A Hybrid Data-Driven Web-Based UI-UX Assessment Model

Today, a large proportion of end user information systems have their Graphical User Interfaces (GUI) built with web-based technology (JavaScript, CSS, and HTML). Some of these web-based systems include: Internet of Things (IOT), Infotainment (in vehicles), Interactive Display Screens (for digital menu boards, information kiosks, digital signage displays at bus stops or airports, bank ATMs, etc.), and web applications/services (on smart devices). As such, web-based UI must be evaluated in order to improve upon its ability to perform the technical task for which it was designed. This study develops a framework and a processes for evaluating and improving the quality of web-based user interface (UI) as well as at a stratified level. The study develops a comprehensive framework which is a conglomeration of algorithms such as the multi-criteria decision making method of analytical hierarchy process (AHP) in coefficient generation, sentiment analysis, K-means clustering algorithms and explainable AI (XAI)

Overløpskontroll i avløpsnett med forskjellige modelleringsteknikker og internet of things

Increased urbanization and extreme rainfall events are causing more frequent instances of sewer overflow, leading to the pollution of water resources and negative environmental, health, and fiscal impacts. At the same time, the treatment capacity of wastewater treatment plants is seriously affected. The main aim of this Ph.D. thesis is to use the Internet of Things and various modeling techniques to investigate the use of real-time control on existing sewer systems to mitigate overflow. The role of the Internet of Things is to provide continuous monitoring and real-time control of sewer systems. Data collected by the Internet of Things are also useful for model development and calibration. Models are useful for various purposes in real-time control, and they can be distinguished as those suitable for simulation and those suitable for prediction. Models that are suitable for a simulation, which describes the important phenomena of a system in a deterministic way, are useful for developing and analyzing different control strategies. Meanwhile, models suitable for prediction are usually employed to predict future system states. They use measurement information about the system and must have a high computational speed. To demonstrate how real-time control can be used to manage sewer systems, a case study was conducted for this thesis in Drammen, Norway. In this study, a hydraulic model was used as a model suitable for simulation to test the feasibility of different control strategies. Considering the recent advances in artificial intelligence and the large amount of data collected through the Internet of Things, the study also explored the possibility of using artificial intelligence as a model suitable for prediction. A summary of the results of this work is presented through five papers. Paper I demonstrates that one mainstream artificial intelligence technique, long short-term memory, can precisely predict the time series data from the Internet of Things. Indeed, the Internet of Things and long short-term memory can be powerful tools for sewer system managers or engineers, who can take advantage of real-time data and predictions to improve decision-making. In Paper II, a hydraulic model and artificial intelligence are used to investigate an optimal in-line storage control strategy that uses the temporal storage volumes in pipes to reduce overflow. Simulation results indicate that during heavy rainfall events, the response behavior of the sewer system differs with respect to location. Overflows at a wastewater treatment plant under different control scenarios were simulated and compared. The results from the hydraulic model show that overflows were reduced dramatically through the intentional control of pipes with in-line storage capacity. To determine available in-line storage capacity, recurrent neural networks were employed to predict the upcoming flow coming into the pipes that were to be controlled. Paper III and Paper IV describe a novel inter-catchment wastewater transfer solution. The inter-catchment wastewater transfer method aims at redistributing spatially mismatched sewer flows by transferring wastewater from a wastewater treatment plant to its neighboring catchment. In Paper III, the hydraulic behaviors of the sewer system under different control scenarios are assessed using the hydraulic model. Based on the simulations, inter-catchment wastewater transfer could efficiently reduce total overflow from a sewer system and wastewater treatment plant. Artificial intelligence was used to predict inflow to the wastewater treatment plant to improve inter-catchment wastewater transfer functioning. The results from Paper IV indicate that inter-catchment wastewater transfer might result in an extra burden for a pump station. To enhance the operation of the pump station, long short-term memory was employed to provide multi-step-ahead water level predictions. Paper V proposes a DeepCSO model based on large and high-resolution sensors and multi-task learning techniques. Experiments demonstrated that the multi-task approach is generally better than single-task approaches. Furthermore, the gated recurrent unit and long short-term memory-based multi-task learning models are especially suitable for capturing the temporal and spatial evolution of combined sewer overflow events and are superior to other methods. The DeepCSO model could help guide the real-time operation of sewer systems at a citywide level.publishedVersio

