• Journal of Wetlands Research
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• v.16 no.1
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• pp.103-112
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• 2014

Area average rainfall estimation is important to determine the exact amount of the available water resources and the essential input data for rainfall-runoff analysis. Like that, the necessary criterion for accurate area average rainfall estimate is the uniform spatial distribution of raingauge network. In this study, we suggest the spatial distribution evaluation methodology of raingauge network to estimate better area average rainfall and after the suggested method is applied to Han River and Geum River basin. The spatial distribution of rainfall network can be quantified by the nearest neighbor index. In order to evaluate the effects of the spatial distribution of rainfall network by each basin, area average rainfall was estimated by arithmetic mean method, the Thiessen's weighting method and estimation theory for 2013's rainfall event, and evaluated the involved errors by each cases. As a result, it can be found that the estimation error at the best basin of spatial distribution was lower than the worst basin of spatial distribution.

• The Journal of Information Systems
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• v.25 no.2
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• pp.153-171
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• 2016

Purpose This study suggests the development of forecasting model for local cable TV advertisement. In order to verify the expected effect of the suggestion, using the causal loop map of System Dynamics, the factors affecting the prospects of cable TV commercial market were divided into 5 groups. Then targeting 97 people involved in the cable TV commercial market in Busan, Ulsan, and Gyeongnam, a survey was conducted on their perception of the current status of local advertisement market and future prospect. Design/methodology/approach The analysis of the collected data shows that workers in advertising and advertisers perceive the influence of cable TV as an advertising media to be high, while clearly understanding the problems of cable TV commercial market. Based on this the effects on the prospects of cable TV commercial market were analyzed and a forecasting method called Weighted Moving Average was applied. In order to improve accuracy of the added value of Weighted Moving Average, the 5 factors were divided into qualitative factors and quantitative factors, and using Multi-attribute Decision Making method, all the factors were normalized and weighting factors were deduced. The result of simulating the prospects of cable TV commercial market using Weighted Moving Average, both qualitative and quantitative factors showed downward turn in the market prospect for the following 10 years. Findings The result reflects generally negative perception of advertisement viewers about the prospects of cable TV commercial market. Compared to the previous studies on domestic cable TV commercials that focused on policy suggestions and surveys on perception of current status, this study has its significance in that it used scientific method and simulation for verification.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.284-286
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• 2009

In this paper, we consider a cognitive radio system operating as secondary user. It uses an empty channel that is not currently used by primary users having the license to the channel. In the previous works, secondary user looks for an empty channel by choosing any channel in order or randomly and by sensing the channel to distinguish whether primary users are using. But if primary user is fixed type, we will find an empty channel faster than the mentioned channel selecting methods by using a method considering prior information about cases that primary user used the channel, since it is possible to analogize the channel access possibility of primary user according to regular time and position. Therefore, we propose a channel searching method based on the channel list for the purpose of reducing the channel searching time and improving throughput of secondary users. Firstly, we determine a weighting value of each channel based on the history of channel activities of primary users. This value is added to current channel state buffer and we search an empty channel from channel with smallest value to one with the biggest value. Finally, we compare the performances of the proposed method with those of the sequential channel searching and the random channel searching methods in terms of the average channel searching time and the average number of transmissions of secondary user.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.323-335
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• 2023

In causal analysis of high dimensional data, it is important to reduce the dimension of covariates and transform them appropriately to control confounders that affect treatment and potential outcomes. The augmented inverse probability weighting (AIPW) method is mainly used for estimation of average treatment effect (ATE). AIPW estimator can be obtained by using estimated propensity score and outcome model. ATE estimator can be inconsistent or have large asymptotic variance when using estimated propensity score and outcome model obtained by parametric methods that includes all covariates, especially for high dimensional data. For this reason, an ATE estimation using an appropriate dimension reduction method and semiparametric model for high dimensional data is attracting attention. Semiparametric method or sparse sufficient dimensionality reduction method can be uesd for dimension reduction for the estimation of propensity score and outcome model. Recently, another method has been proposed that does not use propensity score and outcome regression. After reducing dimension of covariates, ATE estimation can be performed using matching. Among the studies on ATE estimation methods for high dimensional data, four recently proposed studies will be introduced, and how to interpret the estimated ATE will be discussed.

• Industrial Engineering and Management Systems
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• v.7 no.2
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• pp.126-132
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• 2008

The bin packing problem (BPP) is an NP-Complete Problem. The problem can be described as there are $N=\{1,2,{\cdots},n\}$ which is a set of item indices and $L=\{s1,s2,{\cdots},sn\}$ be a set of item sizes sj, where $0<sj{\leq}1$, ${\forall}j{\in}N$. The objective is to minimize the number of bins used for packing items in N into a bin such that the total size of items in a bin does not exceed the bin capacity. Assume that the bins have capacity equal to one. In the past, many researchers put on effort to find the heuristic algorithms instead of solving the problem to optimality. Then, the quality of solution may be measured by the asymptotic worst-case ratio or the average-case ratio. The First Fit Decreasing (FFD) is one of the algorithms that its asymptotic worst-case ratio equals to 11/9. Many researchers prove the asymptotic worst-case ratio by using the weighting function and the proof is in a lengthy format. In this study, we found an easier way to prove that the asymptotic worst-case ratio of the First Fit Decreasing (FFD) is not more than 11/9. The proof comes from two ideas which are the occupied space in a bin is more than the size of the item and the occupied space in the optimal solution is less than occupied space in the FFD solution. The occupied space is later called the weighting function. The objective is to determine the maximum occupied space of the heuristics by using integer programming. The maximum value is the key to the asymptotic worst-case ratio.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.359-375
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• 2017

