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International Carpathian control Conference (ICCC)

作者： Jakub Jura Pavel Trnka Matous Cejnek Dept. of Automatic Control and Engineering Informatics Czech Technical University in Prague Prague Czech Republic

This contribution describes the use of Natural Language Processing (NLP) methods for the lexical analysis of requirements for control, sensors, and information systems in the Agriculture 4.0 domain. The analysis is presented on an orchard 4.0 *** proposed orchard includes a sensor network (containing mainly measurements of hydrometeorological and soil variables), camera monitoring of conditions, and yield, support for autonomous robotic care and harvesting based on machine vision, prediction of appropriate times for interventions, etc. Requirements specification for mentioned system is written in natural language.A sentence splitting, Tokenization, Lemmatization, and POS (Part-of-Speech) tagging methods are applied to the mentioned structured requirements of the system and Use Case description. From these and by means of NLP, the candidates of classes, attributes, operations, and associations of the UML (Unified Modeling Language) class diagram are filtered and the UML model is synthesized. This paper presents the application of software engineering methods to support the development of complex heterogeneous sensors, information, and control systems.

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MIRROR: miRNA regulation-level differential network to study sex and ethnic disparities in cancer

MIRROR: miRNA regulation-level differential network to study...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Caterina Alfano Marco Filetti Lorenzo Farina Manuela Petti Dept. of Experimental Medicine Sapienza University of Rome Rome Italy Phase 1 Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Dept. of Computer Control and Management Engineering PhD Program in Network Oncology and Precision Medicine Sapienza University of Rome Rome Italy Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Cancer disparities are adverse differences in cancer measures that exist among certain population groups. Given that the role they play not only in the disease prognosis but also in therapy response, there is an urgent need to understand what causes them. Most studies investigate these disparities by analyzing transcriptomic data and in particular miRNAs for their regulatory role, but only focusing on expression levels. To face this challenge we propose MIRROR, a new method which analyzes a differential co-expression network of miRNAs between patients’ cohorts, to study the role they play at the target genes’ level. Doing so, we can study the altered molecular mechanism that are linked to cancer disparities. The application of MIRROR to two different cases of cancer disparities has demonstrated its efficacy in identifying molecular players involved in the considered disparity, presenting itself as a viable option to approach this challenge.

关键词： Proteins Technological innovation Transcriptomics Medical treatment Focusing Mirrors Prostate cancer Prognostics and health management Faces Cancer

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Energy Consumption Model for LoRaWAN via Field Experiments for Smart Agriculture

Energy Consumption Model for LoRaWAN via Field Experiments f...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Satoshi Yamazaki Taiki Ogura Dept. of Control and Computer Engineering National Institute of Technology Numazu College Numazu Japan Dept. of Electrical Electronics and Information Engineering Nagaoka University of Technology Niigata Japan

This paper proposes a scheme to model the energy consumption of LoRaWAN, which is a popular example of low-power wide-area networks (LPWANs), nodes via the results of outdoor field experiments by assuming regional smart agriculture as a use case for internet of things (IoT). Specifically, we derive an experimental approximation formula to estimate the battery lifetime by introducing parameters such as spread factor and payload length. The validity of the proposed scheme is demonstrated by confirming that the results obtained by the approximate formula, the experimental and theoretical results generally agree, regardless of the node state and spread factor. Furthermore, we show that the obtained approximate formula can be used to identify the current consumption value in the sleep state that should be achieved to achieve the desired battery lifetime.

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System Identification-informed Transparent and Explainable Machine Learning with Application to Power Consumption Forecasting

System Identification-informed Transparent and Explainable M...

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Electrical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Hua-Liang Wei Dept. Automatic Control and Systems Engineering Energy Institute Centre of Machine Intelligence Centre for Signal Processing and Cpmplex Systems The University of Sheffield Sheffield UK

System identification (SysID) is the art and science of dealing with dynamic data modelling problems from systems science perspectives. It has been an active field and is still very active today, due to its wide range of applications, especially its basic principles of finding transparent, interpretable and parsimonious models for different purposes. The past decades have witnessed the explosive growth in machine learning (ML) and its applications in all areas of science and engineering. Meanwhile, there has been an increasing demand for the development of transparent, explainable and/or interpretable ML models. This paper proposes a new framework for developing System Identification-informed Transparent and Explainable MAchine Learning (SITEMAL) models. A case study, involving a real power consumption dataset, is presented to demonstrate the application of the proposed modelling framework and its performance for power consumption forecasting.

