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Update of approximations in ordered information systems under variations of attribute and object set 属性和对象集变化下有序信息系统中近似值的更新
Pub Date : 2022-01-26 DOI: 10.1007/s43674-021-00011-x
Yan Li, Xiaoxue Wu, Qiang Hua

Many collected data from real world applications often evolve when new attributes or objects are inserted or old ones are removed. The set approximations of ordered information systems (OIS) need to be updated from time to time for further data reduction, analysis, or decision-making. Incremental approaches are feasible and efficient techniques for updating approaches when any variation occurs. In this paper, considering OIS for multi-criteria classification problems, we discuss the principles of incrementally updating approximations in dominance relation based method in four different types of dynamic environments which combine the changes of both attribute set and object set. In each dynamic environment, the corresponding updating principles and algorithm are given with detail proofs. The experimental results and analysis on UCI data sets show that the proposed incremental approach outperforms the non-incremental method and the integration of current incremental algorithms in the implementation efficiency.

许多从现实世界应用程序收集的数据通常在插入新属性或对象或删除旧属性或对象时发生变化。有序信息系统(OIS)的集合近似值需要不时更新,以进行进一步的数据缩减、分析或决策。增量方法是在发生任何变化时更新方法的可行且有效的技术。在本文中,考虑多准则分类问题的OIS,我们讨论了在四种不同类型的动态环境中,在基于优势关系的方法中,在属性集和对象集的变化相结合的情况下,增量更新近似的原理。在各种动态环境下,给出了相应的更新原理和算法,并给出了详细的证明。实验结果和对UCI数据集的分析表明,所提出的增量方法在实现效率上优于非增量方法和现有增量算法的集成。
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引用次数: 0
Vibration analysis for fault detection in wind turbines using machine learning techniques 基于机器学习技术的风力发电机故障检测振动分析
Pub Date : 2022-01-10 DOI: 10.1007/s43674-021-00029-1
Javier Vives

The implementation of machine learning techniques allows to prevent in advance the degeneration of any component present in a wind turbine, as well as the detection and diagnosis of sudden failures. This methodology allows automatic and autonomous learning to predict, detect and diagnose electrical and mechanical failures in wind turbines. Four different failure states have been simulated due to bearing vibrations in wind turbines, comparing traditional techniques, such as frequency analysis, as well as the implementation of AI, using the KNN and SVM methodology. This contribution evaluates different methodologies for monitoring, supervision and fault diagnosis based on the implementation of machine learning algorithms adapted to the different components and faults of the wind turbine. Implementing these techniques, allows to anticipate a breakdown, reduce downtime and costs, especially if they are offshore.

机器学习技术的实现允许预先防止风力涡轮机中存在的任何部件的退化,以及检测和诊断突然故障。这种方法允许自动和自主学习来预测、检测和诊断风力涡轮机的电气和机械故障。使用KNN和SVM方法,比较了频率分析等传统技术以及人工智能的实现,模拟了风力涡轮机中轴承振动导致的四种不同故障状态。这一贡献基于适用于风力涡轮机不同部件和故障的机器学习算法的实现,评估了用于监测、监督和故障诊断的不同方法。实施这些技术可以预测故障,减少停机时间和成本,尤其是在海上。
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引用次数: 5
From linear to non-linear: investigating the effects of right-rail results on complex SERPs 从线性到非线性:研究右轨结果对复杂SERP的影响
Pub Date : 2022-01-10 DOI: 10.1007/s43674-021-00028-2
Yunqiu Shao, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma

Modern search engine result pages (SERPs) become increasingly complex with heterogeneous information aggregated from various sources. In many cases, these SERPs also display results in the right rail besides the traditional left-rail result lists, which change the linear result list to a non-linear panel and might influence user search behavior patterns. While user behavior on the traditional ranked result list has been well studied in existing works, it still lacks a thorough investigation of the effects caused by the right-rail results, especially on complex SERPs. To shed light on this research question, we conducted a user study, which collected participants’ eye movements, detailed interaction behavioral logs, and feedback information. Based on the collected data, we analyze the influence of right-rail results on users’ examination patterns, search behavior, perceived workload, and satisfaction. We further construct a user model to predict users’ examination behavior on non-linear SERPs. Our work contributes to understanding the effects of the right-rail results on users’ interaction patterns, benefiting other related research, such as the evaluation and UI optimization of search systems.

