Semi-active control for structural vibration based on predictive algorithm [Chinese title = 基于预测算法的结构振动半主动控制]

Based on Newmark-β method, a structural vibration response is predicted. Through finding the appropriate control force parameters within certain ranges to optimize the objective function, the predictive control of the structural vibration is achieved. At the same time, the numerical simulation analysis of a two-storey frame structure with magneto-rheological (MR) dampers under earthquake records is carried out, and the parameter influence on structural vibration reduction is discussed. The results demonstrate that the semi-active control based on Newmark-β predictive algorithm is better than the classical control strategy based on full-state feedback control and has remarkable advantages of structural vibration reduction and control robustness.

A new predictive current control technique for multilevel converters

Multilevel inverters provide an attractive solution for power electronics when both reduced harmonic contents and high voltages are required. In this paper, a novel predictive current control technique is proposed for a three-phase multilevel inverter, which controls the capacitors voltages and load currents with low switching losses. The advantage of this contribution is that the technique can be applied to more voltage levels without significantly changing the control circuit. The three-phase three-level inverter with a pure inductive load has been implemented to track reference currents using analogue circuits and programmable logic device.

Dengue fever and el nino-southern oscillation in Queensland, Australia : a time series predictive model

Background It remains unclear over whether it is possible to develop an epidemic forecasting model for transmission of dengue fever in Queensland, Australia. Objectives To examine the potential impact of El Niño/Southern Oscillation on the transmission of dengue fever in Queensland, Australia and explore the possibility of developing a forecast model of dengue fever. Methods Data on the Southern Oscillation Index (SOI), an indicator of El Niño/Southern Oscillation activity, were obtained from the Australian Bureau of Meteorology. Numbers of dengue fever cases notified and the numbers of postcode areas with dengue fever cases between January 1993 and December 2005 were obtained from the Queensland Health and relevant population data were obtained from the Australia Bureau of Statistics. A multivariate Seasonal Auto-regressive Integrated Moving Average model was developed and validated by dividing the data file into two datasets: the data from January 1993 to December 2003 were used to construct a model and those from January 2004 to December 2005 were used to validate it. Results A decrease in the average SOI (ie, warmer conditions) during the preceding 3–12 months was significantly associated with an increase in the monthly numbers of postcode areas with dengue fever cases (β=−0.038; p = 0.019). Predicted values from the Seasonal Auto-regressive Integrated Moving Average model were consistent with the observed values in the validation dataset (root-mean-square percentage error: 1.93%). Conclusions Climate variability is directly and/or indirectly associated with dengue transmission and the development of an SOI-based epidemic forecasting system is possible for dengue fever in Queensland, Australia.

A prospective study of personality features predictive of early adolescent alcohol misuse

Personality factors implicated in alcohol misuse have been extensively investigated in adult populations. Fewer studies have clarified the robustness of personality dimensions in predicting early onset alcohol misuse in adolescence. The aim of this study was to examine the predictive utility of two prominent models of personality (Cloninger, 1987; Eysenck & Eysenck, 1975) in emergent alcohol misuse in adolescence. One hundred and 92 secondary school students (mean age = 13.8 years, SD = 0.5) were administered measures of personality (Revised Junior Eysenck Personality Questionnaire – abbreviated; Temperament scale of Junior Temperament and Character Inventory) and drinking behavior (quantity and frequency of consumption, Alcohol Use Disorders Identification Test) at Time 1. At 12-month follow-up, 170 students (88.5%) were retained. Hierarchical multiple regressions revealed the dimensions of psychoticism, extraversion, and Novelty-Seeking to be the most powerful predictors of future alcohol misuse in adolescents. Results provide support for the etiological relevance of these dimensions in the development of early onset alcohol misuse. Findings can be used to develop early intervention programs that target personality risk factors for alcohol misuse in high-risk youth.

Implementing an emergency department patient admission predictive tool : insights from practice

Purpose: This paper aims to show that identification of expectations and software functional requirements via consultation with potential users is an integral component of the development of an emergency department patient admissions prediction tool. ---------- Design/methodology/approach: Thematic analysis of semi-structured interviews with 14 key health staff delivered rich data regarding existing practice and future needs. Participants included emergency department staff, bed managers, nurse unit managers, directors of nursing, and personnel from health administration. ---------- Findings: Participants contributed contextual insights on the current system of admissions, revealing a culture of crisis, imbued with misplayed communication. Their expectations and requirements of a potential predictive tool provided strategic data that moderated the development of the Emergency Department Patient Admissions Prediction Tool, based on their insistence that it feature availability, reliability and relevance. In order to deliver these stipulations, participants stressed that it should be incorporated, validated, defined and timely. ---------- Research limitations/implications: Participants were envisaging a concept and use of a tool that was somewhat hypothetical. However, further research will evaluate the tool in practice. ---------- Practical implications: Participants' unsolicited recommendations regarding implementation will not only inform a subsequent phase of the tool evaluation, but are eminently applicable to any process of implementation in a healthcare setting. ---------- Originality/value: The consultative process engaged clinicians and the paper delivers an insider view of an overburdened system, rather than an outsider's observations.

