• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.763-768
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• 2012

Active window system which can reduce the environmental noises, such as traffic noise and construction noise, from an open window into a room was proposed in the previous works. The key idea of the proposed active window system was that the control sources are approximately collocated with the primary noise source in terms of the acoustic power for global noise reduction throughout the interior room. Moreover, because it is important not to intrude into the living space in the building environment, no error sensors were used and an open-loop control method using control sources at the window frame and the reference sensors outside the room was used for the proposed system. The open-loop control gain was calculated by the interior room model assumed as the semi-infinite space, and the interior sound field was estimated by Rayleigh integral equation under the baffled window model assumption. However, windows with a finite thickness should were considered for the calculation of the open-loop control gain of the active window system since these are representative of most window cases. Therefore, the finite thickness window model based on the Sgard's model was derived and the open-loop control gain using the interior sound field estimated by that model was calculated for active window system. To compare the performance of these two models, a scale-model experiment was performed in an anechoic chamber according to noise source directions. Experimental results showed that the performance for the thickness window model is better than the baffled window model as the angle with respect to the perpendicular direction is larger.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.909-914
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• 2007

In this paper, an overload detecting algorithm for an excavator is presented. The proposed overload detecting algorithm is based on the time series analysis especially moving window. The main purpose of this paper is to prevent a damage or crack from the fatigue in advance. 16 channel sensors data are considered and maximum stress is computed by a sensor fusion method every moving window. After the maximum stress every window is compared with a given threshold, this overload detecting algorithm decides overload or not.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.146-151
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• 2019

This paper reports a sliding window filtering approach for ground moving targets with cross-correlated sensor noise and uncertainty. In addition, the effect of uncertain parameters during a tracking error on the model performance is considered. A distributed fusion sliding window filter is also proposed. The distributed fusion filtering algorithm represents the optimal linear combination of local filters under the minimum mean-square error criterion. The derivation of the error cross-covariances between the local sliding window filters is the key to the proposed method. Simulation results of the motion of the ground moving target a demonstrate high accuracy and computational efficiency of the distributed fusion sliding window filter.

• Current Optics and Photonics
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• v.7 no.1
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• pp.73-82
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• 2023

In this study, we demonstrate ultrahigh-resolution spectral-domain optical coherence tomography with a 200-kHz line rate using a superluminescent diode with a -3-dB bandwidth of 100 nm at 849 nm. To increase the line rate, a subset of the total number of camera pixels is used. In addition, a partial-spectrum detection method is used to obtain OCT images within an imaging depth of 2.1 mm while maintaining ultrahigh axial resolution. The partially detected spectrum has a flat-topped intensity profile, and side lobes occur after fast Fourier transformation. Consequently, we propose and apply the super-Gaussian window function as a new window function, to reduce the side lobes and obtain a result that is close to that of the axial-resolution condition with no window function applied. Upon application of the super-Gaussian window function, the result is close to the ultrahigh axial resolution of 4.2 ㎛ in air, corresponding to 3.1 ㎛ in tissue (n = 1.35).

• 한국태양에너지학회:학술대회논문집
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• 2009.11a
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• pp.32-37
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• 2009

An energy loss through the window system occupies about 10 to 30 percent on energy consumption of the whole building. That is the reason, several elements for a building composition of window system are the weakest from the heat. Insulation performance increases for the reducing heat loss. Heat transfer through the window system that is reducing heat transfer through conduction, convection and radiation. Insulation performance reinforcement methods classify improving heat specific quality of window system and improving efficiency of whole window system. The most application method among each methods is reducing emission ratio of the window system(Low-E glass), increasing a number of glazing(multiple window) and a method of vacuuming between glazing and glazing. Therefore this study is investigated a sort of glazing and specific character, U-value calculation with changing glazing thickness and calculation of temperature distribution and U-value with a glazing charging gas kind from double glazing. For a conclusion, an aspect of U-value figure at the smallest value case of vacuum glazing with Low-E coating. That means insulation efficiency is the best advantage during a building plan selecting vacuum glazing with Low-E coating for a energy saving aspect. In this way, U-value become different the number of glazing, coating whether or not and selecting injection gas. Therefore selecting of glazing is very important after due consideration by a characteristic and use of building and consideration of strong point and weak point.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.538-547
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• 2022

