Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning-based Electric Vehicle User Behavior Prediction

Electric Vehicles (EV s) have grown rapidly over the past decade but the integration of large-scale electric vehicles into the power grid is still a challenge. Overloading and harmonic distortions because of EV charging may impact the grid stability and may create grid issues. Power industry needs to get ready for the EV roll-out. One of the key actions to prepare for greater EV penetration and mitigate grid issues is smart charge scheduling of EVs to manage the charging load efficiently. Charge scheduling of EVs highly depends on EV users' behavior, so prediction of users' behavior is critical. Two of the important parameters, i.e., energy consumption and session duration are predicted with the Mean Absolute Error (MAE) of 1.45 kWh and 66.5 minutes respectively. These predictions are based on weekly, monthly and yearly historical public charging dataset of Adaptive Charging Network (ACN) implemented at Caltech USA. Ensemble machine learning models with base models as Random Forest (RF), Support Vector Machine (SVM) and XGBoost are used to improve the results. In contrast with the existing literature, this work utilizes the predicted session duration and weather data from local weather station along with the nearby events data and public holidays. Prediction errors are reduced in comparison to existing works with the improved data processing pipeline and feature selection.
PMDB: A Range-Based Key-Value Store on Hybrid NVM-Storage Systems

Emerging Nov-Volatile Memory (NVM) may replace DRAM as main memory in future computers. However, data will likely still be stored on storage due to the enormous large size of available data. We investigate how key-value stores can be efficiently designed and implemented in a hybrid system, called NVM-Storage system, consisting of NVM as memory and traditional storage. We first discuss the performance trade-offs among Put, Get, and Range Query of the existing designs. Then, we propose PMDB, a range-based key-value store on NVM-Storage systems. PMDB achieves good performance for Put, Get and Range Query at the same time by utilizing a range-based data management and deploying a light-weight index on NVM. We compare PMDB with the state-of-the-art schemes including SLM-DB [21] and MatrixKV [40] for hybrid NVM-storage systems. Evaluation results indicate that in workloads with mixed Put, Get and Range Queries, PMDB outperforms existing key-value stores by <inline-formula><tex-math notation="LaTeX">$1.16\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>. </mml:mo><mml:mn>16</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq1-3202755.gif"/></alternatives></inline-formula> – <inline-formula><tex-math notation="LaTeX">$2.49\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mo>. </mml:mo><mml:mn>49</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq2-3202755.gif"/></alternatives></inline-formula>.
Dams in series with continuous release

In a previous paper (Moran (1956)) the theory of a dam with a continuous release was developed for a situation in which the input was defined by an additive homogeneous random process of gamma distribution type, and the release was defined to occur continuously at a fixed rate when the content of the dam was non-zero. In the present paper we modify these conditions by assuming first that the input is defined by a general additive homogeneous process with non-negative increments with finite second moment, and secondly that the rate of release is proportional to the content of the dam. This modification to the release rule ensures that the content of the dam is never zero and the theory is then so simplified that the distributional properties of the contents of a sequence of dams in series can be easily found.
Convolutional Neural Network based Framework for Automatic Lung Cancer Detection from Lung CT Images

Lung cancer is one of the major causes of death across the globe. Medical interventions with modern healthcare facilities are widely used to cure lung cancer. However, it is indispensable to have research on early detection of lung cancer as it has potential to save lives of people. With innovations in machine learning Computer Aided Design (CAD) systems for automatic detection of lung cancer has become an important solution. Particularly deep learning models such as Convolutional Neural Network (CNN) is found to have necessary mechanisms to learn features from Computed Tomography (CT) scan images and detect the probability of lung cancer. In this paper, we propose a CNN based model for automatic detection of lung cancer provided lung CT scan image. We proposed an algorithm known as CNN based Automatic Lung Cancer Detection (CNN-ALCD) which is based on supervised learning phenomenon. The learned model is capable of detecting lung cancer from any newly arrived test sample. The proposed solution has different mechanisms such as preprocessing, building CNN with different layers, training the CNN model and performing lung cancer detection. Empirical study revealed that the proposed CNN based model outperforms many existing neural network-based methods with highest accuracy 94.11%. Therefore, the proposed system can be integrated with a Clinical Decision Support System (CDSS) in healthcare units for automatic diagnosis of lung cancer.
Design of a Q-learning based Smart Grid and smart Water scheduling model based on Heterogeneous Task Specific Offloading process

