Objective Model Selection in Physics: Exploring the Finite Information Quantity Approach

Traditional methods for selecting models in experimental data analysis are susceptible to researcher bias, hindering exploration of alternative explanations and potentially leading to overfitting. The Finite Information Quantity (FIQ) approach offers a novel solution by acknowledging the inherent limitations in information processing capacity of physical systems. This framework facilitates the development of objective criteria for model selection (comparative uncertainty) and paves the way for a more comprehensive understanding of phenomena through exploring diverse explanations. This work presents a detailed comparison of the FIQ approach with ten established model selection methods, highlighting the advantages and limitations of each. We demonstrate the potential of FIQ to enhance the objectivity and robustness of scientific inquiry through three practical examples: selecting appropriate models for measuring fundamental constants, sound velocity, and underwater electrical discharges. Further research is warranted to explore the full applicability of FIQ across various scientific disciplines.

Structural failure analysis with CMS-based ground motion selection using innovative cost function and weight factors

The selection and scaling of ground motion records is considered a primary and essential task in performing structural analysis and design.Conventional methods involve using ground motion models and a conditional spectrum to select ground motion records based on the target spectrum.This research demonstrates the influence of adopting different weighted factors for various period ranges during matching selected ground motions with the target hazard spectrum.The event data from the Next Generation Attenuation West 2(NGA-West 2)database is used as the basis for ground motion selection,and hazard de-aggregation is conducted to estimate the event parameters of interest,which are then used to construct the target intensity measure(IM).The target IMs are then used to select ground motion records with different weighted vector-valued objective functions.The weights are altered to account for the relative importance of IM in accordance with the structural analysis application of steel moment resisting frame(SMRF)buildings.Instead of an ordinary objective function for the matching spectrum,a novel model is introduced and compared with the conventional cost function.The results indicate that when applying the new cost function for ground motion selection,it places higher demands on structures compared to the conventional cost function.Moreover,submitting more weights to the first-mode period of structures increases engineering demand parameters.Findings demonstrate that weight factors allocated to different period ranges can successfully account for period elongation and higher mode effects.

Research of CBR, DM and smart algorithms based design methods for high-rise building structure form-selection

First, the high-rise building structure design process is divided into three relevant steps, that is, scheme generation and creation, performance evaluation, and scheme optimization. Then with the application of relational database, the case database of high-rise structures is constructed, the structure form-selection designing methods such as the smart algorithm based on CBR, DM, FINS, NN and GA is presented, and the original forms system of this method and its general structure are given. CBR and DM are used to generate scheme candidates; FINS and NN to evaluate and optimize the scheme performance; GA to create new structure forms. Finally, the application cases are presented, whose results fit in with the real project. It proves by combining and using the expert intelligence, algorithm intelligence and machine intelligence that this method makes good use of not only the engineering project knowledge and expertise but also much deeper knowledge contained in various engineering cases. In other words, it is because the form selection has a strong background support of vast real cases that its results prove more reliable and more acceptable. So the introduction of this method provides an effective approach to improving the quality, efficiency, automatic and smart level of high-rise structures form selection design.

Structural form selection of the high-rise buildingwith the improved BP neural network

As civil engineering technology development,the structural form selection is more and more critical in design of high-rise buildings.However,structural form selection involves expertise knowledge and changes with the environment which makes the task arduous.An approach utilizing improved back propagation(BP)neural network optimized by the Levenberg-Marquardt(L-M)algorithm is proposed to extract the main controlling factors of structural form selection.Then,an intelligent expert system with artificial neural network is constructed to design high-rise buildings structure effectively.The experiment tests the model in 15 well-known architecture samples and get the prediction accuracy of 93.33%.The results show that the method is feasible and can help designers select the appropriate structural form.

