Slope deformation partitioning and monitoring points optimization based on cluster analysis

The scientific and fair positioning of monitoring locations for surface displacement on slopes is a prerequisite for early warning and forecasting.However,there is no specific provision on how to effectively determine the number and location of monitoring points according to the actual deformation characteristics of the slope.There are still some defects in the layout of monitoring points.To this end,based on displacement data series and spatial location information of surface displacement monitoring points,by combining displacement series correlation and spatial distance influence factors,a spatial deformation correlation calculation model of slope based on clustering analysis was proposed to calculate the correlation between different monitoring points,based on which the deformation area of the slope was divided.The redundant monitoring points in each partition were eliminated based on the partition's outcome,and the overall optimal arrangement of slope monitoring points was then achieved.This method scientifically addresses the issues of slope deformation zoning and data gathering overlap.It not only eliminates human subjectivity from slope deformation zoning but also increases the efficiency and accuracy of slope monitoring.In order to verify the effectiveness of the method,a sand-mudstone interbedded CounterTilt excavation slope in the Chongqing city of China was used as the research object.Twenty-four monitoring points deployed on this slope were monitored for surface displacement for 13 months.The spatial location of the monitoring points was discussed.The results show that the proposed method of slope deformation zoning and the optimized placement of monitoring points are feasible.

NEW APPROACH FOR RELIABILITY-BASED DESIGN OPTIMIZATION:MINIMUM ERROR POINT

Conventional reliability-based design optimization （RBDO） requires to use the most probable point （MPP） method for a probabilistic analysis of the reliability constraints. A new approach is presented, called as the minimum error point （MEP） method or the MEP based method, for reliability-based design optimization, whose idea is to minimize the error produced by approximating performance functions. The MEP based method uses the first order Taylor＇s expansion at MEP instead of MPP. Examples demonstrate that the MEP based design optimization can ensure product reliability at the required level, which is very imperative for many important engineering systems. The MEP based reliability design optimization method is feasible and is considered as an alternative for solving reliability design optimization problems. The MEP based method is more robust than the commonly used MPP based method for some irregular performance functions.

Fatty liver index vs waist circumference for predicting non-alcoholic fatty liver disease

AIM: To determine the discriminatory performance of fatty liver index (FLI) for non-alcoholic fatty liver disease (NAFLD).METHODS: The data of 5052 subjects aged over 18 years were analyzed. FLI was calculated from body mass index, waist circumference (WC), triglyceride, and gamma glutamyl transferase data. Logistic regression analysis was conducted to determine the association between FLI and NAFLD. The discriminatory performance of FLI in the diagnosis of NAFLD was evaluated by receiver operating characteristic analysis. Area under the curves (AUCs) and related confidence intervals were estimated. Optimal cutoff points of FLI in the diagnosis of NAFLD were determined based on the maximum values of Youden’s index.RESULTS: The mean age of men and women in the study population were 44.8 ± 16.8 and 43.78 ± 15.43, respectively (P = 0.0216). The prevalence of NAFLD was 40.1% in men and 44.2% in women (P < 0.0017). FLI was strongly associated with NAFLD, so that even a one unit increase in FLI increased the chance of developing NAFLD by 5.8% (OR = 1.058, 95%CI: 1.054-1.063, P < 0.0001). Although FLI showed good performance in the diagnosis of NAFLD (AUC = 0.8656 (95%CI: 0.8548-0.8764), there was no significant difference with regards to WC (AUC = 0.8533, 95%CI: 0.8419-0.8646). The performance of FLI was not significantly different between men (AUC = 0.8648, 95%CI: 0.8505-0.8791) and women (AUC = 0.8682, 95%CI: 0.8513-0.8851). The highest performance with regards to age was related to the 18-39 age group (AUC = 0.8930, 95%CI: 0.8766-0.9093). The optimal cutoff points of FLI were 46.9 in men (sensitivity = 0.8242, specificity = 0.7687, Youden’s index = 0.5929) and 53.8 in women (sensitivity = 0.8233, specificity = 0.7655, Youden’s index = 0.5888).CONCLUSION: Although FLI had acceptable discriminatory power in the diagnosis of NAFLD, WC was a simpler and more accessible index with a similar performance.

