Different Number Of Parameters Research Articles

Water quality analysis of Sumurup River in Gunungkidul, Indonesia, using the water quality index

Sumurup River is one of the rivers in Gunungkidul, Indonesia, that continuously flows water throughout the year and traverses different land use types. Consequently, it receives numerous waste inputs. Given the limited water resources in the area, it is necessary to assess the water quality regularly. Moreover, this river was previously a drinking water supply, but it can now only be used for purposes with lower requirements, e.g., bathing and washing. The water quality was assessed using three scenarios with different number of parameters in the composite and two indices: the Indonesian Modification of Water Quality Index (INA-WQI) and the Weighted Arithmetic Water Quality Index (WA-WQI). Parameters observed were pH, DO, BOD, COD, NH3, NO3, total phosphate, TSS, TDS, and fecal coliform. Results showed that the safe limits of pH, BOD, COD, NH3, NO3, total phosphate, TSS, and fecal coliform for drinking water (Class I) were exceeded at several sampling points. In contrast, DO and TDS were below their maximum allowable concentrations at all points. WQI assessment produced varying index values categorized as fair to good (INA-WQI) or excellent to unsuitable for drinking (WA-WQI). Further, the different composite scenarios had a significant effect on WA-WQI but not on INA-WQI scores.

EFFICIENCY COMPARISON OF NETWORKS IN HANDWRITTEN LATIN CHARACTERS RECOGNITION WITH DIACRITICS

The aim of the article is to analyze and compare the performance and accuracy of architectures with a different number of parameters on the example of a set of handwritten Latin characters from the Polish Handwritten Characters Database (PHCD). It is a database of handwriting scans containing letters of the Latin alphabet as well as diacritics characteristic of the Polish language. Each class in the PHCD dataset contains 6,000 scans for each character. The research was carried out on six proposed architectures and compared with the architecture from the literature. Each of the models was trained for 50 epochs, and then the accuracy of prediction was measured on a separate test set. The experiment thus constructed was repeated 20 times for each model. Accuracy, number of parameters and number of floating-point operations performed by the network were compared. The research was conducted on subsets such as uppercase letters, lowercase letters, lowercase letters with diacritics, and a subset of all available characters. The relationship between the number of parameters and the accuracy of the model was indicated. Among the examined architectures, those that significantly improved the prediction accuracy at the expense of a larger network size were selected, and a network with a similar prediction accuracy as the base one, but with twice as many model parameters was selected.

Methodology for searching multi-criteria solutions based on digital twins

Aim. To justify and propose a methodology for searching multi-criteria Pareto-optimal solutions based on the use of socio-economic models or digital twins of complex dynamical systems.Tasks. To substantiate the relevance of using predictive methods for making managerial decisions in conditions of rapid changes and the need to use for these purposes recommendation systems using socio-economic models or digital twins. To propose a methodology for searching multi-criteria Pareto-optimal solutions and to evaluate its accuracy on mathematical and socioeconomic models. To formulate recommendations and limitations on the use of this methodology.Methods. General scientific methods (analysis, synthesis, grouping), mathematical methods of searching for Pareto-optimal solutions and generation of LP -sequences, results of mathematical modelling and numerical experiments were used in the research.Results. The methodology of searching for multicriteria Pareto-optimal managerial decisions is proposed, proposals and limitations on its application are formulated.Conclusions. In conditions of rapid changes it is important to be able to make managerial decisions that take into account the influence of many factors and, accordingly, many criteria characterising the socio-economic system, and at the same time it is necessary to make these managerial decisions in accordance with the logic “from the future”. The method of searching for multi-criteria Pareto-optimal solutions based on the use of socio-economic models or digital twins is proposed. The accuracy of this method is investigated on mathematical models of different degrees of nonlinearity with different number of parameters when solving production multi-parametric problems.

