Optimal design of traffic signal controller using neural networks and fuzzy logic systems

This paper aims at optimally adjusting a set of green times for traffic lights in a single intersection with the purpose of minimizing travel delay time and traffic congestion. Neural network (NN) and fuzzy logic system (FLS) are two methods applied to develop intelligent traffic timing controller. For this purpose, an intersection is considered and simulated as an intelligent agent that learns how to set green times in each cycle based on the traffic information. The training approach and data for both these learning methods are similar. Both methods use genetic algorithm to tune their parameters during learning. Finally, The performance of the two intelligent learning methods is compared with the performance of simple fixed-time method. Simulation results indicate that both intelligent methods significantly reduce the total delay in the network compared to the fixed-time method.

Design of an optimal ANFIS traffic signal controller by using cuckoo search for an isolated intersection

An optimal design of Adaptive Neuro-Fuzzy Inference System (ANFIS) traffic signal controller is presented in this paper. The proposed controller aims to adjust a set of green times for traffic lights in a single intersection with the purpose of minimizing travel delay time and traffic congestion. The ANFIS controller is trained, to learned how to set green times for each traffic phase. This intelligent controller uses the Cuckoo Search (CS) algorithm to tune its parameters during the learning pried. Evaluating the performance of the proposed controller in comparison with the performance of a FLS controller (FLC) with predefined rules and membership functions, and also three fixed-Time controllers, illustrates the better performance of the optimal ANFIS controller against the other benchmark controllers.

Application of cuckoo search for design optimization of heat exchangers

A wide variety of evolutionary optimization algorithms have been used by researcher for optimal design of shell and tube heat exchangers (STHX). The purpose of optimization is to minimize capital and operational costs subject to efficiency constraints. This paper comprehensively examines performance of genetic algorithm (GA) and cuckoo search (CS) for solving STHX design optimization. While GA has been widely adopted in the last decade for STHX optimal design, there is no report on application of CS method for this purpose. Simulation results in this paper demonstrate that CS greatly outperforms GA in terms of finding admissible and optimal configurations for STHX. It is also found that CS method not only has a lower computational requirement, but also generates the most consistent results.

Effectiveness of evolutionary algorithms for optimization of heat exchangers

This paper comprehensively investigates performance of evolutionary algorithms for design optimization of shell and tube heat exchangers (STHX). Genetic algorithm (GA), firefly algorithm (FA), and cuckoo search (CS) method are implemented for finding the optimal values for seven key design variables of the STHX model. ε-NTU method and Bell-Delaware procedure are used for thermal modeling of STHX and calculation of shell side heat transfer coefficient and pressure drop. The purpose of STHX optimization is to maximize its thermal efficiency. Obtained results for several simulation optimizations indicate that GA is unable to find permissible and optimal solutions in the majority of cases. In contrast, design variables found by FA and CS always lead to maximum STHX efficiency. Also computational requirements of CS method are significantly less than FA method. As per optimization results, maximum efficiency (83.8%) can be achieved using several design configurations. However, these designs are bearing different dollar costs. Also it is found that the behavior of the majority of decision variables remains consistent in different runs of the FA and CS optimization processes.

Patient and nurse preferences for nurse handover-using preferences to inform policy: a discrete choice experiment protocol

INTRODUCTION: Nursing bedside handover in hospital has been identified as an opportunity to involve patients and promote patient-centred care. It is important to consider the preferences of both patients and nurses when implementing bedside handover to maximise the successful uptake of this policy. We outline a study which aims to (1) identify, compare and contrast the preferences for various aspects of handover common to nurses and patients while accounting for other factors, such as the time constraints of nurses that may influence these preferences.; (2) identify opportunities for nurses to better involve patients in bedside handover and (3) identify patient and nurse preferences that may challenge the full implementation of bedside handover in the acute medical setting. METHODS AND ANALYSIS: We outline the protocol for a discrete choice experiment (DCE) which uses a survey design common to both patients and nurses. We describe the qualitative and pilot work undertaken to design the DCE. We use a D-efficient design which is informed by prior coefficients collected during the pilot phase. We also discuss the face-to-face administration of this survey in a population of acutely unwell, hospitalised patients and describe how data collection challenges have been informed by our pilot phase. Mixed multinomial logit regression analysis will be used to estimate the final results. ETHICS AND DISSEMINATION: This study has been approved by a university ethics committee as well as two participating hospital ethics committees. Results will be used within a knowledge translation framework to inform any strategies that can be used by nursing staff to improve the uptake of bedside handover. Results will also be disseminated via peer-reviewed journal articles and will be presented at national and international conferences.

Channel estimation for two-way MIMO relay systems in frequency-selective fading environments

© 2002-2012 IEEE. In this paper, we investigate the channel estimation problem for two-way multiple-input multiple-output (MIMO) relay communication systems in frequency-selective fading environments. We apply the method of superimposed channel training to estimate the individual channel state information (CSI) of the first-hop and second-hop links for two-way MIMO relay systems with frequency-selective fading channels. In this algorithm, a relay training sequence is superimposed on the received signals at the relay node to assist the estimation of the second-hop channel matrices. The optimal structure of the source and relay training sequences is derived to minimize the mean-squared error (MSE) of channel estimation. Moreover, the optimal power allocation between the source and relay training sequences is derived to improve the performance of channel estimation. Numerical examples are shown to demonstrate the performance of the proposed superimposed channel training algorithm for two-way MIMO relay systems in frequency-selective fading environments.