现代搜索引擎结果页(SERP)由于从各种来源聚合的异构信息而变得越来越复杂。在许多情况下,除了传统的左栏结果列表之外,这些SERP还在右栏中显示结果,这将线性结果列表更改为非线性面板,并且可能影响用户搜索行为模式。虽然在现有的工作中已经对传统排名结果列表上的用户行为进行了很好的研究,但它仍然缺乏对正确轨道结果造成的影响的彻底调查,尤其是对复杂SERP的影响。为了阐明这个研究问题,我们进行了一项用户研究,收集了参与者的眼球运动、详细的互动行为日志和反馈信息。基于收集的数据,我们分析了右轨结果对用户考试模式、搜索行为、感知工作量和满意度的影响。我们进一步构建了一个用户模型来预测用户在非线性SERP上的考试行为。我们的工作有助于理解正确轨道结果对用户交互模式的影响,有利于其他相关研究,如搜索系统的评估和UI优化。
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引用次数: 2
Advances in Computational Intelligence: 21st Mexican International Conference on Artificial Intelligence, MICAI 2022, Monterrey, Mexico, October 24–29, 2022, Proceedings, Part I 计算智能的进展:第21届墨西哥国际人工智能会议,MICAI 2022,蒙特雷,墨西哥,2022年10月24日至29日,会议录,第一部分
Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-19493-1
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引用次数: 1
Advances in Computational Intelligence: 21st Mexican International Conference on Artificial Intelligence, MICAI 2022, Monterrey, Mexico, October 24–29, 2022, Proceedings, Part II 计算智能的进展:第21届墨西哥国际人工智能会议,MICAI 2022,蒙特雷,墨西哥,2022年10月24日至29日,会议录,第二部分
Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-19496-2
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引用次数: 0
A support vector approach based on penalty function method 一种基于罚函数法的支持向量方法
Pub Date : 2021-12-17 DOI: 10.1007/s43674-021-00026-4
Songfeng Zheng

Support vector machine (SVM) models are usually trained by solving the dual of a quadratic programming, which is time consuming. Using the idea of penalty function method from optimization theory, this paper combines the objective function and the constraints in the dual, obtaining an unconstrained optimization problem, which could be solved by a generalized Newton method, yielding an approximate solution to the original model. Extensive experiments on pattern classification were conducted, and compared to the quadratic programming-based models, the proposed approach is much more computationally efficient (tens to hundreds of times faster) and yields similar performance in terms of receiver operating characteristic curve. Furthermore, the proposed method and quadratic programming-based models extract almost the same set of support vectors.

支持向量机(SVM)模型通常通过求解二次规划的对偶来训练,这是耗时的。利用优化理论中罚函数法的思想,将对偶中的目标函数和约束条件相结合,得到了一个无约束优化问题,该问题可以用广义牛顿法求解,得到了原始模型的近似解。对模式分类进行了广泛的实验,与基于二次规划的模型相比,所提出的方法在计算上更高效(速度快几十到几百倍),并且在接收机工作特性曲线方面产生了类似的性能。此外,所提出的方法和基于二次规划的模型提取了几乎相同的支持向量集。
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引用次数: 0
Solutions of Yang Baxter equation of symplectic Jordan superalgebras 辛Jordan超代数的Yang-Baxter方程的解
Pub Date : 2021-12-17 DOI: 10.1007/s43674-021-00017-5
Amir Baklouti, Warda Bensalah, Khaled Al-Motairi

We establish in this paper the equivalence between the existence of a solution of the Yang Baxter equation of a Jordan superalgebras and that of symplectic form on Jordan superalgebras.

本文建立了Jordan超代数的Yang-Baxter方程的一个解的存在性与Jordan超代数上辛形式的解的等价性。
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引用次数: 0
An eigenvector approach for obtaining scale and orientation invariant classification in convolutional neural networks 卷积神经网络中获得尺度和方向不变分类的特征向量方法
Pub Date : 2021-12-17 DOI: 10.1007/s43674-021-00023-7
Swetha Velluva Chathoth, Asish Kumar Mishra, Deepak Mishra, Subrahmanyam Gorthi R. K. Sai

