Machine learning model based on non-convex penalized huberized-SVM

The support vector machine(SVM)is a classical machine learning method.Both the hinge loss and least absolute shrinkage and selection operator(LASSO)penalty are usually used in traditional SVMs.However,the hinge loss is not differentiable,and the LASSO penalty does not have the Oracle property.In this paper,the huberized loss is combined with non-convex penalties to obtain a model that has the advantages of both the computational simplicity and the Oracle property,contributing to higher accuracy than traditional SVMs.It is experimentally demonstrated that the two non-convex huberized-SVM methods,smoothly clipped absolute deviation huberized-SVM(SCAD-HSVM)and minimax concave penalty huberized-SVM(MCP-HSVM),outperform the traditional SVM method in terms of the prediction accuracy and classifier performance.They are also superior in terms of variable selection,especially when there is a high linear correlation between the variables.When they are applied to the prediction of listed companies,the variables that can affect and predict financial distress are accurately filtered out.Among all the indicators,the indicators per share have the greatest influence while those of solvency have the weakest influence.Listed companies can assess the financial situation with the indicators screened by our algorithm and make an early warning of their possible financial distress in advance with higher precision.

Drag Coefficient of a Non-Convex Polygonal Plate during Free Fall

Waterside creatures or aquatic organisms use a fin or web to generate a thrust force. These fins or webs have a non-convex section, referred to as a non-convex shape. We investigate the drag force acting on a non-convex plate during unsteady motion. We perform the experiment in a water tank during free fall. We fabricate the non-convex plate by cutting isosceles triangles from the side of a convex hexagonal plate. The base angle of the triangle is between 0° to 45°. The base angle is 0 indicates the convex hexagonal thin plate. We estimate the drag coefficient with the force balance acting on the model based on the image analysis technique. The results indicate that increasing the base angle by more than 30° increased the drag coefficient. The drag coefficient during unsteady motion changed with the growth of the vortex behind the model. The vortex has small vortices in the shear layer, which is related to the Kelvin-Helmholtz instabilities.

Impact Force Localization and Reconstruction via ADMM-based Sparse Regularization Method

In practice,simultaneous impact localization and time history reconstruction can hardly be achieved,due to the illposed and under-determined problems induced by the constrained and harsh measuring conditions.Although l_(1) regularization can be used to obtain sparse solutions,it tends to underestimate solution amplitudes as a biased estimator.To address this issue,a novel impact force identification method with l_(p) regularization is proposed in this paper,using the alternating direction method of multipliers(ADMM).By decomposing the complex primal problem into sub-problems solvable in parallel via proximal operators,ADMM can address the challenge effectively.To mitigate the sensitivity to regularization parameters,an adaptive regularization parameter is derived based on the K-sparsity strategy.Then,an ADMM-based sparse regularization method is developed,which is capable of handling l_(p) regularization with arbitrary p values using adaptively-updated parameters.The effectiveness and performance of the proposed method are validated on an aircraft skin-like composite structure.Additionally,an investigation into the optimal p value for achieving high-accuracy solutions via l_(p) regularization is conducted.It turns out that l_(0.6)regularization consistently yields sparser and more accurate solutions for impact force identification compared to the classic l_(1) regularization method.The impact force identification method proposed in this paper can simultaneously reconstruct impact time history with high accuracy and accurately localize the impact using an under-determined sensor configuration.

An Efficient Smoothing and Thresholding Image Segmentation Framework with Weighted Anisotropic-lsotropicTotalVariation

In this paper,we design an efficient,multi-stage image segmentation framework that incorporates a weighted difference of anisotropic and isotropic total variation(AITV).The segmentation framework generally consists of two stages:smoothing and thresholding,thus referred to as smoothing-and-thresholding(SaT).In the first stage,a smoothed image is obtained by an AITV-regularized Mumford-Shah(MS)model,which can be solved efficiently by the alternating direction method of multipliers(ADMMs)with a closed-form solution of a proximal operator of the l_(1)-αl_(2) regularizer.The convergence of the ADMM algorithm is analyzed.In the second stage,we threshold the smoothed image by K-means clustering to obtain the final segmentation result.Numerical experiments demonstrate that the proposed segmentation framework is versatile for both grayscale and color images,effcient in producing high-quality segmentation results within a few seconds,and robust to input images that are corrupted with noise,blur,or both.We compare the AITV method with its original convex TV and nonconvex TVP(O<p<1)counterparts,showcasing the qualitative and quantitative advantages of our proposed method.

