1,103 research outputs found
Distributed State Estimation With Phasor Measurement Units (Pmu) For Power Systems
Wide-area monitoring for the power system is a key tool for preventing the power system from system wide failure. State Estimation (SE) is an essential and practical monitoring tool that has been widely used to provide estimated values for each quantity within energy management systems (EMS) in the control center. However, monitoring larger power systems coordinated by regional transmission operators has placed an enormous operational burden on current SE techniques. A distributed state estimation (DSE) algorithm with a hierarchical structure designed for the power system industry is much more computationally efficient and robust especially for monitoring a wide-area power system. Moreover, considering the deregulation of the power system industry, this method does not require sensitive data exchange between smaller areas that may be competing entities. The use of phasor measurement units (PMUs) in the SE algorithm has proven to improve the performance in terms of accuracy and converging speed. Being able to synchronize the measurements between different areas, PMUs are perfectly suited for distributed state estimation. This dissertation investigates the benefits of the DSE using PMU over a serial state estimator in wide area monitoring. A new method has been developed using available PMU data to calculate the reference angle differences between decomposed power systems in various situations, such as when the specific PMU data of the global slack bus cannot be obtained. The algorithms were tested on six bus, I standard 30 bus and I 118-bus test cases. The proposed distributed state estimator has also been implemented in a test bed to work with a power system real-time digital simulator (RTDS) that simulates the physical power system. PMUs made by SEL and GE are used to provide real-time inputs to the distributed state estimator. Simulation results demonstrated the benefits of the PMU and distributed SE techniques. Additionally a constructed test bed verified and validated the proposed algorithms and can be used for different smart grid tests
Multiagent model and mean field theory of complex auction dynamics
Acknowledgements We are grateful to Ms Yinan Zhao for providing the data and to Yuzhong Chen and Cancan Zhou for discussions and suggestions. This work was supported by ARO under Grant No. W911NF-14-1-0504 and by NSFC under Grants Nos. 11275003 and 61174165. The visit of QC to Arizona State University was partially sponsored by the State Scholarship Fund of China.Peer reviewedPublisher PD
Knowledge Pyramid: A Novel Hierarchical Reasoning Structure for Generalized Knowledge Augmentation and Inference
Knowledge graph (KG) based reasoning has been regarded as an effective means
for the analysis of semantic networks and is of great usefulness in areas of
information retrieval, recommendation, decision-making, and man-machine
interaction. It is widely used in recommendation, decision-making,
question-answering, search, and other fields. However, previous studies mainly
used low-level knowledge in the KG for reasoning, which may result in
insufficient generalization and poor robustness of reasoning. To this end, this
paper proposes a new inference approach using a novel knowledge augmentation
strategy to improve the generalization capability of KG. This framework
extracts high-level pyramidal knowledge from low-level knowledge and applies it
to reasoning in a multi-level hierarchical KG, called knowledge pyramid in this
paper. We tested some medical data sets using the proposed approach, and the
experimental results show that the proposed knowledge pyramid has improved the
knowledge inference performance with better generalization. Especially, when
there are fewer training samples, the inference accuracy can be significantly
improved.Comment: 10 pages,8 figure
QueryNet: Attack by Multi-Identity Surrogates
Deep Neural Networks (DNNs) are acknowledged as vulnerable to adversarial
attacks, while the existing black-box attacks require extensive queries on the
victim DNN to achieve high success rates. For query-efficiency, surrogate
models of the victim are used to generate transferable Adversarial Examples
(AEs) because of their Gradient Similarity (GS), i.e., surrogates' attack
gradients are similar to the victim's ones. However, it is generally neglected
to exploit their similarity on outputs, namely the Prediction Similarity (PS),
to filter out inefficient queries by surrogates without querying the victim. To
jointly utilize and also optimize surrogates' GS and PS, we develop QueryNet, a
unified attack framework that can significantly reduce queries. QueryNet
creatively attacks by multi-identity surrogates, i.e., crafts several AEs for
one sample by different surrogates, and also uses surrogates to decide on the
most promising AE for the query. After that, the victim's query feedback is
accumulated to optimize not only surrogates' parameters but also their
architectures, enhancing both the GS and the PS. Although QueryNet has no
access to pre-trained surrogates' prior, it reduces queries by averagely about
an order of magnitude compared to alternatives within an acceptable time,
according to our comprehensive experiments: 11 victims (including two
commercial models) on MNIST/CIFAR10/ImageNet, allowing only 8-bit image
queries, and no access to the victim's training data. The code is available at
https://github.com/Sizhe-Chen/QueryNet.Comment: QueryNet reduces queries by about an order of magnitude against SOTA
black-box attack
Environmental regulation, innovation quality and firmsâ competitivity-Quasi-natural experiment based on Chinaâs carbon emissions trading pilot
In the study of the âPorter Hypothesisâ, scholars explored the
impact of different forms of innovation on the firmsâ competitivity,
but did not distinguish between innovations on the difference
in patent quality. In addition, relevant research only regards
innovation as a mediator between environmental regulation and
competitivity, and doesnât take into account innovation induced
by environmental regulation, can only promote competitivity
under the constraints of environmental regulation. That is to say,
environmental regulation not only induces innovation, but also
moderates innovation to promote competitivity. In view of this,
we use panel data of A-share listed firms in China from 2006 to
2016, and adopt propensity score matching and different in different
(PSM-DID) model to empirically test the inductive effect and
moderating effect. The results show that CETS cannot only
improve the quantity and quality, but also significantly enhance
the firmsâ market value; innovation itself cannot enhance the
firmsâ market value, but the interaction with CETS can promote
the firmsâ market value. In addition, the CETS has a stronger
inductive effect on innovation of state-owned shares firms, but
the positive moderating effect on high-quality innovation and
competitivity only exists in non-state-owned shares firms
Stochastic stability of viscoelastic systems
Many new materials used in mechanical and structural engineering exhibit viscoelastic properties, that is, stress depends on the past time history of strain, and vice versa. Investigating the behaviour of viscoelastic materials under dynamical loads is of great theoretical and practical importance for structural design, vibration reduction, and other engineering applications. The objective of this thesis is to find how viscoelasticity affects the stability of structures under random loads.
