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

作者： Rayon, Nathan Menser, Kobi Catalano, Louis Vega, Jonathan Najarro, Julio Sevil, Hakki Erhan Department of Computer Science University of West Florida FL32514 United States Department of Mechanical Engineering University of West Florida FL32514 United States Catalano Aerospace LLC FL32502 United States Department of Intelligent Systems & Robotics University of West Florida FL32514 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper introduces a new unmanned aerial system (UAS) design featuring coaxial rotors and tethered operation with a base station. The coaxial rotor configuration enhances stability and maneuverability, particularly in challenging environments. The tethered connection to a base station ensures prolonged flight duration and enables continuous power and data transmission. Moreover, the UAS incorporates a visual question answering (VQA) based target identification system. This system leverages advanced computer vision algorithms to analyze visual input from onboard cameras, coupled with natural language processing techniques to interpret and respond to queries regarding identified targets. The integration of VQA technology enables critical decision-making decisions through providing crucial information identify about targets in real-time scenarios. The new design has a significant potential for applications in surveillance, reconnaissance, search and rescue, and disaster response, as this design was conceptualized to provide persistent Intelligence, Reconnaissance & Surveillance (ISR) capabilities. Further analysis of the concept design was conducted to investigate the feasibility of novel design characteristics. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Reconnaissance aircraft

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Gradual domain adaptation via manifold-constrained distributionally robust optimization 24

Gradual domain adaptation via manifold-constrained distribut...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Amirhossein Saberi Amir Najafi Amin Behjati Ala Emrani Yasaman Zolfi Mahdi Shadrooy Abolfazl Motahari Babak H. Khalaj Department of Electrical Engineering and Sharif Center for Information Systems and Data Science Sharif University of Technology Tehran Iran Department of Computer Engineering Sharif University of Technology Tehran Iran

ISBN: (纸本)9798331314385

The aim of this paper is to address the challenge of gradual domain adaptation within a class of manifold-constrained data distributions. In particular, we consider a sequence of T ≥ 2 data distributions P1, ..., PT undergoing a gradual shift, where each pair of consecutive measures Pi, Pi+1 are close to each other in Wasserstein distance. We have a supervised dataset of size n sampled from P0, while for the subsequent distributions in the sequence, only unlabeled i.i.d. samples are available. Moreover, we assume that all distributions exhibit a known favorable attribute, such as (but not limited to) having intra-class soft/hard margins. In this context, we propose a methodology rooted in Distributionally Robust Optimization (DRO) with an adaptive Wasserstein radius. We theoretically show that this method guarantees the classification error across all Pis can be suitably bounded. Our bounds rely on a newly introduced compatibility measure, which fully characterizes the error propagation dynamics along the sequence. Specifically, for inadequately constrained distributions, the error can exponentially escalate as we progress through the gradual shifts. Conversely, for appropriately constrained distributions, the error can be demonstrated to be linear or even entirely eradicated. We have substantiated our theoretical findings through several experimental results.

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The Effect of Imbalanced Data and Parameter Selection via Genetic Algorithm Long Short-Term Memory (LSTM) for Financial Distress Prediction

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IAENG International Journal of Applied Mathematics 2023年第3期53卷 25页

作者： Adisa, Juliana Adeola Ojo, Samuel Owolawi, Pius Adewale Pretorius, Agnieta Ojo, Sunday Olusegun Tshwane University of Technology Department of Computer Systems Engineering Soshanguve Campus South Africa Department of Computer Systems Engineering Tshwane University of Technology Soshanguve Campus South Africa Faculty of ICT Tshwane University of Technology Soshanguve Campus South Africa Computer Science Department of Information Technology Durban University of Technology Durban South Africa

