Anaesthesia monitoring using fuzzy logic

Objective. Humans have a limited ability to accurately and continuously analyse large amount of data. In recent times, there has been a rapid growth in patient monitoring and medical data analysis using smart monitoring systems. Fuzzy logic-based expert systems, which can mimic human thought processes in complex circumstances, have indicated potential to improve clinicians' performance and accurately execute repetitive tasks to which humans are ill-suited. The main goal of this study is to develop a clinically useful diagnostic alarm system based on fuzzy logic for detecting critical events during anaesthesia administration. Method. The proposed diagnostic alarm system called fuzzy logic monitoring system (FLMS) is presented. New diagnostic rules and membership functions (MFs) are developed. In addition, fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS), and clustering techniques are explored for developing the FLMS' diagnostic modules. The performance of FLMS which is based on fuzzy logic expert diagnostic systems is validated through a series of offline tests. The training and testing data set are selected randomly from 30 sets of patients' data. Results. The accuracy of diagnoses generated by the FLMS was validated by comparing the diagnostic information with the one provided by an anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist's and FLMS's diagnoses. When detecting hypovolaemia, a substantial level of agreement was observed between FLMS and the human expert (the anaesthetist) during surgical procedures. Conclusion. The diagnostic alarm system FLMS demonstrated that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in delivering decision support to anaesthetists.

Short term load forecasting using interval type-2 fuzzy logic systems

Accurate Short Term Load Forecasting (STLF) is essential for a variety of decision making processes. However, forecasting accuracy may drop due to presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. This paper proposes the application of Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) for the problem of STLF. IT2 FLSs, with extra degrees of freedom, are an excellent tool for handling prevailing uncertainties and improving the prediction accuracy. Experiments conducted with real datasets show that IT2 FLS models appropriately approximate future load demands with an acceptable accuracy. Furthermore, they demonstrate an encouraging degree of accuracy superior to feedforward neural networks used in this study.

Planning with events and states

We present an overall planning system in which specifications can be described in terms of events and states. The underlying feature of this system is temporal logic, and its expressive power alloys one to deal with simultaneous actions and interacting actions. Moreover, one can represent both goal-oriented positive constraints and prevention-oriented negative constraints. The planning system can generate hierarchical plans and the overall model is capable of handling interacting agents.

Urbanheart surgery - a logic of design alternatives

In 1972 Sir Leslie Martin in his essay “The Grid as Generator”, advocated “a strong theoretical basis for [planning and] urban design” (Carolin P, 2000, p4) by methodically shifting design parameters regarding the way “in which buildings [could be] placed on the land” Martin was able to demonstrate how the generation of alternatives could “allow wider scope for decisions and objectives” to be considered and discussed (Carmona M, & Tiesdell S 2007, p81). Operating within a conventional design studio yet drawing of Sir Leslie Martin’s logic, ie developing an informed understanding of a problem by identifying a finite world of design ‘alternatives’, the following paper outlines a studio based program at the School of Architecture and Building, Deakin University, referred to as the ‘UrbanHeart Surgery’. While most atelier-based courses operate largely on an ad-hoc basis where students often work within self imposed competitive isolation, Urbanheart adopts a more open yet structured approach where students work in design collaboratives to generate a matrix of alternative design scenarios. The program actively integrates postgraduate students from Architecture, Urban Design and Planning into a design research culture and allows them to engage in critical discourse by working on strategic design projects in three areas significant to the future development of the state of Victoria: Metropolitan Urbanism, Urbanism on the Periphery and Regional Urbanism.

How can the impact of cultural development work in local government be measured? Towards more effective planning and evaluation strategies

The field of cultural development in local government is relatively new, with most councils only having dedicated staff or teams within the last ten to fifteen years. Challenges are associated with that newness in the areas of planning, goal setting and formal evaluation of achievements in relation to goals. How can the best decisions be made about what is needed? How can the outcomes of that work be evaluated? What should be measured and how? This paper explores these challenges and presents some solutions. Program Logic is introduced as a methodology for effective planning and evaluation of cultural development work in local government. The need for both performance and outcome evaluation of cultural development work is discussed, as are the levels of evaluation required; considering the contribution of individual workers, departments, whole of council and the overall community outcomes. Factors beyond the influence of local government, which impact the outcomes of arts initiatives, are also considered in arguing that more sophisticated evaluation processes are required.

Interval type-2 fuzzy logic systems for load forecasting : a comparative study

Accurate short term load forecasting (STLF) is essential for a variety of decision-making processes. However, forecasting accuracy can drop due to the presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. This paper proposes the application of Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) for the problem of STLF. IT2 FLSs, with additional degrees of freedom, are an excellent tool for handling uncertainties and improving the prediction accuracy. Experiments conducted with real datasets show that IT2 FLS models precisely approximate future load demands with an acceptable accuracy. Furthermore, they demonstrate an encouraging degree of accuracy superior to feedforward neural networks and traditional type-1 Takagi-Sugeno-Kang (TSK) FLSs.

Prediction interval construction using interval type-2 fuzzy logic systems

This study proposes a novel non-parametric method for construction of prediction intervals (PIs) using interval type-2 Takagi-Sugeno-Kang fuzzy logic systems (IT2 TSK FLSs). The key idea in the proposed method is to treat the left and right end points of the type-reduced set as the lower and upper bounds of a PI. This allows us to construct PIs without making any special assumption about the data distribution. A new training algorithm is developed to satisfy conditions imposed by the associated confidence level on PIs. Proper adjustment of premise and consequent parameters of IT2 TSK FLSs is performed through the minimization of a PI-based objective function, rather than traditional error-based cost functions. This new cost function covers both validity and informativeness aspects of PIs. A metaheuristic method is applied for minimization of the non-linear non-differentiable cost function. Quantitative measures are applied for assessing the quality of PIs constructed using IT2 TSK FLSs. The demonstrated results for four benchmark case studies with homogenous and heterogeneous noise clearly show the proposed method is capable of generating high quality PIs useful for decision-making.

Wire - a formal intermediate language for binary analysis

Wire is a intermediate language to enable static program analysis on low level objects such as native executables. It has practical benefit in analysing the structure and semantics of malware, or for identifying software defects in closed source software. In this paper we describe how an executable program is disassembled and translated to the Wire intermediate language. We define the formal syntax and operational semantics of Wire and discuss our justifications for its language features. We use Wire in our previous work Malwise, a malware variant detection system. We also examine applications for when a formally defined intermediate language is given. Our results include showing the semantic equivalence between obfuscated and non obfuscated code samples. These examples stem from the obfuscations commonly used by malware.

Fuzzy logic optimized wireless sensor network routing protocol

Wireless sensor networks (WSNs) are used in health monitoring, tracking and security applications. Such networks transfer data from specific areas to a nominated destination. In the network, each sensor node acts as a routing element for other sensor nodes during the transmission of data. This can increase energy consumption of the sensor node. In this paper, we propose a routing protocol for improving network lifetime and performance. The proposed protocol uses type-2 fuzzy logic to minimize the effects of uncertainty produced by the environmental noise. Simulation results show that the proposed protocol performs better than a recently developed routing protocol in terms of extending network lifetime and saving energy and also reducing data packet lost.