• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.49 no.2
• /
• pp.28-34
• /
• 2012

Various feature extraction algorithms are widely applied to real-time image processing applications for extracting significant features from images. Feature extraction algorithms are mostly combined with image processing algorithms mostly for image tracking and recognition. Feature extraction function is used to supply feature information to the other image processing algorithms and it is mainly implemented in a preprocessing stage. Nowadays, image processing applications are faced with embedded system implementation for a real-time processing. In order to satisfy this requirement, it is necessary to reduce execution time so as to improve the performance. Reducing the time for executing a feature extraction function dose not only extend the execution time for the other image processing algorithms, but it also helps satisfy a real-time requirement. This paper explains FAST (Feature from Accelerated Segment Test algorithm) of E. Rosten and presents FPGA-based embedded hardware accelerator architecture. The proposed acceleration scheme can be implemented by using approximately 2,217 Flip Flops, 5,034 LUTs, 2,833 Slices, and 18 Block RAMs in the Xilinx Vertex IV FPGA. In the Modelsim - based simulation result, the proposed hardware accelerator takes 3.06 ms to extract 954 features from a image with $640{\times}480$ pixels and this result shows the cost effectiveness of the propose scheme.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.30 no.6
• /
• pp.306-318
• /
• 2018

In order to evaluate the seismic capacity of massive vertical type breakwaters which have intensively been deployed along the coast of South Korea over the last two decades, we carry out the preliminary numerical simulation against the PoHang, GyeongJu, Hachinohe 1, Hachinohe 2, Ofunato, and artificial seismic waves based on the measured time series of ground acceleration. Numerical result shows that significant sliding can be resulted in once non-negligible portion of seismic energy is shifted toward the longer period during its propagation process toward the ground surface in a form of shear wave. It is well known that during these propagation process, shear waves due to the seismic activity would be amplified, and non-negligible portion of seismic energy be shifted toward the longer period. Among these, the shift of seismic energy toward the longer period is induced by the viscosity and internal friction intrinsic in the soil. On the other hand, the amplification of shear waves can be attributed to the fact that the shear modulus is getting smaller toward the ground surface following the descending effective stress toward the ground surface. And the weakened intensity of soil as the number of attacking shear waves are accumulated can also contribute these phenomenon (Das, 1993). In this rationale, we constitute the numerical model using the model by Hardin and Drnevich (1972) for the weakened shear modulus as shear waves go on, and shear wave equation, in the numerical integration of which $Newmark-{\beta}$ method and Modified Newton-Raphson method are evoked to take nonlinear stress-strain relationship into account. It is shown that the numerical model proposed in this study could duplicate the well known features of seismic shear waves such as that a great deal of probability mass is shifted toward the larger amplitude and longer period when shear waves propagate toward the ground surface.

• Journal of Korea Water Resources Association
• /
• v.53 no.9
• /
• pp.661-670
• /
• 2020

The water supply system has a wider installation range and various components of it than other infrastructure, making it difficult to secure stability against earthquakes. Therefore, it is necessary to develop methods for evaluating the seismic performance of water supply systems. Ground Motion Prediction Equation (GMPE) is used to evaluate the seismic performance (e.g, failure probability) for water supply facilities such as pump, water tank, and pipes. GMPE is calculated considering the independent variables such as the magnitude of the earthquake and the ground motion such as PGV (Peak Ground Velocity) and PGA (Peak Ground Acceleration). Since the large magnitude earthquake data has not accumulated much to date in Korea, this study tried to select a suitable GMPE for the domestic earthquake simulation by using the earthquake data measured in Korea. To this end, GMPE formula is calculated based on the existing domestic earthquake and presented the results. In the future, it is expected that the evaluation will be more appropriate if the determined GMPE is used when evaluating the seismic performance of domestic waterworks. Appropriate GMPE can be directly used to evaluate hydraulic seismic performance of water supply networks. In other words, it is possible to quantify the damage rate of a pipeline during an earthquake through linkage with the pipe failure probability model, and it is possible to derive more reasonable results when estimating the water outage or low-pressure area due to pipe damages. Finally, the quantifying result of the seismic performance can be used as a design criteria for preparing an optimal restoration plan and proactive seismic design of pipe networks to minimize the damage in the event of an earthquake.

