98 research outputs found
Development and application of synchronized wide-area power grid measurement
Phasor measurement units (PMUs) provide an innovative technology for real-time monitoring of the operational state of entire power systems and significantly improve power grid dynamic observability. This dissertation focuses on development and application of synchronized power grid measurements. The contributions of this dissertation are as followed:First, a novel method for successive approximation register analog to digital converter control in PMUs is developed to compensate for the sampling time error caused by the division remainder between the desirable sampling rate and the oscillator frequency. A variable sampling interval control method is presented by interlacing two integers under a proposed criterion. The frequency of the onboard oscillator is monitored in using the PPS from GPS.Second, the prevalence of GPS signal loss (GSL) on PMUs is first investigated using real PMU data. The correlation between GSL and time, spatial location, solar activity are explored via comprehensive statistical analysis. Furthermore, the impact of GSL on phasor measurement accuracy has been studied via experiments. Several potential solutions to mitigate the impact of GSL on PMUs are discussed and compared.Third, PMU integrated the novel sensors are presented. First, two innovative designs for non-contact PMUs presented. Compared with conventional synchrophasors, non-contact PMUs are more flexible and have lower costs. Moreover, to address nonlinear issues in conventional CT and PT, an optical sensor is used for signal acquisition in PMU. This is the first time the utilization of an optical sensor in PMUs has ever been reported.Fourth, the development of power grid phasor measurement function on an Android based mobile device is developed. The proposed device has the advantages of flexibility, easy installation, lower cost, data visualization and built-in communication channels, compared with conventional PMUs.Fifth, an identification method combining a wavelet-based signature extraction and artificial neural network based machine learning, is presented to identify the location of unsourced measurements. Experiments at multiple geographic scales are performed to validate the effectiveness of the proposed method using ambient frequency measurements. Identification accuracy is presented and the factors that affect identification performance are discussed
Timestamp Error Detection and Estimation for PMU Data based on Linear Correlation between Relative Phase Angle and Frequency
Time synchronization is essential to synchro-phasor-based applications. However, Timestamp Error (TE) in synchrophasor data can result in application failures. This paper proposes a method for TE detection based on the linear correlation between frequency and relative phase angle. The TE converts the short-term relative phase angle from noise-like signal to one that linear with the frequency. Pearson Correlation Coefficient (PCC) is applied to measure the linear correlation and then detect the timestamp error. The time error is estimated based on the variation of frequency and relative phase angle. Case studies with actual synchrophasor data demonstrate the effectiveness of TE detection and excellent accuracy of TE estimation
GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding
Humans subconsciously engage in geospatial reasoning when reading articles.
We recognize place names and their spatial relations in text and mentally
associate them with their physical locations on Earth. Although pretrained
language models can mimic this cognitive process using linguistic context, they
do not utilize valuable geospatial information in large, widely available
geographical databases, e.g., OpenStreetMap. This paper introduces GeoLM, a
geospatially grounded language model that enhances the understanding of
geo-entities in natural language. GeoLM leverages geo-entity mentions as
anchors to connect linguistic information in text corpora with geospatial
information extracted from geographical databases. GeoLM connects the two types
of context through contrastive learning and masked language modeling. It also
incorporates a spatial coordinate embedding mechanism to encode distance and
direction relations to capture geospatial context. In the experiment, we
demonstrate that GeoLM exhibits promising capabilities in supporting toponym
recognition, toponym linking, relation extraction, and geo-entity typing, which
bridge the gap between natural language processing and geospatial sciences. The
code is publicly available at https://github.com/knowledge-computing/geolm.Comment: Accepted to EMNLP23 mai
A Micro–Macro Damage Mechanics-based Model for Fatigue Damage and Life Prediction of Fiber-reinforced Composite Laminates
