ArgoMoon: the Italian cubesat for Artemis1 mission

2021 ◽  
Author(s):  
Simone Pirrotta ◽  
Biagio Cotugno ◽  
Valerio di Tana ◽  
Silvio Patruno ◽  
Francesca Ingiosi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Flight Control ◽  
Power Distribution ◽  
New Technology ◽  
Recognition Algorithm ◽  
Deep Space ◽  
Ground Segment ◽  
Space Agency ◽  
Flight Operations ◽  
High Level

<p>In order to increase the scientific and technological return of the Artemis I mission, NASA has directed the SLS Program to accommodate Secondary Payloads on board of the Space Launch System (SLS), to be deployed with the Orion capsule; among them, ArgoMoon cubesat has been selected as European contribution. It is a 6U platform designed by Argotec on behalf of the Italian Space Agency (ASI) and will be released from the launch vehicle Interim Cryogenic Propulsion Stage (ICPS). The main objectives of the satellite are: i) taking photographs to document the ICPS after the deployment of the Orion capsule and the deployment of the other secondary payloads mounted on-board; ii) taking photographs of the Earth and the Moon; iii) validate guidance and autonomous targeting technology and iv) verifying a new technology for power distribution, satellite data acquisition and processing suitable for nanosatellite volume. In fact, the cubesat will be the first national spacecraft working in near Deep Space and operated through a Ground Segment mainly based in Italy.</p> <p>ArgoMoon design is based on the HAWK platform, designed by Argotec following an “all in-house” concept. Some of the main features of this platform are the focus on rad-hard subsystem components, a high level of autonomy capability supported by artificial intelligence, and the scalability towards larger bus sizes.</p> <p>Early after deployment, ArgoMoon will be able to operate autonomously and perform SLS tracking and proximity flight navigation, making use of a complex image recognition algorithm based on artificial intelligence. These operations are carried out by two optical payloads and the obtained photography will be used to support the NASA and payload communities in providing information regarding the status of their deployment and the condition of the second stage as it completes the final phase of its mission. After that, ArgoMoon will be operative for another six months for technological validation and Moon observation purposes.</p> <p>During the communication windows throughout the entire satellite lifetime, ArgoMoon will be operated and monitored entirely by the Argotec Mission Control Centre, connected to the Deep Space Network (DSN). The Flight Control Team (FCT) will follow the flight operations with in-house developed software able to plan and validate in orbit activities, verifying the on-board.</p> <p>After a successfully integration and test campaign, the cubesat has been shipped to USA for the filling activities on the propulsion tanks and the final delivery to NASA for the integration in the SLS, expected in July 2021.</p> <p>The results of this mission will strongly contribute to future of Space Exploration based on small satellite platforms in Deep Space.</p>

scholarly journals RESEARCH ON THE TRAINING OF ELECTROMECHANICAL COMPLEX TALENTS IN HIGHER VOCATIONAL COLLEGES UNDER THE BACKGROUND OF ARTIFICIAL INTELLIGENCE

2021 ◽  
Author(s):  
AN-YAN CAO ◽  
QIAN LV ◽  
LI-GONG CUI
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Control ◽  
Manufacturing Industry ◽  
New Technology ◽  
Intelligent Manufacturing ◽  
Training System ◽  
Teaching Mode ◽  
Chinese Characteristics ◽  
High Level ◽  
Intelligence Development

The new technology in artificial intelligence has a far-reaching impact on the intelligent development of the manufacturing industry. These new skill requirements are bound to promote the reform of vocational education in the field of intelligent control. It is urgent to train a group of highly skilled compound talents who can adapt to the development of artificial intelligence and intelligent manufacturing and can engage in the field of intelligent control technology. In this background, this paper takes Binzhou Polytechnic as an example, which is a high level vocational college with Chinese characteristics. We investigate the current situation and development prospect of artificial intelligence and intelligent manufacturing and analyze the problems and development opportunities of mechatronics technology specialty under the background of artificial intelligence development. We put forward some suggestions on the personnel training system of mechatronics technology from the aspects of specialty orientation and the integration and optimization of training specifications, the development of specialty curriculum system, the reform of teaching mode, and the construction of innovative teacher team.

Automation Surprise

2017 ◽  
Vol 7 (1) ◽  
pp. 28-41 ◽  
Author(s):  
Robert J. de Boer ◽  
Karel Hurts
Keyword(s):  
Flight Control ◽  
Representative Sample ◽  
Aviation Safety ◽  
Control Mode ◽  
Cognitive Errors ◽  
Laboratory Studies ◽  
Airline Pilots ◽  
Flight Operations ◽  
Widespread Phenomenon ◽  
Few Data

Abstract. Automation surprise (AS) has often been associated with aviation safety incidents. Although numerous laboratory studies have been conducted, few data are available from routine flight operations. A survey among a representative sample of 200 Dutch airline pilots was used to determine the prevalence of AS and the severity of its consequences, and to test some of the factors leading to AS. Results show that AS is a relatively widespread phenomenon that occurs three times per year per pilot on average but rarely has serious consequences. In less than 10% of the AS cases that were reviewed, an undesired aircraft state was induced. Reportable occurrences are estimated to occur only once every 1–3 years per pilot. Factors leading to a higher prevalence of AS include less flying experience, increasing complexity of the flight control mode, and flight duty periods of over 8 hr. It is concluded that AS is a manifestation of system and interface complexity rather than cognitive errors.

