• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.653-658
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• 2023

In systems like the combat management system of a naval ship or smart city of civilians, where many sensors and actuators are connected, the middle-ware DDS (Data Distribution Service) is mainly used to transmit large amounts of data. It is scalable and can effectively respond to the increase in sensors or equipment connected to the system in the future. The engineering control system (ECS), which plays an important role similar to the combat management system of a naval ship, still uses Server-Client model with industrial protocols such as Modbus and CAN (Controller Area Network) bus, to transmit data, which is unfavorable in terms of scalability. However, as automation and unmanned systems advance, more sensors and actuators are expected to be added, necessitating substantial program modification. DDS can effectively address such situations. The purpose of this study is to confirm the development possibility of an integrated monitoring and control system of a ship by using OpenDDS, which follows the OMG (Object Management Group) standard among the middle-ware DDS used in the combat management system. To achieve this goal, field equipment simulators and an ECS server were configured to perform field equipment data input/output and simulation using DDS was performed. The ECS prototype successfully handled data transmission, confirming that DDS is capable of serving as the middle-ware for the ECS of a ship.

• Tunnel and Underground Space
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• v.33 no.5
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• pp.399-413
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• 2023

The present study conducted a numerical modeling of CO₂ injection at the Janggi Basin using the TOUGH-FLAC simulator, and examined the hydro-mechanical stability of the aquifer and the fault. Based on the site investigations and a 3D geological model of the target area, we simulated the injection of 32,850 tons of CO₂ over a 3-year period. The analysis of CO₂ plume with different values of the aquifer permeability revealed that assuming a permeability of 10^-14 m² the CO₂ plume exhibited a radial flow and reached the fault after 2 years and 9 months. Conversely, a higher permeability of 10^-13 m² resulted in predominant westward flow along the reservoir, with negligible impact on the fault. The pressure changes around the injection well remained below 0.6 MPa over the period, and the influence on the hydro-mechanical stability of the reservoir and fault was found to be insignificant.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.305-314
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• 2023

In this study, a mathematical model of a 9.77 G/T small fishing vessel was established based on captive model tests. The powering and manoeuvring performances of the vessel in the harbor and coastal sea were focused on, so captive model tests were conducted up to the full-scale speed of 8 knots. Propeller open water, resistance, and self-propulsion tests of a 1/3.5-scaled model ship were performed in a towing tank, and the full-scale powering performance was predicted. Hydrodynamic coefficients in the mathematical model were obtained by rudder open water, horizontal planar motion mechanism tests of the same model ship. In particular, in static drift and pure yaw tests which were conducted at a speed of 2 to 8 knots, the linear hydrodynamic coefficients varied with the ship speed. The effect of the ship speed on the linear coefficients was considered in the mathematical model, and manoeuvring motions, such as turning circles and zig-zags, were simulated with various approach speeds and analyzed.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.31-38
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• 2024

The digital phase-locked loop(DPLL) is one of the circuits composed of a digital detector, digital loop filter, voltage-controlled oscillator, and divider as a fundamental circuit, widely used in many fields such as electrical and circuit fields. A state estimator using various mathematical algorithms is used to improve the performance of a digital phase-locked loop. Traditional state estimators have utilized Kalman filters of infinite impulse response state estimators, and digital phase-locked loops based on infinite impulse response state estimators can cause rapid performance degradation in unexpected situations such as inaccuracies in initial values, model errors, and various disturbances. In this paper, we propose a two-layer Frobenius norm-based finite impulse state estimator to design a new digital phase-locked loop. The proposed state estimator uses the estimated state of the first layer to estimate the state of the first layer with the accumulated measurement value. To verify the robust performance of the new finite impulse response state estimator-based digital phase locked-loop, simulations were performed by comparing it with the infinite impulse response state estimator in situations where noise covariance information was inaccurate.

• Resources Recycling
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• v.33 no.3
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• pp.38-47
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• 2024

Molybdenite is the primary molybdenum resource and is extracted via flotation due to its unique hydrophobic surface. Meanwhile, the grade and crystal size of mined molybdenite are decreasing. As a result, the size of the molybdenum ore required for liberation is decreasing, and the flotation process's feed size input is also decreasing. Therefore, in order to secure molybdenum, it is necessary to perform research on the flotation for the fine molybdenite. In this study, we developed a method to enhance the flotation efficiency of fine molybdenite particles in the range of 5-30 ㎛. The methodology involved implementing bubble size reduction and particle aggregation. Through simulations of bubble-particle collision probability and flotation experiments, we were able to find the ranges of bubble size and particle aggregate size that make fine particles float more effectively. This range provided the conditions for effective flotation of fine molybdenite particles. Therefore, we will implement the flotation conditions established in this study for fine molybdenum ore to improve the flotation process in molybdenum mineral processing plants in the future.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.2
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• pp.200-208
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• 2024

