Accelerating Mems Design Process Through Machine Learning from Pixelated Binary Images

This study presents a co-design project that invites participants with little or no background in artificial intelligence (AI) and machine learning (ML) to design their ideal virtual assistants (VAs) for everyday (/daily) use. VAs are differently designed and function when integrated into people’s daily lives (e.g., voice-controlled VAs are designed to blend in based on their natural qualities). To further understand users’ ideas of their ideal VA designs, participants were invited to generate designs of personal VAs. However, end users may have unrealistic expectations of future technologies. Therefore, design fiction was adopted as a method of guiding the participants’ image of the future and carefully managing their realistic, as well as unrealistic, expectations of future technologies. The result suggests the need for a human–AI relationship based on controls with various dimensions (e.g., vocalness degree and autonomy level) instead of specific features. The design insights are discussed in detail. Additionally, the co-design process offers insights into how users can participate in AI/ML designs.

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An Artificial Intelligence Based Algorithm for Object-Oriented Design

Volume 3: 22nd Design Automation Conference ◽

10.1115/96-detc/dac-1429 ◽

1996 ◽

Author(s):

Jean-Luc Segapeli ◽

Annie Cavarero

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Design Process ◽

Object Oriented ◽

Design Methods ◽

Application Domain ◽

Object Oriented Design ◽

Object Classes ◽

Entity Relationship ◽

Composition Process

Abstract It is possible to classify the Object-Oriented design methods in two sets. The first set, which is the most numerous, uses an entity relationship approach to build a static class schema. Then this schema is completed by the use of different models (dynamic, functional). The second set tries to obtain a class schema but they don’t provide a guide to build it. So if different designers work on the same application, it is impossible to obtain the same schema. So, in this paper we want to prove that we have defined a new design process (generalization process) which is based upon a set of rules to guide the users and the designers to build a representation of their application. The originality of our process lays upon works developped in machine learning and artificial intelligence. We try to translate the expertize of users or designers given through examples into a class schema. Therefore we have defined a new algorithm of clustering to organize examples into a hierarchy of classes. This process is included in a project called C.O.D. (Class and Object Definition). The project is composed of different processes which take into account the expertize level of the designers and their knowledge about the application domain: specialization process, which is based upon generic application and fuzzy object classes; composition process, which uses a functional application definition with an algorithm to build classes and links between classes; generalization process, which is developed in this paper.

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Mapping Through Listening

Computer Music Journal ◽

10.1162/comj_a_00255 ◽

2014 ◽

Vol 38 (3) ◽

pp. 34-48 ◽

Cited By ~ 16

Author(s):

Baptiste Caramiaux ◽

Jules Françoise ◽

Norbert Schnell ◽

Frédéric Bevilacqua

Keyword(s):

Machine Learning ◽

Design Process ◽

Design Principle ◽

Machine Learning Techniques ◽

Digital Music ◽

Learning Techniques ◽

The Relationship ◽

Interactive Installations ◽

Perception Action ◽

Fundamental Design

Gesture-to-sound mapping is generally defined as the association between gestural and sound parameters. This article describes an approach that brings forward the perception–action loop as a fundamental design principle for gesture–sound mapping in digital music instrument. Our approach considers the processes of listening as the foundation—and the first step—in the design of action–sound relationships. In this design process, the relationship between action and sound is derived from actions that can be perceived in the sound. Building on previous work on listening modes and gestural descriptions, we propose to distinguish between three mapping strategies: instantaneous, temporal, and metaphorical. Our approach makes use of machine-learning techniques for building prototypes, from digital music instruments to interactive installations. Four different examples of scenarios and prototypes are described and discussed.

