Advanced Wide-Area Monitoring System Design, Implementation, and Application

Wide-area monitoring systems (WAMSs) provide an unprecedented way to collect, store and analyze ultra-high-resolution synchrophasor measurements to improve the dynamic observability in power grids. This dissertation focuses on designing and implementing a wide-area monitoring system and a series of applications to assist grid operators with various functionalities. The contributions of this dissertation are below: First, a synchrophasor data collection system is developed to collect, store, and forward GPS-synchronized, high-resolution, rich-type, and massive-volume synchrophasor data. a distributed data storage system is developed to store the synchrophasor data. A memory-based cache system is discussed to improve the efficiency of real-time situation awareness. In addition, a synchronization system is developed to synchronize the configurations among the cloud nodes. Reliability and Fault-Tolerance of the developed system are discussed. Second, a novel lossy synchrophasor data compression approach is proposed. This section first introduces the synchrophasor data compression problem, then proposes a methodology for lossy data compression, and finally presents the evaluation results. The feasibility of the proposed approach is discussed. Third, a novel intelligent system, SynchroService, is developed to provide critical functionalities for a synchrophasor system. Functionalities including data query, event query, device management, and system authentication are discussed. Finally, the resiliency and the security of the developed system are evaluated. Fourth, a series of synchrophasor-based applications are developed to utilize the high-resolution synchrophasor data to assist power system engineers to monitor the performance of the grid as well as investigate the root cause of large power system disturbances. Lastly, a deep learning-based event detection and verification system is developed to provide accurate event detection functionality. This section introduces the data preprocessing, model design, and performance evaluation. Lastly, the implementation of the developed system is discussed

Learning in AI Processor

AI processor, which can run artificial intelligence algorithms, is a state-of-the-art accelerator,in essence, to perform special algorithm in various applications. In particular,these are four AI applications: VR/AR smartphone games, high-performance computing, Advanced Driver Assistance Systems and IoT. Deep learning using convolutional neural networks (CNNs) involves embedding intelligence into applications to perform tasks and has achieved unprecedented accuracy [1]. Usually, the powerful multi-core processors and the on-chip tensor processing accelerator unit are prominent hardware features of deep learning AI processor. After data is collected by sensors, tools such as image processing technique, voice recognition and autonomous drone navigation, are adopted to pre-process and analyze data. In recent years, plenty of technologies associating with deep learning Al processor including cognitive spectrum sensing, computer vision and semantic reasoning become a focus in current research

Resonant tunneling diode oscillators for optical communications

The ability to use resonant tunneling diodes (RTDs) as both transmitters and receivers is an emerging topic, especially with regards to wireless communications. Successful data transmission has been achieved using electronic RTDs with carrier frequencies exceeding 0.3 THz. Specific optical-based RTDs, which act as photodetectors, have been developed by adjusting the device structure to include a light absorption layer and small optical windows on top of the device to allow direct optical access. This also allows the optical signal to directly modulate the RTD oscillation. Both types of RTD oscillators will allow for seamless integration of high frequency radio and optical fiber networks.European Union's Horizon research and innovation programme [645369

Modeling the Light Curves of the Luminous Type Ic Supernova 2007D

SN 2007D is a nearby (redshift z = 0.023146), luminous Type Ic supernova (SN) having a narrow light curve (LC) and high peak luminosity. Previous research based on the assumption that it was powered by the 56Ni cascade decay suggested that the inferred 56Ni mass and the ejecta mass are ~1.5 M ⊙ and ~3.5 M ⊙, respectively. In this paper, we employ some multiband LC models to model the R-band LC and the color (V − R) evolution of SN 2007D to investigate the possible energy sources powering them. We find that the pure 56Ni model is disfavored; the multiband LCs of SN 2007D can be reproduced by a magnetar whose initial rotational period P 0 and magnetic field strength B p are (or ) ms and (or ) G, respectively. By comparing the spectrum of SN 2007D with that of some superluminous SNe (SLSNe), we find that it might be a luminous SN like several luminous gap-filler optical transients that bridge ordinary and SLSNe, rather than a genuine SLSN

Modeling the Light Curves of the Luminous Type Ic Supernova 2007D

SN~2007D is a nearby (redshift $z = 0.023146$), luminous Type Ic supernova (SN) having a narrow light curve (LC) and high peak luminosity. Previous research based on the assumption that it was powered by the $^{56}$Ni cascade decay suggested that the inferred $^{56}$Ni mass and the ejecta mass are $\sim 1.5$M$_{\odot}$ and $\sim 3.5$M$_{\odot}$, respectively. In this paper, we employ some multiband LC models to model the $R$-band LC and the color ($V-R$) evolution of SN~2007D to investigate the possible energy sources powering them. We find that the pure $^{56}$Ni model is disfavored; the multiband LCs of SN~2007D can be reproduced by a magnetar whose initial rotational period $P_{0}$ and magnetic field strength $B_p$ are $7.28_{-0.21}^{+0.21}$ (or $9.00_{-0.42}^{+0.32}$) ms and $3.10_{-0.35}^{+0.36}\times 10^{14}$ (or $2.81_{-0.44}^{+0.43}\times 10^{14}$) G, respectively. By comparing the spectrum of SN~2007D with that of some superluminous SNe (SLSNe), we find that it might be a luminous SN like several luminous ``gap-filler" optical transients that bridge ordinary and SLSNe, rather than a genuine SLSN.Comment: 11 pages, 5 figures, 1 table, accepted for publication in Ap

Revisiting the Knowledge Injection Frameworks

In recent years, large language models (LLMs), such as GPTs, have attained great impact worldwide. However, how to adapt these LLMs to better suit the vertical domain-specific tasks by utilizing external knowledge remains not completely solved. Indeed, there have emerged a few works on this line where most of them rely on an alignment heuristic that is built to inject the corresponding knowledge tuple into the associated text sample. However, despite the promise, we identify a pivotal problem in this work ubiquitously. Simply put, we find that injecting unaligned (i.e., random) knowledge tuple into the LLMs achieves comparable (and sometimes better) results than the aligned knowledge being injected. We therefore take a thorough investigation of this frustrating finding on a variety of related prior work and further provide a chain of potential interpretations for the phenomenon. Based on all that, we offer a simple remediated technique. Briefly, the core of this technique is rooted in an ideological emphasis on the pruning and purification of the external knowledge base to be injected into LLMs. At last, we show that by integrating this technique into most (if not all) knowledge injection frameworks and recent LLMs, it manages to overcome the aforementioned sanity problem and further pushes the boundary of the performance of the domain-adaptive LLMs.Comment: 9 pages, 6 figures, accepted by EMNLP 2023 Mai