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A Robust Observer with Gyroscopic Bias Correction for Rotational Dynamics

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IFAC-PapersOnLine 2023年第2期56卷 1641-1648页

作者： Erjen Lefeber Marcus Greiff Anders Robertsson Dept. of Mech. Engineering Eindhoven University of Technology Eindhoven The Netherlands Department of Automatic Control Lund University Lund Sweden Mitsubishi Electric Research Laboratories Cambridge MA U.S.A.

We propose an observer for rotational dynamics subject to directional and gyroscopic measurements, which simultaneously estimates the gyroscopic biases and attitude rates. We show uniform almost global asymptotic and local exponential stability of the resulting error dynamics, implying robustness against bounded disturbances. This robustness is quantified with respect to a popular nonlinear complementary filter in quantitative simulation studies, and we explore how the measurement noise propagates to the asymptotic errors as a function of tuning.

关键词： Nonlinear observers and filter design Lyapunov methods UAVs

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The Goldilocks Framework: Towards Selecting the Optimal Approach to Conducting AI Projects 22

The Goldilocks Framework: Towards Selecting the Optimal Appr...

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1st International Conference on AI engineering - Software engineering for AI, CAIN 2022

作者： Dzhusupova, Rimman Bosch, Jan Olsson, Helena Holmstrom Control & Safety Systems McDermott Dept. of Electrical & Instrumentation The Hague Netherlands Chalmers University of Technology Department of Computer Science and Engineering Gothenburg Sweden Malmö University Department of Computer Science and Media Technology Malmö Sweden

ISBN: (纸本)9781450392754

Artificial intelligence is increasingly becoming important to businesses since many companies have realized the benefits of applying Machine Learning (ML) and Deep Learning (DL) into their operations. Nevertheless, ML/DL technologies' industrial development and deployment examples are still rare and generally confined within a small cluster of large international companies who are struggling to apply ML more broadly and deploy their use cases at a large scale. Meanwhile, current AI market has started offering various solutions and services. Thus, organizations must understand how to acquire AI technology based on their business strategy and available resources. This paper discusses the industrial experience of developing and deploying ML/DL use cases to support organizations in their transformation towards AI. We identify how various factors, like cost, schedule, and intellectual property, can be affected by the choice of approach towards ML/DL project development and deployment within large international engineering corporations. As a research result, we present a framework that covers the trade-offs between those various factors and can support engineering companies to choose the best approach based on their long-term business strategies and, therefore, would help to accomplish their ML/DL project deployment successfully. © 2022 ACM.

关键词： Commerce

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Trust Region Policy Optimization with Optimal Transport Discrepancies: Duality and Algorithm for Continuous Actions

arXiv

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arXiv 2022年

作者： Terpin, Antonio Lanzetti, Nicolas Yardim, Batuhan Dörfler, Florian Ramponi, Giorgia Automatic Control Laboratory ETH Zürich Switzerland Dept. of Computer Science ETH Zürich Switzerland Dept. of Computer Science ETH AI Center Switzerland

Policy Optimization (PO) algorithms have been proven particularly suited to handle the high-dimensionality of real-world continuous control tasks. In this context, Trust Region Policy Optimization methods represent a popular approach to stabilize the policy updates. These usually rely on the Kullback-Leibler (KL) divergence to limit the change in the policy. The Wasserstein distance represents a natural alternative, in place of the KL divergence, to define trust regions or to regularize the objective function. However, state-of-the-art works either resort to its approximations or do not provide an algorithm for continuous state-action spaces, reducing the applicability of the method. In this paper, we explore optimal transport discrepancies (which include the Wasserstein distance) to define trust regions, and we propose a novel algorithm – Optimal Transport Trust Region Policy Optimization (OT-TRPO) – for continuous state-action spaces. We circumvent the infinite-dimensional optimization problem for PO by providing a one-dimensional dual reformulation for which strong duality holds. We then analytically derive the optimal policy update given the solution of the dual problem. This way, we bypass the computation of optimal transport costs and of optimal transport maps, which we implicitly characterize by solving the dual formulation. Finally, we provide an experimental evaluation of our approach across various control tasks. Our results show that optimal transport discrepancies can offer an advantage over state-of-the-art approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Approximation algorithms

