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$p$ to capture the Landau damping process. Instead of including the gradients of all the equation residuals, gPINN$p$ only adds the gradient of the pressure equation residual as one additional constraint. Among the three approaches, the gPINN$p$-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 $2.3\%$ performance degradation, even with ultra-low precision quantization down to 2 bits, corresponding up to $13\times$ compression of the model parameters, and up to $4\times$ 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