Retrieval Single-Doppler Radar Wind with Variational Assimilation Method-Part I: Objective Selection of Functional Weighting Factors

In variational problem, the selection of functional weighting factors (FWF) is one of the key points for discussing many relevant studies. To overcome arbitrariness and subjectivity of the empirical selecting methods used widely at present, this paper tries to put forward an optimal objective selecting method of FWF. The focus of the study is on the weighting factors optimal selection in the variation retrieval single-Doppler radar wind field with the simple adjoint models. Weighting factors in the meaning of minimal variance are calculated out with the matrix theory and the finite difference method of partial differential equation. Experiments show that the result is more objective comparing with the factors obtained with the empirical method.

Selection Method of Multi-Objective Problems Using Genetic Algorithm in Motion Plan of AUV

To research the effect of the selection method of multi — objects genetic algorithm problem on optimizing result, this method is analyzed theoretically and discussed by using an autonomous underwater vehicle (AUV) as an object. A changing weight value method is put forward and a selection formula is modified. Some experiments were implemented on an AUV, TwinBurger. The results shows that this method is effective and feasible.

A New method for selecting multi-scale road network objects

Aimed at solving the problems of road network object selection at any unknown scale, the existing methods on object selection are integrated and extended in this paper, and a new object interpolation method is proposed, which reflects the inheritable and transferable characteristics of related information among multi-scale representation objects, and takes the attribute effects into account. Then the basic idea, the overall framework and the technical flow of the interpolation are put forward, at the samet：me synthetical weight function of the interpolation method is defined and described. The method and technical strategies of object selection are extended, and the key problems are solved, including the dejign of the objective quantitative and structural selections based on the weight values, the interpolation experiment strategies and technical flows, the result of the test shows that the object interpolation method not only inherits the objects at smaller scales, but also takes the attribute effect into account when deriving objects from larger scales according to the road importance, which is a guarantee to objective selection of the road objects at middle scales.

Fuzzy Multi-Objective Decision Model of Supplier Selection with Preference Information

Supplier selection is a multi-objective decision problem, which must be considered many objectives, some objectives are qualitative, and others are quantitative. Meanwhile, manufacturer has preference for different suppliers. In this paper, a new multi-objective decision model with preference information of supplier is established. A practical example of supplier selection problem utilizing this model is studied. The result demonstrates the feasibility and effectiveness of the methods proposed in the paper.

Do Search and Selection Operators Play Important Roles in Multi-Objective Evolutionary Algorithms:A Case Study

Multi-objective Evolutionary Algorithm (MOEA) is becoming a hot research area and quite a few aspects of MOEAs have been studied and discussed. However there are still few literatures discussing the roles of search and selection operators in MOEAs. This paper studied their roles by solving a case of discrete Multi-objective Optimization Problem (MOP): Multi-objective TSP with a new MOEA. In the new MOEA, We adopt an efficient search operator, which has the properties of both crossover and mutation, to generate the new individuals and chose two selection operators: Family Competition and Population Competition with probabilities to realize selection. The simulation experiments showed that this new MOEA could get good uniform solutions representing the Pareto Front and outperformed SPEA in almost every simulation run on this problem. Furthermore, we analyzed its convergence property using finite Markov chain and proved that it could converge to Pareto Front with probability 1. We also find that the convergence property of MOEAs has much relationship with search and selection operators.

Path Selection of Multimodal Transport Based on Multi-Objective Mixed Integer Programming

Based on “One Belt and One Road”, this paper studies the path selection of multimodal transport by using the method of multi-objective mixed integer programming. Therefore, this paper studies the factors of transportation time, transportation cost and transportation safety performance, and establishes a mathematical model. In addition, the method of multi-objective mixed integer programming is used to comprehensively consider the different emphasis and differences of customers on cargo transportation. Then we use planning tools of Microsoft Excel to solve path selection and to determine whether the chosen path is economical and reliable. Finally, a relatively complex road network is built as an example to verify the accuracy of this planning method.

Optimal Test Points Selection Based on Multi-Objective Genetic Algorithm

A new approach to select anoptimal set of test points is proposed. The described method uses fault-wise table and multi-objective genetic algorithm to find the optimal set of test points. First, the fault-wise table is constructed whose entries are measurements associated with faults and test points. The selection of optimal test points is transformed to the selection of the columns that isolate the rows of the table. Then, four objectives are described according to practical test requirements. The multi-objective genetic algorithm is explained. Finally, the presented approach is illustrated by a practical example. The results indicate that the proposed method can efficiently and accurately find the optimal set of test points and is practical for large scale systems.

