Reduction rules for YAWL workflows with cancellation regions and OR-joins

As the need for concepts such as cancellation and OR-joins occurs naturally in business scenarios, comprehensive support in a workflow language is desirable. However, there is a clear trade-off between the expressive power of a language (i.e., introducing complex constructs such as cancellation and OR-joins) and ease of verification. When a workflow contains a large number of tasks and involves complex control flow dependencies, verification can take too much time or it may even be impossible. There are a number of different approaches to deal with this complexity. Reducing the size of the workflow, while preserving its essential properties with respect to a particular analysis problem, is one such approach. In this paper, we present a set of reduction rules for workflows with cancellation regions and OR-joins and demonstrate how they can be used to improve the efficiency of verification. Our results are presented in the context of the YAWL workflow language.     

Lessons learned on development and application of agent-based models of complex dynamical systems

The field of agent-based modelling (ABM) has gained a significant following in recent years, and it is often marketed as an excellent introduction to modelling for the novice modeller or non-programmer. The typical objective of developing an agent-based model is to either increase our mechanistic understanding of a real-world system, or to predict how the dynamics of the real-world system are likely to be affected by changes to internal or external factors. Although there are some excellent ABMs that have been used in a predictive capacity across a number of domains, we believe that the promotion of ABM as an ‘accessible to all’ approach, could potentially lead to models being published that are flawed and therefore generate inaccurate predictions of real-world systems. The purpose of this article is to use our experiences in modelling complex dynamical systems, to reinforce the view that agent-based models can be useful for answering questions of the real-world domain through predictive modelling, but also to emphasise that all modellers, expert and novice alike, must make a concerted effort to adopt robust methods and techniques for constructing, validating and analysing their models, if the result is to be meaningful and grounded in the system of interest.     

Fully automated real time fatigue detection of drivers through Fuzzy Expert Systems

This paper presents a non-intrusive fatigue detection system based on the video analysis of drivers. The system relies on multiple visual cues to characterize the level of alertness of the driver. The parameters used for detecting fatigue are: eye closure duration measured through eye state information and yawning analyzed through mouth state information. Initially, the face is located through Viola–Jones face detection method to ensure the presence of driver in video frame. Then, a mouth window is extracted from the face region, in which lips are searched through spatial fuzzy c-means (s-FCM) clustering. Simultaneously, the pupils are also detected in the upper part of the face window on the basis of radii, inter-pupil distance and angle. The monitored information of eyes and mouth are further passed to Fuzzy Expert System (FES) that classifies the true state of the driver. The system has been tested using real data, with different sequences recorded in day and night driving conditions, and with users belonging to different race and gender. The system yielded an average accuracy of 100% on all the videos on which it was tested.     

Optimization of LSO/LuYAP phoswich detector for small animal PET

LSO/LuYAP phoswich detectors for small animal PET were developed to measure the depth of interaction (DOI), and to improve the spatial resolution at the edge of the field of view (FOV). The aim of this study was to optimize the optical coupling conditions between the crystal and photomultiplier tube (PMT) to maximize the light-collection efficiency, and to develop a method for rejecting scatter events by applying an equal energy window in each crystal layer. The light yields of the phoswich detector were estimated by changing the refractive index of the optical coupling material using a DETECT simulation. The accuracy of the DOI measurement on the phoswich detector, using an optical coupling material with the optimal light yield, were evaluated experimentally and compared with the air condition. The energy window for the photopeak events cannot be applied properly because the light outputs of LSO and LuYAP are different. The LSO/LuYAP photopeaks need to be superposed in order to effectively discriminate the scattered events by applying an equal energy window. The photopeaks of the LSO and LuYAP can be superposed by inserting a reflecting material between the crystals. The optimal coverage ratio of the inserting material was derived from a DETECT simulation, and its performance was investigated. In the simulation result, optimal refractive index of the optical coupling material was 1.7. The average DOI measurement errors of the LSO/LuYAP were 0.6%/3.4% and 4.9%/41.4% in the phoswich detector with and without an optical coupling material, respectively. The photopeaks of the LSO and LuYAP were superposed by covering 75% of the contact surface between the crystals with white Teflon. The DOI measurement errors of the LSO/LuYAP were 0.2%/2.4%. In this study, the optimal condition of the optical coupling material inserted between the crystal and PMT was derived to improve the accuracy of DOI measurement, and a photopeak superposition method of the LSO and LuYAP was developed in order to reject scatter events.     

