290 research outputs found
Convolutional Neural Network-based RoCoF-Constrained Unit Commitment
The fast growth of inverter-based resources such as wind plants and solar
farms will largely replace and reduce conventional synchronous generators in
the future renewable energy-dominated power grid. Such transition will make the
system operation and control much more complicated; and one key challenge is
the low inertia issue that has been widely recognized. However, locational
post-contingency rate of change of frequency (RoCoF) requirements to
accommodate significant inertia reduction has not been fully investigated in
the literature. This paper presents a convolutional neural network (CNN) based
RoCoF-constrained unit commitment (CNN-RCUC) model to guarantee RoCoF stability
following the worst generator outage event while ensuring operational
efficiency. A generic CNN based predictor is first trained to track the highest
locational RoCoF based on a high-fidelity simulation dataset. The RoCoF
predictor is then formulated as MILP constraints into the unit commitment
model. Case studies are carried out on the IEEE 24-bus system, and simulation
results obtained with PSS/E indicate that the proposed method can ensure
locational post-contingency RoCoF stability without conservativeness
Active Linearized Sparse Neural Network-based Frequency-Constrained Unit Commitment
Conventional synchronous generators are gradually being re-placed by
low-inertia inverter-based resources. Such transition introduces more
complicated operation conditions, frequency deviation stability and
rate-of-change-of-frequency (RoCoF) security are becoming great challenges.
This paper presents an active linearized sparse neural network (ALSNN) based
frequency-constrained unit commitment (ALSNN-FCUC) model to guarantee frequency
stability following the worst generator outage case while ensuring operational
efficiency. A generic data-driven predictor is first trained to predict maximal
frequency deviation and the highest locational RoCoF simultaneously based on a
high-fidelity simulation dataset, and then incorporated into ALSNN-FCUC model.
Sparse computation is introduced to avoid dense matrix multiplications. An
active data sampling method is proposed to maintain the bindingness of the
frequency related constraints. Besides, an active ReLU linearization method is
implemented to further improve the algorithm efficiency while retaining
solution quality. The effectiveness of proposed ALSNN-FCUC model is
demonstrated on the IEEE 24-bus system by conducting time domain simulations
using PSS/E
Analysis of Weather and Time Features in Machine Learning-aided ERCOT Load Forecasting
Accurate load forecasting is critical for efficient and reliable operations
of the electric power system. A large part of electricity consumption is
affected by weather conditions, making weather information an important
determinant of electricity usage. Personal appliances and industry equipment
also contribute significantly to electricity demand with temporal patterns,
making time a useful factor to consider in load forecasting. This work develops
several machine learning (ML) models that take various time and weather
information as part of the input features to predict the short-term system-wide
total load. Ablation studies were also performed to investigate and compare the
impacts of different weather factors on the prediction accuracy. Actual load
and historical weather data for the same region were processed and then used to
train the ML models. It is interesting to observe that using all available
features, each of which may be correlated to the load, is unlikely to achieve
the best forecasting performance; features with redundancy may even decrease
the inference capabilities of ML models. This indicates the importance of
feature selection for ML models. Overall, case studies demonstrated the
effectiveness of ML models trained with different weather and time input
features for ERCOT load forecasting
Genetic Fingerprint Concerned with Lymphatic Metastasis of Human Lung Squamous Cancer
Background and objective With the most recent introduction of microarray technology to biology, it becomes possible to perform comprehensive analysis of gene expression in cancer cell. In this study the laser microdissection technique and cDNA microarray analysis were combined to obtain accurate molecular profiles of lymphatic metastasis in patients with lung squamous cell carcinoma. Methods Primary lung squamous cancer tissues and regional lymph nodes were obtained from 10 patients who underwent complete resection of lung cancer. According to the source of lung cancer cells, the samples were classified into three groups: the primary tumor with lymphatic metastasis (TxN+, n=5), the primary tumor without lymphatic metastasis (TxN-, n=5) and matched tumor cells from metastatic lymph nodes (N+, n=5). Total RNA was extracted from laser microdissected tumor samples. Adequate RNA starting material of mRNA from primary tumor or metastatic nodes were labeled and then hybridized into the same microarray containing 6 000 known, named human genes/ESTs. After scanning, data analysis was performed using GeneSpringTM6.2. Results A total of 37 genes were found to be able to separate TxN+ from TxN-. TxN+ have higher levels of genes concerned with structural protein, signal transducer, chaperone and enzyme. TxN- have higher levels of genes coding for cell cycle regulator, transporter, signal transducer and apoptosis regulator. Interestingly, there were no differentially expressed genes between N+ and TxN+. Conclusion The acquisition of the metastatic phenotype might occur early in the development of lung squamous cancer. We raise the hypothesis that the gene-expression signature described herein is valuable to elucidate the molecular mechanisms regarding lymphatic metastasis and to look for novel therapeutic targets
Learning Sparse Neural Networks with Identity Layers
The sparsity of Deep Neural Networks is well investigated to maximize the
performance and reduce the size of overparameterized networks as possible.
