• Journal of the Korean Society of Propulsion Engineers
• /
• v.23 no.1
• /
• pp.116-123
• /
• 2019

Thermal batteries are also called molten-salt batteries as the electrolyte is mainly composed of molten salt. The molten-salt electrolyte is a solid that does not conduct electricity at room temperature, but when it is melted by a pyrotechnic heat source, it becomes an excellent ionic conductor. Thermal batteries are a kind of pyrotechnic battery because they operate only when the solid electrolyte is melted by the heat energy provided by pyrotechnic materials. Pyrotechnic components used in a thermal battery include heat sources, fuse strips, and an igniter. The reliability of these pyrotechnic components critically affects the reliability and performance of the battery that must supply electricity stably to guided munitions even under extreme environmental conditions. Different igniter types offer different advantages: notch-type igniters offer improved ignition probability, whereas film-type igniters offer improved safety. The addition of metal oxides to the heat paper could improve the burn rate, and the ignition reliability could be greatly improved by using it with a flame igniter at the same time. Using a two-step reduction process, high-purity Fe particles in coral form can be safely obtained.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.7
• /
• pp.353-362
• /
• 2019

Thermodynamic analysis of cascade refrigeration systems has attracted considerable research attention. On the other hand, a system evaluation based on thermodynamic analyses of the individual parts, including the evaporator, condenser, intercooler, expansion valve, etc., has received less attention. In this study, performance analysis was conducted on a cascade refrigeration system, which has an individual cooling and refrigeration evaporator, and equips the intercooler and air-cooled condenser in a series in a lower cycle. The thermo-fluid design was then performed on the major components of the system - upper condenser, lower condenser, cooling evaporator, refrigeration evaporator, intercooler, compressor, electronic expansion valve - of 15 kW refrigeration, and 8 kW cooling capacity using R-410A. A series of simulations were conducted on the designed system. The change in outdoor temperature from 26 C to 38 C resulted in the cooling capacity of the lower evaporator remaining approximately the same, whereas it decreased by 9% at the upper evaporator and by 63% at the intercooler. The COP decreased with increasing outdoor temperature. In addition, the COP of the cycle with the intercooler operation was higher that of the cycle without the intercooler operation. Furthermore, the increase in the upper condenser size by two fold increased the upper evaporator by 4%. On the other hand, the lower evaporator capacity remained the same. The COP of the upper cycle increased with increasing upper condenser size, whereas that of the lower cycle remained almost the same. When the size of the lower condenser was increased 2.8 fold, the intercooler capacity increased by 8%, whereas those of upper and the lower evaporator remained approximately the same. Furthermore, the COP of the lower cycle increased with an increase in the lower condenser. On the other hand, the change of the upper condenser was minimal.

• The Korean Journal of Applied Statistics
• /
• v.34 no.3
• /
• pp.309-327
• /
• 2021

Dynamic treatment regimes (DTRs) are decision-making rules designed to provide personalized treatment to individuals in multi-stage randomized trials. Unlike classical methods, in which all individuals are prescribed the same type of treatment, DTRs prescribe patient-tailored treatments which take into account individual characteristics that may change over time. The Q-learning method, one of regression-based algorithms to figure out optimal treatment rules, becomes more popular as it can be easily implemented. However, the performance of the Q-learning algorithm heavily relies on the correct specification of the Q-function for response, especially in observational studies. In this article, we examine a number of double-robust weighted least-squares estimating methods for Q-learning in high-dimensional settings, where treatment models for propensity score and penalization for sparse estimation are also investigated. We further consider flexible ensemble machine learning methods for the treatment model to achieve double-robustness, so that optimal decision rule can be correctly estimated as long as at least one of the outcome model or treatment model is correct. Extensive simulation studies show that the proposed methods work well with practical sample sizes. The practical utility of the proposed methods is proven with real data example.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.27 no.1
• /
• pp.53-59
• /
• 2021

The marine transport industry generally applies new technologies later than other transport industries, such as airways and railways. Vessels require efficient operation, and their performance and lifespan depend on the level of maintenance and management. Many studies have shown that corrective maintenance (CM) and time-based maintenance (TBM) have restrictions with respect to enabling efficient maintenance of workload and cost to improve operational efficiency. Predictive maintenance (PdM) is an advanced technology that allows monitoring the condition and performance of a target machine to predict its time of failure and helps maintain the key machinery in optimal working conditions at all times. This study presents the development of a marine predictive maintenance (MPdM; maritime predictive maintenance) method based on applying PdM to the marine environment. The MPdM scheme is designed by considering the special environment of the marine transport industry and the extreme marine conditions. Further, results of the study elaborates upon the concept of MPdM and its necessity to advancing marine transportation in the future.

• Journal of Wetlands Research
• /
• v.23 no.1
• /
• pp.74-84
• /
• 2021

Active stormwater management is essential to minimize the impact of urban development and improve the hydrological cycle system. In recent years, the Low Impact Development (LID) technique for urban stormwater management is attracting attention as a reasonable alternative. The Storm Water Management Model (SWMM) is actively used in urban hydrological cycle improvement projects as it provides simulation functions for various GI (Green Infra) facilities through its LID module. However, in order to simulate GI facilities using SWMM, there are many difficulties in setting up complex watersheds and deploying GI facilities. In this study, a model that can evaluate the performance of GI facilities is proposed while implementing the core hydrological process of GI facilities. Since the proposed model operates based on hydrological routing, it can not only reflect the infiltration, storage, and evapotranspiration of GI facilities, but also quantitatively evaluate the effect of improving urban hydrological cycle by GI facilities. The applicability of the proposed model was verified by comparing the results of the proposed model with the results of SWMM. In addition, a discussion of errors occurring in the SWMM's permeable pavement system simulation is included.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.34 no.6
• /
• pp.409-415
• /
• 2021

