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10th International Electric Drives Production conference (EDPC)

作者： Meiners, Moritz Mayr, Andreas Kuhn, Marlene Raab, Bernhard Franke, Joerg Friedrich Alexander Univ Erlangen Nuremberg FAU Inst Factory Automat & Prod Syst FAPS Nurnberg Germany

ISBN: (纸本)9781728184586

With the increasing demand for electric vehicles, automobile manufacturers and suppliers need to adjust their value chains to meet future requirements. In wire harness assembly, which is part of the production of vehicle electrical systems, the joining of wires and connecting elements, usually realized by crimping, is one of the most complex and quality-critical processes. For quality assessment, the crimp height and pull-out force are measured as primary quality criteria. In the context of the increased safety requirements of autonomous driving, monitoring the crimp connections should be automated, holistic, and in real-time for each wire-crimp connection during production. However, the high complexity of internal and external influences as well as the variety of crimp connections complicates the operation of conventional process monitoring systems based on hard-coded criteria. In this context, data-driven approaches using the methods of artificial intelligence are moving into focus. the emerging development of information technology opens up new perspectives for process monitoring systems with inherent intelligence. To provide a proof of concept for an intelligent process monitoring, in this paper experiments are carried out on a crimping station, provoking different types of error conditions. the resulting dataset is then analyzed using deep learning. Finally, a comparison of the approaches shown is carried out, followed by an outlook on future research activities.

关键词： artificial intelligence machine learning deep learning crimping quality control process curve autoencoder

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Searching for waveforms on spatially-filtered epileptic ECoG

Searching for waveforms on spatially-filtered epileptic ECoG

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International ieee/EMBS conference on Neural Engineering, CNE

作者： Carlos H. Mendoza-Cardenas Austin J. Brockmeier University of Delaware Newark Delaware USA

Seizures are one of the defining symptoms in patients with epilepsy, and due to their unannounced occurrence, they can pose a severe risk for the individual that suffers it. New research efforts are showing a promising future for the prediction and preemption of imminent seizures, and with those efforts, a vast and diverse set of features have been proposed for seizure prediction algorithms. However, the data-driven discovery of nonsinusoidal waveforms for seizure prediction is lacking in the literature, which is in stark contrast with recent works that show the close connection between the waveform morphology of neural oscillations and the physiology and pathophysiology of the brain, and especially its use in effectively discriminating between normal and abnormal oscillations in electrocorticographic (ECoG) recordings of epileptic patients. Here, we explore a scalable, energy-guided waveform search strategy on spatially-projected continuous multi-day ECoG data sets. Our work shows that data-driven waveform learning methods have the potential to not only contribute features with predictive power for seizure prediction, but also to facilitate the discovery of oscillatory patterns that could contribute to our understanding of the pathophysiology and etiology of seizures.

关键词： learning systems Electric potential Neural activity Neural engineering Morphology Epilepsy Search problems

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HemCNN: Deep learning enables decoding of fNIRS cortical signals in hand grip motor tasks

HemCNN: Deep Learning enables decoding of fNIRS cortical sig...

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International ieee/EMBS conference on Neural Engineering, CNE

作者： Pablo Ortega Aldo Faisal Brain & Behaviour Lab London UK Brain & Behaviour Lab Imperial College London London UK

We solve the fNIRS left/right hand force decoding problem using a data-driven approach by using a convolutional neural network architecture, the HemCNN. We test HemCNN's decoding capabilities to decode in a streaming way the hand, left or right, from fNIRS data. HemCNN learned to detect which hand executed a grasp at a naturalistic hand action speed of 1 Hz, outperforming standard methods. Since HemCNN does not require baseline correction and the convolution operation is invariant to time translations, our method can help to unlock fNIRS for a variety of real-time tasks. Mobile brain imaging and mobile brain machine interfacing can benefit from this to develop real-world neuroscience and practical human neural interfacing based on BOLD-like signals for the evaluation, assistance and rehabilitation of force generation, such as fusion of fNIRS with EEG signals.

关键词： Neuroimaging Neuroscience Convolution Force Real-time systems Decoding Task analysis

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Cerebellar control of saccadic adaptation using a spiking neural network model integrated into the Neurorobotics platform

Cerebellar control of saccadic adaptation using a spiking ne...