• Journal of Intelligence and Information Systems
• /
• v.24 no.1
• /
• pp.25-52
• /
• 2018

This study performs social network analysis based on consumer sentiment related to a location in Seoul using data reflecting consumers' web search activities and emotional evaluations associated with commerce. The study focuses on large commercial districts in Seoul. In addition, to consider their various aspects, social network indexes were combined with the trading area's public data to verify factors affecting the area's sales. According to R square's change, We can see that the model has a little high R square value even though it includes only the district's public data represented by static data. However, the present study confirmed that the R square of the model combined with the network index derived from the social network analysis was even improved much more. A regression analysis of the trading area's public data showed that the five factors of 'number of market district,' 'residential area per person,' 'satisfaction of residential environment,' 'rate of change of trade,' and 'survival rate over 3 years' among twenty two variables. The study confirmed a significant influence on the sales of the trading area. According to the results, 'residential area per person' has the highest standardized beta value. Therefore, 'residential area per person' has the strongest influence on commercial sales. In addition, 'residential area per person,' 'number of market district,' and 'survival rate over 3 years' were found to have positive effects on the sales of all trading area. Thus, as the number of market districts in the trading area increases, residential area per person increases, and as the survival rate over 3 years of each store in the trading area increases, sales increase. On the other hand, 'satisfaction of residential environment' and 'rate of change of trade' were found to have a negative effect on sales. In the case of 'satisfaction of residential environment,' sales increase when the satisfaction level is low. Therefore, as consumer dissatisfaction with the residential environment increases, sales increase. The 'rate of change of trade' shows that sales increase with the decreasing acceleration of transaction frequency. According to the social network analysis, of the 25 regional trading areas in Seoul, Yangcheon-gu has the highest degree of connection. In other words, it has common sentiments with many other trading areas. On the other hand, Nowon-gu and Jungrang-gu have the lowest degree of connection. In other words, they have relatively distinct sentiments from other trading areas. The social network indexes used in the combination model are 'density of ego network,' 'degree centrality,' 'closeness centrality,' 'betweenness centrality,' and 'eigenvector centrality.' The combined model analysis confirmed that the degree centrality and eigenvector centrality of the social network index have a significant influence on sales and the highest influence in the model. 'Degree centrality' has a negative effect on the sales of the districts. This implies that sales decrease when holding various sentiments of other trading area, which conflicts with general social myths. However, this result can be interpreted to mean that if a trading area has low 'degree centrality,' it delivers unique and special sentiments to consumers. The findings of this study can also be interpreted to mean that sales can be increased if the trading area increases consumer recognition by forming a unique sentiment and city atmosphere that distinguish it from other trading areas. On the other hand, 'eigenvector centrality' has the greatest effect on sales in the combined model. In addition, the results confirmed a positive effect on sales. This finding shows that sales increase when a trading area is connected to others with stronger centrality than when it has common sentiments with others. This study can be used as an empirical basis for establishing and implementing a city and trading area strategy plan considering consumers' desired sentiments. In addition, we expect to provide entrepreneurs and potential entrepreneurs entering the trading area with sentiments possessed by those in the trading area and directions into the trading area considering the district-sentiment structure.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_3
• /
• pp.1043-1060
• /
• 2023

