Spritzgießen 2019

2019 ◽  
Keyword(s):  
Machine Learning ◽  
Affective Computing ◽  
Technology Innovation ◽  
Künstliche Intelligenz

Inhalt Plenarvorträge Produktentwicklung einmal anders – effizient, flexibel, agil! Dr. rer. nat. S. Lambertz, Freudenberg Technology Innovation, Weinheim 1 Spreu und Weizen – Welche Automobilzulieferer schaffen den Strukturwandel, welche nicht? M.-R. Faerber, Managing Partner der Struktur Management Partner GmbH, Köln 7 Wenn Sinneswahrnehmungen digital werden und Technik fühlen lernt – Trends und Anwendungen des Affective Computing Dr.-Ing. J. Garbas, Fraunhofer IIS, Erlangen 9 Kurzberichte aus der Forschung Machine Learning zur Erkennung von Veränderungen beim Spritzgiessprozess Prof. Dr. F. Ehrig, Prof. Dr. G. Schuster, HSR Hochschule für Technik Rapperswil, Rapperswil, Schweiz 19 Steigerung von Produkt- und Prozessqualität beim Spritzgießen durch künstliche Intelligenz M.Sc. A. Schulze Struchtrup, M.Sc. M. Janßen, Prof. Dr.-Ing. R. Schiffers, Institut für Produkt Engineering, Universität Duisburg-Essen 27 I4.0 Pilotfabrik für die smarte Kunststoffverarbeitung Prof...

scholarly journals Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5135
Author(s):  
Ngoc-Dau Mai ◽  
Boon-Giin Lee ◽  
Wan-Young Chung
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Temporal Lobe ◽  
Affective Computing ◽  
Electrode Placement ◽  
Entropy Measure ◽  
Support Vector ◽  
Emotion Classification ◽  
Eeg Signals ◽  
Computing Method

In this research, we develop an affective computing method based on machine learning for emotion recognition using a wireless protocol and a wearable electroencephalography (EEG) custom-designed device. The system collects EEG signals using an eight-electrode placement on the scalp; two of these electrodes were placed in the frontal lobe, and the other six electrodes were placed in the temporal lobe. We performed experiments on eight subjects while they watched emotive videos. Six entropy measures were employed for extracting suitable features from the EEG signals. Next, we evaluated our proposed models using three popular classifiers: a support vector machine (SVM), multi-layer perceptron (MLP), and one-dimensional convolutional neural network (1D-CNN) for emotion classification; both subject-dependent and subject-independent strategies were used. Our experiment results showed that the highest average accuracies achieved in the subject-dependent and subject-independent cases were 85.81% and 78.52%, respectively; these accuracies were achieved using a combination of the sample entropy measure and 1D-CNN. Moreover, our study investigates the T8 position (above the right ear) in the temporal lobe as the most critical channel among the proposed measurement positions for emotion classification through electrode selection. Our results prove the feasibility and efficiency of our proposed EEG-based affective computing method for emotion recognition in real-world applications.

Machine Learning for Affective Computing

2015 ◽  
Author(s):  
Rafael Calvo ◽  
Sidney D'Mello ◽  
Jonathan Gratch ◽  
Arvid Kappas ◽  
Ashish Kapoor
Keyword(s):  
Machine Learning ◽  
Affective Computing

Künstliche Intelligenz zur Entwicklung von Screening-Parametern im Bereich der kornealen Biomechanik

2019 ◽  
Vol 236 (12) ◽  
pp. 1423-1427 ◽  
Author(s):  
Sven Reisdorf
Keyword(s):  
Machine Learning ◽  
Künstliche Intelligenz ◽  
Corvis St ◽  
Screening Parameter

ZusammenfassungMachine Learning stellt insbesondere dann eine sinnvolle Alternative dar, wenn eine Datenanalyse mit wissensbasierten analytischen Methoden sehr aufwendig und schwierig ist. In solchen Fällen bietet sich auch eine Kombination aus analytischen Methoden und empirischen Methoden mittels künstlicher Intelligenz (KI) an. Die Entwicklung verschiedener Auswertefunktionen des Corvis ST ist hierfür ein konkretes Beispiel. In diesem Beitrag wird die Entwicklung dreier Screening-Parameter mittels KI beschrieben. Der Artikel zeigt, wie diese Entwicklungen im Bereich der Erkennung von klinischem und subklinischem Keratokonus sowie Glaukom-Screening klinisch hilfreich sind.