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International ieee/EMBS conference on Neural Engineering, CNE

作者： Junya Inoue Alberto Antonietti Alessandra Pedrocchi Faculty of Science University of Tuebingen Tuebingen Germany Politecnico di Milano Milano Italy

the cerebellum is essential for motor learning and adaptation since it can change the relationship between sensory input and motor output. Cerebellar activity contributes to the motor execution with the aim of optimizing the resulting performance. this optimization is driven by the error information, that the cerebellum receives through the sensory feedback. In this study, we aim at studying the cerebellar contribution in the saccadic adaptation, fine and rapid control of eye movements. We set up a virtual experiment where an iCub robot, controlled by a cerebellar-inspired spiking neural network, was challenged in a saccadic adaptation task. the virtual robot, at first, made inaccurate saccades and therefore attempted to adjust them, trial after trial, based on sensory error feedback. the spiking neural network model was equipped with plasticity mechanisms, that allowed the robot to change and adapt the amplitude of the saccade movement after a few trials. Interestingly, we observed that for this task a single plasticity mechanism was sufficient to produce a fast adaptation, while in other cerebellar-driven tasks, the addition of more plasticities granted an improvement of the adaptation performances.

关键词： Cerebellum Adaptation models Neural networks Neural engineering Robot sensing systems Task analysis Optimization

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Convergence of Intelligent data Acquisition and Advanced Computing systems

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SENSORS 2021年第7期21卷 2262页

作者： Stamatescu, Grigore Sachenko, Anatoliy Popescu, Dan Univ Politehn Bucuresti Dept Automat & Ind Informat 313 Splaiul Independentei Bucharest 060042 Romania Kazimierz Pulaski Univ Technol & Humanities Radom Dept Informat Ul Malczewskiego 29 PL-26600 Radom Poland West Ukrainian Natl Univ Res Inst Intelligent Comp Syst 3 Peremoga Sq UA-46020 Ternopol Ukraine

the special issue welcomed submissions as extended versions of conference articles published in the proceedings of the “10th ieee International conference on Intelligent data Acquisition and Advanced Computing systems: Technology and Applications (IDAACS’2019)” allowing the authors to expand on their theoretical and experimental contributions. Potential topics of the special issue that were initially defined were mapped onto the core areas and special streams of the IDAACS’2019 conference, and included but were not limited to: advanced instrumentation and data acquisition systems, advanced mathematical methods for data acquisition and computing systems, computational intelligence for instrumentation and data acquisition systems, data analysis and modeling, intelligent distributed systems and remote control, intelligent information systems, data mining, and ontology, Internet of things, pattern recognition, digital image, and signal processing, intelligent instrumentation and data acquisition systems in advanced manufacturing for Industry 4, intelligent robotics and sensors, machine learning, application for smart buildings and smart cities. [2] presents a system that combines scalar, ground level measurements, from sensor networks with aerial robotic platforms (UAVs) as data collection and communication relay infrastructure. data are colected through existing building automation system (BAS) infrastructure with wireless components for sensing, room temperature sensors, and control, motor valves for the heating elements.

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Lα-regularization-based sparse semi-supervised learning for data with complex distributions 8

Lα-regularization-based sparse semi-supervised learning for...

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8th ieee data driven control and learning systems conference, ddcls 2019

作者： Zhang, Qi Chu, Tianguang School of Information Technology Management University of International Business Economics Beijing100029 China College of Engineering Peking University Beijing100871 China

ISBN: (纸本)9781728114545

We consider in this paper two kinds of complex data, i.e., multimodal and mixmodal data, and aim to develop data-driven learning models to exploit the intrinsic characteristics of the data. An $l-{\alpha}$-regularization-based sparse semi-supervised graph embedding (SSGE) model for feature extraction upon multimodal and mixmodal data is presented by incorporating the hierarchical locality with non-convex sparsity, facilitating better interpretation of the projections. Semi-supervised projection learning and $l-{\alpha}$-regularization-based sparse subspace learning are implemented successively, with feasible solving algorithms presented. Experiments for face recognition verifies the feasibility and effectiveness of the proposed SSGE model in both multimodal and mixmodal situations. © 2019 ieee.

关键词： Face recognition

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Proceedings of 2020 ieee PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020

Proceedings of 2020 IEEE PES Innovative Smart Grid Technolog...

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10th ieee PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020

ISBN: (纸本)9781728171005

the proceedings contain 240 papers. the topics discussed include: analysis of the electrical properties of SimBench low voltage benchmark network models;aggregator’s business models: challenges faced by different roles;analysis of deep-heat energy wells for heat pump systems;on assessment of the scenario clustering in stochastic bidding: a full-cycle perspective;energy consumption management in buildings in the context of voluntary and mandatory demand response programs in smart grids;supplementary damping control design for large scale pv power plant at transmission level interconnection;reinforcement learning based energy management algorithm for energy trading and contingency reserve application in a microgrid;data driven framework for load profile generation in medium voltage networks via transfer learning;and impact of unbalanced electric vehicle charging on low-voltage grids.

