The Influence of the Moving Image on Music-Induced Emotions

The combination of music and the moving image is prevalent in our society, occurring in visual art forms and media such as movies, music videos, and commercials. The relationship between the two has received much interest in existing research, but the focus has mainly been on the influence that music may have on the experience of the moving image. The present study adopts the reverse approach, and examines the potential impact the moving image may have on emotions felt and perceived while listening to music. Sixty-six participants were presented with three music excerpts that were introduced either alone or paired with one of two videos aimed to elicit strong emotions (either awe or being moved). The music excerpts were played a second time (without visual accompaniment) to investigate whether a possible influence of previously presented visual information was persistent. The results revealed that the moving image did have an intensifying effect on emotions induced (both awe and being moved) while listening to music. A significant negative effect on perceived valence and enjoyment was found for the least congruent music & video pairing. Interestingly, this incongruent pairing had the most persistent influence, being the only significant carry-over effect during the second presentation of music alone

Information-based Preprocessing of PLC Data for Automatic Behavior Modeling

Cyber-physical systems (CPS) offer immense optimization potential for manufacturing processes through the availability of multivariate time series data of actors and sensors. Based on automated analysis software, the deployment of adaptive and responsive measures is possible for time series data. Due to the complex and dynamic nature of modern manufacturing, analysis and modeling often cannot be entirely automated. Even machine- or deep learning approaches often depend on a priori expert knowledge and labelling. In this paper, an information-based data preprocessing approach is proposed. By applying statistical methods including variance and correlation analysis, an approximation of the sampling rate in event-based systems and the utilization of spectral analysis, knowledge about the underlying manufacturing processes can be gained prior to modeling. The paper presents, how statistical analysis enables the pruning of a dataset's least important features and how the sampling rate approximation approach sets the base for further data analysis and modeling. The data's underlying periodicity, originating from the cyclic nature of an automated manufacturing process, will be detected by utilizing the fast Fourier transform. This information-based preprocessing method will then be validated for process time series data of cyber-physical systems' programmable logic controllers (PLC)

Root Cause Analysis Of Productivity Losses In Manufacturing Systems Utilizing Ensemble Machine Learning

In today’s rapidly evolving landscape of automation and manufacturing systems, the efficient resolution of productivity losses is paramount. This study introduces a data-driven ensemble approach, utilizing the cyclic multivariate time series data from binary sensors and signals from Programmable Logic Controllers (PLCs) within these systems. The objective is to automatically analyze productivity losses per cycle and pinpoint their root causes by assigning the loss to a system element. The ensemble approach introduced in this publication integrates various methods, including information theory and machine learning behavior models, to provide a robust analysis for each production cycle. To expedite the resolution of productivity losses and ensure short response times, stream processing becomes a necessity. Addressing this, the approach is implemented as data-stream analysis and can be transferred to batch processing, seamlessly integrating into existing systems without the need for extensive historical data analysis. This method has two positive effects. Firstly, the result of the analysis ensures that the period of lower productivity is reduced by identifying the likely root cause of the productivity loss. Secondly, these results are more reliable due to the ensemble approach and therefore avoid dependency on technical experts. The approach is validated using a semi-automated welding manufacturing system, an injection molding automation system, and a synthetically generated test PLC dataset. The results demonstrate the method's efficacy in offering a data-driven understanding of process behavior and mark an advancement in autonomous manufacturing system analysis

The Influence of the Moving Image on Music-Induced Emotions

The combination of music and the moving image is prevalent in our society, occurring in visual art forms and media such as movies, music videos, and commercials. The relationship between the two has received much interest in existing research, but the focus has mainly been on the influence that music may have on the experience of the moving image. The present study adopts the reverse approach, and examines the potential impact the moving image may have on emotions felt and perceived while listening to music. Sixty-six participants were presented with three music excerpts that were introduced either alone or paired with one of two videos aimed to elicit strong emotions (either awe or being moved). The music excerpts were played a second time (without visual accompaniment) to investigate whether a possible influence of previously presented visual information was persistent. The results revealed that the moving image did have an intensifying effect on emotions induced (both awe and being moved) while listening to music. A significant negative effect on perceived valence and enjoyment was found for the least congruent music & video pairing. Interestingly, this incongruent pairing had the most persistent influence, being the only significant carry-over effect during the second presentation of music alone