Smart cities require deployment & management of multiple Internet-of-Things (IoT) devices that can sense, communicate & perform information-specific actions. One of the most important tasks for deployment of these devices is controlling their scheduling operations. Existing smart city deployment models are either highly complex, or cannot be scaled beyond a certain set of devices, which limits their deployment capabilities. Moreover, most of these deployments are suited for a limited number of nodal traffic, which further affects their scalability levels. To overcome these issues, this text proposes design of a Q-learning based task scheduling Model that can uses Edge-Based Heterogeneous Task Specific Offloading (HTSO) with Genetic Algorithm (GA) based optimizations. The Q-learning model initially clusters tasks based on their importance-levels, which are determined by the number of real-time request types. High-capacity edge nodes and edge computing devices have their sleep patterns determined by these clusters depending on their relative importance levels. All of these choices are made by VMs in the edge devices, which helps to keep all of the edge computers in sync with one another. The Q-Learning Model may determine task-edge device mapping schedules based on priority levels of tasks and the computing efficiency of edge devices. Finding computationally-free edge devices using the GA Model, then assigning them duties depending on priority levels for those activities. Combining Q-Learning with GA allows the model to improve its computing efficiency for Smart Grid & Smart Water scheduling scenarios. This is accompanied with reduced average access latency, average response delay, and energy usage levels. The model was deployed on multiple smart city scenarios, and its performance was compared w.r. t. different standard deployment models. Based on this comparison, it was observed that the proposed model was able to improve scheduling efficiency by 15.5% for smart grids, and 8.3% for smart water scheduling, increase scheduling speed by 3.9% for smart grids, and 4.5% for smart water scheduling, while maintaining high computational efficiency of 99.5% for both the applications. Due to these advantages, the proposed model is capable of deployment for a wide variety of smart city use cases.
A Comparison between the Hoskins-Bretherton Model of Frontogenesis and the Analysis of an Intense Surface Frontal Zone

Abstract The Hoskins-Bretherton model of frontogenesis employed here represents the counterpart of the two-dimensional Eady problem expressed in geostrophic coordinate space. The fundamental characteristics of the model solution are shown to be derivable from the properties of the nonlinear one-dimensional advection equation and the linearized Eady problem. Detailed comparisons are made between the predictions of this model and the analysis of an intense frontal zone presented by Sanders. Qualitative agreement is found in details of the horizontal wind field and potential temperature distributions. The major discrepancy occurs in the vertical velocity field: the most intense vertical velocities occur at midlevel in the model and are significantly smaller in magnitude than the rising narrow jet above the analyzed zone of maximum cyclonic relative vorticity. The presence of this jet is responsible for the most significant frontogenetical properties of the front associated with vertical tilting of potential .
The Coronavirus Pandemic and the Transformation of Telehealth in the Context of Artificial Intelligence

The pandemic is seriously affecting individuals' wellbeing, occupations, economies, and practices. This pandemic has shaken the world dramatically and framed a moment to think about the future, incorporating our relationship with nature. Since the COVID-19 pandemic started, it's been relied upon to drive remarkable development in telehealth, especially for demonstrative patients, to stay at home and talk with specialists through virtual stations, helping with diminishing the spread of the disease to mass and the clinical staff on the ground zero. The novel coronavirus epidemic has changed our way of living, society, and human services framework. This study proposed the application of artificial intelligence to make its classification. The outcomes of the proposed systems are equated with pre-existing algorithms to highlight the benefits of test time minimization and classification error. Furthermore, this study tries to analyse corona time series data on the level of classification and found that the decision tree algorithm gives the best accuracy of approx. 100% with zero error and zero standard deviation with 7098 milliseconds.
Study on Technique of Super Critial CO_2 Extraction of Oleoresin Cumin

Cumin seed was taken as experimental material of extraction condition.This paper systemically studid the influence of pressure,temperature,degree fluidof CO 2 on the extraction rate.The better technological parameter was that the pressure was 35MPa and the temperature was 50℃ and the grind degree 40 and tluid of CO 2 25 l/h,The bases for batch process were provided. \;
Transformer Purchase Price Prediction Method Based on Long Short Term Memory Model with Complex Factor Input

Traditional Long Short Term Memory (LSTM) model can effectively improve the price prediction problem, but the LSTM model only accepts a single element input, and the power equipment price prediction involves multiple factors. Based on this, this paper proposes a multi-factor input-based LSTM model for transformer purchase price prediction, firstly, by changing the mapping structure of input layer, loop layer and output layer in the LSTM model, and then reasonably introducing the loop dropout restriction mechanism to ensure that the model structure is efficiently adapted to multi-factor input and low data training time. Finally, experimental simulations are conducted on a real price data set, and the validation results show that this algorithm can efficiently improve the price prediction accuracy and model robustness compared with traditional models.
A Sample Data Augmentation Method for EMG Gesture Recognition

