Applied Computer Sciences in Engineering

This paper deals with methods for identification of drone activities based on its sensor data. Several unsupervised and supervised approaches are proposed and tested for the task of activity analysis. We demonstrate that sensor data, although quite correlated, are still prone to standard dimensionality reduction techniques that in fact make the problem hard for unsupervised methods. On the other hand, a supervised model based on deep neural network is capable of learning the task from human operator data reformulated as a classification problem.

Unmanned Aerial Vehicle (UAV) are increasingly playing an important role in various fields of application and their development, with the advancement and decreasing cost of technology, became simpler. In this paper we present a mathematical model for a quadrotor and different control strategies. The control task is based on the discretization of the system by Tustin’s Method to achieve the angular stabilization of the quadrotor. The proposed approach shows the standard single loop and nested loop model, hierarchizing the mid-level attitude control, evaluating the performance of a P, PD and P-P controllers obtained by linearizing the plant’s dynamics around an equilibrium point. Simulation results compared the proposed controllers providing a possible solution for future implementation.

A key to the advancement of robotics is to create more complex, flexible and sturdier robot structures and controllers. In order to accomplish this task it is crucial to first develop a 3D model that allows to make a robot well-designed and easily reconfigurable, where one can change its structure, add and remove degrees of freedom of legs, create and simulate new patterns of locomotion, and other adjustments. This research demonstrates the development of the embedded hexapod robot Aduka, a mobile robot powered by an embedded system. Using BeagleBone Black, other single boards and an operating system FreeBSD it can be remotely controlled by computers and mobile devices for hazardous environment inspections.

The conventional way to perform the sampling process of a signal requires the application of a filter to remove high frequency components in order to avoid the effect of frequency overlap. However, in many cases filtering the signal may mean important loss of information about signal details. This paper shows that under certain conditions on the sampling system and the sampled signal, it is possible to recover all the signal frequency components according to a sub-sampling and reconstruction process inspired in compress sensing. The study considered pulse signals with pulse duration much smaller than the total length of the signal.

The world today combines a larger number of megacities, which are control nodes of the whole world. These megacities must provide to their inhabitants an efficient way to transport and that is the reason of this paper. It is intended to give a judgment of the acquisition, handling and processing of information that may be useful for an intelligent traffic system.

The general task in speaker identification for audio forensics is to identify the unknown speaker within an audio proof, who is suspected of a crime. Here, the voice of each person within a group of suspects is compared to the audio proof with the aim to determining which of them corresponds to the source. In this paper, a semi-supervised speaker identification method is proposed, which does not require a training stage. Also, the feature extraction is based on the use of cochleagrams for the previously selected words. The system can identify one or multiple suspects which have high similarity to the audio proof, or give a null response if none of the suspects satisfies a similarity threshold. The results of the proposed method are compared with the respective results of the same method but using spectrograms instead of cochleagrams. The performance of our system is measured through a confusion matrix (true and false positives, and true and false negatives) and global results are given in terms of overall accuracy and kappa index. According to several tests, our system has an overall accuracy higher than 0.97 and a kappa index around 0.78; this means a high confidence in the results of identification and rejection.

Robotic Process Automation (RPA) emerges as software based solution to automate rules-based business processes that involve routine tasks, structured data and deterministic outcomes. Recent studies report the benefits of the application of RPA in terms of productivity, costs, speed and error reduction. Most of these applications were carried out on back office business process where the customer is not directly involved, therefor a case study was conducted on a BPO provider to verify the benefits and results of applying RPA to a service business process with front and back office activities. The results show that productivity improvement is the main benefit of RPA, nevertheless time reduction was not achieved on this case.

Considering the problem of handling devices with management limitations, the solution can be based on protocol conversion through finite-state converter in combination with the SNMP proxy agent functions and using the serial ports of legacy equipment and small devices with processing restrictions as sensors on a Ad Hoc network or Internet of Things. It reviews the framework of reference of management network, giving details of the challenges for the mentioned devices and the state of art of the existing solutions. This paper approximates to the proposed solution due to the research is in progress.

This paper presents a proposal of obstacle evasion oriented to mobile robots in clustering tasks. For this case, polar coordinates are set for the movement of the mobile, the possible obstacles in the path are determined and imaginary boundaries are generated in each possible obstacle in order to delimit the path of the mobile between them. The algorithm developed under the Netlogo programming environment makes it possible to perform evasion and reach the clustering point efficiently.