Music Emotions Recognition by Machine Learning With Cognitive Classification Methodologies

2017 ◽  
Vol 11 (4) ◽  
pp. 80-92
Author(s):  
Junjie Bai ◽  
Kan Luo ◽  
Jun Peng ◽  
Jinliang Shi ◽  
Ying Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Affective Computing ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Cognitive Computing ◽  
Bayes Classifier ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
Neuro Fuzzy

Music emotions recognition (MER) is a challenging field of studies addressed in multiple disciplines such as musicology, cognitive science, physiology, psychology, arts and affective computing. In this article, music emotions are classified into four types known as those of pleasing, angry, sad and relaxing. MER is formulated as a classification problem in cognitive computing where 548 dimensions of music features are extracted and modeled. A set of classifications and machine learning algorithms are explored and comparatively studied for MER, which includes Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Neuro-Fuzzy Networks Classification (NFNC), Fuzzy KNN (FKNN), Bayes classifier and Linear Discriminant Analysis (LDA). Experimental results show that the SVM, FKNN and LDA algorithms are the most effective methodologies that obtain more than 80% accuracy for MER.

Assessing Animal Emotion and Behavior Using Mobile Sensors and Affective Computing

2019 ◽  
pp. 49-77
Author(s):  
Heath Yates ◽  
Brent Chamberlain ◽  
William Baldwin ◽  
William H. Hsu ◽  
Dana Vanlandingham
Keyword(s):  
Machine Learning ◽  
Virtual Reality ◽  
Affective Computing ◽  
Use Cases ◽  
Mobile Sensors ◽  
Smart Surveillance ◽  
Computing Framework ◽  
And Behavior ◽  
Work Done

Affective computing is a very active and young field. It is driven by several promising areas that could benefit from affective intelligence such as virtual reality, smart surveillance, perceptual interfaces, and health. This chapter suggests new design for the detection of animal affect and emotion under an affective computing framework via mobile sensors and machine learning. The authors review existing literature and suggest new use cases by conceptual reevaluation of existing work done in affective computing and animal sensors.

Panasonic übernimmt Blue Yonder für 6 Milliarden Euro

2021 ◽  
Vol 73 (17) ◽  
pp. 33-33
Author(s):  
Jörg Rode
Keyword(s):  
Machine Learning ◽  
Künstliche Intelligenz

Scottsdale/Osaka. Der japanische Elektronikkonzern Panasonic kauft für knapp 6 Mrd. Euro Blue Yonder. Das ist einer der weltgrößten Anbieter von Retail-Software – darunter Programme für Prognose und Auto-Dispo. Blue Yonder setzt stark auf Künstliche Intelligenz (KI) in Form von Machine Learning.

Logistik-Digitalisierung: Politik der kleinen Schritte

2019 ◽  
Vol 33 (07-08) ◽  
pp. 22-24
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Internet Of Things ◽  
Künstliche Intelligenz ◽  
Neue Technologien

Interview | Der globale SAP-Logistikpartner leogistics unterstützt Unternehmen bei der Transition aus der alten in die neue IT-Welt, in der Digital Supply Chain, beim Transportmanagement und der Werks- und Standortlogistik. Im Gespräch mit dieser Zeitschrift analysiert leogistics-CEO André Käber die Hürden der Digitalisierung und spricht über konkrete Chancen, die sich durch neue Technologien rund um das Internet of Things (IoT), Künstliche Intelligenz (KI) und Machine Learning für die Optimierung der Werks- und Transportlogistik nutzen lassen.

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