Desarrollo e investigación de modelos de puntuación de colecciones basados en la plataforma analítica Deductor

Este artículo resuelve el problema de la construcción e investigación de modelos de puntuación de colecciones. Se destaca la relevancia de resolver este problema sobre la base de las tecnologías de modelado inteligente: árboles de decisión, regresión logística y redes neuronales. Los datos iniciales de los modelos fueron un conjunto de 14 columnas y 5779 filas. La construcción de los modelos se realizó en plataforma Deductor. Cada modelo fue probado en el conjunto de 462 registros. Para todos los modelos se construyó la correspondiente matriz de clasificación y se calcularon los errores de 1º y 2º tipo, así como el error general de los modelos. En términos de minimizar estos errores, la regresión logística mostró los peores resultados y la red neuronal mostró los mejores. Además, se evaluó la efectividad de los modelos construidos según criterios de «ingresos» y «tiempo». Por el tiempo que cuesta el modelo de regresión logística supera a otros modelos. Sin embargo, en términos de ingresos, el modelo de red neuronal fue el mejor. Así, los resultados mostraron que para minimizar el tiempo dedicado al trabajo con los deudores es recomendable utilizar un modelo logístico. Sin embargo, para maximizar las ganancias y minimizar los errores de clasificación, es apropiado utilizar un modelo de red neuronal. Esto indica su eficacia y posibilidad de uso práctico en sistemas de puntuación inteligentes

Desarrollo e investigación de modelos de puntuación de colecciones basados en la plataforma analítica Deductor

This article solves the problem of collection scoring models constructing and researching. The relevance of solving this problem on the intelligent modeling technologies basis: decision trees, logistic regression and neural networks is noted. The initial data for the models was a set of 14 columns and 5779 rows. The models construction was performed in Deductor platform. Each model was tested on the set of 462 records. For all models, the corresponding classification matrix were constructed and the1st and 2nd kind errors were calculated, as well as the general error of the models. In terms of minimizing these errors, logistic regression showed the worst results, and the neural network showed the best. In addition, the constructed models effectiveness was evaluated according to «income» and «time» criteria. By the time costs the logistic regression model exceeds other models. However, in terms of income the neural network model was the best. Thus, the results showed that in order to minimize the time spent on work with debtors it is advisable to use a logistic model. However, to maximize profits and minimize classification errors, it is appropriate to use a neural network model. This indicates its effectiveness and practical use possibility in intelligent scoring systems.Este artículo resuelve el problema de la construcción e investigación de modelos de puntuación de colecciones. Se destaca la relevancia de resolver este problema sobre la base de las tecnologías de modelado inteligente: árboles de decisión, regresión logística y redes neuronales. Los datos iniciales de los modelos fueron un conjunto de 14 columnas y 5779 filas. La construcción de los modelos se realizó en plataforma Deductor. Cada modelo fue probado en el conjunto de 462 registros. Para todos los modelos se construyó la correspondiente matriz de clasificación y se calcularon los errores de 1º y 2º tipo, así como el error general de los modelos. En términos de minimizar estos errores, la regresión logística mostró los peores resultados y la red neuronal mostró los mejores. Además, se evaluó la efectividad de los modelos construidos según criterios de «ingresos» y «tiempo». Por el tiempo que cuesta el modelo de regresión logística supera a otros modelos. Sin embargo, en términos de ingresos, el modelo de red neuronal fue el mejor. Así, los resultados mostraron que para minimizar el tiempo dedicado al trabajo con los deudores es recomendable utilizar un modelo logístico. Sin embargo, para maximizar las ganancias y minimizar los errores de clasificación, es apropiado utilizar un modelo de red neuronal. Esto indica su eficacia y posibilidad de uso práctico en sistemas de puntuación inteligentes

The development of modern automated image processing and transfer systems for agriculture unmanned aerial vehicles

