we investigate the resource management challenges in next-generation mobile crowdsensing networks with the goal of minimizing task completion latency while ensuring coverage performance, i.e., an essential metric to ensure comprehensive data collection across the monitored area, yet it has been commonly overlooked in existing studies. To this end, we formulate a weighted latency and coverage gap minimization problem via jointly optimizing user selection, subchannel allocation, and sensing task allocation. The formulated minimization problem is a non-convex mixed-integer programming issue. To facilitate the analysis, we decompose the original optimization problem into two subproblems. One focuses on optimizing sensing task and subband allocation under fixed sensing user selection, which is optimally solved by the Hungarian algorithm via problem reformulation. Building upon these findings, we introduce a time-efficient two-sided swapping method to refine the scheduled user set and enhance system performance. Extensive numerical results demonstrate the effectiveness of our proposed approach compared to various benchmark strategies. Copyright © 2025, The Authors. All rights reserved.

关键词： Integer programming

Accelerating Stable Matching between Workers and Spatial-Temporal Tasks for Dynamic MCS: A Stagewise Service Trading Approach

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arXiv 2025年

作者： Qi, Houyi Liwang, Minghui Wang, Xianbin Fu, Liqun Hong, Yiguang Li, Li Cheng, Zhipeng School of Informatics Xiamen University Fujian China Department of Control Science and Engineering Canada Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China Department of Electrical and Computer Engineering Western University ON Canada School of Future Science and Engineering Soochow University Jiangsu China

Designing proper incentives in mobile crowdsensing (MCS) networks represents a critical mechanism in engaging distributed mobile users (workers) to contribute heterogeneous data for diverse applications (tasks). We develop a novel stagewise trading framework to reach efficient and stable matching between tasks and workers, upon considering the diversity of tasks and the dynamism of MCS networks. This framework integrates futures and spot trading stages, where in the former, we propose futures trading-driven stable matching and pre-path-planning (FT-SMP3) for long-term task-worker assignment and pre-planning of workers’ paths based on historical statistics and risk analysis. While in the latter, we investigate spot trading-driven DQN path planning and onsite worker recruitment (ST-DP2WR) mechanism to enhance workers’ and tasks’ practical utilities by facilitating temporary worker recruitment. We prove that our proposed mechanisms support crucial properties such as stability, individual rationality, competitive equilibrium, and weak Pareto optimality theoretically. Also, comprehensive evaluations confirm the satisfaction of these properties in practical network settings, demonstrating our commendable performance in terms of service quality, running time, and decision-making overheads. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk analysis