Beyond statistical procedures for predictive modelling: Data mining algorithms and support for university research at QUT

In a seminal data mining article, Leo Breiman [1] argued that to develop effective predictive classification and regression models, we need to move away from the sole dependency on statistical algorithms and embrace a wider toolkit of modeling algorithms that include data mining procedures. Nevertheless, many researchers still rely solely on statistical procedures when undertaking data modeling tasks; the sole reliance on these procedures has lead to the development of irrelevant theory and questionable research conclusions ([1], p.199). We will outline initiatives that the HPC & Research Support group is undertaking to engage researchers with data mining tools and techniques; including a new range of seminars, workshops, and one-on-one consultations covering data mining algorithms, the relationship between data mining and the research cycle, and limitations and problems with these new algorithms. Organisational limitations and restrictions to these initiatives are also discussed.

Autonomous underwater vehicle trajectory design coupled with predictive ocean models : a case study

Data collection using Autonomous Underwater Vehicles (AUVs) is increasing in importance within the oceano- graphic research community. Contrary to traditional moored or static platforms, mobile sensors require intelligent planning strategies to manoeuvre through the ocean. However, the ability to navigate to high-value locations and collect data with specific scientific merit is worth the planning efforts. In this study, we examine the use of ocean model predictions to determine the locations to be visited by an AUV, and aid in planning the trajectory that the vehicle executes during the sampling mission. The objectives are: a) to provide near-real time, in situ measurements to a large-scale ocean model to increase the skill of future predictions, and b) to utilize ocean model predictions as a component in an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. We present an algorithm designed to generate paths for AUVs to track a dynamically evolving ocean feature utilizing ocean model predictions. This builds on previous work in this area by incorporating the predicted current velocities into the path planning to assist in solving the 3-D motion planning problem of steering an AUV between two selected locations. We present simulation results for tracking a fresh water plume by use of our algorithm. Additionally, we present experimental results from field trials that test the skill of the model used as well as the incorporation of the model predictions into an AUV trajectory planner. These results indicate a modest, but measurable, improvement in surfacing error when the model predictions are incorporated into the planner.

Alcohol expectancy changes over a 12-week cognitive–behavioral therapy program are predictive of treatment success

This study examines if outcome expectancies (perceived consequences of engaging in certain behavior) and self- efficacy expectancies (confidence in personal capacity to regulate behavior) contribute to treatment outcome for alcohol dependence. Few clinical studies have examined these constructs. The Drinking Expectancy Profile (DEP), a psychometric measure of alcohol expectancy and drinking refusal selfefficacy, was administered to 298 alcohol-dependent patients (207 males) at assessment and on completion of a 12-week cognitive–behavioral therapy alcohol abstinence program. Baseline measures of expectancy and self-efficacy were not strong predictors of outcome. However, for the 164 patients who completed treatment, all alcohol expectancy and self-efficacy factors of the DEP showed change over time. The DEP scores approximated community norms at the end of treatment. Discriminant analysis indicated that change in social pressure drinking refusal self-efficacy, sexual enhancement expectancies, and assertion expectancies successfully discriminated those who successfully completed treatment from those who did not. Future research should examine the basis of expectancies related to social functioning as a possible mechanism of treatment response and a means to enhance treatment outcome.

Network protocols and predictive control strategies for distributed real-time control applications

A trend in design and implementation of modern industrial automation systems is to integrate computing, communication and control into a unified framework at different levels of machine/factory operations and information processing. These distributed control systems are referred to as networked control systems (NCSs). They are composed of sensors, actuators, and controllers interconnected over communication networks. As most of communication networks are not designed for NCS applications, the communication requirements of NCSs may be not satisfied. For example, traditional control systems require the data to be accurate, timely and lossless. However, because of random transmission delays and packet losses, the control performance of a control system may be badly deteriorated, and the control system rendered unstable. The main challenge of NCS design is to both maintain and improve stable control performance of an NCS. To achieve this, communication and control methodologies have to be designed. In recent decades, Ethernet and 802.11 networks have been introduced in control networks and have even replaced traditional fieldbus productions in some real-time control applications, because of their high bandwidth and good interoperability. As Ethernet and 802.11 networks are not designed for distributed control applications, two aspects of NCS research need to be addressed to make these communication networks suitable for control systems in industrial environments. From the perspective of networking, communication protocols need to be designed to satisfy communication requirements for NCSs such as real-time communication and high-precision clock consistency requirements. From the perspective of control, methods to compensate for network-induced delays and packet losses are important for NCS design. To make Ethernet-based and 802.11 networks suitable for distributed control applications, this thesis develops a high-precision relative clock synchronisation protocol and an analytical model for analysing the real-time performance of 802.11 networks, and designs a new predictive compensation method. Firstly, a hybrid NCS simulation environment based on the NS-2 simulator is designed and implemented. Secondly, a high-precision relative clock synchronization protocol is designed and implemented. Thirdly, transmission delays in 802.11 networks for soft-real-time control applications are modeled by use of a Markov chain model in which real-time Quality-of- Service parameters are analysed under a periodic traffic pattern. By using a Markov chain model, we can accurately model the tradeoff between real-time performance and throughput performance. Furthermore, a cross-layer optimisation scheme, featuring application-layer flow rate adaptation, is designed to achieve the tradeoff between certain real-time and throughput performance characteristics in a typical NCS scenario with wireless local area network. Fourthly, as a co-design approach for both a network and a controller, a new predictive compensation method for variable delay and packet loss in NCSs is designed, where simultaneous end-to-end delays and packet losses during packet transmissions from sensors to actuators is tackled. The effectiveness of the proposed predictive compensation approach is demonstrated using our hybrid NCS simulation environment.