Event-based Sediment Yield Modeling using Artificial Neural Network

In the present study, a back propagation feedforward artificial neural network (ANN) model was developed for the computation of event-based temporal variation of sediment yield from the watersheds. The training of the network was performed by using the gradient descent algorithm with automated Bayesian regularization, and different ANN structures were tried with different input patterns. The model was developed from the storm event data (i.e. rainfall intensity, runoff and sediment flow) registered over the two small watersheds and the responses were computed in terms of runoff hydrographs and sedimentographs. Selection of input variables was made by using the autocorrelation and cross-correlation analysis of the data as well as by using the concept of travel time of the watershed. Finally, the best fit ANN model with suitable combination of input variables was selected using the statistical criteria such as root mean square error (RMSE), correlation coefficient (CC) and Nash efficiency (CE), and used for the computation of runoff hydrographs and sedimentographs. Further, the relative performance of the ANN model was also evaluated by comparing the results obtained from the linear transfer function model. The error criteria viz. Nash efficiency (CE), error in peak sediment flow rate (EPS), error in time to peak (ETP) and error in total sediment yield (ESY) for the storm events were estimated for the performance evaluation of the models. Based on these criteria, ANN based model results better agreement than the linear transfer function model for the computation of runoff hydrographs and sedimentographs for both the watersheds.     

Experimental evaluation of two complementary decentralized event-based control methods

Event-based control aims at reducing the feedback communication effort among the sensors, controllers and actuators in control loops to time instants at which the feedback of information is necessary to meet a desired control performance. This paper presents a new method for the decentralized event-based control of physically interconnected systems and shows its experimental evaluation. The novel method is based on two complementary approaches, called the global and the local approach, which jointly ensure the ultimate boundedness of the closed-loop system. The global approach steers the state of each subsystem into a target region, whereas the local approach keeps the state in this set in spite of exogenous disturbances and the effect of the interconnections to other subsystems. This event-based control method is applied to a continuous flow process to show its practical implementation and to evaluate the analytical results on the basis of experiments.     

A new method for tuning PI controllers with symmetric send-on-delta sampling strategy

In this paper we present a new method for tuning PI controllers with symmetric send-on-delta (SSOD) sampling strategy. First we analyze the conditions that produce oscillations in event based systems considering SSOD sampling strategy. The Describing Function is the tool used to address the problem. Once the conditions for oscillations are established, a new robustness to oscillation performance measure is introduced which entails with the concept of phase margin, one of the most traditional measures of relative stability in closed-loop control systems. Therefore, the application of the proposed robustness measure is easy and intuitive. The method is tested by both simulations and experiments. Additionally, a Java application has been developed to aid in the design according to the results presented in the paper.     

Two posture analysis approaches and their application in a modified Rapid Upper Limb Assessment evaluation

This paper presents two posture risk quantification methods: first, an event-based method where the most common and the worst postures are estimated in a task; second, a time-based method where posture distributions are calculated from random samples of observed postures in the task. A ‘click-on-screen’ posture data entry method was developed for the time-based posture analysis method to make the observation process easier and to reduce possible posture categorization bias. Both methods were used to quantify various work posture parameters among a study cohort of 733 subjects from a prospective epidemiological study of upper extremity musculoskeletal disorders. Composite posture indices using a modified Rapid Upper Limb Assessment (RULA) method were also computed using data obtained by the two posture analysis methods. Results showed that both methods were able to distinguish jobs with large differences in certain posture measures. However, they did not produce the same results and could not be used interchangeably. Risk evaluation criteria should be developed, either for specific posture parameters or as a composite index, with a well-defined postural analysis method, so that users can follow exact procedures and obtain comparable results. The event-based method is easy to use and may suit practitioners better, while the time-based method adds more information to the measurement and may suit users who want more detailed information about posture exposure.     

ARCHITECT: A layered framework for classifying technologies of event-based systems

In the past decade, event-based systems (EBS) have received increasing attention from various communities. Central to these systems is the notion of event, which is often generally considered as “a happening of interest”. An EBS encompasses a large range of functionalities on various technological levels (e.g., language, execution, or communication). Existing approaches vary in the scope of implemented functionality and underlying technical concepts. For this reason, comparing EBS solutions is a challenging task. Furthermore, the lack of a unified view on EBS poses challenges to system architects in choosing and combining technologies for building event-based systems.     

A practical tuning methodology for event-based PI control

This paper is focused on the tuning of an event-based PI controller for first-order plus time delay systems (FOPTD). In this work, a novel design and combination of a controller and event generator with an easy-to-use tuning methodology is presented. The event generator combines the Smith predictor structure with the symmetric send-on-delta (SSOD) sampling scheme to compensate the delay and trigger the events. The controller has an adaptive structure with the purpose of improving the set-point tracking and guaranteeing stability under conditions of uncertainty. The approach is focused on FOPTD systems but can be easily extended to higher order systems. Stability and robustness analyses are conducted, and the experimental results verify the effectiveness of the approach.     

Listener latency profiling: Measuring the perceptible performance of interactive Java applications

When developers need to improve the performance of their applications, they usually use one of the many existing profilers. These profilers generally capture a profile that represents the execution time spent in each method. The developer can thus focus her optimization efforts on the methods that consume the most time. In this paper we argue that this type of profile is insufficient for tuning interactive applications. Interactive applications respond to user events, such as mouse clicks and key presses. The perceived performance of interactive applications is directly related to the response time of the program.In this paper we present listener latency profiling, a profiling approach with two distinctive characteristics. First, we call it latency profiling because it helps developers to find long latency operations. Second, we call it listener profiling because it abstracts away from method-level profiles to compute the latency of the various listeners. This allows a developer to reason about performance with respect to listeners, also called observers, the high level abstraction at the core of any interactive Java application.We present our listener latency profiling approach, describe LiLa, our implementation, validate it on a set of microbenchmarks, and evaluate it on a complex real-world interactive application. We then perform case studies where we use LiLa to tune the perceptible performance of two interactive applications, and we show that LiLa is able to pinpoint performance problems even if their causes are embedded in the largest interactive Java application we are aware of.