• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.332-339
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• 2014

Face classification has wide applications in security and surveillance. However, this technique presents various challenges caused by pose, illumination, and expression changes. Face recognition with long-distance images involves additional challenges, owing to focusing problems and motion blurring. Multiple frames under varying spatial or temporal settings can acquire additional information, which can be used to achieve improved classification performance. This study investigates the effectiveness of multi-frame decision-level fusion with photon-counting linear discriminant analysis. Multiple frames generate multiple scores for each class. The fusion process comprises three stages: score normalization, score validation, and score combination. Candidate scores are selected during the score validation process, after the scores are normalized. The score validation process removes bad scores that can degrade the final output. The selected candidate scores are combined using one of the following fusion rules: maximum, averaging, and majority voting. Degraded facial images are employed to demonstrate the robustness of multi-frame decision-level fusion in harsh environments. Out-of-focus and motion blurring point-spread functions are applied to the test images, to simulate long-distance acquisition. Experimental results with three facial data sets indicate the efficiency of the proposed decision-level fusion scheme.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.29-29
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• 2014

A fundamental problem in space physics is to explain the origin of energetic charged particles in space close to the Earth and the significant temporal variations of their flux. The particles are primarily electrons and protons although energetic heavy ions such as O+ are sometimes non-negligible. By "energetic" we mean a rather broad energy range of particles from a few tens of keV to well above MeV. Drastic variations of the particle fluxes (by >3 orders of magnitude) occur over both a short time scale like a few minutes and a long time scale like the 11-year sunspot cycle. In this talk I will focus on relativistic energy electrons (~MeV) trapped within the Earth's magnetosphere. They are a primary element of the space weather since they can cause damage to satellites, so often called "killer electrons". Considering that the source particles in both the solar wind and the ionosphere are relatively cold (~eV), the quasi-permanent existence of these very energetic particles close to the Earth has been a surprise to space physicists for decades. Complex electromagnetic processes such as wave-particle interactions within the magnetosphere are believed to play a major role in generating these killer electrons. While detailed physics remains an active research area, for this lecture I will introduce a synthesized picture of how solar activities are related to wave-particle interaction physics inside the magnetosphere. This can be applied to other astrophysical systems.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.211-232
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• 2007

In this paper we investigate some issues and solutions related to the design of a Data Warehouse (DW), storing several aggregate measures about trajectories of moving objects. First we discuss the loading phase of our DW which has to deal with overwhelming streams of trajectory observations, possibly produced at different rates, and arriving in an unpredictable and unbounded way. Then, we focus on the measure presence, the most complex measure stored in our DW. Such a measure returns the number of distinct trajectories that lie in a spatial region during a given temporal interval. We devise a novel way to compute an approximate, but very accurate, presence aggregate function, which algebraically combines a bounded amount of measures stored in the base cells of the data cube. We conducted many experiments to show the effectiveness of our method to compute such an aggregate function. In addition, the feasibility of our innovative trajectory DW was validated with an implementation based on Oracle. We investigated the most challenging issues in realizing our trajectory DW using standard DW technologies: namely, the preprocessing and loading phase, and the aggregation functions to support OLAP operations.

• Journal of Internet Computing and Services
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• v.20 no.3
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• pp.85-92
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• 2019

In this paper, we extract the quantitative relation data of activities from the workflow event log file recorded in the XES standard format and connect them to rediscover the workflow process model. Extract the workflow process patterns and proportions with the rediscovered model. There are four types of control-flow elements that should be used to extract workflow process patterns and portions with log files: linear (sequential) routing, disjunctive (selective) routing, conjunctive (parallel) routing, and iterative routing patterns. In this paper, we focus on four of the factors, disjunctive routing, and conjunctive path. A framework implemented by the authors' research group extracts and arranges the activity data from the log and converts the iteration of duplicate relationships into a quantitative value. Also, for accurate analysis, a parallel process is recorded in the log file based on execution time, and algorithms for finding and eliminating information distortion are designed and implemented. With these refined data, we rediscover the workflow process model following the relationship between the activities. This series of experiments are conducted using the Large Bank Transaction Process Model provided by 4TU and visualizes the experiment process and results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5465-5480
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• 2018

