Tecnura

15-31

Authors: Jesser James Marulanda Durango, Andrés Escobar Mejía, Luis Alfonso Alzate Gómez

Context: One of the functions of an Active Power Filter (APF) is to calculate current set points (i.e. harmonic reduction, power factor correction and current and voltage balancing) that compensate the power source. There are several algorithms in the literature that perform this function, so the objective in this article is to present the results obtained from the implementation of five algorithms proposed in the literature, under conditions of distorted voltages. Method: The methodology consists of the implementation of the following algorithms: Instantaneous reactive power; unit power factor; perfect harmonic compensation; PQR algorithm; and synchronous frame of reference in the MATLAB simulation program. Then, we calculate the total harmonic distortion (THD), the effective value of the line current and its fundamental component, and the power factor (PF) at the three-phase source, using an electrical system composed of linear and non-linear loads with similar characteristics to a real circuit. We will take values before and after the connection to the network of the active power filter, considering each of the algorithms separately and maintaining the same conditions in the system for each of them. Other quantities are also calculated in the power system to make the comparison. Results: The main currents before and after the active power filter connection are displayed, in addition to the harmonic distortion of the currents and the power factor. We also present the waveform of the inverter continuous voltage with each of the algorithms used, in order to evaluate the performance of a voltage controller that requires filter. Conclusions: In operating conditions with distorted voltages in the power supply network, the algorithm with the best performance is the perfect harmonic compensation algorithm, since it uses a low-pass filter to mitigate the effects of the harmonics of the voltages on the calculation of the currents of compensation.

45-52

Authors: Jeison Marín Alfonso, Leonardo Betancur Agudelo, Henry Arguello Fuentes

Context: It is possible to capture High-resolution 3D hyper-spectral images in a single 2D image through techniques based on compressed sensing. A variety of architectures have proposed Compressive Spectral Imaging (CSI) technique during the last years. An optical camera designed to capture spatio-spectral information of the scene prints projections towards a Focal Plane Array (FPA) giving the capability of storing or transmitting them. Afterwards, the original image can be reconstructed via an -norm-based optimization algorithm. The size in bytes of the FPA measurement is less than the original image; for that reason, this FPA is considered a 2D compressed version of the original 3D image. Objective: To perform a further compression of the FPA measurement for four CSI architectures, in order to increase transfer rates or to decrease storing sizes. Method: In this work, the design of the further compression using arithmetic coding is presented for four CSI architectures, and an inverse transformation is proposed. This transformation is applied to the FPA based on the structure of the optical filters and the coded apertures of the cameras used in the CSI, allowing an increasing in the compression rate. Results: Results show that the compression rate rises between 1 and 2 points in three of the architectures. Conclusions: Despite data loss in the process of transformation-quantification-compression-decompression of the FPA, the quality of the reconstructed data cube (expressed in terms of the PSNR between the reconstructed image and the original one) remains close to the original version with no further compression.

68-78

Authors: Hans Raul Marquez, Lizeth Camila Salgado, Cesar Hernández

Context: The developed scheme allows carrying out the assignment of several frequency channels (both contiguous and not contiguous) available to the secondary users that require a higher bandwidth, under an environment of equality. Objective: The following paper develops a multichannel assignment which allows taking in a more efficient way the spectral opportunities in cognitive radio networks. Method: The developed assignment model is composed by the K-Means algorithm, which is in charged of carrying out the grouping of channels through clusters for the best parameters, and another algorithm in charge of establishing an equal criteria for all the secondary users that wish to transmit. The reached results were evaluated with experimental spectral occupancy data taken from the GSM frequency band. The developed model was compared with the MFA-CRN algorithm. Results: The obtained measurements correspond to the average bandwidth, the average delay, and the fairness calculation in the assignment of several channels. The developed assignment model shows an improvement in the assignment of a higher transmission average bandwidth for each secondary user while maintaining the fairness criteria in the channel assignments. Conclusion: Despite the increasing in the number of handoffs, it is also observed that metrics such as average bandwidth, average throughput and average delay are never negatively impacted by this increase in handoffs.

88-101

Authors: Gloria Amaris, Humberto Ávila, Thomas Guerrero

Context: Climate change effects, human interventions, and river characteristics are factors that increase the risk on the population and the water resources. However, negative impacts such as flooding, and river droughts may be previously identified using appropriate numerical tools. Objectives: The annual volume (Millions of m3/year) time series of the Magdalena River was analyzed by an ARIMA model, using the historical time series of the Calamar station (Instituto de Hidrología, Meteorología y Estudios Ambientales de Colombia - IDEAM), and looking for matching the modelling hypothesis with the data measured in the river. Methods: The ARIMA model is considered one of the most implemented approaches in hydrology and studies related to climatic variability because it considers non-stationary information. Results: The maximum volume forecasted of the Magdalena River from 2013 to 2024 oscillates between 289,695 million m3 and 309,847 million m3. The minimum volume forecast for the same period ranges from 179,123 million m3 to 157,764 million m3, with a decreasing trend of 106 million m3 in 100 years. Conclusions: The simulated results obtained with the ARIMA model compared to the observed data showed a fairly good adjustment of the minimum and maximum magnitudes. This allows concluding that it is a good tool for estimating minimum and maximum volumes, even though this model is not capable of simulating the exact behaviour of an annual volume time series.

111-129

Authors: Francisco Javier Moreno, Nataly Castrillón Charari, Camilo Taborda Zuluaga

Context: One of the strategies for managing large volumes of data is distributed and parallel computing. Among the tools that allow applying these characteristics are some Data Base Management Systems (DBMS), such as Oracle, DB2, and SQL Server. Method: In this paper we present a case study where we evaluate the performance of an SQL query in two of these DBMS. The evaluation is done through various forms of data distribution in a computer network with different degrees of parallelism. Results: The tests of the SQL query evidenced the performance differences between the two DBMS analyzed. However, more thorough testing and a wider variety of queries are needed. Conclusions: The differences in performance between the two DBMSs analyzed show that when evaluating this aspect, it is necessary to consider the particularities of each DBMS and the degree of parallelism of the queries.