The occurrence a fire on a training ship with a large number of people on board can lead to severe casualties. Hence the Seafarers' Act and Safety Life At Sea(SOLAS) emphasizes the importance of the abandon ship drill. Therefore, in this study, the training ship of Mokpo National Maritime University, Segero, which has a large number of people on board, was selected as the target ship and the likelihood and severity of fire accidents on each deck were predicted through the preliminary hazard analysis(PHA) qualitative risk assessment. Additionally, assuming a fire in a high-risk area, a simulation of evacuation time and population density was performed to quantitatively predict the risk. The the total evacuation time was predicted to be the longest at 501s in the meal time scenario, in which the population distribution was concentrated in one area. Depending on the scenario, some decks had relatively high population densities of over 1.4pers/m², preventing stagnation in the number of evacuees. The results of this study are expected to be used as basic data to develop training scenarios for training ships by quantifying evacuation time and population density according to various evacuation scenarios, and the research can be expanded in the future through comparison of mathematical models and experimental values.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.4
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• pp.267-277
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• 2024

The Sortie Generation Rate (SGR) is a critical performance indicator for carrier-based aircraft and is a key factor for the carrier design process. This study aims to analyze the factors that affect SGR and establish a representative Sortie Generation Process (SGP) along with simulation results to calculate SGR for a naval ship equipped to carry aircraft. Detailed SGR factors are identified from the perspectives of the aircraft, aviation personnel, and aircraft carrier during the flight preparation stage, and the SGP is established accordingly. As a representative, Korean Navy's CVX basic design is chosen for detailed analysis. The physical dimension and spots for the deck design with time and probabilistic data of SGP are considered to develop a queueing network model for SGR calculation. To consider the specific probabilistic features, the model was solved with discrete event simulation tools(SimPy and AnyLogic) where the results show great agreement. Such findings on SGR factors and calculation are expected to be incorporated in the future development of SGR calculation algorithms and also present guidelines for proper design of aircraft carrier based on concrete operation concept.

• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.80-87
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• 2010

This study was conducted to develop a reservoir modelling workflow to reproduce the heterogeneous distribution of effective permeability that impacts on the performance of SAGD (Steam Assisted Gravity Drainage), the in-situ bitumen recovery technique in the Athabasca Oil Sands. Lithologic facies distribution is the main cause of the heterogeneity in bitumen reservoirs in the study area. The target formation consists of sand with mudstone facies in a fluvial-to-estuary channel system, where the mudstone interrupts fluid flow and reduces effective permeability. In this study, the lithologic facies is classified into three classes having different characteristics of effective permeability, depending on the shapes of mudstones. The reservoir modelling workflow of this study consists of two main modules; facies modelling and permeability modelling. The facies modelling provides an identification of the three lithologic facies, using a stochastic approach, which mainly control the effective permeability. The permeability modelling populates mudstone volume fraction first, then transforms it into effective permeability. A series of flow simulations applied to mini-models of the lithologic facies obtains the transformation functions of the mudstone volume fraction into the effective permeability. Seismic data contribute to the facies modelling via providing prior probability of facies, which is incorporated in the facies models by geostatistical techniques. In particular, this study employs a probabilistic neural network utilising multiple seismic attributes in facies prediction that improves the prior probability of facies. The result of using the improved prior probability in facies modelling is compared to the conventional method using a single seismic attribute to demonstrate the improvement in the facies discrimination. Using P-wave velocity in combination with density in the multiple seismic attributes is the essence of the improved facies discrimination. This paper also discusses sand matrix porosity that makes P-wave velocity differ between the different facies in the study area, where the sand matrix porosity is uniquely evaluated using log-derived porosity, P-wave velocity and photographically-predicted mudstone volume.

• Journal of the Korean Society for Marine Environment & Energy
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• v.10 no.2
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• pp.67-85
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• 2007

A three-dimensional hydrodynamic model with the fine grid is applied to simulate the barotropic tides, tidal currents, residual currents and salinity dispersions in the Yellow Sea and the East China Sea. Data inputs include seasonal hydrography, mean wind and river input, and oceanic tides. Computed tidal distributions of four major tides($M_2,\;S_2,\;K_1$ and $O_1$) are presented and results are in good agreement with the observations in the domain. The model reproduces well the tidal charts. The tidal residual current is relatively strong around west coast of Korea including the Cheju Island and southern coast of China. The current by $M_2$ has a maximum speed of 10 cm/s in the vicinity of Cheju Island with a anti-clockwise circulation in the Yellow Sea. General tendency of the current, however, is to flow eastward in the South Sea. Surface residual current simulated with $M_2$ and with $M_2+S_2+K_1+O_1$ tidal forcing shows slightly different patterns in the East China Sea. The model shows that the southerly wind reduces the southward current created by freshwater discharge. In summer during high runoff(mean discharge about $50,000\;m^3/s$ of Yangtze), low salinity plume-like structure(with S < 30.0 psu) extending some 160 km toward the northeast and Changjiang Diluted Water(CDW), below salinity 26 psu, was found within about 95 km. The offshore dispersion of the Changjiang outflow water is enhanced by the prevailing southerly wind. It is estimated that the inertia of the river discharge cannot exclusively reach the around sea of Cheju Island. It is noted that spatial and temporal distribution of salinity and the other materials are controlled by mixture of Changjiang discharge, prevailing wind, advection by flowing warm current and tidal current.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.