Online risk assessment using a bank of Kalman Filters and event tree

Early detection of faults in a process plant is important in order to prevent of happening catastrophic events which might cause deaths, economic, and environmental losses. Recently, on-line calculation of risk and its use for monitoring of process faults were proposed by [Bao et al., 2011]. In this study, a new methodology is proposed which brings more clarity in the calculation of risk from online monitoring of process data. In the proposed methodology, process faults have been classified into two groups: hardware failure and disturbance type faults. First a “Bank of Kalman Filters” is used to detect and diagnose possible failures occurred in the system. Based on the fault category, if it is a disturbance type fault, the estimated states are used directly to calculate the probability of fault. On the other hand, for hardware failure, residuals obtained from Kalman Filter are used to update the probabilities of the affected gates of the “Event Tree”, and the probability of occurrence of a catastrophic event is calculated. Next, the risk of operating system under the current condition is calculated using the updated probability and severity. Results show that using the combination of “bank of Kalman Filter” and “Event Tree Analysis” brings more clarity to risk calculation and improves the detection time of the failure. Based on the calculated risk, operators can prioritize the faults and take appropriate action to the most critical one which ensures process safety

Connectivity Analysis in Vehicular Ad-hoc Network based on VDTN

In the last decade, user demand has been increasing exponentially based on modern communication systems. One of these new technologies is known as mobile ad-hoc networking (MANET). One part of MANET is called a vehicular ad-hoc network (VANET). It has different types such as vehicle-to-vehicle (V2V), vehicular delay-tolerant networks, and vehicle-to-infrastructure (V2I). To provide sufficient quality of communication service in the Vehicular Delay-Tolerant Network (VDTN), it is important to present a comprehensive survey that shows the challenges and limitations of VANET. In this paper, we focus on one type of VANET, which is known as VDTNs. To investigate realistic communication systems based on VANET, we considered intelligent transportation systems (ITSs) and the possibility of replacing the roadside unit with VDTN. Many factors can affect the message propagation delay. When road-side units (RSUs) are present, which leads to an increase in the message delivery efficiency since RSUs can collaborate with vehicles on the road to increase the throughput of the network, we propose new methods based on environment and vehicle traffic and present a comprehensive evaluation of the newly suggested VDTN routing method. Furthermore, challenges and prospects are presented to stimulate interest in the scientific community

Design of an event-based early warning system for process operations

This thesis proposes a new methodology to design an event-based warning system as an alternative to the conventional variable-based alarm system. This study initially explores the options for grouping process variables for alarm allocation. Several grouping methods are discussed and an event-based grouping procedure is detailed. Selection of the key variables for a group is performed considering the information that the variables contain to distinguish between an abnormal and a normal condition. The information theory is used to quantify the information content of a variable about an event to select the key variables. The cross-correlation analysis between pairs of key variables is used to identify the redundant variables. Simulation study using the model of a continuous stirred tank reactor (CSTR) is used to demonstrate the methodology. The proposed event-based early warning system utilizing online measurements is detailed in the thesis. In this approach, warnings are assigned to plant abnormal events instead of individual variables. To assess the likelihoods of undesirable events, the Bayesian Network is used; the event likelihoods are estimated in real time utilizing online measurements. Diagnostic analysis is conducted to identify root-causes of events. By assigning warning to events, the methodology results in significantly lower number of warnings compared to traditional variable-based warning (alarms) system. It also enables early warning of a possible event along with an efficient diagnosis of the root-causes of the event. Experimental testing using a level control system is presented to demonstrate the efficacy of the proposed method. Simulation study using the model of a CSTR is also presented to demonstrate the performance of the algorithm. Both, experimental and simulation studies, have shown promising results

A role-based software architecture to support mobile service computing in IoT scenarios

The interaction among components of an IoT-based system usually requires using low latency or real time for message delivery, depending on the application needs and the quality of the communication links among the components. Moreover, in some cases, this interaction should consider the use of communication links with poor or uncertain Quality of Service (QoS). Research efforts in communication support for IoT scenarios have overlooked the challenge of providing real-time interaction support in unstable links, making these systems use dedicated networks that are expensive and usually limited in terms of physical coverage and robustness. This paper presents an alternative to address such a communication challenge, through the use of a model that allows soft real-time interaction among components of an IoT-based system. The behavior of the proposed model was validated using state machine theory, opening an opportunity to explore a whole new branch of smart distributed solutions and to extend the state-of-the-art and the-state-of-the-practice in this particular IoT study scenario.Peer ReviewedPostprint (published version