SUPPORT VECTOR MACHINE FOR DETERMINING EULER ANGLES IN AN INERTIAL NAVI-GATION SYSTEM

The paper discusses the improvement of the accuracy of an inertial navigation system created on the basis of MEMS sensors using machine learning (ML) methods. As input data for the classifier, we used information obtained from a developed laboratory setup with MEMS sensors on a sealed platform with the ability to adjust its tilt angles. To assess the effectiveness of the models, test curves were constructed with different values of the parameters of these models for each core in the case of a linear, polynomial radial basis function. The inverse regularization parameter was used as a parameter. The proposed algo-rithm based on MO has demonstrated its ability to correctly classify in the presence of noise typical for MEMS sensors, where good classification results were obtained when choosing the optimal values of hy-perparameters.

On multidimensional Jackson's theorem

We have found the best linear polynomial methods of approximation of continuous periodic functions of multiple variables in uniform metric with concave modulus of continuity.

Data Analysis and Forecasting of COVID-19 Pandemic in Kuwait

The first COVID 19 case of Kuwait was announced on 24th February, 2020 and the daily new cases increases exponentially since then until May, 2020 when the first wave started to decline. The same exponential dynamics has been observed between January and March, 2021. The forecast of new cases and death recorded daily is crucial so that health experts and citizens can be guided in order to avoid escalation of the pandemic. We propose a deterministic method to predict the basic reproduction number Ro of first and second wave of COVID-19 cases in Kuwait and also to forecast the daily new cases and death of the pandemic in the country. Forecasting has been done using ARIMA model, Exponential smoothing model, Holts method, Prophet forecasting model and machine learning models like log-linear, polynomial and support vector regressions. The results presented aligned with other methods used to predict Ro in first and second waves and the forecasting clearly shows the trend of the pandemic in Kuwait. The deterministic prediction of Ro is a good forecasting tool available during the exponential phase of the contagion, which shows an increasing trend during the beginning of the first and second waves of the pandemic in Kuwait.

Numerical Integration Implementation Using Trapezoidal Rule Method To Calculate Aproximation Area Of West Java Province

An area can be shaped into a regular shape or an irregular shape. There is an area of irregular shape which is restricted by an unknown function, to determine that area must use a numerical integration. One of numerical integration methods is Trapezoidal Rule by replacing (????) with an integral approach function which can be evaluated, then let the (????) approximated by a linear polynomial in the certain interval, denoted as closed interval . This study is going to calculate the area of West Java Province by using this method with several different number of partitions in each quadrant such as, 9 partitions, 11 partitions, and 36 partitions in for different quadrants. This study provides the final result of the approximate area which will be compared with the actual area based in the error of result. The main finding is the approximate total area will be closer to the actual area followed by the increasing number of partitions.

Lie polynomials in an algebra defined by a linearly twisted commutation relation

We present an elementary approach to characterizing Lie polynomials on the generators [Formula: see text] of an algebra with a defining relation in the form of a twisted commutation relation [Formula: see text]. Here, the twisting map [Formula: see text] is a linear polynomial with a slope parameter, which is not a root of unity. The class of algebras defined as such encompasses [Formula: see text]-deformed Heisenberg algebras, rotation algebras, and some types of [Formula: see text]-oscillator algebras, the deformation parameters of which, are not roots of unity. Thus, we have a general solution for the Lie polynomial characterization problem for these algebras.

Interpolating bit-vector formulas using uninterpreted predicates and Presburger arithmetic

The inference of program invariants over machine arithmetic, commonly called bit-vector arithmetic, is an important problem in verification. Techniques that have been successful for unbounded arithmetic, in particular Craig interpolation, have turned out to be difficult to generalise to machine arithmetic: existing bit-vector interpolation approaches are based either on eager translation from bit-vectors to unbounded arithmetic, resulting in complicated constraints that are hard to solve and interpolate, or on bit-blasting to propositional logic, in the process losing all arithmetic structure. We present a new approach to bit-vector interpolation, as well as bit-vector quantifier elimination (QE), that works by lazy translation of bit-vector constraints to unbounded arithmetic. Laziness enables us to fully utilise the information available during proof search (implied by decisions and propagation) in the encoding, and this way produce constraints that can be handled relatively easily by existing interpolation and QE procedures for Presburger arithmetic. The lazy encoding is complemented with a set of native proof rules for bit-vector equations and non-linear (polynomial) constraints, this way minimising the number of cases a solver has to consider. We also incorporate a method for handling concatenations and extractions of bit-vector efficiently.

