Modelling ion-pair geometries and dynamics in a 1-ethyl-1-methylpyrrolidinium-based ion-conductive crystal

Full conformational and energy explorations are conducted on an organic ionic plastic crystal, 1-ethyl-1-methylpyrrolidium tetrafluoroborate [C2 mpyr][BF4 ]. The onsets of various stages of dynamic behaviour, which appear to account for low-temperature solid-solid phase transitions, are investigated by using quantum-chemical simulations. It is suggested that pseudorotation of the pyrrolidine ring occurs in the first instance; the partial rotation of the entire cation subsequently occurs and may be accompanied by reorientation of the ethyl chain as the temperature increases further. A cation-anion configuration, whereby BF4 (-) interacts with the C2 mpy cation from the side of the ring, is the most likely structure in the low-temperature phase IV region. These interpretations are supported by (13) C nuclear magnetic resonance chemical-shift analysis.

Prediction interval-based modelling of polymerization reactor: a new modelling strategy for chemical reactors

Precise and reliable modelling of polymerization reactor is challenging due to its complex reaction mechanism and non-linear nature. Researchers often make several assumptions when deriving theories and developing models for polymerization reactor. Therefore, traditional available models suffer from high prediction error. In contrast, data-driven modelling techniques provide a powerful framework to describe the dynamic behaviour of polymerization reactor. However, the traditional NN prediction performance is significantly dropped in the presence of polymerization process disturbances. Besides, uncertainty effects caused by disturbances present in reactor operation can be properly quantified through construction of prediction intervals (PIs) for model outputs. In this study, we propose and apply a PI-based neural network (PI-NN) model for the free radical polymerization system. This strategy avoids assumptions made in traditional modelling techniques for polymerization reactor system. Lower upper bound estimation (LUBE) method is used to develop PI-NN model for uncertainty quantification. To further improve the quality of model, a new method is proposed for aggregation of upper and lower bounds of PIs obtained from individual PI-NN models. Simulation results reveal that combined PI-NN performance is superior to those individual PI-NN models in terms of PI quality. Besides, constructed PIs are able to properly quantify effects of uncertainties in reactor operation, where these can be later used as part of the control process. © 2014 Taiwan Institute of Chemical Engineers.

A dynamic time warped clustering technique for discrete event simulation-based system analysis

Abstract This paper introduces a novel approach for discrete event simulation output analysis. The approach combines dynamic time warping and clustering to enable the identification of system behaviours contributing to overall system performance, by linking the clustering cases to specific causal events within the system. Simulation model event logs have been analysed to group entity flows based on the path taken and travel time through the system. The proposed approach is investigated for a discrete event simulation of an international airport baggage handling system. Results show that the method is able to automatically identify key factors that influence the overall dwell time of system entities, such as bags that fail primary screening. The novel analysis methodology provides insight into system performance, beyond that achievable through traditional analysis techniques. This technique also has potential application to agent-based modelling paradigms and also business event logs traditionally studied using process mining techniques.

Dynamic cloud service selection using an adaptive learning mechanism in multi-cloud computing

Cloud service selection in a multi-cloud computing environment is receiving more and more attentions. There is an abundance of emerging cloud service resources that makes it hard for users to select the better services for their applications in a changing multi-cloud environment, especially for online real time applications. To assist users to efficiently select their preferred cloud services, a cloud service selection model adopting the cloud service brokers is given, and based on this model, a dynamic cloud service selection strategy named DCS is put forward. In the process of selecting services, each cloud service broker manages some clustered cloud services, and performs the DCS strategy whose core is an adaptive learning mechanism that comprises the incentive, forgetting and degenerate functions. The mechanism is devised to dynamically optimize the cloud service selection and to return the best service result to the user. Correspondingly, a set of dynamic cloud service selection algorithms are presented in this paper to implement our mechanism. The results of the simulation experiments show that our strategy has better overall performance and efficiency in acquiring high quality service solutions at a lower computing cost than existing relevant approaches.

