Liquefied hydrogen is attracting attention as an energy source of the future due to its hydrogen storage rate and low risk. However, the disadvantage is that the unit price is high due to technical difficulties in production, transportation, and storage. This study was conducted to improve the design accuracy and development period of needle valves, which are important parts with a wide technical application range among liquefied hydrogen equipment. Since the needle valve must discharge an appropriate flow rate of the liquefied fluid, it is important to determine the needle valve design parameters suitable for the target flow rate. Computational Fluid Dynamics and Artificial Neural Network technology used to determine the design variables of fluid flow were applied to improve the setting and analysis time of the parameter. In addition, procedures and methods for applying the design parameter of needle valves to Convolutional Neural Networks were presented. The procedure and appropriate conditions for selecting parameters and functional conditions of the Convolutional Neural Network were presented, and the accuracy of predicting the flow coefficient according to the design parameter was secured 95%. It is judged that this method can be applied to other structures and machines.

In this study we present a new approach to estimating termite populations size. So far, termite researchers have been using the mark-capture-recapture method. This method has a disadvantage that measurement time is long and error range is large. To this end, we built an agent-based model to simulate termite tunneling behavior. Using this model, we made simulated tunnel patterns that are determined by three variables: the number of simulated termites (N), the passing probability of two encountering termites (P), and the distance that termites move soil parcels (D). To explore whether the N value can be estimated with a partial termite tunnel pattern, we generated four groups of tunnel patterns that are partially obscured in complete tunnel pattern image: (1) A pattern group in which the outer area of the tunnel pattern is obscured (I-pattern), (2) a pattern group in which half of the tunnel pattern is obscured (H-pattern), (3) a pattern group in which the inner region of the tunnel pattern is obscured (O-pattern), and (4) a pattern group combining I- and O-pattern (IO-pattern). For each group, 80% of the tunnel patterns were learned through a convolution neural network (CNN) and the remaining 20% of the patterns were used for estimating N value. The estimation results showed that the N estimates for the IO-pattern are the most accurate and are in the order I-, H-, and O-patterns. This means that the termite population size can be estimated based on tunnel information near the center of the colony.

Three CNN (Convolutional Neural Network) models of GoogLeNet, VGGNet, and Alexnet were evaluated to select the best deep learning based image analysis mothod that can detect pavement distresses of pothole, spalling, and punchout on expressway. Education data was obtained using pavement surface images of 11,056km length taken by Gopro camera equipped with an expressway patrol car. Also, deep learning framework of Caffe developed by Berkeley Vision and Learning Center was evaluated to use the three CNN models with other frameworks of Tensorflow developed by Google, and CNTK developed by Microsoft. After determing the optimal CNN model applicable for the distress detection, the analyzed images and corresponding GPS locations, distress sizes (greater than distress length of 150mm), required repair material quantities are trasmitted to local maintenance office using LTE wireless communication system through ICT center in Korea Expressway Corporation. It was found out that the GoogLeNet, AlexNet, and VGG-16 models coupled with the Caffe framework can detect pavement distresses by accuracy of 93%, 86%, and 72%, respectively. In addition to four distress image groups of cracking, spalling, pothole, and punchout, 22 different image groups of lane marking, grooving, patching area, joint, and so on were finally classified to improve the distress detection rate.

Ground penetrating radar (GPR) is a typical sensor system for underground objects detection area. The multichannel GPR devices can give more detail and informative three-dimensional (3D) data for classification underground objects. Spatial information of underground objects can be well characterized in the three-dimensional GPR block data which consists of several B-scan and C-scan data. In this article underground object classification method is proposed by using 3D GRP data. Deep learning technique is recently being adopted into this field due to its powerful image classification capacity. The 3D GRP block data is then used to train deep three-dimensional convolution neural network (3D CNN). The proposed method successfully classifies cavity, pipe, manhole and subsoils having small false positive errors. The suggested method is experimentally validated by area data collected on urban roads in Seoul, South Korea.