A multidirectional damage model was proposed to predict fatigue damage evolution and final failure of composite laminates in this paper. A damage characterization model for composite laminates was established to characterize the influence of three main damage modes on the damaged mechanical behavior of composite laminates at micro–macro level. The damage evolution model was also established based on damage mechanics to predict the evolution of the three damage modes and stiffness degradation of composite laminates by means of damage characterization model. Then, a relationship between residual stiffness and residual strength was introduced, from which the residual strength could be obtained according to the predicted residual stiffness. When the residual strength is calculated to decrease to the maximum applied stress of fatigue loading after several cycles, the composite laminate was assumed to fail, and accordingly the fatigue life could be obtained. In order to verify the model, the predicted stiffness degradation and fatigue life of two cross-ply laminates under fatigue loadings with different stress levels were compared to experimental results. The standard derivation of stiffness degradation and average errors of fatigue between prediction results and experimental results were less than 0.1 and 8.26%, respectively, indicating the effectiveness and reliability of proposed model
Kick Bad Guys Out! Zero-Knowledge-Proof-Based Anomaly Detection in Federated Learning
Federated learning (FL) systems are vulnerable to malicious clients that
submit poisoned local models to achieve their adversarial goals, such as
preventing the convergence of the global model or inducing the global model to
misclassify some data. Many existing defense mechanisms are impractical in
real-world FL systems, as they require prior knowledge of the number of
malicious clients or rely on re-weighting or modifying submissions. This is
because adversaries typically do not announce their intentions before
attacking, and re-weighting might change aggregation results even in the
absence of attacks. To address these challenges in real FL systems, this paper
introduces a cutting-edge anomaly detection approach with the following
features: i) Detecting the occurrence of attacks and performing defense
operations only when attacks happen; ii) Upon the occurrence of an attack,
further detecting the malicious client models and eliminating them without
harming the benign ones; iii) Ensuring honest execution of defense mechanisms
at the server by leveraging a zero-knowledge proof mechanism. We validate the
superior performance of the proposed approach with extensive experiments
Compound Attention and Neighbor Matching Network for Multi-contrast MRI Super-resolution
Multi-contrast magnetic resonance imaging (MRI) reflects information about
human tissue from different perspectives and has many clinical applications. By
utilizing the complementary information among different modalities,
multi-contrast super-resolution (SR) of MRI can achieve better results than
single-image super-resolution. However, existing methods of multi-contrast MRI
SR have the following shortcomings that may limit their performance: First,
existing methods either simply concatenate the reference and degraded features
or exploit global feature-matching between them, which are unsuitable for
multi-contrast MRI SR. Second, although many recent methods employ transformers
to capture long-range dependencies in the spatial dimension, they neglect that
self-attention in the channel dimension is also important for low-level vision
tasks. To address these shortcomings, we proposed a novel network architecture
with compound-attention and neighbor matching (CANM-Net) for multi-contrast MRI
SR: The compound self-attention mechanism effectively captures the dependencies
in both spatial and channel dimension; the neighborhood-based feature-matching
modules are exploited to match degraded features and adjacent reference
features and then fuse them to obtain the high-quality images. We conduct
experiments of SR tasks on the IXI, fastMRI, and real-world scanning datasets.
The CANM-Net outperforms state-of-the-art approaches in both retrospective and
prospective experiments. Moreover, the robustness study in our work shows that
the CANM-Net still achieves good performance when the reference and degraded
images are imperfectly registered, proving good potential in clinical
applications.Comment: This work has been submitted to the IEEE for possible publication.
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Magnetic Field Based Wireless GMD/EMP-E3 Impact Monitoring Device:
A system and methods for monitoring an impact of geomagnetic disturbances (GMDs) or an E3 component of electromagnetic pulses (EMP-E3), involving a transducer generating a transduced signal in response to a magnetic field of a current carrying element of a transmission line. The transduced signal reflects harmonic characteristics of the current carrying element, and is amplified and filtered, then digitally converted. Excessive impact is detected when a threshold condition is met with respect to a total harmonic distortion (THD) and/or a change in THD. The THD can be calculated from amplitudes of harmonic components of interest. The amplitudes can be calculated in various ways, including Fourier transforming the digital signal to locate peaks in the resulting spectral lines, or using a phase sensitive detection algorithm in which the digital signal is multiplied by a phase swept reference signal and then integrated
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