Refine
Year of publication
Document Type
- Conference Proceeding (82)
- Article (peer reviewed) (9)
- Book (8)
- Contribution to a Periodical (7)
- Other (4)
- Part of a Book (3)
Language
- English (113) (remove)
Is part of the Bibliography
- no (113)
Keywords
- Speech Recognition (18)
- CFT (6)
- Database (5)
- Microsoft (5)
- Sql server (5)
- Fault trees (4)
- Safety-critical systems (4)
- Software Development (4)
- Augmented Reality (AR) (3)
- Automatic Speech Recognition (3)
- Cognitive Mapping (3)
- Machine Learning (3)
- Mobile Robot (3)
- Multimedia, Case Study, E-learning, blended learning (3)
- Pattern Recognition (3)
- Software safety (3)
- 5G (2)
- Automatic Evaluation (2)
- Compositional safety analysis (2)
- Computer vision (2)
- FMEA (2)
- GMM (2)
- Java (2)
- Robotic Research (2)
- Robotics (2)
- Safety (2)
- Safety Analysis (2)
- Sonar Sensor (2)
- automatic assessment (2)
- automatic speech recognition (2)
- automatic summarization (2)
- body cameras (2)
- meeting summarization (2)
- mobile-access edge computing (2)
- productivity (2)
- speaker identification (2)
- spoken language understanding (2)
- user interaction (2)
- visualization (2)
- 3D Point-Sets (1)
- 3D Rotations (1)
- Acoustic applications (1)
- Adaption models (1)
- Algorithms (1)
- Analysis models (1)
- Approximate dynamic program (1)
- Aspect-oriented programming (AOP) (1)
- Austria (1)
- Automated qualification (1)
- Automated recertification (1)
- Automation and Mechatronics (1)
- Automotive Applications (1)
- Automotive Engineering (1)
- Biomedical acoustics (1)
- Business process, ERP systems, Austria (1)
- Calibration Pattern (1)
- Camera (1)
- Camera calibration (1)
- Case Study (1)
- Children's Speech (1)
- Cleft Lip and palate (1)
- Color segmentation (1)
- Componentware (1)
- Computer Science Education (1)
- Cyber-physical Systems (1)
- Data Fusion (1)
- Data selection (1)
- Demand Side Management (1)
- Dependability Engineering (1)
- District heating systems (1)
- Driver Assistent Systems (1)
- Dual Quaternion (1)
- Dual Quaternion (1)
- E-Governance (1)
- E-Learning (1)
- Embedded real-time systems (1)
- Endoscopic Surgery Robot (1)
- FDTA-A (1)
- Failure Mode (1)
- Failure propagation (1)
- Failure-Dependent Timing Analysis (1)
- Fault tolerance (1)
- Fault/anomaly detection (1)
- Feature Tracking (1)
- Flow type annotations (1)
- Function charts (1)
- Germany (1)
- Graphical User Interfaces (GUI) (1)
- Hand-Eye Calibration (1)
- Heating control (1)
- Home Automation (1)
- Household Appliance Usage Prediction (1)
- Humanoid Robot (1)
- ICP Algorithm (1)
- ITIL, configuration management, automated documentation (1)
- Industrial CPS (1)
- Industrial Informatics (1)
- Integrated System (1)
- Integrated model-based safety analysis (1)
- Iterative Closest Point (1)
- Iterative algorithms (1)
- Iterative closest point algorithm (1)
- Java Server (1)
- Lattice Generation (1)
- Layered architecture (1)
- Layout (1)
- Localization (1)
- Lung cancer technologies (1)
- Machine learning (1)
- Mapping (1)
- Meeting Summarization (1)
- Metric-Topological Map-Building (1)
- Mobile Robots (1)
- Mode trees (1)
- Model driven engineering languages (1)
- Model-Driven Architecture (1)
- Model-Driven Software Development (MDSD) (1)
- Mono Poly (1)
- Multimedia (1)
- Multimedia, Case Study, E-learning, blended learning, Software Engineering (1)
- Multimedia, content representation, information types, interaction types (1)
- Multiparty meetings processing (1)
- NILM (1)
- Noise robustnes (1)
- Numerical Stability (1)
- Obstacle Avoidance (1)
- Page Rank (1)
- Pathologic Speech (1)
- Pathology (1)
- Pattern recognition (1)
- Port dependency traces (1)
- Problem Solving Strategies (1)
- Prosodic Feature (1)
- Rapid Application Development (RAD) (1)
- Real Time Pricing (RTP) (1)
- Real time systems (1)
- Recurrence Plot (1)
- Reward Function (1)
- Safety-critical embedded systems (1)
- Scale Estimation (1)
- Secondary Education (1)
- Service Robot (1)
- Service robots (1)
- Simultaneous Localization (1)
- Smart Meter, Advanced Metering Infrastructure, AMI (1)
- Software Maintenence (1)
- Software architecture (1)
- Speech Processing Applications (1)
- Speech Recognition Software (1)
- Speech Recognition System (1)
- Speech Recognizer (1)
- Speech intelligibility (1)
- Speech processing (1)
- Speech recognition (1)
- Speech understanding and dialogue (1)
- Stereo Self-Calibration (1)
- Summarization (1)
- System availability (1)
- Target Temperature (1)
- Temporal Orderr (1)
