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The optimization of full-scale biogas plant operation is of great importance to make biomass a competitive source of renewable energy. The implementation of innovative control and optimization algorithms, such as Nonlinear Model Predictive Control, requires an online estimation of operating states of biogas plants. This state estimation allows for optimal control and operating decisions according to the actual state of a plant. In this paper such a state estimator is developed using a calibrated simulation model of a full-scale biogas plant, which is based on the Anaerobic Digestion Model No.1. The use of advanced pattern recognition methods shows that model states can be predicted from basic online measurements such as biogas production, CH4 and CO2 content in the biogas, pH value and substrate feed volume of known substrates. The machine learning methods used are trained and evaluated using synthetic data created with the biogas plant model simulating over a wide range of possible plant operating regions. Results show that the operating state vector of the modelled anaerobic digestion process can be predicted with an overall accuracy of about 90%. This facilitates the application of state-based optimization and control algorithms on full-scale biogas plants and therefore fosters the production of eco-friendly energy from biomass.
We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.
As the recognition of emotion from speech has matured to a degree where it becomes applicable in real-life settings, it is time for a realistic view on obtainable performances. Most studies tend to overestimation in this respect: acted data is often used rather than spontaneous data, results are reported on pre-selected prototypical data, and true speaker disjunctive partitioning is still less common than simple cross-validation. A considerably more realistic impression can be gathered by inter-set evaluation: we therefore show results employing six standard databases in a cross-corpora evaluation experiment. To better cope with the observed high variances, different types of normalization are investigated. 1.8k individual evaluations in total indicate the crucial performance inferiority of inter- to intra-corpus testing.
Sensing and acquiring reliable physical values are the fundamentals, not only for a predictive maintenance or quality assessment, but especially for big data analysis and sophisticated Industry 4.0 applications. In steel plants, physical values are distributed over the complete process chain of steel making while the environmental conditions are harsh with respect to high temperature, aggressive fluids, water, shock and dust. These conditions render the use of electronic devices focusing a consumer market impossible.The developed self-contained smart measuring system presented in this paper survives in harsh environments and is composed of small-sized modules providing miscellaneous functionalities. The high degree of modularity in hard-and software facilitates a cost-effective adaptation to many applications, like vibration monitoring, temperature logging or torque measurement. Different onboard measurement components are available yet, namely high sensitive bridge amplifiers for strain measurements, accelerometers, gyroscopes, orientation-, temperature- and humidity sensors as well as an unique system identification. A communication module enables wireless transmission of the acquired data via Bluetooth or NFC online. Moreover, different power supply features are supported by the power module: Ultra-low power modes for long-life battery use, recharging of lithium cells, and an inductive power supply for wireless power transfer for applications with moving or rotating components. The heart of the system is a powerful ARM based microcontroller which enables an intelligent analysis of the data in situ. This is especially important where data size and complexity is the relevant factor for example in area-wide sensor networks.
