scholarly journals On the Use of Artificial Intelligence to Define Tank Transfer Functions

2021 ◽  
Author(s):  
Pál Schmitt ◽  
Charles Gillan ◽  
Ciaran Finnegan
Keyword(s):  
Artificial Intelligence ◽  
Transfer Functions ◽  
Third Order ◽  
Nonlinear Functional ◽  
Functional Relationships ◽  
Wave Characteristics ◽  
Highly Nonlinear ◽  
Wide Range ◽  
First Results ◽  
Time Trace

Experimental test facilities are generally characterised using linear transfer functions to relate the wavemaker forcing amplitude to wave elevation at a probe located in the wavetank. Second and third order correction methods are becoming available but are limited to certain ranges of waves in their applicability. Artificial intelligence has been shown to be a suitable tool to find even highly nonlinear functional relationships. This paper reports on a numerical wavetank implemented using the OpenFOAM software package which is characterised using artificial intelligence. The aim of the research is to train neural networks to represent non-linear transfer functions mapping a desired surface-elevation time-trace at a probe to the wavemaker input required to create it. These first results already demonstrate the viability of the approach and the suitability of a single setup to find solutions over a wide range of sea states and wave characteristics.

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

2020 ◽  
Vol 142 (11) ◽  
Author(s):  
Jorge F. Arinez ◽  
Qing Chang ◽  
Robert X. Gao ◽  
Chengying Xu ◽  
Jianjing Zhang
Keyword(s):  
Artificial Intelligence ◽  
Manufacturing Systems ◽  
Hierarchical Organization ◽  
System Level ◽  
Current Status ◽  
Modeling And Control ◽  
Highly Nonlinear ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
Collaboration Process

Abstract Today’s manufacturing systems are becoming increasingly complex, dynamic, and connected. The factory operations face challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in artificial intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) review the state-of-the-art applications of AI to representative manufacturing problems, (2) provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human–robotic collaboration, process monitoring, diagnosis, and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control.

A new multi-layer irrotational Boussinesq-type model for highly nonlinear and dispersive surface waves over a mildly sloping seabed

2018 ◽  
Vol 842 ◽  
pp. 323-353 ◽  
Author(s):  
Z. B. Liu ◽  
K. Z. Fang ◽  
Y. Z. Cheng
Keyword(s):  
Transfer Functions ◽  
Velocity Profiles ◽  
High Accuracy ◽  
Linear Velocity ◽  
Staggered Grid ◽  
Type Model ◽  
Layer Model ◽  
Third Order ◽  
Highly Nonlinear ◽  
Linear And Nonlinear

A new multi-layer irrotational Boussinesq-type model is proposed for both linear and nonlinear surface water waves over mildly sloping seabeds. The model is formulated in terms of computational horizontal and vertical velocity components within each layer and satisfies exact kinematic and dynamic free-surface conditions as well as kinematic seabed conditions. Using a Stokes-type expansion, a theoretical analysis of the new multi-layer model is carried out to examine both linear and nonlinear properties, including wave celerity, velocity profiles, shoaling amplitude, second- and third-order transfer functions and amplitude dispersion. The dispersive coefficients in the governing equations are determined by optimizing the linear celerity or linear velocity profiles. For example, the four-layer model shows extremely high accuracy and is applicable up to $kh=667$–800 (where $k$ is the wavenumber and $h$ is a typical water depth) with a 1 % error in wave phase celerity, and up to $kh=352$–423 with a 1 % error in the linear velocity components. The super- and subharmonic transfer functions are extremely accurate up to $kh=300$ (1 % error), the third-order harmonics and amplitude dispersion are accurate up to $kh=477$ (1 % error), and the shoaling property is optimized to cover the range of $0<kh<300$, which presents a 0.06 % tolerance error in shoaling amplitude. The high-accuracy nature of the model increases its suitability for simulating random wave propagation from extremely deep to shallow waters over mildly sloping topographies. The model is implemented numerically on a non-staggered grid via a composite fourth-order Adams–Bashforth–Moulton time integration. The numerical results show good agreement with both the analytical solutions and experimental data.

scholarly journals Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining

2021 ◽  
Author(s):  
Michael D. T. McDonnell ◽  
Daniel Arnaldo ◽  
Etienne Pelletier ◽  
James A. Grant-Jacob ◽  
Matthew Praeger ◽  
...  
Keyword(s):  
Short Pulse ◽  
Surface Texturing ◽  
Laser Surface Texturing ◽  
Laser Machining ◽  
Laser Materials ◽  
Parameter Optimisation ◽  
Short Pulse Laser ◽  
Alternative Approach ◽  
Highly Nonlinear ◽  
Wide Range