The paper contains results of analytic research of unmanned aerial vehicles using in agriculture. The main problems arising in the creation and subsequent large volumes of high-resolution images real time transfer in unmanned aerial vehicles are highlighted. The Automated image processing and transfer system using new methods of information compression on unmanned aerial vehicles board is proposed. The paper considers the issues of consider the problems of constructing new orderings of Walsh functions and constructing fast compression algorithms in synthesized systems of discrete Walsh functions. For processing and subsequent transmission of information from UAVs recommended to use the fast DWT procedure, it allows for a hardware implementation capable of the real-time conversion performing due to its simplicity. The introduction of the proposed solutions for UAVs in agriculture allows to increase accurasy of electronic cartographic material, to keep electronic records of agricultural operations, to carry out operational monitoring of the crops state and to respond quickly for violations and deviations, to predict crop yields and plan their activities for short-term and long-term prospects

Hydrogen Production through Autothermal Reforming of Ethanol: Enhancement of Ni Catalyst Performance via Promotion

Autothermal reforming of bioethanol (ATR of C2H5OH) over promoted Ni/Ce0.8La0.2O1.9 catalysts was studied to develop carbon-neutral technologies for hydrogen production. The regulation of the functional properties of the catalysts was attained by adjusting their nanostructure and reducibility by introducing various types and content of M promoters (M = Pt, Pd, Rh, Re; molar ratio M/Ni = 0.003–0.012). The composition–characteristics–activity correlation was determined using catalyst testing in ATR of C2H5OH, thermal analysis, N2 adsorption, X-ray diffraction, transmission electron microscopy, and EDX analysis. It was shown that the type and content of the promoter, as well as the preparation mode (combined or sequential impregnation methods), determine the redox properties of catalysts and influence the textural and structural characteristics of the samples. The reducibility of catalysts improves in the following sequence of promoters: Re < Rh < Pd < Pt, with an increase in their content, and when using the co-impregnation method. It was found that in ATR of C2H5OH over bimetallic Ni-M/Ce0.8La0.2O1.9 catalysts at 600 °C, the hydrogen yield increased in the following row of promoters: Pt < Rh < Pd < Re at 100% conversion of ethanol. The introduction of M leads to the formation of a NiM alloy under reaction conditions and affects the resistance of the catalyst to oxidation, sintering, and coking. It was found that for enhancing Ni catalyst performance in H2 production through ATR of C2H5OH, the most effective promotion is with Re: at 600 °C over the optimum 10Ni-0.4Re/Ce0.8La0.2O1.9 catalyst the highest hydrogen yield 65% was observed

Desarrollo e investigación de modelos de puntuación de colecciones basados en la plataforma analítica Deductor

This article solves the problem of collection scoring models constructing and researching. The relevance of solving this problem on the intelligent modeling technologies basis: decision trees, logistic regression and neural networks is noted. The initial data for the models was a set of 14 columns and 5779 rows. The models construction was performed in Deductor platform. Each model was tested on the set of 462 records. For all models, the corresponding classification matrix were constructed and the1st and 2nd kind errors were calculated, as well as the general error of the models. In terms of minimizing these errors, logistic regression showed the worst results, and the neural network showed the best. In addition, the constructed models effectiveness was evaluated according to «income» and «time» criteria. By the time costs the logistic regression model exceeds other models. However, in terms of income the neural network model was the best. Thus, the results showed that in order to minimize the time spent on work with debtors it is advisable to use a logistic model. However, to maximize profits and minimize classification errors, it is appropriate to use a neural network model. This indicates its effectiveness and practical use possibility in intelligent scoring systems.Este artículo resuelve el problema de la construcción e investigación de modelos de puntuación de colecciones. Se destaca la relevancia de resolver este problema sobre la base de las tecnologías de modelado inteligente: árboles de decisión, regresión logística y redes neuronales. Los datos iniciales de los modelos fueron un conjunto de 14 columnas y 5779 filas. La construcción de los modelos se realizó en plataforma Deductor. Cada modelo fue probado en el conjunto de 462 registros. Para todos los modelos se construyó la correspondiente matriz de clasificación y se calcularon los errores de 1º y 2º tipo, así como el error general de los modelos. En términos de minimizar estos errores, la regresión logística mostró los peores resultados y la red neuronal mostró los mejores. Además, se evaluó la efectividad de los modelos construidos según criterios de «ingresos» y «tiempo». Por el tiempo que cuesta el modelo de regresión logística supera a otros modelos. Sin embargo, en términos de ingresos, el modelo de red neuronal fue el mejor. Así, los resultados mostraron que para minimizar el tiempo dedicado al trabajo con los deudores es recomendable utilizar un modelo logístico. Sin embargo, para maximizar las ganancias y minimizar los errores de clasificación, es apropiado utilizar un modelo de red neuronal. Esto indica su eficacia y posibilidad de uso práctico en sistemas de puntuación inteligentes

