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Inteligencia Artificial - GIA

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SEMGROMI—a semantic grouping algorithm to identifying microservices using semantic similarity of user stories

Vera-Rivera, F. H. | 2023-05-12

Microservices is an architectural style for service-oriented distributed computing, and is being widely adopted in several domains, including autonomous vehicles, sensor networks, IoT systems, energy systems, telecommunications networks and telemedicine systems. When migrating a monolithic system to a microservices architecture, one of the key design problems is the “microservice granularity definition”, i.e., deciding how many microservices are needed and allocating computations among them. This article describes a semantic grouping algorithm (SEMGROMI), a technique that takes user stories, a well-known functional requirements specification technique, and identifies number and scope of candidate microservices using semantic similarity of the user stories’ textual description, while optimizing for low coupling, high cohesion, and high semantic similarity. Using the technique in four validation projects (two state-of-the-art projects and two industry projects), the proposed technique was compared with domain-driven design (DDD), the most frequent method used to identify microservices, and with a genetic algorithm previously proposed as part of the Microservices Backlog model. We found that SEMGROMI yields decompositions of user stories to microservices with high cohesion (from the semantic point of view) and low coupling, the complexity was reduced, also the communication between microservices and the estimated development time was decreased. Therefore, SEMGROMI is a viable option for the design and evaluation of microservices-based applications. The proposed semantic similarity-based technique (SEMGROMI) is part of the Microservices Backlog model, which allows to evaluate candidate microservices graphically and based on metrics to make design-time decisions about the architecture of the microservicesbased application.

Microservices backlog – a genetic programming technique for identification and evaluation of microservices from user stories.

Vera-Rivera, F. H. | 2017

The microservice granularity directly affects the quality attributes and usage of computational resources of the system, determining optimal microservice granularity is an open research topic. Microservices granularity is defined by the number of operations exposed by the microservice, the number of microservices that compose the whole application, and its complexity and dependencies. This paper describes "Microservice Backlog (MB)", a semiautomatic model for defining and evaluating the granularity of microservice-based applications; MB uses genetic programming technique to calculate at design time the granularity of each microservice from the user stories in the "product backlog" or release planning; the genetic algorithm combined coupling, cohesion, granularity, semantic similarity, and complexity metrics to define the number of microservices, and the user stories associated with each microservice. MB decomposes the candidate microservices, allowing to analyze graphically the size of each microservice, as well as its complexity, dependencies, coupling, cohesion metrics, and the number of calls or requests between microservices. The resulting decomposition (number of microservices and their granularity) performed by MB shows less coupling, higher cohesion, less complexity, fewer user stories associated with each microservice, and fewer calls among microservices. MB was validated against three existing methods, using two state-of-the-art applications (Cargo Tracking and JPet-Store), and one real-life applications (Foristom Conferences). The development team and/or architect can use metrics to identify the critical points of the system and determine at design time how the microservice-based application will be implemented.

Performance, Emission, and Economic Perspectives of a Diesel Engine Fueled with a Mixture of Hydroxy Gas and Biodiesel from Waste Palm Cooking Oil

Pardo García, Carlos Eduardo | 2021

This investigation experimentally examines the influence of hydroxy gas fumigation in a diesel engine fueled with a biodiesel blend derived from waste palm cooking oil (B10). For the experimental tests, a fixed rotation speed of 2000 rpm and a load condition of 50%, 75%, and 100% have been established. Hydroxy gas (HHO) has been added through the engine's air intake system at a flow of 0.5 lpm, 0.75 lpm, and 1 lpm. Results have demonstrated the positive effect of HHO fumigation on the combustion performance of the B10 blend. Moreover, a reduction of 4.3% in the BSFC and a 2.64% increase in peak pressure in B10 due to the presence of HHO have been observed. On the other hand, a decrease of 8.7%, 9.9%, and 22.8% in CO2, HC, and smoke opacity emissions has been evidenced with the addition of HHO in B10. B10 implementation has promoted NOx emission escalation. However, this increase has been only 1.23% compared to pure diesel. In conclusion, HHO enrichment favors combustion performance and emissions minimization, which represents a significant opportunity to mitigate the negative effect of the lower calorific power of these types of fuels.

