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

作者： Banerjee, Avijit Tevetzidis, Ilias Satpute, Sumeet Gajanan Nikolakopoulos, George Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents a nonlinear dynamic inversion-based motion plan for a levitating robotic satellite emulation platform. The frictionless motion of such a levitating platform has dynamic equivalency with a satellite’s realistic movement in space, thus evolving as an efficient demonstration tool as a hardware-in-loop satellite motion simulator. A brief design description of the emulation platform unified with various sensor and actuator units is presented, and an associated nonlinear mathematical model representing its coupled dynamic motion is described. The proposed control design architecture manipulates the associated nonlinear dynamics, which appear due to the coupling of translational and rotational movement of the robotic platform, thereby establishing an efficient trajectory tracking capability for various operating regions without the additional requirement of gain scheduling. In order to realize the control requirement through a set of ON-OFF thruster-based actuation units, which closely resembles the reaction control system of realistic satellite design, an optimal thrust allocation logic is proposed that accounts for various physical limitations of thrusters such as thrusters as its magnitude constraint and minimum on time. The efficacy of the proposed modelling and control scheme is experimentally validated in different realistic motion planning scenarios, such as point-to-point manoeuvres with terminal objectives and a superior path-following capability. The experimental studies are supported with related results. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Motion planning

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Fusion of Multiple Audio Descriptors for the Recognition of Dysarthric Speech 27

Fusion of Multiple Audio Descriptors for the Recognition of ...

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27th Conference on the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2024

作者： Bharti, Komal Das, Pradip K. IIT Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331506032

Dysarthria is a motor speech disorder majorly caused from brain stroke or severe accidents affecting the left hemisphere of the brain. Due to the injury, speech muscles and tongue movement become weak, leading to highly unintelligible speech. In the past decade, the performance of Automatic Speech Recognition (ASR) systems have improved signif-icantly for normal speech, but when it comes to dysarthric speech, performance is far from satisfactory. In this pa-per we implement dysarthric speech recognition system in a speaker-adaptive manner, leveraging additional acoustic features to help the model effectively learn and adapt to each speaker's unique speech patterns. Feature selection is based on MFCC combined with different variants of Jitter and Shimmer. Despite the challenges posed by limited data and considerable variability among the speakers, our system achieves an average Word Recognition Accuracy (WRA) of 83.11 % on the UASpeech database, which is a significant improvement over previous studies. In addition to ASR, we also analyze dysarthric speakers with very low intelligibility rates and found some prominent insights into their speech characteristics. © 2024 IEEE.

关键词： Feature Selection

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WGSDMM+GA: A genetic algorithm-based service clustering methodology assimilating dirichlet multinomial mixture model with word embedding

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Future Generation computer Systems 2023年第1期145卷 254-266页

作者： Agarwal, Neha Sikka, Geeta Awasthi, Lalit Kumar Computer Science and Engineering Department Indian Institute of Information Technology Karnataka Raichur584135 India Department of Computer Science and Engineering National Institute of Technology Delhi Delhi110036 India 246174 India

Advancement of web 2.0 results in the expeditious growth of services in repositories and service portals, which raises the demand for service management. With the clusters of services, different processes like discovery, selection, ranking, etc., can be expedited efficiently. K-Means clustering is highly recommended for service clustering but it can be stuck in the local optimum values. Developers generally use short text to describe the functionality of Web services. For effective service clustering, it is required that relevant features of services are mapped in vector space based on semantic relations. Gibbs Sampling algorithm for Dirichlet Multinomial Mixture model (GSDMM) model is able to provide good performance on short text because it works on the assumption that there will be one topic related to one document. In this paper, a novel technique for web service clustering, i.e., WGSDMM+GA, is proposed in which relevant features, semantic relations, and their mappings in vector space are analyzed based on an assimilated approach of Word2Vec and GSDMM models. As K-means clustering cannot provide effective results due to local optima problem, GA-based clustering is applied in which fitness function is based on Manhattan distance. The proposed model is applied to five real-time datasets, and the performance is evaluated based on standard evaluation measures. Experimental results illustrate that WGSDMM+GA yields better results, and the accuracy score is increased by 23%, 17%,17%, 21%, and 25% on five real-time datasets. © 2023 Elsevier B.V.