Comparison of Two Types of Control Structures for Benzene Chlorine Reactive Distillation Systems

The ＂neat＂ operation of the two-reactant reactive distillation column has Oetter steady-state economics, while It presents a challenge for design, optimization, and control of the process. Based on the optimal economic design, the dual-composition control structure and dual-temperature control structure are designed respectively for the benzene chlorine consecutive reactive distillation process. The effectiveness and robustness are analyzed comparably for the disturbance resistance in terms of changes of production rate and feed composition. Results show that dual-temperature control with propose selection of tray temperatures and the optimal profile of the set point can provide better transient process performance than the composition control structure.

Saddle Point Optimality Criteria of Interval Valued Non-Linear Programming Problem

The present paper aims to develop the Kuhn-Tucker and Fritz John criteria for saddle point optimality of interval-valued nonlinear programming problem.To achieve the study objective,we have proposed the definition of minimizer and maximizer of an interval-valued non-linear programming problem.Also,we have introduced the interval-valued Fritz-John and Kuhn Tucker saddle point problems.After that,we have established both the necessary and sufficient optimality conditions of an interval-valued non-linear minimization problem.Next,we have shown that both the saddle point conditions(Fritz-John and Kuhn-Tucker)are sufficient without any convexity requirements.Then with the convexity requirements,we have established that these saddle point optimality criteria are the necessary conditions for optimality of an interval-valued non-linear programming with real-valued constraints.Here,all the results are derived with the help of interval order relations.Finally,we illustrate all the results with the help of a numerical example.

Scheduling and control of failure prone systems with demand uncertainty and job overlaps

Given that the overlapping of jobs is permitted, the paper studies the scheduling and control of failure prone production systems, i.e. so-called settings with demand uncertainty and job overlaps. Because a variable demand resource is involved in the production and corrective maintenance control problems of the system, which switched randomly between zero and a maximum level, it is difficult to obtain the analytical solutions of the optimal single hedging point policy. An asymptotic optimal scheduling policy is presented and a double hedging point policy is offered to control simultaneously the production rate and the corrective maintenance rate of the system. The corresponding analytical solutions and approximate solutions are obtained. Considering the relationship of production, corrective maintenance and demand variable, an approximate optimal single hedging point control policy is proposed. Numerical results are presented.

Optimal time point for the transplantation of neural stem cells induced to differentiate with retinoic acid

Previous studies have demonstrated that differentiated neural stem cells （NSCs） are more suitable for transplantation than non-differentiated NSCs. In this study, NSCs were expanded in vitro for two passages, induced with retinoic acid to differentiate, and harvested between 1 6 days later. They were subsequently cultured in artificial cerebrospinal fluid for an additional 3 days, dudng which their growth and morphology was monitored. NSCs induced for 4 days exhibited a peak rate of cells differentiating into neurons and robust growth. Our results indicate that the optimal time point for transplanting NSCs is following a 4-day period of induced differentiation.

The Application of ODE Method in Constrained Optimization

in this paper,a new method to solve the general constrained optimization problem is proposed,theproblem of finding the local optimal points of the nonlinear programming problem with equality and inequality constraints is considered by solving the ODE d.e.Ordinary oprential Equation)with aPPropriatenumerical procedure.Moreover,the rate of conveyance to optimal points is quadratic.Some numerical resultis given to show the efficiency of the method proposed in this poper.

Design of a grating by a joint optimization method for a phase-shifting point diffraction interferometer

A grating is an important element of a phase-shifting point diffraction interferometer, and the grating constant and duty cycle have a great impact on the interferometer, so the design of a grating becomes significant. In order to measure the projection objective with a numerical aperture of 0.2, we present a joint optimization method of a pinhole and grating based on scalar diffraction and the finite difference time domain method. The grating constant and the film thickness are selected, and the duty cycle of the grating is optimized. The results show that in the grating processing the material chromium is adopted, the thickness is 200 nm, and the grating constant is 15 μm. When the duty cycle is 55%, the interference fringe contrast is the greatest. The feasibility of the design result is further verified by experiment.

Position optimization of particular solution sources for distributed source boundary point method by volume velocity-matching

Choosing particular solution source and its position have great influence on accu- racy of sound field prediction in distributed source boundary point method. An optimization method for determining the position of particular solution sources is proposed to get high accu- racy prediction result. In this method, tripole is chosen as the particular solution. The upper limit frequency of calculation is predicted by setting 1% volume velocity relative error limit using vibration velocity of structure surface. Then, the optimal position of particular solution sources, in which the relative error of volume velocity is minimum, is determined within the range of upper limit frequency by searching algorithm using volume velocity matching. The transfer matrix between pressure and surface volume velocity is constructed in the optimal position. After that, the sound radiation of structure is calculated by the matrix. The results of numerical simulation show that the calculation error is significantly reduced by the proposed method. When there are vibration velocity measurement errors, the calculation errors can be controlled within 5% by the method.

