A SUPERLINEARLY CONVERGENT SPLITTING FEASIBLE SEQUENTIAL QUADRATIC OPTIMIZATION METHOD FOR TWO-BLOCK LARGE-SCALE SMOOTH OPTIMIZATION

This paper discusses the two-block large-scale nonconvex optimization problem with general linear constraints.Based on the ideas of splitting and sequential quadratic optimization(SQO),a new feasible descent method for the discussed problem is proposed.First,we consider the problem of quadratic optimal(QO)approximation associated with the current feasible iteration point,and we split the QO into two small-scale QOs which can be solved in parallel.Second,a feasible descent direction for the problem is obtained and a new SQO-type method is proposed,namely,splitting feasible SQO(SF-SQO)method.Moreover,under suitable conditions,we analyse the global convergence,strong convergence and rate of superlinear convergence of the SF-SQO method.Finally,preliminary numerical experiments regarding the economic dispatch of a power system are carried out,and these show that the SF-SQO method is promising.

Fish forewarning of comprehensive toxicity in water environment based on Bayesian sequential method

Environmental impact of pollutants can be analyzed effectively by acquiring fish behavioral signals in water with biological behavior sensors. However, a variety of factors, such as the complexity of biological organisms themselves, the device error and the environmental noise, may compromise the accuracy and timeliness of model predictions. The current methods lack prior knowledge about the fish behavioral signals corresponding to characteristic pollutants, and in the event of a pollutant invasion, the fish behavioral signals are poorly discriminated. Therefore, we propose a novel method based on Bayesian sequential,which utilizes multi-channel prior knowledge to calculate the outlier sequence based on wavelet feature followed by calculating the anomaly probability of observed values. Furthermore, the relationship between the anomaly probability and toxicity is analyzed in order to achieve forewarning effectively. At last, our algorithm for fish toxicity detection is verified by integrating the data on laboratory acceptance of characteristic pollutants. The results show that only one false positive occurred in the six experiments, the present algorithm is effective in suppressing false positives and negatives, which increases the reliability of toxicity detections, and thereby has certain applicability and universality in engineering applications.

Sequential Monte Carlo Method Toward Online RUL Assessment with Applications

Online assessment of remaining useful life(RUL) of a system or device has been widely studied for performance reliability, production safety, system conditional maintenance, and decision in remanufacturing engineering. However,there is no consistency framework to solve the RUL recursive estimation for the complex degenerate systems/device.In this paper, state space model(SSM) with Bayesian online estimation expounded from Markov chain Monte Carlo(MCMC) to Sequential Monte Carlo(SMC) algorithm is presented in order to derive the optimal Bayesian estimation.In the context of nonlinear & non-Gaussian dynamic systems, SMC(also named particle filter, PF) is quite capable of performing filtering and RUL assessment recursively. The underlying deterioration of a system/device is seen as a stochastic process with continuous, nonreversible degrading. The state of the deterioration tendency is filtered and predicted with updating observations through the SMC procedure. The corresponding remaining useful life of the system/device is estimated based on the state degradation and a predefined threshold of the failure with two-sided criterion. The paper presents an application on a milling machine for cutter tool RUL assessment by applying the above proposed methodology. The example shows the promising results and the effectiveness of SSM and SMC online assessment of RUL.

Mixed D-vine copula-based conditional quantile model for stochastic monthly streamflow simulation

Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months.To address this limitation,this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations.This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas.The up-to-down sequential method,which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach,was used to determine the structures of multivariate Dvine copulas.The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station,the inflow control station of the Longyangxia Reservoir in the Yellow River Basin.The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow.This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.

Feasibility evaluation for excavation of Naghshe Jahan Square subway station by underground methods

In recent years, in reaction to the increasing usage of urban areas, the excavation of underground spaces has been developed. One of the most challenging issues encountered by engineers is the construction of subway stations as large underground spaces at shallow depth with soft surrounding soils. In this paper, Naghshe Jahan Square subway station located in Isfahan, Iran, has been simulated by geomechanical fnite difference method(FDM). This station is located under important historical structures. Therefore, the ground displacement and surface settlement induced by the excavation of the subway station should be strictly controlled. Many of such problems are affected by selected excavation method. For these reasons, different underground excavation methods associated with construction have been studied. In this study, sequential excavation method and large-diameter curved pipe roofng method are used and the numerical results of the two methods are compared. The presence of groundwater table obliges us to choose special techniques for the stability of the ground around the subway station during construction; hence compressed air and ground freezing techniques are utilized in the simulations of the subway station. Finally, after choosing appropriate support systems, the large-diameter curved pipe roofng method with 1.5 m spacing between curved pipes is proposed.

