Aircraft interior failure pattern recognition utilizing text mining and neural networks

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Identification of structural damage in flexible structures using system norm and neural networks

Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.

Creating capabilities in local environments and production networks

Includes bibliography

Vertical integration and information sharing — an empirical investigation in the German apparel industry

Despite the astounding success of the fast fashion retailers, the management practices leading to these results have not been subject to extensive research so far. Given this background, we analyze the impact of information sharing and vertical integration on the performance of 51 German apparel companies. We find that the positive impact of vertical integration is mediated by information sharing, i.e. that the ability to improve the information flow is a key success factor of vertically integrated apparel supply chains. Thus, the success of an expansion strategy based on vertical integration critically depends on effective ways to share logistical information.

Context-Aware Adaptive Opportunistic Routing in Mobile Ad-hoc and Sensor Networks

Mobile ad-hoc networks (MANETs) and wireless sensor networks (WSNs) have been attracting increasing attention for decades due to their broad civilian and military applications. Basically, a MANET or WSN is a network of nodes connected by wireless communication links. Due to the limited transmission range of the radio, many pairs of nodes in MANETs or WSNs may not be able to communicate directly, hence they need other intermediate nodes to forward packets for them. Routing in such types of networks is an important issue and it poses great challenges due to the dynamic nature of MANETs or WSNs. On the one hand, the open-air nature of wireless environments brings many difficulties when an efficient routing solution is required. The wireless channel is unreliable due to fading and interferences, which makes it impossible to maintain a quality path from a source node to a destination node. Additionally, node mobility aggravates network dynamics, which causes frequent topology changes and brings significant overheads for maintaining and recalculating paths. Furthermore, mobile devices and sensors are usually constrained by battery capacity, computing and communication resources, which impose limitations on the functionalities of routing protocols. On the other hand, the wireless medium possesses inherent unique characteristics, which can be exploited to enhance transmission reliability and routing performance. Opportunistic routing (OR) is one promising technique that takes advantage of the spatial diversity and broadcast nature of the wireless medium to improve packet forwarding reliability in multihop wireless communication. OR combats the unreliable wireless links by involving multiple neighboring nodes (forwarding candidates) to choose packet forwarders. In opportunistic routing, a source node does not require an end-to-end path to transmit packets. The packet forwarding decision is made hop-by-hop in a fully distributed fashion. Motivated by the deficiencies of existing opportunistic routing protocols in dynamic environments such as mobile ad-hoc networks or wireless sensor networks, this thesis proposes a novel context-aware adaptive opportunistic routing scheme. Our proposal selects packet forwarders by simultaneously exploiting multiple types of cross-layer context information of nodes and environments. Our approach significantly outperforms other routing protocols that rely solely on a single metric. The adaptivity feature of our proposal enables network nodes to adjust their behaviors at run-time according to network conditions. To accommodate the strict energy constraints in WSNs, this thesis integrates adaptive duty-cycling mechanism to opportunistic routing for wireless sensor nodes. Our approach dynamically adjusts the sleeping intervals of sensor nodes according to the monitored traffic load and the estimated energy consumption rate. Through the integration of duty cycling of sensor nodes and opportunistic routing, our protocol is able to provide a satisfactory balance between good routing performance and energy efficiency for WSNs.

Analysis and visualization of the dynamics of research groups in terms of projects and co-authored publications : A case study of library and information science in Argentina

Objective: The present study offers a novel methodological contribution to the study of the configuration and dynamics of research groups, through a comparative perspective of the projects funded (inputs) and publication co-authorships (output). Method: A combination of bibliometric techniques and social network analysis was applied to a case study: the Departmento de Bibliotecología (DHUBI), Universidad Nacional de La Plata, Argentina, for the period 2000-2009. The results were interpreted statistically and staff members of the department, were interviewed. Results: The method makes it possible to distinguish groups, identify their members and reflect group make-up through an analytical strategy that involves the categorization of actors and the interdisciplinary and national or international projection of the networks that they configure. The integration of these two aspects (input and output) at different points in time over the analyzed period leads to inferences about group profiles and the roles of actors. Conclusions: The methodology presented is conducive to micro-level interpretations in a given area of study, regarding individual researchers or research groups. Because the comparative input-output analysis broadens the base of information and makes it possible to follow up, over time, individual and group trends, it may prove very useful for the management, promotion and evaluation of science

Analysis and visualization of the dynamics of research groups in terms of projects and co-authored publications : A case study of library and information science in Argentina

Objective: The present study offers a novel methodological contribution to the study of the configuration and dynamics of research groups, through a comparative perspective of the projects funded (inputs) and publication co-authorships (output). Method: A combination of bibliometric techniques and social network analysis was applied to a case study: the Departmento de Bibliotecología (DHUBI), Universidad Nacional de La Plata, Argentina, for the period 2000-2009. The results were interpreted statistically and staff members of the department, were interviewed. Results: The method makes it possible to distinguish groups, identify their members and reflect group make-up through an analytical strategy that involves the categorization of actors and the interdisciplinary and national or international projection of the networks that they configure. The integration of these two aspects (input and output) at different points in time over the analyzed period leads to inferences about group profiles and the roles of actors. Conclusions: The methodology presented is conducive to micro-level interpretations in a given area of study, regarding individual researchers or research groups. Because the comparative input-output analysis broadens the base of information and makes it possible to follow up, over time, individual and group trends, it may prove very useful for the management, promotion and evaluation of science

Analysis and visualization of the dynamics of research groups in terms of projects and co-authored publications : A case study of library and information science in Argentina

