Scalar Technique Research Articles

Forecasting Time Series AQI Using Machine learning of Haryana Cities Using Machine Learning

In India and throughout the world, air pollution is becoming a severe worry day by day. Governments and the general public have grown more concerned about how air pollution affects human health. Consequently, it is crucial to forecast the air quality with accuracy. In this paper, Machine learning methods SVR and RFR were used to build the hybrid forecast model to predict the concentrations of Air Quality Index in Haryana Cities. The forecast models were built using air pollutants and meteorological parameters from 2019 to 2021 and testing and validation was conducted on the air quality data for the year 2022 of Jind and Panipat city in the State of Haryana. Further, performance of hybrid forecast model was enhanced using scalar technique and performance was evaluated using various coefficient metrics and other parameters. First, the important factors affecting air quality are extracted and irregularities from the dataset are removed. Second, for forecasting AQI various approaches have been used and evaluation is carried out using performance metrics. The experimental results showed that the proposed hybrid model had a better forecast result than the standard Random forest Regression, Support Vector Regression and Multiple Linear Regression.

Internet of medical things‐based multitiered and hybrid architectural framework for effective heart disease prediction model

AbstractCardiovascular and chronic respiratory diseases are becoming a global threat. The alarming fact about Cardio‐vascular diseases (CVD) is that rural areas are recording a high number of heart failures compared to urban areas. The proposed research work intends to create an effective medical setup in country‐wide through Internet of Medical Things (IoMT)‐based multitiered and hybrid architectural framework and Machine Learning (ML) model. The proposed architecture collects the medical data using the IoMT devices. During data preprocessing, categorical data are encoded using one‐hot mechanism and is normalized using standard scalar technique. In addition, feature selection mechanisms are applied to choose the best significant features using hybrid sequence of Filter—Wrapper—Ensemble approach. To reduce the diagnostic barriers in health prediction model, an efficient method of Bayesian optimization for hyperparameter tuning and k‐fold cross‐validation resampling technique is applied to evaluate various conventional and ensemble ML techniques. The prediction results show the high accuracy of 86.47% and 85.81% using Logistic Regression and Extreme Gradient Boosting (XGBoosting) algorithm, respectively. In particular, the accuracy obtained using decision tree for the proposed approach is higher (80.53%) than that of existing approach (73.22%).

Adaptive XGBOOST Hyper Tuned Meta Classifier for Prediction of Churn Customers

In India, the banks have a formidable edge in maintaining their customer retention ratio for past few decades. Downfall makes the private banks to reduce their operations and the nationalised banks merge with other banks. The researchers have used the traditional and ensemble algorithms with relevant feature engineering techniques to better classify the customers. The proposed algorithm uses a Meta classifier instead of an ensemble algorithm with an adaptive genetic algorithm for feature selection. Churn prediction is the number of customers who wants to terminate their services in the banking sector. The model considers twelve attributes like credit score, geography, gender, age, etc, to predict customer churn. The project consists of five modules as follows. First is the pre-processing module that identifies the missing data and fills the value with mean and mode. Second is the data transformation module where, the categorical data is converted into numerical data using label encoding to fasten the computations. The converted numerical data is normalized using the standard scalar technique. The feature selection module identifies the essential attributes using DragonFly and Firefly (Hybrid Fly) algorithms. The classification module designs an intelligent Meta learner, which combines the Ensemble Algorithm Extreme Gradient Boosting (XGBOOST) with base classifiers as “Extra Tree Classifier” and “Logistic Regression” to predict the churn customers.

Scalar Control of Induction motor using Fuzzy logic

The main objective of the paper is to control the speed of an induction machine with scalar control technique using Fuzzy logic controller. Here the PI controller is replaced with Fuzzy Logic controller. By using Fuzzy Logic controller there are wider range of operating conditions can be covered and easier to adapt. For Fuzzy Logic controller some rules are written which play major role to control the speed of induction motor in an effective manner. The errors are evaluated according to rules defined.

Синтез субоптимальных программ калибровки блока акселерометров численными методами

The paper presents the results of synthesis of calibration programs consisting of 9 and 18 measurement positions. The synthesis was performed by numerical methods for scalar (invariant) technique of accelerometer unit calibration. The resulting programs are compared to the existing calibration programs which have been obtained analytically. The results of mathematical simulation and field experiment confirm the theoretical calculations, as well as the effective application of the obtained calibration programs.

