Monitoring therapeutic processes using risk-adjusted multivariate Tukey's CUSUM control chart

Using control charts for monitoring therapeutic processes has become popular lately. As the application of traditional control charts in the therapeutic processes may be misleading due to the inherent differences between patients, a multifactor correlated risk measure is considered in monitoring of these processes. Therefore, using risk-adjusted control charts for monitoring the therapeutic processes is of interest to practitioners. Furthermore, in health care monitoring, statistical models should account for abnormal distributions and outlier data to minimize misinterpretations of monitoring schemes. This study proposes a risk-adjusted multivariate Tukey's cumulative sum (RA-MTCUSUM) control chart. The proposed method is a combination of the accelerated failure time (AFT) regression model, the Tukey's control chart (TCC) featuring robustness against abnormality, and the multivariate cumulative sum (MCUSUM) control chart for monitoring multivariable process. Simulation experiments are performed to evaluate the performance of the proposed control chart using the average run length (ARL) measure. Results show that the RA-MTCUSUM control chart has better performance in comparison with traditional ones for monitoring various distributions (normal and non-normal). Based on the simulation results, outlier data do not disturb the proposed control chart's performance. Moreover, applying the RA-MTCUSUM control chart to a real-world dataset related to sepsis patients of a hospital located in Tehran, Iran indicates that the control chart has more reasonable performance than the traditional control charts in the real applications due to its robustness.     

Direct Power Control of Dfig by Using Nonlinear Model Predictive Controller

This paper proposes a nonlinear model predictive direct power control (PDPC) strategy for a double fed induction generator (DFIG)‐based wind energy generation system. Active and reactive power variations of DFIG are calculated based on machine rules, and a nonlinear model of DFIG is given. A nonlinear model predictive controller (NMPC) is presented based on the useful cost function and constraint that it results in more proximity between simulations and reality. The power and current ripples are reduced and the optimal rotor voltage is generated based on an objective function and the constraints. The rotor voltage vector is calculated in the synchronous reference frame and transferred into the rotor reference frame. Simulation results of a 2 MW DFIG system show good performance of the proposed method during variation of active and reactive powers, machine parameters, and wind speed. Also, the transient responses of active and reactive powers are within a few milliseconds.     

Free vibration analysis of variable thickness thin plates by two-dimensional differential transform method

This paper aims at extending the application of two-dimensional differential transform method (2D-DTM) to study the free vibration of thin plates with arbitrarily varying thickness. First, the differential equation of motion governing thin plates with varying thickness is derived using Hamilton’s principle. Afterward, the 2D-DTM, a numerical method which is capable of reducing the size of computational work and can be applied to various types of differential equations, has been applied to derive the natural frequencies of variable thickness thin plates with different boundary conditions. Several numerical examples have been carried out to demonstrate the applicability and accuracy of the present method in free vibration analysis of both uniform plates and plates with variable thickness.     

An intelligent approach to the lateral forces usage in controlling the vehicle yaw rate

A direct yaw moment control system (DYC) is designed to improve the handling and stability of a four‐wheel‐drive electric vehicle. The main task of this paper is to use the lateral forces in the process of optimally controlling vehicle stability. This is performed by defining a variable optimum region for the slip ratio of each wheel. A hierarchical structure is selected to design the control system. The higher‐level control system controls the yaw rate of the vehicle based on the fuzzy logic technique. The lower‐level control system, installed in each wheel, maintains the slip ratio of the same wheel within an optimum region using the fuzzy logic technique. This optimum region for each wheel is continuously modified based on the impact of the lateral force on the generated control yaw moment and the friction coefficient of the road. Therefore, an algorithm for estimation of the friction coefficient is proposed. Computer simulations are carried out to investigate the effectiveness of the proposed method. This is accomplished by comparison of the results of control methods with a fixed slip ratio region and the results of the proposed method with a variable slip ratio region in some maneuvers. The robustness of the proposed controller against hard braking and noise contamination, as well as the effect of steering wheel angle amplitude, is verified. The simulation results show that the influence of the proposed method on enhancing vehicle performance is significant. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Pareto–Koopmans efficiency in two‐stage network data envelopment analysis in the presence of undesirable intermediate products and nondiscretionary factors

Many studies have recently been conducted on the evaluation of system performance with a two‐stage network structure in data envelopment analysis (DEA) literature. One of the topics of interest to researchers has been the mitigation of undesirable products or nondiscretionary factors into their corresponding possible production set (PPS) and their impact on overall efficiency calculations. Determination of decision‐making units (DMUs) with Pareto–Koopmans efficiency status is decisive in identifying benchmark units. The calculated overall efficiency status is compromised when both undesirable products and nondiscretionary factors are present. This work utilizes an axiomatic approach. A novel PPS for a two‐stage network in presence of undesirable intermediate products and nondiscretionary exogenous inputs is introduced. Based on this PPS and by focusing on the principle of mathematical dominance, new models for evaluating overall and divisional efficiencies are presented. In addition, by proposing a two‐step network DEA approach, a necessary and sufficient condition for detection of DMUs with Pareto–Koopmans efficiency status is provided. And by introducing a two‐step algorithm, a novel technique for determining overall efficiency conditions is produced. Finally, the proposed technology is applied to a practical example, and outcomes are discussed.     

