this paper evaluates the use of a text mining tool to support learning of science concepts. the tool, called Sobek, extracts relevant information from unstructured data and represents it visually in a graph. Sobek was...
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ISBN:
(纸本)9783319988726;9783319988719
this paper evaluates the use of a text mining tool to support learning of science concepts. the tool, called Sobek, extracts relevant information from unstructured data and represents it visually in a graph. Sobek was used here in an experiment with 36 students in 9th grade who had to learn concepts related to the particulate nature of matter. Students were divided in control (16) and experimental group (20). Students in the experimental group interacted with Sobek after reading a few texts, while the students in the control group carried out the activity in a more traditional way (reading/answering questions). Results from the experiment favored students in the experimental group, which led to the conclusion that Sobek did help students in the learning task.
the continuously expanding demand for faster and more efficient communication, dictates the research and development of more contemporary telecommunication systems, such as the FSO, characterized by their very high da...
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ISBN:
(纸本)9781728111841
the continuously expanding demand for faster and more efficient communication, dictates the research and development of more contemporary telecommunication systems, such as the FSO, characterized by their very high data rates and bandwidth. However, the optical beam, propagating through the atmosphere, suffers from phenomena with negative impact on the performance of the overall communication system. A commonly used technique to counterbalance these obstacles is to use a hybrid FSO/RF or FSO/MMW system. In this work, we further develop this idea, by studying a triple hybrid FSO/MMW/RF system, operating over turbulent atmospheric channel with pointing errors, along with Weibull and Rayleigh fading for the MMW and RF links, respectively. the mathematical expression for the outage performance estimation of the triple hybrid system is derived and its efficiency is verified through the numerical results.
data-driven fault diagnosis and classification for wind turbine systems have received much attention due to a large amount of data available recorded by supervisory control and data acquisition (SCADA) systems and sma...
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ISBN:
(纸本)9781728143958
data-driven fault diagnosis and classification for wind turbine systems have received much attention due to a large amount of data available recorded by supervisory control and data acquisition (SCADA) systems and smart meters. It is of interest but challenging to diagnose and classify multiple faults occurring simultaneously in a system monitored. In this study, a data-driven and supervised machine learning-based fault diagnosis and classification algorithm is addressed by the combination and consolidation among Hilbert-Huang Transformation (HHT), Multi-Linear Principal Component Analysis (MPCA), and Support Vector Machine (SVM) to enhance the feasibility and capability of fault diagnosis and classification for systems subjected to multiple faults. the algorithm proposed is applied to the 4.8 MW wind turbine benchmark model, where multiple actuator faults are taken into considerations. the effectiveness of the methodology is demonstrated by using intensive simulations and comparison studies.
In the face of increasing cyber threats and the expansion of computer connections and applications, strong protection against cyber-attacks has become necessary. Intrusion Detection systems (IDS) are necessary to dete...
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In the evolving landscape of smart grids (SGs), Supervisory control and data Acquisition (SCADA) systems play a pivotal role in ensuring the seamless operation of power networks. However, their critical nature makes t...
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Recent approaches to model-based manipulator control involve data-drivenlearning of the inverse dynamics relationship of a manipulator, eliminating the need for any knowledge of the system model. Ideally, such algori...
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ISBN:
(纸本)9783642215377;9783642215384
Recent approaches to model-based manipulator control involve data-drivenlearning of the inverse dynamics relationship of a manipulator, eliminating the need for any knowledge of the system model. Ideally, such algorithms should be able to process large amounts of data in an online and incremental manner, thus allowing the system to adapt to changes in its model structure or parameters. Locally Weighted Projection Regression (LWPR) and other non-parametric regression techniques have been applied to learn manipulator inverse dynamics. However, a common issue amongst these learning algorithms is that the system is unable to generalize well outside of regions where it has been trained. Furthermore, learning commences entirely from 'scratch,' making no use of any a-priori knowledge which may be available. In this paper, an online, incremental learning algorithm incorporating prior knowledge is proposed. Prior knowledge is incorporated into the LWPR framework by initializing the local linear models with a first order approximation of the available prior information. It is shown that the proposed approach allows the system to operate well even without any initial training data, and further improves performance with additional online training.
the proceedings contain 21 papers. the special focus in this conference is on International conference on Software Process Improvement . the topics include: Gamification in Software Engineering: A Tertiary Study;knowl...
ISBN:
(纸本)9783030335465
the proceedings contain 21 papers. the special focus in this conference is on International conference on Software Process Improvement . the topics include: Gamification in Software Engineering: A Tertiary Study;knowledge Transfer in Software Companies Based on Machine learning;linked data and Musical Information to Improvement the Cultural and Heritage Knowledge Management;distributed System Based on Deep learning for Vehicular Re-routing and Congestion Avoidance;from a Conceptual to a Computational Model of Cognitive Emotional Process for Engineering Students;Algorithm Proposal to control a Robotic Arm for Physically Disable People Using the LCD Touch Screen;multithreading Programming for Feature Extraction in Digital Images;selection and Fusion of Color Channels for Ripeness Classification of Cape Gooseberry Fruits;model driven Automatic Code Generation: An Evolutionary Approach to Disruptive Innovation Benefits;cluster Monitoring and Integration in Technology Company;objectives Patterns Applied to the Business Model of a Public Education System;a datadriven Platform for Improving Performance Assessment of Software Defined Storage Solutions;teaching Approach for the Development of Virtual Reality Videogames;requirements Validation in the Information systems Software Development: An Empirical Evaluation of Its Benefits for a Public Institution in Lima;towards a Social and Human Factor Classification Related to Productivity in Software Development Teams;software Product Quality in DevOps Contexts: A Systematic Literature Review;Reinforcing DevOps Generic Process with a Guidance Based on the Basic Profile of ISO/IEC 29110;a Selection Process of Graph databases Based on Business Requirements.
there have been constant debates on several aspects of the Brazilian secondary education including student's selection process and engagement, and quality of learning among other issues. the Federal Institutes (IF...
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Automatic program repair plays a crucial role in the software development and implementation. While deep learning-based approaches have made significant progress, one inherent challenge is the inefficiency in code rep...
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In recent years, several research works have proposed the analysis of network flow information using machine learning in order to detect threats or anomalous activities. In this sense, NetFlow-based systems stand out ...
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ISBN:
(纸本)9781665406949
In recent years, several research works have proposed the analysis of network flow information using machine learning in order to detect threats or anomalous activities. In this sense, NetFlow-based systems stand out as one of the main sources of network flow information. In these systems, NetFlow collectors provide the flow monitoring information to be analyzed, but the particular information structure and format provided by different collector implementations is a recurring problem. In this paper, a new YANG data model is proposed as a standard model to use NetFlow-based monitoring data. In order to validate the proposal, a NetFlow collector incorporating the proposed NetFlow YANG model has been developed, to be integrated in a network scenario in which network flows are analyzed to detect malicious cryptomining activity. this collector extends an existing one, and provides design patterns to incorporate other existing collectors into this common data model. Our results show how, by using the YANG modeling language, network flow information can be handled and aggregated in a formal and unified way that provides flexibility and facilitates data analysis applied to threat detection.
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