关键词： Genetic algorithms

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Intelligent Reporting System: Engineered with Application Development 11

Intelligent Reporting System: Engineered with Application De...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Singh, Damini Agarwal, Khyati Marwaha, Mansha Mishra, Neha Pathak, Disha Mohini Abes Engineering College Department of Computer Science India

ISBN: (纸本)9798350350357

The traditional system of issue reporting has heavily relied on manual processes that require users to register their complaints with the authorities. This method is time-consuming and often results in inefficiencies due to the lack of a centralized and transparent mechanism. Our idea revolutionizes the traditional approach by employing a cutting-edge solution that grants users the ability to report and track issues directly and effectively, paving the way for a new era of seamless and transparent issue reporting. The latest technological advancements encourage the convergence of various technologies, seamlessly integrating application development, machine learning features, and cloud computing. © 2024 IEEE.

关键词： Application programs

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Segmenting Image and Removing Obtrusive Elements

Segmenting Image and Removing Obtrusive Elements

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2024 International Conference on electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Singh, Anirudh Gupta, Vishank Gupta, Yash Kamboj, Dikshant Singh, Ashwini Kumar Malik, Preeti University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350378092

This Model offers a novel method for processing images that highlights the main subject and removes unnecessary features using YOLO, Darknet, picture segmentation, and Mask R-CNN."The approach accomplishes accurate object detection by using procedures like segmentation using Mask R-CNN, YOLO and Darknet object identification, and backdrop blackening. It is highly skilled at identifying primary items and has been trained on a large photo dataset. Its uses include autonomous driving, object detection, and image retrieval. By drastically lowering background noise, the detection component improves the accuracy of photo object detection. Essentially, this technique is critical to improving object detection accuracy through the reduction of background noise, which is an essential component of image processing. Finding and keeping important components while eliminating unnecessary ones from test cases or camera feeds is the main goal of the Model. © 2024 IEEE.

关键词： Image segmentation

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Design and implementation of a peer-to-peer energy trading scheme in multi-microgrid network with photovoltaics and wind energy

Journal of Energy Systems

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Journal of Energy Systems 2023年第2期7卷 158-172页

作者： Ehjaz, Muhammad Iqbal, Muhammad Zaidi, Syed Sajjad Haider Khan, Bilal Muhammad COMSATS University Electrical and Computer Engineering Department Wah Campus Islamabad Pakistan PNEC National University of Sciences and Technology Electrical Engineering Department Islamabad Pakistan

Expected widespread deployment of Peer-to-Peer energy transactions through affective utilization of Renewable Energy Sources require efficient energy transaction mechanism among the microgrids. We propose a scheme to establish peer-to-peer energy trading in multi-microgrid network by considering photovoltaic and wind energy systems. The research objectives are to minimize overall cost of all microgrids in multi-microgrid network and minimize the loading on centralized power network. Various parameters of photovoltaics and wind energy systems are modeled to explore their impact on P2P energy trading. Energy Management Unit establishes the smart contracts among microgrids, manages power transactions and calculates the cost based on dynamic pricing scheme in the multi-microgrid network. Two different cases are considered with respect to the types of power transaction among the microgrids in the multi-microgrid network and main grid. The effectiveness of the proposed scheme is validated by implementing on local small-scale power distribution system. © 2023 The Author(s).