Adaptive QoS scheduling in wireless cellular networks

Quality of service (QoS) has been always controversial in resource shared networks. Scheduling as a packet prioritizing mechanism at Data Link Layer (DLL) contributes to QoS guarantee provisioning significantly. In this paper, a novel packet scheduler is developed in wireless cellular networks. The proposed scheme provides QoS-guaranteed service for the applications running on the sensor nodes in all the three aspects of QoS, i.e. data rate, packet loss and packet delay with regard to jitter simultaneously. We establish a three-dimensional space with certain basis vectors for QoS and introduce the efficient point of performance in terms of QoS provisioning in that space. Then we develop a generalized metric, the QoS-deviation, which is the Euclidean distance between the QoS work point of flows and the QoS efficient point in the proposed space. Based on this metric, a novel scheduling approach, namely AQDC, is designed which makes it possible to tune the trade-off between QoS provisioning and throughput optimization in an adaptive manner depending on the current Cell QoS-deviation level (CDL). Furthermore, we also develop another scheduler, namely ARTC, which is the residual-time version of the AQDC scheduler. Finally, a QoS-deviation-based CAC policy will be introduced which can be applied to all schedulers without any consideration about their structure and can be employed in cellular packet switched networks.     

Development of novel method for prediction of gas density in operational conditions

The performance of gas industries is extensively function of gas properties such as gas density. Due to this importance in the present work, a novel grid partitioning based fuzzy inference system method applied to predict gas density base on pressure, temperature and molecular weight of gas. To this end, the required experimental data are collected from reliable sources. Different comparison scenarios are used to evaluate the ability of model. The coefficients of determination (R²) for training and testing phases are calculated as 0.9985 and 0.9980 respectively. The determined indexes and graphical evaluations show that predicting model can estimate gas density in high degree of accuracy. According to the obtained results, the predicting model can be used as a simple and powerful software in gas industries to predict different processes.     

Structural characterization and strengthening mechanism of forsterite nanostructured scaffolds synthesized by multistep sintering method

In this study, highly porous forsterite scaffolds with interconnected porosities were synthesized using multi-step sintering (MSS) method. The starting powder was nanosized forsterite, which was synthesized from talc and magnesium carbonate powders. The phase composition, average particle size and morphology of the produced forsterite powder were characterized by X-ray diffraction technique (XRD) and transition electron microscopy (TEM). Forsterite scaffolds were produced by foamy method using polymeric sponges. MSS process including three steps was used to efficiently sinter the forsterite nanopowders without destroying the initial porous structure of polymeric sponges. The results showed that MSS technique is an efficient and appropriate procedure to produce highly porous forsterite scaffolds with pore size in the range of 100–300?μm. The compressive strength, compressive modulus and porosity of C12 specimen (sintered at 1650?°C for 1?h with subsequent annealing at 1000?°C for 1000?min) was 1.88?MPa, 29.2?MPa, and 72.4%, respectively, which is very close to that of cancellous bone. The approach studied in this research can be developed for other nanostructure ceramics to produce highly porous scaffolds with interconnected porosities for load bearing applications.     

ZnO Nanoparticles as Ethanol Gas Sensors and the Effective Parameters on Their Performance

<正>ZnO nanoparticles are synthesized and applied as ethanol gas sensors.In some cases,the sensitivity and response time of these particles are shown to be higher than that has been reported in the literature.It has been investigated that the most possible reason for this higher gas sensing performance can be attributed to the quantity of the activity coefficient of its initial components.However,other effects such as pH and thermal decomposition are of importance as well.Specific ion interaction(SIT) model is applied to derive the mean activity coefficient values of the additives used in synthesis of ZnO nanoparticles.     

Interferometric sensor based on coherent imaging of gratings

A sensitive interferometric sensor scheme that is based on coherent imaging of a first phase grating onto a second phase grating, their periods accurately matched, is suggested. Experimental data, obtained with a setup based on the suggested scheme, are presented. The sensor was found capable of measuring an angular tilt of a mirror less than 0.5 microrad. Compared with a previously suggested measuring scheme, the novelty of the one presented here is the inclusion of a second set of gratings, which eliminates measurement ambiguity. Some characteristics of the sensor scheme are discussed.