• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2012.10a
• /
• pp.887-890
• /
• 2012

Image recognition is one of the most important research directions of pattern recognition. Image based road automatic identification technology is widely used in current society, the intelligence has become the trend of the times. This paper studied the image segmentation algorithm theory and its application in road signs recognition system. With the help of image processing technique, respectively, on road signs automatic recognition algorithm of three main parts, namely, image segmentation, character segmentation, image and character recognition, made a systematic study and algorithm. The experimental results show that: the image segmentation algorithm to establish road signs recognition model, can make effective use of smart phone system and application.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.50 no.4
• /
• pp.180-187
• /
• 2001

This paper proposes an efficient evolutionary programming algorithm for solving a generation expansion planning(GEP) problem known as a highly-nonlinear dynamic problem. Evolutionary programming(EP) is an optimization algorithm based on the simulated evolution (mutation, competition and selection). In this paper, new algorithm is presented to enhance the efficiency of the EP algorithm for solving the GEP problem. By a domain mapping procedure, yearly cumulative capacity vectors are transformed into one dummy vector, whose change can yield a kind of trend in the cost value. To validate the proposed approach, this algorithm is tested on two cases of expansion planning problems. Simulation results show that the proposed algorithm can provide successful results within a resonable computational time compared with conventional EP and dynamic programming.

• Proceedings of the KSRS Conference
• /
• 2007.10a
• /
• pp.57-60
• /
• 2007

Monthly mean data of ${\AA}ngstr{\ddot{o}}m$ exponent and Aerosol optical thickness (AOT) from Sea-viewing Wide Field-of-view Sensor (SeaWiFS) measurements over the East Asian waters were analyzed. Increasing trend of the satellite-derived ${\AA}ngstr{\ddot{o}}m$ exponent from 1998 to 2004 was found while AOT mean was observed stable during the same period. The trend of ${\AA}ngstr{\ddot{o}}m$ exponent is then interpreted as increase in fraction of small aerosol particles to give quantitative estimates on the variability of aerosols. The mean increase is evaluated to be $4{\sim}5%$ over the 7-year period in terms of the contribution of small particles to the total AOT, or sub-micron fraction (SMF). Possibilities of the observed trend arising from the sensor calibration or algorithm performance are carefully checked, which confirm our belief that this observed trend is rather a real fact than an artifact due to data processing. Another time series of SMF data (2000-2005) estimated from the fine-mode fraction (FMF) of Moderate Resolution Imaging Spectroradiometer (MODIS) supports this observation yet with different calibration system and retrieval algorithms.

• Economic and Environmental Geology
• /
• v.32 no.1
• /
• pp.19-31
• /
• 1999

In this study, we have estimated lineaments in the Euiseong area, Kyungbuk Province, from Landsat TM by applying the algorithm developed by Kim and Won et al. which can effectively reduce the look direction bias associated with the Sun's azimuth angle. Fratures over the study area were also mapped in the field at 57 selected sites to compare them with the results from the satellite image. The trends of lineaments estimated from the Landsat TM images are characterized as $N50^{\circ}$~70W, NS~$N10^{\circ}$W, and $N10^{\circ}$~$60^{\circ}$E trends. The spatial distribution of lineaments is also studied using a circular grid, and the results show that the area can be divided into two domains : domain A in which NS~$N20^{\circ}$E direction is dominant, and domain B in which west-north-west direction is prominent. The trends of lineaments can also be classified into seven groups. Among them, only C, D and G trends are found to be dominant based upon Donnelly's nearest neighbor analysis and correlations of lineament desities. In the color composite image produced by overlaying the lineament density map of these C-, D-, and G-trends, G-trend is shown to be developed in the whole study area while the eastern part of the area is dominated by D-trend. C-trend develops extensively over the whole are except the southeastern part. The orientation of fractures measured at 35 points in the field shows major trends of NS~$N30^{\circ}$E, $N50^{\circ}$~$80^{\circ}$W, and N80$^{\circ}$E~EW, which agree relatively well with the lineaments estimated form the satellite image. The rose diagram analysis fo field data shows that WNW-ESE trending discontinuities are developed in the whole area while discontinuities of NS~$N20^{\circ}$E are develped only in the estern part, which also coincide with the result from the satellite image. The combined results of lineaments from the satellite image and fracture orientation of field data at 22 points including 18 minor faults in Sindong Group imply that the WNW-ESE trend is so prominent that Gumchun and Gaum faults are possibly extended up to the lower Sindong Group in the study area.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.10
• /
• pp.55-65
• /
• 2023

