The prediction of complex product quality has been extensively studied in the last decades. However, due to the high dimensionality and diversity of quality features, the control optimization of feature parameters remains a significant challenge. For complex products, fault detection techniques are required to accurately predict product quality, and significant influencing quality features must also be identified for their control optimization. In this paper, we propose a novel approach combining the Least Absolute Shrinkage and Selection Operator (LASSO) method with Bayesian Networks (BN) for the detection, identification and control of product quality metrics. Specifically, for complex products with high-dimensional features, the identification of key quality features is achieved initially by the LASSO method to obtain more accurate quality prediction. Subsequently, the optimal production range is determined through the utilization of a Bayesian network to achieve the optimization of product quality. The experimental results show that processing fewer but critical features can not only obtain satisfactory prediction accuracy, but also save computational time. Furthermore, this method offers practical operational guidance for product quality prediction and control in complex product industries.
Electric vehicle technology has experienced rapid development. However, there are still obstacles on the information technology side, one of which is the separation of information systems that cannot be exchanged easily. This problem arises because each electric vehicle brand makes its own product ecosystem. In contrast, a system that can work efficiently is using data between companies that can be operated together known as interoperability. The most efficient level of information interoperability can be formed from the development of data ontologies. This article aims to explore ontologies to enhance interoperability in the batteries domain, which is a key component of electric vehicles using systematic literature review (SLR) analysis to examine article documents in Scopus from 2014 to 2024. The content analysis of the articles was carried out based on the interoperability layer model classified into domains, zones, and information purposes in two research questions. Research shows that challenges to achieving interoperability are harmonized information representation, diverse domains, and diverse technology applications landscape. This suggests that further research into ontologies for the interoperability battery domain studies is needed to fill the gap.
Japanese capacitor companies have a high share of the global market. High reliability of electronic components plays an important role in ensuring high safety and performance of the entire system. This study focuses on corporate R&D activities related to electrolytic capacitors. This paper proposes a method for analyzing the number of patents that are the result of technological development and for deriving advancedness through textual analysis of patent information. The method was also used to investigate the advanced nature of technological development. The results show that although the number of patents is at a high level, there are issues with the advancedness of the technology.
Recently, the logistics sector has come to recognize the importance of sustainability, especially with regard to automated e-fulfillment centers that mainly rely on automation and robotics technology. Practitioners are increasingly recognizing the importance of efficiently maintaining order fulfillment while minimizing the costs associated with renewable energy. Thus, practitioners need decisional support to optimize their renewable energy resources and prevent wastage, all while ensuring effective operations. This study uses fuzzy association rules mining (FARM) to reveal links that are hidden between environmental data and the production of renewable energy. “IF-then” rules will be produced by the FARM, providing decision support in the field. The proposed model focuses on solar power as a case study. By utilizing the extracted rules, users can identify the most influential factors that impact solar power generation. By targeting those crucial factors, practitioners can effectively determine the ideal quantity of solar panels, allowing them to prevent the accumulation of unused solar panels while still maintaining optimal operational performance.