Medical waste management is becoming increasingly important, specifically in light of the current COVID-19 pandemic, as hospitals, clinics, quarantine centers, and medical research institutes are generating tons of medical waste every day. Previously, a traditional incineration process was utilized for managing medical waste, but the lack of landfill sites, and accompanying environmental concerns endanger public health. Consequently, an innovative sterilization shredding system was developed to resolve this problem. In this research, we focused on the design and numerical analysis of a shredding system for hazardous and infectious medical waste, to establish its operational performance. The shredding machine's components were modeled in a CAD application, and finite element analysis (FEA) was conducted using ABAQUS software. Static, fatigue, and dynamic loading conditions were used to analyze the structural stability of the cutting blade. The blade geometry proved to be effective based on the cutting force applied to shred medical waste. The dynamic stability of the structure was verified using modal analysis. Furthermore, an S-N curve was generated using a high cycle fatigue study, to predict the expected life of the cutting blade. Resultantly, an appropriate shredder system was devised to link with a sterilization unit, which could be beneficial in reducing the volume of medical waste and disposal time, thereof, thus eliminating environmental issues, and potential health hazards.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.1
• /
• pp.184-192
• /
• 2022

Valve internal leakage is caused by damage to the internal parts of the valve, resulting in accidents and shutdowns of the piping system. This study investigated the possibility of a real-time leak detection method using the acoustic emission (AE) signal generated from the piping system during the internal leakage of a butterfly valve. Datasets of raw time-domain AE signals were collected and postprocessed for each operation mode of the valve in a systematic manner to develop a data-driven model for the detection and classification of internal leakage, by applying machine learning algorithms. The aim of this study was to determine whether it is possible to treat leak detection as a classification problem by applying two classification algorithms: support vector machine (SVM) and convolutional neural network (CNN). The results showed different performances for the algorithms and datasets used. The SVM-based binary classification models, based on feature extraction of data, achieved an overall accuracy of 83% to 90%, while in the case of a multiple classification model, the accuracy was reduced to 66%. By contrast, the CNN-based classification model achieved an accuracy of 99.85%, which is superior to those of any other models based on the SVM algorithm. The results revealed that the SVM classification model requires effective feature extraction of the AE signals to improve the accuracy of multi-class classification. Moreover, the CNN-based classification can be a promising approach to detect both leakage and valve opening as long as the performance of the processor does not degrade.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_4
• /
• pp.1889-1900
• /
• 2022

Roads are an essential factor in the physical functioning of modern society. The spatial information of the road has much longer update cycle than the traffic situation information, and it is necessary to generate the information faster and more accurately than now. In this study, as a way to achieve that goal, the Pan-sharpening technique was applied to satellite images of Kompsat 3 and 3A to improve spatial resolution. Then, the data were used for road extraction using the semantic segmentation technique, which has been actively researched recently. The acquired Kompsat 3/3A pan-sharpened images were trained by putting it into a U-Net based segmentation model along with Massachusetts road data, and the applicability of the images were evaluated. As a result of training and verification, it was found that the model prediction performance was maintained as long as certain conditions were maintained for the input image. Therefore, it is expected that the possibility of utilizing satellite images such as Kompsat satellite will be even higher if rich training data are constructed by applying a method that minimizes the impact of surrounding environmental conditions affecting models such as shadows and surface conditions.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.35 no.1
• /
• pp.1-12
• /
• 2023

Along with the growing interest in renewable energy development, Korea's west coast is one of the favorable regions for tidal power. Tidal power using tidal barrages that work like hydroelectric dams is a representative method of tidal power through long-term operation, but the promotion of tidal power projects is being delayed or stopped due to impacts on ecological changes, reproduction, water column processes and hydrology. In order to reduce the high construction cost and environmental cost problems caused by tidal power using tidal barrages, in this study, field experiments were conducted to develop and verify the performance of tidal power generation devices applicable to sea areas where dykes are already installed. As a result of conducting five cases of experiments using two water tanks, pipe lines, open channels, and water turbine and generator, the possibility of developing a power generation system capable of generating more than 10 kW output and more than 60% efficiency were confirmed. The results of this study can be used for small-scale tidal power by utilizing the existing dykes of the west coast.

• Journal of agriculture & life science
• /
• v.50 no.3
• /
• pp.31-41
• /
• 2016

This study was conducted to develop a handy earth auger for use in sloppy and rugged forest terrains in order to reduce labor cost which comprises a major part of the production costs in forest afforestation projects. The first prototype is developed consist of two parts, the soil-digging screw and the battery power source. The specifications of the first prototype screw are: length of 170mm, a top diameter of 60mm, bottom diameter of 47mm, 23° angle for each helix, and a 50mm awl-head tip. The use of a single line of screw was selected for reduced weight. In addition, a power source of rotary DC Motor(WD-6G2425, WONILL, Korea) with a maximum torque of 30kgf-cm, rotation of 20-30rpm, K6G30C decelerator with a reduction ratio of 30:1 which could be used with no load for 48 was operated. In consideration of its weight, a lithium battery was utilized in line with the goal of developing a lightweight auger. In order to evaluate the performance of the first prototype, test sites were selected as 6 areas. The rotational force was found to be highest in area A(Solid area), followed by areas F(Mounted slope 40° area) and E(Mounted slope 30° area). It was also observed that in general, the rotational force increased along with the increase in soil depth with the maximum rotational force recorded at 10cm.