Classify Epileptic EEG Signals Using Extreme Support Vector Machine for Ictal and Muscle Artifact Detection

EEG signals aids in diagnosing various wave signals recorded by the activities of the brain. It also produces unavoidable artifacts, in the recording process. The purpose of this study therefore is to detect ictal and artefact signals, with the aim of reducing interpretation errors especially those related to the muscle which are quite difficult to distinguish. The data used are EEG signal recording results obtained from Rumah Sakit Universitas Airlangga. It consisted of two classes, namely ictal and muscle artefact. The signal decomposition method used is a wavelet transform, known as DWT. While the extraction feature utilized, consist of quartile, maximum, minimum, mean and standard deviation. This study also utilized the SVM with linear, polynomial, RBF and ELM (ESVM) kernels. Research results shows that the ESVM classification time is faster than the SVM and other kernels. However, the values of accuracy, sensitivity, specificity and AUC are not better.

Determining the influence of transformation changes in the life cycle on the assessment of effectiveness of an ecologistic system project

The issue of assessing the effectiveness of a project of an ecologistic system using the criterion of «a discounted payback period», which takes into consideration the transformational changes in the project life cycle, was considered. The specific features of the life cycle of a project of an ecologistic system, in the structure of which it is proposed to include environmentally-oriented regeneration and revitalization phases, were explored. The phases of a life cycle were divided into stages, between which consecutive and parallel relations were established. The project life cycle consists of time intervals, during which from one to three stages of the project phases can run in parallel. A model of the life cycle of an ecologistic system, which shows the relations between time intervals and cash flows that correspond to the stages of the project life cycle phases, was developed. A mathematical formula for calculating the discounted payback period of a project, which takes into consideration the specific features of the formation of cash flows of separate phases of the life cycle of an ecologistic system, was proposed. The application of the formula is possible when assuming the constancy of cash flows of the stages of operational and the regeneration phases, which corresponds to the conditions of uncertainty of their forecasting at the beginning of the project. The functional dependences between the discounted payback period and cash flows during the phases of a project life cycle were studied. Depending on a phase of the life cycle, the dependence is expressed by a linear, polynomial, or power function. The identification of functional dependences makes it possible to study the dynamics of changes in the discounted payback period with changes in project cash flows, which can be used in forecasting the effectiveness of an ecologistic system project.

Analysis of the industrial quality of three sugarcane cultivars at “santa rosalía de la chontalpa” sugarcane mill

Objective: To evaluate the industrial quality of three sugarcane cultivars in a template cycle at the supply area of “Santa Rosalía de la Chontalpa” sugarcane mill. Design / Methodology / Approach: An experiment was established under a factorial design 3x3 (3 cultivars: CP 72-2086, MEX 79-431 and MEX 69-290; x 3 sampling dates: 330, 390 and 450 DDS, Spanish equivalent for days after sowing) on an Eutric Fluvisol soil. In each plantation, a sample of 10 stems with three replications was collected to determine the industrial quality by polarimetry. Results: The industrial quality of the evaluated cultivars only differed statistically in terms of the percentage of purity, MEX 79-431 was the one that presented the lowest value for this variable. At 450 DDS, the highest value was observed for °Brix (17.28), POL percentage (14.92), purity (86.44%). The values obtained in the present study for the quality of juice in the evaluated cultivars are within the range of the standard values established for Mexico. Limitations / Implications: Polarimetry is still the method used by most of the sugar mills in Mexico, even if other more environmental-friendly methodologies exist. Findings / Conclusions: The trend line that best fit to MEX 69-290 and MEX 79-431, for °Brix, POL and purity, was a linear polynomial and to CP 72-2086, a polynomial quadratic. Fresh stems and reducing sugars showed best fit with an inverse polynomial. °Brix presented strong and positive correlation with POL (R = 0.99**); and strong and negative with reducer sugars (R = -0.95**) and fresh stem humidity (R = -0.91**).

Models to estimate longitudinal compressive strength of Brazilian hardwood based on apparent density

As wood is an orthotropic and natural material, there are several properties required for its use in civil construction. The apparent density has been used to estimate physical and mechanical properties of wood, as it is easy to determine experimentally, unlike other determinations, which involve the use of equipment available only in large research centers. Using the Brazilian standard ABNT NBR 7190 and linear and non-linear regression models, this research aimed to evaluate their accuracy in estimating the compressive strength parallel to the fibers (fc0) as well as their characteristic value (fc0,k). This study considered 72 tropical wood species from native forests that were divided into the 4 strength classes of this standard. For the set formed by all species, the linear polynomial model was the best fit, resulting in a determination coefficient of just over 70%.