177 research outputs found
Optimum Network of Battery Storage to Support Electric Vehicle Charging Infrastructure in Smart Cities
Smart mobility and transportation is a critical component of smart cities. One barrier to the smart transportation is a lack of charging stations that can empower a huge amount of electric vehicles, especially the autonomous one. Battery storage technology provides an opportunity; however, how battery storage can serve a crucial role in enabling fast-charging stations to fulfill customer demand and providing a profit for charging station operators is unclear. This paper reports a discrete event simulation (DES) model to determine the optimum network of battery storage system considering costs and charging stations. A case study of Detroit Area in the State of Michigan is provided to demonstrate the usage of the model. Results show that lithium-ion batteries cost the most whereas zinc-air batteries cost the least. Findings suggest that a highly condensed charging station network provide higher benefit and result in lower total cost through battery units connected to a microgrid
Discovering Predictable Latent Factors for Time Series Forecasting
Modern time series forecasting methods, such as Transformer and its variants,
have shown strong ability in sequential data modeling. To achieve high
performance, they usually rely on redundant or unexplainable structures to
model complex relations between variables and tune the parameters with
large-scale data. Many real-world data mining tasks, however, lack sufficient
variables for relation reasoning, and therefore these methods may not properly
handle such forecasting problems. With insufficient data, time series appear to
be affected by many exogenous variables, and thus, the modeling becomes
unstable and unpredictable. To tackle this critical issue, in this paper, we
develop a novel algorithmic framework for inferring the intrinsic latent
factors implied by the observable time series. The inferred factors are used to
form multiple independent and predictable signal components that enable not
only sparse relation reasoning for long-term efficiency but also reconstructing
the future temporal data for accurate prediction. To achieve this, we introduce
three characteristics, i.e., predictability, sufficiency, and identifiability,
and model these characteristics via the powerful deep latent dynamics models to
infer the predictable signal components. Empirical results on multiple real
datasets show the efficiency of our method for different kinds of time series
forecasting. The statistical analysis validates the predictability of the
learned latent factors
An approach to fault diagnosis for rotating machinery based on feature reconstruction with LCD and t-SNE
It is crucial to effectively and accurately diagnose fault of rotating machinery. However, high dimension characteristic of features, which are extracted from vibration signals of Rotating machinery, makes it difficult to recognize accurately fault mode. To resolve this problem, t-distributed stochastic neighbor embedding (t-SNE) is introduced to reduce the dimensionality of the feature vector in this paper. Therefore, the article proposes a method for fault diagnosis of Rotating machinery based on local characteristic decomposition-sample entropy (LCD-SampEn), t-SNE and random forest (RF). Firstly, original vibration signals of rotating machinery are decomposed to a number of ISCs by the LCD. Then, feature vector is obtained through calculating SampEn of each ISC. Subsequently, the t-SNE is used to reduce the dimension of the feature vectors. Finally, the reconstructed feature vectors are applied to the RF for implementing the classification of fault patterns. Two cases are studied based on the experimental data of bearing and hydraulic pump fault diagnosis, in which the proposed method can achieve 98.22 % and 98.75 % of diagnosis rate respectively. Compared with the pear methods, the proposed approach exhibits the best performance. The results validate the effectiveness and superiority of the present method
Transcriptome Analysis and Ultrastructure Observation Reveal that Hawthorn Fruit Softening Is due to Cellulose/Hemicellulose Degradation
