Mun’s research while affiliated with Pusan National University and other places

A composite solid propellant which generates high propulsive force in a short time is typically composed of an oxidizer, a metal fuel powder and a binder. Among these, the binder is an important component. The binder maintains the mechanical properties of propellant grains and endures several thermal and mechanical stresses in the engine. Several studies have been reported for the development of energetic propellant binders for increasing the propellant′s propulsive force. While several materials have been studied for the synthesis of energetic prepolymers, a nitramine-group-containing prepolymer is a suitable candidate because these types of prepolymers are less toxic and more cost-effective when compared to the traditional glycidyl azide polymers (GAP) and triazole-based prepolymers. Considering the lack of studies for the binder using a nitramine-group-containing prepolymers, we synthesized a nitramine-group-containing monomer and polymerized a nitramine-group-containing prepolymer. The prepolymer was then used for the preparation of the binder and its thermal and mechanical properties, as well as the effect of the plasticizer, were studied. The binder that was prepared using the prepolymer containing a nitramine-group showed very high elongation, tensile strength. Nitrate-ester (NE)-type plasticizer could reduce the glassy transition temperature (Tg)of the binder successfully. Also, high-energy is released due to the decomposition of the nitramine-group at around 245 °C, thus exhibiting the efficiency of the nitramine-group-containing prepolymer as an excellent energetic binder material.

A piping and instrumentation diagram (P&ID) is a key drawing widely used in the energy industry. In a digital P&ID, all included objects are classified and made amenable to computerized data management. However, despite being widespread, a large number of P&IDs in the image format still in use throughout the process (plant design, procurement, construction, and commissioning) are hampered by difficulties associated with contractual relationships and software systems. In this study, we propose a method that uses deep learning techniques to recognize and extract important information from the objects in the image-format P&IDs. We define the training data structure required for developing a deep learning model for the P&ID recognition. The proposed method consists of preprocessing and recognition stages. In the preprocessing stage, diagram alignment, outer border removal, and title box removal are performed. In the recognition stage, symbols, characters, lines, and tables are detected. The objects for recognition are symbols, characters, lines, and tables in P&ID drawings. A new deep learning model for symbol detection is defined using AlexNet. We also employ the connectionist text proposal network (CTPN) for character detection, and traditional image processing techniques for P&ID line and table detection. In the experiments where two test P&IDs were recognized according to the proposed method, recognition accuracies for symbol, characters, and lines were found to be 91.6%, 83.1%, and 90.6% on average, respectively.

Rheumatoid arthritis is an autoimmune disease that causes serious functional loss in patients. Early and accurate diagnosis of rheumatoid arthritis may attenuate its severity. Despite a diagnosis guideline in the 2010 American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) classification criteria for rheumatoid arthritis, the practical difficulties in its diagnosis highlight the need of developing new methods for diagnosing rheumatoid arthritis. The current study aimed to identify rheumatoid arthritis diagnostic biomarkers by using a proteomics approach. Serum protein profiling was conducted using mass spectrometry, and five distinguishable biomarkers were identified therefrom. In the validation study, the five biomarkers were quantitatively verified by multiple reaction monitoring (MRM) analysis. Two proteins, namely serum amyloid A4 and vitamin D binding protein, showed high performance in distinguishing patients with rheumatoid arthritis from healthy controls. Logistic analysis was conducted to evaluate how accurately the two biomarkers distinguish patients with rheumatoid arthritis from healthy controls. The classification accuracy was 86.0% and 81.4% in patients with rheumatoid arthritis and in healthy controls, respectively. Serum amyloid A4 and vitamin D binding protein could be potential biomarkers related to the inflammatory response and joint destruction that accompany rheumatoid arthritis.