With the development of artificial intelligence, the need for accurate, efficient, and convenient gesture recognition techniques has become more pressing. Traditional EMG (electromyogram) algorithms usually use pre-emptive enhancement when doing data enhancement but do not consider whether the enhanced samples are valid. At the same time, adversarial attack gives new ideas to the model. This paper combines the adversarial attack with data augmentation and proposes the “find valid augmented samples” algorithm. Compared to the noise of 82.25% for the original signal GRU model and 75.11% for the ELM model, the results using our method increase to 87.66% and 88.11% respectively. The experimental results show that this algorithm improves the accuracy of the model compared to other algorithms and contributes to gesture recognition technology.
Changes in the Structure of the Tropical Subcloud Layer from the Undisturbed to Disturbed States

Abstract Results of the analysis of measurements of temperature, mixing ratio, and horizontal wind speed are presented. The data were collected using a tethered ballon system during the 1969 BOMEX during periods of undisturbed and disturbed weather conditions. The results show that substantial changes occur in the character and stratification of the lower subcloud layer from the undisturbed to disturbed states. The lapse rate, height of the mixed layer, and estimated surface fluxes under disturbed conditions are to be obtained from the observations. These values are applied to currently suggested parametric relationships of the mixed layer under undisturbed conditions. Totally unrealistic values of vertical motion are obtained. It is concluded that convective cloud scale downdrafts driven by evaporation are extremely important in determining the budget of the mixed layer under disturbed conditions.
Eliminating Discharge Imbalances in Predicting Shallow Water Flows with Shocks

The shallow water equations are widely used in modelling environmental flows. Being a hyperbolic system of differential equations, they admit shocks that represent hydraulic jumps and bores. Although the water surface can be solved satisfactorily with the modern shock-capturing schemes, the predicted flow rate often suffers from imbalances where shocks occur, eg the mass conservation is violated by failing to maintain a constant discharge rate at every cross-section in a steady open channel flow. A total-variation-diminishing Lax-Wendroff scheme is developed, and used to demonstrate how to achieve an exact flux balance. The performance of the proposed methods is inspected through some test cases, which include 1- and 2-dimensional, flat and irregular bed scenarios. The proposed methods are shown to preserve the mass exactly, and can be easily extended to other shock-capturing models.
On sojourn time in Jackson networks of queues

This paper is about representations for equilibrium sojourn time distributions in Jackson networks of queues. For a network with N single-server nodes let hi be the Laplace transform of the residual system sojourn time for a customer ‘arriving' to node i, ‘arrival' meaning external input or internal transfer. The transforms {hi : i = 1, ···, N} are shown to satisfy a system of equations we call the network flow equations. These equations lead to a general recursive representation for the higher moments of the sojourn time variables {Ti : i = 1, ···, N}. This recursion is discussed and then, by way of illustration, applied to the single-server Markovian queue with feedback.
Stabilization of an Underactuated Robot on a Ball

In this work we investigate and derive a nonlinear control law for a spatial model of the underactuated Ballbot system. The spatial Ballbot system is an extended 3D model to the 2D model of a unicycle. The main classes of inverted pendulums are reviewed, and their principle differences are considered. The holonomic nature of the motion of the ball rolling on a plane is discussed, and a new innovative approach considering the motion of the center of the ball as holonomic is described. A spatial 3D kinematic and dynamic model are derived, and a control law for the stabilization and position of the robot are constructed. Partial feedback linearization for an underactuated systems, is used to derive the stabilization controller, and a simple P.D. controller is used for the position control. The stability nature of each controller is investigated, and the overall system is proven to be locally stable. Computer simulation are presented, and the experiments are preformed using the LEGO NXT Mind-Storm kit.
Data Mining Technology Assists in The Construction of The Influencing Factor Model of Learners' Satisfaction in Offline Online and Offline Hybrid Golden Courses

Constructing the influencing factor model of learners' satisfaction in mixed gold classroom is a method to determine the level of product satisfaction. The purpose of this study is to understand the effectiveness of builders in their work and how they can make their work more effective. The construction of influencing factor model is based on the following theory: price, quality, service and other factors can affect human behavior. This theory was developed by Dr. Robert J. Sternberg to explain how people make decisions when facing choices or choices (Sternberg 2006). The construction of influencing factor model assumes that decision-making involves four main components: heuristic, emotional response, cognitive process and behavioral response (Sternberg and grigoren). The main purpose of this study is to determine whether there is a relationship between constructors and student satisfaction.
Investigation of a Method to Extend a 2-Dimensional Gait to 3-Dimensions in a Human Musculoskeletal Model with 70 Muscles