Dependence analysis for calculating the theoretical stress concentration factor in a butt welded joint

Determination of the theoretical stress concentration factor in a butt welded joint by different methods is considered. The results obtained on the basis of analytical dependencies and the finite element method are compared with varying the geometric parameters of the joint. The most accurate dependencies are established for determining the theoretical stress concentration factor for a different number of parameters in a given range of their values. Keywords: butt welded joint, theoretical stress concentration factor, analytical dependencies, finite element method. manshula@mail.ru, a.shlepetinskiy@reph.ru

Determination of Significant Parameters on the Basis of Methods of Mathematical Statistics, and Boolean and Fuzzy Logic

Among the set of parameters for which data are collected for decision-making based on artificial intelligence methods, often only some of the parameters are significant. This article compares methods for determining the significant parameters based on the theory of mathematical statistics, and fuzzy and boolean logic. The testing was conducted on several test data sets with a different number of parameters and different variability of parameter values. It was shown that for data sets with a small number of parameters (<5), the most accurate result was given for a method based on the theory of mathematical statistics and boolean logic. For a data set with a large number of parameters—the most suitable is the method of fuzzy logic.

Evaluation of Parameter Selection in the Bivariate Statistical-based Landslide Susceptibility Modeling (Case Study: the Citarik Sub-watershed, Indonesia)

A landslide susceptibility mapping is essential for landslide hazard mitigation to reduce the associated risk. This paper aims to present the results of the landslide susceptibility modeling in the Citarik sub-watershed using three bivariate statistical-based methods, i.e., frequency ratio (FR), information value (IV), and weight of evidence (WoE). The main objective of this study is to evaluate the significance of the threshold of the area under curve (AUC) value in parameter selection. In this study, 118 landslide pixels were compiled from Google Earth images, unmanned aircraft vehicle (UAV) aerial photos taken just after the landslide, official landslide reports, and field observation. Thirteen landslide causative factors were prepared in Geographic Information System (GIS) environment, derived from various satellite images and maps. The landslide data were divided into two groups, 70% of data as training data and the rest as test data. Two scenarios that involve a different number of parameters were compared to explain the threshold of the AUC value in parameter selection and model accuracy. The result of this study shows that the AUC value threshold of 0.6 for parameter selection cannot be applied in all cases, and the performance of both two scenarios was excellent in assessing landslide susceptibility in this study area. Those three landslide susceptibility zonation maps of the best scenario showed that the sub-watershed's northern, northeastern, south-eastern, and southern parts were under high to very high susceptibility to landslides, including the Cimanggung area where a recent deadly double landslide occurred.

ОVERVIEW OF STATISTICAL METHODS FOR FORECAST DEVELOPMENT

Having a general idea of the nature of the forecast and understanding the general methodology of forecasting in general, it is advisable for analytical departments, economic departments, management units of enterprises, banks, firms (of any socio-economic objects) to develop at least short-term forecasts based on indicators of their activity in modern conditions coopetitions to understand trends in changes in these indicators. As a result of the conducted research, the basic principles that must be observed when developing forecasts are revealed, as well as a detailed description and features of statistical auto-projective forecasting methods are given: random walk models containing a free term or it; models characterizing a deterministic trend with random fluctuations around the trend; moving average models; exponential smoothing models using simple exponential smoothing, linear, quadratic and seasonal smoothing (Brown, Holt, and Winters models); integrated presentation of autoregressive models and moving average models (parametric ARIMA models). In addition, the conducted research confirmed that the significance of the developed forecast and the level of confidence in the obtained future values of the indicators depends on the quality of the developed models. The criteria for checking the quality of the developed forecasts are: the Akaike information criterion, which evaluates the quality of the model compared to each other; the Hannan-Quinn Criterion information criterion, which is used to compare models with a different number of parameters and is an alternative to the Akaike information criterion; the Schwarz-Bayesian information Criterion, which compares the quality of a model relative to each other using a likelihood function; the mean squared error value, the absolute value of the mean squared error and the absolute value of the mean squared error expressed as a percentage.

On $$\mathrm {G}_2$$ and sub-Riemannian model spaces of step and rank three

We give the complete classification of all sub-Riemannian model spaces with both step and rank three. Model spaces in this context refer to spaces where any infinitesimal isometry between horizontal tangent spaces can be integrated to a full isometry. They will be divided into three families based on their nilpotentization. Each family will depend on a different number of parameters, making the result crucially different from the known case of step two model spaces. In particular, there are no nontrivial sub-Riemannian model spaces of step and rank three with free nilpotentization. We also realize both the compact real form $${\mathfrak {g}}_2^c$$ and the split real form $${\mathfrak {g}}_2^s$$ of the exceptional Lie algebra $${\mathfrak {g}}_2$$ as isometry algebras of different model spaces.

