An industrial PID data repository for control loop performance monitoring (CPM)

Control loop performance monitoring methods to detect problems in PID loops are developed and tested using industrial data sets. The data is captured from the process, passed on to the researcher who tries out new detection and diagnosis methods. The data is not generally shared with other researchers working on similar problems. The authors therefore have implemented a data repository to categorise and store the data so that it becomes accessible to all researchers. Existing methods can be compared and enhanced using the data sets. This paper describes the context of CPM as well as the data repository. The repository is set up, hosted and maintained by the South African Council for Automation and Control using a professional web developer.https://www.journals.elsevier.com/ifac-papersonline2019-04-01hj2018Electrical, Electronic and Computer Engineerin

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In this article, we describe and interpret a set of acoustic and linguistic features that characterise emotional/emotion-related user states – confined to the one database processed: four classes in a German corpus of children interacting with a pet robot. To this end, we collected a very large feature vector consisting of more than 4000 features extracted at different sites. We performed extensive feature selection (Sequential Forward Floating Search) for seven acoustic and four linguistic types of features, ending up in a small number of ‘most important ’ features which we try to interpret by discussing the impact of different feature and extraction types. We establish different measures of impact and discuss the mutual influence of acoustics and linguistics

On Presenters and Commenters in YouTube Climate Change Videos

Social media videos can promote viewers responsibility to solve social problems such as climate change. Not all aspiring videos, however, are successful in persuading their viewers on the perils involved in climate change and on the need for pro-environmental behaviour. Our study examined attributes that could explain a video’s persuasiveness and focused on the video presenter traits. Videos on climate change were sourced from YouTube conjointly with the comments they elicited. The presenters in these videos addressed the negative effects and dangers of climate change and the role of human activity in resolving them. Two attributes were manually coded for each video: the type of presenter in the videos– scientist, politician or celebrity, and their presentation style: blaming, stating the problem, or suggesting a solution. A measure of persuasiveness was computed from the YouTubers comments using sentiment analysis. This computation provided a polarity label – positive, negative, or neutral, for all comments, for each video. Subsets of 50 comments per video were manually coded to validate the computational analysis. The findings indicated that a predominant number of positive-polarity comments was elicited by video presenters who were scientists. Videos that proposed potential solutions to climate change elicited a majority of positive polarity. Politicians and celebrity presenters, as well as blame-oriented videos elicited a larger number of negative-polarity comments. These initial findings imply a potential of sentiment analysis of comments to elucidate which attributes can increase a video’s persuasiveness on its viewers. This insight can improve future video production and enhance their influence

Emotion Elicitation in a Computerized Gambling Game

We have designed a novel computer controlled environment that elicits emotions in subjects while they are uttering short identical phrases. The paradigm is based on Damasio's experiment for eliciting apprehension and is implemented in a voice activated computer game. For six subjects we have obtained recordings of dozens of identical sentences, which are coupled to events in the game – gain or loss of points. Prosodic features of the recorded utterances were extracted and classified. The resultant classifier gave 78-85 % recognition of presence/absence of apprehension. 1

Phonetic search methods for large speech databases

“Phonetic Search Methods for Large Databases” focuses on Keyword Spotting (KWS) within large speech databases. The brief will begin by outlining the challenges associated with Keyword Spotting within large speech databases using dynamic keyword vocabularies. It will then continue by highlighting the various market segments in need of KWS solutions, as well as, the specific requirements of each market segment. The work also includes a detailed description of the complexity of the task and the different methods that are used, including the advantages and disadvantages of each method and an in-depth comparison. The main focus will be on the Phonetic Search method and its efficient implementation. This will include a literature review of the various methods used for the efficient implementation of Phonetic Search Keyword Spotting, with an emphasis on the authors’ own research which entails a comparative analysis of the Phonetic Search method which includes algorithmic details. This brief is useful for researchers and developers in academia and industry from the fields of speech processing and speech recognition, specifically Keyword Spotting