Homotopy Method for Non-convex Programming in Unbonded Set

In the past few years, much and much attention has been paid to the method for solving non-convex programming. Many convergence results are obtained for bounded sets. In this paper, we get global convergence results for non-convex programming in unbounded sets under suitable conditions.

Simulation of bacterial flagellar phase transition by non-convex and non-local continuum modeling

Bacterial flagellar filament can undergo a stress-induced polymorphic phase transition in both vitro and vivo environments.The filament has 12 different helical forms(phases) characterized by different pitch lengths and helix radii.When subjected to the frictional force of flowing fluid,the filament changes between a left-handed normal phase and a right-handed semi-coiled phase via phase nucleation and growth.This paper develops non-local finite element method(FEM) to simulate the phase transition under a displacement-controlled loading condition(controlled helix-twist).The FEM formulation is based on the Ginzburg-Landau theory using a one-dimensional non-convex and non-local continuum model.To describe the processes of the phase nucleation and growth,viscosity-type kinetics is also used.The non-local FEM simulation captures the main features of the phase transition:two-phase coexistence with an interface of finite thickness,phase nucleation and phase growth with interface propagation.The non-local FEM model provides a tool to study the effects of the interfacial energy/thickness and loading conditions on the phase transition.

A Positioning Method and Realization on Single Satellites in Different Orbits Using TDOA

The main geolocation technology currently used in COSPAS-SARSAT system is TDOA/FDOA or three-star TDOA,the principle is to determine the location of the signal source by using the difference in arrival time and frequency of the wireless signal between different receivers.Therefore,ground monitoring stations need to be equipped with more than two antenna receiving stations,and multiple satellites should be able to simultaneously relay the distress signal from the target source in order to achieve the geolocation function.However,when the ground receiving system has only one antenna receiving station,or the target source is in a heavily obscured environment,the ground side is unable to receive the forwarded signals from multiple satellites at the same time,which will make it impossible to locate.To address these problems,in this paper,a time-sharing single satellite geolocations method based on different orbits is proposed for the first time.This method uses one or several low-earth orbit satellites(LEO)and mediumearth orbit satellites(MEO)in the visible area,and the receiving station only needs one pair of receiving antennas to complete the positioning.It can effectively compensate for the shortcomings of the traditional TDOA using the same moment and have better positioning accuracy compared with the single satellite in the same orbit.Due to the limited experimental conditions,this paper tests the navigation satellite using different orbit time-sharing single satellite geolocations,and proves that the positioning method has high positioning accuracy and has certain promotion and application value.

Non-Convex Optimization of Resource Allocation in Fog Computing Using Successive Approximation

Fog computing can deliver low delay and advanced IT services to end users with substantially reduced energy consumption.Nevertheless,with soaring demands for resource service and the limited capability of fog nodes,how to allocate and manage fog computing resources properly and stably has become the bottleneck.Therefore,the paper investigates the utility optimization-based resource allocation problem between fog nodes and end users in fog computing.The authors first introduce four types of utility functions due to the diverse tasks executed by end users and build the resource allocation model aiming at utility maximization.Then,for only the elastic tasks,the convex optimization method is applied to obtain the optimal results;for the elastic and inelastic tasks,with the assistance of Jensen’s inequality,the primal non-convex model is approximated to a sequence of equivalent convex optimization problems using successive approximation method.Moreover,a two-layer algorithm is proposed that globally converges to an optimal solution of the original problem.Finally,numerical simulation results demonstrate its superior performance and effectiveness.Comparing with other works,the authors emphasize the analysis for non-convex optimization problems and the diversity of tasks in fog computing resource allocation.

Non-convexity Pricing and Allocating Costs in Stochastic Electricity Markets

Stochastic electricity markets have drawn attention due to fast increase of renewable penetrations.This results in two issues:one is to reduce uplift payments arising from non-convexity under renewable uncertainties,and the other one is to allocate reserve costs based on renewable uncertainties.To resolve the first issue,a convex hull pricing method for stochastic electricity markets is proposed.The dual variables of system-wide constraints in a chance-constrained unit commitment model are shown to reduce expected uplift payments,together with developing a linear program to efficiently calculate such prices.To resolve the second issue,an allocation method is proposed to allocate reserve costs to each renewable power plant by explicitly investigating how renewable uncertainties of each renewable power plant affect reserve costs.The proposed methods are validated in a 24-period 3-unit test example and a 24-period 48-unit utility example.

