基于SOM神经网络的教学认知诊断模型研究

认知诊断模型在评估知识点依赖关系松散型学科知识状态时,因其生成的可达矩阵较为稀疏,易导致认知诊断的效率和准确度降低。而自组织映射(Self-Organizing Maps,SOM)神经网络可在不考虑知识点依赖关系的情况下,通过自动发现样本中的内在规律和本质属性,实现对学生知识状态的分类与评估。基于此,文章构建了基于SOM神经网络的教学认知诊断模型,在“大学英语视听说”课程中开展了此诊断模型的教学应用,并从诊断结果、学习成效、满意度三个方面进行了应用效果分析,结果表明:诊断模型用于知识点依赖关系松散型学科时,能够通过自组织聚类消除学生答题过程中的误差,精准、高效地分类评估学生的知识状态;诊断模型有助于教师实施有针对性的补救教学,在提高学生的学习效率和避免成绩的两极化方面效果显著;学生对诊断模型的整体满意度也较高。文章的研究丰富了教学认知诊断理论,可为个性化学习、精准补救教学提供诊断方法和实践经验。

考虑替代性的SOM神经网络卷烟配方模块分类方法研究

为了提高模块替代决策效率和整个卷烟制造系统柔性与生产效率,提出了一种基于替代度的SOM神经网络模型对卷烟配方模块进行分类,并与历史替代统计结果进行比对。结果表明,替代度能较好地衡量模块间的替代程度,替代度越大,每个类别中的各项质量指标一致性越强,模块质量越相似,越推荐进行相互替代;在以不同替代度标准取值对卷烟配方模块进行分类时,替代度标准值越大,分类越细,选择替代度标准值为3.06作为卷烟配方模块强替代性的标准进行分类时是最合适的,此时每个类别中卷烟配方模块质量具有较高的相似性。基于替代度的SOM神经网络分类结果显示,发生类内替代的比例明显优于一般SOM神经网络算法、两阶段聚类算法和K-means聚类算法,当替代度标准值为3.06时,类内相互替代率可达95.39%,而类间替代率不足5.00%,相同类别模块替代率良好。

Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map

A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-efficiency and have many errors.This study researched the spatial distribution and cluster characteristics of gravels based on digital image processing technology combined with a self-organizing map(SOM)and multivariate statistical methods in the grassland of northern Tibetan Plateau.Moreover,the correlation of morphological parameters of gravels between different cluster groups and the environmental factors affecting gravel distribution were analyzed.The results showed that the morphological characteristics of gravels in northern region(cluster C)and southern region(cluster B)of the Tibetan Plateau were similar,with a low gravel coverage,small gravel diameter,and elongated shape.These regions were mainly distributed in high mountainous areas with large topographic relief.The central region(cluster A)has high coverage of gravels with a larger diameter,mainly distributed in high-altitude plains with smaller undulation.Principal component analysis(PCA)results showed that the gravel distribution of cluster A may be mainly affected by vegetation,while those in clusters B and C could be mainly affected by topography,climate,and soil.The study confirmed that the combination of digital image processing technology and SOM could effectively analyzed the spatial distribution characteristics of gravels,providing a new mode for gravel research.

Waterlogging risk assessment based on self-organizing map(SOM)artificial neural networks:a case study of an urban storm in Beijing

Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annuallyinthe urban area of Beijing, the capital of China. Based on a selforganizing map(SOM) artificial neural network(ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product(GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANNis suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors,producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. The points that were assigned risk grades of IV or Vwere located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.

Fault diagnosis of rocket engine ground testing bed with self-organizing maps(SOMs)

To solve the fault diagnosis problem of liquid propellant rocket engine ground testing bed,a fault diagnosis approach based on self-organizing map(SOM)is proposed.The SOM projects the multidimensional ground testing bed data into a two-dimensional map.Visualization of the SOM is used to cluster the ground testing bed data.The out map of the SOM is divided to several regions.Each region is represented for one fault mode.The fault mode of testing data is determined according to the region of their labels belonged to.The method is evaluated using the testing data of a liquid-propellant rocket engine ground testing bed with sixteen fault states.The results show that it is a reliable and effective method for fault diagnosis with good visualization property.

A novel fractional uplink power control framework for self-organizing networks

Internet of things and network densification bring significant challenges to uplink management.Only depending on optimization algorithm enhancements is not enough for uplink transmission.To control intercell interference,Fractional Uplink Power Control(FUPC)should be optimized from network-wide perspective,which has to find a better traffic distribution model.Conventionally,traffic distribution is geographic-based,and ineffective due to tricky locating efforts.This paper proposes a novel uplink power management framework for Self-Organizing Networks(SON),which firstly builds up pathloss-based traffic distribution model and then makes the decision of FUPC based on the model.PathLoss-based Traffic Distribution(PLTD)aggregates traffic based on the propagation condition of traffic that is defined as the pathloss between the position generating the traffic and surrounding cells.Simulations show that the improvement in optimization efficiency of FUPC with PLTD can be up to 40%compared to conventional GeoGraphic-based Traffic Distribution(GGTD).