Financial companies are grappling with a burning issue about bankruptcy prediction. There are many methods for bankruptcy prediction, including statistical models and machine learning. Real-life datasets are often imbalanced with high dimensionality. Therefore, it is challenging to train a robust model to predict bankruptcy. Thus, we first applied an oversampling technique known as the Synthetic Minority Oversampling Technique (SMOTE) to reduce the skewness of the data. The balanced data was trained with the baseline models, the ensemble classifiers using different combination methods and the long short-term memory (LSTM) model. In addition, we employed an optimization technique called a genetic algorithm (GA) to optimize and determine the learning parameters of an LSTM network. We further determine the effects of using different training/testing ratios on the developed models. An autoencoder long short-term memory (LSTM) model was developed to extract the best feature representation of the input data. A comparative analysis was carried out between the LSTM-GA and autoencoder-LSTM. The results show that the improved LSTM-GA model with an accuracy of 98.11% performs better than other models. Overall, the research work concluded that all models and LSTM have good performances, while the optimized LSTM model via genetic algorithm outperforms the classical machine learning models. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： Genetic algorithms

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Multi-Dataset Multi-Task Learning for COVID-19 Prognosis

arXiv

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arXiv 2024年

作者： Ruffini, Filippo Tronchin, Lorenzo Wu, Zhuoru Chen, Wenting Soda, Paolo Shen, Linlin Guarrasi, Valerio Unit of Computer Systems & Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Hong Kong

In the fight against the COVID-19 pandemic, leveraging artificial intelligence to predict disease outcomes from chest radiographic images represents a significant scientific aim. The challenge, however, lies in the scarcity of large, labeled datasets with compatible tasks for training deep learning models without leading to overfitting. Addressing this issue, we introduce a novel multi-dataset multi-task training framework that predicts COVID-19 prognostic outcomes from chest X-rays (CXR) by integrating correlated datasets from disparate sources, distant from conventional multi-task learning approaches, which rely on datasets with multiple and correlated labeling schemes. Our framework hypothesizes that assessing severity scores enhances the model's ability to classify prognostic severity groups, thereby improving its robustness and predictive power. The proposed architecture comprises a deep convolutional network that receives inputs from two publicly available CXR datasets, AIforCOVID for severity prognostic prediction and BRIXIA for severity score assessment, and branches into task-specific fully connected output networks. Moreover, we propose a multi-task loss function, incorporating an indicator function, to exploit multi-dataset integration. The effectiveness and robustness of the proposed approach are demonstrated through significant performance improvements in prognosis classification tasks across 18 different convolutional neural network backbones in different evaluation strategies. This improvement is evident over single-task baselines and standard transfer learning strategies, supported by extensive statistical analysis, showing great application potential. © 2024, CC BY-NC-SA.

关键词： COVID-19

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Image Caption And Speech Generation Using LSTM And GTTS API

Image Caption And Speech Generation Using LSTM And GTTS API

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Augmented Intelligence and Sustainable systems (ICAISS), International Conference on

作者： K. Bhargav Ram Venkatesh B Sala Pooja Sai Sree Chunduru Anilkumar V. Surya Narayana Reddy Bhavya Kodumuri Department of Computer Science and Engineering BVRIT HYDERABAD College of Engineering for Women Hyderabad India Department of Computer Science and Engineering BVRIT HYDERABAD College of Engineering for Women Hydetabad India Systems Engineer Infosys Hyderabad India Dept of Information Technology GMR Institute Of Technology Rajam Andhra Pradesh Department of Computer Science and Engineering VNR VIGNANA JYOTHI INSTITUTE OF ENGINEERING AND TECHNOLOGY Hyderabad India Department of Computer Science and Engineering Vidya Jyothi Institute of Technology HYderabad India

Image Caption generation is one of the challenging tasks in the field of artificial intelligence. It is used to generate a textual description for a given picture. But due to, the recent advancement in deep learning techniques requires only one single end-to-end model to create a caption for the given image. This paper is intended to provide an image caption and speech generator used to generate a single-line description for a given image and audio/speech for the report generated. The researchers used a sophisticated pipeline of specifically designed models in the previous models. To achieve the proposed model, VGG16 and LSTM models are used to obtain descriptions for the image and GTTS API is used for the audio/speech generation.

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Decision Making in Problem of Irrigation Based on Z-Information 16th

Decision Making in Problem of Irrigation Based on Z-Informat...