AbstractInteractions between light and matter during short-pulse laser materials processing are highly nonlinear, and hence acutely sensitive to laser parameters such as the pulse energy, repetition rate, and number of pulses used. Due to this complexity, simulation approaches based on calculation of the underlying physical principles can often only provide a qualitative understanding of the inter-relationships between these parameters. An alternative approach such as parameter optimisation, often requires a systematic and hence time-consuming experimental exploration over the available parameter space. Here, we apply neural networks for parameter optimisation and for predictive visualisation of expected outcomes in laser surface texturing with blind vias for tribology control applications. Critically, this method greatly reduces the amount of experimental laser machining data that is needed and associated development time, without negatively impacting accuracy or performance. The techniques presented here could be applied in a wide range of fields and have the potential to significantly reduce the time, and the costs associated with laser process optimisation.

scholarly journals Star formation thresholds derived From THINGS

2006 ◽  
Vol 2 (S237) ◽  
pp. 397-397
Author(s):  
F. Bigiel ◽  
F. Walter ◽  
E. de Blok ◽  
E. Brinks ◽  
B. Madore
Keyword(s):  
Star Formation ◽  
Large Scale ◽  
Gravitational Instability ◽  
Critical Density ◽  
Nearby Galaxy ◽  
Uv Emission ◽  
Radial Profiles ◽  
Wide Range ◽  
First Results ◽  
Type Mass

AbstractWe present first results from THINGS (The HI Nearby Galaxy Survey), which consists of high quality HI maps obtained with the VLA of 34 galaxies across a wide range of galaxy parameters (Hubble type, mass/luminosity). We compare the distribution of HI to the UV emission in our sample galaxies. In particular we present radial profiles of the HI (tracing the neutral interstellar medium) and UV (mainly tracing regions of recent star formation) in our sample galaxies. The azimuthally averaged HI profiles are compared to the predicted critical density above which organized large-scale star formation is believed to start (this threshold is based on the Toomre-Q parameter, which in turn is a measure for local gravitational instability).

scholarly journals Corporate Governance of Artificial Intelligence in the Public Interest

Information ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 275
Author(s):  
Peter Cihon ◽  
Jonas Schuett ◽  
Seth D. Baum
Keyword(s):  
Artificial Intelligence ◽  
Corporate Governance ◽  
Law Enforcement ◽  
Nonprofit Organizations ◽  
Public Interest ◽  
Department Of Defense ◽  
The Public ◽  
Wide Range ◽  
The Media ◽  
The U.S

Corporations play a major role in artificial intelligence (AI) research, development, and deployment, with profound consequences for society. This paper surveys opportunities to improve how corporations govern their AI activities so as to better advance the public interest. The paper focuses on the roles of and opportunities for a wide range of actors inside the corporation—managers, workers, and investors—and outside the corporation—corporate partners and competitors, industry consortia, nonprofit organizations, the public, the media, and governments. Whereas prior work on multistakeholder AI governance has proposed dedicated institutions to bring together diverse actors and stakeholders, this paper explores the opportunities they have even in the absence of dedicated multistakeholder institutions. The paper illustrates these opportunities with many cases, including the participation of Google in the U.S. Department of Defense Project Maven; the publication of potentially harmful AI research by OpenAI, with input from the Partnership on AI; and the sale of facial recognition technology to law enforcement by corporations including Amazon, IBM, and Microsoft. These and other cases demonstrate the wide range of mechanisms to advance AI corporate governance in the public interest, especially when diverse actors work together.

scholarly journals Assessment of Linearization Approaches for Multibody Dynamics Formulations

2017 ◽  
Vol 12 (4) ◽  
Author(s):  
Francisco González ◽  
Pierangelo Masarati ◽  
Javier Cuadrado ◽  
Miguel A. Naya
Keyword(s):  
Mechanical System ◽  
Multibody Dynamics ◽  
Equations Of Motion ◽  
Differential Algebraic Equations ◽  
Algebraic Equations ◽  
Practical Applications ◽  
Linearization Methods ◽  
Highly Nonlinear ◽  
Wide Range ◽  
The Way

Formulating the dynamics equations of a mechanical system following a multibody dynamics approach often leads to a set of highly nonlinear differential-algebraic equations (DAEs). While this form of the equations of motion is suitable for a wide range of practical applications, in some cases it is necessary to have access to the linearized system dynamics. This is the case when stability and modal analyses are to be carried out; the definition of plant and system models for certain control algorithms and state estimators also requires a linear expression of the dynamics. A number of methods for the linearization of multibody dynamics can be found in the literature. They differ in both the approach that they follow to handle the equations of motion and the way in which they deliver their results, which in turn are determined by the selection of the generalized coordinates used to describe the mechanical system. This selection is closely related to the way in which the kinematic constraints of the system are treated. Three major approaches can be distinguished and used to categorize most of the linearization methods published so far. In this work, we demonstrate the properties of each approach in the linearization of systems in static equilibrium, illustrating them with the study of two representative examples.