Sharing the floodplain: Mediated modeling for environmental management

Complex ecosystems, such as the Upper Mississippi River (UMR), present major management challenges. Such systems often provide a range of ecosystem services that are differentially valued by stakeholders representing distinct interests (e.g., agriculture, conservation, navigation) or institutions (e.g., federal and state agencies). When no single entity has the knowledge or authority to resolve conflicts over shared resource use, stakeholders may struggle to jointly understand the scope of the problem and to reach reasonable compromises. This paper explores mediated modeling as a group consensus building process for understanding relationships between ecological, economic and cultural well-being in the UMR floodplain. We describe a workshop structure used to engage UMR stakeholders that may be extended to resource use conflicts in other complex ecosystems. We provide recommendations for improving on these participatory methods in structuring future efforts. In conclusion, we suggest that tools which facilitate collaborative learning, such as mediated modeling, need to be incorporated at an institutional level as a vital element of integrated ecosystem management.     

Hyperspectral imaging as an effective tool for prediction the moisture content and textural characteristics of roasted pistachio kernels

The objective of this study was to develop calibration models for prediction of moisture content and textural characteristics (fracture force, hardness, apparent modulus of elasticity and compressive energy) of pistachio kernels roasted in different conditions (temperatures 90, 120 and 150 °C; times 20, 35 and 50 min and air velocities 0.5, 1.5 and 2.5 m/s) using Vis/NIR hyperspectral imaging and multivariate analysis. The effects of different pre-processing methods and spectral treatments such as normalization [multiplicative scatter correction (MSC), standard normal variate transformation (SNV)], smoothing (median filter, Savitzky–Golay and Wavelet) and differentiation (first derivative, D¹ and second derivative, D²) on the obtained data were investigated. The prediction models were developed by partial least square regression (PLSR) and artificial neural network (ANN). The results indicated that ANN models have higher potential to predict moisture content and textural characteristics of roasted pistachio kernels comparing to PLSR models. High correlation was observed between reflectance data and fracture force (R²?=?0.957 and RMSEP?=?3.386) using MSC, Savitzky–Golay and D¹, compressive energy (R²?=?0.907 and RMSEP?=?15.757) using the combination of MSC, Wavelet and D¹, moisture content (R²?=?0.907 and RMSEP?=?0.179) and apparent modulus of elasticity (R²?=?0.921 and RMSEP?=?2.366) employing combination of SNV, Wavelet and D¹, respectively. Moreover, Vis–NIR data correlated well with hardness (R²?=?0.876 and RMSEP?=?5.216) using SNV, Wavelet and D². These results showed the capability of Vis/NIR hyperspectral imaging and the central role of multivariate analysis in developing accurate models for prediction of moisture content and textural properties of roasted pistachio kernels.     

Identification of a pilot scale fluidised-bed coal gasification unit by using neural networks

This work is under the general framework of advanced control system application to a pilot scale gasification reactor. In this paper the identification step of the system and the dynamic analysis have been studied. For system identification the artificial neural network (ANN) has been used and an ANN model was developed.     

The dynamic programming equations for stochastic games with discrete actions

We consider the quasi-variational inequalities associated with a stochastic game with discrete actions and switching costs. We obtain an analytical uniqueness result, and, for a particular case, a regurality estimate.     

Applying neural network analysis on heart rate variability data to assess driver fatigue

Long duration driving is a significant cause of fatigue related accidents on motorways. Fatigue caused by driving for extended hours can acutely impair driver’s alertness and performance. This papers presents an artificial intelligence based system which could detect early onset of fatigue in drivers using heart rate variability (HRV) as the human physiological measure. The detection performance of neural network was tested using a set of electrocardiogram (ECG) data recorded under laboratory conditions. The neural network gave an accuracy of 90%. This HRV based fatigue detection technique can be used as a fatigue countermeasure.