关键词： Energy management

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Compromising the Intelligence of Modern DNNs: On the Effectiveness of Targeted RowPress

Compromising the Intelligence of Modern DNNs: On the Effecti...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Zhou, Ranyang Liu, Jacqueline T. Ahmed, Sabbir Angizi, Shaahin Rakin, Adnan Siraj New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ United States State University of New York Department of Computer Science BinghamtonNY United States

ISBN: (纸本)9783982674100

Recent advancements in side-channel attacks have revealed the vulnerability of modern Deep Neural Networks (DNNs) to malicious adversarial weight attacks. The well-studied RowHammer attack has effectively compromised DNN performance by inducing precise and deterministic bit-flips in the main memory (e.g., DRAM). Similarly, RowPress has emerged as another effective strategy for flipping targeted bits in DRAM. However, the impact of RowPress on deep learning applications has yet to be explored in the existing literature, leaving a fundamental research question unanswered: How does RowPress compare to RowHammer in leveraging bit-flip attacks to compromise DNN performance? This paper is the first to address this question and evaluate the impact of RowPress on DNN applications. We conduct a comparative analysis utilizing a novel DRAM-profile-aware attack designed to capture the distinct bit-flip patterns caused by RowHammer and RowPress. Eleven widely-used DNN architectures trained on different benchmark datasets deployed on a Samsung DRAM chip conclusively demonstrate that they suffer from a drastically more rapid performance degradation under the RowPress attack compared to RowHammer. The difference in the underlying attack mechanism of RowHammer and RowPress also renders existing RowHammer mitigation mechanisms ineffective under RowPress. As a result, RowPress introduces a new vulnerability paradigm for DNN compute platforms and unveils the urgent need for corresponding protective measures. © 2025 EDAA.

关键词： Deep neural networks

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FAS-RIS Communication: Model, Analysis, and Optimization

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9938-9943页

作者： Yao, Junteng Zheng, Jianchao Wu, Tuo Jin, Ming Yuen, Chau Wong, Kai-Kit Adachi, Fumiyuki Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China Huizhou University School of Computer Science and Engineering Huizhou516000 China Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 Singapore University College London Department of Electronic and Electrical Engineering LondonWC1E 6BT United Kingdom Yonsei University Yonsei Frontier Laboratory the School of Integrated Technology Seoul03722 Korea Republic of Sendai Japan

This correspondence investigates the novel fluid antenna system (FAS) technology, combining with reconfigurable intelligent surface (RIS) for wireless communications, where a base station (BS) communicates with a FAS-enabled user with the assistance of a RIS. To analyze this technology, we derive the outage probability based on the block-diagonal matrix approximation (BDMA) model. With this, we obtain the upper bound, lower bound, and asymptotic approximation of the outage probability to gain more insights. Moreover, we design the phase shift matrix of the RIS in order to minimize the system outage probability. Simulation results confirm the accuracy of our approximations and that the proposed schemes outperform benchmarks significantly. © 2025 IEEE.

关键词： Benchmarking

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Intent-Based Network Configuration Using Large Language Models

Intent-Based Network Configuration Using Large Language Mode...

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作者： Tu, Nguyen Nam, Sukhyun Hong, James Won-Ki Department of Computer Science and Engineering POSTECH Pohang Korea Republic of

The increasing scale and complexity of network infrastructure present a huge challenge for network operators and administrators in performing network configuration and management tasks. Intent-based networking has emerged as a solution to simplify the configuration and management of networks. However, one of the most difficult tasks of intent-based networking is correctly translating high-level natural language intents into low-level network configurations. In this paper, we propose a general and effective approach to perform the network intent translation task using large language models with fine-tuning, dynamic in-context learning, and continuous learning. Fine-tuning allows a pretrained large language model to perform better on a specific task. In-context learning enables large language models to learn from the examples provided along with the actual intent. Continuous learning allows the system to improve overtime with new user intents. To demonstrate the feasibility of our approach, we present and evaluate it with two use cases: network formal specification translation and network function virtualization configuration. Our evaluation shows that with the proposed approach, we can achieve high intent translation accuracy as well as fast processing times using small large language models that can run on a single consumer-grade GPU. © 2024 John Wiley & Sons Ltd.

关键词： High level languages

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Fake News Classification: Past, Current, and Future

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computers, Materials & Continua 2023年第11期77卷 2225-2249页

作者： Muhammad Usman Ghani Khan Abid Mehmood Mourad Elhadef Shehzad Ashraf Chaudhry Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science&Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbulTurkey

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.

关键词： Supervised learning algorithms fake news classification online disinformation transformers recurrent neural network(RNN)disinformation transformers recurrent neural network(RNN)

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