Softening, a common phenomenon in many fruits, is a well coordinated and genetically determined process. However, the process of flesh softening during ripening has rarely been described in hawthorn. In this study, we found that ‘Ruanrou Shanlihong 3 Hao’ fruits became softer during ripening, whereas ‘Qiu JinXing’ fruits remained hard. At late developmental stages, the firmness of ‘Ruanrou Shanlihong 3 Hao’ fruits rapidly declined, and that of ‘Qiu JinXing’ fruits remained essentially unchanged. According to transmission electron microscopy (TEM), the middle lamella of ‘Qiu JinXing’ and ‘Ruanrou Shanlihong 3 Hao’ fruit flesh was largely degraded as the fruits matured. Microfilaments in ‘Qiu JinXing’ flesh were arranged close together and were deep in color, whereas those in ‘Ruanrou Shanlihong 3 Hao’ fruit flesh were arranged loosely, partially degraded and light in color. RNA-Seq analysis yielded approximately 46.72 Gb of clean data and 72,837 unigenes. Galactose metabolism and pentose and glucuronate interconversions are involved in cell wall metabolism, play an important role in hawthorn texture. We identified 85 unigenes related to the cell wall between hard- and soft-fleshed hawthorn fruits. Based on data analysis and real-time PCR, we suggest that β-GAL and PE4 have important functions in early fruit softening. The genes Ffase, Gns, α-GAL, PE63, XTH and CWP, which are involved in cell wall degradation, are responsible for the different textures of hawthorn fruits. Thus, we hypothesize that the different textures of ‘Qiu JinXing’ and ‘Ruanrou Shanlihong 3 Hao’ fruits at maturity mainly result from cellulose/hemicelluloses degradation rather than from lamella degradation. Overall, we propose that different types of hydrolytic enzymes in cells interact to degrade the cell wall, resulting in ultramicroscopic Structure changes in the cell wall and, consequently, fruit softening. These results provide fundamental insight regarding the mechanisms by which hawthorn fruits acquire different textures and also lay a solid foundation for further research
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection
The recent decade witnessed a surge of increase in financial crimes across
the public and private sectors, with an average cost of scams of $102m to
financial institutions in 2022. Developing a mechanism for battling financial
crimes is an impending task that requires in-depth collaboration from multiple
institutions, and yet such collaboration imposed significant technical
challenges due to the privacy and security requirements of distributed
financial data. For example, consider the modern payment network systems, which
can generate millions of transactions per day across a large number of global
institutions. Training a detection model of fraudulent transactions requires
not only secured transactions but also the private account activities of those
involved in each transaction from corresponding bank systems. The distributed
nature of both samples and features prevents most existing learning systems
from being directly adopted to handle the data mining task. In this paper, we
collectively address these challenges by proposing a hybrid federated learning
system that offers secure and privacy-aware learning and inference for
financial crime detection. We conduct extensive empirical studies to evaluate
the proposed framework's detection performance and privacy-protection
capability, evaluating its robustness against common malicious attacks of
collaborative learning. We release our source code at
https://github.com/illidanlab/HyFL .Comment: PETs prize challenge versio
Chemical Production of Kopi Luwak
The Civet Cat of family Vivverridae is used to produce a rare coffee product called Kopi Luwak. As a result of Kopi Luwak’s increasing popularity, Civet Cat abuse is prevalent. Our research aims to recreate Kopi Luwak by artificially replicating the conditions of the Civet Cats digestive system. Proteolytic enzymes, acid treatment, and varying incubation conditions will be used to simulate the process
Feature reconstruction based on t-SNE: an approach for fault diagnosis of rotating machinery
It is crucial to effectively and accurately diagnose the faults of rotating machinery. However, the high-dimensional characteristic of the features, which are extracted from the vibration signals of rotating machinery, makes it difficult to accurately recognize the fault mode. To resolve this problem, t-distributed stochastic neighbor embedding (t-SNE) is introduced to reduce the dimensionality of the feature vector in this paper. Therefore, the article describes a proposed method for fault diagnosis of rotating machinery based on local characteristic decomposition-sample entropy (LCD-SampEn), t-SNE and random forest (RF). First, the original vibration signals of rotating machinery are decomposed to a number of intrinsic scale components (ISCs) by the LCD. Next, the feature vector is obtained through calculating SampEn of each ISC. Subsequently, t-SNE is used to reduce the dimension of the feature vectors. Finally, the reconstructed feature vectors are applied to the RF for implementing the classification of the fault patterns. Two cases are studied based on the experimental data of the fault diagnoses of a bearing and a hydraulic pump. The proposed method can achieve a diagnosis rate of 98.22 % and 98.75 % for the bearing and the hydraulic pump, respectively. Compared with the other methods, the proposed approach exhibits the best performance. The results validate the effectiveness and superiority of the proposed method