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Physics-Informed Neural Networks: A Step Towards Data-Driven Optimization of Additive Manufacturing

Physics-Informed Neural Networks: A Step Towards Data-Driven...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Fabio Depaoli Stefano Felicioni Francesco Ponzio Alessandro Aliberti Enrico Macii Federica Bondioli Elisa Padovano Santa Di Cataldo Dept. of Control and Computer Engineering Politecnico di Torino Italy Dept. of Applied Science and Technology Politecnico di Torino Italy Interuniv. Dept. of Reg. and Urban Studies and Planning Politecnico di Torino Italy

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

Laser powder bed fusion (L-PBF) is the most popular Additive Manufacturing (AM) process for metals. It builds a 3D object layer-by-layer, by spreading metal powder on top of the previous layer and selectively melting it with a laser. Despite its many advantages, large-scale production may be hampered by the large number of process parameters and the challenges associated with their optimization. We propose an automated parameter selection approach based on process signatures extracted from a parameterized simulation of the process. Specifically, we outline a rapid data-driven simulation method based on Physics-Informed Neural Network (PINN). This approach involves training a neural network to solve the partial differential equation describing the process at varying values of a parameter of interest (for example, the laser power), thus eliminating the need for repeated Finite Elements Method (FEM) simulations. Our preliminary experiments demonstrate the feasibility of our approach.

关键词： Training Powders Three-dimensional displays Neural networks Metals Production Three-dimensional printing Finite element analysis Laser fusion Optimization

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Targeting different defect-oriented fault models in IC testing: an experimental approach

Targeting different defect-oriented fault models in IC testi...

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Euromicro Symposium on Digital System Design

作者： N. Mirabella A. Floridia R. Cantoro M. Grosso M. Sonza Reorda STMicroelearonics s.r.l. Italy Dept. of Control and Computer Engineering Politecnico di Torino Italy

In the field of integrated circuit (IC) testing, the detection of defects is crucial to ensure the reliability and functionality of the final product. Among the variety of fault models that can be used to target the many possible defects in a circuit, delay faults (transition and path delay) have been used for many years. Lately, cell-aware testing (CAT) has been introduced as a different approach that aims to improve the detection of internal defects of standard cells: it involves using specific patterns to detect faults that could not be detected by common fault models (e.g., stuck-at and transition delay fault models). Both delay and cell-aware faults can be caused by several factors, such as manufacturing defects, environmental conditions, and aging effects. In this paper, we investigate the application of test patterns generated with the transition and path delay fault models in comparison with others developed with the cell-aware approach, in terms of fault coverage, pattern count and test generation time. Overall, the study shows that the combination of the path delay fault model and cell-aware testing can lead to improved fault coverage and lower test. The experimental results are presented over a wide range of open-source benchmarks and on a RISC-V design using a proprietary industrial technology library.

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Towards More Human-like AI Communication: A Review of Emergent Communication Research

arXiv

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arXiv 2023年

作者： Brandizzi, Nicolo' Dept. of Computer Control and Management Engineering Sapienza University of Rome via Ariosto 25 Rome00185 Italy

In the recent shift towards human-centric AI, the need for machines to accurately use natural language has become increasingly important. While a common approach to achieve this is to train large language models, this method presents a form of learning misalignment where the model may not capture the underlying structure and reasoning humans employ in using natural language, potentially leading to unexpected or unreliable behavior. Emergent communication (EmCom) is a field of research that has seen a growing number of publications in recent years, aiming to develop artificial agents capable of using natural language in a way that goes beyond simple discriminative tasks and can effectively communicate and learn new concepts. In this review, we present EmCom under two aspects. Firstly, we delineate all the common proprieties we find across the literature and how they relate to human interactions. Secondly, we identify two subcategories and highlight their characteristics and open challenges. We encourage researchers to work together by demonstrating that different methods can be viewed as diverse solutions to a common problem and emphasize the importance of including diverse perspectives and expertise in the field. We believe a deeper understanding of human communication is crucial to developing machines that can accurately use natural language in human-machine interactions. © 2023, CC BY.

关键词： Reinforcement learning

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