Contribution to sample failure probability plot and its solution by Kriging method

To analyze the effect of the region of the model inputs on the model output,a novel concept about contribution to the sample failure probability plot(CSFP) is proposed based on the contribution to the sample mean plot(CSM) and the contribution to the sample variance plot(CSV).The CSFP can be used to analyze the effect of the region of the model inputs on the failure probability.After the definition of CSFP,its property and the differences between CSFP and CSV/CSM are discussed.The proposed CSFP can not only provide the information about which input affects the failure probability mostly,but also identify the contribution of the regions of the input to the failure probability mostly.By employing the Kriging model method on optimized sample points,a solution for CSFP is obtained.The computational cost for solving CSFP is greatly decreased because of the efficiency of Kriging surrogate model.Some examples are used to illustrate the validity of the proposed CSFP and the applicability and feasibility of the Kriging surrogate method based solution for CSFP.

On the Convergence Rate of a Proximal Point Algorithm for Vector Function on Hadamard Manifolds

The proximal point algorithm has many interesting applications,such as signal recovery,signal processing and others.In recent years,the proximal point method has been extended to Riemannian manifolds.The main advantages of these extensions are that nonconvex problems in classic sense may become geodesic convex by introducing an appropriate Riemannian metric,constrained optimization problems may be seen as unconstrained ones.In this paper,we propose an inexact proximal point algorithm for geodesic convex vector function on Hadamard manifolds.Under the assumption that the objective function is coercive,the sequence generated by this algorithm converges to a Pareto critical point.When the objective function is coercive and strictly geodesic convex,the sequence generated by this algorithm converges to a Pareto optimal point.Furthermore,under the weaker growth condition,we prove that the inexact proximal point algorithm has linear/superlinear convergence rate.

Solution to economic dispatch loading effect by using catfish problem with valve-point PSO algorithm

This paper proposes application of a catfish particle swarm optimization （PSO） algorithm to economic dispatch （ED） problems. The ED problems considered in this paper include valve-point loading effect, power balance constraints, and generator limits. The conventional PSO and catfish PSO algorithms are applied to three different test systems and the solutions obtained are compared with each other and with those reported in literature. The comparison of solutions shows that catfish PSO outperforms the conventional PSO and other methods in terms of solution quality though there is a slight increase in computational time.

Cheeger's cut, maxcut and the spectral theory of1-Laplacian on graphs

This is primarily an expository paper surveying up-to-date known results on the spectral theory of1-Laplacian on graphs and its applications to the Cheeger cut, maxcut and multi-cut problems. The structure of eigenspace, nodal domains, multiplicities of eigenvalues, and algorithms for graph cuts are collected.

Two-Agent Scheduling on a Bounded Parallel-Batching Machine with Makespan and Maximum Lateness Objectives

This paper studies the two-agent scheduling on a bounded parallel-batching machine.In the problem,there are two agents A and B each having their own job sets with the restriction that the processing times of jobs of agent B are equal.The jobs of different agents can be processed in a common batch.Moreover,each agent has its own objective function to be minimized.The objective function of agent A is the makespan of its jobs,and the objective function of agent B is the maximum lateness of its jobs.We present a polynomial-time algorithm for finding all Pareto optimal solutions of this two-agent parallel-batching scheduling problem.

An adaptive fast search algorithm for block motion estimation in H.264

Motion estimation is an important issue in H.264 video coding systems because it occupies a large amount of encoding time.In this paper,a novel search algorithm which utilizes an adaptive hexagon and small diamond search (AHSDS) is proposed to enhance search speed.The search pattern is chosen according to the motion strength of the current block.When the block is in active motion,the hexagon search provides an efficient search means;when the block is inactive,the small diamond search is adopted.Simulation results showed that our approach can speed up the search process with little effect on distortion performance compared with other adaptive approaches.

Inference for Optimal Split Point in Conditional Quantiles

In this paper we show the occurrence of cubic-root asymptotics in misspecified conditional quantile models where the approximating functions are restricted to be binary decision trees. Inference procedure for the optimal split point in the decision tree is conducted by inverting a t-test or a deviation measure test, both involving Chemoff type limiting distributions. In order to avoid estimating the nuisance parameters in the complicated limiting distribution, subsampling is proved to deliver the correct confidence interval/set.