The safety case and the lessons learned for the reliability and maintainability case

This paper examine the safety case and the lessons learned for the reliability and maintainability case

Modeling and real-time control of urban drainage systems: A review

Urban drainage systems (UDS) may be considered large-scale systems given their large number of associated states and decision actions, making challenging their real-time control (RTC) design. Moreover, the complexity of the dynamics of the UDS makes necessary the development of strategies for the control design. This paper reviews and discusses several techniques and strategies commonly used for the control of UDS. Moreover, the models to describe, simulate, and control the transport of wastewater in UDS are also reviewed.This work has been partially supported by Mexichem, Colombia through the project “Drenaje Urbano y Cambio Climático: Hacia los Sistemas de Alcantarillado del Futuro.” Fase II, with reference No. 548-2012, the scholarships of Colciencias No. 567-2012 and 647-2013, and the project ECOCIS (Ref. DPI2013-48243-C2-1-R).Peer Reviewe

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Human-centered automation of air traffic control operations in the terminal area

Cover titleNovember 2, 1994Series statement handwritten on coverProposal for the Interdepartmental Doctoral Program in Human Factors and AutomationIncludes bibliographical references (p. 70-73)Introduction: Air Traffic Control operations are described extensively in the ATC manuals such as the Airman's Information Manual [1] and the ATC Controller's Handbook [2]. Mathematical analysis has also been conducted for the ATC operations as evident in the many theses that have been published in ATC research [3, 4, 5]. A brief description is due here however in order to provide a background for the following document. There are six major ATC functions in the terminal area and a summary of their description in Sadoune's thesis [5] follows: Flow Management: The flow management purpose is to provide efficient transition between the en-route corridors and the terminal area through the metering fixes. The en-route corridors are the airways connecting the airports, the terminal area is the designated space around the airport, and the metering fixes are the points at which aircraft enter the terminal area under the flow control process called metering. The flow management system is capable of delivering the aircraft to the metering fix at predetermined time, altitude, and speed, minimizing fuel consumption and flight time. Beyond the metering fix however the concern in no longer fuel and cost, it is the separation between the aircraft and the landing schedule. Ground-based flight path generation is needed at that point. Runway Scheduling: The runway capacity is the limiting factor of the flow of traffic at congested airports. There are many reasons why runways are not used efficiently in the current tactical practice. These include the independent scheduling of landings and takeoffs, the ad hoc fashion in which takeoffs are inserted between landings, and the common use of the first-come-first-serve approach which is fair but not optimal. Runway scheduling is a queuing process and can be optimized for maximum throughput, long term service, and minimum delays of aircraft, taking into account fuel consumption, duration of flight, and other factors. The difficulty is in the dynamic nature of the schedule where modifications are needed as new entrants arrive or as environmental conditions change. The determination of the runway capacity and its improvement through the use of advanced technologies are discussed in Flow Control: Through traffic redistribution the flow control process helps smooth the demand fluctuations leading to a controlled number of aircraft simultaneously present in the terminal area. Two processes accomplish flow control: metering and holding. Metering divides the approach to the airport into successive stages between metering fixes. The flow management system delivers the aircraft to the metering fixes at the predetermined time, altitude, and speed. Holding points are assigned where holding aircraft are stacked and isolated from traffic. Holding aircraft circle in holding patterns awaiting landing clearance. Therefore, while metering moves the delays resulting from the runway capacity upstream, holding extends the flight path in time to accommodate arrival delays. These practices however can result in idle runway time in favor of more flow control leading to less efficient use of the runway. Flight Path Generation: There are standard routes both from the terminal area entry points to the runway for approach and from the runway to the en-route corridors for departure. These predefined routes can be used at low traffic flow rates, and add to the precision since automatic flight control systems are capable of flying along them automatically. However they are not optimal in using the space, or in exploiting the aircraft capabilities, or in maximizing the runway capacity. Automated flight path generation allows the incorporation of the space organization, the ATC separation criteria, the landing and takeoff schedule, the aircraft dynamics and performance limitations, and the maneuvering characteristics of the pilot in generating more optimal and flexible paths. This subject will be emphasized further in this document. Path Conformance Monitoring: In order to supervise the execution of the flight path plan, the radar surveillance system provides vague and non-precise measurement of the position of the aircraft. The controllers base their estimates of the conformance on 2-dimensional radar displays, and have to wait few intervals to estimate the direction of the aircraft. To adjust for the path conformance error the controllers issue heading, altitude, and speed clearances (vectors) to the pilots. Communication between controllers and pilots is done via radio transmission. Errors result from misunderstanding between the pilot and the controller, pilot response, as well as wind and unexpected atmospheric disturbances. Again new technologies and more automation are expected to improve the path conformance capabilities. These include better surveillance using satellites, digital data links for communication between the controller and the pilot, and display of the path to the pilot on board the aircraft. Questions of resolution and threshold of the conformance error become critical to the automation of the monitoring function. Hazard Monitoring: This includes detecting possible collisions between aircraft and with the ground. There is a trade off between false alarms and missed alarms in setting the threshold for the hazard alarm. Namely the more conservative the alarm threshold is set, the less is the risk of collision due to a missed alarm. But the disturbance to the traffic flow caused by the large number of false alarms is higher