Roughsets-based Approach for Predicting Battery Life in IoT

Internet of Things (IoT) and related applications have successfully contributed towards enhancing the value of life in this planet. The advanced wireless sensor networks and its revolutionary computational capabilities have enabled various IoT applications become the next frontier, touching almost all domains of life. With this enormous progress, energy optimization has also become a primary concern with the need to attend to green technologies. The present study focuses on the predictions pertinent to the sustainability of battery life in IoT frameworks in the marine environment. The data used is a publicly available dataset collected from the Chicago district beach water. Firstly, the missing values in the data are replaced with the attribute mean. Later, one-hot encoding technique is applied for achieving data homogeneity followed by the standard scalar technique to normalize the data. Then, rough set theory is used for feature extraction, and the resultant data is fed into a Deep Neural Network (DNN) model for the optimized prediction results. The proposed model is then compared with the state of the art machine learning models and the results justify its superiority on the basis of performance metrics such as Mean Squared Error, Mean Absolute Error, Root Mean Squared Error, and Test Variance Score.

The phase space mechanism for selectivity in a symmetric potential energy surface with a post-transition-state bifurcation

Chemical selectivity is a phenomenon displayed by potential energy surfaces (PES) that is relevant for many organic chemical reactions whose PES feature a valley-ridge inflection point in the region between two sequential index-1 saddles. In this letter we describe the underlying dynamical phase space mechanism that qualitatively determines the product distributions resulting from bifurcating reaction pathways. We show that selectivity is a consequence of the heteroclinic and homoclinic connections established between the invariant manifolds of the families of unstable periodic orbits (UPOs) present in the system. The geometry of the homoclinic and heteroclininc connections is determined using the technique of Lagrangian descriptors, a trajectory-based scalar technique with the capability of unveiling the geometrical template of phase space structures that characterizes transport.

Elastic wave-equation datuming

Wave-equation datuming (WED) techniques have demonstrated superiority when waves occur on the acquisition surface nonvertically, and traditional static corrections based on the time shift become inaccurate. Meanwhile, as for multicomponent data, those scalar techniques can hardly maintain the vector characteristics for the following multicomponent data processing flows. Considering this, we have developed an elastic-wave datuming approach to handle the static corrections for multicomponent data. Different from those existing scalar WED techniques, the multicomponent data are first decomposed into multicomponent P- and S-wave data. Then, the decomposed data are transformed into the [Formula: see text]-[Formula: see text] domain, and they are extrapolated from the acquisition surface to the datum using the one-way elastic-wave continuation. Finally, the datumed multicomponent data are reconstructed at the output datum by adding up the datumed P- and S-wave data. This elastic WED can guarantee that the same wave modes on different components are equally datumed, and the data remain multicomponent so that they are still applicable to multicomponent-joint processing techniques. Finally, several test examples involved in this paper have proved our method’s effectiveness in multicomponent data datuming application.

Secure and Robust Telemedicine using ECC on Radix-8 with Formal Verification

The scalar multiplication techniques used in Elliptic curve cryptography (ECC) are having the scope for gaining the computation efficiency. This is possible through the reduction of precomputed operations. Finding the more efficient technique compares to the most recent or efficient one is a research gap for all schemes. The manuscript presents an application oriented work for Telemedicine using ECC. It is based on robust application on reduced computational complexity. The methodology we apply for the same is Scalar Multiplication without precomputation on Radix-8. Introduced software and the hardware performance are reporting a big advantage over all the related proposed techniques. The reason to cover this problem is to provide a path on a fascinating area of ECC on a smaller key size be applicable for all applications on a same level of security strengths. The smaller length key gives the higher speed and shorter clock cycle to initiate the operation.

A Moment-Based Notion of Time Dependence for Functional Time Series

This paper addresses the fundamental topic of time dependence for time series when data points are given as functions. We construct a notion of time dependence through the scores of the principal components, which allows us to adapt various scalar time series techniques to the functional data context. In particular, we define dependence based on the autocovariances and cumulants of the scores, covering short and long memory scenarios. This notion naturally applies to linear processes. To justify this moment based approach we investigate the asymptotic properties of the estimator of the functional principal components and show its consistency under short and long memory. Finally, the applicability of our notion is illustrated through several statistical problems in functional time series: estimation of the functional autoregressive model, estimation of the long-run covariance function and testing for short memory against the long memory alternative.