Evaluation of Performance Parameters and Emissions of a Diesel Engine Fueled with Preheated Palm Oil Mill Effluent Biodiesel Blends

Pardo García, Carlos Eduardo | 2021

The current research analyzes the effect of fuel preheating on performance parameters and emission characteristics in a stationary diesel engine fuelled with palm oil mill effluent biodiesel blends. The investigation has been carried out on an experimental test bench under four-engine load conditions. The fuel injection temperature range is set between 35-75 °C. From the results obtained, it has been possible to demonstrate that the preheating process allows the reduction of fuel consumption and an increase in the efficiency of the engine powered with POME 5 and POME 10 blends. The preheating process has the potential to reduce by 14 % BSFC and increase by 17% the BTE of the engine. Additionally, the results have showed a reduction potential of 17.2%, 16.5%, and 15.6% in CO2, HC, and smoke opacity emissions when preheating processes are used in POME biodiesel blends. In general, this type of strategy is a promising solution to incentivize the use of biodiesel blends from palm oil mill effluent.

Evaluation of Energy Losses in Auxiliary Systems and Mechanisms Subject to Friction in a Stationary Diesel Engine

Pardo García, Carlos Eduardo | 2021

This research has developed a methodology to estimate energy losses associated with auxiliary systems and mechanisms subject to friction in a stationary diesel engine. The study methodology is based on mathematical models used to calculate engine friction forces and power losses. The analysis involves the cooling, the lubrication, the fuel injection system, and the engine's valve train, bearings, and piston mechanism. A stationary diesel engine with four load conditions and three rotational speed conditions has been taken as a reference for the study. The analysis of the results has showed that fuel injection is the auxiliary system with the highest energy consumption. In general, the cooling system, the lubrication, and the injection represent loss energy equivalent to 0.3%, 0.5%, and 0.9% of the chemical energy of the fuel. The interaction between the cylinder liner and the piston skirt is the main source of energy loss associated with friction processes. For valve train mechanisms, bearings and pistons it has been evidenced that these represent 1.25%, 1.91%, and 5.26% of the chemical energy of the fuel. In general, the methodology proposed in this research is a tool that allows diagnosing energy losses in auxiliary systems and engine mechanisms, which is useful for evaluating future strategies focused on energy improvement.

Analysis of the Tribological and Dynamic Characteristics of the Piston Compression Ring Under Different Engine Operating Conditions

Pardo García, Carlos Eduardo | 2021

In this investigation, a study of the tribological and dynamic characteristics present in the compression ring is executed for different engine conditions. A series of numerical simulations has been carried out using the OpenFOAM software for the study. The simulation conditions have been based on the parameters measured on a diesel engine test bench. Different ranges of rotation speed, load, and lubrication oil temperature have been established for the analysis. The results show that the parameters of rotation speed and engine load significantly influence the tribological and dynamic conditions of the piston compression ring. An increase of 425 rpm and 136 Nm in the rotational speed and load of the engine causes an increase of 13% and 34% in the power loss due to friction. The Top Dead Center and the Bottom Dead Center are the locations most prone to experience critical wear and tear. Increasing the lubricating oil temperature increases the maximum friction force by 9%. In general, the proposed methodology allows for establishing a direct relationship between the operating conditions and the tribological characteristics in the compression ring that directly affect the efficiency and useful life of the engine.

Analysis of the characteristics of combustion, emissions, and energy efficiency of blends of heptanol with pure diesel fuel in compression ignition engine(Article)

García, C.P | 2021-09

The objective of this research is to study the influence of the heptanol content in conventional diesel fuel in different proportions for the evaluation of the combustion parameters, emissions, and energy efficiency. For the above, three heptanol blends identified as HP4%, HP8%, and HP16% have been used with a percentage of heptanol of 4%, 8%, and 16%, respectively. The tests have beencarried out on a single-cylinder stationary diesel engine at a constant load (9 Nm) and a rotational speed of 2800 rpm, 3200 rpm, and 3600 rpm. The results show that the percentage of heptanol causes an increase in the maximum values of the pressure in the combustion chamber and the heat release rate. In general, the presence of heptanol in conventional diesel produces an increase of 3.9% and 4.8% in the maximum values of these parameters. Additionally, the HP4%, HP8%, and HP16% heptanol blends allow reducing CO2, NOx, and smoke opacity emissions by 11.7%, 6.7%, and 37.1%, respectively. Due to the closeness of the physicochemical properties between the blends of heptanol and conventional diesel, a similarity between the energy and exergy performance between both fuels has been obtained. The above demonstrates the potential of heptanol to be used as a renewable fuel without the need for modifications to the engine. In addition, they have the ability to reduce NOx emissions, which is a problem in various types of biodiesel.

