Regularised Layerwise Weight Norm Based Skin Lesion Features Extraction and Classification

Melanoma is the most lethal malignant tumour,and its prevalence is increasing.Early detection and diagnosis of skin cancer can alert patients to manage precautions and dramatically improve the lives of people.Recently,deep learning has grown increasingly popular in the extraction and categorization of skin cancer features for effective prediction.A deep learning model learns and co-adapts representations and features from training data to the point where it fails to perform well on test data.As a result,overfitting and poor performance occur.To deal with this issue,we proposed a novel Consecutive Layerwise weight Con-straint MaxNorm model(CLCM-net)for constraining the norm of the weight vector that is scaled each time and bounding to a limit.This method uses deep convolutional neural networks and also custom layer-wise weight constraints that are set to the whole weight matrix directly to learn features efficiently.In this research,a detailed analysis of these weight norms is performed on two distinct datasets,International Skin Imaging Collaboration(ISIC)of 2018 and 2019,which are challenging for convolutional networks to handle.According to thefindings of this work,CLCM-net did a better job of raising the model’s performance by learning the features efficiently within the size limit of weights with appropriate weight constraint settings.The results proved that the proposed techniques achieved 94.42%accuracy on ISIC 2018,91.73%accuracy on ISIC 2019 datasets and 93%of accuracy on combined dataset.

Adaptive weighting impact angle optimal guidance law considering seeker's FOV angle constraints

In this paper, a new adaptive optimal guidance law with impact angle and seeker’s field-of-view(FOV) angle constraints is proposed. To this end, the generalized optimal guidance law is derived first. A changeable impact angle weighting(IAW) coefficient is introduced and used to modify the guidance law to make it adaptive for all guidance constraints. After integrating the closed-form solution of the guidance command with linearized engagement kinematics, the analytic predictive models of impact angle and FOV angle are built, and the available range of IAW corresponding to constraints is certain. Next, a calculation scheme is presented to acquire the real-time value of IAW during the entire guidance process. When applying the proposed guidance law, the IAW will keep small to avoid a trajectory climbing up to limit FOV angle at an initial time but will increase with the closing target to improve impact position and angle accuracy, thereby ensuring that the guidance law can juggle orders of guidance accuracy and constraints control.

Mixed-Weights Least-Squares Stable Predictive Control Algorithm with Soft and Hard Constraints

Mixed-weight least-squares (MWLS) predictive control algorithm, compared with quadratic programming (QP) method, has the advantages of reducing the computer burden, quick calculation speed and dealing with the case in which the optimization is infeasible. But it can only deal with soft constraints. In order to deal with hard constraints and guarantee feasibility, an improved algorithm is proposed by recalculating the setpoint according to the hard constraints before calculating the manipulated variable and MWLS algorithm is used to satisfy the requirement of soft constraints for the system with the input constraints and output constraints. The algorithm can not only guarantee stability of the system and zero steady state error, but also satisfy the hard constraints of input and output variables. The simulation results show the improved algorithm is feasible and effective.

基于空间约束的裂缝密度反演方法

常规利用法向弱度、切向弱度间接预测裂缝密度的方法存在较大误差。为此,提出一种针对碳酸盐岩(HTI介质)裂缝密度直接反演的方法。在振幅随炮检距和方位角变化(AVAZ)反演的基础上,引入反距离加权插值法(IDW)以优化传统各向异性全变分(ATV)多道反演的横向差分算子,充分利用相邻多个地震道之间的相关性,提高反演算法的横向连续性和稳定性;将横向约束、低频约束及稀疏约束共同用于构建反演目标函数,采用交替方向乘子法(ADMM)优化求解。利用裂缝密度直接反演和反距离加权插值的优点,先对Marmousi II模型测试,验证了该方法的有效性和抗噪性;再将该方法应用于塔里木盆地YM区块的实际数据,验证了该方法的可行性。该方法对裂缝密度预测结果更准确、可靠,可在类似地区推广应用。