Europe

2020 ◽  
pp. 649-666
Author(s):  
Andrea Renda
Keyword(s):  
Artificial Intelligence ◽  
Digital Technology ◽  
Policy Framework ◽  
Expert Group ◽  
Ethical Guidelines ◽  
Ethical Implications ◽  
Strong Focus ◽  
Ethics Guidelines ◽  
Definition Of ◽  
High Level

This chapter assesses Europe’s efforts in developing a full-fledged strategy on the human and ethical implications of artificial intelligence (AI). The strong focus on ethics in the European Union’s AI strategy should be seen in the context of an overall strategy that aims at protecting citizens and civil society from abuses of digital technology but also as part of a competitiveness-oriented strategy aimed at raising the standards for access to Europe’s wealthy Single Market. In this context, one of the most peculiar steps in the European Union’s strategy was the creation of an independent High-Level Expert Group on AI (AI HLEG), accompanied by the launch of an AI Alliance, which quickly attracted several hundred participants. The AI HLEG, a multistakeholder group including fifty-two experts, was tasked with the definition of Ethics Guidelines as well as with the formulation of “Policy and Investment Recommendations.” With the advice of the AI HLEG, the European Commission put forward ethical guidelines for Trustworthy AI—which are now paving the way for a comprehensive, risk-based policy framework.

scholarly journals Interventional radiology and artificial intelligence in radiology: Is it time to enhance the vision of our medical students?

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Pierre Auloge ◽  
Julien Garnon ◽  
Joey Marie Robinson ◽  
Sarah Dbouk ◽  
Jean Sibilia ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Interventional Radiology ◽  
Clinical Practice ◽  
Medical Students ◽  
Medical Curriculum ◽  
New Technology ◽  
Large Population ◽  
The Other ◽  
Clinical Phase ◽  
Preclinical Phase

Abstract Objectives To assess awareness and knowledge of Interventional Radiology (IR) in a large population of medical students in 2019. Methods An anonymous survey was distributed electronically to 9546 medical students from first to sixth year at three European medical schools. The survey contained 14 questions, including two general questions on diagnostic radiology (DR) and artificial intelligence (AI), and 11 on IR. Responses were analyzed for all students and compared between preclinical (PCs) (first to third year) and clinical phase (Cs) (fourth to sixth year) of medical school. Of 9546 students, 1459 students (15.3%) answered the survey. Results On DR questions, 34.8% answered that AI is a threat for radiologists (PCs: 246/725 (33.9%); Cs: 248/734 (36%)) and 91.1% thought that radiology has a future (PCs: 668/725 (92.1%); Cs: 657/734 (89.5%)). On IR questions, 80.8% (1179/1459) students had already heard of IR; 75.7% (1104/1459) stated that their knowledge of IR wasn’t as good as the other specialties and 80% would like more lectures on IR. Finally, 24.2% (353/1459) indicated an interest in a career in IR with a majority of women in preclinical phase, but this trend reverses in clinical phase. Conclusions Development of new technology supporting advances in artificial intelligence will likely continue to change the landscape of radiology; however, medical students remain confident in the need for specialty-trained human physicians in the future of radiology as a clinical practice. A large majority of medical students would like more information about IR in their medical curriculum; almost a quarter of students would be interested in a career in IR.

scholarly journals A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jane Scheetz ◽  
Philip Rothschild ◽  
Myra McGuinness ◽  
Xavier Hadoux ◽  
H. Peter Soyer ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
New Zealand ◽  
Radiation Oncology ◽  
Online Survey ◽  
New Technology ◽  
Medical Liability ◽  
Healthcare Outcomes ◽  
Medical Workforce ◽  
Artificial Intelligence Technology ◽  
Workforce Needs

AbstractArtificial intelligence technology has advanced rapidly in recent years and has the potential to improve healthcare outcomes. However, technology uptake will be largely driven by clinicians, and there is a paucity of data regarding the attitude that clinicians have to this new technology. In June–August 2019 we conducted an online survey of fellows and trainees of three specialty colleges (ophthalmology, radiology/radiation oncology, dermatology) in Australia and New Zealand on artificial intelligence. There were 632 complete responses (n = 305, 230, and 97, respectively), equating to a response rate of 20.4%, 5.1%, and 13.2% for the above colleges, respectively. The majority (n = 449, 71.0%) believed artificial intelligence would improve their field of medicine, and that medical workforce needs would be impacted by the technology within the next decade (n = 542, 85.8%). Improved disease screening and streamlining of monotonous tasks were identified as key benefits of artificial intelligence. The divestment of healthcare to technology companies and medical liability implications were the greatest concerns. Education was identified as a priority to prepare clinicians for the implementation of artificial intelligence in healthcare. This survey highlights parallels between the perceptions of different clinician groups in Australia and New Zealand about artificial intelligence in medicine. Artificial intelligence was recognized as valuable technology that will have wide-ranging impacts on healthcare.

