Feasibility of Data-driven EMG Signal Generation using a Deep Generative Model

A generative model is a statistical model capable of generating new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical traces, also known as an event log. Two types of generative business process models have been developed in previous work: data-driven simulation models and deep learning models. Until now, these two approaches have evolved independently, and their relative performance has not been studied. This paper fills this gap by empirically comparing a data-driven simulation approach with multiple deep learning approaches for building generative business process models. The study sheds light on the relative strengths of these two approaches and raises the prospect of developing hybrid approaches that combine these strengths.

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Data-driven affective product design using complete three-dimensional surface data

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-211947 ◽

2021 ◽

pp. 1-19

Author(s):

Zeng Wang ◽

Weidong Liu ◽

Minglang Yang

Keyword(s):

Mathematical Model ◽

Product Design ◽

Design Method ◽

Three Dimensional ◽

Automatic Generation ◽

Generative Model ◽

Data Driven ◽

Recognition Model ◽

3D Surface ◽

Surface Data

As the main part of design display and evaluation, product three-dimensional (3D) form is the core object in affective product design. However, previous research has not yet addressed the development of technical models and method involving complete 3D surface data, and thus cannot guarantee the quality of affective product design. By using the techniques of triangular mesh model, spherical harmonic and conditional variational auto-encoder, this paper proposes a data-driven affective product design method composed of several technical models using complete 3D surface data. These models include: mathematical model for quantifying 3D form, recognition model for recognizing customer’s affective responses, and generative model for generating new 3D forms. For affective product design, the mathematical model achieves the acquisition and processing of complete 3D surface data, the recognition model improves the objectivity and accuracy of recognition by integrating the 3D form data into the calculation process of emotion recognition, and the generative model realizes the automatic generation of new 3D forms in response to emotional data based on the recognition results. Each model provides technical support for realizing the acquisition, processing and generation of complete 3D surface data of product form, and ensures the systematic and completeness of the proposed method for the affective product design involving 3D form innovation. The feasibility of the method is verified by an example of car design, and the results show that it is an effective affective product design method involving 3D form innovation.

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Data-driven topology design using a deep generative model

Structural and Multidisciplinary Optimization ◽

10.1007/s00158-021-02926-y ◽

2021 ◽

Author(s):

Shintaro Yamasaki ◽

Kentaro Yaji ◽

Kikuo Fujita

Keyword(s):

Structural Design ◽

High Performance ◽

Design Methodology ◽

Generative Model ◽

Topology Design ◽

Training Data ◽

Data Driven ◽

Design Domain ◽

Numerical Examples ◽

New Material

AbstractIn this paper, we propose a sensitivity-free and multi-objective structural design methodology called data-driven topology design. It is schemed to obtain high-performance material distributions from initially given material distributions in a given design domain. Its basic idea is to iterate the following processes: (i) selecting material distributions from a dataset of material distributions according to eliteness, (ii) generating new material distributions using a deep generative model trained with the selected elite material distributions, and (iii) merging the generated material distributions with the dataset. Because of the nature of a deep generative model, the generated material distributions are diverse and inherit features of the training data, that is, the elite material distributions. Therefore, it is expected that some of the generated material distributions are superior to the current elite material distributions, and by merging the generated material distributions with the dataset, the performances of the newly selected elite material distributions are improved. The performances are further improved by iterating the above processes. The usefulness of data-driven topology design is demonstrated through numerical examples.

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Range-GAN: Range-Constrained Generative Adversarial Network for Conditioned Design Synthesis

10.1115/detc2021-69963 ◽

2021 ◽

Author(s):

Amin Heyrani Nobari ◽

Wei Chen ◽

Faez Ahmed

Keyword(s):

