Design and Characterization for Regenerative Shock Absorbers

L'abstract è presente nell'allegato / the abstract is in the attachmen

A Robust Data-driven Process Modeling Applied to Time-series Stochastic Power Flow

In this paper, we propose a robust data-driven process model whose hyperparameters are robustly estimated using the Schweppe-type generalized maximum likelihood estimator. The proposed model is trained on recorded time-series data of voltage phasors and power injections to perform a time-series stochastic power flow calculation. Power system data are often corrupted with outliers caused by large errors, fault conditions, power outages, and extreme weather, to name a few. The proposed model downweights vertical outliers and bad leverage points in the measurements of the training dataset. The weights used to bound the influence of the outliers are calculated using projection statistics, which are a robust version of Mahalanobis distances of the time series data points. The proposed method is demonstrated on the IEEE 33-Bus power distribution system and a real-world unbalanced 240-bus power distribution system heavily integrated with renewable energy sources. Our simulation results show that the proposed robust model can handle up to 25% of outliers in the training data set.Comment: Submitted to the IEEE Transactions on Power System

Deep Learning Based Parking Vacancy Detection for Smart Cities

Parking shortage is a major problem in modern cities. Drivers cruising in search of a parking space directly translate into frustration, traffic congestion, and excessive carbon emission. We introduce a simple and effective deep learning-based parking space notification (PSN) system to inform drivers of new parking availabilities and re-occupancy of the freed spaces. Our system is particularly designed to target areas with severe parking shortages (i.e., nearly all parking spaces are occupied), a situation that allows us to convert the problem of detecting parking vacancies into recognizing vehicles leaving from their stationary positions. Our PSN system capitalizes on a calibrated Mask R-CNN model and a unique adaptation of the IoU concept to track the changes of vehicle positions in a video stream. We evaluated PSN using videos from a CCTV camera installed at a private parking lot and publicly available YouTube videos. The PSN system successfully captured all new parking vacancies arising from leaving vehicles with no false positive detections. Prompt notification messages were sent to users via cloud messaging services

IPO7 promotes pancreatic cancer progression via regulating ERBB pathway

Background: Importin 7 (IPO7) belongs to the Importin β family and is implicated in the progression of diverse human malignancies. This work is performed to probe the role of IPO7 in pancreatic cancer development and its potential downstream mechanisms. Methods: IPO7 expression in PC and paracancerous tissues were measured using Immunohistochemistry (IHC) staining and qRT-PCR. Western blotting was utilized to detect the expression level of IPO7 in PC cells and immortalize the pancreatic ductal epithelial cell line. After constructing the IPO7 overexpression and knockdown models, the effect of IPO7 on the proliferation of PC cells was analyzed by the CCK-8 and EdU assay. The migration and invasion of PC cells were examined by wound healing assay and Transwell experiment. The apoptosis rate of PC cells was analyzed by flow cytometry and TUNEL assay. The Gene Set Enrichment Analysis (GSEA) was used to determine the enrichment pathways of IPO7. The effect of IPO7 on the ERBB2 expression was determined using Western blotting. A xenograft mouse model was applied to investigate the carcinogenic effect of IPO7 in vivo. Results: IPO7 expression was remarkably elevated in the cancer tissues of PC patients. IPO7 overexpression remarkably enhanced PC cell proliferation, migration and invasion and suppressed apoptosis, while knockdown of IPO7 exerted the opposite effect. Mechanistically, IPO7 facilitated the malignant phenotype of PC cells by up-regulating ERBB2 expression. In addition, knockdown of IPO7 inhibited tumor growth and lung metastasis in vivo. Conclusion: IPO7 can act as an oncogenic factor and accelerate PC progression by modulating the ERBB pathway

An Efficient Multifidelity Model for Assessing Risk Probabilities in Power Systems under Rare Events

Risk assessment of power system failures induced by low-frequency, high-impact rare events is of paramount importance to power system planners and operators. In this paper, we develop a cost-effective multi-surrogate method based on multifidelity model for assessing risks in probabilistic power-flow analysis under rare events. Specifically, multiple polynomial-chaos-expansion-based surrogate models are constructed to reproduce power system responses to the stochastic changes of the load and the random occurrence of component outages. These surrogates then propagate a large number of samples at negligible computation cost and thus efficiently screen out the samples associated with high-risk rare events. The results generated by the surrogates, however, may be biased for the samples located in the low-probability tail regions that are critical to power system risk assessment. To resolve this issue, the original high-fidelity power system model is adopted to fine-tune the estimation results of low-fidelity surrogates by reevaluating only a small portion of the samples. This multifidelity model approach greatly improves the computational efficiency of the traditional Monte Carlo method used in computing the risk-event probabilities under rare events without sacrificing computational accuracy

Semi-implicit Continuous Newton Method for Power Flow Analysis

This paper proposes a semi-implicit version of continuous Newton method (CNM) for power flow analysis. The proposed method succeeds the numerical robustness from the implicit CNM (ICNM) framework while prevents the iterative solution of nonlinear systems, hence revealing higher convergence speed and computation efficiency. The intractability of ICNM consists in its nonlinear implicit ordinary-differential-equation (ODE) nature. We circumvent this by introducing intermediate variables, hence converting the implicit ODEs into differential algebraic equations (DAEs), and solve the DAEs with a linear scheme, the stiffly accurate Rosenbrock type method (SARM). A new 4-stage 3rd-order hyper-stable SARM, together with a 2nd-order embedded formula to control the step size, is constructed. Case studies on system 9241pegase verified the alleged performance