Heterogeneous Scheduling of Deep Neural Networks for Low-power Real-time Designs

Deep neural networks have become the readiest answer to a range of application challenges including image recognition, stock analysis, natural language processing, and biomedical applications such as seizure detection. All while outperforming prior leading solutions that relied heavily on hand-engineered techniques. However, deployment of these neural networks often requires high-computational and memory-intensive solutions. These requirements make it challenging to deploy Deep Neural Networks (DNNs) in embedded, real-time low-power applications where classic architectures, GPUs and CPUs, still impose significant power burden. Systems-on-Chip (SoC) with Field-programmable Gate Arrays (FPGAs) can be used to improve performance and allow more fine-grain control of resources than CPUs or GPUs, but it is difficult to find the optimal balance between hardware and software to improve DNN efficiency. In the current research literature there have been few proposed solutions to address optimizing hardware and software deployments of DNNs in embedded low-power systems. To address the computation resource restriction and low-power needs for deploying these networks, we describe and implement a domain-specific metric model for optimizing task deployment on differing platforms, hardware and software. Next, we propose a DNN hardware accelerator called Scalable Low-power Accelerator for real-time deep neural Networks (SCALENet) that includes multithreaded software workers. Finally, we propose a heterogeneous aware scheduler that uses the DNN-specific metric models and the SCALENet accelerator to allocate a task to a resource based on solving a numerical cost for a series of domain objectives. To demonstrate the applicability of our contribution, we deploy nine modern deep network architectures, each containing a different number of parameters within the context of two different neural network applications: image processing and biomedical seizure detection. Utilizing the metric modeling techniques integrated into the heterogeneous aware scheduler and the SCALENet accelerator, we demonstrate the ability to meet computational requirements, adapt to multiple architectures, and lower power by providing an optimized task to resource allocation. Our heterogeneous aware scheduler improves power saving by decreasing power consumption by 10% of the total system power, does not affect the accuracy of the networks, and still meets the real-time deadlines. We demonstrate the ability to achieve parity with or exceed the energy efficiency of NVIDIA GPUs when evaluated against Jetson TK1 with embedded GPU SoC and with a 4× power savings in a power envelope of 2.0W. When compared to existing FPGA-based accelerators, SCALENet’s accelerator and heterogeneous aware scheduler achieves a 4× improvement in energy efficiency.

Binary multi-verse optimization algorithm for global optimization and discrete problems

Multi-verse optimizer is one of the recently proposed nature-inspired algorithms that has proven its efficiency in solving challenging optimization problems. The original version of Multi-verse optimizer is able to solve problems with continuous variables. This paper proposes a binary version of this algorithm to solve problems with discrete variables such as feature selection. The proposed Binary Multi-verse optimizer is equipped with a V-shaped transfer function to covert continuous values to binary, and update the solutions over the course of optimization. A comparative study is conducted to compare Binary Multi-verse optimizer with other binary optimization algorithms such as Binary Bat Algorithm, Binary Particle Swarm Optimization, Binary Dragon Algorithm, and Binary Grey Wolf Optimizer. As case studies, a set of 13 benchmark functions including unimodal and multimodal is employed. In addition, the number of variables of these test functions are changed (5, 10, and 20) to test the proposed algorithm on problems with different number of parameters. The quantitative results show that the proposed algorithm significantly outperforms others on the majority of benchmark functions. Convergence curves qualitatively show that for some functions, proposed algorithm finds the best result at early iterations. To demonstrate the applicability of proposed algorithm, the paper considers solving feature selection and knapsack problems as challenging real-world problems in data mining. Experimental results using seven datasets for feature selection problem show that proposed algorithm tends to provide better accuracy and requires less number of features compared to other algorithms on most of the datasets. For knapsack problem 17 benchmark datasets were used, and the results show that the proposed algorithm achieved higher profit and lower error compared to other algorithms.