- Tool chains (1)
- Tool-Based Analysis (1)
- Total Laryngectomy (1)
- Translation Vector (1)
- Unconstrained NonlinearOptimization (1)
- V-I trajectory (1)
- Vector Quantization (1)
- Vehicle control platform (1)
- WCET (1)
- World Coordinate System (1)
- academic spoken English (1)
- acoustic modeling (1)
- alcohol intoxication (1)
- automotive applications (1)
- binomial distribution (1)
- browsing (1)
- business process (1)
- channel compensation (1)
- computer-assisted language learning (1)
- corpus description (1)
- discrete time Markov Chains (1)
- e-learning (1)
- edge cloud (1)
- edge cloud computing (1)
- edge device migration (1)
- embedded systems (1)
- endoscopic camera (1)
- evaluation (1)
- experiences (1)
- fMLLR (1)
- fault trees (1)
- first passage time (1)
- hand- eye calibration (1)
- integer linear programming (1)
- key phrase extraction (1)
- key phrase ranking (1)
- key phrases (1)
- keyword generation (1)
- keyword spotting (1)
- learning by doing (1)
- matrix representation (1)
- meetings (1)
- minimalinvasive surgery (1)
- mobility management (1)
- multiparty meetings processing (1)
- neural networks (1)
- non-native prosody (1)
- occupation time (1)
- pathologic speech (1)
- ranking (1)
- recurrence time (1)
- rhythm (1)
- robustness (1)
- speaker verification (1)
- spectral features (1)
- speech recognition (1)
- summarization (1)
- summarization evaluation (1)
- system fusion (1)
- task migration (1)
- vektor quantization (1)
Institute
- Fakultät für Informatik (113) (remove)
Fault and anomaly detection in district heating substations: A survey on methodology and data sets
(2023)
District heating systems are essential building blocks for affordable, low-carbon heat supply. Early detection and elimination of faults is crucial for the efficiency of these systems and necessary to achieve the low temperatures targeted for 4th generation district heating systems. Especially methods for fault and anomaly detection in district heating substations are currently of high interest, as faults in substations can be repaired quickly and inexpensively, and smart meter data are becoming widely available. In this paper, we review recent scientific publications presenting data-driven approaches for fault and anomaly detection in district heating substations with a focus on methods and data sets. Our review indicates that researchers use a wide variety of methods, mostly focusing on unsupervised anomaly detection rather than fault detection. This is due to a lack of labeled data sets, preventing the use of supervised learning methods and quantitative analysis. Together with the lack of publicly available data sets, this impedes the accurate comparison of individual methods. To overcome this impediment, increase the comparability of different methods and foster competition, future research should focus on establishing publicly available data sets, and industry-relevant metrics as benchmarks.
Mobility management is a key feature of mobile edge computing. We present an edge cloud infrastructure testbed to explore various mobility scenarios. The design objection of this testbed has been a flexible open platform based on commodity hardware that can easily be scaled with more edge devices and compute resources to perform various edge cloud experiments. As first experiments on our testbed, we have investigated the feasibility of task migration among edge devices caused by edge device overload and unpredictable user movements. We describe the migration process and present some measurements to demonstrate the feasibility.
Parameter free Non-intrusive Load Monitoring (NILM) algorithms are a major step toward real-world NILM scenarios. The identification of appliances is the key element in NILM. The task consists of identification of the appliance category and its current state. In this paper, we present a param- eter free appliance identification algorithm for NILM using a 2D representation of time series known as unthresholded Recurrence Plots (RP) for appliance category identification. One cycle of voltage and current (V-I trajectory) are transformed into a RP and classified using a Spacial Pyramid Pooling Convolutional Neural Network architecture. The performance of our approach is evaluated on the three public datasets COOLL, PLAID and WHITEDv1.1 and compared to previous publications. We show that compared to other approaches using our architecture no initial parameters have to be manually tuned for each specific dataset.