RELIABILITY-BASED DESIGN OF COMPOSITES UNDER THE MIXED UNCERTAINTIES AND THE OPTIMIZATION ALGORITHM

This paper proposed a reliability design model for composite materials under the mixture of random and interval variables. Together with the inverse reliability analysis technique, the sequential single-loop optimization method is applied to the reliability-based design of composites. In the sequential single-loop optimization, the optimization and the reliability analysis are decoupled to improve the computational efficiency. As shown in examples, the minimum weight problems under the constraint of structural reliability are solved for laminated composites. The Particle Swarm Optimization （PSO） algorithm is utilized to search for the optimal solutions. The design results indicate that, under the mixture of random and interval variables, the method that combines the sequential single-loop optimization and the PSO algorithm can deal effectively with the reliability-based design of composites.

Effects of long-term phosphorus fertilization and straw incorporation on phosphorus fractions in subtropical paddy soil

Study on soil phosphorus（P） fraction is an important aspect in probing the mechanisms of soil P accumulation in farmland and mitigating its losing risk to the environment. We used a sequential extraction method to evaluate the impacts of long-term fertilization and straw incorporation on inorganic, organic, and residual P（Pi, Po, and Pre） fractions in the plow layer（0–20 cm） of acidic paddy soil in southern China. The experiment comprised of six treatments：（i） no fertilizer control（CK）;（ii） straw incorporation and green manure（SG）;（iii） nitrogen and P fertilizer（NP）;（iv） NP＋SG;（v） NP＋K fertilizer（NPK）; and（vi） NPK＋SG. The results showed that, compared to the initial total soil P content（TSP, 600 mg kg^–1 in 1990）, long-term（20 years） combined continuous P fertilizer and SG significantly increased P accumulation（by 13–20%） while single fertilization（39.3 kg P ha^–1 yr^–1） could maintain soil P status at the most. The average soil P fractions comprised of extractable Pi, Po, and Pre by 51.7, 33.4, and 14.9% in total soil P, respectively. With comparison of no fertilizer addition（CK）, long-term single fertilization significantly（P〈0.05） increased the accumulation of Na HCO3^–, Na OH^–, and HCl^– extractable Pi fractions accounting for two- to three-fold, while SG increased the accumulation of Na HCO3^– and Na OH^– extractable Piand Po accounting for 12–60%. Though the mobilization of Pre fractions was not significant（P〉0.05）, our data indicate that SG may partially substitute for fertilizer P input and minimizing soil P accumulation and subsequent environmental risk in the subtropical paddy soil.

SEQUENTIAL CONVEX PROGRAMMING METHODS FOR SOLVING LARGE TOPOLOGY OPTIMIZATION PROBLEMS： IMPLEMENTATION AND COMPUTATIONAL RESULTS

In this paper, we describe a method to solve large-scale structural optimization problems by sequential convex programming （SCP）. A predictor-corrector interior point method is applied to solve the strictly convex subproblems. The SCP algorithm and the topology optimization approach are introduced. Especially, different strategies to solve certain linear systems of equations are analyzed. Numerical results are presented to show the efficiency of the proposed method for solving topology optimization problems and to compare different variants.

A PARALLEL NUMERICAL MODEL OF SOLVING N-S EQUATIONS BY USING SEQUENTIAL REGULARIZATION METHOD

A parallel numerical model was established for solving the Navier-Stokesequations by using Sequential Regu-larization Method (SRM). The computational domain is decomposedinto P sub-domains in which the difference formulae were obtained from the governing equations. Thedata were exchannged at the virtual boundary of sub-domains in parallel computation. Theclose-channel cavity flow was solved by the implicit method. The driven square cavity flow wassolved by the explicit method. The results were compared well those given by Ghia.

Efcient autofocus method for sequential automatic capturing of high-magnification microscopic images

This letter presents an autofocus （AF） method to position a high-magnification microscope lens that automatically captures hundreds of images from a single moving slide. These images are taken by a mobile clinic unit in a rural location, and are later automatically processed and revised by a remote specialist. This process requires high focus precision to enable image processing techniques to achieve proper results. Low focusing times are also required for the system to be operative. We propose a novel method that combines two focus measures with an adapted searching scheme to cope with both constraints.

Development and psychometric evaluation of the nurse behavior toward confirmed and suspected HIV/AIDS patients (NB-CSHAP) scale

There is a scarcity of literature discussing nurses’ behaviors toward caring for suspected or confirmed human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) patients. The development of a scale specific to measure nurses’ behaviors will allow health institutions to assess the disposition of their nurses in terms of HIV care. This study aims to present the rigors of developing and validating a reliable instrument to contextualize these nurses’ behaviors. This study utilized a sequential exploratory mixed method design to develop the NB-CSHAP scale. Thematic analysis was done on the qualitative data from the interviews with persons living with HIV/AIDS (PLHA) from which items were selected to be included in the scale. Exploratory factor analysis was utilized to extract the factors and Cronbach’s alpha was used to assess the reliability of the instrument. Four factors were extracted and are categorized as either caring or discriminatory behaviors. These include: (1) service-oriented, (2) openhanded, (3) perceptive, and (4) discriminatory. The scale has an internal consistency of 0.73. The scale shows acceptable psychometric properties, hence can be used to assess the nurses’ behaviors in caring for confirmed or suspected HIV clients. The scale may be used by health institutions to determine the quality of the patient care provided by their nurses to clients with confirmed or suspected HIV .

