Revista Ingenieria Solidaria



Revista Ingenieria Solidaria

Ingeniería Solidaria

eISSN: 2357-6014 H index: 14
Editor: Gloria Jeanette Rincón Aponte Coeditor: Vijender Kumar Solanki
Start : 2005 Periodicity : Continuous edition Language : English
Scope : Scientific, electronic, international, open access Journal whose objective is the communication of applied and sustainable research articles with a social vocation of unpublished works, covering all areas of engineering and technology. The publication does not generate any cost for the authors.
Indexed in : Clarivate, MIAR, Google, Latindex Directory, Biblat, Publindex, REdIB, EGlobal, EBSCOhost.

  • Design of real-time cow behavior monitoring system based on wireless sensor networks and K-Means clustering algorithm
    por Duc-Tan Tran el septiembre 6, 2021 a las 12:00 am

    Introduction: The present article is the product of the research whose code CS20.04, carried out during 2020. This work was supported by the Institute of Information Technology (IOIT), Vietnam Academy of Science and Technology (VAST). Problem: Animal monitoring is a significant problem in the agricultural sector. The primary purpose is to monitor the health of animals regularly. Consequently, animal welfare and product quality could be improved, leading to an improvement in profit. Cow behavior recognition system was considered as the right solution for cow monitoring. The requirements for this kind of system are economical, high performance, and real-time. Objective: The research objective is to design a real-time cow monitoring system based on wireless sensor networks and the K-means clustering algorithm. Methodology: A wireless sensor node was designed to measure the collar-mounted acceleration data using an accelerometer. Firstly, the collected data were classified into three classes based on the VeDBA (Vector of Dynamic Body Acceleration) feature using the K-means algorithm. Then, the thresholds for VeDBA in the previous step were used to classify new data. Results: Three behaviors (including feeding, lying, and standing) were classified in real-time with the accuracy of classification about 89%.   Conclusion: The proposed system could be adapted in monitoring cow in real-time, the behavior classification could be implemented on the microcontroller. The results confirmed the reliability of the proposed system. Originality: The behavior classification could be implemented on the microcontroller for the first time in monitoring cow. Limitations: Only three behaviors were classified in the experiment.

  • Cyberbullying detection on multi-modal data using pre-trained deep learning architectures
    por subbaraju pericherla el septiembre 6, 2021 a las 12:00 am

    Cyberbullying is a big challenging task in the social media era. The forms of bullying are increasing with the increase of digital technologies. In the past, most of the bullying happened through text messages. Now bullies take advantage of technology, they try bullying others in different forms such as images, videos, and emojis. In this paper, we proposed an approach to identify cyberbullying on both text and image data combinations. We used RoBERTa and Xception deep learning architectures to generate word embeddings from the text data and the image respectively. LightGBM classifier is used to classify bullying and non-bullying tweets. The experiments conducted on 2100 samples of combined data of text and image. The proposed approach efficiently classifies bullying data with F1-score of 80% and outperforms as compared to existing approaches.

  • Optimal LQG controller to adjust the rudder supplying water to the turbine of small and medium hydro power plants
    por Giang Le Ngoc el septiembre 6, 2021 a las 12:00 am

    Introduction: This paper is the result of the research “Optimal LQG controller to open rudder supplying water to turbine of small and medium hydro power plants” developed in the Electric Power University in Vietnam in 2019. Problem: To maintain the frequency of the emitted voltage of the generator at the nominal value of 50 Hz, the authors present a solution to apply the optimal control theory to create a command to control the rudder angle to adjust the water flow into the turbine. Objective: The article presented about the order value formation algorithm, to stabilize the frequency of transmission voltage at 50 Hz standard value. Methodology: In this paper, the laws to control the rudder supplying water to turbine of small and medium hydro power plants are synthesized by optimal control theory. To establish optimal control rules, the paper proposes using the Kalman filter to estimate the state of the object. By that, the frequency of generated voltage will be stabilized under changing load. The efficacy of the steady and dynamic performance of the control strategy was verified using Matlab/Simulink software. Results: The proposed system can compensate for power fluctuations and is effective in terms of power regulation. Conclusion: The algorithm presented in the paper is the basis for setting up the software when when designing and manufacturing the turbine - generator combination. Applying this algorithm, the process of adjusting the transmitted power according to the required load will be performed with quality. Originality: This paper’s contribution lies in its employment of an effective optimal LQG control for varying operating conditions. Limitations: The authors need to spend more time to the development and and test this algorithm and and implementing it in practice.

  • Design of a comprehensive methodology for the lean manufacturing implementation in the colombian context
    por Luz Angela Ospina Jiménez el septiembre 6, 2021 a las 12:00 am

    Abstract: Introduction: This article is the product of the research "design of a comprehensive methodology for the implementation of lean manufacturing in the Colombian context", carried out during the years 2019, 2020 and 2021 in Bogotá, Cundinamarca. Problem: Existing Lean Manufacturing implementation methodologies do not take into account the organizational aspects of the culture of the country in which it is being developed, thus making its full implementation difficult. Objective: Propose a comprehensive implementation methodology that allows increasing the successful application of Lean Manufacturing in the Colombian industry. Methodology: This article presents the review and analysis of the existing implementation methodologies and the results of the analysis of problems in implementation processes in the Colombian industry, as well as the comprehensive implementation methodology designed. Results: To achieve the successful implementation of lean manufacturing in the Colombian context, an adequate follow-up of the implementation process is required, the commitment of the company's senior managers, the definition of appropriate leaders for the implementation process and the adequate training of personnel. Conclusion: This project seeks to propose a flexible methodology that adjusts to organizational conditions. Originality: This research integrates key success factors associated with the Colombian context in the implementation methodology. Limitations: The sample size of the diagnosis of the degree of implementation of Lean Manufacturing in the Colombian industry from which the key success factors to be included in the methodology are taken.

  • Clustering Framework to Cope with COVID-19 for Cities in Turkey
    por Didem Guleryuz el septiembre 6, 2021 a las 12:00 am

    Introduction: This article is the product of the research “Clustering Framework to Cope with COVID-19 for Cities in Turkey”, developed at Bayburt University in 2021. Problem: Turkey's risk map, presented in January 2021, to take local decisions in tackling the COVID-19 pandemic was based on confirmed cases only. Health, socio-economic and environmental indicators are also important for management decisions of COVID-19. The risk map to be designed by adding these indicators will support more effective decisions. Objective: The research aims to propose a clustering scheme to design a risk map of cities for Turkey. Methodology: The unsupervised clustering algorithm suggested dividing the cities of Turkey into clusters, considering health, socio-economic, environmental indicators, and the spread pattern of COVID-19. Results: We found that cities are clustered into five groups while megacity Istanbul alone formed a cluster, three of Turkey's largest cities formed another cluster. Other clusters consist of 19, 26, and 32 cities, respectively. The most important determinants which have predictive power are identified. Conclusion: The suggested clustering method can be a decision support system for policymakers to determine the differences and similarities of cities in quarantine decisions and normalization phases for the following periods of the pandemic. Originality: To the best of our knowledge, this study differs from previous studies because countries were grouped in previous studies only considering the confirmed cases. In this study, cities were clustered in terms of the health, socio-economic, and environmental indicators to make decisions locally. Limitations: The distribution of confirmed cases by age could be added, especially to make decisions about education, but this data is not officially announced.  

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