Parameter Estimation in Electrical Distribution Systems with limited Measurements using Regression Methods

This paper presents novel methods for parameter identification in electrical grids with small numbers of spatially distributed measuring devices, which is an issue for distribution system operators managing aged and not properly mapped underground Low Voltage (LV) grids, especially in Germany. For this purpose, the total impedance of individual branches of the overall system is estimated by measuring currents and voltages at a subset of all system nodes over time. It is shown that, under common assumptions for electrical distsribution systems, an estimate of the total impedance can be made using readily computable proxies. Different regression methods are then used and compared to estimate the total impedance of the respective branches, with varying weights of the input data. The results on realistic LV feeders with different branch lengths and number of unmeasured segments are discussed and multiple influencing factors are investigated through simulations. It is shown that estimates of the total impedances can be obtained with acceptable quality under realistic assumptions

Machine-learning-based Bayesian state estimation in electrical energy systems

In many algorithmic applications in electrical power grids, state estimation (SE) represents the first step of a process chain. In SE, sensor measurements are processed to infer the most probable grid state. Classical methods such as weighted least squares (WLSs) based approaches use statistical methods that can be based on sensor noise and erroneous measurements. With these methods, only point estimates are made, which results in a lack of knowledge about prediction uncertainties. In this study, machine-learning-based methods for determining the actual state of the grid are proposed. Bayesian optimisation is applied to find the optimal hyperparameter configurations for neural networks (NNs) for SE tasks. The application of Bayesian inference using Bayesian NNs is proposed, which allows the prediction of point estimates as well as uncertainty intervals for the system states. The advantages of using Bayesian approaches in comparison to classical SE methods like WLS are shown

Identification and Validation of Esophageal Squamous Cell Carcinoma Targets for Fluorescence Molecular Endoscopy

Dysplasia and intramucosal esophageal squamous cell carcinoma (ESCC) frequently go unnoticed with white-light endoscopy and, therefore, progress to invasive tumors. If suitable targets are available, fluorescence molecular endoscopy might be promising to improve early detection. Microarray expression data of patient-derived normal esophagus (n = 120) and ESCC samples (n = 118) were analyzed by functional genomic mRNA (FGmRNA) profiling to predict target upregulation on protein levels. The predicted top 60 upregulated genes were prioritized based on literature and immunohistochemistry (IHC) validation to select the most promising targets for fluorescent imaging. By IHC, GLUT1 showed significantly higher expression in ESCC tissue (30 patients) compared to the normal esophagus adjacent to the tumor (27 patients) (p n = 17) and high-grade dysplasia (HGD, n = 13) is higher (p n = 7) and to the normal esophagus adjacent to the tumor (n = 5). The sensitivity and specificity of 2-DG 800CW to detect HGD and ESCC is 80% and 83%, respectively (ROC = 0.85). We identified and validated GLUT1 as a promising molecular imaging target and demonstrated that fluorescent imaging after topical application of 2-DG 800CW can differentiate HGD and ESCC from LGD and normal esophagus

C-Met targeted fluorescence molecular endoscopy in Barrett's esophagus patients and identification of outcome parameters for phase-I studies

Fluorescence molecular endoscopy (FME) is an emerging technique in the field of gastroenterology that holds potential to improve diagnosis and guide therapy, by serving as a ‘red-flag’ endoscopic imaging technique. Here, we investigated the safety, feasibility and optimal method of administration of EMI-137, targeting c-Met, during FME in Barrett’s Esophagus (BE) and report several outcome parameters for early phase FME studies. Methods: FME was performed in 15 Barrett’s neoplasia patients. EMI-137 was administered to three cohorts of five patients: 0.13 mg/kg intravenously (IV); 0.09 mg/kg IV or topically at a dose of 200 µg/cm BE (n=1) or 100 µg/cm BE (n=4). Fluorescence was visualized in vivo, quantified in vivo using multi-diameter single-fiber reflectance, single-fiber fluorescence (MDSFR/SFF) spectroscopy and correlated to histopathology and immunohistochemistry. EMI-137 localization was assessed using fluorescence microscopy. Results: FME using different IV and topical doses o