Comparative analysis of three methods of risk assessment for repetitive movements of the upper limbs: OCRA index, ACGIH(TLV), and strain index

PurposeLiterature proposes different methods for risk assessment due to repeated movements of upper limbs. The results are sometimes different even if methods are applied in the same work conditions. This study, based only on hand/wrist work involving ulnar deviation, compares three methods among those most used for distal upper extremity (DUE) work-related musculoskeletal disorders (WMSDs): OCRA Index (O.I.), Strain Index (S.I.) and ACGIH(TLV). Design/methodology/approachThe work analyzes the algorithms of the methods in terms of number of parameters considered in the formulas, range of variability for each one, and their interaction in determining the final results. Starting to calculate maximum frequency that each method allows in no-risk conditions (varying force and duty cycle), specific scenarios were also created varying one parameter at a time among those determining risk (i.e. force, posture, recovery time …) for a better comprehension of the different algorithms proposed and the provided final results. FindingsThe results are generally different: in the 21 scenarios analyzed, all the methods indicate the same level of risk in only 6 cases when using the formula for the calculation of the hand activity level (HAL) in ACGIH(TLV), and in only 3 cases when using the table. Force bears a higher relative weight for ACGIH(TLV) compared to the others methods: the number of exertions per second allowed in the no-risk zone decreases markedly as the applied force increases. The S.I. was noted to generally be the most precautionary among those examined regarding the frequencies of movements allowed in the no-risk range. The use of categorical multiplier instead of linear, made the results mainly different in the intermediate risk conditions. Originality/valueThe differences in results are due not only to the different number of parameters considered by methods but also to the different risk limit values inside each algorithm. A greater amount of epidemiological data would be desirable in order to better define the conditions to be considered harmful for a worker performing repeated movements of the upper limbs.

Defining appropriate reaction scheme for hydrotreating of vegetable oil through proper calculation of kinetic parameters

Kinetic models reported in the literature were used to derive a new general kinetic model for the hydrotreating of vegetable oils. The proposed model includes ten reaction rate coefficients, three more than the literature models. Experimental data obtained at 360 °C with a CoMo/Al2O3 catalyst and at 420 °C with a NiW/SiO2-Al2O3 catalyst were used for model validations. A series of statistical tools and sensitivity analysis were applied to find the best set of kinetic parameters that assured the convergence to the global minimum of the objective function. To carry out a fair comparison of kinetic models due to the different number of parameters, the Akaike and Bayesian criteria were used. It was found that some reaction pathways that were omitted by the literature models have significant reaction rates, so that the proposed model exhibits better performance.

Comparison of viscoelastic models with a different number of parameters for transient simulations

AbstractThe numerical and analytical models used for transient simulations, and hence for the pressurized pipe system diagnosis, require the definition of a rheological component related to the pipe material. The introduction and the following widespread use of polymeric material pipes, characterized by a viscoelastic behavior, increased the complexity and the number of parameters involved in this component with respect to metallic materials. Furthermore, since tests on specimens are not reliable, a calibration procedure based on transient test is required to estimate the viscoelastic parameters. In this paper, the trade-off between viscoelastic component accuracy and simplicity is explored, based on the Akaike criterion. Several aspects of the calibration procedure are also examined, such as the use of a frequency domain numerical model and of different standard optimization algorithms. The procedure is tested on synthetic data and then it is applied to experimental data, acquired during transients on a high density polyethylene pipe. The results show that the best model among those used for the considered system implements the series of a spring with three Kelvin–Voigt elements.

Correlation measure of hesitant fuzzy soft sets and their application in decision making

Hesitant fuzzy soft set (HFSS) allows each element to have different number of parameters and the values of those parameters are represented by multiple possible membership values. HFSS is considered as a powerful tool to represent uncertain information in group decision-making process. In this study, we introduce the concept of correlation coefficient for HFSS and some of its properties. Using correlation coefficient of HFSS, we develop correlation efficiency which shows the significance of the HFSS. We also propose an algorithm to apply correlation coefficient in decision-making problem, where information is presented in hesitant fuzzy environment. In order to extend the application of HFSS, we propose correlation coefficient in the framework of interval-valued hesitant fuzzy soft set (IVHFSS). We also introduce correlation efficiency in the context of IVHFSS. Then the proposed algorithm is extended using IVHFSS for solving decision-making problems. Finally, two examples that are semantically meaningful in real life are illustrated to show the effectiveness of the proposed algorithms.

