Compressed air is a widespread but costly energy carrier. Leaks account for 10 compressed air consumption in production facilities and their removal offers high potential for cost reduction. The turbulent flow from a leak causes broadband acoustic emissions. These are exploited for leak detection using a narrowband ultrasonic microphone that is insensitive to audible noise. A parabolic mirror or an acoustic horn is utilized to enhance the directivity and the received signal power of the microphone. The microphone is mounted on mobile service robots. In the project "Robotair" an automated guided vehicle (AGV) and a remotely controlled micro aerial vehicle (MAV) were used for robotized inspection of production facilities. In order to detect leaks, predefined areas are scanned. A leak is detected and localized based on the sensed peak amplitude of the ultrasound signal. The corresponding pose of the sensor facing the leak is determined based on the selflocalization of the robot. Leak localization is carried out by triangulation of two sensor poses. Tests were conducted in an automobile production facility to evaluate the performance of the system. A leak was placed in an assembly line and multiple measurements were taken from two positions with the AGV and the MAV. The leaks were successfully detected and localized.

Compressed air is a widespread but costly energy carrier. Leaks account for 10 compressed air consumption in production facilities and their removal offers high potential for cost reduction. The turbulent flow from a leak causes broadband acoustic emissions. These are exploited for leak detection using a narrowband ultrasonic microphone that is insensitive to audible noise. A parabolic mirror or an acoustic horn is utilized to enhance the directivity and the received signal power of the microphone. The microphone is mounted on mobile service robots. In the project "Robotair" an automated guided vehicle (AGV) and a remotely controlled micro aerial vehicle (MAV) were used for robotized inspection of production facilities. In order to detect leaks, predefined areas are scanned. A leak is detected and localized based on the sensed peak amplitude of the ultrasound signal. The corresponding pose of the sensor facing the leak is determined based on the selflocalization of the robot. Leak localization is carried out by triangulation of two sensor poses. Tests were conducted in an automobile production facility to evaluate the performance of the system. A leak was placed in an assembly line and multiple measurements were taken from two positions with the AGV and the MAV. The leaks were successfully detected and localized.

Industrial Engineering What you need to know to engineer the global service economy. As customers and service providers create new value through globally interconnected service enterprises, service engineers are finding new opportunities to innovate, design, and manage the service operations and processes of the new service-based economy. Introduction to Service Engineering provides the tools and information a service engineer needs to fulfill this critical new role. The book introduces engineers as well as students to the fundamentals of the theory and practice of service engineering, covering the characteristics of service enterprises, service design and operations, customer service and service quality, web-based services, and innovations in service systems. Readers explore such key aspects of service engineering as: * The role of service science in developing a smarter planet * Service enterprises, including: enterprise value creation, architecture of service organizations, service enterprise modeling, and the application of methods of systems engineering to services * Service design, including collaborative e-service systems and the new service development process * Service operations and management, including service call centers * Service quality, from design operations to customer relations * Web-based services and technology in the global e-organization * Innovation in service systems from service engineering to integrative solutions, service-oriented architecture solutions, and technology transfer streams With chapters written by fifty-seven specialists and edited by bestselling authors Gavriel Salvendy and Waldemar Karwowski, Introduction to Service Engineering uses numerous examples, problems, and real-world case studies to help readers master the knowledge and the skills required to succeed in service engineering.

This paper proposes a framework for Composite Alternative Recommendation Development (CARD), which supports composite product and service definitions, top-k decision optimization, and dynamic preference learning. Composite services are characterized by a set of sub-services, which, in turn, can be composite or atomic. Each atomic and composite service is associated with metrics, such as cost, duration, and enjoyment ranking. The framework is based on the Composite Recommender Knowledge Base, which is composed of views, including Service Metric Views that specify services and their metrics; Recommendation Views that specify the ranking definition to balance optimality and diversity; parametric Transformers that specify how service metrics are defined in terms of metrics of its subservices; and learning sets from which the unknown parameters in the transformers are iteratively learned. Also introduced in the paper is the top-k selection criterion that, based on a vector of utility metrics, provides the balance between the optimality of individual metrics and the diversity of recommendations. To exemplify the framework, specific views are developed for a travel package recommender system.

We study the case of integrating situational reasoning into a mobile service recommendation system. Since mobile Internet services are rapidly proliferating, finding and using appropriate services require profound service descriptions. As a consequence, for average mobile users it is nowadays virtually impossible to find the most appropriate service among the many offered. To overcome these difficulties, task navigation systems have been proposed to guide users towards best-fitting services. Our goal is to improve the user experience of such task navigation systems making them context-aware (i.e. to optimize service navigation by taking the user's situation into account). We propose the integration of a situational reasoning engine that applies classification-based inference to qualitative context elements, gathered from multiple sources and represented using ontologies. The extended task navigator enables the delivery of situation-aware recommendations in a proactive way. Initial experiments with the extended system indicate a considerable improvement of the navigator's usability.