Determinación de la predisposición a la diabetes mellitus basado en red neuronal Fuzzy

This article solves the problem of determining the occurrence of diabetes mellitus in pregnant women. The aim of the study is to find an effective method based on the machine learning method, which will simplify the procedure and increase the speed of determining this disease with a high degree of accuracy. The currently existing methods for determining diabetes mellitus cannot allow constantly monitoring the presence of diabetes mellitus in pregnant women due to the complexity of the procedure, as well as the long time it takes to process the analysis results. Therefore, the use of modern technologies, in particular machine learning methods, will get rid of these disadvantages and allow to continuously monitor the possible occurrence of the disease. In this article, the subject area is analyzed and the relevance of the research topic is considered, the initial data preparation and collection for a neuro-fuzzy model constructing are carried out. In addition, intelligent models based on various machine learning methods have been constructed. The best method based on a fuzzy neural network has been chosen, which allows to classify the available data with a high degree of accuracy. A use-case diagram for solving practical problems of determining the presence of diabetes mellitus has been developed.Este artículo resuelve el problema de determinar la aparición de diabetes mellitus en mujeres embarazadas. El objetivo del estudio es encontrar un método eficaz basado en el método de aprendizaje automático, que simplificará el procedimiento y aumentará la velocidad de determinación de esta enfermedad con un alto grado de precisión. Los métodos existentes actualmente para la determinación de diabetes mellitus no pueden permitir monitorear constantemente la presencia de diabetes mellitus en mujeres embarazadas debido a la complejidad del procedimiento, así como al largo tiempo que lleva procesar los resultados de los análisis. Por lo tanto, el uso de tecnologías modernas, en particular métodos de aprendizaje automático, eliminará estas desventajas y permitirá monitorear continuamente la posible aparición de la enfermedad. En este artículo se analiza el área temática y se considera la relevancia del tema de investigación, se realiza la preparación y recolección de datos iniciales para la construcción de un modelo neuro-borroso. Además, se han construido modelos inteligentes basados ​​en varios métodos de aprendizaje automático. Se ha elegido el mejor método basado en una red neuronal difusa, que permite clasificar los datos disponibles con un alto grado de precisión. Se ha desarrollado un diagrama de casos de uso para resolver problemas prácticos de determinación de la presencia de diabetes mellitus

Hydrogen Production through Bi-Reforming of Methane: Improving Ni Catalyst Performance via an Exsolution Approach

Hydrogen production through the bi-reforming of methane over exsolution-derived Ni catalysts has been studied. Nickel-based catalysts were prepared through the activation of (CeM)1−xNixOy (M = Al, La, Mg) solid solutions in a reducing gaseous medium. Their performance and resistance to coking under the reaction conditions were controlled by regulating their textural, structural, morphological, and redox properties through adjustments to the composition of the oxide matrix (M/Ce = 0–4; x = 0.2–0.8; y = 1.0–2.0). The role of the M-dopant type in the genesis and properties of the catalysts was established. The efficiency of the catalysts in the bi-reforming of methane increased in the following series of M: M-free 1−xNixOy catalysts. At 800 °C the optimum Ce0.6Mg0.2Ni0.2O1.6 catalyst provided a stable H2 yield of 90% at a high level of CO2 and CH4 conversions (>85%)

