• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.193-195
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• 2006

In order to minimize the influence of aircraft noise in the vicinity of domestic airports, the establishment of proper land-use plan according to the influence scope of aircraft noise in the opening part of preparing a housing site around domestic airports is needed. For the purpose of doing it, the environmental impact assessment accompanied by the accurate prediction of aircraft noise distribution is preceded, and this paper describes the improvement plan for performing the trustworthy environmental impact assessment of aircraft noise in the neighborhood of domestic airports.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.289-291
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• 2006

The noise pollution caused by air traffic is one of the popular environmental issues, which consistently gives rise to public discussion today especially among local residents near airports. But, because there is a growing tendency that large residential areas are constructed in flatlands around airports due to a high population density, the importance of the EIA in connection with aircraft noise is increased to take precautionary measures. Therefore, this paper examines the present status and the improvement plan of aircraft noise impact assessment.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.626-629
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• 2008

The present study investigates the effect of the flight patterns of military aircraft for takeoff and approach on noise map. Aircraft noise modeling and simulation have been made on a Korean military airport by INM(Integrated Noise Model). The flight path of a military aircraft is modeled with takeoff, overfly, approach and touch-and-go modes. The present INM simulations are conducted for various cases with change of different takeoff and approach modes. It show that the change of takeoff and approach modes can cause considerable noise effects on the noise influence region around the airport.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.1C
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• pp.81-89
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• 2003

For improving the performance of noise suppression in tactical communication environments, an enhanced MELP vocoder is suggested, in which an acoustic noise suppressor is integrated into the front end of the MELP algorithm, and an FEC code into the channel side of the MELP algorithm. The acoustic noise suppressor is the modified IS-127 EVRC noise suppressor which is adapted for the MELP vocoder. As for FEC, the turbo code, which consists of rate-113 encoding and BCJR-MAP decoding algorithm, is utilized. In acoustic noise environments, the lower the SNR becomes, the more the effects of noise suppression is increased. Moreover, The suggested system has greater noise suppression effects in stationary noise than in non-stationary noise, and shows its superiority by 0.24 in MOS test to the original MELP vocoder. When the interleave size is one MELP frame, BER 10-6 is accomplished at channel bit SNR 4.2 ㏈. The iteration of decoding at 3 times is suboptimal in its complexity vs. performance. Synthetic quality is realized as more than MOS 2.5 at channel bit SNR 2 ㏈ in subjective voice quality test, when the interleave size is one MELP frame and the iteration of decoding is more than 3 times.

• Korean Journal of Optics and Photonics
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• v.25 no.5
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• pp.273-278
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• 2014

We present an algorithm for surface reconstruction from the second-derivative data for free-form aspherics, which uses a subaperture scanning system that measures the local surface profile and determines the three second-derivative values at those local sampling points across the free-form surface. The three second-derivative data were integrated to get a map of x- and y-slopes, which went through a second Southwell integration step to reconstruct the surface profile. A synthetic free-form surface 200 mm in diameter was simulated. The simulation results show that the reconstruction error is 19 nm RMS residual difference. Finally, the sensitivity to noise is diagnosed for second-derivative Gaussian random noise with a signal to noise ratio (SNR) of 16, the simulation results proving that the suggested method is robust to noise.

• Molecules and Cells
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• v.44 no.12
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• pp.883-892
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• 2021

Genome-wide chromosome conformation capture (3C)-based high-throughput sequencing (Hi-C) has enabled identification of genome-wide chromatin loops. Because the Hi-C map with restriction fragment resolution is intrinsically associated with sparsity and stochastic noise, Hi-C data are usually binned at particular intervals; however, the binning method has limited reliability, especially at high resolution. Here, we describe a new method called HiCORE, which provides simple pipelines and algorithms to overcome the limitations of single-layered binning and predict core chromatin regions with three-dimensional physical interactions. In this approach, multiple layers of binning with slightly shifted genome coverage are generated, and interacting bins at each layer are integrated to infer narrower regions of chromatin interactions. HiCORE predicts chromatin looping regions with higher resolution, both in human and Arabidopsis genomes, and contributes to the identification of the precise positions of potential genomic elements in an unbiased manner.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.277-282
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• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.3
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• pp.143-151
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• 2024

In this study, we present a mapping framework for 3D spatial reconstruction of digital twin model using navigation and perception sensors mounted on an Autonomous Surface Vehicle (ASV). For improving the level of realism of digital twin models, 3D spatial information should be reconstructed as a digitalized spatial model and integrated with the components and system models of the ASV. In particular, for the 3D spatial reconstruction, color and 3D point cloud data which acquired from a camera and a LiDAR sensors corresponding to the navigation information at the specific time are required to map without minimizing the noise. To ensure clear and accurate reconstruction of the acquired data in the proposed mapping framework, a image preprocessing was designed to enhance the brightness of low-light images, and a preprocessing for 3D point cloud data was included to filter out unnecessary data. Subsequently, a point matching process between consecutive 3D point cloud data was conducted using the Generalized Iterative Closest Point (G-ICP) approach, and the color information was mapped with the matched 3D point cloud data. The feasibility of the proposed mapping framework was validated through a field data set acquired from field experiments in a inland water environment, and its results were described.