Numerical and Experimental Analysis of the Potential Fuel Savings and Reduction in CO Emissions by Implementing Cylinder Bore Coating Materials Applied to Diesel Engines

Orjuela Abril, Martha Sofia | 2021-02-18

Currently, internal combustion engines contribute to the problem of global warming due to their need to use products derived from fossil resources. To mitigate the above problem, this study proposes the use of coatings on the cylinder bore in order to reduce fuel consumption and polluting emissions. Therefore, in the present study a numerical model is developed in which the tribological behavior, heat fluxes, and leakage of the combustion gases in the chamber are considered to evaluate the influence of the coating. Nickel nanocomposite (NNC) and diamond-like carbon (DLC) coatings are considered in the study. The results demonstrate that the NNC coating produces a 32% reduction in the total friction force of the compression ring. The estimated maximum temperatures for the lubricating oil were 214, 202, and 194 ◦C for the DLC, steel, and NNC materials. Increasing the temperature in the DLC coating can cause a reduction in the tribological performance of the lubricant. The estimates made show that the implementation of the NNC coating allows a maximum reduction of 5.28 ton of fuel and 39.30 kg of CO emissions, which are based on the global fleet of diesel engines forecast for the year 2025 (corresponding to one hundred and eighty million engines) and a test time of 1800 s. The proposed numerical model allows future analyses to be carried out for other types of materials used as coatings. Additionally, the model can be expanded and adapted to consider other systems that involve friction processes in the engine.

Influence of compression rings on the dynamic characteristics and sealing capacity of the combustion chamber in diesel engines

Hernández Comas, Brando | 2021-03-02

Internal combustion engines are widely implemented in several applications; however, they still face significant challenges due to the sealing capacity of the compression rings. Gas leakage through the crankcase, also known as blow-by, directly impacts power losses, overall efficiency, and global emissions. Therefore, the present study investigates the influence of parameters such as the ring gap, ring masses, and twist angle of the compression rings on the sealing capacity of the combustion chamber. A mathematical model is proposed to account for geometric, dynamic, and operational characteristics in a single-cylinder diesel engine. The results indicated that the greatest gas losses to the crankcase occur during the compression and combustion stages as a consequence of extreme pressure conditions. Specifically, at least 0.5% of the gases locked in the combustion chamber are released on each cycle, while increasing the mass of the compression rings boosts the gas leakage due to higher inertial forces in the rings. In contrast, a positive twist angle of the compression rings reduced the combustion gases leakage by 7.33 × 10−5 g/cycle. Additionally, a combined reduction in the gap of both compression rings minimized the leakage flows by 37%. In conclusion, the proposed model served as a robust tool to evaluate different parameters on the sealing capacity of the combustion chamber that contribute to minimizing global emissions. Secondary piston motion and ring distortion represent significant opportunities in future studies.

Numerical simulation to analyze the physical behavior of centrifugal pumps as a turbine

Prada Botia, Gaudy Carolina | 2021-05

In this research, a methodology based on the development of numerical simulations is proposed to analyze the physical behavior of centrifugal pumps such as a turbine. Numerical simulations were carried out using OpenFOAM software. For the validation of the numerical model, the construction of an experimental test bench was carried out. The analysis carried out involves the evaluation of performance parameters of the pump as a turbine, such as head, power, and efficiency. Additionally, the effect of the rotation speed on the previous parameters is evaluated. From the results obtained, it was shown that the maximum relative error was 4%, 3.4%, and 3.8% for the head, power, and efficiency parameters, respectively. In general, it was evidenced that the proposed numerical simulation has the ability to describe the real trends of the pump as a turbine for different flow conditions. In addition, an 11% increase in rotational speed was shown to cause a 12%, 1.9%, and 3% increase in head, power, and maximum efficiency. The proposed methodology is considered an adequate tool to analyze performance and identify the best efficiency point of pump systems such as a turbine. In this way, greater energy use is guaranteed.