The time history dependence of viscoelasticity renders the equations of motion of viscoelastic bodies in the form of integro-partial differential equations, which are more difficult to study compared to those of elastic bodies.
The method of stochastic averaging, which has been proved to be an effective tool in the study of dynamical systems, is applied to simplify some single degree-of-freedom linear viscoelastic systems parametrically excited by wide-band noise and narrow-band noise. The solutions of the averaged systems are diffusion processes characterized by ItĂ´ differential equations. Therefore, the stability of the solutions is determined in the sense of the moment Lyapunov exponents and Lyapunov exponents, which characterize the moment stability and the almost-sure stability, respectively. The moment Lyapunov exponents may be obtained by solving the averaged ItĂ´ equations directly, or by solving the eigenvalue problems governing the moment Lyapunov exponents.
Monte Carlo simulation is applied to study the behaviour of stochastic dynamical systems numerically. Estimating the moments of solutions through sample average may lead to erroneous results under the circumstances that systems exhibit large deviations. An improved algorithm for simulating the moment Lyapunov exponents of linear homogeneous stochastic systems is presented. Under certain conditions, the logarithm of norm of a solution converges weakly to normal distribution after suitably normalized. This property, along with the results of KomlĂłs-Major-TusnĂĄdy for sums of independent random variables, are applied to construct the algorithm. The numerical results obtained from the improved algorithm are used to determine the accuracy of the approximate analytical moment Lyapunov exponents obtained from the averaged systems. In this way the effectiveness of the stochastic averaging method is confirmed.
The world is essentially nonlinear. A single degree-of-freedom viscoelastic system with cubic nonlinearity under wide-band noise excitation is studied in this thesis. The approximated nonlinear stochastic system is obtained through the stochastic averaging method. Stability and bifurcation properties of the averaged system are verified by numerical simulation. The existence of nonlinearity makes the system stable in one of the two stationary states
SwG-former: Sliding-window Graph Convolutional Network Integrated with Conformer for Sound Event Localization and Detection
Sound event localization and detection (SELD) is a joint task of sound event
detection (SED) and direction of arrival (DoA) estimation. SED mainly relies on
temporal dependencies to distinguish different sound classes, while DoA
estimation depends on spatial correlations to estimate source directions. To
jointly optimize two subtasks, the SELD system should extract spatial
correlations and model temporal dependencies simultaneously. However, numerous
models mainly extract spatial correlations and model temporal dependencies
separately. In this paper, the interdependence of spatial-temporal information
in audio signals is exploited for simultaneous extraction to enhance the model
performance. In response, a novel graph representation leveraging graph
convolutional network (GCN) in non-Euclidean space is developed to extract
spatial-temporal information concurrently. A sliding-window graph (SwG) module
is designed based on the graph representation. It exploits sliding-windows with
different sizes to learn temporal context information and dynamically
constructs graph vertices in the frequency-channel (F-C) domain to capture
spatial correlations. Furthermore, as the cornerstone of message passing, a
robust Conv2dAgg function is proposed and embedded into the SwG module to
aggregate the features of neighbor vertices. To improve the performance of SELD
in a natural spatial acoustic environment, a general and efficient SwG-former
model is proposed by integrating the SwG module with the Conformer. It exhibits
superior performance in comparison to recent advanced SELD models. To further
validate the generality and efficiency of the SwG-former, it is seamlessly
integrated into the event-independent network version 2 (EINV2) called
SwG-EINV2. The SwG-EINV2 surpasses the state-of-the-art (SOTA) methods under
the same acoustic environment
A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images
Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality
- âŚ