Modernización del sistema de seguridad y Control de equipo por medio de transponders: Desarrollo del software de administración del inventario general del hospital

Tesis (Ingeniería Biomédica), Instituto Politécnico Nacional, UPIBI, 2009, 1 archivo PDF, (97 páginas). tesis.ipn.m

Compliant Cross-Axis Joints: A Tailoring Displacement Range Approach via Lattice Flexures and Machine Learning

Compliant joints are flexible elements that allow displacement due to the elastic deformations they experience under the action of external loading. The flexible parts responsible for these displacements are known as flexure hinges. Displacement, or motion range, in compliant joints depends on the stiffness of the flexure hinges and can be tailored through various parameters, including the overall dimensions, the base material, and the distribution within the hinge. Considering the distribution, we propose the stiffness modification of a compliant cross-axis joint via the use of lattice mechanical metamaterials. Due to the wide range of parameters that influence the stiffness of a lattice, different machine learning algorithms (artificial neural networks, support vector machine, and Gaussian progress regression) were proposed to forecast these parameters. Here, the machine learning algorithm with the best forecasting was the Gaussian progress regression; this algorithm has the advantage of well-tuning even with small regression databases, allowing these functions to more easily adjust to suit specific data, even if the dataset is small. Hexagonal, re-entrant, and square lattices were studied as flexure hinges. For each, the effect of the unit cell size and its orientation with respect to the principal axis on the effective stiffness were studied via computational and laboratory experiments on additively manufactured samples. Finite element predictions resulted in good agreement with the experimentally obtained data. As a result, using lattice-flexure hinges led to increments in displacement ranging from double to ten times those obtained with solid hinges. The most suitable machine learning algorithm was the Gaussian progress regression, with a maximum error of 0.12% when compared to the finite element analysis results

Design of an Aluminum Alloy Using a Neural Network-Based Model

Lightweight materials are in constant progress due to the new requirements of mobility. At the same time, it is mandatory to meet the internal standards of the original equipment manufacturers to guarantee product quality, and market regulations are necessary to reduce or eliminate pollution emissions. In order to reach these technical requirements, the design is optimized, and new materials and alloys are evaluated. The search for these new types of materials is long and expensive. For this search, new technologies have emerged, such as integrated computational materials engineering, which is a valuable tool to forecast through simulation alloy characteristics that meet specific requirements without fabrication. This research develops an artificial neural network to establish the chemical composition of a new aluminum alloy based on the desired manufacturing characteristics as well as fatigue strength. For this, the proposed artificial neural network was trained with the chemical composition of preexisting aluminum-based alloys and the resulting desired mechanical properties. The significant contribution of the proposed research consists not only of the neural network high-performance forecasting but also the fact that for to train and validate it, not only simulations of its responses to the different possibilities of alloys were tried but also validated through an experimental laboratory test performed by uniaxial machine. The proposed artificial neural network results show an average correlation of 99.33% between its forecasting and laboratory testing

Design and construction of a quantitative model for the management of technology transfer at the Mexican elementary school system

Nowadays, schools in Mexico have financial autonomy to invest in infrastructure, although they must adjust their spending to national education projects. This represents a challenge, since it is complex to predict the effectiveness that an ICT (Information and Communication Technology) project will have in certain areas of the country that do not even have the necessary infrastructure to start up. To address this problem, it is important to provide schools with a System for Technological Management (STM), that allows them to identify, select, acquire, adopt and assimilate technologies. In this paper, the implementation of a quantitative model applied to a STM is presented. The quantitative model employs parameters of schools, regarding basic infrastructure such as essential services, computer devices, and connectivity, among others. The results of the proposed system are presented, where from the 5 possible points for the correct transfer, only 3.07 are obtained, where the highest is close to 0.88 with the availability of electric energy and the lowest is with the internet connectivity and availability with a 0.36 and 0.39 respectively which can strongly condition the success of the program.Hoy en día, las escuelas en México cuentan con autonomía financiera para hacer inversión en infraestrutura, aunque deben ajustar sus gastos a los proyectos nacionales de educación. Esto representa un reto, ya que resulta complejo preveer la efectividad que tendrá un proyecto en TIC (Tecnologías de la Información y la Comunicación) en ciertas zonas del país que ni siquiera cuentan con la infraestructura necesaria para su puesta en marcha. Para abordar este problema, es importante dotar a las escuelas de un Sistema de Gestión Tecnológica (STM) que les permita identificar, seleccionar, adquirir, adoptar y asimilar tecnologías. En este trabajo se presenta la implementación de un modelo cuantitativo aplicado a un STM. El modelo cuantitativo emplea parámetros de escuelas con respecto a infraestructura básica como servicios esenciales, dispositivos informáticos, conectividad, entre otros. Se presentan los resultados del sistema propuesto, donde de los 5 posibles puntos para la transferencia correcta sólo se obtienen 3,07, el más alto es cercano a 0,88 con la disponibilidad de energía eléctrica, y el menor es con la conectividad y disponibilidad de Internet con A 0,36 y 0,39 respectivamente, lo que puede condicionar fuertemente el éxito de los programas

Design and construction of a quantitative model for the management of technology transfer at the Mexican elementary school system