INFLUENCE OF SPATIAL INTERACTIONS OF INCLUSIONS ON THE EFFECTIVE ELASTIC TENSOR OF CRACKED POROUS MEDIUM

The determination of effective stiffness tensor of microinhomogeneous and, in general, macroscopically homogeneous composite medium is related to so-called problem of many-body interaction. Solution to the problem can be found only as an approximation. In this paper we consider a solution to such a problem for porous-cracked medium that is a terrigenous rock having anisotropic elastic properties. The elastic anisotropy is a result of many factors including anisotropic properties of clay minerals and preferential orientation of non-isometric heterogeneities. Different Effective Medium Theories for calculating effective stiffness tensor of cracked porous medium use so called Effective Field Hypothesis (H1, H2 and H3). For example, T-matrix method, Mori-Tanaka method, General Singular Approximation method, and Effective Field Methods use the Effective Field Hypothesis. Thus, different methods produce similar results. When constructing models of rock’s effective properties the rock is treated a composite “made by nature”. In this case of importance is a proper approximation of the real medium by a parametric model medium that reflects specific features of rock’s microstructure. The microstructure is a result of rock evolution. Therefore, the model of the medium and the model parameters play very important roles in the modelling. To prove this statement, two models of a cracked- porous medium’s properties were created using two different methods: the T-matrix method and General Singular Approximation Method. The methods were applied for two different parametric models of one and the same rock. The models were build based on visual analysis of rock’s thin sections. Each of the constructed models has different number of parameters. The parameters are also different. However, a common feature of the two models is that for rocks of this type it is necessary to take into account a rigidity of contact between mineral grains and organic material. Besides, a connectivity of different heterogeneities should be also parametrized. For each model a set of parameters was found and a porosity interval where the models produce similar results in terms of elastic wave velocities is determined.

A nonlinear term selection method for improving synchronous machine parameters estimation

Reliable synchronous machine modeling is key to accurate power system planning, operation and post-event analysis, especially in the emerging smart grids. In the literature, various models of a synchronous machine with different number of parameters have been used while little attention has been paid to the significance of each parameter in an originally nonlinear model. In this paper, first, a shrinkage and term selection method is extended to the identification of nonlinear systems. Then, the extended method is applied to the synchronous machine identification problem in order to determine which parameters have more substantial impacts on the machine response, i.e., the model parameters are partitioned into well- and ill-conditioned sets. It is shown that the ill-conditioned parameters can be set to typical values to allow for significant improvements in the identifiability and speed of convergence of the estimated parameters without loosing the capability to characterize the system. As a result, the parameter estimation is done for a reduced-order optimization problem, which leads to a more reliable estimation with lower variances and faster convergence, especially in on-line measurements. The performance and effectiveness of the proposed nonlinear term selection method is demonstrated using numerical simulations and compared to the results of two existing approaches.

Training radial basis function networks using biogeography-based optimizer

Training artificial neural networks is considered as one of the most challenging machine learning problems. This is mainly due to the presence of a large number of solutions and changes in the search space for different datasets. Conventional training techniques mostly suffer from local optima stagnation and degraded convergence, which make them impractical for datasets with many features. The literature shows that stochastic population-based optimization techniques suit this problem better and are reliably alternative because of high local optima avoidance and flexibility. For the first time, this work proposes a new learning mechanism for radial basis function networks based on biogeography-based optimizer as one of the most well-regarded optimizers in the literature. To prove the efficacy of the proposed methodology, it is employed to solve 12 well-known datasets and compared to 11 current training algorithms including gradient-based and stochastic approaches. The paper considers changing the number of neurons and investigating the performance of algorithms on radial basis function networks with different number of parameters as well. A statistical test is also conducted to judge about the significance of the results. The results show that the biogeography-based optimizer trainer is able to substantially outperform the current training algorithms on all datasets in terms of classification accuracy, speed of convergence, and entrapment in local optima. In addition, the comparison of trainers on radial basis function networks with different neurons size reveal that the biogeography-based optimizer trainer is able to train radial basis function networks with different number of structural parameters effectively.

