Revolutionizing Aerospace and Defense: The Impact of AI and Robotics on Modern Warfare

Shetty, Dasharathraj K; Prerepa, Gayathri; Naik, Nithesh; Bhat, Ravi; Sharma, J.N.; Mehrotra, Piyush

doi:10.1145/3590837.3590856

Cited by 4 publications

(3 citation statements)

References 55 publications

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“…The significance of these technical breakthroughs is highlighted in research [90], which highlights how they reduce costs, streamline design and prototyping processes, and increase manufacturing efficiency in a variety of industries.…”

Section: Challenges In Ai Adoption For Fault Diagnosismentioning

confidence: 99%

Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions

Baroud

2024

J. Appl. Data Sci.

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Integrating Artificial Intelligence (AI) within Industry 4.0 has propelled the evolution of fault diagnosis and predictive maintenance (PdM) strategies, marking a significant shift towards smarter maintenance paradigms in the mechatronics sector. With the advent of Industry 4.0, mechatronic systems have become increasingly sophisticated, highlighting the critical need for advanced maintenance methodologies that are both efficient and effective. This paper delves into the confluence of cutting-edge AI techniques, including machine learning (ML) and deep learning (DL), with multi-agent systems (MAS) to enhance fault diagnosis precision and facilitate PdM in the context of Industry 4.0. Specifically, we explore the use of various ML models, including Support Vector Machines (SVMs) and Random Forests (RFs), and DL architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which have been effectively oriented to analyses complex industrial data. Initially, the study examines the progress in AI algorithms that accelerate fault identification by leveraging data from system operations, sensors, and historical trends. AI-enabled fault diagnosis rapidly detects irregularities and discerns the fundamental causes, thereby minimizing downtime and enhancing system reliability and efficiency. Furthermore, this paper underscores the adoption of AI-driven PdM approaches, emphasizing prognostics that predict the Remaining Useful Life (RUL) of machinery. This predictive capability allows for the strategic scheduling of maintenance activities, optimizing resource use, prolonging the lifespan of expensive assets, and refining the management of spare parts inventory. The tangible advantages of employing AI for fault diagnosis and PdM are showcased through a case study from authentic mechatronics implementations. This case study highlights successful implementations, documenting real-world challenges such as data integration issues and system interoperability, and elaborates on the strategies deployed to navigate these obstacles. The results demonstrate improved operational reliability and cost savings and shed light on the pragmatic considerations and solutions that facilitate the adoption of AI and MAS in industrial applications. The paper also navigates the challenges and prospective research avenues in applying AI within the mechatronics domain of Industry 4.0, setting the stage for ongoing innovation and exploration in this transformative domain.

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Section: Challenges In Ai Adoption For Fault Diagnosismentioning

confidence: 99%

Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions

Baroud

2024

J. Appl. Data Sci.

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“…In the aerospace and defense sectors, the integration of artificial intelligence (AI) and robotics, key components of mechatronics, has revolutionized traditional practices. Shetty et al (2022) discusses how these technological advancements have led to cost savings, faster design and prototyping, and improved manufacturing efficiency in these industries. The development of unmanned autonomous aircraft, for instance, has reduced time and cost, minimized human error, and decreased environmental pollution.…”

Section: The Significance Of Mechatronics In Advancing Modern Industrymentioning

confidence: 99%

Mechatronics in modern industrial applications: Delving into the integration of electronics, mechanics, and informatics

Abimbola Oluwatoyin Adegbite,

Chinedu Nnamdi Nwasike,

Nwabueze Kelvin Nwaobia

et al. 2023

World J. Adv. Res. Rev.

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This study delves into the integration of electronics, mechanics, and informatics within the realm of mechatronics, emphasizing its pivotal role in modern industrial applications. The primary objective is to dissect the synergistic interplay of these disciplines and assess their collective impact on various industrial sectors. Employing a systematic literature review as the core methodology, the research draws on a wide array of peer-reviewed articles and academic journals, focusing on publications from the last two decades to ensure relevance and contemporaneity. Key findings reveal that mechatronics has significantly evolved from its conceptual inception to a cornerstone technology in industries such as manufacturing, automotive, and healthcare. The study highlights the advancements in mechatronic systems, including the integration of AI and IoT, which have led to enhanced efficiency, precision, and adaptability in industrial processes. Furthermore, the research identifies the economic, technological, and environmental impacts of these systems, underscoring their role in driving innovation and sustainability. The study concludes with actionable recommendations for industry stakeholders and policymakers, emphasizing the need for continuous investment in R&D, workforce training, and the development of ethical standards and regulations. It also proposes future research directions, particularly focusing on the scalability, sustainability, and long-term societal impacts of mechatronic systems. This research contributes to a deeper understanding of mechatronics in the industrial domain, offering insights into its current applications and future potential.

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“…In today's interconnected digital landscape, the evolution of cyber threats has reached unprecedented levels of sophistication, posing substantial challenges to conventional cybersecurity measures [1]. As the frequency and complexity of these threats continue to surge, the integration of artificial intelligence (AI) emerges as a pivotal solution, offering a transformative approach to fortifying defenses against evolving cyber perils [2]. This paper presents "AI Sentry," an innovative paradigm that harnesses the power of AI to revolutionize cybersecurity through intelligent threat detection and proactive mitigation.…”

Section: Introductionmentioning

confidence: 99%

Ai Sentry: Reinventing Cybersecurity Through Intelligent Threat Detection

Rangaraju

2023

EPHIJSE

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In recent years, the escalating complexity and frequency of cyber threats have presented a formidable challenge to traditional cybersecurity measures. The emergence of artificial intelligence (AI) technologies has revolutionized the landscape, offering a promising solution to fortify defenses against evolving threats. This paper introduces AI Sentry, an innovative approach to cybersecurity that leverages the power of AI for intelligent threat detection and prevention. AI Sentry embodies a paradigm shift in cybersecurity, integrating machine learning, neural networks, and advanced algorithms to proactively identify, analyze, and mitigate potential threats in real time. By continuously learning from vast datasets and adapting to new attack vectors, AI Sentry enhances its ability to recognize anomalous patterns and behaviors, thereby thwarting sophisticated cyber assaults. The core strength of AI Sentry lies in its capability to detect anomalies and predict threats with a high degree of accuracy, surpassing the limitations of traditional signature-based systems. Through anomaly detection, behavioral analysis, and contextual understanding, AI Sentry not only identifies known threats but also anticipates zero-day attacks and previously unseen malicious activities. This paper delves into the technical underpinnings of AI Sentry, elucidating its architecture, data processing techniques, machine learning models, and the orchestration of various AI components. Furthermore, it explores the ethical considerations and challenges associated with AI-powered cybersecurity, including issues of privacy, bias mitigation, and transparency in decision-making.

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Revolutionizing Aerospace and Defense: The Impact of AI and Robotics on Modern Warfare

Cited by 4 publications

References 55 publications

Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions

Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions

Mechatronics in modern industrial applications: Delving into the integration of electronics, mechanics, and informatics

Ai Sentry: Reinventing Cybersecurity Through Intelligent Threat Detection

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