Curricula framework for a digital transformation master’s in science and engineering

Vera Contreras, Milton Jesús | 2021-06-09

Digital transformation is an interdisciplinary work that has the objective to analyze data in real-time for better decision making, where it involves the modeling and the simulation of physical-mathematical and social processes and the monitoring of machinery that using information and communication technologies. Nowadays the fourth industrial revolution technologies are the driver for this transformation and soon will be quantum computing and other physics and mathematics advances. Therefore, in this academic-scientific work, a curricular framework is proposed for a master's program in digital transformation for science and engineering; the framework follows the literature review and analysis to formulate the denomination and justification for the master's degree. The results are (i) a general three-level model as a roadmap for the specific curricular design of a master’s in digital transformation for science and engineering; (ii) the recommendation that a master's program has a research focus in the two categories established by the Colombian standard (research and practitioner); (ii) finally, a political, economic, social, technological, environmental, and legal analysis justifies a master’s in digital transformation for science and engineering. The research model details the phases that were carried out to characterize and identify the curriculum framework that will be the basis for formalizing the master's degree document.

GeoMotor: Design with nature. Recognition of geometries using a convolutional neural-network approach (CNN)

Villa Carrero, Juan Manuel | 2020-12-02

This article conceptualizes a solid regular called GeoMotor capable of moving and directing the sediments of a mountain river and changing its geography. The GeoMotor manages to manipulate the directional growth of sediments in an artificial environment, unveiling emerging architectural structures. For this, an analog simulation of the mountain river flow was performed and provide data to understand the phenomenon. Subsequently, this data was used to train a neural network that recognizes the emerging architectural patterns. As future work, it is planned to improve the models to offer functionalities beyond the orthodox practices of traditional architectonic models

Un algoritmo recursivo de reconocimiento de patrones

Puerto Cuadros, Eduard Gilberto | 2017-07-25

En este trabajo, proponemos un algoritmo de reconocimiento de patrones basado en el funcionamiento sistemático del neocórtex humano, en procesos de reconocimiento de patrones. El algoritmo explota la idea de recursividad, basada en la jerarquía neocortical y en la desagregación/integración del patrón en el proceso de reconocimiento. El algoritmo propuesto es probado para analizar sus capacidades de reconocimiento de patrones.

Administración, control y seguimiento de la pretemporada en el fútbol por medio de un sistema de información - Sinplafut1.0

vera rivera, jose luis | 2019-10-07

La pre-temporada es la parte inicial de los procesos de adaptación deportiva que generalmente realizan todos los equipos de alto rendimiento, en Colombia solo en primera división hay 32 equipos de futbol profesional. La presente investigación tiene como objetivo el de desarrollar un sistema de información para mejorar y agilizar con la planificación de la pre-temporada en el futbol usando tecnologías de la información y la comunicación y técnicas modernas del entrenamiento deportivo. Se utilizó un tipo de investigación tecnológica aplicada dentro de un trabajo interactivo e interdisciplinario, entre expertos en entrenamiento deportivo y expertos en el desarrollo de sistemas de información y tecnologías, lo que permitió innovar en el proceso de la planificación de la pretemporada en el fútbol, para equipos, clubes, preparadores físicos, entrenadores y directores técnicos tanto a nivel aficionado como profesional. Esto permitió agilizar, gestionar y controlar de una mejor manera esta planificación, siendo está novedosa usando las siguientes tecnologías: metodologías de desarrollo ágil, los sistemas de información, la computación en la nube, la programación web, la computación móvil y las analíticas web. Con base en estas teorías se analizó, diseñó e implementó el sistema de información. Con el uso de tecnologías los entrenadores pueden tener información más precisa de sus procesos de entrenamiento, poseen indicadores que les permiten evaluar el rendimiento y estado de sus jugadores para obtener mejores resultados.

A recursive patterns matching model for the dynamic pattern recognition problem

Puerto Cuadros, Eduard Gilberto | 2018-06-04

This paper defines a new recursive pattern matching model based on the theory of the systemic functioning of the human brain, called pattern recognition theory of mind, in the context of the dynamic pattern recognition problem. Dynamic patterns are characterized by having properties that change in intervals of time, such as a pedestrian walking or a car running (the negation of a dynamic pattern is a static pattern). Novel contributions of this paper include: (1) Formally develop the concepts of dynamic and static pattern, (2) design a recursive pattern matching model, which exploits the idea of recursivity and time series in the recognition process, and the unbundling/integration of pattern to recognize, and (3) develop strategies of pattern matching from two major orientations: recognition of dynamic patterns oriented by characteristic, or oriented by perception. The model is instantiated in several cases, to analyze its performance.