基于离散粒子群算法的管道保温结构优化研究

针对目前管道保温结构优化算法不稳定、结果优化程度不高的问题,建立以经济效益为目标函数,以满足国家散热损失标准等条件为约束函数的离散型数学模型。以BPSO算法为基础改变其位置更新规则,防止种群进化失效;采用自适应权重增加粒子的全局和局部搜索能力;充分利用模拟退火算法的思想避免出现早熟现象。应用改进的算法分别对普通蒸汽管道和核电站的蒸汽管道进行系统仿真实验。结果表明,该算法能够在满足国家散热损失标准等条件下取得最优解,可以为管道保温结构提供合理的优化方案。

顾及起算点误差的区域参考框架约束方法研究

利用Helmert转换模型,首先对起算点先验坐标可能存在的粗差进行探测并剔除,其次依据剩余起算点坐标的相似变换构建最小约束条件,最后实现整网坐标框架的确定。选取中国境内及周边IGS站和陆态网GNSS基准站单周观测数据,采用IGS站ITRF2014坐标分别构建参数加权约束、最小约束、顾及IGS站先验坐标误差的Helmert模型约束,然后利用不同的基准约束平差方法获取整网坐标成果。实验结果表明,利用Helmert模型能有效削弱起算点坐标粗差对网形扭曲的影响,在一定程度上可提高区域参考框架的精度和可靠性。

基于课程学习权重集成的贝叶斯结构学习算法研究

从大量复杂的数据中学习贝叶斯网络(Bayesian network,BN)一直是一个难点问题,本文借鉴课程学习的思想,引入了一种适合于BN中节点之间互相影响程度的测量,然后划分课程阶段,分阶段构造无向图骨架,并利用优化函数对骨架进行优化;通过集成策略,将各个集成学习结果所得到的课程权重进行集合,并通过边过滤来减少错误边的出现;最后,通过爬山搜索构建BN结构。实验结果表明,在4个标准数据集上,本文所提方法具有较高的精确度和稳定性。与多种传统贝叶斯结构学习(Bayesian network structure learning,BNSL)方法相比,本文所提方法性能平均提高了37.18%。本文分析结果可为BNSL的增量学习过程进一步提供参考。

复杂背景下基于SIFT算法的局部遮挡人脸识别

由于复杂的背景环境中存在大量噪声点、平滑点以及失真点,影响人脸识别效果,提出基于SIFT算法的局部遮挡人脸识别方法。考虑到噪声因素的影响建立尺度不变高斯函数,通过该函数描述原始图像的噪声干扰,查找平滑程度最高的像素点,以像素均值作为参考,对像素点及周围区域实行平滑约束。建立包含眼睛、鼻子、嘴巴以及耳朵的面部特征子集,与标准图像比对,采用最大期望算法提取重叠度最高的面部特征,作为人脸识别的参照标准。采用两个向量的内积衡量人脸图像与参考值之间的相似度,计算存在和不存在局部遮挡的特征权重,建立置信区间,代入面部特征权重实施置信对比,完成人脸识别。实验结果表明,所提方法的识别精准度较高,在灰度变化、噪声以及局部遮挡的情况下均能够保证识别精度。

Solving single-frequency phase ambiguity using parameter weights fitting and constrained equation ambiguity resolution methods

Based on the structural characteristics of the double-differenced normal equation, a new method was proposed to resolve the ambiguity float solution through a selection of parameter weights to construct an appropriate regularized matrix, and a singular decomposition method was used to generate regularization parameters. Numerical test results suggest that the regularized ambiguity float solution is more stable and reliable than the least-squares float solution. The mean square error matrix of the new method possesses a lower correlation than the variance- covariance matrix of the least-squares estimation. The size of the ambiguity search space is reduced and the search efficiency is improved. The success rate of the integer ambiguity searching process is improved significantly when the ambiguity resolution by using constraint equation method is used to determine the correct ambiguity integer- vector. The ambiguity resolution by using constraint equation method requires an initial input of the ambiguity float solution candidates which are obtained from the LAMBDA method in the new method. In addition, the observation time required to fix reliable integer ambiguities can be significantly reduced.