A visual language and environment for enterprise system modelling and automation

Objective: We want to support enterprise service modelling and generation using a more end user-friendly metaphor than current approaches, which fail to scale to large organisations with key issues of "cobweb" and "labyrinth" problems and large numbers of hidden dependencies. Method: We present and evaluate an integrated visual approach for business process modelling using a novel tree-based overlay structure that effectively mitigate complexity problems. A tree-overlay based visual notation (EML) and its integrated support environment (MaramaEML) supplement and integrate with existing solutions. Complex business architectures are represented as service trees and business processes are modelled as process overlay sequences on the service trees. Results: MaramaEML integrates EML and BPMN to provide complementary, high-level business service modelling and supports automatic BPEL code generation from the graphical representations to realise web services implementing the specified processes. It facilitates generated service validation using an integrated LTSA checker and provides a distortion-based fisheye and zooming function to enhance complex diagram navigation. Evaluations of EML show its effectiveness. Conclusions: We have successfully developed and evaluated a novel tree-based metaphor for business process modelling and enterprise service generation. Practice implications: a more user-friendly modelling approach and support tool for business end users.

SecDSVL: a domain-specific visual language to support enterprise security modelling

Enterprise security management requires capturing different security and IT systems' details, analyzing and enforcing these security details, and improving employed security to meet new risks. Adopting structured models greatly helps in simplifying and organizing security specification and enforcement processes. However, existing security models are generally limited to specific security details and do not deliver a comprehensive security model. They also often do not have user-friendly notations, being complicated extensions of existing modeling languages (such as UML). In this paper, we introduce a comprehensive Security Domain Specific Visual Language (SecDSVL), which enables capturing of key security details to support enterprise systems security management process. We discuss our SecDSVL, tool support and the model-based enterprise security management approach it supports, give a usage example, and present evaluation experiments of SecDSVL.

Modelling personal preferences for Top-N movie recommendations

Modelling the temporal dynamics of personal preferences is still under-developed despite the rapid development of personalization. In this paper, we observe that the user preference styles tend to change regularly following certain patterns in the context of movie recommendation systems. Therefore, we propose a Preference Pattern model to capture the user preference styles and their temporal dynamics, and apply this model to improve the accuracy of the Top-N movie recommendations. Precisely, a preference pattern is defined as a set of user preference styles sorted in a time order. The basic idea is to model user preference styles and their temporal dynamics by constructing a representative subspace with an Expectation-Maximization (EM)-like algorithm, which works in an iterative fashion by refining the global and the personal preference styles simultaneously. Then, the degree which the recommendations match the active user's preference styles, can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results indicate that the proposed model is robust to the data sparsity problem, and can significantly outperform the state-of-the-art algorithms on the Top-N movie recommendations in terms of accuracy.

Modelling and analysis of type 4 wind turbine generator system for utilization in frequency regulation studies

Green energy targets for coming decades advocates high penetration of wind energy in main energy matrix which also pose incendiary threat to stability and reliability of modern electric grid if their dynamic performance aspects are not assessed beforehand. Considering increasing interest in dynamic performance along with ancillary service assessment related to frequency regulation, development of suitable generic modeling has gained high priority. This paper presents modeling of type 4 full converter wind turbine generator system suitable for frequency regulation focusing on active power control. Complete model is a modification of WECC generic model with additional aerodynamic and pitch control model. Descriptions of individual sub models are presented and performance results are compared manufacturer specific GE type 4 WTG generic model by means of simulations in the MATLAB ® Power System Block set.

Dynamic virtual workspace for teleoperation of heterogeneous robots in non-deterministic environments

