本文提出了一种基于密度聚类的三支K-Means算法。针对传统的K-Means算法在选取初始聚类中心时往往依赖于随机选择和无法处理不确定性数据对象的问题,本文采用基于密度聚类算法优化初始聚类中心的选择,并优化了截断距离的选取,最后使用三支决策的方法对聚类结果进行处理。实验结果表明,与传统的K-Means算法相比,改进的K-Means算法在聚类中表现出更高的聚类精度和稳定性。This paper proposes a three-branch K-Means algorithm based on density clustering. In view of the problem that the traditional K-Means algorithm often relies on random selection and cannot handle uncertain data objects when selecting initial clustering centers, this paper uses a density-based clustering algorithm to optimize the selection of initial clustering centers, and optimizes the selection of truncation distance. Finally, a three-branch decision method is used to process the clustering results. The experimental results show that the improved K-Means algorithm exhibits higher clustering accuracy and stability in clustering compared to the traditional K-Means algorithm.
电池以其卓越的储能能力,成为现代科技与生活不可或缺的能源核心,因此对电池老化状态精准诊断和预测是非常重要的。对于一般预测方法而言,电化学模型依赖电池内部机制进行预测,但高度敏感于材料、结构及工作条件变化。机器学习模型依赖高质量大数据与先进算法估算电池健康,但受限于数据质量和算法选择。鉴于上述模型的不足,本文提出了一种创新的电池健康预测模型——基于置信规则库的预测模型。通过构建一系列基于不确定性和模糊性处理的规则,有效应对电池内部状态的复杂性和外部环境的多变性。经实验验证该模型能够提高预测的准确性和可靠性,为锂离子电池健康状态估计及寿命预测领域提供了新的思路和方法,有望在未来能源管理和电池维护中发挥重要作用。Batteries, with their exceptional energy storage capabilities, have emerged as an indispensable energy core in modern technology and daily life. Consequently, accurate diagnosis and prediction of battery aging status are of paramount importance. Traditional prediction methods, such as electrochemical models, rely on the internal mechanisms of batteries for prediction but are highly sensitive to changes in materials, structures, and operating conditions. On the other hand, machine learning models estimate battery health based on high-quality big data and advanced algorithms, yet they are constrained by data quality and algorithm selection. Given the limitations of these models, this paper introduces an innovative battery health prediction model—a prediction model based on a Belief Rule Base. By constructing a series of rules that address uncertainty and fuzziness, it effectively tackles the complexity of the battery’s internal state and the variability of the external environment. Experimental validation demonstrates that this model enhances prediction accuracy and reliability, offering new insights and methodologies for lithium-ion battery health state estimation and lifetime prediction. It is anticipated to play a pivotal role in future energy management and battery maintenance.
准确地预测多年冻土区地表冻结天数对于当前地表环境有重大意义。当前地表冻结地区融化时间推后,融化结束时间提前,总体冻结时长增加。对于传统置信规则库来说,只考虑了其精准性的问题,而对于地表冻结时长天数,其可解释性具有重大意义,需要考虑水汽、气体以及在融化过程之中的能量交换过程。因此本文使用因子分析法,对于影响地表冻结天数的因素提取因子,然后使用可解释性置信规则库,对地表冻结天数进行可解释性预测,使用中国东北地区的数据进行案例研究,结果表明可解释性置信规则库可以对多年冻土区地表冻结天数进行有效预测,验证了模型的有效性。Accurate prediction of the number of days of surface freezing in perennial permafrost regions is of great significance for the current surface environment. Currently, the thawing time is pushed back in the surface freezing region, the thawing end time is advanced, and the overall freezing duration increases. For the traditional belief rule base, only its accuracy is considered, but for the surface freezing duration days, its interpretability is of great significance, and it needs to consider the water vapor, gas, and the energy exchange process during the melting process. Therefore, this paper uses the factor analysis method to extract the factors for the number of days of surface freezing, and then uses the interpretable confidence rule base to predict the number of days of surface freezing with interpretability, and uses the data from Northeast China to conduct a case study, and the results show that the interpretable belief rule base can effectively predict the number of days of surface freezing in the perennial permafrost region, which verifies the effectiveness of the model.
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