Data-Driven Modeling of Landau Damping by Physics-Informed Neural Networks
Kinetic approaches are generally accurate in dealing with microscale plasma
physics problems but are computationally expensive for large-scale or
multiscale systems. One of the long-standing problems in plasma physics is the
integration of kinetic physics into fluid models, which is often achieved
through sophisticated analytical closure terms. In this study, we successfully
construct a multi-moment fluid model with an implicit fluid closure included in
the neural network using machine learning. The multi-moment fluid model is
trained with a small fraction of sparsely sampled data from kinetic simulations
of Landau damping, using the physics-informed neural network (PINN) and the
gradient-enhanced physics-informed neural network (gPINN). The multi-moment
fluid model constructed using either PINN or gPINN reproduces the time
evolution of the electric field energy, including its damping rate, and the
plasma dynamics from the kinetic simulations. For the first time, we introduce
a new variant of the gPINN architecture, namely, gPINN to capture the Landau
damping process. Instead of including the gradients of all the equation
residuals, gPINN only adds the gradient of the pressure equation residual as
one additional constraint. Among the three approaches, the gPINN-constructed
multi-moment fluid model offers the most accurate results. This work sheds new
light on the accurate and efficient modeling of large-scale systems, which can
be extended to complex multiscale laboratory, space, and astrophysical plasma
physics problems.Comment: 11 pages, 7 figure
San Bruno, puerta a los cerros: arquitectura como vínculo entre el ciudadano y su entorno natural
Artículo de gradoSe realiza un proyecto urbano a escala de tres barrios: Egipto, El Parejo y La Peña. igualmente se realiza un proyecto urbano a menor escala en el sector San Bruno (Egipto) y un proyecto arquitectónico dentro de este, en la entrada a los Cerros Orientales de Bogotá, se propone una casa del árbol.1. INTRODUCCIÓN
1.1 DISPOSITIVOS DE APROPIACIÓN DEMOCRATICA
2. METODOLOGÍA
3. RESULTADOS
3.1 ETAPAS DE DESARROLLO
3.2 BARRIO EGIPTO, EL PAREJO Y LA PEÑA
3.3 SECTOR SAN BRUNO
3.4 MEMORIA Y ACCESIBILIDAD
3.5 BOSQUE DE COLUMNAS
3.5.1 ACTIVA
3.5.2 PASIVA
3.5.3 PRODUCTIVA
4. LA CASA DEL ARBOL
5. DISCUSIÓN
6. CONCLUSION
7. REFERENCIAS
8. ANEXOSPregradoArquitect
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT
Transformer based architectures have become de-facto models used for a range
of Natural Language Processing tasks. In particular, the BERT based models
achieved significant accuracy gain for GLUE tasks, CoNLL-03 and SQuAD. However,
BERT based models have a prohibitive memory footprint and latency. As a result,
deploying BERT based models in resource constrained environments has become a
challenging task. In this work, we perform an extensive analysis of fine-tuned
BERT models using second order Hessian information, and we use our results to
propose a novel method for quantizing BERT models to ultra low precision. In
particular, we propose a new group-wise quantization scheme, and we use a
Hessian based mix-precision method to compress the model further. We
extensively test our proposed method on BERT downstream tasks of SST-2, MNLI,
CoNLL-03, and SQuAD. We can achieve comparable performance to baseline with at
most performance degradation, even with ultra-low precision
quantization down to 2 bits, corresponding up to compression of the
model parameters, and up to compression of the embedding table as
well as activations. Among all tasks, we observed the highest performance loss
for BERT fine-tuned on SQuAD. By probing into the Hessian based analysis as
well as visualization, we show that this is related to the fact that current
training/fine-tuning strategy of BERT does not converge for SQuAD
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