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16th International Conference on Application of Fuzzy systems, Soft Computing and Artificial Intelligence Tools, ICAFS 2023

作者： Zeinalova, Lala M. Muravyova, Elena Bogdanova, Diana Department of Computer Engineering Azerbaijan State University of Oil and Industry 20 Azadlig Avenue Baku1010 Azerbaijan Department of Automated Technological and Information Systems Ufa State Petroleum Technological University Sterlitamak Russia Department of Computational Mathematics and Cybernetics Ufa University of Science and Technology Ufa Russia

ISBN: (纸本)9783031762826

In this paper an intelligent irrigation system is considered. Irrigation regimes for agricultural crops provide the determination of scientifically based norms of irrigation, depending on the soil, physiological, climatic and technical characteristics of the irrigated array. In modern irrigation systems, the parameters of irrigation regimes are calculated similarly to traditional methods. To solve the problem of efficient irrigation, it is necessary to take into account the weak formalizability, which makes it difficult to use accurate mathematical models to describe the control system. The solution to this problem would be partially possible if there was a large number of qualified experts who, taking into account the linguistic nature of the variables, are able to determine the optimal irrigation rates, using their experience without constructing any functional dependencies. Due to the absence of such reference experts in intelligent irrigation planning systems, mathematical models based on Soft Computing should come forward. Our model is based on Z-information, which has such an important qualitative attribute as reliability. The concept of Z-number relates to the issue of reliability of information, especially in the realms of decision analysis. Z-number valuations take into account the uncertаinty of the experts` opinion in the estimаtion of the options. We apply an interpolation approach for Z-rules for the solution of problem of approximate reasoning. A practical example of evaporation is given to show the applicability of approach in irrigation process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Fuzzy logic

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Efficient Drinking Water Quality Analysis using Machine Learning Model with Hyper-Parameter Tuning 7

Efficient Drinking Water Quality Analysis using Machine Lear...

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7th International Conference on Intelligent Computing and Control systems, ICICCS 2023

作者： Maheswari, M Uma Sudharsanan, R. Arthy, M. Jenefer, Anne Oormila, L. Samuthira Pandi, V. RMD Engineering College Department of Electrical and Electronics Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Information Technology Tamil Nadu Ramapuram India SRM Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu India Agni College of Technology Department of Computer Science Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Electronics Ad Communication Engineering Tamilnadu Ramapuram India

ISBN: (纸本)9798350397253

The conventional approaches to determining the quality of water include costly and lengthy statistical and laboratory testing;hence, the concept of real-time monitoring is no longer applicable in the modern day. The disastrous implications of poor water quality demand that there be a remedy that is both more expedient and more realistic. This research study examines a variety of supervised machine learning algorithms in order to identify the quality of the water. A variety of variables, such as the pH value, the hardness, the solids, the chloramines, the sulfates, the conductivity, the organic carbon, the trihalomethanes, the turbidity, and the potability of the water, are essential for determining the quality of the water. Random Forest (RF) and Decision Tree (DT) are used in order to ascertain the caliber of the water that is suitable for human consumption. The standard laboratory approach for testing the quality of water is one that takes a lot of time and may be somewhat expensive at times. In a very short length of time, the algorithms that are proposed in this study are able to provide an estimation of the quality of drinking water. DT has a height accuracy F1 score of 99%, whereas RF has a score of 87.86% and a precision of 82.36%. The difference in these two scores is due to the fact that DT's precision is lower. The suggested method demonstrates its potential for usage in real-time water quality monitoring systems by achieving appropriate accuracy with a minimal set of parameters. To prove the usefulness of this program, this is essential. © 2023 IEEE.

关键词： Water quality

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Society 5.0: a simulation optimisation study of dynamic scheduling for a grocery store

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International Journal of Simulation and Process Modelling 2021年第2-3期17卷 178-186页

作者： Mykoniatis, Konstantinos Angelopoulou, Anastasia Nichols, Samira Shirzaei Department of Industrial and Systems Engineering Auburn University AuburnAL36849 United States TSYS School of Computer Science Columbus State University ColumbusGA31907 United States Computer Information Systems and Analytics College of Business University of Central Arkansas AR72035 United States

Society 5.0 refers to a technology-based human-centred society that integrates cyber-physical systems and uses advanced technology to improve everyday life. Queuing systems, such as grocery stores, will transition to Society 5.0. Waiting time in the queue of such systems makes it difficult to determine the necessary number of resources to operate cost-effectively and keep customers satisfied. This work studies a grocery store with two types of resources: cashier-staffed billing counters and self-checkout machines. The objective of the study is to dynamically schedule the required number of resources such that the arriving customers experience reduced waiting times in the queue, while the store maintains low operational costs. The work uses the Simio simulation software and the OptQuest simulation optimisation engine to determine the staffing levels and achieve the aforementioned objectives. The optimisation experiment results showed that using dynamic resource scheduling can assist in maintaining satisfactory customer waiting times and operational costs. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Discrete event simulation

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MMDF-ESI: Multi-Modal Deep Fusion of EEG and MEG for Brain Source Imaging 16th

MMDF-ESI: Multi-Modal Deep Fusion of EEG and MEG for Brai...