In this paper, we apply KeyBERT(Keyword extraction with Bidirectional Encoder Representations of Transformers) algorithm-driven topic extraction and topic frequency analysis to deep learning and reinforcement learning research to discover the rapidly changing trends in them. First, we crawled abstracts of research papers on deep learning and reinforcement learning, and temporally divided them into two groups. After pre-processing the crawled data, we extracted topics using KeyBERT algorithm, and then analyzed the extracted topics in terms of topic occurrence frequency. This analysis reveals that there are distinct trends in research work of all analyzed algorithms and applications, and we can clearly tell which topics are gaining more interest. The analysis also proves the effectiveness of the utilized topic extraction and topic frequency analysis in research trend analysis, and this trend analysis scheme is expected to be used for research trend analysis in other research fields. In addition, the analysis can provide insight into how deep learning will evolve in the near future, and provide guidance for select research topics and methodologies by informing researchers of research topics and methodologies which are recently attracting attention.

• Journal of Information Processing Systems
• /
• v.18 no.5
• /
• pp.711-718
• /
• 2022

In this paper, an accurate segmentation algorithm of video dynamic background image (VDBI) based on improved wavelet transform is proposed. Based on the smooth processing of VDBI, the traditional wavelet transform process is improved, and the two-layer decomposition of dynamic image is realized by using two-dimensional wavelet transform. On the basis of decomposition results and information enhancement processing, image features are detected, feature points are extracted, and quantum ant colony algorithm is adopted to complete accurate segmentation of the image. The maximum SNR of the output results of the proposed algorithm can reach 73.67 dB, the maximum time of the segmentation process is only 7 seconds, the segmentation accuracy shows a trend of decreasing first and then increasing, and the global maximum value can reach 97%, indicating that the proposed algorithm effectively achieves the design expectation.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2007.05a
• /
• pp.397-399
• /
• 2007

This paper presents multiple regression analysis and data update to improve present spatial electric load forecasting algorithm of the DISPLAN. Spatial electric load forecasting considers a local economy, the number of local population and load characteristics. A Case study is performed for Jeon-Ju and analyzes a trend of the spatial load for the future 20 years. The forecasted information can contribute to an asset management of distribution systems.

• Interaction and multiscale mechanics
• /
• v.6 no.2
• /
• pp.197-210
• /
• 2013

A fundamental trend of processor architecture evolving towards exaflops is fast increasing floating point performance (so-called "free" flops) accompanied by much slowly increasing memory and network bandwidth. In order to fully enjoy the "free" flops, a numerical algorithm of PDEs should request more flops per byte or increase arithmetic intensity. A meshfree/GFEM approximation can be the class of the algorithm. It is shown in a GFEM without extra dof that the kind of approximation takes advantages of the high performance of manycore GPUs by a high accuracy of approximation; the "expensive" method is found to be reversely hardware-efficient on the emerging architecture of manycore.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.7
• /
• pp.1817-1839
• /
• 2024

To solve the problem of insufficient recognition of key nodes in the existing software defect detection process, this paper proposes a key node recognition algorithm based on node expansion degree and improved K-shell position, shortened as SDD_KNR. Firstly, the calculation formula of node expansion degree is designed to improve the degree that can measure the local defect propagation capability of nodes in the software network. Secondly, the concept of improved K-shell position of node is proposed to obtain the improved K-shell position of each node. Finally, the measurement of node defect propagation capability is defined, and the key node recognition algorithm is designed to identify the key function nodes with large defect impact range in the process of software defect detection. Using real software systems such as Nano, Cflow and Tar to design three sets of experiments. The corresponding directed weighted software function invoke networks are built to simulate intentional attack and defect source infection. The proposed SDD_KNR algorithm is compared with the BC algorithm, K-shell algorithm, KNMWSG algorithm and NMNC algorithm. The changing trend of network efficiency and the strength of node propagation force are analyzed to verify the effectiveness of the proposed SDD_KNR algorithm.

• Journal of information and communication convergence engineering
• /
• v.8 no.3
• /
• pp.323-327
• /
• 2010

Recently, the television market trend is change to HD television and the need of the study on HD image enhancement is increased rapidly. To enhancement of image quality, the retinex algorithm is commonly used. That's why we studied how to accelerate the retinex algorithm with CUDA on GPGPU (general purpose graphics processing unit). Calculating average part in retinex algorithm is similar to pyramidal calculation. We parallelize this recursive pyramidal average calculating for all layers, map the average data into the 2D plane and reduce the calculating time dramatically. Sequential C code takes 8948ms to get the average values for all layers in $1024{\times}1024$ image, but proposed method takes only only about 0.9ms for the same image. We are going to study about the real-time HD video rendering and image enhancement.