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Multi-terminal HVDC projects are increasingly developed in recent years to enhance the flexibility of energy transmission. Differing from the point-to-point HVDC systems, it is more challenging to design the multi-terminal HVDC system and ensure stable interoperability among converter systems, especially when the converters are manufactured from different vendors. Although impedance-based stability analysis allows for analyzing such systems based on black-box models, it is still difficult to utilize those black-box models to identify the root cause of potential instability. To tackle this challenge, this paper proposes a multi-level sensitivity analysis approach using frequency-domain sensitivity functions based on the impedance-based stability criterion. The proposed method differs from the classical sensitivity analysis based on state-space or transfer-function models, as it is purely based on black-box impedance models. Case studies on a four-terminal HVDC system are carried out for stability and sensitivity analysis based on the impedance measurement in PSCAD, through which the most sensitive HVDC station can be identified. The proposed theory and the analyzed results are finally validated by electromagnetic transient simulations.

This article proposes a microwave sensing and imaging method for multiphase flow monitoring and metering in oil and gas pipes. The ultrawideband (UWB) synthetic aperture radar (SAR) technique is used to create a high-resolution image of the pipe cross section. The image is, then, used to estimate each phase volume by extracting the edge of each phase. Additionally, the generalized impulsization technique is presented and applied to reconstruct a sharp-image, and decrease the error in flow rate estimation. Furthermore, a novel technique to enhance the detectability of weak targets in the images is proposed. Finally, a novel sectional image reconstruction technique is also applied to improve the imaging and metering of the stratified flows. All the proposed techniques are evaluated through experiments. This study demonstrates the ability to accurately estimate the crude oil flow rate with a maximum error of 3.8%. These results show that UWB SAR is capable of providing a reliable and noninvasive solution for multiphase flow metering.

Globally, lung cancer is responsible for nearly one in five cancer deaths. The National Lung Screening Trial (NLST) demonstrated the efficacy of low-dose computed tomography (LDCT) to identify early-stage disease, setting the basis for widespread implementation of lung cancer screening programs. However, the specificity of LDCT lung cancer screening is suboptimal, with a significant false positive rate. Representing this imaging-based screening process as a sequential decision making problem, we combined multiple machine learning-based methods to learn a partially-observable Markov decision process that simultaneously optimizes lung cancer detection while enhancing test specificity. Using NLST data, we trained a dynamic Bayesian network as an observational model and used inverse reinforcement learning to discover a rewards function based on experts' decisions. Our resultant predictive model decreased the false positive rate while maintaining a high true positive rate at a level comparable to human experts. Our model also detected a number of lung cancers earlier.

Nowadays, visual analytics is mainly performed by programming approaches and viewing the results on a desktop monitor. However, due to the capabilities of smart glasses, new user interactions and representation possibilities become possible. This refers especially to 3D visualizations in the medical field, as well as, the industry domain, as valuable depth information can be related to the complex real-world structures and related data, which is also denoted as immersive analytics. However, the applicability of immersive analytics and its drawbacks, especially in the context of mixed reality, are quite unexplored. In order to validate the feasibility of immersive analytics for the aforementioned purposes, we designed and conducted a usability study with 60 participants. More specifically, we evaluated the effects of spatial sounds, performance changes from one analytics task to another, expert status, and compared an immersive analytics approach (i.e., a mixed-reality application) with a desktop-based solution. Participants had to solve several data analytics tasks (outlier's detection and cluster recognition) with the developed mixed-reality application. Thereby, the performance measures regarding time, errors, and movement patterns were evaluated. The separation into groups (low performer vs. high performer) was performed using a mental rotation pretest. When solving analytic tasks in mixed reality, participants changed their movement patterns in the mixed reality setting significantly, while the use of spatial sounds reduced the handling time significantly, but did not affect the movement patterns. Furthermore, the usage of mixed reality for cluster recognition is significantly faster than the desktop-based solution (i.e., a 2D approach). Moreover, the results obtained with self-developed questionnaires indicate 1) that wearing smart glasses are perceived as a potential stressor and 2) that the utilization of sounds is perceived very differently by the participants. Altogether, industry and researchers should consider immersive analytics as a suitable alternative compared to the traditional approaches.