Titanium in biomedical applications—properties and fabrication: a review

Ti and Ti-based alloys have unique properties such as high strength, low density and excellent corrosion resistance. These properties are essential for the manufacture of lightweight and high strength components for biomedical applications. In this paper, Ti properties such as metallurgy, mechanical properties, surface modification, corrosion resistance, biocompatibility and osseointegration in biomedical applications have been discussed. This paper also analyses the advantages and disadvantages of various Ti manufacturing processes for biomedical applications such as casting, powder metallurgy, cold and hot working, machining, laser engineering net shaping (LEN), superplastic forming, forging and ring rolling. The contributions of this research are twofold, firstly scrutinizing the behaviour of Ti and Ti-based alloys in-vivo and in-vitro experiments in biomedical applications to determine the factors leading to failure, and secondly strategies to achieve desired properties essential to improving the quality of patient outcomes after receiving surgical implants. Future research will be directed toward manufacturing of Ti for medical applications by improving the production process, for example using optimal design approaches in additive manufacturing and investigating alloys containing other materials in order to obtain better medical and mechanical characteristics.

Stable operation of distribution networks using D-STATCOM considering uncertainties in load compositions

This paper presents a novel control design for D-STATCOM to ensure grid code-compatible performance of distributed wind generators. The approach considered in this paper is to find the smallest upper bound on the H norm of the uncertain system and to design an optimal linear quadratic Gaussian (LQG) controller based on this bound. The change in the model due to variations of induction motor (IM) load compositions in the composite load is considered as an uncertain term in the design algorithm. The performance of the designed controller is demonstrated on a distribution test system representative of the Kumamoto area in Japan. It is found that the proposed controller enhances voltage stability of the distribution system under varying operating conditions

D-STATCOM control in distribution networks with composite loads to ensure grid code compatible performance of photovoltaic generators

This paper proposes an optimal linear quadratic Gaussian (LQG) controller for D-STATCOM to improve the dynamic performance of distribution networks with photovoltaic generators. The controller is designed based on the H∞ norm of the uncertain system. The change in system model due to the variation of load compositions in the composite load is considered as an uncertain term in the design algorithm. The performance of the designed controller is demonstrated on a widely used test system. Simulation results indicate that the proposed controller can be a potential solution for improving the voltage stability of distribution networks.

Association between serum concentration of 25-hydroxyvitamin D and the risk of hip arthroplasty for osteoarthritis: result from a prospective cohort study

OBJECTIVES: There is ongoing debate regarding the optimal serum concentrations of 25-hydroxy-vitamin D for musculoskeletal health, including osteoarthritis (OA). The aim of this prospective cohort study was to determine whether serum 25-hydroxy-vitamin D concentrations were associated with the risk of hip arthroplasty for OA. DESIGN: This study examined 9135 participants from the Australian Diabetes, Obesity and Lifestyle Study who had serum 25-hydroxy-vitamin D measured in 1999-2000 and were aged ≥40 years at the commencement of arthroplasty data collection. The incidence of hip arthroplasty for OA during 2002-2011 was determined by linking cohort records to the Australian Orthopaedic Association National Joint Replacement Registry. RESULTS: Over an average 9.1 (standard deviation (SD) 2.7) years of follow-up, 201 hip arthroplasties for OA were identified (males n = 90; females n = 111). In males, a one-standard-deviation increase in 25-hydroxy-vitamin D was associated with a 25% increased incidence (HR 1.25, 95% CI 1.02-1.56), with a dose response relationship evident by quartiles of 25-hydroxy-vitamin D concentration (P for trend 0.04). These results were independent of age, body mass index (BMI), ethnicity, smoking status, physical activity, season of blood collection, latitude, hypertension and diabetes, area level disadvantage or after excluding those with extreme low 25-hydroxy-vitamin D concentrations. No significant association was observed in women (HR 1.10, 95% CI 0.87, 1.39). CONCLUSIONS: Increasing serum 25-hydroxy-vitamin D concentrations were associated with an increased risk of hip arthroplasty for OA in males, while no significant association was observed in females. The mechanism for the association warrants further investigation.

Information-agnostic coflow scheduling with optimal demotion thresholds

Previous coflow scheduling proposals improve the coflow completion time (CCT) over per-flow scheduling based on prior information of coflows, which makes them hard to apply in practice. State-of-art information-agnostic coflow scheduling solution Aalo adopts Discretized Coflow-aware Least-Attained-Service (D-CLAS) to gradually demote coflows from the highest priority class into several lower priority classes when their sent-bytes-count exceeds several predefined demotion thresholds. However, current design standards of these demotion thresholds are crude because they do not analyze the impacts of different demotion thresholds on the average coflow delay. In this paper, we model the D-CLAS system by an M/G/1 queue and formulate the average coflow delay as a function of the demotion thresholds. In addition, we prove the valley-like shape of the function and design the Down-hill searching (DHS) algorithm. The DHS algorithm locates a set of optimal demotion thresholds which minimizes the average coflow delay in the system. Real-data-center-trace driven simulations indicate that DHS improves average CCT up to 6.20× over Aalo.