In biology, a mutation is a change in the nucleotide sequence of the genome of a microorganisms (e.g., virus, bacteria). Mutations play an important role in biological processes of microorganisms such as drug resistance. Analyzing these mutations becomes a challenge because microorganisms are described by a big amount of data that must be processed based on reference microorganisms data. Nowadays, scientists in health areas make these analyses manually or using standalone software that provide results in plain text formats, which limit their interpretations. In this paper, we present an approach for analyzing microorganism pathogens. The analysis is performed using the information contained in the nucleotide sequence and comparing it to reference sequences. This approach allows users to calculate changes of nucleotide and amino acid from selected sequences obtained using conventional Sanger and cloning sequencing techniques. The results of our approach are deployed using different visualization techniques that facilitate results interpretation.

This research document is focused on the design an implementation of a low cost Automatic Weather Station (AWS) in order to measure the environmental variables which are related to the wind an photovoltaic energy generation. This kind of structure, a weather station, entails more elements than sensors variables which are going to be measure, in such a way that it would be difficult because of data collection system, the information storage system process, the location of the AWS, the visualization system and the time of synchronization. This Process includes the proper selection of the sensors and each element, the analysis of the conditioning system, the construction of the entire prototype and the startup of this according to the environmental conditions of the position that will have the AWS.

In the Software industry, big software projects are carried out with hundreds of developers. The fast change in technologies and development environments increase the complexity. Usually, there are project teams with a project leader. However, it is very difficult to know the profile of each developer. The development tasks also have their profile. Hence, it is necessary to assign each task to the most suitable developer. The erroneous assigning of tasks can cause delays and increase the project costs. Thus, a bad assigning of tasks can cause stress and low productivity. Therefore, we make a proposal to enhance tasks assignment to developers regarding the task and developer profiles. The task profile includes characteristics such as: knowledge, kind of task, complexity, experience, etc., in other aspects as codification: paradigm, programming language, version, etc. Using algorithms with similarity coefficients, we look for the best match to assign the tasks to developers. In this work, we used five techniques of similarity coefficient in order to find results and to recommend the best solution for this problem. We conclude that with the Sokal and Sneath technique we obtain better results to solve the problem.

In this paper we combine two string searching related problems: the approximate string matching under parameters $$\delta $$ and $$\gamma $$, and the order preserving matching problem. Order-preserving matching regards the internal structure of the strings rather than their absolute values while matching under $$\delta $$ and $$\gamma $$ distances permit a level of error. We formally define the $$\delta \gamma $$–order-preserving matching problem. We designed two algorithms for it based on the segment tree and the Fenwick tree, respectively. Also, we design and implement in C++ and an experimental setup to compare these algorithms with the naive solution and the updateBA algorithm introduced in [22]. The data structure based algorithms show better experimental performance due to their better lower bound of $$\varOmega (n \lg n)$$ complexity.

In this work, a high-frequency trading strategy using Deep Neural Networks (DNNs) is presented. The input information consists of: (i). Current time (hour and minute); (ii). The last n one-minute pseudo-returns, where n is the sliding window size parameter; (iii). The last n one-minute standard deviations of the price; (iv). The last n trend indicator, computed as the slope of the linear model fitted using the transaction prices inside a particular minute. The DNN predicts the next one-minute pseudo-return, this output is later transformed to obtain a the next predicted one-minute average price. This price is used to build a high-frequency trading strategy that buys (sells) when the next predicted average price is above (below) the last closing price.

Clustering is one of the most successful techniques for territorial design, location-allocation problems etc. In this type of problems, the parameters are usually optimized by means of a single objective. However, real applications are far from being solved without the application of multi-objective approaches. In this paper we present a bi-objective partitioning proposal to solve problems that involve census-based variables for territorial design (TD), known to be a high complexity computational problems. Two quality measures for partitioning are chosen, which are simultaneously optimized. The first quality measure obeys a geometrical concept of distances, whereas the second measure focuses in the calculation of balance for a descriptive variable. A formulation is included with a flexible notation for the second objective about variable population and this is our main contribution. Furthermore, our model allows for implementations in several languages and it is possible to reach quality solutions within a reasonable computation time.Experimental tests show that it is possible to get results in the Pareto frontier, which is constructed with the approximate solutions generated by the chosen metaheuristic. In this case, one pilot test and its associated Pareto’s front, is presented. These solutions are non-dominated and non-comparable with a similar mechanism on which the minima of a Hasse Diagram are reached.