Nowadays, schools in Mexico have financial autonomy to invest in infrastructure, although they must adjust their spending to national education projects. This represents a challenge, since it is complex to predict the effectiveness that an ICT (Information and Communication Technology) project will have in certain areas of the country that do not even have the necessary infrastructure to start up. To address this problem, it is important to provide schools with a System for Technological Management (STM), that allows them to identify, select, acquire, adopt and assimilate technologies. In this paper, the implementation of a quantitative model applied to a STM is presented. The quantitative model employs parameters of schools, regarding basic infrastructure such as essential services, computer devices, and connectivity, among others. The results of the proposed system are presented, where from the 5 possible points for the correct transfer, only 3.07 are obtained, where the highest is close to 0.88 with the availability of electric energy and the lowest is with the internet connectivity and availability with a 0.36 and 0.39 respectively which can strongly condition the success of the program

Design and construction of a quantitative model for the management of technology transfer at the Mexican elementary school system

Nowadays, schools in Mexico have financial autonomy to invest in infrastructure, although they must adjust their spending to national education projects. This represents a challenge, since it is complex to predict the effectiveness that an ICT (Information and Communication Technology) project will have in certain areas of the country that do not even have the necessary infrastructure to start up. To address this problem, it is important to provide schools with a System for Technological Management (STM), that allows them to identify, select, acquire, adopt and assimilate technologies. In this paper, the implementation of a quantitative model applied to a STM is presented. The quantitative model employs parameters of schools, regarding basic infrastructure such as essential services, computer devices, and connectivity, among others. The results of the proposed system are presented, where from the 5 possible points for the correct transfer, only 3.07 are obtained, where the highest is close to 0.88 with the availability of electric energy and the lowest is with the internet connectivity and availability with a 0.36 and 0.39 respectively which can strongly condition the success of the program.Hoy en día, las escuelas en México cuentan con autonomía financiera para hacer inversión en infraestrutura, aunque deben ajustar sus gastos a los proyectos nacionales de educación. Esto representa un reto, ya que resulta complejo preveer la efectividad que tendrá un proyecto en TIC (Tecnologías de la Información y la Comunicación) en ciertas zonas del país que ni siquiera cuentan con la infraestructura necesaria para su puesta en marcha. Para abordar este problema, es importante dotar a las escuelas de un Sistema de Gestión Tecnológica (STM) que les permita identificar, seleccionar, adquirir, adoptar y asimilar tecnologías. En este trabajo se presenta la implementación de un modelo cuantitativo aplicado a un STM. El modelo cuantitativo emplea parámetros de escuelas con respecto a infraestructura básica como servicios esenciales, dispositivos informáticos, conectividad, entre otros. Se presentan los resultados del sistema propuesto, donde de los 5 posibles puntos para la transferencia correcta sólo se obtienen 3,07, el más alto es cercano a 0,88 con la disponibilidad de energía eléctrica, y el menor es con la conectividad y disponibilidad de Internet con A 0,36 y 0,39 respectivamente, lo que puede condicionar fuertemente el éxito de los programas

Hand Movement Classification Using Burg Reflection Coefficients

Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input for the control of prosthetic devices has become a hot topic of research. The challenge of classifying these signals relies on the accuracy of the proposed algorithm and the possibility of its implementation in hardware. This paper considers the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with the focus lying on the signal model and time domain characteristics for better classification accuracy. The proposal considers a simple preprocessing technique that produces signals suitable for feature extraction and the Burg reflection coefficients to form learning and classification patterns. These coefficients yield a competitive classification rate compared to the time domain features used. Sometimes, the feature extraction from electromyographic signals has shown that the procedure can omit less useful traits for machine learning models. Using feature selection algorithms provides a higher classification performance with as few traits as possible. The algorithms achieved a high classification rate up to 100% with low pattern dimensionality, with other kinds of uncorrelated attributes for hand movement identification

Development of an Intelligent System for the Monitoring and Diagnosis of the Well-Being

Today, society is more aware of their well-being and health, making wearable devices a new and affordable way to track them continuously. Smartwatches allow access to daily vital physiological measurements, which help people to be aware of their health status. Even though these technologies allow the following of different health conditions, their application in health is still limited to the following physical parameters to allow physicians treatment and diagnosis. This paper presents LM Research, a smart monitoring system mainly composed of a web page, REST APIs, machine learning algorithms, psychological questionnaire, and smartwatches. The system introduces the continuous monitoring of the users’ physical and mental indicators to prevent a wellness crisis; the mental indicators and the system’s continuous feedback to the user could be, in the future, a tool for medical specialists treating well-being. For this purpose, it collects psychological parameters on smartwatches and mental health data using a psychological questionnaire to develop a supervised machine learning wellness model that predicts the wellness of smartwatch users. The full construction of the database and the technology employed for its development is presented. Moreover, six machine learning algorithms (Decision Tree, Random Forest, Naive Bayes, Neural Networks, Support Vector Machine, and K-nearest neighbor) were applied to the database to test which classifies better the information obtained by the proposed system. In order to integrate this algorithm into LM Research, Random Forest being the one with the higher accuracy of 88%