Introduction de la vidéosurveillance intelligente à domicile:Perception des acteurs du système de santé à l’égard de leur pratique, de l’accessibilité et de l’image transmise

Introduction : Les personnes âgées vivant à domicile font des chutes qui peuvent entraîner des conséquences graves. La possibilité de les détecter et d’intervenir rapidement grâce à la vidéosurveillance intelligente constitue une avenue prometteuse. Objectif : L’étude vise à explorer la perception et la récep-tivité des acteurs du système de santé face à l’utilisation de cette technologie sous trois aspects : la transmission des images, l’accessibilité de leur clientèle à cette technologie et son introduction dans leur pratique. Méthodologie : Sept groupes de discussion focalisée ont permis de recueillir le point de vue de 31 participants. Une analyse de contenu avec le logiciel N’Vivo a été réalisée. Résultats : Les participants estiment que les images de chute pourraient parve-nir au CLSC, au proche aidant, au 911 ou à une centrale de surveillance. Ils considèrent que des critères d’admissibilité seraient nécessaires pour favoriser l’accessibilité à la vidéosurveillance intelligente qui, croient-ils, pourrait améliorer les interventions en termes de gestion des urgences lors de chutes et de déter-mination de leurs causes. Conclusion : Les participants sont favorables à la technologie proposée, mais ils requerraient une période d’adaptation afin d’ajuster leur pratique. Ils suggèrent la réalisation d’un projet pilote qui validerait cette technologie.

Revêtement intelligent à base des silices mésoporeuses fonctionnalisées pour le relargage stimulé d’agents antimicrobiens

Les biofilms bactériens sont composés d’organismes unicellulaires vivants au sein d’une matrice protectrice, formée de macromolécules naturelles. Des biofilms non désirés peuvent avoir un certain nombre de conséquences néfastes, par exemple la diminution du transfert de chaleur dans les échangeurs de chaleurs, l’obstruction de membranes poreuses, la contamination des surfaces coques de navires, etc. Par ailleurs, les bactéries pathogènes qui prolifèrent dans un biofilm posent également un danger pour la santé s’ils croissent sur des surfaces médicales synthétiques comme des implants biomédicaux, cathéters ou des lentilles de vue. De plus, la croissance sur le tissu naturel par certaines souches des bactéries peut être fatale, comme Pseudomonas aeruginosa dans les poumons. Cependant, la présence de biofilms reste difficile à traiter, car les bactéries sont protégées par une matrice extracellulaire. Pour tenter de remédier à ces problèmes, nous proposons de développer une surface antisalissure (antifouling) qui libère sur demande des agents antimicrobiens. La proximité et la disposition du système de relargage placé sous le biofilm, assureront une utilisation plus efficace des molécules antimicrobiennes et minimiseront les effets secondaires de ces dernières. Pour ce faire, nous envisageons l’utilisation d’une couche de particules de silice mésoporeuses comme agents de livraison d’agents antimicrobiens. Les nanoparticules de silice mésoporeuses (MSNs) ont démontré un fort potentiel pour la livraison ciblée d’agents thérapeutiques et bioactifs. Leur utilisation en nano médecine découle de leurs propriétés de porosité intéressantes, de la taille et de la forme ajustable de ces particules, de la chimie de leur surface et leur biocompatibilité. Ces propriétés offrent une flexibilité pour diverses applications. De plus, il est possible de les charger avec différentes molécules ou biomolécules (de tailles variées, allant de l’ibuprofène à l’ARN) et d’exercer un contrôle précis des paramètres d’adsorption et des cinétiques de relargage (désorption). Mots Clés : biofilms, nanoparticules de silice mésoporeuses, microfluidique, surface antisalissure.

Urban management revolution : intelligent management systems for ubiquitous cities

A successful urban management support system requires an integrated approach. This integration includes bringing together economic, socio-cultural and urban development with a well orchestrated transparent and open decision making mechanism. The paper emphasises the importance of integrated urban management to better tackle the climate change, and to achieve sustainable urban development and sound urban growth management. This paper introduces recent approaches on urban management systems, such as intelligent urban management systems, that are suitable for ubiquitous cities. The paper discusses the essential role of online collaborative decision making in urban and infrastructure planning, development and management, and advocates transparent, fully democratic and participatory mechanisms for an effective urban management system that is particularly suitable for ubiquitous cities. This paper also sheds light on some of the unclear processes of urban management of ubiquitous cities and online collaborative decision making, and reveals the key benefits of integrated and participatory mechanisms in successfully constructing sustainable ubiquitous cities.