One of the main problems in the big cities is traffic, both vehicles and motorcycles. Currently Bogotá D.C. (Capital of Colombia), has more than 8 million inhabitants, of whom the majority travels in private and public vehicles. In the interior of the city, it has been possible to identify sectors in which mobility generates a frequent chaos, given the number of vehicles that have to move there, including light vehicles and also cargo vehicles. The application of the Ant Colony algorithm is proposed to simulate the traffic at a specific time in an intersection in order to determine if it is possible to increase the flow of vehicles that circulate through said intersection, decreasing the time of exit of the vehicles. At the end of the article we present the results of the simulation which allow us to take a stand against the research and to confirm that the research process and, above all, the simulation process, allow us to make a decision about the way in which the times have been adjusted Of traffic lights at most of the intersections of the city of Bogotá.

Information extracted from environmental sounds has been of great importance to the analysis of ecological complexity in natural ecosystems. However, the study of these sounds does not have a universal protocol for the sampling and reduction of large quantities of data that it produces. This paper proposes to use a neural network to optimize the sampling of soundscapes of three Colombian ecosystems. The neural network is trained to identify meaningful temporal windows for audio recording from previously gathered data. This method simplifies the acoustic complexity analysis.

This paper presents an analysis of the behavior of the IEEE 30-node distribution system in presence of short duration variations; specifically sags and swells are assessed before and after implementation of SVC (Static Var Compensator) and DSTATCOM (Distribution Static Compensator). The magnitude for sags and swells is selected according to what is specified in standard IEEE 1159: 2009 and NTC 5000: 2013. An algorithm is implemented in MATLAB to find the power flow solution based on the Newton-Raphson method, in which the SVC and DSTATCOM compensators are included. To validate the algorithm, a test for a 5-bus system, is conducted. In the IEEE 30-bus system, load values are changed in order to generate the sags and swells under analysis; the case study focuses on the behavior of node 10. The findings suggest that although the values of power delivered (or absorbed) by the SVC and DSTATCOM compensators for the same event are different, all nodes maintain voltage levels between 0.9 and 1.1 p.u.

This paper deals with the implementation of an OFDM system which uses wavelets as the carriers through the Discrete Wavelet Transform. It analyzes performance in terms of the Bit Error Rate (BER) for different values of signal-to-noise ratio in an Additive White Gaussian Noise (AWGN) wireless transmission environment. Additionally, it simulates the BER response for different bandwidths in the wavelet-based system, in order to reduce it, lessening it’s BER performance until it equals the FFT-based, thus we achieved an improvement in spectral effieciency. To ensure the same testing conditions, both systems were implemented using the open source GnuRadio Software-Defined Radio (SDR) platform.

This article presents an application of the Particle Swarm Optimization (PSO) on the optimization of the power flow in an IEEE system with 14 nodes, which has some nodes with distributed generation. In first place, the mathematical model used for the optimization of the electricity generation costs is defined. Afterwards, this model is applied in a study case with the IEEE system with 14 nodes and distributed generation.

Interest in fresh food has increased around the world. However, according to FAO losses of perishable food can reach 50%, depending on the logistics capabilities of the supply chain. A management model for transportation and warehouse capacities for the perishable food supply chain is proposed. The expansion of own capacity is evaluated in comparison to contracting 3PL in two scenarios: with and without cold chain. The model was developed within the system dynamics paradigm, modelled in iThink and evaluated through a case study of the supply chain of mango in Cundinamarca-Bogotá. The seasonality of the supply and its discrepancy with the demand is included in the model. The model allows the study of the logistic performance, quality, costs and responsiveness of the Mango Supply Chain.