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Availability Attacks on Computing systems through Alteration of Environmental control: Smart Malware Approach 19

Availability Attacks on Computing Systems through Alteration...

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10th ACM/ieee International conference on Cyber-Physical systems (ICCPS)

作者： Chung, Keywhan Kalbarczyk, Zbigniew T. Iyer, Ravishankar K. Univ Illinois Urbana IL 61820 USA

ISBN: (纸本)9781450362856

In this paper, we demonstrate the feasibility of smart malware that advances state-of-the-art attacks by (i) indirectly attacking a computing infrastructure through a cyber-physical system (CPS) that manages the environment in which the computing enterprise operates, (ii) disguising its malicious actions as accidental failures, and (iii) self-learning attack strategies from cyber-physical system measurement data. We address all aspects of the malware, including the construction of the self-learning malware and the launch of a failure injection attack. We validate the attacks in a data-driven CPS simulation environment developed as part of this study.

关键词： Cyber Security Cyber Physical systems CPS Malware

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Towards Safe Machine learning for CPS Infer Uncertainty from Training data 19

Towards Safe Machine Learning for CPS Infer Uncertainty from...

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10th ACM/ieee International conference on Cyber-Physical systems (ICCPS)

作者： Gu, Xiaozhe Easwaran, Arvind Nanyang Technol Univ Energy Res Inst Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Entineering Singapore Singapore

ISBN: (纸本)9781450362856

Machine learning (ML) techniques are increasingly applied to decision-making and control problems in Cyber-Physical systems among which many are safety-critical, e.g., chemical plants, robotics, autonomous vehicles. Despite the significant benefits brought by ML techniques, they also raise additional safety issues because 1) most expressive and powerful ML models are not transparent and behave as a black box and 2) the training data which plays a crucial role in ML safety is usually incomplete. An important technique to achieve safety for ML models is "Safe Fail", i.e., a model selects a reject option and applies the backup solution, a traditional controller or a human operator for example, when it has low confidence in a prediction. data-driven models produced by ML algorithms learn from training data, and hence they are only as good as the examples they have learnt. As pointed in [17], ML models work well in the "training space" (i.e., feature space with sufficient training data), but they could not extrapolate beyond the training space. As observed in many previous studies, a feature space that lacks training data generally has a much higher error rate than the one that contains sufficient training samples [31]. therefore, it is essential to identify the training space and avoid extrapolating beyond the training space. In this paper, we propose an efficient Feature Space Partitioning Tree (FSPT) to address this problem. Using experiments, we also show that, a strong relationship exists between model performance and FSPT score.

关键词： Machine learning Safety Safe Fail

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Embedded On-line System for Electrical Energy Measurement and Forecasting in Buildings 10

Embedded On-line System for Electrical Energy Measurement an...

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10th ieee International conference on Intelligent data Acquisition and Advanced Computing systems - Technology and Applications (IDAACS)

作者： Nichiforov, Cristina Stamatescu, Grigore Stamatescu, Iulia Arghira, Nicoleta Fagarasan, Ioana Iliescu, Sergiu Stelian Univ Politehn Bucuresti Dept Automat Control & Ind Informat Bucharest Romania Graz Univ Technol Inst Tech Informat Graz Austria

ISBN: (纸本)9781728140681

Fine grained measurement of electrical energy consumption in commercial buildings is essential for improved fault diagnosis and control with impact on the overall operation as well as user comfort. An open system architecture is presented for data collection, processing and communication of measured energy patterns at the local and aggregated level. the implementation is based on an embedded development board with current and voltage sensors, supported by open-source software and packages. Suitable user and programmatic interfaces allow reliable bidirectional connection to external automation equipment and information systems. such as the Building Management systems (BMS). Recent advances in advanced algorithms for time series pre-processing and data-driven modelling allow good quality in situ predictions for the collected measurements. A relevant example consists of neural network based learning systems which are able to provide accurate hour-ahead and day-ahead predictions that contribute to reduction of peak demand with economic and environmental impact. Integration of such platforms in higher-level CyberPhysical Energy systems (CPES) is further discussed.

关键词： energy monitoring internet of things load forecasting neural networks smart buildings

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