Stuttering is a complex speech disorder identified by repetitions, prolongations of sounds, syllables or words and blockswhile speaking. Specific stuttering behaviour differs strongly,thus needing personalized therapy. Therapy sessions requirea high level of concentration by the therapist. We introduce STAN, a system to aid speech therapists in stuttering therapysessions. Such an automated feedback system can lower the cognitive load on the therapist and thereby enable a more consistent therapy as well as allowing analysis of stuttering over the span of multiple therapy sessions.
Real-world domestic electricity demand datasets are the key enabler for developing and evaluating machine learning algorithms that facilitate the analysis of demand attribution and usage behavior. Breaking down the electricity demand of domestic households is seen as the key technology for intelligent smart-grid management systems that seek an equilibrium of electricity supply and demand. For the purpose of comparable research, we publish DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany. The dataset contains recordings of 15 homes over a period of up to 3.5 years, wherein total 50 appliances have been recorded at a frequency of 1 Hz. Recorded appliances are of significance for load-shifting purposes such as dishwashers, washing machines and refrigerators. One home also includes three-phase mains readings that can be used for disaggregation tasks. Additionally, DEDDIAG contains manual ground truth event annotations for 14 appliances, that provide precise start and stop timestamps. Such annotations have not been published for any long-term electricity dataset we are aware of.
Mobile-access edge clouds provide distributed com-pute capacities for low-latency applications. 5G technology willpave the way for such mobile deployment scenarios. In thispaper, we propose an edge cloud infrastructure that supportslow-latency video analysis connected with bandwidth reductionfor a moving group of persons. As example, we consider a mobilebody camera scenario that monitors the situation in a certain areaand transmits it to an operations center. Our discussion focuseson three aspects: mobility support, low-latency video processing,and bandwidth reduction. For this, we propose a mobile edgecloud infrastructure with a central cloud. In order to optimizevideo processing we optimize the edge cloud device assignmentof the cameras depending on their movement by reassigning itto another cloud device. This requires live migration of ongoingvideo analysis between edge devices. Finally we discuss the useof a mobile central cloud.
The classical results of the binomial and negative binomial probability distribution are generalized by means of homogeneous Discrete Time Markov Chains to series of stochastically independent random trials. These have not only two possible outcomes but two groups of them -- different kinds of successes and failures with occurrence probabilities depending on the outcome of the previous trial. This generalization allows a uniform view of occupation time, first passage time and recurrence time. Our results are consequently derived and presented in matrix form, the probabilities as well as the moments. They can be applied to all Discrete Time Markov Chains, especially in computer capacity planning, performability and economics.
Generating a more detailed understanding of domestic electricity demand is a major topic for energy suppliers and householders in times of climate change.
Over the years there have been many studies on consumption feedback systems to inform householders, disaggregation algorithms for Non-Intrusive-Load-Monitoring (NILM), Real-Time-Pricing (RTP) to promote supply aware behavior through monetary incentives and appliance usage prediction algorithms. While these studies are vital steps towards energy awareness, one of the most fundamental challenges has not yet been tackled: Automated detection of start and stop of usage cycles of household appliances. We argue that most research efforts in this area will benefit from a reliable segmentation method to provide accurate usage information.
We propose a SVM-based segmentation method for home appliances such as dishwashers and washing machines. The method is evaluated using manually annotated electricity measurements of five different appliances recorded over two years in multiple households.
Time series are series of values ordered by time. This kind of data can be found in many real world settings. Classifying time series is a difficult task and an active area of research. This paper investigates the use of transfer learning in Deep Neural Networks and a 2D representation of time series known as Recurrence Plots. In order to utilize the research done in the area of image classification, where Deep Neural Networks have achieved very good results, we use a Residual Neural Networks architecture known as ResNet. As preprocessing of time series is a major part of every time series classification pipeline, the method proposed simplifies this step and requires only few parameters. For the first time we propose a method for multi time series classification: Training a single network to classify all datasets in the archive with one network. We are among the first to evaluate the method on the latest 2018 release of the UCR archive, a well established time series classification benchmarking dataset.
An apparatus and method for analyzing availability of a system including subsystems each having at least one failure mode with a corresponding failure effect on the system are provided. The apparatus includes a degraded mode tree generation unit configured to automatically generate a degraded mode tree. The degraded mode tree includes at least one degraded mode element representing a degraded system state of the system that deviates from a normal operation state of the system based on a predetermined generic system meta model stored in a database including Failure Mode and Effects Analysis elements representing subsystems, failure modes, failure effects, and diagnostic measures. The apparatus also includes a processor configured to evaluate the generated degraded mode tree for calculation of the availability of the system.