The present simulation model represents and analyzes the dynamics of the recoverable solid waste stream in the Bogotá’s recycling chain, characterizing the amount of waste collected per day, according to a number of recyclers that are in the daily activity.This study is intended to provide guidance for decision-making district order, in politics and public operation of the recycling system as an input frequency in training and collection routes.The model was developed through continuous simulation, using the computational tool Ithink, with which we represent the study system, which allows us to perform the analysis of the same, projections of the quantity of recyclable waste and capacity of collection by Part of the recyclers starting in 2017 until the year 2020.The main achievement of the model is to be able to determine the most important variables to increase the efficiency in the collection of recyclable materials, which are given by the increase of the population of the recycling sector and its capacity of collection, transport, and storage. In addition to allowing to measure, the impact of the separation at the source on the efficiency of the collection system of recyclable solid waste generated per day, and whether or not this influences the amount of waste entering the landfill.

This paper presents a system dynamics approach to the corruption problem in Colombia. The main causes were identified in service systems and their relation to drug trafficking. Each of these problems were modeled following the System Dynamics methodology, building causal diagrams and the simulation models that represent the dynamics of the factors that influence corruption such as: economic, political, social, psychological, and cultural factors. These factors are integrated in a general simulation model involving fuzzy logic in the inputs, which allows validating the behavior modeled and identify aspects to reducing the corruption in Colombia.

This paper presents a simulation approach of the problem of scheduling customers in a queuing networks using a fuzzy inference system. Usually, in the queuing systems there are rules as round robin, equiprobable, shortest queue, among others, to schedule customers, however the condition of the system like the cycle time, utilization and the length of queue is difficult to measure. We propose a fuzzy inference system in order to determine the status in the system using input variables like the length queue and utilization. Our simulation shows an improvement in the performance measures compared with traditional scheduling policies.

This paper presents a simple methodology for estimating confidence intervals of quantiles in queueing systems. The paper investigates the actual probability density function of quantile estimators resulting of independent replications. Furthermore, we present a methodology, based on the concepts of bootstrapping, i.e., re-sampling and sub-sampling, to calculate the variability of an estimator without running different independent replications. Contrary to what overlapping and non-overlapping batching procedures suggest, we propose to randomly select data points to form a sub-sample, instead of selecting time-consecutive data points. The results of this study suggest that this proposal reduces the correlation between sub-samples (or batches) and overcomes the issue of normality.

This paper presents the development of a controlled robotic hand. The movement of the robot hand can be controlled by electromyography signals carrying out the steps of sensing, filtering, acquisition, digitization and processing of electromyographic signals together with the steps of power and control and drive equipment, thus achieving the development of a robotic hand. The robot controlled by Arduino nano, aiming to develop a control system with electromyography signals capable to control external devices such as prostheses and robots.

In this article, we show a prototype of an irrigation system for oil palm plantations in Colombia. We make use of a predictive control model to improve the irrigation schedule. This model takes weather predictions from the Internet and uses them in the control model as a new variable to take into account when it comes to deciding whether the crops should be irrigated or not. A soil moisture sensor is used to both validate if the decision was correct and maintain the crop within the desired range of soil moisture. With this work, we want to give farmers the opportunity to embrace the Internet of Things (IoT) as a technology that can make their job easier and with better productivity in their plantations.

This article describes the prospective in the Colombian industry related to the “Internet of things” and the concept of industry 4.0, using an analysis of different platforms implementation throughout several fields of the Colombian industries.

Monitoring has always been of crucial importance for industries, and it has been useful when getting notifications about critical parameters reaching threshold and alerts pertaining to failure of a specific device becomes a necessity. Traditional approaches are used to solve this problem, nevertheless generally deploy and maintenance the solutions based on that approaches are high-cost.The Internet of Things (IoT) is a novel paradigm that covers a wide range of topics in the information-based era. One of the sectors which is particularly optimistic about the IoT future growth is the industry. This work aims to develop an IoT-based power-monitoring prototype as an alternative to the existing solutions. According with the last, a three-tier architecture for Industrial IoT applications is described and is used to implement the prototype. The presented solution is low-cost, enables real-time power-monitoring and allows to show the benefits of Internet of Things over Industrial tasks.

Overcoming the barriers to access technology has been one of the main objectives that many institutes have planned for the inclusion of people with some type of disability. The technological developments have innumerable possibilities to create research projects that are inclusive, that is to say that it does not have difficulties to be used by people in a condition of visual disability, to achieve this, it is presented a solution to the problems of localization in Indoor space by using technology that is already available in any mobile phone through the use of Bluetooth Low Energy 4.0 and an application made in Android.