Hydrogen Production through Bi-Reforming of Methane: Improving Ni Catalyst Performance via an Exsolution Approach

Hydrogen production through the bi-reforming of methane over exsolution-derived Ni catalysts has been studied. Nickel-based catalysts were prepared through the activation of (CeM)1−xNixOy (M = Al, La, Mg) solid solutions in a reducing gaseous medium. Their performance and resistance to coking under the reaction conditions were controlled by regulating their textural, structural, morphological, and redox properties through adjustments to the composition of the oxide matrix (M/Ce = 0–4; x = 0.2–0.8; y = 1.0–2.0). The role of the M-dopant type in the genesis and properties of the catalysts was established. The efficiency of the catalysts in the bi-reforming of methane increased in the following series of M: M-free < La < Al < Mg, correlating with the structural behavior of the nickel active component and the anti-coking properties of the support matrix. The preferred M-type and M/Ce ratio determined the best performance of (CeM)1−xNixOy catalysts. At 800 °C the optimum Ce0.6Mg0.2Ni0.2O1.6 catalyst provided a stable H2 yield of 90% at a high level of CO2 and CH4 conversions (>85%)

Nanoscale control during synthesis of Me/La2O3, Me/CexGd1-xOy and Me/CexZr 1-xOy (Me = Ni, Pt, Pd, Rh) catalysts for autothermal reforming of methane

Supported catalysts Me/La2O3, Me/CexGd1−xOy and Me/CexZr1−xOy (Me = Ni, Pt, Pd, Rh) were developed for the autothermal reforming of methane (ATR of CH4). The influence of support composition (La2O3, CexGd1−xOy, x = 0.50–0.90 and CexZr1−xOy, x = 0.33–0.67), type and content of the active component (5–30 wt% Ni; 0.5–1.5 wt% Pt, Pd or Rh) on the nanostructure of catalysts and their performance in the ATR of CH4 was investigated. The properties and structure of the catalysts in the course of their preparation and operation in the reaction were systematically characterized by means of X-ray diffraction, BET N2 adsorption/desorption, H2 temperature-programmed reduction, transmission electron microscopy and X-ray photoelectron spectroscopy techniques. The state and particle size of Ni-containing species were regulated by the support composition and Ni content. In case of the La2O3 support, the strong interaction between NiO and La2O3 led to the formation of two binary oxides LaNiO3 and La2NiO4 in the fresh samples, the composition of which was regulated by the Ni content. In case of the CexGd1−xOy and CexZr1−xOy supports, in contrast to the La2O3 support, nickel oxide and ceria-based solid solution were formed in the fresh samples. The catalyst evolution under reaction condition was studied. The conversion of methane and product (H2, CO) yields considerably increased when Ce0.8Gd0.2Oy or Ce0.5Zr0.5Oy instead of La2O3 were used as catalyst supports: at 850 °C the yields of ∼35% H2 and ∼41% CO at CH4 conversion ∼76% were observed for the 10 wt%Ni/La2O3, while the yields of ∼49% H2 and ∼66% CO at CH4 conversion ∼97% were observed for the 10 wt% Ni/Ce0.5Zr0.5O2, which correlates with the increase of reducibility of Ni species as a result of weakening of the Ni–support interaction. The optimal value of metal content for the catalyst performance also depends on the support composition. The best ATR of CH4 performance is provided by 10 wt% Ni/Ce0.5Zr0.5O2 and 1 wt% Rh/Ce0.8Gd0.2O2 catalysts.The presented research has received funding from the European Union 7th Framework Programme (FP7/2007–2013) under grant agreement No. 262840.Peer Reviewe