This review examines the principles, limitations, and recent advancements in elastic modulus measurement using nanoindentation. The importance of accurate contact area prediction is discussed, along with the Oliver-Pharr method and its limitations. The Continuous Stiffness Measurement (CSM) technique is presented as a significant improvement, allowing continuous measurement of mechanical properties throughout the indentation process. For ultra-thin films, the Li and Vlassak method, which incorporates Yu's solution and the concept of effective thickness, is highlighted as a means to correct for substrate effects. Recent developments in artificial neural network-based models for elastic modulus prediction are also explored. These advancements have greatly expanded the applicability of nanoindentation in semiconductor and MEMS device reliability assessment.

We propose a real-time information propagation arithmetic neural network (PANN) that minimizes the loss of power generation output of the system in the event of sudden changes in the module due to strong external typhoons or earthquakes at the solar power generation facility site. In addition, we propose a new double-sided module reflector that can reduce the local loss of power generation efficiency of the single-sided module reflector that is currently widely distributed, as well as the environmental pollution and inconvenience of maintenance work of the existing double-sided module. We present a computational network that can detect the faulty solar panel in real-time by checking the fault status of the installed solar panel and using a real-time computation method through a node-to-node diffusion method. In particular, this method recognizes the power loss part due to sudden changes in the module in real time and can take emergency measures for various nonlinear field facilities through a neural structure that finds the optimal distance up, down, left, and right. To confirm the characteristics of the loss reduction control of the field facility, we confirmed that the system was configured as a 7-degree-of-freedom control model using the PANN neural network learning structure method and improved the power generation output. PANN (Propagation Arithmetic Neural Networks) and various module systems are proposed for the real-time recovery of faulty solar panels and improving module system efficiency.

As industry and technology go through advancement, it is hard to search new materials which satisfy various standards through conventional trial-and-error based research methods. Crystal Graph Convolutional Neural Network(CGCNN) is a neural network which uses material’s features as train data, and predicts the material properties(formation energy, bandgap, etc.) much faster than first-principles calculation. This report introduces how to train the CGCNN model which predicts the formation energy using open database. It is anticipated that with a simple programming skill, readers could construct a model using their data and purpose. Developing machine learning model for materials science is going to help researchers who should explore large chemical and structural space to discover materials efficiently.

This study examines the feasibility of the image deep learning method using convolution neural networks (CNNs) to maintain a porcelain insulator. Data augmentation is performed to prevent over-fitting, and the classification performance is evaluated by training the age, material, region, and pollution level of the insulator using image data in which the background and labelling are removed. Based on the results, it was difficult to predict the age, but it was possible to classify 76% of the materials, 60% of the pollution level, and more than 90% of the regions. From the results of this study, we identified the potential and limitations of the CNN classification for the four groups currently classified. However, it was possible to detect discoloration of the porcelain insulator resulting from physical, chemical, and climatic factors. Based on this, it will be possible to estimate the corrosion of the cap and discoloration of the porcelain caused by environmental deterioration, abnormal voltage, and lightning.