Monitoring environmental variables in buildings is a first step to implement and provide services related to energy savings, comfort and safety among others issues. In this work we design and implement an Energy Monitoring System in order to achieve monitoring of environmental variables in buildings that use decentralized air conditioning systems. In particular, we focus on variables related with human comfort, such as room temperature, humidity, $$CO_2$$ and energy consumption of the air conditioning system. The system was implemented using low cost components and an Internet of Things Architecture. The field tests show the reliability of the proposed system.

Fuzzy logic emerges as an important tool in modelling control systems. For this reason, it is necessary to find methodologies involving fuzzy inference and allow to obtain levels of accuracy and interpretability accordance with design requirements. This paper proposes the generalization of a Fuzzy Inference System based on Boolean and Kleenean relations (FIS-BKR) from the conceptual expansion of the virtual actuator.

Currently, there are many programs that support entrepreneurship as a way to improve economic and social conditions of a country. In this article is designed the model of a fuzzy logic system with the aim of providing a tool that allows generating an initial indicator of the viability of a product or business proposal, based on the model design Canvas and using as a reference the EMIS Benchmarking Score proposed by the Euromoney Institutional Investor Company (EMIS), an organization that provides financial information of companies. The implementation of this prototype taken from experimental data achieved a correlation of 88%, which implies that the model is stable and it fits with the established metrics for the benchmark.

This paper shows some distance measures based on memberships and centroids for comparing fuzzy variables which are commonly used in fuzzy logic systems and rule-based models. An application example is provided, and some interpretation issues are explained.

In this paper the performance of k Nearest Neighbors and Multilayer Perceptron algorithm the is used in a classical task in the branch of the Civil Engineering: predict the level of service in the road. The use of fuzzy similarity quality measure method for calculating the weights of the features allows to performance of KNN and MLP in the case of mixed data (features with discrete or real domains). Experimental results show that this approach is better than other methods used to calculate the weight of the features. The results of the predictions of the level of service show the effectiveness of the method in the solution of problems of traffic engineering.

This paper presents a method for random variable generation based on the cumulative membership function. The proposed method uses fuzzy numbers and uniformly distributed random numbers to obtain a random variable, mainly used in simulation models.

In the anesthesiology area, supporting for surgical interventions are relevant to make these procedures pain-free and comfortable for the patients. Nowadays, complexity in those methods can be simple, where medical doctors perform entire work, or assistance systems for making decisions in this task. The purpose of this paper is to present the comparison between the decision making in anesthesiology process given by medical personnel and a fuzzy logic system output based on the same information. Results based on Kappa index show that fuzzy system can provide information with almost perfect agreement about quantities of gas that have to be supplied to patients in anesthesiology action.

Range extender is the core component of E-REV, its start-stop control determines the operation modes of vehicle. This paper based on a certain type of E-REV, optimized the control strategy for range extender start-stop with different driving cycle conditions and target mileage, and conducted the modeling and co-simulation of E-REV with Advisor and Simulink software. The simulation results with chosen driving cycle conditions indicated that certain target mileage, by correcting the battery SOC of range extender start-stop moment can reduce the running time of the range extender, reached the purpose of meeting the vehicle mileage and reducing consumption and emission at the same time.

Diamond-like carbon (DLC) thin films have been investigated for a wide range of applications due to their excellent electrical and mechanical properties. In the last decade, several researches and development activities have been conducted on the use of these thin films as piezoresistors in MEMS pressure sensors. This paper provides an overview on the design of a piezoresistive pressure sensor constituted of a silicon circular diaphragm with four DLC thin-film piezoresistors arranged in the Wheatstone bridge configuration. The sensor was designed from analytical formulas found in the literature.

This paper shows an estimate of some power quality indices for three different lighting technologies connected in the same electrical circuit. The devices considered are compact fluorescent lamp with electronic ballast, high intensity discharge metal halide lamp, and LED lamp; including nonlinear models of lamps for power quality analysis. These models are obtained by parameter identification, using optimization from the actual measurement of the line current of each lamp. Power analysis are based on IEEE 1459-2010 standard.

