Author name cluster

Joern Ploennigs

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

AAAI Conference 2023 System Paper

AI Model Factory: Scaling AI for Industry 4.0 Applications

Dhaval Patel
Shuxin Lin
Dhruv Shah
Srideepika Jayaraman
Joern Ploennigs
Anuradha Bhamidipati
Jayant Kalagnanam

This demo paper discusses a scalable platform for emerging Data-Driven AI Applications targeted toward predictive maintenance solutions. We propose a common AI software architecture stack for building diverse AI Applications such as Anomaly Detection, Failure Pattern Analysis, Asset Health Forecasting, etc. for more than a 100K industrial assets of similar class. As a part of the AI system demonstration, we have identified the following three key topics for discussion: Scaling model training across multiple assets, Joint execution of multiple AI applications; and Bridge the gap between current open source software tools and the emerging need for AI Applications. To demonstrate the benefits, AI Model Factory has been tested to build the models for various industrial assets such as Wind turbines, Oil wells, etc. The system is deployed on API Hub for demonstration.

PDF Details DOI

AAAI Conference 2017 System Paper

From Semantic Models to Cognitive Buildings

Joern Ploennigs
Anika Schumann

Today’s operation of buildings is either based on simple dashboards that are not scalable to thousands of sensor data or on rules that provide very limited fault information only. In either case considerable manual effort is required for diagnosing building operation problems related to energy usage or occupant comfort. We present a Cognitive Building demo that uses (i) semantic reasoning to model physical relationships of sensors and systems, (ii) machine learning to predict and detect anomalies in energy ﬂow, occupancy and user comfort, and (iii) speech-enabled Augmented Reality interfaces for immersive interaction with thousands of devices. Our demo analyzes data from more than 3, 300 sensors and shows how we can automatically diagnose building operation problems.

PDF Details

ECAI Conference 2014 Conference Paper

Exploiting the Semantic Web for Systems Diagnosis

Anika Schumann
Freddy Lécué
Joern Ploennigs

Diagnosis is the task of explaining abnormal behaviors of systems like telecommunication, transportation or energy systems. Given a sequence of observations the problem is to determine, online, all faults that are in line with these observations. Many approaches tackle this problem but they either require domain expertise or a formal description of how observations and faults are connected. This limits their scope to the diagnosis of well-understood faults. We address the problem of diagnosing faults that may occur for the first time and present a new diagnosis approach that integrates techniques for analyzing semantic descriptions of observations and faults.

Details

ECAI Conference 2014 Conference Paper

Extending Semantic Sensor Networks for Automatically Tackling Smart Building Problems

Joern Ploennigs
Anika Schumann
Freddy Lécué

Sensor systems are constantly growing in all application areas and become elements of our environment. Semantic Sensor Networks (SSN) support this development and provide standardized semantic access for reasoning on this information. Unfortunately they do not model internal system knowledge or simple correlations between sensors and hence they cannot be used to automatically perform analytics tasks based on sensor data only. We show how SSN ontology can be extended and demonstrate its benefits for the task of diagnosing smart building problems using real-world data.

Details