基于SOM3.4.2平台的iLoboke仿真足球机器人研究

2015年,iLobokeⅠ首次以标准化产品的形态推向市场,其搭载全局视觉、决策调度、无线通信和车载控制系统等先进技术,通过足球对抗的形式,实现高速动态下的多机器人协同。随着科技的不断进步,iLoboke足球机器人在2018年已经升级到第四代产品。这一代产品更加开放,支持更广泛的扩展编程,为用户提供更多的自由度和创造力。无论是足球领域还是其他领域,机器人的应用将逐渐渗透到人们的生活中,为我们创造更便利的生活和更广阔的发展空间。该文旨在利用SOM3.4.2平台,对iLoboke足球机器人控制进行研究,利用微粒算法规划移动路径,实现机器人在仿真环境中的足球运动,科学高效地预判传球。

Combining Self-Organizing Map and Lipschitz Condition for Estimation in Direction of Arrival

There are many DOA estimation methods based on different signal features, and these methods are often evaluated by experimental results, but lack the necessary theoretical basis. Therefore, a direction of arrival (DOA) estimation system based on self-organizing map (SOM) and designed for arbitrarily distributed sensor array is proposed. The essential principle of this method is that the map from distance difference of arrival (DDOA) to DOA is Lipschitz continuity, it indicates the similar topology between them, and thus Kohonen SOM is a suitable network to classify DOA through DDOA. The simulation results show that the DOA estimation errors are less than 1° for most signals between 0° to 180°. Compared to MUSIC, Root-MUSIC, ESPRIT, and RBF, the errors of signals under signal-to-noise ratios (SNR) declines from 20 dB to 2 dB are robust, SOM is better than RBF and almost close to MUSIC. Further, the network can be trained in advance, which makes it possible to be implemented in real-time.

鼻内镜下腺样体切除术对SOM患儿并发症的影响

目的探讨鼻内镜下腺样体切除术在分泌性中耳炎(SOM)患儿中的应用效果。方法选择2019年1月至2022年5月收治的SOM患儿94例,根据随机数表法将其分为参考组和观察组,每组47例。参考组采用鼓膜置管术治疗,观察组予以鼻内镜下腺样体切除术联合鼓膜置管术治疗治疗。观察2组听力水平[听性脑干反应(ABR)阀值、ABRI波潜伏期]、疗效、炎性反应[C-反应蛋白(CRR)、肺瘤坏死因子α(TNF-α)]、复发及并发症发生情况。结果2组术后12个月ABR阈值和ABR I波潜伏期水平均较本组术前低,差异有统计学意义(P<0.05),但2组间比较差异无统计学意义(P>0.05);观察组总有效率稍高于参考组,但2组差异无统计学意义(P>0.05);2组术后3 d的血清CRP、TNF-α水平均低于本组术前,且观察组较参考组低,差异有统计学意义(P<0.05);观察组术后12个月的病情复发率及术后12个月内的并发症发生率均明显较参考组低,差异有统计学意义(P<0.05)。结论鼻内镜下腺样体切除术联合鼓膜置管术治疗SOM患儿的疗效确切,能够减轻SOM患儿炎性反应,并可减少病情复发及并发症发生率。

融合SOM神经网络与K-means聚类算法的用户信用画像研究

为提高现阶段基于K-Means聚类算法的用户信用画像模型的准确性和实时性,提出一种融合自组织映射(SOM,Self-Organizing Map)神经网络与K-Means聚类算法的改进方法。通过SOM对用户数据进行降维和特征提取,直接获得最优聚类数目后再用K-Means算法进行聚类分析。通过真实在线借贷平台数据对所提方法进行验证,结果表明,该方法可提升用户信用画像分析的质量,更好地满足金融数据分析中对实时管理和风险控制的要求,为金融机构提供精准的决策支持。

Equilibrium reconstruction method for self-organized plasmas on reversed field pinches with polarimeter-interferometer

In the reversed field pinch(RFP),plasmas exhibit various self-organized states.Among these,the three-dimensional(3D)helical state known as the“quasi-single-helical”(QSH)state enhances RFP confinement.However,accurately describing the equilibrium is challenging due to the presence of 3D structures,magnetic islands,and chaotic regions.It is difficult to obtain a balance between the available diagnostic and the real equilibrium structure.To address this issue,we introduce KTX3DFit,a new 3D equilibrium reconstruction code specifically designed for the Keda Torus eXperiment(KTX)RFP.KTX3DFit utilizes the stepped-pressure equilibrium code(SPEC)to compute 3D equilibria and uses polarimetric interferometer signals from experiments.KTX3DFit is able to reconstruct equilibria in various states,including axisymmetric,doubleaxis helical(DAx),and single-helical-axis(SHAx)states.Notably,this study marks the first integration of the SPEC code with internal magnetic field data for equilibrium reconstruction and could be used for other 3D configurations.