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Multilayer Machine Learning-Assisted Optimization-Based Robust Design and Its Applications to Antennas and Arrays

10.36227/techrxiv.12681947 ◽

2020 ◽

Author(s):

Weiqi Chen ◽

Qi Wu ◽

Chen Yu ◽

Haiming Wang ◽

Wei Hong

Keyword(s):

Machine Learning ◽

Design Process ◽

Robust Design ◽

Design Method ◽

Computation Time ◽

Design Parameters ◽

Worst Case Analysis ◽

Worst Case ◽

And Performance ◽

Robust Design Method

An efficient multilayer machine learning-assisted optimization (ML-MLAO)-based robust design method is proposed for antenna and array applications. Machine learning methods are introduced into multiple layers of the robust design process, including worst-case analysis (WCA), maximum input tolerance hypervolume (MITH) searching, and robust optimization, considerably accelerating the whole robust design process. First, based on a surrogate model mapping between the design parameters and performance, WCA is performed using a genetic algorithm to ensure reliability. MITH searching is then carried out using a double-layer MLAO (DL-MLAO) framework to find the MITH of the given design point. Next, based on the training set obtained using DL-MLAO, correlations between the design parameters and the MITH are learned. The robust design is carried out using surrogate models for both the performance and the MITH, and these models are updated online following the ML-MLAO scheme. Furthermore, two examples, including an array synthesis problem and an antenna design problem, are used to verify the proposed ML-MLAO method. Finally, the numerical results and computation time are discussed to demonstrate the effectiveness of the proposed method.

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Machine Learning for Additive Manufacturing

Encyclopedia ◽

10.3390/encyclopedia1030048 ◽

2021 ◽

Vol 1 (3) ◽

pp. 576-588

Author(s):

Dean Grierson ◽

Allan E. W. Rennie ◽

Stephen D. Quayle

Keyword(s):

Machine Learning ◽

Additive Manufacturing ◽

Design Process ◽

Manufacturing Processes ◽

Wide Range ◽

Production Techniques ◽

End Use ◽

Industrial Adoption ◽

Further Development ◽

Modelling Data

Additive manufacturing (AM) is the name given to a family of manufacturing processes where materials are joined to make parts from 3D modelling data, generally in a layer-upon-layer manner. AM is rapidly increasing in industrial adoption for the manufacture of end-use parts, which is therefore pushing for the maturation of design, process, and production techniques. Machine learning (ML) is a branch of artificial intelligence concerned with training programs to self-improve and has applications in a wide range of areas, such as computer vision, prediction, and information retrieval. Many of the problems facing AM can be categorised into one or more of these application areas. Studies have shown ML techniques to be effective in improving AM design, process, and production but there are limited industrial case studies to support further development of these techniques.

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Machine Learning for Accelerating the Design Process of Double-Double Composite Structures

Composite Structures ◽

10.1016/j.compstruct.2022.115233 ◽

2022 ◽

pp. 115233

Author(s):

Zilan Zhang ◽

Zhizhou Zhang ◽

Francesco Di Caprio ◽

Grace X. Gu

Keyword(s):

Machine Learning ◽

Design Process ◽

Composite Structures

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Machine Learning Prediction and Reliability Analysis Applied to Subsea Spool and Jumper Design

10.1115/omae2021-62033 ◽

2021 ◽

Author(s):

Mengdi Song ◽

Massyl Gheroufella ◽

Paul Chartier

Keyword(s):

Machine Learning ◽

Reliability Analysis ◽

Design Process ◽

Predictive Modelling ◽

Machine Learning Algorithms ◽

Case Scenario ◽

Worst Case ◽

Deterministic Solution ◽

Number Of Bends ◽

Subsea Pipelines

Abstract In subsea pipelines projects, the design of rigid spool and jumper can be a challenging and time-consuming task. The selected spool layout for connecting the pipelines to the subsea structures, including the number of bends and leg lengths, must offer the flexibility to accommodate the pipeline thermal expansion, the pipe-lay target box and misalignments associated with the post-lay survey metrology and spool fabrication. The analysis results are considerably affected by many uncertainties involved. Consequently, a very large amount of calculations is required to assess the full combination of uncertainties and to capture the worst-case scenario. Rather than applying the deterministic solution, this paper uses machine learning prediction to significantly improve the efficiency of the design process. In addition, thanks to the fast predictive model using machine learning algorithms, the uncertainty quantification and propagation analysis using probabilistic statistical method becomes feasible in terms of CPU time and can be incorporated into the design process to evaluate the reliability of the outputs. The latter allows us to perform a systematic probabilistic design by considering a certain level of acceptance on the probability of failure, for example as per DNVGL design code. The machine learning predictive modelling and the reliability analysis based upon the probability distribution of the uncertainties are introduced and explained in this paper. Some project examples are shown to highlight the method’s comprehensive nature and efficient characteristics.

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