Combined Selection Index for New Yorkshire Dam Line with High Prolificacy

The combined selection index used in the breeding of new Yorkshire dam line with high prolificacy according to breeding objects was formulated as /：2.272E- BVNB-0.056EBVDAYS. After 5 generations breeding, the two main selected traits such as total number of born and age at 100 kg weight was 12.17 piglets/litter and 165.18 d, respectively. The genetic improvements per generates was 0.156 and -2.198, respec- tively. The breeding objects of the new Yorkshire dam line with high prolificacy were basically reached. It indicated that the methods and index could be used in pig breeding.

Application of fuzzy optimal selection of similar slopes to the evaluation of slope stability

The numerical calculation method is widely used in the evaluation of slope stability,but it cannot take the randomness and fuzziness into account that exist in rock and soil engineering objectively.The fuzzy optimization theory is thus introduced to the evaluation of slope stability by this paper and a method of fuzzy optimal selection of similar slopes is put forward to analyze slope stability.By comparing the relative membership degrees that the evaluated object sample of slope is similar to the source samples of which the stabilities are detected clearly,the source sample with the maximal relative membership degree will be chosen as the best similar one to the object sample,and the stability of the object sample can be evaluated by that of the best similar source sample.In the process many uncertain influential factors are considered and characteristics and knowledge of the source samples are obtained.The practical calculation indicates that it can achieve good results to evaluate slope stability by using this method.

Double adaptive selection strategy for MOEA/D

Since most parameter control methods are based on prior knowledge, it is difficult for them to solve various problems.In this paper, an adaptive selection method used for operators and parameters is proposed and named double adaptive selection(DAS) strategy. Firstly, some experiments about the operator search ability are given and the performance of operators with different donate vectors is analyzed. Then, DAS is presented by inducing the upper confidence bound strategy, which chooses suitable combination of operators and donates sets to optimize solutions without prior knowledge. Finally, the DAS is used under the framework of the multi-objective evolutionary algorithm based on decomposition, and the multi-objective evolutionary algorithm based on DAS(MOEA/D-DAS) is compared to state-of-the-art MOEAs. Simulation results validate that the MOEA/D-DAS could select the suitable combination of operators and donate sets to optimize problems and the proposed algorithm has better convergence and distribution.

面向目标检测的视觉注意机制波段选择研究

近年来,波段选择在高光谱图像降维处理中得到了广泛地应用,然而常用的数据降维方法并没能将与人类视觉系统相关的信息进行有效利用,如果将人类与生俱来的视觉注意机制能力应用到高光谱图像中目标的视觉显著性特征的增强或识别,对于高光谱图像的目标检测研究无疑会产生相当的促进作用。研究提出引入视觉注意机制理论应用于波段选择研究,构建面向目标检测应用的视觉注意机制波段选择模型。通过分析计算波段图幅的目标与背景的可识别程度,量化所在波段对地物目标与背景的判别能力,提出了基于目标视觉可识别度的波段选择方法;利用LC显著性算法进行空间域的视觉显著性目标分析,计算背景与目标的显著性差异绝对值,提出基于LC显著目标结构分布的波段选择方法。将这两种方法结合提出的改进子空间划分方法,建立面向目标检测的视觉注意机制波段选择模型,并经高光谱遥感AVIRIS San Diego公开数据集进行目标检测实验验证,结果表明所提出的基于视觉注意机制的波段选择模型对于目标检测应用具有较好的检测效果,实现了数据降维和高效的计算处理。