This paper deals with the development of a multiobjective optimization algorithm programmed in Matlab, which allows to obtain a voltage vs frequency ratio (V/F) that decreases the voltage value as the frequency is reduced by maintaining the total harmonic distortion (THD) at levels very close to 0% over the entire frequency range of operation. The multiobjective algorithm is applied to a common-source cascaded H-bridge multilevel converter with maximum 15 steps per line voltage. The inverter is simulated by supplying a three-phase induction motor.

In this paper the effects of voltage unbalance in both the Complex Current Unbalance Factor (CCUF) and the Torque Ripple Factor (TRF) of a three-phase induction motor have been discussed. The effects of 13060 points of voltage unbalance taken around the limit of standards (VUF = 2%) have been analyzed, since the largest number of international standards use this value as the limit. For the analysis, the single cage model of three-phase induction motor was used. The voltage unbalance has been characterized through four parameters: type, module of Complex Voltage Unbalance Factor (VUF), angle of Complex Voltage Unbalance Factor (θ_CVUF) and positive sequence voltage (V₁). The influence of each parameter in both module and angle of CCUF (CUF and θ_CCUF), and the Torque Ripple Factor (TRF) have been analyzed on those 13060 points. Results show that only VUF and V₁ parameters have a big influence in CUF and TRF.

This paper proposes a control method to provide the function of Solar Panel Emulation (SPE) to a low-cost programmable DC source. The proposed control operates with base on the error between measurement of the conductance connected at the output of the SPE and the conductance obtained evaluating the I-V characteristic of the panel model. The principle of operation is that the output of the controller modifies the value of the voltage introduced into the W-Lambert function to obtain the corresponding current enforcing the regulation of the measured conductance. In this paper, a controller composed by a nonlinear static gain and a conventional PI controller is proposed. The controller architecture configures a Wiener system structure which ensures the stable convergence of the real SPE variables to the values defined by the model. Simulation results are presented evaluating the dynamic performance of the system for irradiance and load variations. SPE method is implemented using simple measurement and conditioning circuits interacting with an application developed in LabVIEW through a data acquisition card. This implementation provides a complete solution highly applicable in research and industrial development.

This paper presents a proposal to charge and discharge valve-regulated lead-acid (VRLA) battery arrays integrated into microgrids by using the bidirectional Cûk converter with a multi-mode multi-loop control scheme based on sliding-mode control. The proposed control integrates the constant current - constant voltage - constant voltage charge method (CC-CV-CV) which is implemented using an inner current loop based on the sliding mode control of the input current of the converter and two nested outer loops based on the voltage and current of the battery. Conversely, the discharge is studied using two operational modes, one using a current reference given by a high level on a hierarchical structure and other using a current reference given by an outer Proportional-Integral (PI) controller regulating the voltage of the DC bus. The modelling of the bidirectional Cûk converter in these applications and its operational modes besides the analysis of the inner loop and the design criteria used in the synthesis of the outer loops are addressed. Simulation results are presented validating the control proposal.

This work addresses the planning of the collection of waste animal tissue in a Colombian rendering company. Over a week, the rendering company visits more than 800 slaughterhouses, butchers, and supermarkets in the Aburra’s Valley, the metropolitan area of Medellín (Colombia) to supply their plant (located in the outskirts of the city) with raw material that are transformed into value-added products. The underlying vehicle routing problem have several distinguishing features: periodicity, consistency, clustered customers and heterogeneous fleet. To solve this rich VRP we present a two-phase heuristic. The first phase of the heuristic groups the collection points using a capacitated concentrator location problem (CCLP). Then, in the second phase a mixed integer program schedules the visits of the collection points in each cluster to balance the number of visits performed daily based on the capacities of the available vehicles. These two phases aim at getting consistent and evenly spread visits during the week. Preliminary results with the data of the current operation reveal a savings potential of 5 out of 15 vehicles, and a better spread of the visits over the planning horizon.

The objective with the present paper is to find a solution method for the inventory routing problem, in which have been added several characteristics that resemble their behavior to the current companies’ reality. Between them the hard time windows, a dynamic component and a success probability applied to the routes between the nodes. In that case we use the dynamic programming with finite stages in order to optimize the routing of vehicles in each period, taking into account the dynamical changes implicit in the nodes and the other IRP characteristics.