The convolution neural networks are well known for their efficiency in detecting and classifying objects once adequately trained. Though they address shift in-variance up to a limit, appreciable rotation and scale in-variances are not guaranteed by many of the existing CNN architectures, making them sensitive towards input image or feature map rotation and scale variations. Many attempts have been made in the past to acquire rotation and scale in-variances in CNNs. In this paper, an efficient approach is proposed for incorporating rotation and scale in-variances in CNN-based classifications, based on eigenvectors and eigenvalues of the image covariance matrix. Without demanding any training data augmentation or CNN architectural change, the proposed method, ‘Scale and Orientation Corrected Networks (SOCN)’, achieves better rotation and scale-invariant performances. SOCN proposes a scale and orientation correction step for images before baseline CNN training and testing. Being a generalized approach, SOCN can be combined with any baseline CNN to improve its rotational and scale in-variance performances. We demonstrate the proposed approach’s scale and orientation invariant classification ability with several real cases ranging from scale and orientation invariant character recognition to orientation invariant image classification, with different suitable baseline architectures. The proposed approach of SOCN, though is simple, outperforms the current state of the art scale and orientation invariant classifiers comparatively with minimal training and testing time.

卷积神经网络以其在充分训练后检测和分类对象的效率而闻名。尽管它们在一定程度上解决了方差的变化,但许多现有的CNN架构并不能保证方差的显著旋转和缩放,这使得它们对输入图像或特征图的旋转和缩放变化很敏感。过去已经进行了许多尝试来获得细胞神经网络的轮换和方差规模。在本文中,基于图像协方差矩阵的特征向量和特征值,提出了一种在基于CNN的分类中结合方差中的旋转和尺度的有效方法。在不需要任何训练数据扩充或CNN架构更改的情况下,所提出的“尺度和方向校正网络(SOCN)”方法实现了更好的旋转和尺度不变性能。SOCN提出了在基线CNN训练和测试之前对图像进行尺度和方向校正的步骤。作为一种通用方法,SOCN可以与任何基线CNN相结合,以提高其旋转和方差尺度性能。我们在从尺度和方向不变的字符识别到方向不变的图像分类的几个实际案例中,用不同的合适的基线架构,证明了所提出的方法的尺度和方向无关的分类能力。所提出的SOCN方法虽然简单,但与现有技术的尺度和方向不变分类器相比,在最小的训练和测试时间下,其性能要好。
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引用次数: 2
BCK codes BCK代码
Pub Date : 2021-12-17 DOI: 10.1007/s43674-021-00018-4
Hashem Bordbar

In this paper, we initiate the study of the notion of the BCK-function on an arbitrary set A, and providing connections with x-functions and x-subsets for (x in X) where X is a BCK-algebra. Moreover, using the notion of order in a BCK-algebra, the BCK-code C is introduced and besides a new structure of order in C is investigated. Finally, we show that the structure of the BCK-algebra X and the BCK-code C which is generated by X, with their related orders are the same.

在本文中,我们开始研究任意集A上BCK函数的概念,并为(x In x)提供了x函数和x子集的连接,其中x是BCK代数。此外,利用BCK代数中阶的概念,引入了BCK码C,并研究了C中一种新的阶结构。最后,我们证明了BCK代数X和由X生成的BCK码C的结构及其相关阶是相同的。
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引用次数: 2
Caristi type mappings and characterization of completeness of Archimedean type fuzzy metric spaces Caristi型映射与阿基米德型模糊度量空间完备性的刻画
Pub Date : 2021-12-17 DOI: 10.1007/s43674-021-00014-8
J. Martínez-Moreno, D. Gopal, Vladimir Rakočević, A. S. Ranadive, R. P. Pant

This paper deals with some issues of fixed point concerning Caristi type mappings introduced by Abbasi and Golshan (Kybernetika 52:929–942, 2016) in fuzzy metric spaces. We enlarge this class of mappings and prove completeness characterization of corresponding fuzzy metric space. The paper includes a comprehensive set of examples showing the generality of our results and an open question.

本文讨论了Abbasi和Golshan(Kybernetika 52:929–9421916)在模糊度量空间中引入的Caristi型映射的不动点的一些问题。我们扩大了这类映射,并证明了相应的模糊度量空间的完备性刻画。本文包括一组全面的例子,显示了我们的结果的普遍性和一个悬而未决的问题。
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引用次数: 1
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Advances in computational intelligence
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