Learning-based Predictive Control Approach for Real-time Management of Cyber-physical Systems

Cyber-physical systems (CPSs) are composed of heterogeneous, and networked hardware and software components tightly integrated with physical elements [72]. Large-scale CPSs are composed of complex components, subject to uncertainties [89], as though their design and development is a challenging task. Achieving reliability and real-time adaptation to changing environments are some of the challenges involved in large-scale CPSs development [51]. Addressing these challenges requires deep insights into control theory and machine learning. This research presents a learning-based control approach for CPSs management, considering their requirements, specifications, and constraints. Model-based control approaches, such as model predictive control (MPC), are proven to be efficient in the management of CPSs [26]. MPC is a control technique that uses a prediction model to estimate future dynamics of the system and generate an optimal control sequence over a prediction horizon. The main benefit of MPC in CPSs management comes from its ability to take the predictions of system’s environmental conditions and disturbances into account [26]. In this dissertation, centralized and distributed MPC strategies are designed for the management of CPSs. They are implemented for the thermal management of a CPS case study, smart building. The control goals are optimizing system efficiency (lower thermal power consumption in the building), and improving users’ convenience (maintaining desired indoor thermal conditions in the building). Model-based control strategies are advantageous in the management of CPSs due to their ability to provide system robustness and stability. The performance of a model-based controller strongly depends on the accuracy of the model as a representation of the system dynamics [26]. Accurate modeling of large-scale CPSs is difficult (due to the existence of unmodeled dynamics and uncertainties in the modeling process); therefore, modelbased control approach is not practical for these systems [6]. By incorporating machine learning with model-based control strategies, we can address CPS modeling challenges while preserving the advantages of model-based control methods. In this dissertation, a learning-based modeling strategy incorporated with a model-based control approach is proposed to manage energy usage and maintain thermal, visual, and olfactory performance in buildings. Neural networks (NNs) are used to learn the building’s performance criteria, occupant-related parameters, environmental conditions, and operation costs. Control inputs are generated through the model-based predictive controller and based on the learned parameters, to achieve the desired performance. In contrast to the existing building control systems presented in the literature, the proposed management system integrates current and future information of occupants (convenience, comfort, activities), building energy trends, and environment conditions (environmental temperature, humidity, and light) into the control design. This data is synthesized and evaluated in each instance of decision-making process for managing building subsystems. Thus, the controller can learn complex dynamics and adapt to the changing environment, to achieve optimal performance while satisfying problem constraints. Furthermore, while many prior studies in the filed are focused on optimizing a single aspect of buildings (such as thermal management), and little attention is given to the simultaneous management of all building objectives, our proposed management system is developed considering all buildings’ physical models, environmental conditions, comfort specifications, and occupants’ preferences, and can be applied to various building management applications. The proposed control strategy is implemented to manage indoor conditions and energy consumption in a building, simulated in EnergyPlus software. In addition, for comparison purposes, we designed and simulated a baseline controller for the building under the same conditions