For all organizations in the manufacturing and service sectors, transportation has become an important topic for the economic decision-making process. This is due to the high quality and versatility standards that today the industries have established to follow a strict guideline focused on Just-In-Time (JIT). In this context, the present paper extends on the analysis and improvement of a real-world routing problem of a Mexican company dedicated to the manufacturing of plastic products. The value chain of the company has its origin at the gathering (picking-up) of raw materials and components from different suppliers located in the United States. Currently, a third-party company (carrier) has been hired to perform the pickup process, however there is uncertainty regarding the optimality of its routes and their associated costs. The present research analyses the current routing scheme that is performed by the carrier to verify compliance of the requirements of the company which are minimization of costs and travel times. Then, the Capacitated Vehicle Routing Problem (CVRP) is performed for improvement of the current routing scheme. In addition, a new location for a depot (or collection center) is proposed to allow the company to manage its own raw material, avoiding the need to consider a third-party company for the picking-up services by means of the p-Median Problem (PMP). Optimization for the CVRP and PMP was performed with a specialized software and the results obtained by this research present an improved cost-efficient routing scheme for the picking-up process of the plastic company.

Activities in mining complexes contain multiple decisions that affect the operations of the system for the extraction, transformation, transport and storage of various subsoil components. The purpose of this research is the planning of continuous flow production systems for mixed products, in non-metallic mining extraction processes, considering bottlenecks and capacity planning. This paper presents a model for production, based on mathematical optimization, that facilitates the planning and management of operations in the area of extraction, crushing and transformation of a quarry of aggregates for construction, considering the resources and the constraints that allow to define effective strategies in the increase of the productivity of the lines of low production environment by scenarios. This research develops an analysis of bottlenecks and contrasts the nature of the production system by means of a mathematical model of optimization, which considers the capacities and balances in the flows of the Limestone production line. The mathematical model that maximizes profits can be adapted to systems of continuous flow production in mining complexes where their products are part of a reverse logistics process, analysis of alternatives of extraction, transformation and transport.

In this paper, we present a genetic algorithm embedded with mathematical optimization to solve a green inventory routing problem with interval fuel consumption. Using the idea of column generation in which only attractive routes are generated to the mathematical problem, we develop a genetic algorithm that allow us to determine speedily attractive routes that are connected to a mathematical model. We code our genetic algorithm using the idea of a integer number that represents all the feasible set of routes in which the maximum number allowed is the binary number that represents if a customer is visited or not. We approximate the fuel consumption as an interval number in which we want to minimize the overall fuel consumption of distribution. This is the first approximation made in the literature using this type of methodology so we cannot compare our approach with those used in the literature.

In this paper, we solve the Set Covering Problem with a meta-optimization approach. One of the most popular models among facility location models is the Set Covering Problem. The meta-level metaheuristic operates on solutions representing the parameters of other metaheuristic. This approach is applied to an Artificial Bee Colony metaheuristic that solves the non-unicost set covering. The Artificial Bee Colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. This metaheuristic owns a parameter set with a great influence on the effectiveness of the search. These parameters are fine-tuned by a Genetic Algorithm, which trains the Artificial Bee Colony metaheuristic by using a portfolio of set covering problems. The experimental results show the effectiveness of our approach which produces very near optimal scores when solving set covering instances from the OR-Library.

In this paper, we present a novel optimization algorithm named Discrete Bacterial Chemotaxis Optimization Algorithm DBCOA to solve the formation of manufacturing cells (MCs) problem, which consists in assigning machines and parts to a specific cell or family, considering the similarities in their manufacturing processes. The algorithm is based on BFOA with a discrete and hierarchical chemotaxis process that explore the search space to get the best solution. To evaluate the performance of the proposal, we use seven benchmark problems. The results were compared with the optimum solution and the performance of a Genetic Algorithm. In all benchmark problems, the proposed algorithm outperformed the baseline, giving the optimum solution in a short time.

Into the engineering projects is usual to see significant deviations from the planning global duration time to the real achieved. This situation presents some questions about the schedule planning activities effectiveness or control during the execution. The present work develops an algorithm for multi-project scheduling with dynamic resource allocation. The algorithm uses variable total resource availability and priority rules in order to schedule activities and renewable resources, like the manpower or the equipment. System performance has been measured on overall project makespan. The model has been proved using activity networks modeled on dynamic systems software for a case of study in a Bogotá- Colombia electricity distributor. The results have shown a time reduction on the global projects duration time.