Generativity of Self-Organizing Processes and Their Correlative Description in Terms of a Formal Language of Meta-Ordinal Generative Nature, in the Light of the Maximum Ordinality Principle and the Explicit Solution to the “Three-Body Problem”

The main objective of this paper is to demonstrate that the internal processes of Self-Organizing Systems represent a unique and singular process, characterized by their specific generativity. This process can be modeled using the Maximum Ordinality Principle and its associated formal language, known as the “Incipient” Differential Calculus (IDC).

3D Ice Shape Description Method Based on BLSOM Neural Network

When checking the ice shape calculation software,its accuracy is judged based on the proximity between the calculated ice shape and the typical test ice shape.Therefore,determining the typical test ice shape becomes the key task of the icing wind tunnel tests.In the icing wind tunnel test of the tail wing model of a large amphibious aircraft,in order to obtain accurate typical test ice shape,the Romer Absolute Scanner is used to obtain the 3D point cloud data of the ice shape on the tail wing model.Then,the batch-learning self-organizing map(BLSOM)neural network is used to obtain the 2D average ice shape along the model direction based on the 3D point cloud data of the ice shape,while its tolerance band is calculated using the probabilistic statistical method.The results show that the combination of 2D average ice shape and its tolerance band can represent the 3D characteristics of the test ice shape effectively,which can be used as the typical test ice shape for comparative analysis with the calculated ice shape.

Software Reusability Classification and Predication Using Self-Organizing Map (SOM)

Due to rapid development in software industry, it was necessary to reduce time and efforts in the software development process. Software Reusability is an important measure that can be applied to improve software development and software quality. Reusability reduces time, effort, errors, and hence the overall cost of the development process. Reusability prediction models are established in the early stage of the system development cycle to support an early reusability assessment. In Object-Oriented systems, Reusability of software components (classes) can be obtained by investigating its metrics values. Analyzing software metric values can help to avoid developing components from scratch. In this paper, we use Chidamber and Kemerer (CK) metrics suite in order to identify the reuse level of object-oriented classes. Self-Organizing Map (SOM) was used to cluster datasets of CK metrics values that were extracted from three different java-based systems. The goal was to find the relationship between CK metrics values and the reusability level of the class. The reusability level of the class was classified into three main categorizes (High Reusable, Medium Reusable and Low Reusable). The clustering was based on metrics threshold values that were used to achieve the experiments. The proposed methodology succeeds in classifying classes to their reusability level (High Reusable, Medium Reusable and Low Reusable). The experiments show how SOM can be applied on software CK metrics with different sizes of SOM grids to provide different levels of metrics details. The results show that Depth of Inheritance Tree (DIT) and Number of Children (NOC) metrics dominated the clustering process, so these two metrics were discarded from the experiments to achieve a successful clustering. The most efficient SOM topology [2 × 2] grid size is used to predict the reusability of classes.

基于mRMR-SOM的异步电机轴承故障诊断研究

针对异步电机轴承故障诊断问题,提出了一种融合最大相关最小冗余特征选择算法(mRMR)和自组织映射神经网络(SOM)的故障诊断方法,并将其应用于轴承故障诊断的不同阶段。首先,在实验室环境下搭建了异步电机故障诊断试验平台,在不同电机状态下分别采集振动、电流和电压信号,利用统计学方法获取了高维混合特征集;然后,以互信息为背景,利用mRMR根据特征与状态标签间的相关性和特征间的冗余性,筛选了具备强区分能力的特征,以避免计算冗余和后验诊断性能下降;最后,采用SOM对异步电机健康和轴承故障状态进行了分类识别,验证了SOM对异步电机轴承故障诊断的有效性,以及mRMR对故障诊断结果的影响。研究结果表明:基于mRMR-SOM的异步电机轴承故障诊断方法能够准确地区分健康和故障状态,测试集分类准确率达到89%;使用mRMR特征筛选能够将154维特征降低至17维,缩短23.5%的网络收敛时间,并将分类准确率由89%提升至98%;试验结果验证了基于mRMR-SOM的异步电机轴承故障诊断方法对于异步电机轴承故障诊断问题的有效性,且证实其具备良好的诊断效果。