A New Method for State Simplification in Incompletely Specified Sequential Machines

This paper presents a method for state simplification in incompletely specified sequential machines. The new method adopts Inclusive-OR operation of column vectors for multi-level output matrix E_k! Compared with other algorithms in use, this method is theoretically more strict, while its structure is simple and the results obtained are accurate.

A comprehensive review of recent advancements and developments in heat exchanger network synthesis techniques

The rapid development of computational technology and the increasing energy demand have improved heat exchanger network(HEN)synthesis.The HEN synthesis involves several optimizations of matches,distributions of heat loads,and stream splitting of heat units.Thus,obtaining good results at high efficiency has been the main standard for evaluating the techniques in the research area of HEN synthesis.This paper first summarizes and analyzes the main contributions of the existing HEN synthesis techniques.To compare related data quantitively,information on ten typical cases is presented in this paper.Furthermore,recently improved solutions for commonly encountered existing literature cases demonstrate the evolution and competition trends in the field of HEN synthesis.The comparison data presented in this paper not only provide a useful reference for future research but also present the optimization directions.Based on the findings of this study,it is noted that there is still a large room for improvement,and current approaches are incapable of dealing with all HEN cases.Moreover,it is still difficult to escape a local optimum and overcome structural constraints when seeking the global optimum.As a follow-up to the current work,the parallel computing mode and adaptively coordinating the ratio of global and local searching abilities are major development trends for future investigation.

Reliability Assessment of Integrated Electricity and Natural Gas Transmission Systems in Presence of Power-to-gas and Combined Heat and Power Technologies

Assessing the reliability of integrated electricity and gas systems has become an important issue due to the strong dependence of these energy networks through the power-to-gas(P2G)and combined heat and power(CHP)technologies.The current work,initially,presents a detailed energy flow model for the integrated power and natural gas system in light of the P2G and CHP technologies.Considering the simultaneous load flow of networks,a contingency analysis procedure is proposed,and reliability is assessed through sequential Monte Carlo simulations.The current study examines the effect of independent and dependent operation of energy networks on the reliability of the systems.In particular,the effect of employing both P2G and CHP technologies on reliability criteria is evaluated.In addition,a series of sensitivity analysis are performed on the size and site of these technologies to investigate their effects on system reliability.The proposed method is implemented on an integrated IEEE 24-bus electrical power system and 20-node Belgian natural gas system.The simulation procedure certifies the proposed method for reliability assessment is practical and applicable.In addition,the results prove connection between energy networks through P2G and CHP technologies can improve reliability of networks if the site and size of technologies are properly determined.

Rotorcraft flight endurance estimation based on a new battery discharge model

To avoid the numerical complexities of the battery discharge law of electric-powered rotorcrafts,this study uses the Kriging method to model the discharge characteristics of Li-Po batteries under standard conditions.A linear current compensation term and an ambient temperature compensation term based on radial basis functions are then applied to the trained Kriging model,leading to the complete discharged capacity-terminal voltage model.Using an orthogonal experimental design and a sequential method,the coefficients of the current and ambient temperature compensation terms are determined through robust optimization.An endurance calculation model for electric-powered rotorcrafts is then established,based on the battery discharge model,through numerical integration.Laboratory tests show that the maximum relative error of the proposed discharged capacity-terminal voltage model at detection points is 0.0086,and that of the rotorcraft endurance calculation model is 0.0195,thus verifying their accuracy.A flight test further demonstrates the applicability of the proposed endurance model to general electric-powered rotorcrafts.

A novel compensation-based recurrent fuzzy neural network and its learning algorithm

Based on detailed study on several kinds of fuzzy neural networks, we propose a novel compensationbased recurrent fuzzy neural network （CRFNN） by adding recurrent element and compensatory element to the conventional fuzzy neural network. Then, we propose a sequential learning method for the structure identification of the CRFNN in order to confirm the fuzzy rules and their correlative parameters effectively. Furthermore, we improve the BP algorithm based on the characteristics of the proposed CRFNN to train the network. By modeling the typical nonlinear systems, we draw the conclusion that the proposed CRFNN has excellent dynamic response and strong learning ability.

Particle flters for probability hypothesis density flter with the presence of unknown measurement noise covariance

In Bayesian multi-target fltering,knowledge of measurement noise variance is very important.Signifcant mismatches in noise parameters will result in biased estimates.In this paper,a new particle flter for a probability hypothesis density（PHD）flter handling unknown measurement noise variances is proposed.The approach is based on marginalizing the unknown parameters out of the posterior distribution by using variational Bayesian（VB）methods.Moreover,the sequential Monte Carlo method is used to approximate the posterior intensity considering non-linear and non-Gaussian conditions.Unlike other particle flters for this challenging class of PHD flters,the proposed method can adaptively learn the unknown and time-varying noise variances while fltering.Simulation results show that the proposed method improves estimation accuracy in terms of both the number of targets and their states.