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16th International Conference on Brain Informatics, BI 2023

作者： Jiao, Meng Yang, Shihao Wang, Boyu Xian, Xiaochen Semenov, Yevgeniy R. Wan, Guihong Liu, Feng School of Systems and Enterprises Stevens Institute of Technology HobokenNJ United States Massachusetts General Hospital Harvard Medical School BostonMA United States Department of Computer Science Western University LondonON Canada Department of Industrial and Systems Engineering University of Florida GainesvilleFL United States

ISBN: (纸本)9783031430749

Electrophysiological Source Imaging (ESI) aims to reconstruct the underlying electric brain sources based on Electroencephalography (EEG) or Magnetoencephalography (MEG) measurements of brain activities. Due to the ill-posed nature of the ESI problem, faithfully recovering the latent brain sources has been a significant challenge. Classical algorithms have primarily focused on the design of regularization terms according to predefined priors derived from neurophysiological assumptions. Deep learning frameworks attempt to learn the mapping between brain source signals and scalp EEG/MEG measurements in a data-driven manner, and have demonstrated improved performance compared to the classical methods. Given that EEG and MEG can be complementary for measuring the tangential and radial electrical signals of the cortex, combining both modalities is believed to be advantageous in improving the reconstruction performance of ESI. However, the fusion of these two modalities for the ESI problem has not been fully explored in existing deep learning frameworks. In this paper, we propose a Multi-Modal Deep Fusion (MMDF) framework for solving the ESI inverse problem, termed as MMDF-ESI. Our framework integrates both EEG and MEG in a deep learning framework with a specially designed squeeze-and-excitation module. This integration of EEG and MEG is conducted at an early phase in the deep learning framework rather than on the final decision level fusion. Our experimental results show that (1) the localization accuracy of MMDF-ESI consistently outperforms that of using a single modality;(2) MMDF-ESI exhibits excellent stability, characterized by significantly smaller error variance in source reconstruction compared to benchmark methods, particularly for sources with larger extended activation areas and under low signal-to-noise ratio (SNR) conditions;(3) the evaluation on a real EEG/MEG dataset demonstrates that MMDF-ESI enables a more concentrated reconstruction, effectively recoveri

关键词： Inverse problems

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Integrating Multimodal Perception into Ground Mobile Robots

Integrating Multimodal Perception into Ground Mobile Robots

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Ricardo B. Sousa Héber Miguel Sobreira João G. Martins Paulo G. Costa Manuel F. Silva António Paulo Moreira Faculty of Engineering University of Porto (FEUP) Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal ISEP Polytechnic of Porto Porto Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Multimodal perception systems enhance the robustness and adaptability of autonomous mobile robots by integrating heterogeneous sensor modalities, improving long-term localisation and mapping in dynamic environments and human-robot interaction. Current mobile platforms often focus on specific sensor configurations and prioritise cost-effectiveness, possibly limiting the flexibility of the user to extend the original robots further. This paper presents a methodology to integrate multimodal perception into a ground mobile platform, incorporating wheel odometry, 2D laser scanners, 3D Light Detection and Ranging (LiDAR), and RGBD cameras. The methodology describes the electronics design to power devices, firmware, computation and networking architecture aspects, and mechanical mounting for the sensory system based on 3D printing, laser cutting, and bending metal sheet processes. Experiments demonstrate the usage of the revised platform in 2D and 3D localisation and mapping and pallet pocket estimation applications. All the documentation and designs are accessible in a public repository.

关键词： Laser radar Robot vision systems Wheels Three-dimensional printing Cameras Distance measurement Robustness Mobile robots Laser beam cutting Autonomous robots

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