Stock market prediction is a challenging issue for investors. In this paper, we propose a stock price prediction model based on convolutional neural network (CNN) to validate the applicability of new learning methods in stock markets. When applying CNN, 9 technical indicators were chosen as predictors of the forecasting model, and the technical indicators were converted to images of the time series graph. For verifying the usefulness of deep learning for image recognition in stock markets, the predictive accuracies of the proposed model were compared to typical artificial neural network (ANN) model and support vector machine (SVM) model. From the experimental results, we can see that CNN can be a desirable choice for building stock prediction models. To examine the performance of the proposed method, an empirical study was performed using the S&P 500 index. This study addresses two critical issues regarding the use of CNN for stock price prediction: how to use CNN and how to optimize them.

To realize intelligent manufacturing, a controllable factory must be built, and manufacturing competitiveness must be achieved through the improvement of product quality and yield. The yield in the micromanufacturing process is gaining importance as a management factor used in deciding the production cost and product quality as product functions becomes more sophisticated. Because the micromanufacturing process involves manufacturing products through multiple steps, it is difficult to determine the process or equipment that has encountered failure, which can lead to difficulty in securing high yields. This study presents a structural model for building a factory integration system to analyze big data at manufacturing sites and a hierarchical factor analysis methodology to increase product yield and quality in an intelligent manufacturing environment. To improve the product yield, it is necessary to analyze the fault factors that cause low yields and locate and manage the critical processes and equipment factors that affect these fault factors. However, yield management is a difficult problem because there exists a correlation between equipment, and in the sequence of process equipment that the lot passed through, the downstream and the upstream cause complex faults. This study used data‐mining techniques to identify suspected processes and equipment that affect the yield of products in the manufacturing process and to analyze the key factors of the equipment. Ultimately, we propose a methodology to find the key factors of the suspected process and equipment that directly affect the implementation of the intelligent manufacturing scheme and the yield of the product. To verify the effect of key parameters of critical processes and equipment on the yield, the proposed methodology was applied to actual manufacturing sites.

The Smart Factory Equipment Engineering System collects and monitors necessary information in real-time. While putting the product into the equipment, operation conditions are lowered through a Recipe Management System. The working conditions are set by Run-to-Run a system for real-time detection and control through Fault Detection Classification function. In this study, the smart factory equipment system associated with the entire system is proposed by defining and integrating the necessary equipment management functions from a smart factory’s point of view. To do this, detailed analysis and process improvement on products, processes, and production line equipment were conducted and implemented in the smart factory equipment engineering system. The models proposed in this paper have been implemented to the production site of BGA-PCB. It has been confirmed that the models have resulted in significant change, and have qualitative and quantitative impacts on the working methods of equipment. Typically, data collection time, data entry time, and manual writing sheets were greatly reduced.

In the printed circuit board (PCB) manufacturing industry, the yield is an important management factor as it significantly affects the product cost and quality. However, in real situations, it is difficult to ensure a high yield in a manufacturing process, because the products are manufactured through numerous nanoscale manufacturing operations. Thus, for improving the yield, it is necessary to analyze the key process parameters and equipment parameters that result in a low yield. In this study, critical equipment parameters that affect the yield were extracted through a mutual analysis of the equipment parameters (x) and process parameters (y) in the plastic ball grid array (PBGA) manufacturing process. To this end, the study uses the correlation coefficient to apply the heuristic algorithm that extracts critical parameters that keep the redundancy among the equipment parameters to a minimum and exert maximum impact on the critical process parameters. Additionally, by using the general regression neural network technique, the effects of the critical equipment parameters on the process parameters were confirmed. The test results were applied to the PBGA production line and an improvement in the yield was confirmed.