431 research outputs found

    Prospects of Small Hydropower Technology

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    Small hydropower (SHP) belongs to renewable energy technology group and is a form of attractive power generation environmental perspective because of its potential to be found in small rivers and streams. Many countries use the technology of small hydro as a renewable energy source in order to minimize existing environmental effects in the production of electricity and have the maximum use of water, a renewable resource. This technology has shown prominence on the world stage with seemingly insignificant environmental effects on rivers, water channels, and dams

    On the Feasibility of Distinguishing Between Process Disturbances and Intrusions in Process Control Systems using Multivariate Statistical Process Control

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    Process Control Systems (PCSs) are the operat-ing core of Critical Infrastructures (CIs). As such, anomalydetection has been an active research field to ensure CInormal operation. Previous approaches have leveraged networklevel data for anomaly detection, or have disregarded theexistence of process disturbances, thus opening the possibility of mislabelling disturbances as attacks and vice versa. In thispaper we present an anomaly detection and diagnostic systembased on Multivariate Statistical Process Control (MSPC), thataims to distinguish between attacks and disturbances. For this end, we expand traditional MSPC to monitor process leveland controller level data. We evaluate our approach using the Tennessee-Eastman process. Results show that our approachcan be used to distinguish disturbances from intrusions to acertain extent and we conclude that the proposed approach canbe extended with other sources of data for improving results

    find the word that does not belong a framework for an intrinsic evaluation of word vector representations

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    We present a new framework for an intrinsic evaluation of word vector representations based on the outlier detection task. This task is intended to test the capability of vector space models to create semantic clusters in the space. We carried out a pilot study building a gold standard dataset and the results revealed two important features: human performance on the task is extremely high compared to the standard word similarity task, and stateof- the-art word embedding models, whose current shortcomings were highlighted as part of the evaluation, still have considerable room for improvement

    Multivariate Statistical Network Monitoring-Sensor: An effective tool for real-time monitoring and anomaly detection in complex networks and systems

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    Technology evolves quickly. Low-cost and ready-to-connect devices are designed to provide new services and applications. Smart grids or smart health care systems are some examples of these applications. In this totally connected scenario, some security issues arise due to the large number of devices and communications. In this way, new solutions for monitoring and detecting security events are needed to address new challenges brought about by this scenario, among others, the real-time requirement allowing quick security event detection and, consequently, quick response to attacks. In this sense, Intrusion Detection Systems are widely used though their evaluation often relies on the use of predefined network datasets that limit their application in real environments. In this work, a real-time and ready-to-use tool for monitoring and detecting security events is introduced. The Multivariate Statistical Network Monitoring-Sensor is based on the Multivariate Statistical Network Monitoring methodology and provides an alternative way for evaluating Multivariate Statistical Network Monitoring-based Intrusion Detection System solutions. Experimental results based on the detection of well-known attacks in hierarchical network systems prove the suitability of this tool for complex scenarios, such as those found in smart cities or Internet of Things ecosystem

    Group-Wise Principal Component Analysis for Exploratory Intrusion Detection

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    Intrusion detection is a relevant layer of cybersecurity to prevent hacking and illegal activities from happening on the assets of corporations. Anomaly-based Intrusion Detection Systems perform an unsupervised analysis on data collected from the network and end systems, in order to identify singular events. While this approach may produce many false alarms, it is also capable of identifying new (zeroday) security threats. In this context, the use of multivariate approaches such as Principal Component Analysis (PCA) provided promising results in the past. PCA can be used in exploratory mode or in learning mode. Here, we propose an exploratory intrusion detection that replaces PCA with Group-wise PCA (GPCA), a recently proposed data analysis technique with additional exploratory characteristics. A main advantage of GPCA over PCA is that the former yields simple models, easy to understand by security professionals not trained in multivariate tools. Besides, the workflow in the intrusion detection with GPCA is more coherent with dominant strategies in intrusion detection. We illustrate the application of GPCA in two case studies.This work was supported in part by the Spanish Government-MINECO (Ministerio de Economía y Competitividad), using the Fondo Europeo de Desarrollo Regional (FEDER), under Projects TIN2014-60346-R and Project TIN2017-83494-R

    Veintiún competencias laborales que desarrolla el trabajador social en la práctica escolar comunitaria UNAM

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    Este artículo enuncia los hallazgos encontrados en los 126 procesos de prácticas comunitarias realizadas durante el periodo 2007-2010, y que muestran la metodología utilizada con mayor frecuencia por los estudiantes de la licenciatura en Trabajo Social de la Universidad Nacional Autónoma de México (UNAM); se trata de una investigación documental cuantitativa, no experimental, de corte longitudinal y a nivel descriptivo que ofrece las competencias laborales que adquiere el estudiante. Se recolectó la información a través de la técnica de censo por medio de una cédula de información; como resultado presenta un modelo de competencias para el futuro trabajador social, el cual describe las capacidades que enfrenta este profesionista en los distintos tipos de comunidad en el Distrito Federal (DF) y el Estado de México. Esta indagación fue posible realizar gracias al trabajo colegiado de la Universidad Autónoma de Sinaloa (UAS), Universidad de Guadalajara (UdeG) y la Escuela Nacional de Trabajo Social (ENTS) de la UNAM en el marco de XVII Verano de la Investigación Científica y Tecnológica del Pacífico del Programa Delfín de la Academia Mexicana de Ciencias, cuyo hallazgo son las 21 competencias laborales que adquiere un alumno de la licenciatura de Trabajo Social en el ejercicio de la práctica escolar

    Review of Rotary Switched Reluctance Machine Design and Parameters Effect Analysis

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    The switched reluctance machine (SRM) has gained much interest in industrial applications, wind power systems, and electric vehicles. This happened because its main disadvantages, such as the ripple in the torque, were overcome due to continuous research and its advantages, such as simple and robust construction, ability to operate at high speeds and variable speeds, insensitivity to high temperatures, and fault tolerance, have made the SRM the right machine for many applications. The SRM project is apparently similar to the traditional machine design, but diverges on several points due to the unique features of the SRM. Over the years, several authors have proposed different project methodologies for SRM, each with its own particularities and often contradicting each other. Thus, for a beginner designer, the SRM project is a challenge from choosing the right design methodology to choosing the values of some dimensions, which are often empirical. This chapter aims to offer the beginner designer a detailed review of the main SRM design methodologies. In addition, an effect analysis will provide useful insights on how each design variable affects machine performance. The designer will thus have important data on which to base his choices during the SRM design

    Synthesis and characterization of nanocrystalline cellulose derived from Pineapple peel residues

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    Pineapple peel biomass was used as raw material for nanocellulose extraction. The raw material is a residue from the Costa Rican fruit industry. The nanocellulose was obtained by a two-step hydrolysis process. Firstly, the cellulose was hydrolyzed with HCl to obtain microcrystalline cellulose. In the second step, the hydrolysis was carried out using H2SO4 to obtain smaller fragments and decrease the lignin content. A time-dependent study was carried out to determine the particle size decrease depending on the contact time with the H2SO4. The chemical, thermal and morphological properties were analyzed by Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), dynamic light scattering (DLS), zeta potential, atomic force microscopy (AFM) and scanning electron microscopy (SEM). The nanofiber-like cellulose was obtained after 60 minutes of exposure to 65 wt% H2SO4
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