Haoyu Wang

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) is a new remote-sensing technique, and it can be used to estimate near-surface soil moisture from Signal-to-Noise Ratio (SNR) data. Considering the effects of vegetation changes on GNSS-IR in some environments, a non-linear inversion method for soil moisture is proposed. Firstly, the SNR data and satellite elevation angles are solved using Translation, Editing, and Quality Checking. The direct and reflected signals are separated using a low-order polynomial; then, a sinusoidal fitting model of the reflection signal is established; it is used to obtain the amplitude and phase of the SNR interferogram. Finally, an estimation model of vegetation water content and prediction model of the vegetation phase changes are established to modify the original phase and weaken the influence on the vegetation changes. Based on the corrected phase, a Genetic Algorithm Back Propagation Neural Network (BPNN) model is established for soil moisture inversion. According to the GPS monitoring data from the Plate Boundary Observatory H2O network, the experiment indicates that (1) The BPNN is introduced to inverse the soil moisture content, and the non-linear fitting ability of the model is well developed, and the fitting process is stable; (2) the modified phase effectively reduced the effects of vegetation changes on the soil moisture inversion. The correlation coefficient (r) between the inversion results and soil moisture value greatly improved, and the root mean square error and mean absolute error are less than 0.060 and 0.050, respectively. Therefore, the soil moisture problem can be treated as a non-linear event, and the algorithm is feasible and effective.

Deep learning (DL) is moving its step into a growing number of mobile software applications. These software applications, named as DL based mobile applications (abbreviated as mobile DL apps) integrate DL models trained using large-scale data with DL programs. A DL program encodes the structure of a desirable DL model and the process by which the model is trained using training data. Due to the increasing dependency of current mobile apps on DL, software engineering (SE) for mobile DL apps has become important. However, existing efforts in SE research community mainly focus on the development of DL models and extensively analyze faults in DL programs. In contrast, faults related to the deployment of DL models on mobile devices (named as deployment faults of mobile DL apps) have not been well studied. Since mobile DL apps have been used by billions of end users daily for various purposes including for safety-critical scenarios, characterizing their deployment faults is of enormous importance. To fill in the knowledge gap, this paper presents the first comprehensive study to date on the deployment faults of mobile DL apps. We identify 304 real deployment faults from Stack Overflow and GitHub, two commonly used data sources for studying software faults. Based on the identified faults, we construct a fine-granularity taxonomy consisting of 23 categories regarding to fault symptoms and distill common fix strategies for different fault symptoms. Furthermore, we suggest actionable implications and research avenues that can potentially facilitate the deployment of DL models on mobile devices.

Multiport power electronic transformers (PET) are widely used in modern power systems to facilitate flexible interconnection between various ac or dc buses, power sources, and loads. However, due to the wide range of “source-load-storage” operating points, achieving efficient operation of the isolated dc–dc converter in the PET can be challenging. This letter focuses on the modular multiactive bridge (MMAB) converter in the PET, and proposes an optimization modulation scheme based on reactive power minimization (RPM) to improve efficiency. The RPM scheme utilizes a simple reactive power open-loop and active power closed-loop control structure that can be modulated online without the use of look-up tables. Importantly, this generalized method not only significantly improves efficiency, but can also be extended to any number of ports. Finally, experimental results are presented to validate the theoretical analysis and effectiveness of the RPM scheme.