Deep learning architecture for the recursive patterns recognition model

Puerto, E | 2018-12-07

In this work, we propose a deep learning approach for the recursive pattern recognition model, called AR2P (for its acronym in Spanish: "Algoritmo Recursivo de Reconocimiento de Patrones"), by extending its supervised learning capability towards a semi-supervised learning scheme. The deep learning architecture is composed of three phases: the first one, called discovery phase, discovers the atomic descriptors. The second one, called aggregation phase, creates a feature hierarchy (merge of descriptors) from atomic descriptors. Finally, the classification phase carries out the classification of the inputs based on the feature hierarchy. The last phase uses a supervised learning approach, while the first two follow an unsupervised learning approach. In this paper is tested the performance of the proposed model, using a dataset from the UCI Machine Learning Repository.

Predicting student drop-out rates using data mining techniques: A case study

PEREZ GUTIERREZ, BORIS RAINIERO | 2018-12-14

The prevention of students dropping out is considered very important in many educational institutions. In this paper we describe the results of an educational data analytics case study focused on detection of dropout of Systems Engineering (SE) undergraduate students after 6 years of enrollment in a Colombian university. Original data is extended and enriched using a feature engineering process. Our experimental results showed that simple algorithms achieve reliable levels of accuracy to identify predictors of dropout. Decision Trees, Logistic Regression, Naive Bayes and Random Forest results were compared in order to propose the best option. Also, Watson Analytics is evaluated to establish the usability of the service for a non expert user. Main results are presented in order to decrease the dropout rate by identifying potential causes. In addition, we present some findings related to data quality to improve the students data collection process.

International meeting on applied sciences and engineering

Vera-Rivera, F. H. | 2018-12-07

The goal aim objective of the 4th International Meeting on Applied Sciences and Engineering (EISI), is to promote a space for the exchange of knowledge between the academy and the industrial sector of the department of Norte de Santander, Colombia. The assistance of scientific experts in science, engineering, technological development, and innovation allowed generating knowledge and solutions that impacted the socio-economic development of the region and the country.

Desarrollo de aplicaciones basadas en microservicios: tendencias y desafíos de investigación

Vera-Rivera, F. H. | 2019-10

Los microservicios son un enfoque arquitectónico y organizativo del desarrollo de software en el que las aplicaciones están compuestas por pequeños servicios independientes que se comunican a través de un API bien definida y protocolos ligeros. En este trabajo se realizó una revisión de literatura para identificar los desafíos y tendencias de investigación presentes en las fases del proceso de desarrollo y en la gestión de atributos de calidad de este tipo de aplicaciones. Las tendencias se encuentran en la fase de desarrollo y en la fase de diseño; los atributos de calidad más estudiados son la escalabilidad y la calidad del servicio. Los desafíos de la investigación se centran en: definir el nivel de granularidad de los microservicios, modularización y refactorización de servicios, integración con la interfaz de usuario, seguridad, orquestación, monitoreo, gestión y supervisión de microservicios, tolerancia a fallas, recuperación y auto reparación de microservicios.

Using multilayer fuzzy cognitive maps to diagnose autism spectrum disorder

Puerto, E. | 2019-02

Autism Spectrum Disorder (ASD) is comprised of a group of heterogeneous neurodevelopmental conditions, typically characterized by a triad of symptoms consisting of (1) impaired communication, (2) restricted interests, and (3) repetitive and stereotypical behavior pattern. An accurate and early diagnosis of autism can provide the basis for an appropriate educational and treatment program. In this work, we propose a computational model using a Multilayer Fuzzy Cognitive Map (hereafter referred to as MFCM) based on standardized behavioral assessments diagnosing the ASD (MFCM-ASD). The two standards used in the model are: the Autism Diagnostic Observation Schedule, Second Edition (ADOS2), and the Autism Diagnostic Interview Revised (ADIR). The MFCM’s are a soft computing technique characterized by robust properties that make it an effective technique for medical decision support systems. For the evaluation of the MFCM-ASD model, we have used real datasets of diagnosed cases, so as to compare against other method/approaches. Initial experiments demonstrated that the proposed model outperforms conventional Fuzzy Cognitive Maps (FCMs) for ASD diagnosis. Our MFCM-ASD model serves as a diagnostic tool required to support the medical decisions when determining the correct diagnosis of Autism in children with different cognitive characteristics.