基于两阶段行程时间的交通流分配理论

针对目前静态交通流分配理论难以处理网络流量演化的问题,给出了基于交通流反λ基本图的流量分配新模型.通过不断求解新模型更新路段交通状态,明确了利用静态交通流分配模型分析网络交通流演变的具体方法.假设网络路段均处于自由流状态,通过求解得到平衡路段流量,判断是否达到临界流量.将路段流量达到临界流量的路段设定为拥挤状态,重新求解平衡流量,判断是否仍存在达到临界流量的路段.依据上述思路,直到新的模型无解或无新的路段达到临界流量.本文通过定义网络不同级别的拥挤瓶颈,完成对网络流量演化的分析描述.算例验证了新模型与方法的可行性.新理论提供了分析网络交通状态演变的新思路,拓展了静态交通流分配理论.

动画剧本创作中剧本规划的重要作用

通过对国内外经典动画作品特征的分析,结合动画剧本的创作过程提出了剧本规划这一概念,赋予了剧本规划以内涵,并通过案例分析论述了剧本规划的作用和在动画剧本创作中的重要地位。

How to Select the Best Sensors for TDOA and TDOA/AOA Localization?

This paper focuses on the sensor subset optimization problem in time difference of arrival(TDOA) passive localization scenario. We seek for the best sensor combination by formulating a non-convex optimization problem, which is to minimize the trace of covariance matrix of localization error under the condition that the number of selected sensors is given. The accuracy metric is described by the localization error covariance matrix of classical closed-form solution, which is introduced to convert the TDOA nonlinear equations into pseudo linear equations. The non-convex optimization problem is relaxed to a standard semi-definite program(SDP) and efficiently solved in a short time. In addition, we extend the sensor selection method to a mixed TDOA and angle of arrival(AOA) localization scenario with the presence of sensor position errors. Simulation results validate that the performance of the proposed sensor selection method is very close to the exhaustive search method.

Similarity measure application to fault detection of flight system

Fault detection technique is introduced with similarity measure. The characteristics of conventional similarity measure based on fuzzy number are discussed. With the help of distance measure, similarity measure is constructed explicitly. The designed distance-based similarity measure is applicable to general fuzzy membership functions including non-convex fuzzy membership function, whereas fuzzy number-based similarity measure has limitation to calculate the similarity of general fuzzy membership functions. The applicability of the proposed similarity measure to general fuzzy membership structures is proven by identifying the definition. To decide fault detection of flight system, the experimental data （pitching moment coefficients and lift coefficients） are transformed into fuzzy membership functions. Distance-based similarity measure is applied to the obtained fuzzy membership functions, and similarity computation and analysis are obtained with the fault and normal operation coefficients.

Delay-Throughput Tradeoff in Satellite Data Relay Networks with Prioritized User Satellites

The efficient antenna scheduling strategy for data relay satellites(DRSs)is essential to optimize the throughput or delay of the satellite data relay network.However,these two objectives conflict with each other since the user satellites(USs)with higher priorities take up more transmission time of DRSs’antennas for greater throughput but the USs storing more packets cause a severer waiting delay to the whole network.To balance the conflicting metrics for meeting the delay-throughput integrated requirements,we formulate the antenna scheduling as a stochastic non-convex fractional programming,which is challenging to be solved.For the tractability,we equivalently transform the fractional programming to a parametric problem and implement the Lyapunov drift to guarantee the constraint of mean rate stability.By proposing a delay and throughput tradeoff based antenna scheduling algorithm,we further transform the parametric problem to a solvable weight matching problem.Simulation results reveal the feasible region of the preference control parameter for integrated QoS cases and its variation relationship with network delay and throughput.