Generative Model ◽

Inverse Design ◽

Data Driven ◽

Generative Adversarial Network ◽

Design Problems ◽

Design Synthesis ◽

Adversarial Network ◽

Average Improvement ◽

Inverse Design Problem ◽

Alternative Designs

Abstract Typical engineering design tasks require the effort to modify designs iteratively until they meet certain constraints, i.e., performance or attribute requirements. Past work has proposed ways to solve the inverse design problem, where desired designs are directly generated from specified requirements, thus avoid the trial and error process. Among those approaches, the conditional deep generative model shows great potential since 1) it works for complex high-dimensional designs and 2) it can generate multiple alternative designs given any condition. In this work, we propose a conditional deep generative model, Range-GAN, to achieve automatic design synthesis subject to range constraints. The proposed model addresses the sparse conditioning issue in data-driven inverse design problems by introducing a label-aware self-augmentation approach. We also propose a new uniformity loss to ensure generated designs evenly cover the given requirement range. Through a real-world example of constrained 3D shape generation, we show that the label-aware self-augmentation leads to an average improvement of 14% on the constraint satisfaction for generated 3D shapes, and the uniformity loss leads to a 125% average increase on the uniformity of generated shapes’ attributes. This work laid the foundation for data-driven inverse design problems where we consider range constraints and there are sparse regions in the condition space. For further information and code for this paper please refer to http://decode.mit.edu/projects/rangegan/.

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A data-driven generative model for sea surface temperature fields in the tropical Pacific

10.5194/egusphere-egu21-12362 ◽

2021 ◽

Author(s):

Jakob Schlör ◽

Bedartha Goswami

Keyword(s):

Southern Oscillation ◽

Dominant Mode ◽

Equatorial Pacific ◽

Generative Model ◽

Sea Surface ◽

Data Driven ◽

Temperature Fields ◽

Strong Impact ◽

Enso Events ◽

Transition Functions

The most dominant mode of oceanic climate variability on an interannual scale is the El Ni&#241;o-Southern Oscillation (ENSO), which is characterized by anomalous sea surface temperatures (SSTs) in the equatorial Pacific. The SST fields associated with ENSO show strong variability between different events, also known as ENSO diversity. While the diversity of SST patterns have a strong impact on local climate, ecosystem and society, the spatial differences between ENSO events are not yet fully understood.In this work, we present a data-driven approach to model SST anomaly patterns in the Pacific using a deep generative model. In particular, we use a variational autoencoder (VAE) to nonlinearly decompose the monthly SST anomalies into a low dimensional &#8216;latent&#8217; space. VAEs are probabilistic models with neural network transition functions which allow us to model nonlinear features, quantify uncertainty, and include prior knowledge. In our approach, we use mutual information to favor a disentangled latent space with respect to a ground truth derived from correlation-based spatial SST clustering. The VAE-based approach improves upon earlier non-linear dimensionality reduction methods like kernel PCA which only optimize for statistical properties.Our results indicate that the anomalous SST field diversity can be explained primarily by 1) an eastern equatorial Pacific component, 2) a central equatorial Pacific component and 3) a transequatorial component. The components capture underlying spatial correlations to regions in the Northern Pacific and to the basin wide horseshoe pattern. We also observe an asymmetry between the warm and cool phases of the components.

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RANGE-GAN: Design Synthesis Under Constraints Using Conditional Generative Adversarial Networks

Journal of Mechanical Design ◽

10.1115/1.4052442 ◽

2021 ◽

pp. 1-16

Author(s):

Amin Heyrani Nobari ◽

Wei (Wayne) Chen ◽

Faez Ahmed

Keyword(s):

Generative Model ◽

Inverse Design ◽

Data Driven ◽

Generative Adversarial Networks ◽

Design Problems ◽

Design Synthesis ◽

Average Improvement ◽

Multiple Alternative ◽

Inverse Design Problem ◽

Alternative Designs

Abstract Typical engineering design tasks require the effort to modify designs iteratively until they meet certain constraints, i.e., performance or attribute requirements. Past work has proposed ways to solve the inverse design problem, where desired designs are directly generated from specified requirements, thus avoid the trial and error process. Among those approaches, the conditional deep generative model shows great potential since 1) it works for complex high-dimensional designs and 2) it can generate multiple alternative designs given any condition. In this work, we propose a conditional deep generative model, Range-GAN, to achieve automatic design synthesis subject to range constraints. The proposed model addresses the sparse conditioning issue in data-driven inverse design problems by introducing a label-aware self-augmentation approach. We also propose a new uniformity loss to ensure generated designs evenly cover the given requirement range. Through a real-world example of constrained 3D shape generation, we show that the label-aware self-augmentation leads to an average improvement of14% on the constraint satisfaction for generated 3D shapes, and the uniformity loss leads to a 125% average increase on the uniformity of generated shapes' attributes. This work laid the foundation for data-driven inverse design problems where we consider range constraints and there are sparse regions in the condition space.

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