考虑节点重要度的城市轨道交通应急救援点选址研究

为实现对城市轨道交通中关键站点的识别和保护,提出1种考虑节点重要度的应急救援点选址方法,基于复杂网络理论构建城市轨道交通网络,从节点拓扑属性、车站属性、网络连通属性3方面出发建立节点重要度评价方法,构建节点重要度加权下的综合覆盖水平最大、最大救援响应时间最短和建设成本最低的多目标应急救援点选址模型,并以武汉地铁网络为例,基于熵权-TOPSIS法确定网络中共262个节点的重要度,设计变邻域遗传算法进行优化求解。研究结果表明:4种赋权方案下发现,相对不考虑节点重要度,本文方法所得目标在综合覆盖水平上分别提高1.44%,1.06%,2.51%和2.90%,同时可有效降低最大救援响应时间及建设成本,识别与保护关键站点。研究结果可为提升线网整体的应急能力提供指导。

基于距离感知的金属缺陷样本标签分配算法

针对金属表面缺陷检测模型在训练过程中正负样本分配时不考虑金属表面缺陷的宽高比、对目标分布的定位能力差等问题,提出了距离感知的动态标签分配(DDA)算法。DDA不改变原有检测模型的结构也不增加计算开支,根据真实框的几何特性提出新的距离损失计算范式,以优化宽高比悬殊的回归问题,将迭代过程中的回归偏移量解码为预测框坐标,最后计算预测框、锚框和真实框三者之间综合交并比信息,动态地选择正负样本以提高训练精度。在武汉某钢厂冷轧带钢表面缺陷检测中进行了验证,并引入公开的热轧带钢表面缺陷数据集进行了泛化试验,检测效果均有显著改善,对金属表面质量规范有实际应用价值。

基于特征注意力提纯的显著性目标检测模型

近年来,显著性目标检测技术取得了巨大进展,其中如何选择并有效集成多尺度特征扮演了重要角色。针对现有特征集成方法可能导致的信息冗余问题,提出了一种基于特征注意力提纯的显著性检测模型。首先,在解码器中采用一个全局特征注意力引导模块(GAGM)对带有语义信息的深层特征进行注意力机制处理,得到全局上下文信息;然后,通过全局引导流将其送入解码器各层进行监督训练;最后,利用多尺度特征融合模块(FAM)对编码器提取出的多尺度特征与全局上下文信息进行有效集成,并在网格状特征提纯模块(MFPM)中进行进一步细化,以生成清晰、完整的显著图。在5个公开数据集上进行实验,结果表明,所提模型优于现有的其他显著性检测方法,并且处理速度快,当处理320×320尺寸的图像时,能以30帧以上的速度运行。

向量角选择和指标删除的高维多目标进化算法

针对进化算法求解高维多目标优化问题平衡收敛性和多样性所面临的挑战,提出了向量角选择和指标删除的高维多目标进化算法(MOEA/AS-ID)。该算法在环境选择过程中设计了一种包含两种策略的协作机制逐一删除收敛性和多样性差的解以保留精英个体参与下一代的进化。前者基于向量角的选择策略用于选择一对在目标空间具有相似搜索方向的解,后者基于指标的删除策略采用同时兼顾个体收敛性和分布性的I_(SDE)^(+)指标比较被选择的这一对解,然后删除具有较小指标值的解,进而促使种群朝各个方向收敛到帕累托最优前沿,最终平衡解集的收敛性和多样性。在包含各种特征的3组标准测试系列问题DTLZ、SDTLZ、MaF上,MOEA/AS-ID与近年提出的6个涵盖了当前各种类型的高维多目标进化算法执行了广泛的对比仿真实验和数值结果分析。仿真结果和数值分析表明所提算法MOEA/AS-ID求解各种特征的高维多目标优化问题平衡收敛性和多样性的能力具有较强的竞争力。

基于定向采样和自适应选择的免疫算法

针对算法容易陷入局部最优以及无法很好保持多样性等不足,提出一种基于定向采样和自适应选择的免疫算法(DSASIA)。利用在线种群信息动态选择个体;采用自适应比例克隆方式平衡全局和局部搜索能力,确保收敛性;采取定向采样策略识别子代个体,保证多样性。在13个测试函数上与其它4种多目标优化算法进行对比,实验结果表明,DSASIA算法可以较快求出帕累托解集,且解具有更好的多样性和收敛性。