The hybrid algorithm proposed by Liou, Hsieh [1] is a combination between genetic algorithm and particle swarm optimization that can be applied to the flow shop scheduling with constraints of families, setup times, transportation times, batch processing in some machines, and postponement of activities at the end of the work day, F _m |fmls, s _i,k , t _i,j,k , prmu, batch, day|C _max . In this hybrid algorithm, each sequence is a chromosome and at the same time an individual of a swarm, individuals produce new generations and die (like in genetic algorithms), and the decision of which chromosomes will undergo the application of the mutation and crossover operators is made on the basis of the individuals gbest and lbest (like in particle swarm optimization). Finally, the algorithm was benchmarked using Taillard’s instances and was applied in a real case, eleven real sequences of production were measured and the C _max decreased 16.45% on average, this is measured as the percent change of C _max given by the algorithm with respect to the C _max observed in the real case.

In Bogota Colombia early warning systems are a source of useful information on the care and prevention of disasters. This document will be the SIDISAT (district early warning system) network which has different types of sensors that record measurements of different variables such as rainfall, temperature, humidity, shock that could generate disasters or affect the safety, health, or tranquility of citizens, the network of air of Bogota as well as information about the recurrent risks affecting the working area monitoring.

Multiple Sclerosis (MS) is a central nervous system disease widely known for being a non-reversible degeneration process that affects young adults as well as elder people. Such disease affects the equilibrium function of the postural control system required to perform vital tasks such as walking or hold upright position. To palliate the long-term effects associated to MS, physicians perform therapeutic interventions on patients in order to preserve their motor capabilities as best as possible. In order to evaluate the effectiveness of therapeutic interventions, representative measurements or scores are used to assess the actual state of each patient. This work presents an exploratory approach to provide quantitative mediolateral and anteroposterior balance descriptors in MS through Diffusion Analysis on balance data acquired with a low-cost computational system using a Kinect device. The obtained results were compared against a subset of exercises of the Berg Balance Scale.

This article presents the importance of complementary technological tools in the student education and analyzes the impact on mathematics learning of students in fourth grade based on motivation and performance variables involved in each process of instruction conducted in the classroom with the implementation of the interactive tool Kinect Park developed by the authors. The analysis of the students behaviour in their interaction with the educational tool will make it possible to assess the importance of the inclusion of IT in the classroom which for a long time have been guided by traditional methodologies.

This work presents a specific implementation of the Allard approach to the epidemiological time series analysis by ARIMA and SARIMA modeling, intended to describe and predict the epidemic behavior of Varicella in the city of Bogotá D.C. (Colombia). Model selection and preliminary forecast evaluation supported on the official accounts of Varicella incidence rate are performed and reported for the interval 2010–2015. This approach yields a SARMA(3,0,1)(1,0,1) model, whose forecasting results were evaluated against real data of the year 2016. Performance comparison with alternative models and their potential use in the support of epidemic surveillance are also discussed.

Considering the impact of technological advances in mechanical engineering applications, the program offered in this profession at the University of Valle, Cali, Colombia added more than a decade the area of mechatronics. In consequence, it was necessary to adequate current infrastructure to complement the theoretical knowledge acquired in this new area. This paper presents the strategy used to adapt an infrastructure that facilitates learning through experimentation with the “mechatronics principles” course. Many aspects of user-centered design techniques were considered, such as usability, ergonomics, functionality, costs, among others. Thus, a functional prototype of mechatronics workstation was designed and manufactured.

In this work it was developed a new method to detect apneas in neonates infants through ECG and oximetry records.The work has been carried out and evaluated using The Apnea-ECG Database. The developed method analyzes automatically the register of the electrocardiogram and oximeter records and identifies periods of the normal breath and apneic incidents. The base of the proposed method is a system of pattern recognition that identifies the apnea through the analysis based on the domain of the frequency and the time of the ECG signal and the time domain of the oximeter signal. The method was evaluated using a crossed validation with 10 iterations and presents an error of 4.77 %. There is a specificity of 95.78% and a sensibility of 94.61% obtained.