STRESS HYSTERESIS AND DOMAIN EVOLUTION IN THERMOELASTIC TENSION STRIPS

Martensite domain formation, evolution and annihilation are widely observed in stress-induced phase transformation of superelastic NiTi polycrystalline shape memory alloys. By the calculation of the thermodynamic driving force and the incorporation of friction kinetics of the interface, the domain morphology and its evolution were successfully simulated by the interface-tracking technique. The computational results agree well with the experimental observation of tensile strips. Based on theoretical and computational results, we discussed the effects of critical driving force and the existence of metastability on the transition between different domain patterns.

Decentralized Control for Residential Energy Management of a Smart Users' Microgrid with Renewable Energy Exchange

This paper presents a decentralized control strategy for the scheduling of electrical energy activities of a microgrid composed of smart homes connected to a distributor and exchanging renewable energy produced by individually owned distributed energy resources. The scheduling problem is stated and solved with the aim of reducing the overall energy supply from the grid, by allowing users to exchange the surplus renewable energy and by optimally planning users' controllable loads. We assume that each smart home can both buy/sell energy from/to the grid taking into account time-varying non-linear pricing signals. Simultaneously, smart homes cooperate and may buy/sell locally harvested renewable energy from/to other smart homes. The resulting optimization problem is formulated as a non-convex non-linear programming problem with a coupling of decision variables in the constraints. The proposed solution is based on a novel heuristic iterative decentralized scheme algorithm that suitably extends the Alternating Direction Method of Multipliers to a non-convex and decentralized setting. We discuss the conditions that guarantee the convergence of the presented algorithm. Finally, the application of the proposed technique to a case study under several scenarios shows its effectiveness.

Solving a Class of Nonlinear Programming Problems via a Homotopy Continuation Method

In this paper we present a homotopy continuation method for finding the Karush-Kuhn-Tucker point of a class of nonlinear non-convex programming problems. Two numerical examples are given to show that this method is effective. It should be pointed out that we extend the results of Lin et al. （see Appl. Math. Comput., 80（1996）, 209-224） to a broader class of non-convex programming problems.

Application of the p-Median Problem in School Allocation

This paper focus on solving the problem of optimizing students’ orientation. After four years spent in secondary school, pupils take exams and are assigned to the high school. The main difficulty of Education Department Inspection (EDI) of Dakar lies in the allocation of pupils in the suburbs. In this paper we propose an allocation model using the p-median problem. The model takes into account the distance of the standards imposed by international organizations between pupil’s home and school. The p-median problem is a location-allocation problem that takes into account the average (total) distance between demand points (pupil’s home) and facility (pupil’s school). The p-median problem is used to determine the best location to place a limited number of schools. The model has been enhanced and applied to a wide range of school location problems in suburbs. After collecting necessary numerical data to each EDI, a formulation is presented and computational results are carried out.

Logarithm-transform piecewise linearization method for the optimization of fasoline blending processes

Gasoline blending is a key process in a petroleum refinery, as it can yield 60%–70% of a typical refinery's total revenue. This process not only exhibits non-convex nonlinear blending behavior due to the complicated blend mechanism of various component feedstocks with different quality properties, but also involves global optimum searching among numerous blending recipes. Since blend products are required to meet a series of quality requirements and highly-sensitive to the proportion changes of blending feedstocks, global optimization methods for NLP problems are often difficult to be applied because of heavy computational burdens. Thus, piecewise linearization methods are naturally proposed to provide an approximate global optimum solution by adding binary variables into the models and converting the original NLP problems into MILP ones. In this paper, Logarithmtransform piecewise linearization(LTPL) method, an improved piecewise linearization, is proposed. In this method a logarithm transform is applied to convert multi-variable multi-degree constraints into a series of single-variable constraints. As a result, the number of 0–1 variables is greatly reduced. In the final part of this paper, an industrial case study is conducted to demonstrate the effectiveness of LTPL method. In principle, this method would be useful for blending problems with complicated empirical or theoretical models.

On the grouping effect of the l_(1-2)models

This paper aims to study the mathematical properties of the l vmodels that employ measurement matrices with correlated columns.We first show that the l_(1-2)model satisfies the grouping effect which ensures that coefficients corresponding to highly correlated columns in a measurement matrix have small differences.Then we provide the stability analysis based on the sparse approximation property.When the entries of the vectors have different signs,we show that the grouping effect also holds for the constraint l_(1-2)minimization model which is implicated by the linearized Bregman iteration.