3,503 research outputs found
Reducing The Risk Of Floods In Urban Areas With Combined Inland-River System
With the change of the water environment in accordance with climate change, the loss of lives and properties has increased due to urban flood. Although the importance of urban floods has been highlighted quickly, the construction of advancement technology of an urban drainage system combined with inland-river water and its relevant research has not been emphasized in Korea. In addition, without operation in consideration of combined inland-river water, it is difficult to prevent urban flooding effectively. This study, therefore, develops the uncertainty quantification technology of the risk-based water level and the assessment technology of a flood-risk region through a flooding analysis of the combination of inland-river. The study is also conducted to develop forecast technology of change in the water level of an urban region through the construction of very short-term/short-term flood forecast systems. This study is expected to be able to build an urban flood forecast system which makes it possible to support decision making for systematic disaster prevention which can cope actively with climate change
Frequency-Based Decentralized Conservation Voltage Reduction Incorporated Into Voltage-Current Droop Control for an Inverter-Based Islanded Microgrid
Conservation voltage reduction (CVR) aims to decrease load demands by regulating bus voltages at a low level. This paper proposes a new strategy for decentralized CVR (DCVR), incorporated into the current-based droop control of inverter-interfaced distributed energy resources (IDERs), to improve the operational reliability of an islanded microgrid. An controller is developed as an outer feedback controller for each IDER, consisting of controllers for the DCVR and and controllers for power sharing. In particular, the controllers adjust the output voltages of the IDERs in proportion to the frequency variation determined by the controllers. This enables the output voltages to be reduced by the same amount, without communication between the IDERs. The controllers are responsible for reactive power sharing by adjusting the voltages while taking into account the controllers. Small-signal analysis is used to verify the performance of the proposed DCVR with variation in the and droop gains. Case studies are also carried out to demonstrate that the DCVR effectively mitigates an increase in the load demand, improving the operational reliability, under various load conditions determined by power factors and load compositions.11Ysciescopu
Generalized Gumbel-Softmax Gradient Estimator for Various Discrete Random Variables
Estimating the gradients of stochastic nodes is one of the crucial research
questions in the deep generative modeling community, which enables the gradient
descent optimization on neural network parameters. This estimation problem
becomes further complex when we regard the stochastic nodes to be discrete
because pathwise derivative techniques cannot be applied. Hence, the stochastic
gradient estimation of discrete distributions requires either a score function
method or continuous relaxation of the discrete random variables. This paper
proposes a general version of the Gumbel-Softmax estimator with continuous
relaxation, and this estimator is able to relax the discreteness of probability
distributions including more diverse types, other than categorical and
Bernoulli. In detail, we utilize the truncation of discrete random variables
and the Gumbel-Softmax trick with a linear transformation for the relaxed
reparameterization. The proposed approach enables the relaxed discrete random
variable to be reparameterized and to backpropagated through a large scale
stochastic computational graph. Our experiments consist of (1) synthetic data
analyses, which show the efficacy of our methods; and (2) applications on VAE
and topic model, which demonstrate the value of the proposed estimation in
practices
Implicit Kernel Attention
\textit{Attention} computes the dependency between representations, and it
encourages the model to focus on the important selective features.
Attention-based models, such as Transformers and graph attention networks (GAT)
are widely utilized for sequential data and graph-structured data. This paper
suggests a new interpretation and generalized structure of the attention in
Transformer and GAT. For the attention in Transformer and GAT, we derive that
the attention is a product of two parts: 1) the RBF kernel to measure the
similarity of two instances and 2) the exponential of norm to compute
the importance of individual instances. From this decomposition, we generalize
the attention in three ways. First, we propose implicit kernel attention with
an implicit kernel function, instead of manual kernel selection. Second, we
generalize norm as the norm. Third, we extend our attention to
structured multi-head attention. Our generalized attention shows better
performance on classification, translation, and regression tasks
Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models
The proposed method, Discriminator Guidance, aims to improve sample
generation of pre-trained diffusion models. The approach introduces a
discriminator that gives explicit supervision to a denoising sample path
whether it is realistic or not. Unlike GANs, our approach does not require
joint training of score and discriminator networks. Instead, we train the
discriminator after score training, making discriminator training stable and
fast to converge. In sample generation, we add an auxiliary term to the
pre-trained score to deceive the discriminator. This term corrects the model
score to the data score at the optimal discriminator, which implies that the
discriminator helps better score estimation in a complementary way. Using our
algorithm, we achive state-of-the-art results on ImageNet 256x256 with FID 1.83
and recall 0.64, similar to the validation data's FID (1.68) and recall (0.66).
We release the code at https://github.com/alsdudrla10/DG.Comment: International Conference on Machine Learning (ICML23
Improvisation of classification performance based on feature optimization for differentiation of Parkinson’s disease from other neurological diseases using gait characteristics
Most neurological disorders that include Parkinson’s disease (PD) as well as other neurological diseases such as Amyotrophic Lateral Sclerosis (ALS) and Huntington’s disease (HD) have some common abnormalities regarding the movement, vocal, and cognitive behaviors of sufferers. Variations in the manifestation of these types of abnormality help distinguish one disorder from another. In this study, differentiation was performed based on the gait characteristics of patients afflicted by different neurological disorders. In the recent past, many researchers have applied different machine learning and feature selection techniques to the classification of different groups of patients based on common abnormalities. However, in an era of modernization where the focus is on timely low-cost automatization and pattern recognition, such techniques require improvisation to provide high performance. We attempted to improve the performance of such techniques using different feature optimization methods, such as a genetic algorithm (GA) and principal component analysis (PCA), and applying different classification approaches, i.e., linear, nonlinear, and probabilistic classifiers. In this study, gait dynamics data of patients suffering with PD, ALS, and HD were collated from a public database, and a binary classification approach was used by taking PD as one group and adopting ALS+HD as another group. Performance comparison was achieved using different classification techniques that incorporated optimized feature sets obtained from GA and PCA. In comparison with other classifiers using different feature sets, the highest accuracy (97.87%) was obtained using random forest combined with GA-based feature sets. The results provide evidence that could assist medical practitioners in differentiating PD from other neurological diseases using gait characteristics
Posterior-Aided Regularization for Likelihood-Free Inference
The recent development of likelihood-free inference aims training a flexible
density estimator for the target posterior with a set of input-output pairs
from simulation. Given the diversity of simulation structures, it is difficult
to find a single unified inference method for each simulation model. This paper
proposes a universally applicable regularization technique, called
Posterior-Aided Regularization (PAR), which is applicable to learning the
density estimator, regardless of the model structure. Particularly, PAR solves
the mode collapse problem that arises as the output dimension of the simulation
increases. PAR resolves this posterior mode degeneracy through a mixture of 1)
the reverse KL divergence with the mode seeking property; and 2) the mutual
information for the high quality representation on likelihood. Because of the
estimation intractability of PAR, we provide a unified estimation method of PAR
to estimate both reverse KL term and mutual information term with a single
neural network. Afterwards, we theoretically prove the asymptotic convergence
of the regularized optimal solution to the unregularized optimal solution as
the regularization magnitude converges to zero. Additionally, we empirically
show that past sequential neural likelihood inferences in conjunction with PAR
present the statistically significant gains on diverse simulation tasks
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