TY - CHAP A1 - Wenninger, Marc A1 - Bayerl, Sebastian P. A1 - Maier, Andreas A1 - Schmidt, Jochen T1 - Recurrence Plot Spacial Pyramid Pooling Network for Appliance Identification in Non-Intrusive Load Monitoring T2 - 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) N2 - 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. KW - NILM KW - V-I trajectory KW - Recurrence Plot Y1 - 2021 UR - https://doi.org/10.1109/ICMLA52953.2021.00025 SP - 108 EP - 115 ER - TY - CHAP A1 - Bayerl, Sebastian P. A1 - Wenninger, Marc A1 - Schmidt, Jochen A1 - Wolff von Gudenberg, Alexander A1 - Riedhammer, Korbinian T1 - STAN: A stuttering therapy analysis helper T2 - 2021 IEEE Spoken Language Technology Workshop (SLT) N2 - 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. KW - Machine Learning Y1 - 2021 ER - TY - CHAP A1 - Schmidt, Jochen A1 - Wong, C.K. A1 - Yeap, W.K. T1 - Mapping and Localisation with Sparse Range Data T2 - International Conference on Autonomous Robots and Agents (ICARA), pages 497-502, Palmerston North, New Zealand N2 - We present an approach for indoor mapping and localization with a mobile robot using sparse range data, without the need for solving the SLAM problem. The paper consists of two main parts. First, a split and merge based method for dividing a given metric map into distinct regions is presented, thus creating a topological map in a metric framework. Spatial information extracted from this map is then used for self-localization. The robot computes local confidence maps for two simple localization strategies based on distance and relative orientation of regions. The local confidence maps are then fused using an approach adapted from computer vision to produce overall confidence maps. Experiments on data acquired by mobile robots equipped with sonar sensors are presented. KW - Mobile Robots KW - Mapping KW - Localization Y1 - 2018 ER - TY - CHAP A1 - Schmidt, Jochen A1 - Wong, C.K. A1 - Yeap, W.K. T1 - A Split & Merge Approach to Metric-Topological Map-Building T2 - International Conference on Pattern Recognition (ICPR), volume 3, pages 1069-1072, Hong Kong N2 - We present a novel split and merge based method for dividing a given metric map into distinct regions, thus effectively creating a topological map on top of a metric one. The initial metric map is obtained from range data that are converted to a geometric map consisting of linear approximations of the indoor environment. The splitting is done using an objective function that computes the quality of a region, based on criteria such as the average region width (to distinguish big rooms from corridors) and overall direction (which accounts for sharp bends). A regularization term is used in order to avoid the formation of very small regions, which may originate from missing or unreliable sensor data. Experiments based on data acquired by a mobile robot equipped with sonar sensors are presented, which demonstrate the capabilities of the proposed method. KW - Pattern Recognition KW - Metric-Topological Map-Building Y1 - 2018 ER - TY - CHAP A1 - Wong, C.K. A1 - Yeap, W.K. A1 - Schmidt, Jochen T1 - Computing a Network of ASRs Using a Mobile Robot Equipped with Sonar Sensors T2 - International Conference on Robotics, Automation, and Mechatronics (RAM), pages 57-62, Bangkok, Thailand N2 - This paper presents a novel algorithm for computing absolute space representations (ASRs) in Yeap, W.K. and Jefferies, M. (1988) for mobile robots equipped with sonar sensors and an odometer. The robot is allowed to wander freely (i.e. without following any fixed path) along the corridors in an office environment from a given start point to an end point. It then wanders from the end point back to the start point. The resulting ASRs computed in both directions are shown KW - Robotics KW - Automation and Mechatronics Y1 - 2006 ER - TY - CHAP A1 - Yeap, W.K. A1 - Wong, C.K. A1 - Schmidt, Jochen T1 - Initial Experiments with a Mobile Robot on Cognitive Mapping T2 - International Symposium on Practical Cognitive Agents and Robots (PCAR 2006), pages 221-230, Perth, Australia N2 - This paper shows how a mobile robot equipped with sonar sensors and an odometer is used to test ideas about cognitive mapping. The robot first explores an office environment and computes a "cognitive map" which is a network of ASRs [1]. The robot generates two networks, one for the outward journey and the other for the journey home. It is shown that both networks are different. The two networks, however, are not merged to form a single network. Instead, the robot attempts to use distance