How Can Artificial Intelligence Help With Space Missions - A Case Study: Computational Intelligence-Assisted Design of Space Tether for Payload Orbital Transfer Under Uncertainties

Ren, Xiuyan; Chen, Leo

doi:10.1109/access.2019.2951136

Cited by 11 publications

(5 citation statements)

References 19 publications

(31 reference statements)

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“…In fact, some CNN-based research in medical image and signal classification has achieved accuracy that rivals even human experts. AI has lately been used in diverse fields such as facial expression recognition [3], cyber security [4], space missions [5], management [6], online education [7], and chip development [8]. Furthermore, the use of AI has developed content-creation software such as Photoshop or Midjourney, in which AI can now generate images or art just from a description of the scene.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiogram Signals Classification Using Deep-Learning-Based Incorporated Convolutional Neural Network and Long Short-Term Memory Framework

Eleyan,

Alboghbaish

2024

Computers

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Cardiovascular diseases (CVDs) like arrhythmia and heart failure remain the world’s leading cause of death. These conditions can be triggered by high blood pressure, diabetes, and simply the passage of time. The early detection of these heart issues, despite substantial advancements in artificial intelligence (AI) and technology, is still a significant challenge. This research addresses this hurdle by developing a deep-learning-based system that is capable of predicting arrhythmias and heart failure from abnormalities in electrocardiogram (ECG) signals. The system leverages a model that combines long short-term memory (LSTM) networks with convolutional neural networks (CNNs). Extensive experiments were conducted using ECG data from both the MIT-BIH and BIDMC databases under two scenarios. The first scenario employed data from five distinct ECG classes, while the second focused on classifying data from three classes. The results from both scenarios demonstrated that the proposed deep-learning-based classification approach outperformed existing methods.

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Section: Introductionmentioning

confidence: 99%

Electrocardiogram Signals Classification Using Deep-Learning-Based Incorporated Convolutional Neural Network and Long Short-Term Memory Framework

Eleyan,

Alboghbaish

2024

Computers

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“…Presently, digital technologies assisted by artificial intelligence (AI) techniques are at the core of many innovative solutions across several sectors. These sectors include engineering design, research and development, manufacturing, and industrial production [1]- [4]. With a focus on manufacturing and industrial production, a majority of new and emerging AI-based paradigms are mainly aimed at the robust analysis of data and elimination of repetitive jobs on the shop floor for advanced process automation and holistic decision-making.…”

Section: Introductionmentioning

confidence: 99%

Applied Artificial Intelligence in Manufacturing and Industrial Production Systems: PEST Considerations for Engineering Managers

Akinsolu

2023

IEEE Eng. Manag. Rev.

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Presently, artificial intelligence (AI) is playing a leading role in our contemporary world via numerous applications. Despite its many advantages, analytical frameworks highlighting the implications of AI applications are still evolving. Particularly, in manufacturing and industrial production where novel technologies are continuously being harnessed. Consequently, AI and the implications of its applications have relatively remained a grey area for many engineering managers who are key players in the gravitation of manufacturing and industrial production towards the fourth industrial revolution and more recently, the fifth industrial revolution, generally termed as Industry 4.0 (I4.0) and Industry 5.0 (I5.0), respectively. In this study, the implications of AI applications in the general context of manufacturing and industrial production, are presented to provide insight for engineering managers. These implications are discussed via PEST (political, economic, social, and technological) considerations of the broad implications of the adoption of AI techniques in manufacturing and industrial production systems. A new engineering management model has not been proposed in this paper. Rather, a discussion aimed at serving as a tool for the appraisal of the implications of the general applications of AI by engineering managers, who may not be AI specialists or data science experts is presented.

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“…They offer a rich and rapidly growing set of problem solving technologies. Enabling system-level smartness by applying ANI or AGI or AEI technologies is in the center of current cognitive systems research and development, but its future possibilities and implications are seen and judged differently (Ren and Chen, 2019). As shown in Figure 2, four conflicting worldviews and philosophical positions are competing.…”

Section: Introductionmentioning

confidence: 99%

Connectors of smart design and smart systems

Horváth

2021

AIEDAM

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Though they can be traced back to different roots, both smart design and smart systems have to do with the recent developments of artificial intelligence. There are two major questions related to them: (i) What way are smart design and smart systems enabled by artificial narrow, general, or super intelligence? and (ii) How can smart design be used in the realization of smart systems? and How can smart systems contribute to smart designing? A difficulty is that there are no exact definitions for these novel concepts in the literature. The endeavor to analyze the current situation and to answer the above questions stimulated an exploratory research whose first findings are summarized in this paper. Its first part elaborates on a plausible interpretation of the concept of smartness and provides an overview of the characteristics of smart design as a creative problem solving methodology supported by artificial intelligence. The second part exposes the paradigmatic features and system engineering issues of smart systems, which are equipped with application-specific synthetic system knowledge and reasoning mechanisms. The third part presents and elaborates on a conceptual model of AI-based couplings of smart design and smart systems. The couplings may manifest in various concrete forms in real life that are referred to as “connectors” in this paper. The principal types of connectors are exemplified and discussed. It has been found that smart design tends to manifest as a methodology of blue-printing smart systems and that smart systems will be intellectualized the enablers of implementation of smart design. Understanding the affordances of and creating proper connectors between smart design and smart systems need further explorative research.

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How Can Artificial Intelligence Help With Space Missions - A Case Study: Computational Intelligence-Assisted Design of Space Tether for Payload Orbital Transfer Under Uncertainties

Cited by 11 publications

References 19 publications

Electrocardiogram Signals Classification Using Deep-Learning-Based Incorporated Convolutional Neural Network and Long Short-Term Memory Framework

Electrocardiogram Signals Classification Using Deep-Learning-Based Incorporated Convolutional Neural Network and Long Short-Term Memory Framework

Applied Artificial Intelligence in Manufacturing and Industrial Production Systems: PEST Considerations for Engineering Managers

Connectors of smart design and smart systems

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