An acceleration of climate change in recent years has led to increased attention towards 'blue carbon' which refers to the carbon captured by the ocean. However, our comprehension of marine ecosystems is still incomplete. This study classified and analyzed global marine eco-provinces using k-means clustering considering carbon cycling. We utilized five input variables during the past 20 years (2001-2020): Carbon-based Productivity Model (CbPM) Net Primary Production (NPP), particulate inorganic and organic carbon (PIC and POC), sea surface salinity (SSS), and sea surface temperature (SST). A total of nine eco-provinces were classified through an optimization process, and the spatial distribution and environmental characteristics of each province were analyzed. Among them, five provinces showed characteristics of open oceans, while four provinces reflected characteristics of coastal and high-latitude regions. Furthermore, a qualitative comparison was conducted with previous studies regarding marine ecological zones to provide a detailed analysis of the features of nine eco-provinces considering carbon cycling. Finally, we examined the changes in nine eco-provinces for four periods in the past (2001-2005, 2006-2010, 2011-2015, and 2016-2020). Rapid changes in coastal ecosystems were observed, and especially, significant decreases in the eco-provinces having higher productivity by large freshwater inflow were identified. Our findings can serve as valuable reference material for marine ecosystem classification and coastal management, with consideration of carbon cycling and ongoing climate changes. The findings can also be employed in the development of guidelines for the systematic management of vulnerable coastal regions to climate change.

• Journal of Korean Society of Steel Construction
• /
• v.16 no.3 s.70
• /
• pp.305-314
• /
• 2004

Many residential buildings and mixed-use (i.e., residential and commercial) buildings that are currently under construction in the country mainly consist of building clusters rather than single structures. Recent trends show single buildings that actually consist of two houses. The lower part of the building consists of a single dwelling space. However, the upper part of the building is split into two dwellings, considering the aspects of commercialism and appearance, such as ventilation and lighting. These tall and complex buildings not only have low mass and damping. They also depend on wind loads for their structural stability and serviceability, due to the interaction between the building groups and the wind. In architectural design, however, the interaction effects among neighboring houses within a building group have yet to be identified. In addition, it is difficult to predict these interaction effects. In this regard, this thesis aims to model patterns of architecture, which consist of two houses that are existing or under construction. Current structures are investigated by comparing their wind-reduced response interaction effects, based on the measured distance between two buildings, and the acceleration response through the wind tunnel test. The results of this study are expected to provide basic data for wind-induced response interaction effects of building groups. Furthermore, the outcomes are also intended to be used as data for more rational and economical structure design.

• Journal of Korean Society of Steel Construction
• /
• v.17 no.1 s.74
• /
• pp.1-11
• /
• 2005

A bi-level damage detection algorithm that utilizes the dynamic responses of the structure as input and neural network (NN) as pattern classifier is presented. Signal anomaly index (SAI) is proposed to express the amount of changes in the shape of frequency response functions (FRF) or strain frequency response function (SFRF). SAI is calculated using the acceleration and dynamic strain responses acquired from intact and damaged states of the structure. In a bi-level damage identification algorithm, the presence of damage is first identified from the magnitude of the SAI value, then the location of the damage is identified using the pattern recognition capability of NN. The proposed algorithm is applied to an experimental model bridge to demonstrate the feasibility of the algorithm. Numerically simulated signals are used for training the NN, and experimentally-acquired signals are used to test the NN. The results of this example application suggest that the SAI-based pattern recognition approach may be applied to the structural health monitoring system for a real bridge.

• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10b
• /
• pp.1649-1654
• /
• 1991