The Economics of the Soy Kit as an Appropriate Household Technology for Food Entrepreneurs

2021 ◽  
Vol 42 (1) ◽  
pp. 104-115
Author(s):  
Chungmann Kim ◽  
Peter Goldsmith
Keyword(s):  
International Development ◽  
Poverty Reduction ◽  
New Technology ◽  
Research Question ◽  
Development Project ◽  
Small Scale ◽  
Resource Base ◽  
Credit Access ◽  
Feed The Future ◽  
High Level

Background: The ability for women to operate as food entrepreneurs presents opportunities to leverage at-home production technologies that not only support family nutrition but also generate income. To these ends, the Feed the Future Malawi Agriculture Diversification Activity recently launched a development project involving a new technology, the Soy Kit. The Activity, a USAID (United States Agency for International Development) funded effort, sought to improve nutrition utilizing an underutilized local and highly nutritious feedstuff, soybean, through a woman’s entrepreneurship scheme. Objective: The USAID funded effort provides the overarching research question, whether the Soy Kit is a sustainable technology for delivering nutrition and income through a women’s entrepreneurship scheme. If true, then development practitioners will have a valuable tool, and the associated evidence, to address the important crosscutting themes, of nutrition, poverty, entrepreneurship, and women’s empowerment. To answer this research question, the research team first evaluates the underlying production economics of the kit to measure profitability, return on investment, and operational performance. Second, the team qualitatively and quantitatively assesses the kit’s overall appropriateness as a technology for the developing world. Methods: The team follows the schema of Bower and Brown and utilizes descriptive statistics, and financial techniques to conduct an assessment of the economics and technical appropriateness of the Soy Kit technology. Results: The results show a high level of appropriateness across a number of metrics. For example, the payback period from cash flow is under 6 months and the annual return on capital is 163% when entrepreneurs utilize a domestically sourced kit valued at US$80. Conclusion: The technology matches well with the rhythm of household economy, in particular women’s labor availability and resource base. Businesses earn significant returns on capital thus appear to be sustainable without donor subsidy. At the same time, available capital to finance kit entrepreneurs appears to be scarce. More research needs to take place to address the credit access question, in order to make small-scale kit entrepreneur truly self-reliant; the effects on poverty reduction at the household and village level; and nutrition improvement among the consumers.

scholarly journals Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021

AI and Ethics ◽  
2021 ◽  
Author(s):  
Muhammad Ali Chaudhry ◽  
Emre Kazim
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Tutoring Systems ◽  
Policy Makers ◽  
Tutoring Systems ◽  
Research And Practice ◽  
The Past ◽  
Artificial Intelligence In Education ◽  
Potential Impact ◽  
High Level ◽  
Contextualized Learning

AbstractIn the past few decades, technology has completely transformed the world around us. Indeed, experts believe that the next big digital transformation in how we live, communicate, work, trade and learn will be driven by Artificial Intelligence (AI) [83]. This paper presents a high-level industrial and academic overview of AI in Education (AIEd). It presents the focus of latest research in AIEd on reducing teachers’ workload, contextualized learning for students, revolutionizing assessments and developments in intelligent tutoring systems. It also discusses the ethical dimension of AIEd and the potential impact of the Covid-19 pandemic on the future of AIEd’s research and practice. The intended readership of this article is policy makers and institutional leaders who are looking for an introductory state of play in AIEd.

Prediction Models for Truck Accidents at Freeway Ramps in Washington State Using Regression and Artificial Intelligence Techniques

1998 ◽  
Vol 1635 (1) ◽  
pp. 30-36 ◽  
Author(s):  
Wael H. Awad ◽  
Bruce N. Janson
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Linear Regression ◽  
Prediction Models ◽  
Washington State ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Training Process ◽  
Truck Accidents ◽  
High Level

Three different modeling approaches were applied to explain truck accidents at interchanges in Washington State during a 27-month period. Three models were developed for each ramp type including linear regression, neural networks, and a hybrid system using fuzzy logic and neural networks. The study showed that linear regression was able to predict accident frequencies that fell within one standard deviation from the overall mean of the dependent variable. However, the coefficient of determination was very low in all cases. The other two artificial intelligence (AI) approaches showed a high level of performance in identifying different patterns of accidents in the training data and presented a better fit when compared to the regression model. However, the ability of these AI models to predict test data that were not included in the training process showed unsatisfactory results.

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