Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Artificial Neural Networks still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning ANN parameters. In recent years the use of hybrid technologies, combining Artificial Neural Networks and Genetic Algorithms, has been utilized to. In this work, several ANN topologies were trained and tested using Artificial Neural Networks and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out.

Redes para la promoción del desarrollo territorial en el cinturón hortícola platense : Reflexiones y aportes

En el cinturón hortícola platense áun conjunto de instituciones del sector público y privado, establecen redes de diálogo, en la que los sujetos interaccionan, en el marco de procesos de desarrollo regional. El trabajo tiene como objetivo, conocer yá analizar las redes de diálogo que contribuyen al desarrollo regional. Se utilizó una metodología de tipo cualitativa. Los resultados muestran la mayor densidad en las relaciones que se establecen desde distintas instituciones del sector público, para la ejecución de políticas. Asimismo, se visualiza el protagonismo e influencia que desde el sector privado, tienen los vendedores de insumos.

Redes para la promoción del desarrollo territorial en el cinturón hortícola platense : Reflexiones y aportes

En el cinturón hortícola platense áun conjunto de instituciones del sector público y privado, establecen redes de diálogo, en la que los sujetos interaccionan, en el marco de procesos de desarrollo regional. El trabajo tiene como objetivo, conocer yá analizar las redes de diálogo que contribuyen al desarrollo regional. Se utilizó una metodología de tipo cualitativa. Los resultados muestran la mayor densidad en las relaciones que se establecen desde distintas instituciones del sector público, para la ejecución de políticas. Asimismo, se visualiza el protagonismo e influencia que desde el sector privado, tienen los vendedores de insumos.

Redes para la promoción del desarrollo territorial en el cinturón hortícola platense : Reflexiones y aportes

En el cinturón hortícola platense áun conjunto de instituciones del sector público y privado, establecen redes de diálogo, en la que los sujetos interaccionan, en el marco de procesos de desarrollo regional. El trabajo tiene como objetivo, conocer yá analizar las redes de diálogo que contribuyen al desarrollo regional. Se utilizó una metodología de tipo cualitativa. Los resultados muestran la mayor densidad en las relaciones que se establecen desde distintas instituciones del sector público, para la ejecución de políticas. Asimismo, se visualiza el protagonismo e influencia que desde el sector privado, tienen los vendedores de insumos.

Changing the Face of Traditional Education

At large, research universities, a common approach for teaching hundreds of undergraduate students at one time is the traditional, large, lecture-based course. Trends indicate that over the next decade there will be an increase in the number of large, campus courses being offered as well as larger enrollments in courses currently offered. As universities investigate alternative means to accommodate more students and their learning needs, Web-based instruction provides an attractive delivery mode for teaching large, on-campus courses. This article explores a theoretical approach regarding how Web-based instruction can be designed and developed to provide quality education for traditional, on-campus, undergraduate students. The academic debate over the merit of Web-based instruction for traditional, on-campus students has not been resolved. This study identifies and discusses instructional design theory for adapting a large, lecture-based course to the Web.

Mathe-Üben in Studium und Abitur

Fehlende Grundkenntnisse in der Mathematik zählen zu den größten Hindernissen für einen erfolgreichen Start in ein Hochschulstudium. Studienanfänger in einem MINT-Studium bringen inzwischen deutlich unterschiedliche Vorrausetzungen mit: „Mathe-Angst“ gilt als typisches Phänomen und der Übergang in ein selbstbestimmtes Lernverhalten stellt eine große Herausforderung dar. Diese Fall-Studie beschreibt, wie mit Hilfe einer Mathe-App bereits zu Beginn des Studiums aktives Lernen unterstützt und selbstbestimmtes Lernen eingeübt werden kann. Das neue Kurskonzept mit App-Unterstützung stößt an der Hochschule Offenburg auf breite Akzeptanz. Der mobile BYOD-Ansatz ermöglicht Lern-Szenarien, die über PC- bzw.- Laptop-gebundene eLearning-Lösungen nicht realisierbar sind. Der Inhalt des MassMatics-Vorbereitungskurs orientiert sich am Mindestanforderungskatalog des cosh-Arbeitskreises für den Übergang Schule-Hochschule. In der Zwischenzeit wurde der App-gestützte Kurs mit seinen über 500 Aufgaben von mehr als 1000 Studierenden besucht.

