Building a pattern detection model using a deep learning algorithm for data collected from manufacturing sites is an effective way for to perform decision-making and assess business feasibility for enterprises, by providing the results and implications of the patterns analysis of big data occurring at manufacturing sites. To identify the threshold of the abnormal pattern requires collaboration between data analysts and manufacturing process experts, but it is practically difficult and time-consuming. This paper suggests how to derive the threshold setting of the abnormal pattern without manual labelling by process experts, and offers a prediction algorithm to predict the potentials of future failures in advance by using the hybrid Convolutional Neural Networks (CNN)–Long Short-Term Memory (LSTM) algorithm, and the Fast Fourier Transform (FFT) technique. We found that it is easier to detect abnormal patterns that cannot be found in the existing time domain after preprocessing the data set through FFT. Our study shows that both train loss and test loss were well developed, with near zero convergence with the lowest loss rate compared to existing models such as LSTM. Our proposition for the model and our method of preprocessing the data greatly helps in understanding the abnormal pattern of unlabeled big data produced at the manufacturing site, and can be a strong foundation for detecting the threshold of the abnormal pattern of big data occurring at manufacturing sites.

Many countries define policies to manage oceans and coastal areas in order to utilize marine ecosystems strategically. When we reviewed the strategies and policies of various countries in relation to ocean sustainability, we found that biodiversity preservation is a key issue for policies related to sustainable marine development. We investigated the research trends and collaboration status of China, Japan and South Korea regarding marine biodiversity through the analysis of scientific articles using bibliometric analysis. The results showed that Japan collaborated the most with other countries compared to China and South Korea. All three countries collaborated with the Organization for Economic Cooperation and Development (OECD) and Association of Southeast Asian Nations (ASEAN) countries frequently. South Korea showed the strongest inter-collaboration amongst China, Japan and South Korea. Microorganism research is a common research topic in China, Japan and South Korea. Each country demonstrated its own prominent research area, such as local region research in China, deep-sea research in Japan and aquaculture research in South Korea.