Objective: The present study offers a novel methodological contribution to the study of the configuration and dynamics of research groups, through a comparative perspective of the projects funded (inputs) and publication co-authorships (output). Method: A combination of bibliometric techniques and social network analysis was applied to a case study: the Departmento de Bibliotecología (DHUBI), Universidad Nacional de La Plata, Argentina, for the period 2000-2009. The results were interpreted statistically and staff members of the department, were interviewed. Results: The method makes it possible to distinguish groups, identify their members and reflect group make-up through an analytical strategy that involves the categorization of actors and the interdisciplinary and national or international projection of the networks that they configure. The integration of these two aspects (input and output) at different points in time over the analyzed period leads to inferences about group profiles and the roles of actors. Conclusions: The methodology presented is conducive to micro-level interpretations in a given area of study, regarding individual researchers or research groups. Because the comparative input-output analysis broadens the base of information and makes it possible to follow up, over time, individual and group trends, it may prove very useful for the management, promotion and evaluation of science

Floating car data augmentation based on infrastructure sensors and neural networks

The development of new-generation intelligent vehicle technologies will lead to a better level of road safety and CO2 emission reductions. However, the weak point of all these systems is their need for comprehensive and reliable data. For traffic data acquisition, two sources are currently available: 1) infrastructure sensors and 2) floating vehicles. The former consists of a set of fixed point detectors installed in the roads, and the latter consists of the use of mobile probe vehicles as mobile sensors. However, both systems still have some deficiencies. The infrastructure sensors retrieve information fromstatic points of the road, which are spaced, in some cases, kilometers apart. This means that the picture of the actual traffic situation is not a real one. This deficiency is corrected by floating cars, which retrieve dynamic information on the traffic situation. Unfortunately, the number of floating data vehicles currently available is too small and insufficient to give a complete picture of the road traffic. In this paper, we present a floating car data (FCD) augmentation system that combines information fromfloating data vehicles and infrastructure sensors, and that, by using neural networks, is capable of incrementing the amount of FCD with virtual information. This system has been implemented and tested on actual roads, and the results show little difference between the data supplied by the floating vehicles and the virtual vehicles.

The Optimal combination: Grammatical Swarm, Particle Swarm Optimization and Neural Networks.

Social behaviour is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks.

Evaluating alternative industrial network organizations and information systems

Purpose – The purpose of this paper is to present a simulation‐based evaluation method for the comparison of different organizational forms and software support levels in the field of supply chain management (SCM). Design/methodology/approach – Apart from widely known logistic performance indicators, the discrete event simulation model considers explicitly coordination cost as stemming from iterative administration procedures. Findings - The method is applied to an exemplary supply chain configuration considering various parameter settings. Curiously, additional coordination cost does not always result in improved logistic performance. Influence factor variations lead to different organizational recommendations. The results confirm the high importance of (up to now) disregarded dimensions when evaluating SCM concepts and IT tools. Research limitations/implications – The model is based on simplified product and network structures. Future research shall include more complex, real world configurations. Practical implications – The developed method is designed for the identification of improvement potential when SCM software is employed. Coordination schemes based only on ERP systems are valid alternatives in industrial practice because significant investment IT can be avoided. Therefore, the evaluation of these coordination procedures, in particular the cost due to iterations, is of high managerial interest and the method provides a comprehensive tool for strategic IT decision making. Originality/value – Reviewed literature is mostly focused on the benefits of SCM software implementations. However, ERP system based supply chain coordination is still widespread industrial practice but associated coordination cost has not been addressed by researchers.

Disparate connectivity for structural and functional networks is revealed when physical location of the connected nodes is considered

Macroscopic brain networks have been widely described with the manifold of metrics available using graph theory. However, most analyses do not incorporate information about the physical position of network nodes. Here, we provide a multimodal macroscopic network characterization while considering the physical positions of nodes. To do so, we examined anatomical and functional macroscopic brain networks in a sample of twenty healthy subjects. Anatomical networks are obtained with a graph based tractography algorithm from diffusion-weighted magnetic resonance images (DW-MRI). Anatomical con- nections identified via DW-MRI provided probabilistic constraints for determining the connectedness of 90 dif- ferent brain areas. Functional networks are derived from temporal linear correlations between blood-oxygenation level-dependent signals derived from the same brain areas. Rentian Scaling analysis, a technique adapted from very- large-scale integration circuits analyses, shows that func- tional networks are more random and less optimized than the anatomical networks. We also provide a new metric that allows quantifying the global connectivity arrange- ments for both structural and functional networks. While the functional networks show a higher contribution of inter-hemispheric connections, the anatomical networks highest connections are identified in a dorsal?ventral arrangement. These results indicate that anatomical and functional networks present different connectivity organi- zations that can only be identified when the physical locations of the nodes are included in the analysis.

Supply and Demand Side Limitations Affecting the Structure of Agriculture and the Rural Economy. Factor Markets Working Paper No. 21, February 2012

The paper criticises the neo-classical assumptions of perfect factor markets and of complete information, which constitute central elements in labour market theory. Based on literature review and on economic reports from transition economies, as well as developing countries and more advanced economies, this deliverable focuses on the structural impediments and imperfections which often characterise rural labour markets and which may prevent an efficient allocation of labour. According to empirical studies, transactions costs and rigidities hinder the well-functioning of labour markets and constrain labour adjustments. The paper attempts to classify the various limitations of rural labour markets from both supply and demand side, although the distinction is not always clear-cut as some problems occur on both sides. The identification of these issues is extremely important as it allows us to highlight the inefficiencies and the failures in labour markets and to understand their impact on labour allocation. In this context, market intervention is desirable and the paper provides particular support for rural development policies such as investments in human capital. Lastly, labour institutions can play a key role in promoting the well functioning of labour markets, thus it is fundamental that they are well in place.