In this study, to understand the mechanism of human gait, we applied the 2-dimensional gait to a human musculoskeletal model with 70 muscles, and investigated the extension method to 3-dimensional gait. We tested 4 optimization conditions to achieve a 14 degrees of freedom 3-dimensional gait using a musculoskeletal model with 70 muscles, and verified whether the model could achieve gait or not. As a result, gait was achieved only under condition 4, in which the DOFs were added step by step in the frontal axis, vertical axis, and then in the sagittal axis at each joint. The optimization procedure for condition 4 resulted in a 5-second gait. However, the results differed from those of the subject experiments in terms of stride length, average gait speed, and ankle joint angular change, and we discussed points that should be considered in the future. In the future, we would like to reproduce 3-dimensional gait, taking into consideration the points mentioned in the discussion, and contribute to the understanding of the mechanism of human gait.
Envisioning Reliable Diagnosis of Diabetic Retinopathy using U-Net Architecture

For the semantic and instance segmentation of biomedical data, deep neural networks perform better than conventional techniques. However, the computational cost of this approach is high. After training, the network can be made simpler, or an acceptable architecture that offers segmentation with lower accuracy but faster can be selected, lowering the computational cost. We looked into the efficacy and performance of various UNet architectures for the challenge of image segmentation in this study. With the aid of pictures from a retinopathy health survey, the DRIVE dataset was examined. Among the six different models of UNet being compared, R2-UNet delivered the most promising results in terms of accuracy.
Improving numerical simulations of preisach models for accuracy and speed

We present a useful technique to calculate the magnetic response of hard magnetic materials to the application of magnetic fields. We consider that these materials can be described by means of Preisach-based models and the parameters defining the material are distributed according to a Gaussian function. We find that the utilization of a grid in a "cobweb" style can help us to improve both accuracy and speed for this kind of simulation. We show the usefulness of this grid by solving in an easy way inverse type problems.
Sustainability Governance in China: An Analysis of Regional Ecological Efficiency

The Chinese government is committed to sustainability governance to alleviate the shortage of energy and the imbalance between ecological environment and economic development. This paper evaluates and analyzes the sustainability governance performance of China. A bootstrap data envelopment analysis (DEA) is proposed to evaluate sustainability governance performance of 30 provinces based on ecological efficiency in China from 1998 to 2015. The results indicate that the ecological efficiency of China significantly improved as a whole, which is related to the decline in sulfur dioxide emissions. Among these provinces, Jiangsu, Liaoning, and Inner Mongolia exhibited the highest values, while Gansu, Chongqing, and Sichuan had the lowest values. The 30 provinces were divided into four sub-areas. The average ecological efficiency of the eastern area was the highest, followed by the northeast area. Compared to the east area, northeast area, and central area, we find that west area obviously falls behind. As such, the results provide helpful guidance to improve ecological governance performance.
Estimation of Change-points in ARMA-GARCH/IGARCH and General Time Series Models

This paper first develops a general theory for estimating change-points in a general class of linear and nonlinear time series models. Based on a general objective function, it is shown that the estimated change-point converges weakly to the location of the maxima of a double-sided random walk and other estimated parameters are asymptotically normal. When the magnitude d of changed parameters is small, it is shown that the limiting distribution can be approximated by the known distribution as in Yao (1987). This provides a channel to connect our results with those in Picard (1985) and Bai, Lumsdaine and Stock (1998), where the magnitude of changed parameters depends on the sample size n and tends to zero as n ! 1. We then focus on the self-weighted QMLE and the local QMLE of structure-change ARMA-GARCH/IGARCH models. The limiting distribution of the estimated change-point and its approximating distribution are obtained. Some simulation results are reported and one real example is given.
DOUBLE-DIFFUSIVE NATURAL CONVECTION IN A VERTICAL POROUS ANNULUS

A numerical study is carried out on double-diffusive natural convection in a vertical annular porous layer whose vertical walls are at constant temperatures and concentrations. The investigation covers the range 10 < RT < 500, 1 < Le < 10, - 50 < N < 50, 1 < A < 5, 1 < kappa < 10, where RT, Le, N, A, and kappa are the thermal Rayleigh number, Lewis number, buoyancy ratio, aspect ratio, and radius ratio of the enclosure, respectively. The two extreme cases of heat-driven and solute-driven natural convection correlations, valid in the boundary layer regime, are derived to calculate the average Nusselt and Sherwood numbers in terms of the governing parameters of the problem. In order to investigate the effects of the combined thermal and solutal buoyancy forces on the average heat and mass transfer, results have been obtained for a large range of buoyancy ratios N. Streamlines, isotherms, and isosolutes in the system are produced to illustrate the flow structure transition from .