OPTIMAL SELECTION FOR THE WEIGHTED COEFFICIENTS OF THE CONSTRAINED VARIATIONAL PROBLEMS

The aim is to put forward the optimal selecting of weights in variational problemin which the linear advection equation is used as constraint. The selection of the functionalweight coefficients ( FWC) is one of the key problems for the relevant research. It wasarbitrary and subjective to some extent presently. To overcome this difficulty, thereasonable assumptions were given for the observation field and analyzed field, variationalproblems with " weak constraints" and " strong constraints" were considered separately. Bysolving Euler' s equation with the matrix theory and the finite difference method of partialdifferential equation, the objective weight coefficients were obtained in the minimumvariance of the difference between the analyzed field and ideal field. Deduction results showthat theoretically the optimal selection indeed exists in the weighting factors of the costfunction in the means of the minimal variance between the analysis and ideal field in terms ofthe matrix theory and partial differential ( corresponding difference ) equation, if thereasonable assumption from the actual problem is valid and the differnece equation is stable.It may realize the coordination among the weight factors, numerical models and theobservational data. With its theoretical basis as well as its prospects of applications, thisobjective selecting method is probably a way towards the finding of the optimal weightingfactors in the variational problem.

Diagnosis and Resolution of Infeasibility in the Constraint Method for Solving Multi Objective Linear Programming Problems

In this paper we discuss about infeasibility diagnosis and infeasibility resolution, when the constraint method is used for solving multi objective linear programming problems. We propose an algorithm for resolution of infeasibility, which is a combination of interactive, weighting and constraint methods.Numerical examples are provided to illustrate the techniques developed.

经颅直流电刺激协同患侧下肢强制性负重训练对脑卒中Pusher综合征的效果

目的探讨经颅直流电刺激(tDCS)协同患侧下肢强制性负重训练对脑卒中Pusher综合征的疗效。方法选取2021年1月至12月在苏州大学附属第一医院康复科住院的脑卒中Pusher综合征患者60例,随机分为tDCS组、强制组和协同组,各20例。3组均给予常规康复训练,tDCS组加阳极tDCS,强制组加患侧下肢强制性负重训练,协同组加入tDCS协同患侧下肢强制性负重训练,共8周。训练前后采用Berg平衡量表(BBS)、Fugl-Meyer评定量表下肢部分(FMA-LE)、Burke倾斜量表(BLS)及Holden步行功能分级进行评定。结果治疗后,3组BBS、FMA-LE、BLS以及Holden步行功能分级评分均改善(|t|>1.452,P<0.05),协同组各项指标均最优(|F|>1.827,P<0.05)。结论tDCS协同患侧下肢强制性负重训练能有效改善脑卒中Pusher综合征患者下肢功能。

Locating Binding Constraints in LP Problems

In this work, a new method is presented for determining the binding constraints of a general linear maximization problem. The new method uses only objective function values at points which are determined by simple vector operations, so the computational cost is inferior to the corresponding cost of matrix manipulation and/or inversion. This method uses a recently proposed notion for addressing such problems: the average of each constraint. The identification of binding constraints decreases the complexity and the dimension of the problem resulting to a significant decrease of the computational cost comparing to Simplex-like methods. The new method is highly useful when dealing with very large linear programming (LP) problems, where only a relatively small percentage of constraints are binding at the optimal solution, as in many transportation, management and economic problems, since it reduces the size of the problem. The method has been implemented and tested in a large number of LP problems. In LP problems without superfluous constraints, the algorithm was 100% successful in identifying binding constraints, while in a set of large scale LP tested problems that included superfluous constraints, the power of the algorithm considered as statistical tool of binding constraints identification, was up to 90.4%.