Moment bounds on the corrector of stochastic homogenization of non-symmetric elliptic finite difference equations

ABSTRACTWe consider the corrector equation from the stochastic homogenization of uniformly elliptic finite difference equations with random, possibly non symmetric coefficients. Under the assumption that the coefficients are stationary and ergodic in the quantitative form of a logarithmic Sobolev inequality (LSI), we obtain optimal bounds on the corrector and its gradient in dimensions d≥2. Similar estimates have recently been obtained in the special case of diagonal coefficients making extensive use of the maximum principle and scalar techniques. Our new method only invokes arguments that are also available for elliptic systems and does not use the maximum principle. In particular, our proof relies on the LSI to quantify ergodicity and on regularity estimates on the derivative of the discrete Green’s function in weighted spaces. In the critical case d = 2, our argument for the estimate on the gradient of the elliptic Green’s function uses a Calderón–Zygmund estimate in discrete weighted spaces, which we state and prove. As applications, we provide a quantitative two-scale expansion and a quantitative approximation of the homogenized coefficients.

Comparative Analysis of Automatic Parallelization Techniques

With multicore design emerging as the chief design pattern for the microprocessor industry in a major way constant efforts are being made to improve its performance. The only way to extract maximum performance from a multicore based system as of now is through the automatic extraction of threads from sequential applications with the assistance of tools including, compilers and runtime optimizers. The increasing popularity of clusters and other similar forms of distributed computing calls for the requirement of automatic parallelization in compilers. This requirement for techniques through which automatic parallelization can be achieved has set of the development of various different methods with varying efficiencies. The objective of this paper is to test the efficiencies of two such techniques by performing a comparative analysis of the techniques based on various criteria like time complexity for instance. The techniques we are going to work on are namely the Scalar and Array Analysis technique and the Smith waterman technique. As mentioned previously these techniques will be analyzed using various criteria to arrive at a conclusion as to which one of them is superior performance wise.

A new framework for simulating forced homogeneous buoyant turbulent flows

This work proposes a new simulation methodology to study variable density turbulent buoyant flows. The mathematical framework, referred to as homogeneous buoyant turbulence, relies on a triply periodic domain and incorporates numerical forcing methods commonly used in simulation studies of homogeneous, isotropic flows. In order to separate the effects due to buoyancy from those due to large-scale gradients, the linear scalar forcing technique is used to maintain the scalar variance at a constant value. Two sources of kinetic energy production are considered in the momentum equation, namely shear via an isotropic forcing term and buoyancy via the gravity term. The simulation framework is designed such that the four dimensionless parameters of importance in buoyant mixing, namely the Reynolds, Richardson, Atwood, and Schmidt numbers, can be independently varied and controlled. The framework is used to interrogate fully non-buoyant, fully buoyant, and partially buoyant turbulent flows. The results show that the statistics of the scalar fields (mixture fraction and density) are not influenced by the energy production mechanism (shear vs. buoyancy). On the other hand, the velocity field exhibits anisotropy, namely a larger variance in the direction of gravity which is associated with a statistical dependence of the velocity component on the local fluid density.

Passive and reactive scalar measurements in a transient high-Schmidt-number Rayleigh–Taylor mixing layer

An experimental study of mixing induced by Rayleigh–Taylor (RT) instability at an Atwood number (A t ) ~7.5 × 10−4 and Schmidt number (Sc) ~1,000 has been performed. A new transient experimental facility developed on the working principles of the draw-tank facility at Cambridge (Dalziel et al. in J Fluid Mech, 399:1–48, 1999) has been established and enhanced to observe a higher (2×) Reynolds number regime. Water and brine were used to produce the RT density stratification. The evolution of the instability was studied using passive and reactive scalar techniques and quantified using optical diagnostic methods. The data were combined to estimate local and global mixing metrics representative of the mixing mechanism across the mixing layer. In comparison with parameters reported from analogous experiments, the mixing phenomenon at a high Sc shows a strong dependency on the initial conditions prevailing at the onset of the instability and the evidence of a delay in the mixing transition. Values of global and integral mixing parameters did not reach late-time asymptotic values that have been reported previously from steady-state experiments (Texas A&M Water Channel) and may be attributed to the effect of the barrier pull and the overturning mechanism that is thought to hinder the progress of the mixing layer.

