• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.155-169
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• 2016

In this study, we developed a multi-sensor blending short-term rainfall forecasting technique using radar and satellite data during extreme rainfall occurrences in Busan and Gyeongnam region in August 2014. The Tropical Z-R relationship ($Z=32R^{1.65}$) has applied as a optimal radar Z-R relation, which is confirmed that the accuracy is improved during 20mm/h heavy rainfall. In addition, the multi-sensor blending technique has applied using radar and COMS (Communication, Ocean and Meteorological Satellite) data for quantitative precipitation estimation. The very-short-term rainfall forecasting performance was improved in 60 mm/h or more of the strong heavy rainfall events by multi-sensor blending. AWS (Automatic Weather System) and MAPLE data were used for verification of rainfall prediction accuracy. The results have ensured about 50% or more in accuracy of heavy rainfall prediction for 1-hour before rainfall prediction, which are correlations of 10-minute lead time have 0.80 to 0.53, and root mean square errors have 3.99 mm/h to 6.43 mm/h. Through this study, utilizing of multi-sensor blending techniques using radar and satellite data are possible to provide that would be more reliable very-short-term rainfall forecasting data. Further we need ongoing case studies and prediction and estimation of quantitative precipitation by multi-sensor blending is required as well as improving the satellite rainfall estimation algorithm.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.11
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• pp.101-109
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• 2009

Generally, depth cache and pixel cache of 3D graphics are designed by using write-back scheme for efficient use of memory bandwidth. Also, there are write after read operations of same address or only write operations are occurred frequently in 3D graphics cache. If a cache miss is detected, an access to the external memory for write back operation and another access to the memory for handling the cache miss are operated simultaneously. So on frequent cache miss situations, as the memory access bandwidth limited, the access time of the external memory will be increased due to memory bottleneck problem. As a result, the total performance of the processor or the IP will be decreased, also the problem will increase peak power consumption. So in this paper, we proposed a novel early write back cache architecture so as to solve the problems issued above. The proposed architecture controls the point when to access the external memory as to copy the valid data block. And this architecture can improve the cache performance with same hit ratio and same capacity cache. As a result, the proposed architecture can solve the memory bottleneck problem by preventing intensive memory accesses. We have evaluated the new proposed architecture on 3D graphics z cache and pixel cache on a SoC environment where ARM11, 3D graphic accelerator and various IPs are embedded. The simulation results indicated that there were maximum 75% of performance increase when using various simulation vectors.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.96-106
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• 2014

Previous academic research on FPGA tools has relied on simple imaginary models for the targeting architecture. As the first step to overcome such restriction, the issues on analytic placement and legalization which are applied to commercial FPGAs have been brought up, and several techniques to remedy them are presented, and evaluated. First of all, the center of gravity of the placed cells may be far displaced from the center of the chip during analytic placement. A function is proposed to be added to the objective function for minimizing this displacement. And then, the density map is expanded into multiple layers to accurately calculate the density distribution for each of the cell types. Early fixation is also proposed for the memory blocks which can be placed at limited sites in small numbers. Since two flip-flops share control pins in a slice, a compatibility constraint is introduced during legalization. Pre-packing compatible flip-flops is proposed as a proactive step. The proposed techniques are implemented on the K-FPGA fabric evaluation framework in which commercial architectures can be precisely modeled, and modified for enhancement, and validated on twelve industrial strength examples. The placement results show that the proposed techniques have reduced the wire length by 22%, and the slice usage by 5% on average. This research is expected to be a development basis of the optimization CAD tools for new as well as the state-of-the-art FPGA architectures.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.177-186
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• 2018

The mechanism of emotion is complex and influenced by a variety of factors, so that it is crucial to analyze emotion in broad and diversified perspectives. In this study, we classified neutral and negative emotions(sadness, fear, surprise) using arousal evaluation, which is one of the psychological evaluation scales, as well as physiological signals. We have not only revealed the difference between physiological signals coupled to the emotions, but also assessed how accurate these emotions can be classified by our emotional recognizer based on neural network algorithm. A total of 146 participants(mean age $20.1{\pm}4.0$, male 41%) were emotionally stimulated while their physiological signals of the electrocardiogram, blood flow, and dermal activity were recorded. In addition, the participants evaluated their psychological states on the emotional rating scale in response to the emotional stimuli. Heart rate(HR), standard deviation(SDNN), blood flow(BVP), pulse wave transmission time(PTT), skin conduction level(SCL) and skin conduction response(SCR) were calculated before and after the emotional stimulation. As a result, the difference between physiological responses was verified corresponding to the emotions, and the highest emotion classification performance of 86.9% was obtained using the combined analysis of arousal and physiological features. This study suggests that negative emotion can be categorized by psychological and physiological evaluation along with the application of machine learning algorithm, which can contribute to the science and technology of detecting human emotion.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.67-78
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• 2017

Traffic congestion cost is increasing annually. Specifically congestion caused by the CDB traffic contains more than a half of the total congestion cost. Recent advancement in the field of Big Data, AI paved the way to industry revolution 4.0. And, these new technologies creates tremendous changes in the traffic information dissemination. Eventually, accurate and timely traffic information will give a positive impact on decreasing traffic congestion cost. This study, therefore, focused on developing both recurrent and non-recurrent congestion prediction models on urban roads by adopting Recurrent Neural Network(RNN), a tribe in machine learning. Two hidden layers with scaled conjugate gradient backpropagation algorithm were selected, and tested. Result of the analysis driven the authors to 25 meaningful links out of 33 total links that have appropriate mean square errors. Authors concluded that RNN model is a feasible model to predict congestion.