改进ODPSO算法在光伏MPPT中的应用研究

光伏发电系统的输出功率具有明显的非线性特性,易受外界环境因素的影响,现有的最大功率点追踪(maximum power point tracking,MPPT)技术的追踪精度和追踪时间都有待进一步提高。对此,提出了一种结合了整体分布(overall distribution,OD)算法和粒子群优化(particle swarm optimization,PSO)算法的MPPT算法(改进ODPSO-MPPT算法),以解决全局最大功率点追踪问题。对传统PSO-MPPT算法的速度分量加以约束,使其避免陷入局部最优并能准确地追踪最大功率,同时优化传统PSO算法的惯性权重并融合OD算法,使其能在更短时间内找到全局最大功率点。最后,搭建光伏发电系统仿真模型对所提算法进行验证。仿真结果表明,改进ODPSO-MPPT算法在标准测试条件和局部遮阴条件下均能更快速准确地追踪到全局最大功率。

井物性约束下的布格重力异常反演方法与应用

笔者提出在反演中增加井约束范围来控制井约束的强度,通过设置多种地质体情况以及不同井况下的约束反演来分析井数据对于地质体重力密度反演的约束效果,从而获得更高分辨率的反演结果,利用该方法圈定了山东省某地区的6处铁矿位置。

杂交粒子群优化算法反演瑞雷面波频散曲线

针对传统粒子群算法反演面波频散曲线易陷入局部极值的问题,通过非线性的自适应惯性权重、引入压缩因子以及边界条件约束三种优化策略对该算法进行优化,提出一种杂交粒子群优化算法(Hybrid Particle Swarm Optimization,HPSO)进行瑞雷面波频散曲线反演,提高了反演的精度、减少了反演的多解性。通过典型地质模型的无噪声、含噪声及多模态理论频散曲线反演,验证了该方法的有效性、抗噪能力和多模态频散曲线联合反演能力;与边界约束粒子群算法反演结果的对比分析表明,HPSO具有更高的反演精度;最后,对实测数据进行了反演,验证了HPSO的适用性。

含CVaR及增广ε-约束法的多目标光储充电站容量优化配置

电动汽车充电需求和光伏出力的不确定性使光储充电站的收益存在一定的不确定性。为了量化该不确定性给光储充电站收益带来的影响,即收益的CVaR值,建立了含收益和CVaR的光储充电站多目标容量优化配置模型。模型中CVaR函数为条件风险价值量化的投资商的预期收益,收益期望值函数为考虑充电需求和光伏出力典型场景下的收益与其概率乘积之和,结合增广ε-约束法以收益为主目标,将预期收益CVaR次要目标作为约束。求解模型获得不同风险偏好下收益期望值和预期收益CVaR的pareto前沿以及对应的配置容量,采用熵权-TOPSIS法筛选出客观决策方案。相较于传统风险管理中的线性加权法,增广ε-约束法处理目标后可得到更具分布性以及边界最优性的前沿,能够映射出多目标问题的实际pareto前沿,提供收益期望值和预期收益CVaR划分更细致的投资方案。

基于加权模糊的时序约束Petri网电网故障诊断方法

电网发生故障后充分利用报警信息对故障元件进行判别,是保障电力系统安全运行的重要环节。针对现有Petri网时序推理与置信度推理相分开、计算过程复杂且未充分利用时间信息等问题,提出了一种基于加权模糊的时序约束Petri网电网故障诊断方法。将时序约束引入到Petri网的库所与变迁中,根据保护与断路器之间的动作顺序关系建立加权模糊的时序约束Petri网模型。定义置信度与时间区间分层推理运算,实现置信度推理与时序推理同步进行,在置信度推理过程中充分考虑到时间信息的影响。通过对多组算例仿真比较得出,所提方法能够有效判别出故障元件,具有较高的准确性和容错性。

基于AHP的绿色公路选线制约因素权重研究

绿色公路是以周边环境为底色,以生态为先,节约资源,道路与环境相协调为目标,将绿色发展理念融入到公路建设的全过程、全方位,实现低碳、高效能为特征的公路交通。在选线阶段,已经不能仅仅将低碳技术的技术性能作为比选的唯一指标,公路建设的生态环境效益也是一个必须考虑的因素,即公路建设需要综合考虑低碳技术的技术性、经济性和生态环境效益,建立综合决策方法。利用层次分析法(AHP),综合考虑到多种选线影响因素,探讨青海省同赛高速公路秉承绿色公路理念,分析选线中的制约因素,研究各影响因素在选线中的权重,为类似研究提供参考。