Quaternion Structural Similarity: A New Quality Index for Color Images

One of the most important issues for researchers developing image processing algorithms is image quality. Methodical quality evaluation, by showing images to several human observers, is slow, expensive, and highly subjective. On the other hand, a visual quality matrix (VQM) is a fast, cheap, and objective tool for evaluating image quality. Although most VQMs are good in predicting the quality of an image degraded by a single degradation, they poorly perform for a combination of two degradations. An example for such degradation is the color crosstalk (CTK) effect, which introduces blur with desaturation. CTK is expected to become a bigger issue in image quality as the industry moves toward smaller sensors. In this paper, we will develop a VQM that will be able to better evaluate the quality of an image degraded by a combined blur/desaturation degradation and perform as well as other VQMs on single degradations such as blur, compression, and noise. We show why standard scalar techniques are insufficient to measure a combined blur/desaturation degradation and explain why a vectorial approach is better suited. We introduce quaternion image processing (QIP), which is a true vectorial approach and has many uses in the fields of physics and engineering. Our new VQM is a vectorial expansion of structure similarity using QIP, which gave it its name-Quaternion Structural SIMilarity (QSSIM). We built a new database of a combined blur/desaturation degradation and conducted a quality survey with human subjects. An extensive comparison between QSSIM and other VQMs on several image quality databases-including our new database-shows the superiority of this new approach in predicting visual quality of color images.

Implementation of Scalar Control Technique in SVPWM Switched Three–Level Inverter Fed Induction Motor Using DSP Controller

The main objective of the paper is to control the speed of an induction machine with scalar control technique. The scalar control technique (V/F) is used because of simplicity of control algorithm implementation and the response is better and accurate with closed loop slip compensation. An IGBT based three level diode clamped inverter (DCI) topology has been used instead of conventional two level inverter in order to reduce the harmonic content and to increase the device capability to handle the double the voltage of its rating. DOI: http://dx.doi.org/10.11591/ijpeds.v1i2.98

The dynamic multi-period vehicle routing problem

This paper considers the dynamic multi-period vehicle routing problem which deals with the distribution of orders from a depot to a set of customers over a multi-period time horizon. Customer orders and their feasible service periods are dynamically revealed over time. The objectives are to minimize total travel costs and customer waiting, and to balance the daily workload over the planning horizon. This problem originates from a large distributor operating in Sweden. It is modeled as a mixed integer linear program, and solved by means of a three-phase heuristic that works over a rolling planning horizon. The multi-objective aspect of the problem is handled through a scalar technique approach. Computational results show that the proposed approach can yield high quality solutions within reasonable running times.

A new control strategy for tracking peak power in a wind or wave energy system

This paper proposes a novel control strategy for tracking peak power in a wind or wave energy system using a squirrel cage induction generator. It eliminates wind speed measurement or estimation and uses a simple scalar technique by exploiting the cubic nature of the power curve. The method works even when air velocity is varying dynamically.

Fuzzy Satisfying Multiobjective Thermal Power Dispatch Based on Surrogate Worth Trade-off Method

In multiobjective optimization, trade-off analysis plays an important role in determining the best search direction to reach a most preferred solution. As in many cases, the objectives contradict with each other and cannot be handled by conventional scalar optimization techniques. This article deals with the multiobjective optimization problem relating to real and reactive power scheduling of thermal power generating units. In deciding the most preferred solution, operating cost, emission pollutants are the objectives undertaken to be minimized simultaneously while satisfying the security constraints. The real and reactive power line flows are obtained with the help of generalized Z-bus distribution factors (GZBDF). The non-inferior solutions, along with the trade-off functions between the conflicting objectives, are generated by implementing the ε-constraint method. Exploiting fuzzy set theory to access the indifference band, interaction with the decision maker is obtained via surrogate worth trade-off (SWT) functions of the membership functions of the objectives. The surrogate worth trade-off functions are constructed in the functional space and then transformed into the decision space, so the surrogate worth trade-off functions of objectives relate the decision maker's preferences to non-inferior solutions. The goal/objectives being of fuzzy nature can be quantified by defining their membership functions. The effectiveness of the method is demonstrated on IEEE 11-bus system, which comprises 3-generators.

A Design Framework for Scalar Feedback in MIMO Broadcast Channels

Joint linear beamforming and scheduling are performed in a system where limited feedback is present at the transmitter side. The feedback conveyed by each user to the base station consists of channel direction information (CDI) based on a predetermined codebook and a scalar metric with channel quality information (CQI) used to perform user scheduling. In this paper, we present a design framework for scalar feedback in MIMO broadcast channels with limited feedback. An approximation on the sum rate is provided for the proposed family of metrics, which is validated through simulations. For a given number of active users and average SNR conditions, the base station is able to update certain transmission parameters in order to maximize the sum-rate function. On the other hand, the proposed sum-rate function provides a means of simple comparison between transmission schemes and scalar feedback techniques. Particularly, the sum rate of SDMA and time division multiple access (TDMA) is compared in the following extreme regimes: large number of users, high SNR, and low SNR. Simulations are provided to illustrate the performance of various scalar feedback techniques based on the proposed design framework.