We developed fog detection algorithm (KNU_FDA) based on the optical and textural properties of fog using satellite (COMS) and ground observation data. The optical properties are dual channel difference (DCD: BT3.7 - BT11) and albedo, and the textural properties are normalized local standard deviation of IR1 and visible channels. Temperature difference between air temperature and BT11 is applied to discriminate the fog from other clouds. Fog detection is performed according to the solar zenith angle of pixel because of the different availability of satellite data: day, night and dawn/dusk. Post-processing is also performed to increase the probability of detection (POD), in particular, at the edge of main fog area. The fog probability is calculated by the weighted sum of threshold tests. The initial threshold and weighting values are optimized using sensitivity tests for the varying threshold values using receiver operating characteristic analysis. The validation results with ground visibility data for the validation cases showed that the performance of KNU_FDA show relatively consistent detection skills but it clearly depends on the fog types and time of day. The average POD and FAR (False Alarm Ratio) for the training and validation cases are ranged from 0.76 to 0.90 and from 0.41 to 0.63, respectively. In general, the performance is relatively good for the fog without high cloud and strong fog but that is significantly decreased for the weak fog. In order to improve the detection skills and stability, optimization of threshold and weighting values are needed through the various training cases.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.13 no.4
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• pp.145-149
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• 1980

Various methods have been so far proposed to find out the directions and spectra of sound waves from the sources for provisions of noise controls. The conventional methods are generally classified into three systems such as, single microphone system, moving microphone system and multi-microphone system, which composes a resultant super directivity by giving a appropriate delay and a weighting coefficient in the output of each microphone. In case of using a single microphone there is a difficulty in providing it with desirable super directivity in the low frequency range, while in case of using multi-microphone system there has been a disadvantage that the measurement of directivity could not separately be done with the spectrum analysing. And in case of the use of a moving microphone system it needs a condition that the sound source to be detected should be stationary state and in rest. However here we introduce a method that the spectral analysing and the directivity of synthesis can be separately carried out by using a linear array of many microphones, in which each output of the microphone is multiplied by appropriate weighting coefficient and all of those products are summed after passing through adequate filters. The resultant signal is then sampled with an adequate sampling frequency and taken average for processing.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.10
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• pp.1172-1180
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• 2007

OFDM system has very good high spectral efficiency and the robustness to the frequency-selective fading. Because of the high PAPR, OFDM signals can be distorted in nonlinear HPA(High Power Amplifier). So, to overcome the nonlinear distortion, it is very important to reduce the IMD value. With respect to the BER performance, IMD reduction method is better than the PAPR reduction method. However, IMD reduction method has much more system complexity because of the additional FFT processor in transmitter. In this paper, we study the OFDM communication system based on the IMD reduction method using SPW method. A new IMD reduction method is proposed to reduce the computational complexity. SPW method is to divide the input OFDM data into several sub-blocks and to multiply phase weighting values with each sub-blocks for the reduction of PAPR or IMD. Unlike the conventional method, the system size and computational complexity can be reduced.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.53-63
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• 2019

To provide a hand gesture interface through deep learning in mobile environments, research on the light-weighting of networks is essential for high recognition rates while at the same time preventing degradation of execution speed. This paper proposes a method of real-time recognition of written characters in the air using a finger on mobile devices through the light-weighting of deep-learning model. Based on the SSD (Single Shot Detector), which is an object detection model that utilizes MobileNet as a feature extractor, it detects index finger and generates a result text image by following fingertip path. Then, the image is sent to the server to recognize the characters based on the learned OCR model. To verify our method, 12 users tested 1,000 words using a GALAXY S10+ and recognized their finger with an average accuracy of 88.6%, indicating that recognized text was printed within 124 ms and could be used in real-time. Results of this research can be used to send simple text messages, memos, and air signatures using a finger in mobile environments.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.502-504
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• 2005

TFRC는 전송률 산출을 위해 Loss Rate와 RTT정보를 TCP-equation에 적용한다. 그러나 무선망과 같이 지연 변화가 큰 환경의 경우 RTT 값이 정확한 망상황을 표현한다고 할 수 없다. 모바일 단말의 경우 움직임에 따라 지연정보는 계속 변화하게 되며 순방향, 역방향 지연정보간의 차이도 발생하게 된다. 본 논문에서는 지연 변화가 큰 환경에 맞는 지연정보인 One-way Delay를 사용하여 TFRC의 전송률 예측방법을 개선한다. One-way Delay 정보를 이용하여 Average Weighting을 거쳐 TCP-equation에 적용하여 전송률을 산출함으로써 정확한 Bandwidth의 예측이 가능함을 시뮬레이션을 통해 나타낸다.