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Item Towards model-free pressure control in water distribution networks.(MDPI, 2020-09-26)Pressure control in water distribution networks (WDNs) is one of the interventions commonly employed to improve the reliability and sustainability of water supply. Various approaches have been proposed to solve the problem of pressure control. However, most schemes that have been proposed rely on the accuracy of a model in order to precisely control a real WDN. Therefore, any deviation between a model and real WDN parameters could render the results of control schemes useless. As a result, this work proposes the utilisation of the reinforcement learning (RL) technique to control nodes pressure in WDNs without solving the model. Quadratic approximation emulators of WDNs and RL agents are used in the proposed scheme. The effectiveness of the proposed scheme is tested on two WDNs networks and the results are compared with the conventional optimisation scheme that is commonly used for simulation cases. The results show that the proposed scheme is able to achieve the desired results when compared to the benchmark optimisation procedure. However, unlike the optimisation procedure, the proposed scheme achieved the results without the numerical solution of the WDNs. Therefore, this scheme could be used in situations where the model of a network is not well defined.Item OMNIVIL—An autonomous mobile manipulator for flexible production.(MDPI, 2020-12-17)Flexible production is a key element in modern industrial manufacturing. Autonomous mobile manipulators can be used to execute various tasks: from logistics, to pick and place, or handling. Therefore, autonomous robotic systems can even increase the flexibility of existing production environments. However, the application of robotic systems is challenging due to their complexity and safety concerns. This paper addresses the design and implementation of the autonomous mobile manipulator OMNIVIL. A holonomic kinematic design provides high maneuverability and the implemented sensor setup with the underlying localization strategies are robust against typical static and dynamic uncertainties in industrial environments. For a safe and efficient human–robot collaboration (HRC), a novel workspace monitoring system (WMS) is developed to detect human co-workers and other objects in the workspace. The multilayer sensor setup and the parallel data analyzing capability provide superior accuracy and reliability. An intuitive zone-based navigation concept is implemented, based on the workspace monitoring system. Preventive behaviors are predefined for a conflict-free interaction with human co-workers. A workspace analyzing tool is implemented for adaptive manipulation, which significantly simplifies the determination of suitable platform positions for a manipulation task.Item Autosynpose: Automatic generation of synthetic datasets for 6D object pose estimation.(IOP Press, 2020-06-07)We present an automated pipeline for the generation of synthetic datasets for six-dimension (6D) object pose estimation. Therefore, a completely automated generation process based on predefined settings is developed, which enables the user to create large datasets with a minimum of interaction and which is feasible for applications with a high object variance. The pipeline is based on the Unreal 4 (UE4) game engine and provides a high variation for domain randomization, such as object appearance, ambient lighting, camera-object transformation and distractor density. In addition to the object pose and bounding box, the metadata includes all randomization parameters, which enables further studies on randomization parameter tuning. The developed workflow is adaptable to other 3D objects and UE4 environments. An exemplary dataset is provided including five objects of the Yale- CMU-Berkeley (YCB) object set. The datasets consist of 6 million subsegments using 97 rendering locations in 12 different UE4 environments. Each dataset subsegment includes one RGB image, one depth image and one class segmentation image at pixel-level.Item Severity classification of parkinson’s disease based on permutation-variable importance and persistent entropy.(MDPI, 2021-02-19)Parkinson’s disease (PD) is a neurodegenerative disease that causes chronic and progressive motor dysfunction. As PD progresses, patients show different symptoms at different stages of the disease. The severity assessment is inefficient and subjective when it comes to artificial diagnosis. However, abnormal gait was contingent and the subject selection was limited. Therefore, few-shot learning based on small sample sets is critical to solving the problem of insufficient sample data in PD patients. Using datasets from PhysioNet, this paper presents a method based on permutation-variable importance (PVI) and persistent entropy of topological imprints and uses support vector machine (SVM) as a classifier to achieve the severity classification of PD patients. The method includes the following steps: (1) Take the data as gait cycles and calculate the gait characteristics of each cycle. (2) Use the random forest (RF) method to obtain the leading factors differentiating the gait of patients at different severity levels. (3) Use time-delay embedding to map the data into a topological space, and use the topological data analysis based on permutation homology to obtain the persistent entropy. (4) Use the Borderline-SMOTE (BSM) method to balance the sample data. (5) Use the SVM to classify the samples for the severity levels of PD. An accuracy of 98.08% was achieved by 10-fold cross-validation, so our method can be used as an effective means of computer-aided diagnosis of PD and has important practical value.Item A study of cutaneous perception parameters for designing haptic symbols towards information transfer.(MDPI, 2021-09-03)Vibrotactile displays can substitute for sensory channels of individuals experiencing temporary or permanent impairments in balance, vision, or hearing, and can enhance the user experience in professional or entertainment situations. This massive range of potential uses necessitates primary research on human vibrotactile perception. One leading aspect to consider when developing such displays is how to develop haptic patterns or symbols to represent a concept. In most settings, individual patterns are sorted as alphabets of haptic symbols which formulate tactons. Tactons are structured and perceivable tactile patterns (i.e., messages) that transfer information to users by employing the sense of touch. Hence, haptic patterns are critical when designing vibrotactile displays, as they not only affect the rate of information transfer but also determine the design of the displays (e.g., the number and the placement of tactors engaged) and how the information is encoded to achieve separability. Due to this significance, this paper presents an overview study on the cutaneous perception parameters (i.e., intensity, loci, frequency, duration, illusions, and combinations of these) for designing haptic symbols to identify mutual best-practices and knowledge gaps for future work. The study also provides developers from different scientific backgrounds with access to complex notions when engaging this specialized topic (i.e., the use of cutaneous perception parameters towards information transfer). Finally, it offers recommendations on defining which parameters to engage for a specific task or pattern.
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