Energy storage plays an important role in the adoption of renewable energy to help solve climate change problems. Lithium-ion batteries (LIBs) are an excellent solution for energy storage due to their properties. In order to ensure the safety and efficient operation of LIB systems, battery management systems (BMSs) are required. The current design and functionality of BMSs suffer from a few critical drawbacks including low computational capability and limited data storage. Recently, there has been some effort in researching and developing smart BMSs utilizing the cloud platform. A cloud-based BMS would be able to solve the problems of computational capability and data storage in the current BMSs. It would also lead to more accurate and reliable battery algorithms and allow the development of other complex BMS functions. This study reviews the concept and design of cloud-based smart BMSs and provides some perspectives on their functionality and usability as well as their benefits for future battery applications. The potential division between the local and cloud functions of smart BMSs is also discussed. Cloud-based smart BMSs are expected to improve the reliability and overall performance of LIB systems, contributing to the mass adoption of renewable energy.

Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods.

Clustering is an unsupervised machine learning method with many practical applications that has gathered extensive research interest. It is a technique of dividing data elements into clusters such that elements in the same cluster are similar. Clustering belongs to the group of unsupervised machine learning techniques, meaning that there is no information about the labels of the elements. However, when knowledge of data points is known in advance, it will be beneficial to use a semi-supervised algorithm. Within many clustering techniques available, fuzzy C-means clustering (FCM) is a common one. To make the FCM algorithm a semi-supervised method, it was proposed in the literature to use an auxiliary matrix to adjust the membership grade of the elements to force them into certain clusters during the computation. In this study, instead of using the auxiliary matrix, we proposed to use multiple fuzzification coefficients to implement the semi-supervision component. After deriving the proposed semi-supervised fuzzy C-means clustering algorithm with multiple fuzzification coefficients (sSMC-FCM), we demonstrated the convergence of the algorithm and validated the efficiency of the method through a numerical example.

The co-authorship recommendation problem is attractive since it helps researchers extend collaboration to improve the quality of scientific articles as well as promote innovation. This problem involves suggesting authors join research groups based on their research interests, areas of expertise, and past collaborative experiences to write scientific articles together. In this paper, we tackle the co-authorship recommendation problem by modeling it as a co-authorship network, where each author is represented as a vertex, and each collaboration between two authors is represented as an edge. Since the number of author pairs without collaboration is much larger than those with collaboration, datasets created from co-authorship networks are typically two-class imbalanced datasets. Accordingly, we propose an improved algorithm of AdaBoost combined with the W-SVM algorithm, called Im.AdaBoost.W-SVM, to solve the classification problem with two-class imbalanced datasets. To evaluate the performance of our Im.AdaBoost.W-SVM algorithm for the co-authorship recommendation problem, we collected author and article data from the website www.sciencedirect.com through ScienceDirect APIs and created two-class imbalanced datasets. Our experimental results for our self-built co-authorship datasets with different sizes and imbalance ratios showed that our Im.AdaBoost.W-SVM algorithm outperforms the AdaBoost.DecisionTree and AdaBoost.W-SVM algorithms for the co-authorship recommendation problem.

Fuzziness Measure, Quantified Sematic Mapping and Interpolative Method of Approximate Reasoning in Medical Expert Systems.