Application of the analytic hierarchy process (AHP) in multi-criteria analysis of the selection of intelligent building systems

The availability of innumerable intelligent building (IB) products, and the current dearth of inclusive building component selection methods suggest that decision makers might be confronted with the quandary of forming a particular combination of components to suit the needs of a specific IB project. Despite this problem, few empirical studies have so far been undertaken to analyse the selection of the IB systems, and to identify key selection criteria for major IB systems. This study is designed to fill these research gaps. Two surveys: a general survey and the analytic hierarchy process (AHP) survey are proposed to achieve these objectives. The first general survey aims to collect general views from IB experts and practitioners to identify the perceived critical selection criteria, while the AHP survey was conducted to prioritize and assign the important weightings for the perceived criteria in the general survey. Results generally suggest that each IB system was determined by a disparate set of selection criteria with different weightings. ‘Work efficiency’ is perceived to be most important core selection criterion for various IB systems, while ‘user comfort’, ‘safety’ and ‘cost effectiveness’ are also considered to be significant. Two sub-criteria, ‘reliability’ and ‘operating and maintenance costs’, are regarded as prime factors to be considered in selecting IB systems. The current study contributes to the industry and IB research in at least two aspects. First, it widens the understanding of the selection criteria, as well as their degree of importance, of the IB systems. It also adopts a multi-criteria AHP approach which is a new method to analyse and select the building systems in IB. Further research would investigate the inter-relationship amongst the selection criteria.

Fusion of in-vehicle sensor data to develop Intelligent Driver Training System (IDTS)

The over represented number of novice drivers involved in crashes is alarming. Driver training is one of the interventions aimed at mitigating the number of crashes that involve young drivers. To our knowledge, Advanced Driver Assistance Systems (ADAS) have never been comprehensively used in designing an intelligent driver training system. Currently, there is a need to develop and evaluate ADAS that could assess driving competencies. The aim is to develop an unsupervised system called Intelligent Driver Training System (IDTS) that analyzes crash risks in a given driving situation. In order to design a comprehensive IDTS, data is collected from the Driver, Vehicle and Environment (DVE), synchronized and analyzed. The first implementation phase of this intelligent driver training system deals with synchronizing multiple variables acquired from DVE. RTMaps is used to collect and synchronize data like GPS, vehicle dynamics and driver head movement. After the data synchronization, maneuvers are segmented out as right turn, left turn and overtake. Each maneuver is composed of several individual tasks that are necessary to be performed in a sequential manner. This paper focuses on turn maneuvers. Some of the tasks required in the analysis of ‘turn’ maneuver are: detect the start and end of the turn, detect the indicator status change, check if the indicator was turned on within a safe distance and check the lane keeping during the turn maneuver. This paper proposes a fusion and analysis of heterogeneous data, mainly involved in driving, to determine the risk factor of particular maneuvers within the drive. It also explains the segmentation and risk analysis of the turn maneuver in a drive.

Research Monograph: Managing ubiquitous eco cities: telecommunication infrastructure networks, technology convergence and intelligent urban management systems

Efficient and effective urban management systems for Ubiquitous Eco Cities require having intelligent and integrated management mechanisms. This integration includes bringing together economic, socio-cultural and urban development with a well orchestrated, transparent and open decision making mechanism and necessary infrastructure and technologies. In Ubiquitous Eco Cities telecommunication technologies play an important role in monitoring and managing activities over wired, wireless or fibre-optic networks. Particularly technology convergence creates new ways in which the information and telecommunication technologies are used and formed the back bone or urban management systems. The 21st Century is an era where information has converged, in which people are able to access a variety of services, including internet and location based services, through multi-functional devices such as mobile phones and provides opportunities in the management of Ubiquitous Eco Cities. This research paper discusses the recent developments in telecommunication networks and trends in convergence technologies and their implications on the management of Ubiquitous Eco Cities and how this technological shift is likely to be beneficial in improving the quality of life and place of residents, workers and visitors. The research paper reports and introduces recent approaches on urban management systems, such as intelligent urban management systems, that are suitable for Ubiquitous Eco Cities.

Intelligent urban management systems : online decision support systems for ubiquitous cities

A successful urban management support system requires an integrated approach. This integration includes bringing together economic, socio-cultural and urban development with a well orchestrated, transparent and open decision making mechanism. The chapter emphasizes the importance of integrated urban management to better tackle the climate change, and to achieve sustainable urban development and sound urban growth management. This chapter introduces recent approaches on urban management systems, such as intelligent urban management systems, that are suitable for ubiquitous cities. The chapter discusses the essential role of online collaborative decision making in urban and infrastructure planning, development and management, and advocates transparent, fully democratic and participatory mechanisms for an effective urban management system that is particularly suitable for ubiquitous cities. This chapter also sheds light on some of the unclear processes of urban management of ubiquitous cities and online collaborative decision making, and reveals the key benefits of integrated and participatory mechanisms in successfully constructing sustainable ubiquitous cities.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.