Strain-Rate Effect on the Tensile Behaviour of High Strength Alloys

2011 ◽  
Vol 82 ◽  
pp. 124-129 ◽  
Author(s):  
Ezio Cadoni ◽  
Matteo Dotta ◽  
Daniele Forni ◽  
Stefano Bianchi
Keyword(s):  
Strain Rate ◽  
Rate Sensitivity ◽  
High Strength ◽  
Constitutive Law ◽  
Tensile Behaviour ◽  
Strain Rates ◽  
Cross Sectional ◽  
Split Hopkinson Tensile Bar ◽  
Wide Range ◽  
First Results

In this paper the first results of the mechanical characterization in tension of two high strength alloys in a wide range of strain rates are presented. Different experimental techniques were used for different strain rates: a universal machine, a Hydro-Pneumatic Machine and a JRC-Split Hopkinson Tensile Bar. The experimental research was developed in the DynaMat laboratory of the University of Applied Sciences of Southern Switzerland. An increase of the stress at a given strain increasing the strain-rate from 10-3 to 103 s-1, a moderate strain-rate sensitivity of the uniform and fracture strain, a poor reduction of the cross-sectional area at fracture with increasing the strain-rate were shown. Based on these experimental results the parameters required by the Johnson-Cook constitutive law were determined.

The Research and Application of Artificial Intelligence in the Field of Intelligent Robot

2014 ◽  
Vol 898 ◽  
pp. 763-766
Author(s):  
Zhi Hao Li
Keyword(s):  
Artificial Intelligence ◽  
Environmental Changes ◽  
Vision System ◽  
Mineral Exploration ◽  
Intelligent Robot ◽  
Application Range ◽  
Intelligent Robots ◽  
External Data ◽  
Wide Range ◽  
Artificial Intelligence Application

The research and application of artificial intelligence has a very wide range in intelligent robot field. Intelligent robot can not only make use of artificial intelligence gain access to external data, information, (such as stereo vision system, face recognition and tracking, etc.), and then deal with it so as to exactly describe external environment, and complete a task independently, owing the ability of learning knowledge, but also have self-many kinds of artificial intelligence like judgment and decision making, processing capacity and so on. It can make corresponding decision according to environmental changes. Its application range is expanding. In deep sea exploration, star exploration, mineral exploration, heavy pollution, domestic service, entertainment clubs, health care and so on, the figure of intelligent robots artificial intelligence application can all be seen.

APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN MS. PAC-MAN GAME: A REVIEW

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Tse Guan Tan ◽  
Jason Teo
Keyword(s):  
Artificial Intelligence ◽  
Content Analysis ◽  
Video Games ◽  
Analysis Method ◽  
Engineering Economics ◽  
Game Ai ◽  
Content Analysis Method ◽  
Wide Range ◽  
Computer Based ◽  
Future Direction

AbstrakTeknik Kecerdasan Buatan (AI) berjaya digunakan dan diaplikasikan dalam pelbagai bidang, termasukpembuatan, kejuruteraan, ekonomi, perubatan dan ketenteraan. Kebelakangan ini, terdapat minat yangsemakin meningkat dalam Permainan Kecerdasan Buatan atau permainan AI. Permainan AI merujukkepada teknik yang diaplikasikan dalam permainan komputer dan video seperti pembelajaran, pathfinding,perancangan, dan lain-lain bagi mewujudkan tingkah laku pintar dan autonomi kepada karakter dalampermainan. Objektif utama kajian ini adalah untuk mengemukakan beberapa teknik yang biasa digunakandalam merekabentuk dan mengawal karakter berasaskan komputer untuk permainan Ms Pac-Man antaratahun 2005-2012. Ms Pac-Man adalah salah satu permainan yang digunakan dalam siri pertandinganpermainan diperingkat antarabangsa sebagai penanda aras untuk perbandingan pengawal autonomi.Kaedah analisis kandungan yang menyeluruh dijalankan secara ulasan dan sorotan literatur secara kritikal.Dapatan kajian menunjukkan bahawa, walaupun terdapat berbagai teknik, limitasi utama dalam kajianterdahulu untuk mewujudkan karakter permaianan Pac Man adalah kekurangan Generalization Capabilitydalam kepelbagaian karakter permainan. Hasil kajian ini akan dapat digunakan oleh penyelidik untukmeningkatkan keupayaan Generalization AI karakter permainan dalam Pasaran Permainan KecerdasanBuatan. Abstract Artificial Intelligence (AI) techniques are successfully used and applied in a wide range of areas, includingmanufacturing, engineering, economics, medicine and military. In recent years, there has been anincreasing interest in Game Artificial Intelligence or Game AI. Game AI refers to techniques applied incomputer and video games such as learning, pathfinding, planning, and many others for creating intelligentand autonomous behaviour to the characters in games. The main objective of this paper is to highlightseveral most common of the AI techniques for designing and controlling the computer-based charactersto play Ms. Pac-Man game between years 2005-2012. The Ms. Pac-Man is one of the games that used asbenchmark for comparison of autonomous controllers in a series of international Game AI competitions.An extensive content analysis method was conducted through critical review on previous literature relatedto the field. Findings highlight, although there was various and unique techniques available, the majorlimitation of previous studies for creating the Ms. Pac-Man game characters is a lack of generalizationcapability across different game characters. The findings could provide the future direction for researchersto improve the Generalization A.I capability of game characters in the Game Artificial Intelligence market.

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