Existing methods focus on pruning parameters in the training process by using
thresholds and metrics. Meanwhile, feature similarity between different layers
has not been discussed sufficiently before, which could be rigorously proved to
be highly correlated to the network sparsity in this paper. Inspired by
interlayer feature similarity in overparameterized models, we investigate the
intrinsic link between network sparsity and interlayer feature similarity.
Specifically, we prove that reducing interlayer feature similarity based on
Centered Kernel Alignment (CKA) improves the sparsity of the network by using
information bottleneck theory. Applying such theory, we propose a plug-and-play
CKA-based Sparsity Regularization for sparse network training, dubbed CKA-SR,
which utilizes CKA to reduce feature similarity between layers and increase
network sparsity. In other words, layers of our sparse network tend to have
their own identity compared to each other. Experimentally, we plug the proposed
CKA-SR into the training process of sparse network training methods and find
that CKA-SR consistently improves the performance of several State-Of-The-Art
sparse training methods, especially at extremely high sparsity. Code is
included in the supplementary materials
Enhancing medical vision-language contrastive learning via inter-matching relation modelling
Medical image representations can be learned through medical vision-language
contrastive learning (mVLCL) where medical imaging reports are used as weak
supervision through image-text alignment. These learned image representations
can be transferred to and benefit various downstream medical vision tasks such
as disease classification and segmentation. Recent mVLCL methods attempt to
align image sub-regions and the report keywords as local-matchings. However,
these methods aggregate all local-matchings via simple pooling operations while
ignoring the inherent relations between them. These methods therefore fail to
reason between local-matchings that are semantically related, e.g.,
local-matchings that correspond to the disease word and the location word
(semantic-relations), and also fail to differentiate such clinically important
local-matchings from others that correspond to less meaningful words, e.g.,
conjunction words (importance-relations). Hence, we propose a mVLCL method that
models the inter-matching relations between local-matchings via a
relation-enhanced contrastive learning framework (RECLF). In RECLF, we
introduce a semantic-relation reasoning module (SRM) and an importance-relation
reasoning module (IRM) to enable more fine-grained report supervision for image
representation learning. We evaluated our method using four public benchmark
datasets on four downstream tasks, including segmentation, zero-shot
classification, supervised classification, and cross-modal retrieval. Our
results demonstrated the superiority of our RECLF over the state-of-the-art
mVLCL methods with consistent improvements across single-modal and cross-modal
tasks. These results suggest that our RECLF, by modelling the inter-matching
relations, can learn improved medical image representations with better
generalization capabilities.Comment: 11 pages, 5 figures. Under revie
STGIN: Spatial-Temporal Graph Interaction Network for Large-scale POI Recommendation
In Location-Based Services, Point-Of-Interest(POI) recommendation plays a
crucial role in both user experience and business opportunities. Graph neural
networks have been proven effective in providing personalized POI
recommendation services. However, there are still two critical challenges.
First, existing graph models attempt to capture users' diversified interests
through a unified graph, which limits their ability to express interests in
various spatial-temporal contexts. Second, the efficiency limitations of graph
construction and graph sampling in large-scale systems make it difficult to
adapt quickly to new real-time interests. To tackle the above challenges, we
propose a novel Spatial-Temporal Graph Interaction Network. Specifically, we
construct subgraphs of spatial, temporal, spatial-temporal, and global views
respectively to precisely characterize the user's interests in various
contexts. In addition, we design an industry-friendly framework to track the
user's latest interests. Extensive experiments on the real-world dataset show
that our method outperforms state-of-the-art models. This work has been
successfully deployed in a large e-commerce platform, delivering a 1.1% CTR and
6.3% RPM improvement.Comment: accepted by CIKM 202
A KIM-compliant potfit for fitting sloppy interatomic potentials : application to the EDIP model for silicon
Fitted interatomic potentials are widely used in atomistic simulations thanks to their ability to compute the energy and forces on atoms quickly. However, the simulation results crucially depend on the quality of the potential being used. Force matching is a method aimed at constructing reliable and transferable interatomic potentials by matching the forces computed by the potential as closely as possible, with those obtained from first principles calculations. The potfit program is an implementation of the force-matching method that optimizes the potential parameters using a global minimization algorithm followed by a local minimization polish. We extended potfit in two ways. First, we adapted the code to be compliant with the KIM Application Programming Interface (API) standard (part of the Knowledgebase of Interatomic Models Project). This makes it possible to use potfit to fit many KIM potential models, not just those prebuilt into the potfit code. Second, we incorporated the geodesic Levenberg–Marquardt (LM) minimization algorithm into potfit as a new local minimization algorithm. The extended potfit was tested by generating a training set using the KIM Environment-Dependent Interatomic Potential (EDIP) model for silicon and using potfit to recover the potential parameters from different initial guesses. The results show that EDIP is a “sloppy model” in the sense that its predictions are insensitive to some of its parameters, which makes fitting more difficult. We find that the geodesic LM algorithm is particularly efficient for this case. The extended potfit code is the first step in developing a KIM-based fitting framework for interatomic potentials for bulk and two-dimensional materials. The code is available for download via https://www.potfit.net
- …