Recently introduced image classification methods using Transformers show remarkable performance improvements over conventional neural network-based methods. In order to effectively consider regional features, research has been actively conducted on how to apply transformers by dividing image areas into multiple window areas, but learning of inter-window relationships is still insufficient. In this paper, to overcome this problem, we propose a transformer structure that can reflect the relationship between windows in learning. The proposed method computes the importance of each window region through compression and a fully connected layer based on self-attention operations for each window region. The calculated importance is scaled to each window area as a learned weight of the relationship between the window areas to re-calibrate the feature value. Experimental results show that the proposed method can effectively improve the performance of existing transformer-based methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.7
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• pp.1667-1679
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• 2010

This thesis proposes an adaptive variable-sized matching window method using the characteristic points of the image and a method to increase the reliability of the cross-consistency check to raise the correctness of the final disparity image. The proposed adaptive variable-sized window method segments the image with the color information, finds the characteristic points in each segmented image, and varies the size of the matching window according to the existence of the characteristic points inside the window. Also the proposed cross-consistency check method processes the two cases with the cost values corresponding to the best disparity and the second-best disparity: when the cost values themselves are too large and when the difference between the two cost values are too small. The two proposed methods were experimented with the four test images provided by the Middleburry site. As the results from the experiments, the proposed adaptive variable-sized matching window method decreased up to 18.2% of error ratio and the proposed cross-consistency check method increased up to 7.4% of reliability.

• KIEAE Journal
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• v.16 no.2
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• pp.79-85
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• 2016

Purpose: It is difficult to calculate frame U-value because of the two reason. First is selection of air properties in cavity. Second is calculation method in window frame. For this reason, it is important to decide cavity properties in window frame. However, international standards offered different method(ISO 15099, ISO 10077) and air properties was changed according to the two methods. The aim of this study was to suggest method for deriving accurate frame U-value using international standard methods and CFD simulation. Method: First, this study conducted analysis calculation method of ISO 15099 and ISO 10077. And, CFD simulation conducted based on same condition. Finally, ISO calculation and CFD simulation results were verified through comparison with real experiment results. Result: The results show that effective conductivity of ISO 15099 was the highest value. ISO 10077 and CFD result followed. The convergent values of ISO 10077 was the highest. ISO 15099 and CFD followed. ISO calculation reflecting CFD simulation results will reduce error with experimental results.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.4
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• pp.52-59
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• 2010

MAC frame aggregation is a method that combines multiple MPDUs (MAC protocol data units) into one PPDU (PHY protocol data units) to enhance network performance at the MAC layer. In ad hoc networks, TCP underperforms due to the congestion window overshooting problem and thus by setting CWL (congestion window limit) TCP performance can be improved. In this paper, we investigate the problem of setting CWL for TCP performance optimization in ad hoc networks with MAC frame aggregation.

• Journal of the Korean Solar Energy Society
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• v.33 no.6
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• pp.54-61
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• 2013

This is study on the glazing performance of the apartment house to predict energy saving rate when the early design stage by calculating heating load. there are various factors of the window type in apartment building to save energy such as window's U-value, SC or SHGC, window wall ration, frame factor, sunshade coefficient and so on. In this study, we analyzed the heating load focused on the U-value, SC and window wall ration using variable heating degree days method for a small and middle size units $59m^2$, $84m^2$, respectively. Each cases were calculated heating load of the real models compared to standard model to predict energy saving rate. From those cases it was drew the conclusion that were window's U-value, SC and window wall ration for the small and middle size units to expect 10% energy saving rate at least.