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Due to the increasing concerns about the environmental and economic issues of traditional ships, all-electric ships with energy storage and renewable energy integration have become more and more appealing for the forthcoming future. In this paper, an optimal energy storage system (ESS) capacity determination method for a marine ferry ship is proposed; this ship has diesel generators and PV panels. ESSs sizing optimization and power system scheduling optimization are simultaneously conducted and it is converted to a mixed-integer quadratic programming (MIQP) model with special modeling techniques. The case study shows that the proposed method is flexible and effective, and the relationships between the ESSs size and the discharge rate, life cycle times, or initial investment cost are investigated.

Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the captured sensory data, and also predict their failures in advance, which can greatly help to take appropriate actions for maintenance and avoid serious consequences in industrial systems. In recent years, deep learning methods are being widely introduced into FDP due to the powerful feature representation ability, and its rapid development is bringing new opportunities to the promotion of FDP. In order to facilitate the related research, we give a summary of recent advances in deep learning techniques for industrial FDP in this paper. Related concepts and formulations of FDP are firstly given. Seven commonly used deep learning architectures, especially the emerging generative adversarial network, transformer, and graph neural network, are reviewed. Finally, we give insights into the challenges in current applications of deep learning-based methods from four different aspects of imbalanced data, compound fault types, multimodal data fusion, and edge device implementation, and provide possible solutions, respectively. This paper tries to give a comprehensive guideline for further research into the problem of intelligent industrial FDP for the community.

Abstract-NoSQL (Not only SQL) data stores become a vital component in many big data computing platforms due to its inherent horizontal scalability. HBase is an open-source distributed NoSQL store that is widely used by many Internet enterprises to handle their big data computing applications (e.g. Facebook handles millions of messages each day with HBase). Optimizations that can enhance the performance of HBase are of paramount interests for big data applications that use HBase or Big Table like key-value stores. In this paper we study the problems inherent in misconfiguration of HBase clusters, including scenarios where the HBase default configurations can lead to poor performance. We develop HConfig, a semiautomated configuration manager for optimizing HBase system performance from multiple dimensions. Due to the space constraint, this paper will focus on how to improve the performance of HBase data loader using HConfig. Through this case study we will highlight the importance of resource adaptive and workload aware auto-configuration management and the design principles of HConfig. Our experiments show that the HConfig enhanced bulk loading can significantly improve the performance of HBase bulk loading jobs compared to the HBase default configuration, and achieve 2~3.7x speedup in throughput under different client threads while maintaining linear horizontal scalability.

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