Optimal wavelengths obtained from laws analogous to the Wien’s law for monospectral and bispectral methods, and general methodology for multispectral temperature measurements taking into account global transfer function including non-uniform emissivity of surfaces

In this paper, it is shown how to select the optimal wavelengths minimizing the relative error and the standard deviation of the temperature. Furthermore, it is shown that the optimal wavelengths in mono-spectral and bi-spectral methods (for a Planck’s law) can be determined by laws analogous to the displacement Wien’s law. The simplicity of these laws can thus allow real-time selection of optimal wavelengths for a control/optimization of industrial processes, for example. A more general methodology to obtain the optimal wavelengths selection in a multi-spectral method (taking into account the spectral variations of the global transfer function including the emissivity variations) for temperature measurement of surfaces exhibiting non-uniform emissivity, is also presented. This latter can then find an interest in glass furnaces temperature measurement with spatiotemporal non-uniformities of emissivity, the control of biomass pyrolysis, the surface temperature measurement of buildings or heating devices, for example. The goal consists of minimizing the standard deviation of the estimated temperature (optimal design experiment). For the multi-spectral method, two cases will be treated: optimal global and optimal constrained wavelengths selection (to the spectral range of the detector, for example). The estimated temperature results obtained by different models and for different number of parameters and wavelengths are compared. These different points are treated from theoretical, numerical and experimental points of view.

Modeling the exchange rate of the euro against the dollar using the ARCH/GARCH models

The analysis of time series with conditional heteroskedasticity (changeable time variability, conditional variance instability, the phenomenon called volatility) is the main task of ARCH and GARCH models. The aim of these models is to calculate some of the volatility indicators needed for financial decisions. This paper examines the performance of generalized autoregressive conditional heteroscedasticity (GARCH) model in modeling the daily changes of the log exchange rate of the euro against the dollar. Several GARCH models have been applied for modeling the daily log exchange rate returns of the euro, with a different number of parameters. The characteristic of estimated GARCH models is that the obtained coefficients of lagged squared residuals and the conditional variance parameters in the equation of conditional variance have a strong statistical significance. The sum of these two coefficients' estimates is close to a unit, which is typical for GARCH models that are applied on the data of financial assets returns. This means that the shocks in the conditional variance equation will be long lasting. The great value of the sum of these two coefficients shows that the high rates of positive or negative returns leads to a large forecasted value of the variance in the prolonged period. The asymmetrical EGARCH (1,1) model has showed the best results in modeling the euro exchange rate returns. The asymmetry term in the conditional variance equation of this model is negative and statistically significant. A negative value of this term suggests that the positive shock has less impact on the conditional variance than the negative shocks. The asymmetric EGARCH (1,1) model provides evidence of a leverage effect.

Biosorption of Pb(II), Cu(II), and Ni(II) ions onto novel lowcost P. eldarica leaves-based biosorbent: isotherm, kinetics, and operational parameters investigation

In this study, the novel low-cost Pinus eldarica leaves-based biosorbent (Pe) was introduced to remove heavy metal ions from aqueous media. The Pe ability of Pb(II), Cu(II), and Ni(II) ions biosorption was investigated in aqueous solutions. The maximum sorption capacity was 40, 26, and 33 mg g−1 for Pb(II), Cu(II), and Ni(II), respectively, that was so promising. Experiments were carried out in lab-scale batch reactors as function of initial pH (pH0), initial concentration of heavy metal ions (C0), and contact time (t). The statistical analysis was applied to determine the significant parameters, rank of parameter influences, and type of influences. The adsorption kinetics data were modeled using the pseudo-first, pseudo-second-order, and intraparticle diffusion kinetics equations. The pseudo-first-order and pseudo-second-order kinetics equations described the adsorption kinetics very well. Finally, the equilibrium data were described by 15 isotherm models with different number of parameters from two to five. The results revealed that the Langmuir and Fritz–Schlunder models provided the best correlation of the experimental data.