TY  - JOUR
A1  - Gablonski, Thorsten-Christian
A1  - Pryss, Rüdiger
A1  - Probst, Thomas
A1  - Vogel, Carsten
A1  - Andreas, Sylke
T1  - Intersession-Online: A smartphone application for systematic recording and controlling of intersession experiences in psychotherapy
JF  - J — Multidisciplinary Scientific Journal
N2  - Mobile health technologies have become more and more important in psychotherapy research and practice. The market is being flooded by several psychotherapeutic online services for different purposes. However, mobile health technologies are particularly suitable for data collection and monitoring, as data can be recorded economically in real time. Currently, there is no appropriate method to assess intersession experiences systematically in psychotherapeutic practice. The aim of our project was the development of a smartphone application framework for systematic recording and controlling of intersession experiences. Intersession-Online, an iOS- and Android-App, offers the possibility to collect data on intersession experiences easily, to provide the results to therapists in an evaluated form and, if necessary, to induce or interrupt intersession experiences with the primary aim to improve outcome of psychotherapy. In general, the smartphone application could be a helpful, evidence-based tool for research and practice. Overall speaking, further research to investigate the efficacy of Intersession-Online is necessary.
KW  - intersession experiences
KW  - intersession processes
KW  - psychotherapy
KW  - mobile app
KW  - smartphone app
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-285597
SN  - 2571-8800
VL  - 2
IS  - 4
SP  - 480
EP  - 495
ER  - 
TY  - JOUR
A1  - Kammerer, Klaus
A1  - Hoppenstedt, Burkhard
A1  - Pryss, Rüdiger
A1  - Stökler, Steffen
A1  - Allgaier, Johannes
A1  - Reichert, Manfred
T1  - Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings
JF  - Sensors
N2  - o build, run, and maintain reliable manufacturing machines, the condition of their components has to be continuously monitored. When following a fine-grained monitoring of these machines, challenges emerge pertaining to the (1) feeding procedure of large amounts of sensor data to downstream processing components and the (2) meaningful analysis of the produced data. Regarding the latter aspect, manifold purposes are addressed by practitioners and researchers. Two analyses of real-world datasets that were generated in production settings are discussed in this paper. More specifically, the analyses had the goals (1) to detect sensor data anomalies for further analyses of a pharma packaging scenario and (2) to predict unfavorable temperature values of a 3D printing machine environment. Based on the results of the analyses, it will be shown that a proper management of machines and their components in industrial manufacturing environments can be efficiently supported by the detection of anomalies. The latter shall help to support the technical evangelists of the production companies more properly.
KW  - anomaly detection
KW  - sensor data
KW  - machine learning
KW  - production machines
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193885
SN  - 1424-8220
VL  - 19
IS  - 24
ER  -