152 research outputs found
Investigation of energy storage system and demand side response for distribution networks
PhD ThesisThe UK government has a target of achieving an 80% reduction in CO2 emissions with respect to the values from 1990 by 2050. Therefore, renewables based distributed generations (DGs) coupled with substantial electrification of the transport and heat sectors though low carbon technologies (LCTs), will be essential to achieve this target. The anticipated proliferation of these technologies will necessitate major opportunities and challenges to the operation and planning of future distribution networks.
Smartgrid technologies and techniques, such as energy storage systems (ESSs), demand side response (DSR) and real time thermal ratings (RTTRs), provide flexible, economic and expandable solutions to these challenges without resorting to network reinforcement. This research investigates the use of ESS and DSR in future distribution networks to facilitate LCTs with a focus on the management and resolution of thermal constraints and steady state voltage limit violation problems. Firstly, two control schemes based on sensitivity factors and cost sensitivity factors are proposed. Next, the impacts of a range of sources of uncertainties, arising from existing and future elements of the electrical energy system, are studied. The impacts of electric vehicle charging are investigated with Monte Carlo simulation (MCS). Furthermore, to deal with uncertainties efficiently, a scheduling scheme based on robust optimization (RO) is developed. Two approaches have been introduced to estimate the trade-off between the cost and the probability of constraint violations. Finally, the performance of this scheme is evaluated.
The results of this research show the importance of dealing with uncertainties appropriately. Simulation results demonstrate the capability and effectiveness of the proposed RO based scheduling scheme to facilitate DG and LCTs, in the presence of a range of source of uncertainties. The findings from this research provide valuable solution and guidance to facilitate DG and LCTs using ESS, DSR and RTTR in future distribution networks
PPGAN: Privacy-preserving Generative Adversarial Network
Generative Adversarial Network (GAN) and its variants serve as a perfect
representation of the data generation model, providing researchers with a large
amount of high-quality generated data. They illustrate a promising direction
for research with limited data availability. When GAN learns the semantic-rich
data distribution from a dataset, the density of the generated distribution
tends to concentrate on the training data. Due to the gradient parameters of
the deep neural network contain the data distribution of the training samples,
they can easily remember the training samples. When GAN is applied to private
or sensitive data, for instance, patient medical records, as private
information may be leakage. To address this issue, we propose a
Privacy-preserving Generative Adversarial Network (PPGAN) model, in which we
achieve differential privacy in GANs by adding well-designed noise to the
gradient during the model learning procedure. Besides, we introduced the
Moments Accountant strategy in the PPGAN training process to improve the
stability and compatibility of the model by controlling privacy loss. We also
give a mathematical proof of the differential privacy discriminator. Through
extensive case studies of the benchmark datasets, we demonstrate that PPGAN can
generate high-quality synthetic data while retaining the required data
available under a reasonable privacy budget.Comment: This paper was accepted by IEEE ICPADS 2019 Workshop. This paper
contains 10 pages, 3 figure
Enhancing Large Language Model with Decomposed Reasoning for Emotion Cause Pair Extraction
Emotion-Cause Pair Extraction (ECPE) involves extracting clause pairs
representing emotions and their causes in a document. Existing methods tend to
overfit spurious correlations, such as positional bias in existing benchmark
datasets, rather than capturing semantic features. Inspired by recent work, we
explore leveraging large language model (LLM) to address ECPE task without
additional training. Despite strong capabilities, LLMs suffer from
uncontrollable outputs, resulting in mediocre performance. To address this, we
introduce chain-of-thought to mimic human cognitive process and propose the
Decomposed Emotion-Cause Chain (DECC) framework. Combining inducing inference
and logical pruning, DECC guides LLMs to tackle ECPE task. We further enhance
the framework by incorporating in-context learning. Experiment results
demonstrate the strength of DECC compared to state-of-the-art supervised
fine-tuning methods. Finally, we analyze the effectiveness of each component
and the robustness of the method in various scenarios, including different LLM
bases, rebalanced datasets, and multi-pair extraction.Comment: 13 pages, 5 figure
FedBA: Non-IID Federated Learning Framework in UAV Networks
With the development and progress of science and technology, the Internet of
Things(IoT) has gradually entered people's lives, bringing great convenience to
our lives and improving people's work efficiency. Specifically, the IoT can
replace humans in jobs that they cannot perform. As a new type of IoT vehicle,
the current status and trend of research on Unmanned Aerial Vehicle(UAV) is
gratifying, and the development prospect is very promising. However, privacy
and communication are still very serious issues in drone applications. This is
because most drones still use centralized cloud-based data processing, which
may lead to leakage of data collected by drones. At the same time, the large
amount of data collected by drones may incur greater communication overhead
when transferred to the cloud. Federated learning as a means of privacy
protection can effectively solve the above two problems. However, federated
learning when applied to UAV networks also needs to consider the heterogeneity
of data, which is caused by regional differences in UAV regulation. In
response, this paper proposes a new algorithm FedBA to optimize the global
model and solves the data heterogeneity problem. In addition, we apply the
algorithm to some real datasets, and the experimental results show that the
algorithm outperforms other algorithms and improves the accuracy of the local
model for UAVs
Key Aquatic Environmental Factors Affecting Ecosystem Health of Streams in the Dianchi Lake Watershed, China
AbstractStreams in a lake watershed are important landscape corridors which link the lake and terrestrial ecosystems. Therefore, the ecosystem health of streams is usually used to indicate aquatic biodiversity of the lake ecosystem, as well as being affected by aquatic environmental factors in response to changes in land use cover of the terrestrial ecosystem due to natural geographic characteristics of the watershed with the closure of ridge lines. This study was carried out at a shallow freshwater lake watershed in the Yunnan-Guizhou Plateau of China, the Dianchi Lake watershed (DLW). Field survey of periphytic algal and macrozoobenthic biodiversity during July and August of 2009, as well as monthly monitoring of water temperature, pH, TSS, DO, TN, TP, NH3N, NO3N, CODMn, BOD, TOC, and the heavy metals Zn (II), Cd (II), Pb (II), Cu (II), and Cr (VI) from January to December 2009 was carried out in 29 streams flowing into Dianchi lake. Multivariate statistical techniques such as factor analysis and canonical correspondence analysis were applied to analyze the structure of the aquatic community in relation to aquatic environmental factors in order to provide controlling objectives for integrated watershed management and improvement of stream rehabilitation in the DLW. The results showed that the structure of the periphytic algal and macrozoobenthic communities were dominated by pollution-tolerant genera, namely the bacillariophytes Navicula and the annelids Tubificidae respectively, and TN, NH3N and TP were key aquatic environmental factors affecting the ecosystem health of streams in the DLW
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