De-noising of Power Quality Disturbance Detection Based on Ensemble Empirical Mode Decomposition Threshold Algorithm

Actual power quality signal which is often affected by noise pollution impacts the analysis results of the disturbance signal. In this paper, EEMD (Ensemble Empirical Mode Decomposition)-based threshold de-noising method is proposed for power quality signal with different SNR (Signal-to-Noise Ratio). As a comparison, we use other four thresholds, namely, the heuristic threshold, the self-adaptive threshold, the fixed threshold and the minimax threshold to filter the noises from power quality signal. Through the analysis and comparison of three characteristics of the signal pre-and-post de-noised, including waveforms, SNR and MSE (Mean Square Error), furthermore the instantaneous attribute of corresponding time by HHT (Hilbert Huang Transform). Simulation results show that EEMD threshold de-noising method can make the waveform close to the actual value. The SNR is higher and the MSE is smaller compared with other four thresholds. The instantaneous attribute can reflect the actual disturbance signal more exactly. The optimal threshold EEMD-based algorithm is proposed for power quality disturbance signal de-noising. Meanwhile, EEMD threshold de-noising method with adaptivity is suitable for composite disturbance signal de-noising

Improved spectral processing for a multi-mode pulse compression Ka–Ku-band cloud radar system

Cloud radars are widely used in observing clouds and precipitation. However, the raw data products of cloud radars are usually affected by multiple factors, which may lead to misinterpretation of cloud and precipitation processes. In this study, we present a Doppler-spectra-based data processing framework to improve the data quality of a multi-mode pulse-compressed Ka–Ku radar system. Firstly, non-meteorological signal close to the ground was identified with enhanced Doppler spectral ratios between different observing modes. Then, for the Doppler spectrum affected by the range sidelobe due to the implementation of the pulse compression technique, the characteristics of the probability density distribution of the spectral power were used to identify the sidelobe artifacts. Finally, the Doppler spectra observations from different modes were merged via the shift-then-average approach. The new radar moment products were generated based on the merged Doppler spectrum data. The presented spectral processing framework was applied to radar observations of a stratiform precipitation event, and the quantitative evaluation shows good performance of clutter or sidelobe suppression and spectral merging.</p

Improved spectral processing for a multi-mode pulse compression Ka-Ku-band cloud radar system

Cloud radars are widely used in observing clouds and precipitation. However, the raw data products of cloud radars are usually affected by multiple factors, which may lead to misinterpretation of cloud and precipitation processes. In this study, we present a Doppler-spectra-based data processing framework to improve the data quality of a multi-mode pulse-compressed Ka-Ku radar system. Firstly, non-meteorological signal close to the ground was identified with enhanced Doppler spectral ratios between different observing modes. Then, for the Doppler spectrum affected by the range sidelobe due to the implementation of the pulse compression technique, the characteristics of the probability density distribution of the spectral power were used to identify the sidelobe artifacts. Finally, the Doppler spectra observations from different modes were merged via the shift-then-average approach. The new radar moment products were generated based on the merged Doppler spectrum data. The presented spectral processing framework was applied to radar observations of a stratiform precipitation event, and the quantitative evaluation shows good performance of clutter or sidelobe suppression and spectral merging.Peer reviewe

Groundwater remediation at a former oil service site

As an intern with URS Corporation, I participated in several remediation and wastewater treatment projects during the year 2004. A groundwater remediation project was selected to present in this record of study for my Doctor of Engineering degree not only because I spent more time on it than any other project, but also because it represents the broadness and depth of a typical URS remediation project. In this report, findings from previous environmental investigations were summarized and used for computer modeling and remediation strategy evaluation. Computer models were used to simulate site conditions and assist in remedy design for the site. Current pump-and-treat systems were evaluated by the model under various scenarios. Recommendations were made for the pump-and-treat system to control the contaminant plume. Various remediation technologies were evaluated and compared for their applicability at the site. A combination of on-site remediation and downgradient plume control was chosen as the site remediation strategy. Treatability studies and additional modeling work are needed for the remediation system design and optimization

pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing

As a privacy-preserving collaborative machine learning paradigm, federated learning (FL) has attracted significant interest from academia and the industry alike. To allow each data owner (a.k.a., FL clients) to train a heterogeneous and personalized local model based on its local data distribution, system resources and requirements on model structure, the field of model-heterogeneous personalized federated learning (MHPFL) has emerged. Existing MHPFL approaches either rely on the availability of a public dataset with special characteristics to facilitate knowledge transfer, incur high computation and communication costs, or face potential model leakage risks. To address these limitations, we propose a model-heterogeneous personalized Federated learning approach based on feature Extractor Sharing (pFedES). It incorporates a small homogeneous feature extractor into each client's heterogeneous local model. Clients train them via the proposed iterative learning method to enable the exchange of global generalized knowledge and local personalized knowledge. The small local homogeneous extractors produced after local training are uploaded to the FL server and for aggregation to facilitate easy knowledge sharing among clients. We theoretically prove that pFedES can converge over wall-to-wall time. Extensive experiments on two real-world datasets against six state-of-the-art methods demonstrate that pFedES builds the most accurate model, while incurring low communication and computation costs. Compared with the best-performing baseline, it achieves 1.61% higher test accuracy, while reducing communication and computation costs by 99.6% and 82.9%, respectively.Comment: 12 pages, 10 figures. arXiv admin note: text overlap with arXiv:2310.1328