In 1995 the VSOP satellite, which is called MUSES-B in Japan, will be launched under the VLBI Space Observatory Programme(VSOP) promoted by ISAS(Institute of Space and Astronautical Science) of Japan. We are now developing the GPS Receiver(GPSR) and On-board Orbit Determination System. This paper describes the GPS(Global Positioning System), VSOP, GPSR(GPS Receiver system) configuration and the results of the GPS system analysis. The GPSR consists of three GPS antennas and 5 channel receiver package. In the receiver package, there are two 16 bits microprocessing units. The power consumption is 25 Watts in average and the weight is 8.5 kg. Three GPS antennas on board enable GPSR to receive GPS signals from any NAVSTARs(GPS satellites) which are visible. NAVSATR's visibility is described as follows. The VSOP satellite flies from 1, 000 km to 20, 000 km in height on the elliptical orbit around the earth. On the other hand, the orbit of NAVSTARs are nearly circular and about 20, 000 km in height. GPSR can't receive the GPS signals near the apogee, because NAVSTARs transmit the GPS signals through the NAVSTAR's narrow beam antennas directed toward the earth. However near the perigee, GPSR can receive from 12 to 15 GPS signals. More than 4 GPS signals can be received for 40 minutes, which are related to GDOP(Geometric Dillusion Of Precision of selected NAVSTARs). Because there are a lot of visible NAVSTARs, GDOP is small near the perigee. This is a favorqble condition for GPSR. Orbit determination system onboard VSOP satellite consists of a Kalman filter and a precise orbit propagator. Near the perigee, the Kalman filter can eliminate the orbit propagation error using the observed data by GPSR. Except a perigee, precise onboard orbit propagator propagates the orbit, taking into account accelerations such as gravities of the earth, the sun, the moon, and other acceleration caused by the solar pressure. But there remain some amount of calculation and integration errors. When VSOP satellite returns to the perigee, the Kalman filter eliminates the error of the orbit determined by the propagator. After the error is eliminated, VSOP satellite flies out towards an apogee again. The analysis of the orbit determination is performed by the covariance analysis method. Number of the states of the onboard filter is 8. As for a true model, we assume that it is based on the actual error dynamics that include the Selective Availability of GPS called 'SA', having 17 states. Analytical results for position and velocity are tabulated and illustrated, in the sequel. These show that the position and the velocity error are about 40 m and 0.008 m/sec at the perigee, and are about 110 m and 0.012 m/sec at the apogee, respectively.

• Structural Engineering and Mechanics
• /
• v.75 no.4
• /
• pp.507-518
• /
• 2020

With the rapid development of rail transit, rail transit noise needs to be paid more and more attention. In order to accurately and effectively analyze the characteristics of low-frequency noise, a prediction model of vibration of box girder was established based on the hybrid FE-SEA method. When the train speed is 140 km/h, 200 km/h and 250 km/h, the vibration and noise of the box girder induced by the vertical wheel-rail interaction in the frequency range of 20-500 Hz are analyzed. Detailed analysis of the energy level, sound pressure contribution, modal analysis and vibration loss power of each slab at the operating speed of 140 km /h. The results show that: (1) When the train runs at a speed of 140km/h, the roof contributes more to the sound pressure at the far sound field point. Analyzing the frequency range from 20 to 500 Hz: The top plate plays a very important role in controlling sound pressure, contributing up to 70% of the sound pressure at peak frequencies. (2) When the train is traveling at various speeds, the maximum amplitude of structural vibration and noise generated by the viaduct occurs at 50 Hz. The vibration acceleration of the box beam at the far field point and near field point is mainly concentrated in the frequency range of 31.5-100 Hz, which is consistent with the dominant frequency band of wheel-rail force. Therefore, the main frequency of reducing the vibration and noise of the box beam is 31.5-100 Hz. (3) The vibration energy level and sound pressure level of the box bridge at different speeds are basically the same. The laws of vibration energy and sound pressure follow the rules below: web

• Smart Media Journal
• /
• v.10 no.3
• /
• pp.31-38
• /
• 2021

It is hard to predict when and where a fall accident will happen. Also, if rapid follow-up measures on it are not performed, a fall accident leads to a threat of life, so studies that can automatically detect a fall accident have become necessary. Among automatic fall-accident detection techniques, a fall detection scheme using an IMU (inertial measurement unit) sensor attached to a wrist is difficult to detect a fall accident due to its movement, but it is recognized as a technique that is easy to wear and has excellent accessibility. To overcome the difficulty in obtaining fall data, this study proposes an algorithm that efficiently learns less data through machine learning such as KNN (k-nearest neighbors) and SVM (support vector machine). In addition, to improve the performance of these mathematical classifiers, this study utilized feature data aquired in the frequency space. The proposed algorithm analyzed the effect by diversifying the parameters of the model and the parameters of the frequency feature extractor through experiments using standard datasets. The proposed algorithm could adequately cope with a realistic problem that fall data are difficult to obtain. Because it is lighter than other classifiers, this algorithm was also easy to implement in small embedded systems where SIMD (single instruction multiple data) processing devices were difficult to mount.