information implicit in the shape of each ASR to find its way home. At random positions in the homeward journey, the robot calculates its orientation towards home. The robot's performances for both problems are evaluated and found to be surprisingly accurate. KW - Mobile Robot KW - Cognitive Mapping Y1 - 2006 ER - TY - CHAP A1 - Schmidt, Jochen A1 - Wong, C.K. A1 - Yeap, W.K. T1 - Spatial Information Extraction for Cognitive Mapping with a Mobile Robot. T2 - Conference on Spatial Information Theory: COSIT'07, Melbourne, Australia. Volume 4736 of Lecture Notes in Computer Science N2 - When animals (including humans) first explore a new environment, what they remember is fragmentary knowledge about the places visited. Yet, they have to use such fragmentary knowledge to find their way home. Humans naturally use more powerful heuristics while lower animals have shown to develop a variety of methods that tend to utilize two key pieces of information, namely distance and orientation information. Their methods differ depending on how they sense their environment. Could a mobile robot be used to investigate the nature of such a process, commonly referred to in the psychological literature as cognitive mapping? What might be computed in the initial explorations and how is the resulting “cognitive map” be used for localization? In this paper, we present an approach using a mobile robot to generate a “cognitive map”, the main focus being on experiments conducted in large spaces that the robot cannot apprehend at once due to the very limited range of its sensors. The robot computes a “cognitive map” and uses distance and orientation information for localization. KW - Mobile Robot KW - Sonar Sensor KW - Robotic Research Y1 - 2018 ER - TY - CHAP A1 - Wong, C.K. A1 - Yeap, W.K. A1 - Schmidt, Jochen T1 - Using a Mobile Robot for Cognitive Mapping T2 - International Joint Conference on Artificial Intelligence (IJCAI), pages 2243-2248, Hyderabad, India, 2007 N2 - When animals (including humans) first explore a new environment, what they remember is fragmentary knowledge about the places visited. Yet, they have to use such fragmentary knowledge to find their way home. Humans naturally use more powerful heuristics while lower animals have shown to developa varietyof methodsthat tend to utilize two key pieces of information,namely distance and orientation information. Their methods differ depending on how they sense their environment. Could a mobile robot be used to investigate the nature of such a process, commonly referred to in the psychological literature as cognitive mapping? What might be computed in the initial explorations and how is the resulting “cognitive map” be used to return home? In this paper, we presented a novel approach using a mobile robot to do cognitive mapping. Our robot computes a “cognitive map” and uses distance and orientation information to find its way home. The process developed provides interesting insights into the nature of cognitive mapping and encourages us to use a mobile robot to do cognitive mapping in the future, as opposed to its popular use in robot mapping. KW - Cognitive Mapping KW - Sonar Sensor KW - Robotic Research Y1 - 2007 ER - TY - CHAP A1 - Wong, C.K. A1 - Yeap, W.K. A1 - Schmidt, Jochen T1 - Our Next Generation of Robotics Researchers? Teaching Robotics at Primary School Level. T2 - Readings in Technology and Education: Proceedings of International Conference on Information Communication Technologies in Education N2 - In this paper, we present our experience in designing and teaching of our first robotics course for students at primary school level. The course was carried out over a comparatively short period of time, namely 6 weeks, 2 hours per week. In contrast to many other projects, we use robots that researchers used to conduct their research and discuss problems faced by these researchers. Thus, this is not a behavioural study but a hands-on learning experience for the students. The aim is to highlight the development of autonomous robots and artificial intelligence as well as to promote science and robotics in schools. KW - Robotic Research KW - Teaching Robotics KW - Software Development Y1 - 2009 ER - TY - CHAP A1 - Schmidt, Jochen A1 - Vogt, F. A1 - Niemann, H. T1 - Nonlinear Refinement of Camera Parameters using an Endoscopic Surgery Robot T2 - Proceedings of IAPR Conference on Machine Vision Applications (MVA) N2 - 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. KW - Endoscopic Surgery Robot KW - endoscopic camera KW - minimalinvasive surgery Y1 - 2018 ER -