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Institute
Das Buch bietet eine umfassende und praxisorientierte Einführung in die wesentlichen Grundlagen und Konzepte der Informatik. Es umfasst den Stoff, der typischerweise in den ersten Semestern eines Informatikstudiums vermittelt wird, vertieft Zusammenhänge, die darüber hinausgehen und macht sie verständlich. Die Themenauswahl orientiert sich an der langfristigen Relevanz für die praktische Anwendung. Praxisnah und aktuell werden die Inhalte für Studierende der Informatik und verwandter Studiengänge sowie für im Beruf stehende Praktiker vermittelt. Die vorliegende fünfte Auflage wurde grundlegend überarbeitet und aktualisiert.
Shifting energy peak load is a subject that plays a huge role in the currently changing energy market, where renewable energy sources no longer produce the exact amount of energy demanded.
Matching demand to supply requires behavior Changes on the customerside, which can be achieved by incentives suchas Real-Time-Pricing (RTP).
Various studies show that such incentives cannot be utilized without a complexity reduction, e.g., by smart home automation systems that inform the customer about possible savings or automatically schedule appliances to off-peak load phases.
We propose a probabilistic appliance usage prediction based on historical energy data that can be used to identify the times of day where an appliance will be used and therefore make load shift recommendations that suite the customer’s usage profile. A huge issue is how to provide a valid performance evaluation for this particular problem.
We will argue why the commonly used accuracy metric is not suitable, and suggest to use other metrics like the area under the Receiver Operating Characteristic (ROC) curve, Matthews Correlation Coefficient (MCC) or F1-Score instead.
The IT architecture for meter reading and utility services is at the core of new business models and has a decisive role as an enabler for resource efficiency measures. The communication architecture used by those services has significant impact on cost, flexibility and speed of new service rollout. This article describes how the dominant system model for meter reading came about, what alternative models exist, and what trade-offs those models have for rollout of new services by different stakeholders. Control of a self learning home automation system by dynamic tariff information (Real-Time-Pricing) is given as an application example.
Zahlensysteme und binäre Arithmetik – Nachricht und Information – Codierung und Datenkompression – Verschlüsselung – Schaltalgebra, Schaltnetze und Elemente der Computerhardware – Rechnerarchitekturen – Rechnernetze – Betriebssysteme – prozedurale und objektorientierte Programmierung (C und Java) – Automatentheorie und formale Sprachen – Berechenbarkeit und Komplexität – Suchen und Sortieren – Bäume und Graphen – Software-Engineering – Datenbanken – Anwendungsprogrammierung im Internet (HTML, CSS, JavaScript und PHP) – Deep Learning mit neuronalen Netzen
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.
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.
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.
There are many nearest neighbor algorithms tailor made for ICP,but most of them require Special input data like range Images or triangle meshes.
We focus on efficient nearest neighbor algorithms that do not impose this limitation, and thus can also be used with 3-D point sets generated by structure-frommotion techniques. We shortly present the evaluated algorithms and introduce the modifications we made to improve their efficiency.
In particular, several enhancements to the well-known k-D tree algorithm are described. The first part of our Performance Analysis consists of Experiments on synthetic point sets, whereas the second part features experiments with the ICP algorithm on real point sets. Both parts are completed by a thorough evaluation of the obtained results.
This contribution introduces MOBSY, a fully integrated, autonomous mobile service robot system. It acts as an automatic dialogue-based receptionist for visitors to our institute.
MOBSY incorporates many techniques from different research areas into one working stand-alone system. The techniques involved range from computer vision over speech understanding to classical robotics.
Along with the two main aspects of vision and speech, we also focus on the integration aspect, both on the methodological and on the technical level.
We describe the task and the techniques involved. Finally, we discuss the experiences that we gained with MOBSY during a live performance at our institute.
Dieser Artikel stellt ein Konzept f¨ur eine Online-Vorlesung im Rahmen der virtuellen Hochschule Bayern vor.
Zunächst werden kurz die fachlichen Inhalte erl¨autert, die es zu vermitteln gilt.
Nach dieser Einführung wird auf die Umsetzung der Inhalte in eine dem neuen Medium Internet angemessene Form eingegangen.
Dies betrifft vor allem die ausgewählten Techniken, die zur Wissensvermittlung eingesetzt werden.
Ein besonderer Schwerpunkt liegt auf der Möglichkeit durch Interaktivität das Wissen experimentell zu vertiefen und somit eine Verbindung zwischen dem theoretisch Gelernten und dem praktisch Erfahrenen herzustellen.
Weiterhin wird eine Vielfalt an Kommunikationsm¨oglichkeiten vorgestellt, da Kommunikation eine wichtige soziale Komponente im Lernprozess und h¨aufig entscheidend f¨ur den Erfolg ist.
We present an Approach for non linea roptimization of the parameters of an endoscopic camera mounted on a surgery robot. The goal is to generate a depth map for each image in order to enhance the quality of medical light fields.
The pose information provided by the robot is used as an initialization, where especially the orientation isi naccurate. Refinement of intrinsic and extrinsic camera parameters is performed by minimizing the back-projectionerror of 3-D points that are reconstructed by triangulation from image Feature stracked over an image sequence.
Optimization of the camera parameters results in an enhancement of Rendering Quality in two ways: More accurate parameters lead to better interpolation as well as to better depth maps for approximating the scenegeometry.