茶树炭疽菌CfSOM1基因的鉴定与生物信息学分析

Som1是cAMP-PKA信号途径下游的一个转录因子,在病原真菌发育和致病过程中可能发挥重要作用。为探究茶树炭疽菌SOM1基因的潜在功能,通过生物信息学方法在茶树炭疽菌N425基因组中鉴定SOM1同源基因,并对其编码蛋白的亲缘关系、理化性质、功能域和互作蛋白等进行分析。结果显示:在茶树炭疽菌N425基因组中鉴定得到一个SOM1同源基因CfSOM1,其编码蛋白CfSom1共含839个氨基酸,包括1个HTH二级结构、1个LisH结构域和2个NLS序列;CfSom1与炭疽菌属中的同源蛋白亲缘关系最近,与酵母菌中同源蛋白的亲缘关系最远;CfSom1被预测与6个核孔蛋白、c AMP依赖蛋白激酶催化亚基CpkA、拓扑异构酶II关联蛋白Ldb1、含WD结构域蛋白Swd1、Uba ts-n结构域蛋白Swa2、转录因子StuA和Cdtf1存在互作关系。这些研究成果将为后续展开对CfSOM1在茶树炭疽菌侵染茶树过程中的功能研究奠定基础。

Spatial Protection and Development of Traditional Mountain Settlements from the Perspective of Self-organization : A Case Study of Anju Ancient Town in Tongliang District, Chongqing

Through literature research, field research and urban design analysis methods, based on the self-organization theory, the evolution characteristics of the settlement space of Anju Ancient Town with significant characteristics of traditional mountain settlements were analyzed, and then its internal and external influencing factors were summarized. Finally, appropriate protection and development strategies were proposed, such as reasonably planning functional zones, breaking through the single structure, and creating a good living cultural environment. The innovation of this study is that it integrates the self-organization theory into the cognition of traditional mountain settlement space, and discusses the connection between the mountain settlement space and the traditional regional context under the modern development concept, so as to seek the benign protection and reasonable development of traditional mountain settlement space.

基于特征子空间的SOM天基状态检测算法

在天基信息网中,资源监控系统采集到的性能指标数据量大、冗余特征较多,导致状态检测准确率低、检测时间长等问题。针对以上问题,提出了基于特征子空间的SOM状态检测算法。将特征提取算法嵌入到SOM神经网络模型中,使得网络在训练的同时提取每个类别对应的属性特征,构成状态对应的特征子空间。并利用状态的特征子空间计算特征对于类别的贡献度,优化SOM神经网络的目标函数,进而提高模型对卫星计算任务执行单元状态检测的速度与准确率。仿真了在不同状态检测模型下的检测准确率、检测灵敏度以及检测时间。结果表明,提出的状态检测模型在检测准确率、检测灵敏度以及检测时间等方面都具有较好的性能。

矿区土壤SOM含量的遥感估算和时空动态变化分析

在煤炭开采过程中,由于矿区地表土壤结构发生变化,地表的养分、水分含量减少,从而引起土壤SOM含量(Soil Organic Matter)的含量减少。基于使用了TM(Thematic Mapper)数据作为SOM含量的遥感数据源,同时利用了MODIS(Moderate-resolution Imaging Spectroradiometer)数据进行SOM含量的估算,并对SOM含量的时空动态变化进行分析。结果表明,使用MODIS数据作为SOM含量的遥感数据源可以获得准确可靠的结果。

云制造下基于时空熵权TOPSIS和SOM聚类的区域绿色制造发展水平测度研究

当前,中国正经历以数字化和绿色化为主题的第四次工业革命时期。在数字经济腾飞,智能制造转型的浪潮中,各地制造业积极响应国家号召,涌现出不少新兴智能绿色制造企业。本文通过时空熵权TOPSIS评价法对2012~2021年中国30个省份的绿色制造水平进行了测度,构建了绿色排放、绿色质效、科技创新、资源利用、绿色治理五个维度的指标。研究发现:1) 在东、中、西部地区中,东部地区绿色制造发展水平常年处于领先水平,而中、西部地区相对较低,其中,“四绿”的建设发展是影响区域绿色制造发展水平的重要因素;2) 整体趋势显示,2012~2015年,不论是东部还是中西部地区,绿色制造发展指数呈下降趋势,直到2016年,“绿色制造”概念的提出及“四绿”建设扭转了这一趋势,缓解了经济发展与环境保护矛盾;3) 历年绿色制造发展水平可划分为四个梯队,第一梯队数量逐年增加,但第四梯队仍较高,主要集中在中、西部,尤其是西部,中、西绿色制造发展水平不足问题依然突出。最后,论文提出了云制造资源优化配置的管理建议。