Aplicaciones de los modelos de predicción bio-inspirados en la administración

Las organizaciones y sus entornos son sistemas complejos. Tales sistemas son difíciles de comprender y predecir. Pese a ello, la predicción es una tarea fundamental para la gestión empresarial y para la toma de decisiones que implica siempre un riesgo. Los métodos clásicos de predicción (entre los cuales están: la regresión lineal, la Autoregresive Moving Average y el exponential smoothing) establecen supuestos como la linealidad, la estabilidad para ser matemática y computacionalmente tratables. Por diferentes medios, sin embargo, se han demostrado las limitaciones de tales métodos. Pues bien, en las últimas décadas nuevos métodos de predicción han surgido con el fin de abarcar la complejidad de los sistemas organizacionales y sus entornos, antes que evitarla. Entre ellos, los más promisorios son los métodos de predicción bio-inspirados (ej. redes neuronales, algoritmos genéticos /evolutivos y sistemas inmunes artificiales). Este artículo pretende establecer un estado situacional de las aplicaciones actuales y potenciales de los métodos bio-inspirados de predicción en la administración.

Intrusion detection techniques in wireless local area networks

This research investigates wireless intrusion detection techniques for detecting attacks on IEEE 802.11i Robust Secure Networks (RSNs). Despite using a variety of comprehensive preventative security measures, the RSNs remain vulnerable to a number of attacks. Failure of preventative measures to address all RSN vulnerabilities dictates the need for a comprehensive monitoring capability to detect all attacks on RSNs and also to proactively address potential security vulnerabilities by detecting security policy violations in the WLAN. This research proposes novel wireless intrusion detection techniques to address these monitoring requirements and also studies correlation of the generated alarms across wireless intrusion detection system (WIDS) sensors and the detection techniques themselves for greater reliability and robustness. The specific outcomes of this research are: A comprehensive review of the outstanding vulnerabilities and attacks in IEEE 802.11i RSNs. A comprehensive review of the wireless intrusion detection techniques currently available for detecting attacks on RSNs. Identification of the drawbacks and limitations of the currently available wireless intrusion detection techniques in detecting attacks on RSNs. Development of three novel wireless intrusion detection techniques for detecting RSN attacks and security policy violations in RSNs. Development of algorithms for each novel intrusion detection technique to correlate alarms across distributed sensors of a WIDS. Development of an algorithm for automatic attack scenario detection using cross detection technique correlation. Development of an algorithm to automatically assign priority to the detected attack scenario using cross detection technique correlation.

Aerial image analysis using spiking neural networks with application to power line corridor monitoring

Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques.

Anomaly detection in online social networks : using data-mining techniques and fuzzy logic

This research is a step forward in improving the accuracy of detecting anomaly in a data graph representing connectivity between people in an online social network. The proposed hybrid methods are based on fuzzy machine learning techniques utilising different types of structural input features. The methods are presented within a multi-layered framework which provides the full requirements needed for finding anomalies in data graphs generated from online social networks, including data modelling and analysis, labelling, and evaluation.

Multi-objective models and techniques for analysing the absolute capacity of railway networks

Railway capacity determination and expansion are very important topics. In prior research, the competition between different entities such as train services and train types, on different network corridors however have been ignored, poorly modelled, or else assumed to be static. In response, a comprehensive set of multi-objective models have been formulated in this article to perform a trade-off analysis. These models determine the total absolute capacity of railway networks as the most equitable solution according to a clearly defined set of competing objectives. The models also perform a sensitivity analysis of capacity with respect to those competing objectives. The models have been extensively tested on a case study and their significant worth is shown. The models were solved using a variety of techniques however an adaptive E constraint method was shown to be most superior. In order to identify only the best solution, a Simulated Annealing meta-heuristic was implemented and tested. However a linearization technique based upon separable programming was also developed and shown to be superior in terms of solution quality but far less in terms of computational time.