This work presents a technique for computing dense disparity maps from a binocular stereo camera system. The methods are applied in an Augmented Reality setting for combining real and virtual worlds with proper occlusions. The proposed stereo correspondence technique is based oil area matching and facilitates an efficient strategy by using the concept of a three-dimensional similarity accumulator whereby occlusions are detected and object boundaries are extracted correctly. The main contribution of this paper is the way we fill the accumulator using absolute differences of images and computing a mean filter on these difference images. This. is. where the main advantages of the accumulator approach can be exploited, since all entries can be computed in parallel and thus extremely efficient. Additionally, we-perform an asymmetric correction step and a post-processing of the disparity maps that maintains object edges.
In this paper we address the problem of using quaternions in unconstrained nonlinear optimization of 3-D rotations. Quaternions representing rotations have four elements but only three degrees of freedom, since they must be of norm one.
This constraint has to be taken into account when applying e.g. the Levenberg-Marquardt algorithm, a method for unconstrained nonlinear optimization widely used in computer vision. We propose an easy to use method for achieving this.
Experiments using our parametrization in photo grammetric bündle -adjustment are presented at the end of the paper.
We describe an Augmented Reality system using the corners of a color cube for camera calibration. In the augmented image the cube is replaced by a computer generated virtual object.
The cube is localized in an image by the CSC color segmentation algorithm. The camera projection matrix is estimated with a linear method that is followed by a nonlinear refinement step.
Because of possible missclassifications of the segmented color regions and the minimum number of point correspondences used for calibration, the estimated pose of the cube may be very erroneous for some frames; therefore we perform outlier detection and treatment for rendering the virtual object in an acceptable manner.
Es wird ein System aus der Augmented Reality (Erweiterte Realiäat, AR) vorgestellt, das zur Kamerakalibrierung einen farbigen Würfel verwendet, der außerdem als Platzhalter für ein virtuelles Objekt dient.
Zur Erkennung des Würfels in einer Szene werden Methoden aus der Farbbildverarbeitung verwendet, wie der Color Structure Code (CSC) und die Klassifikation der entstehenden Regionen nach ihrer Farbe. Zur Beschleunigung der Segmentierung wird ein hierarchisches Verfahren eingesetzt.
Die typischerweise gewünschten Einsatzgebiete für Dienstleistungsroboter, z. B. Krankenhäuser oder Seniorenheime, stellen sehr hohe Anforderungen an die Mensch-Maschine-Schnittstelle.
Diese Erfordernisse gehen im Allgemeinen über die Möglichkeiten der Standardsensoren, wie Ultraschalloder Infrarotsensoren, hinaus. Es müssen daher ergänzende Verfahren zum Einsatz kommen.
Aus der Sicht der Mustererkennung sind die Nutzung des Rechnersehens und des natürlichsprachlichen Dialogs von besonderem Interesse. Dieser Beitrag stellt das mobile System MOBS Y vor. MOBS Y ist ein vollkommen integrierter autonomer mobiler Dienstleistungsroboter.
Er dient als ein automatischer dialogbasierter Empfangsservice für Besucher unseres Instituts.
MOBSY vereinigt vielfältige Methoden aus unterschiedlichsten Forschungsgebieten in einem eigenständigen System. Die zum Einsatz kommenden Methoden aus dem Bereich der Bildverarbeitung reichen dabei von Objektklassifikation über visuelle Selbstlokalisierung und Rekalibrierung bis hin zu multiokularer Objektverfolgung.
Die Dialogkomponente umfasst Methoden der Spracherkennung, des Sprachverstehens und die Generierung von Antworten. Im Beitrag werden die zu erfüllende Aufgabe und die einzelnen Verfahren dargestellt.
MOBSY is a fully integrated autonomous mobile service robot system.
It acts as an automatic dialogue based receptionist for visitors of our institute. MOBSY incorporates many techniques from different research areas into one working stand-alone system. Especially the computer vision and dialogue aspects are of main interest from the pattern recognition’s point of view.
To summarize shortly, the involved techniques range from object classification over visual self-localization and recalibration to object tracking with multiple cameras. A dialogue component has to deal with speech recognition, understanding and answer generation. Further techniques needed are navigation, obstacle avoidance, and mechanisms to provide fault tolerant behavior.
This contribution introduces our mobile system MOBSY. Among the main aspects vision and speech, we focus also on the integration aspect, both on the methodological and on the technical level. We describe the task and the involved techniques.
Finally, we discuss the experiences that we gained with MOBSY during a live performance at the 25th anniversary of our institute
Zahlensysteme und binäre Arithmetik
Nachricht und Information
Codierung und Datenkompression
Verschlüsselung
Schaltalgebra, Schaltnetze und Elemente der Computerhardware
Rechnerarchitekturen
Rechnernetze
Betriebssysteme
Datenbanken
Automatentheorie und formale Sprachen
Berechenbarkeit und Komplexität
Suchen und Sortieren
Bäume und Graphen
prozedurale und objektorientierte Programmierung (C und Java)
Anwendungsprogrammierung im Internet (HTML, CSS, JavaScript und PHP)
Software-Engineering
We propose a reinforcement learning approach to heating control in home automation, that can acquire a set of rules enabling an agent to heat a room to the desired temperature at a defined time while conserving as much energy as possible. Experimental results are presented that show the feasibility of our method.