• Journal of Broadcast Engineering
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• v.17 no.1
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• pp.179-190
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• 2012

In Distributed Video Coding(DVC), a low complexity encoder can be realized by shifting complex processes of encoder such as motion estimation to decoder. Since not only motion estimation/compensation processes but also channel decoding process needs to be performed at DVC decoder, the complexity of a decoder is significantly increased in consequence. Therefore, various fast channel decoding methods are proposed for the most computationally complex part, which is the channel decoding process in DVC decoding. As the channel decoding process becomes faster using various methods, however, the complexity of the other processes are relatively highlighted. For instance, the complexity of side information generation process in the DVC decoder is relatively increased. In this paper, therefore, a fast method for the DVC decoding is proposed by using adaptive search range method in side information generation process. Experimental results show that the proposed method achieves time saving of about 63% in side information generation process, while its rate distortion performance is degraded only by about 0.17% in BDBR.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.609-617
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• 2016

In general, Personal Navigation Systems (PNSs) can be defined systems to acquire pedestrian positional information. GPS is an example of PNS. However, GPS can only be used where the GPS signal can be received. Pedestrian Dead Reckoning (PDR) can estimate the positional information of pedestrians using Inertial Measurement Unit (IMU). Therefore, PDR can be used for GPS-disabled areas. This paper proposes a PDR scheme considering various movement types over GPS-disabled areas as combat environments. We propose a movement distance estimation scheme and movement direction estimation scheme as pedestrian's various movement types such as walking, running and crawling using IMU. Also, we propose a fusion algorithm between GPS and PDR to mitigate the lack of accuracy of positional information at the entrance to the building. The proposed algorithm has been tested in a real test bed. In the experimental results, the proposed algorithms exhibited an average position error distance of 5.64m and position error rate in goal point of 3.41% as a pedestrian traveled 0.6km.

• Journal of the Korean Institute of Gas
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• v.2 no.1
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• pp.28-39
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• 1998

This study investigated the natural convection and radiation in a rectangular enclosure with ceiling vent experimentally and numerically. A heat source is located on the center of the bottom surface. The analysis was peformed a pure convection and is combination of natural convection and radiation. The shape of the considered two dimensional model is a square whose center of ceiling($30\%$) is opened. The numerical simulations are carried out for the pure natural convection case and the combined heat transfer case by using the SIMPLE algorithm. For the turbulent flow, Reynolds stresses are closed by the standard $k-{\epsilon}$ model and the wall function is used to determine the wall boundary conditions. The experiment was performed on the same geometrical shape as the computations. The radiative heat transfer is analized by the S-N discrete ordinates method. The results of pure natural convection are compared with those of combined heat transfer by the velocity vectors, stream lines, isothermal lines. The results obtained are as follows 1. Comparing the results of pure convection with those of the combined convection-radiation through the shape of stream lines, isothermal lines are similar to each other. 2. The temperature fields obtained by numerical method are compared to those obtained by experimental one, and it is found that they are showed mean relative error $8.5\%$. 3. Visualization bt smoke is similar to computational results.

• Journal of Navigation and Port Research
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• v.47 no.4
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• pp.231-238
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• 2023

The position of a light buoy is always flexible due to the influence of external forces such as tides and wind. The position can be checked through AIS (Automatic Identification System) or RTU (Remote Terminal Unit) for AtoN. As a result of analyzing the position data for the last five years (2017-2021) of a light buoy, the average position error was 15.4%. It is necessary to detect position error data and obtain refined position data to prevent navigation safety accidents and management. This study aimed to detect position error data and obtain refined position data by DBSCAN Clustering position data obtained through AIS or RTU for AtoN. For this purpose, 21 position data of Gunsan Port No. 1 light buoy where RTU was installed among western waters with the most position errors were DBSCAN clustered using Python library. The minPts required for DBSCAN Clustering applied the value commonly used for two-dimensional data. Epsilon was calculated and its value was applied using the k-NN (nearest neighbor) algorithm. As a result of DBSCAN Clustering, position error data that did not satisfy minPts and epsilon were detected and refined position data were acquired. This study can be used as asic data for obtaining reliable position data of a light buoy installed with AIS or RTU for AtoN. It is expected to be of great help in preventing navigation safety accidents.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.366-377
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• 2016

Water use efficiency (WUE) is considered as an important ecological indicator which may provide information on the process-structure relationships associated with energy-matter-information flows in ecosystem. The WUE at ecosystem-level can be defined as the ratio of gross primary productivity (GPP) to evapotranspiration (ET). In this study, KoFlux's long-term (2007-2015) eddy covariance measurements of $CO_2$ and water vapor fluxes were used to examine the WUE of needle fir plantation in Korea National Arboretum. Our objective is to ascertain the seasonality and inter-annual variability in WUE of this needle fir plantation so that the results may be assimilated into the development of a holistic ecological indicator for resilience assessment. Our results show that the WUE of needle fir plantation is characterized by a concave seasonal pattern with a minimum ($1.8-3.3g\;C{\cdot}(kg\;H_2O)^{-1}$) in August and a maximum ($5.1-11.4g\;C{\cdot}(kg\;H_2O)^{-1}$) in February. During the growing season (April to October), WUE was on average $3.5{\pm}0.3g\;C\;(kg\;H_2O)^{-1}$. During the dormant seasons (November to March), WUE showed more variations with a mean of $7.4{\pm}1.0g\;C{\cdot}(kg\;H_2O)^{-1}$. These values are in the upper ranges of WUE reported in the literature for coniferous forests in temperate zone. Although the growing season was defined as the period from April to October, the actual length of the growing season (GSL) varied each year and its variation explained 62% of the inter-annual variability of the growing season WUE. This is the first study to quantify long-term changes in ecosystem-level WUE in Korea and the results can be used to test models, remote-sensing algorithms and resilience of forest ecosystem.