Robots are ever increasing in a variety of different workplaces providing an array of benefits such alternative solutions to traditional human labor. While developing fully autonomous robots is the ultimate goal in many robotic applications the reality is that there still exist many situationswere robots require some level of teleoperation in order to achieve assigned goals especially when deployed in non-deterministic environments. For instance teleoperation is commonly used in areas such as search and rescue, bomb disposal and exploration of inaccessible or harsh terrain. This is due to a range of factors such as the lack of ability for robots to quickly and reliably navigate unknown environments or provide high-level decision making especially intime critical tasks. To provide an adequate solution for such situations human-in-the-loop control is required. When developing human-in-the-loop control it is important to take advantage of the complimentary skill-sets that both humans and robots share. For example robots can performrapid calculations, provide accurate measurements through hardware such as sensors and store large amounts of data while humans provide experience, intuition, risk management and complex decision making capabilities. Shared autonomy is the concept of building robotic systems that take advantage of these complementary skills-sets to provide a robust an efficient robotic solution. While the requirement of human-in-the-loop control exists Human Machine Interaction (HMI) remains an important research topic especially the area of User Interface (UI) design.In order to provide operators with an effective teleoperation system it is important that the interface is intuitive and dynamic while also achieving a high level of immersion. Recent advancements in virtual and augmented reality hardware is giving rise to innovative HMI systems. Interactive hardware such as Microsoft Kinect, leap motion, Oculus Rift, Samsung Gear VR and even CAVE Automatic Virtual Environments [1] are providing vast improvements over traditional user interface designs such as the experimental web browser JanusVR [2]. This combined with the introduction of standardized robot frameworks such as ROS and Webots [3] that now support a large number of different robots provides an opportunity to develop a universal UI for teleoperation control to improve operator efficiency while reducing teleoperation training.This research introduces the concept of a dynamic virtual workspace for teleoperation of heterogeneous robots in non-deterministic environments that require human-in-the-loop control. The system first identifies the connected robots through the use kinematic information then determines its network capabilities such as latency and bandwidth. Given the robot type and network capabilities the system can then provide the operator with available teleoperation modes such as pick and place control or waypoint navigation while also allowing them to manipulate the virtual workspace layout to provide information from onboard camera’s or sensors.

Full converter based wind turbine generator system generic modelling: Variations and applicability

Wind energy system integration can lead to adverse effects on modern electric grid so it is imperative toassess their dynamic performance before actual plant startup. Transmission system operators all over theworld stress the need for a proper wind turbine generator model for dynamic performance as well asancillary service assessments. Due to the bulk power system assessment requirements, developmentof suitable generic modeling has gained high priority. Generic modeling of type 4 full converter wind turbinegenerator system for application in frequency ancillary service investigations under varying windspeed and varying reference power has been presented in this study. Prevalent generic model, manufacturerspecific proprietary generic model along with detailed wind turbine model with synchronous generatoris also provided to highlight various modelling framework difference. Descriptions of individualsub models of proposed generic model are presented in detail and performance results are comparedand validated with GE’s proprietary generic model and detailed WTG model by means of simulationsin the MATLAB Power System Block set.

Rainfall-runoff modeling using dynamic evolving neural fuzzy inference system with online learning

This is an open access article under the CC BY-NC-ND license.Neuro-Fuzzy Systems (NFS) are computational intelligence tools that have recently been employed in hydrological modeling. In many of the common NFS the learning algorithms used are based on batch learning where all the parameters of the fuzzy system are optimized off-line. Although these models have frequently been used, there is a criticism on such learning process as the number of rules are needed to be predefined by the user. This will reduce the flexibility of the NFS architecture while dealing with different data with different level of complexity. On the other hand, online or local learning evolves through local adjustments in the model as new data is introduced in sequence. In this study, dynamic evolving neural fuzzy inference system (DENFIS) is used in which an evolving, online clustering algorithm called the Evolving Clustering Method (ECM) is implemented. ECM is an online, maximum distance-based clustering method which is able to estimate the number of clusters in a data set and find their current centers in the input space through its fast, one-pass algorithm. The 10-minutes rainfall-runoff time series from a small (23.22 km2) tropical catchment named Sungai Kayu Ara in Selangor, Malaysia, was used in this study. Out of the 40 major events, 12 were used for training and 28 for testing. Results obtained by DENFIS were then compared with the ones obtained by physically-based rainfall-runoff model HEC-HMS and a regression model ARX. It was concluded that DENFIS results were comparable to HEC-HMS and superior to ARX model. This indicates a strong potential for DENFIS to be used in rainfall-runoff modeling.