• Journal of Korean Society of Forest Science
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• v.87 no.2
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• pp.300-307
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• 1998

It is not an easy task for forest managers to make sound decisions on forest management and operations planning because of huge sets of spatial and temporal data and complex decision-making processes involved. However, as an efficient tool, GIS techniques enable them to enhance broad understandings on forest inventory and management conditions. In this study, we developed a GIS model for field use in forest management. In building the model, we have chosen MapInfo version 4.0 as the basic engine of the model. The model also includes an interface module to help forest managers use MapInfo functions easily. It handles MapInfo functions required to manage inventory data and analyze spatial distributions of forest stands. For testing field applicability of the model, we have build field data sets for a district of Chunchun National Forest. Then, we tested functions through quarrying stand attributes and constructing thematic maps. In this paper, the structures and functions of the model as well as the results of field applications are discussed.

• Bulletin of the Korean Space Science Society
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• 2007.10a
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• pp.139-143
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• 2007

Electric propulsion has become a cost effective and sound engineering solution for many space applications. The success of SMART-1 and MUSES-C developed by European Space Agency (ESA) and Japan Aerospace Exploration Agency (JAXA) each proved that even small spacecraft could accomplish planetary mission with electric propulsion systems. A small electric propulsion system which is Hall effect thruster like SMART-1 is under development by SaTReC and GDPL (Glow Discharge Plasma Lab.) in KAIST for the next microsatellite, STSAT-3. To achieve optimized propulsion system, it is very necessary to understand plasma motions of Hall effect thruster. In this paper, we try to approach comprehensive plasma model with the particle simulation complementary to Particle In Cell (PIC) simulation. We think these two different approaches will help experimenters to optimize Hall effect thruster performances.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.469-474
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• 2002

In recent years, modularization of engine parts has increased the application of plastic products in air intake systems. Plastic intake manifolds provide many advantages including reduced weight, contracted cost, and lower intake air temperatures. These manifolds, however, have some weakness when compared with customary aluminium intake manifolds, in that they have low sound transmission loss because of their lower material density. This low transmission loss of plastic intake manifolds causes several problems related to flow noise, especially when the throttle is opened quickly. The physical processes, responsible for this flow noise, include turbulent fluid motion and relative motion of the throttle to the airflow. The former is generated by high-speed airflow in the splits between the throttle valve and the inner-surface of the throttle body and surge-tank, which can be categorized into the quadrupole source. The latter induces the unsteady force on the flow, which can be classified into the dipole source. In this paper, the mechanism of noise generation from the turbulence is only investigated as a preliminary study. Stochastic noise source synthesis method is adopted for the analysis of turbulence-induced, i.e. quadrupole noise by throttle at quick opening state. The method consists of three procedures. The first step corresponds to the preliminary time-averaged Navier-Stokes computation with a $k-\varepsilon$ turbulence model providing mean flow field characteristics. The second step is the synthesis of time-dependent turbulent velocity field associated with quadrupole noise sources. The final step is devoted to the determination of acoustic source terms associated with turbulent velocity. For the first step, we used market available analysis tools such as STAR-CD, the trade names of fluid analysis tools available on the market. The steady state flows at three open angle of throttle valve, i.e. 20, 35 and 60 degree, are numerically analyzed. Then, time-dependent turbulent velocity fields are produced by using the stochastic model and the flow analysis results. Using this turbulent velocity field, the turbulence-originated noise sources, i.e. the self-noise and shear-noise sources are synthesized. Based on these numerical results, it is found that the origin of the turbulent flow and noise might be attributed to the process of formulation and the interaction of two vortex lines formed in the downstream of the throttle valve. These vortex lines are produced by the non-uniform splits between the throttle valve and inner cylinder surface. Based on the analysis, we present the low-noise design of the inner geometry of throttle body.

• Journal of Navigation and Port Research
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• v.37 no.4
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• pp.401-407
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• 2013

In parallel with the development of ship equipment, bridge systems have been improved, but marine accidents due to human error have not been reduced. Recently, research in nautical bridge equipment has focused on suitable ergonomic designs in order to reduce these errors due to human factors. In a bridge of a ship, there are numerous auditory signals that deliver important information clearly to the sailors. However, only a few studies have been conducted related to the human recognition of these auditory signals. There are three types of auditory signals: voice alarms, abstract sounds, and auditory icons. This study was conducted in order to design more appropriate auditory icons using a sensibility evaluation method. The auditory icons were rated to have five warning situations (engine failure, fire, steering failure, low power, and collision) using the Semantic Differential Method. It is expected that the results of this study will be used as basic data for auditory displays inside bridges and for integrated bridge alarm systems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.570-575
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• 2019

Active muffler is implemented by applying active noise control technique to reduce exhaust noise of automobile muffler. Conventional Filtered_x LMS algorithm has a problem that the degree of control filter becomes very large and convergence deteriorates when acoustic feedback is present. The recursive LMS algorithm can compensate for this problem because it can be easily diverted in the adaptive filter adaptation process. In this paper, the structure of the primary path and the secondary path transfer function is designed as the IIR filter to improve the convergence performance and the computational burden, and the stabilization filter algorithm is applied to secure stability which is a disadvantage of the IIR filter structure. The stabilization filter algorithm plays a role of pulling the pole into the unit circle to prevent the pole of the transfer function corresponding to the acoustic feedback from diverging during the adaptation process. In this way, the computational burden of the active muffler system and the convergence performance can be improved. In order to show the usefulness of the proposed system, we compared the performance of the proposed Filtered_x LMS algorithm with the performance of the proposed system for the exhaust sound of a diesel engine, which is a variable environment. Compared to conventional algorithm, proposed algorithm's computational burden is less than half, and convergence performances are more than 4 times.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.2
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• pp.152-163
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• 2022

In this paper, we present a method to enable an unmanned aerial vehicle to drop the sonobuoy, an essential element of anti-submarine warfare, in an optimal deployment. To this end, an environment simulating the distribution of sound detection performance was configured through the Unity game engine, and the environment directly configured using Unity ML-Agents and the reinforcement learning algorithm written in Python from the outside communicated with each other and learned. In particular, reinforcement learning is introduced to prevent the accumulation of wrong actions and affect learning, and to secure the maximum detection area for the sonobuoy while the vehicle flies to the target point in the shortest time. The optimal placement of the sonobuoy was achieved by applying the Deep Deterministic Policy Gradient (DDPG) algorithm. As a result of the learning, the agent flew through the sea area and passed only the points to achieve the optimal placement among the 70 target candidates. This means that an autonomous aerial vehicle that deploys a sonobuoy in the shortest time and maximum detection area, which is the requirement for optimal placement, has been implemented.