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Class imbalance refers to the significant difference in the number of samples from different classes within a dataset, making it challenging to identify minority class samples correctly. This issue is prevalent in real-world classification tasks, such as software defect prediction, medical diagnosis, and fraud detection. The synthetic minority oversampling technique (SMOTE) is widely used to address class imbalance issue, which is based on interpolation between randomly selected minority class samples and their neighbors. However, traditional SMOTE and most of its variants only interpolate between existing samples, which may be affected by noise samples in some cases and synthesize samples that lack diversity. To overcome these shortcomings, this paper proposes the Minimum Enclosing Ball SMOTE (MEB-SMOTE) method from a geometry perspective. Specifically, MEB is innovatively introduced into the oversampling method to construct a representative point. Then, high-quality samples are synthesized by interpolation between this representative point and the existing samples. The rationale behind constructing a representative point is discussed, demonstrating that the center of MEB is more suitable as the representative point. To exhibit the superiority of MEB-SMOTE, experiments are conducted on 15 real-world imbalanced datasets. The results indicate that MEB-SMOTE can effectively improve the classification performance on imbalanced datasets.

Parameter extraction of photovoltaic (PV) models is crucial for the planning, optimization, and control of PV systems. Although some methods using meta-heuristic algorithms have been proposed to determine these parameters, the robustness of solutions obtained by these methods faces great challenges when the complexity of the PV model increases. The unstable results will affect the reliable operation and maintenance strategies of PV systems. In response to this challenge, an improved RIME algorithm with enhanced exploration and exploitation is proposed for robust and accurate parameter identification for various PV models. Specifically, the differential evolution mutation operator is integrated in the exploration phase to enhance the population diversity. Meanwhile, a new exploitation strategy incorporating randomization and neighborhood strategies simultaneously is developed to maintain the balance of exploitation width and depth. The improved RIME algorithm is applied to estimate the optimal parameters of the single-diode model (SDM), double-diode model (DDM), and triple-diode model (TDM) combined with the Lambert-W function for three PV cell and module types including RTC France, Photo Watt-PWP 201 and S75. According to the statistical analysis in 100 runs, the TEIMRE achieves more accurate and robust parameter estimations than other techniques to various PV models in varying environmental conditions. All of our source codes are publicly available at https://github.com/dirge1/TERIME.

Uncertainty reduction is vital for improving system reliability and reducing risks. To identify the best target for uncertainty reduction, uncertainty importance measure is commonly used to prioritize the significance of input variable uncertainties. Then, designers will take steps to reduce the uncertainties of variables with high importance. However, for variables with minimal uncertainty, the cost of controlling their uncertainties can be unacceptable. Therefore, uncertainty magnitude should also be considered in developing uncertainty reduction strategies. Although variance-based methods have been developed for this purpose, they are dependent on statistical moments and have limitations when dealing with highly-skewed distributions that are commonly encountered in practical applications. Motivated by this problem, we propose a new uncertainty importance measure based on cumulative residual entropy. The proposed measure is moment-independent based on the cumulative distribution function, which can handle the highly-skewed distributions properly. Numerical implementations for estimating the proposed measure are devised and verified. A real-world engineering case considering highly-skewed distributions is introduced to show the procedure of developing uncertainty reduction strategies considering uncertainty magnitude and corresponding cost. The results demonstrate that the proposed measure can present a different uncertainty reduction recommendation compared to the variance-based approach because of its moment-independent characteristic.

Global sensitivity analysis (GSA) can provide rich information for controlling output uncertainty. In practical applications, segmented models are commonly used to describe an abrupt model change. For segmented models, the complicated uncertainty propagation during the transition region may lead to different importance rankings of different GSA methods. If an unsuitable GSA method is applied, misleading results will be obtained, resulting in suboptimal or even wrong decisions. In this paper, four GSA indices, i.e., Sobol index, mutual information, delta index and PAWN index, are applied for a segmented fire spread model (Dry Eucalypt). The results show that four GSA indices give different importance rankings during the transition region since segmented characteristics affect different GSA indices in different ways. We suggest that analysts should rely on the results of different GSA indices according to their practical purpose, especially when making decisions for segmented models during the transition region.

Accelerated degradation testing (ADT) is an effective way to evaluate the lifetime and reliability of highly reliable products. Markovian stochastic processes are usually applied to describe the degradation process. However, the degradation processes of some products are non-Markovian due to the interaction with environments. Besides, owing to the differences in materials and manufacturing processes, products from the same population exhibit diverse degradation paths. Motivated by this issue, an ADT model with memory effects and unit-to-unit variability (UtUV) is proposed in this article. The memory effect in the degradation process is captured by the fractional Brownian motion (FBM) and the UtUV is considered in the acceleration model. Then, the lifetime and reliability under the normal operating condition are presented. To give an accurate estimation of the memory effect, a statistical inference method is devised based on the expectation maximization (EM) algorithm. The effectiveness of the proposed method is verified by a simulation case and a microwave case. It is shown that the estimation of the memory effect obtained by the EM algorithm is much more accurate than the traditional method. Moreover, without considering UtUV in the ADT model, the estimation of the memory effect can be highly biased. The proposed ADT model is superior in both deterministic degradation trend predictions and degradation boundary quantification compared to existing models.

Baroreflex is critical to maintain the blood pressure homeostasis, and the quantification of the baroreflex regulation function (BRF) can provide guidance for disease diagnosis, treatment and healthcare. Current quantification of the BRF such as baroreflex sensitivity cannot represent the BRF systematically. From the perspective of complex systems, we regard that the BRF is the emergence result of the diverse states and interactions in the physiological mechanisms. Therefore, the three-layer emergence is constructed in this work, which is from the physiological mechanisms to the physiological indexes and then to the BRF. On this basis, since the entropy in statistical physics macroscopically measures the diversity of the system's states, a new index called the PhysioEnt is proposed to represent the BRF and quantify the physical relationships between the BRF and four physiological indexes, baroreflex sensitivity, heart rate, heart rate variability, and systolic blood pressure. Based on the proposed method, some new findings with medical significance are obtained, including the mechanisms that aging and obesity affect the resting-condition BRF are different, and the resting-condition BRFs of men and older people depend more on the physiological processes among organs/tissues. Based on the measurable indexes, the proposed method would support the individualized medicine prospectively., Comment: 25 pages, 7 figures