Topology, control and development of high power multilevel converters

Thesis (M. Ing.) -- University of Stellenbosch, 1998.One copy microfiche.Full text to be digitised and attached to bibliographic record

Natural balancing of multicell converters

Thesis (PhD)--University of Stellenbosch, 2004.ENGLISH ABSTRACT: Multilevel converters were developed as a result of a growing need for higher power converters. This dissertation addresses a specific multilevel topology called the multicell topology. A problem associated with this topology is cell capacitor voltage unbalance. This dissertation addresses the issue of natural balancing of multicell converters. The topology is mathematically analysed and a theory is developed to explain the natural balancing mechanism. The study of the natural balancing property includes a detailed harmonic-, steady-state- and time constant analysis. The theory is verified by a comparison between the theoretical-, simulated- and experimental results.AFRIKAANSE OPSOMMING: Veelvlakkige omsetters het ontstaan as gevolg van ’n behoefte aan ho¨er drywing omsetters. Hierdie proefskrif handel spesifiek oor die veelsellige omsetter topologie. ’n Probleem wat met hierdie topologie geassosieer word is selkapasitor onbalans. Hierdie proefskrif ondersoek die natuurlike balansering van veelsellige omsetters. Die topologie word wiskundig geanaliseer en ’n teorie word geformuleer om die natuurlike balanseringsmeganisme te verduidelik. Die ondersoek van die natuurlike balanseringseienskap bevat ’n volledige harmoniese-, bestendige toestand- en tydkonstante analise. Die teorie is gekontroleer deur teoretiese-, simulasie- en eksperimentele resultate te vergelyk

Simultaneous Sleep Stage and Sleep Disorder Detection from Multimodal Sensors Using Deep Learning

Sleep scoring involves the inspection of multimodal recordings of sleep data to detect potential sleep disorders. Given that symptoms of sleep disorders may be correlated with specific sleep stages, the diagnosis is typically supported by the simultaneous identification of a sleep stage and a sleep disorder. This paper investigates the automatic recognition of sleep stages and disorders from multimodal sensory data (EEG, ECG, and EMG). We propose a new distributed multimodal and multilabel decision-making system (MML-DMS). It comprises several interconnected classifier modules, including deep convolutional neural networks (CNNs) and shallow perceptron neural networks (NNs). Each module works with a different data modality and data label. The flow of information between the MML-DMS modules provides the final identification of the sleep stage and sleep disorder. We show that the fused multilabel and multimodal method improves the diagnostic performance compared to single-label and single-modality approaches. We tested the proposed MML-DMS on the PhysioNet CAP Sleep Database, with VGG16 CNN structures, achieving an average classification accuracy of 94.34% and F1 score of 0.92 for sleep stage detection (six stages) and an average classification accuracy of 99.09% and F1 score of 0.99 for sleep disorder detection (eight disorders). A comparison with related studies indicates that the proposed approach significantly improves upon the existing state-of-the-art approaches