Energy-efficient task scheduling on multi-core server is a fundamental issue in green cloud computing. Multi-core processors are widely used in mobile devices, personal computers, and servers. Existing energy efficient task scheduling methods chiefly focus on reducing the energy consumption of the processor itself, and assume that the cores of the processor are controlled independently. However, the cores of some processors in the market are divided into several voltage islands, in each of which the cores must operate on the same status, and the cost of the server includes not only energy cost of the processor but also the energy of other components of the server and the cost of user waiting time. In this paper, we propose a cost-aware scheduling algorithm ICAS for computation intensive tasks on multi-core server. Tasks are first allocated to cores, and optimal frequency of each core is computed, and the frequency of each voltage island is finally determined. The experiments' results show the cost of ICAS is much lower than the existing method.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.556-570
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• 2021

The existing video expression recognition methods mainly focus on the spatial feature extraction of video expression images, but tend to ignore the dynamic features of video sequences. To solve this problem, a multi-mode convolution neural network method is proposed to effectively improve the performance of facial expression recognition in video. Firstly, OpenFace 2.0 is used to detect face images in video, and two deep convolution neural networks are used to extract spatiotemporal expression features. Furthermore, spatial convolution neural network is used to extract the spatial information features of each static expression image, and the dynamic information feature is extracted from the optical flow information of multiple expression images based on temporal convolution neural network. Then, the spatiotemporal features learned by the two deep convolution neural networks are fused by multiplication. Finally, the fused features are input into support vector machine to realize the facial expression classification. Experimental results show that the recognition accuracy of the proposed method can reach 64.57% and 60.89%, respectively on RML and Baum-ls datasets. It is better than that of other contrast methods.

• Korean Journal of Ecology and Environment
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• v.57 no.1
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• pp.51-59
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• 2024

This note summarizes the application of zooplankton indices for water quality management and estimation based on main research topics of articles focusing on wetland ecosystems, topics that are remained poorly investigated in S. Korea. The aquatic ecosystem-based consists of indices that respond to different target environmental factors, including environmental disturbance. Among the major indicator species and biota, we reviewed that management strategy for the wetland environment has to be focused more on small-sizes, in terms of zooplankton ecology and indices. The ecology of zooplankton communities in freshwater ecosystem has been the focus of an increasing number of studies since 2019, and considerable progress has been made in understanding the major mechanisms involved in regulating their abundance, diversity and spatio-temporal patterns. Even though studies on the freshwater ecosystem in Korea have a long history, a few of studies on zooplankton biota were conducted at wetlands. We suggested the candidate zooplankton indices proposed by the U.S. EPA and EU to suit Korean conditions. In the step of selecting metrics, the best available metrics are species-related variables, such as composition and abundance, as well as richness and diversity. Overall, in spite of several limitations, the development of a plankton-based multivariate assessment method in Korea wetlands is possible using mostly field research data. Later, it could be improved based on qualitative metrics on zooplankton, and with the emergence of further survey data. The present information can be used as basic information for researchers who are dealing with aquatic environments and its interaction with organisms.

• CELLMED
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• v.14 no.2
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• pp.2.1-2.10
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• 2024

Through big data analysis of the 'Joseonwangjosilok', this study examines the perception of ginseng among the ruling class and its utilization during the Joseon era. It aims to provide foundational data for the development of ginseng into a high-value cultural commodity. The focus of this research, the Joseonwangjosilok, comprises 1,968 volumes in 948 books, spanning a record of 518 years. Data was collected through web crawling on the website of the National Institute of Korean History, followed by frequency analysis of significant words. To assess the interest in ginseng across the reigns of 27 kings during the Joseon era, ginseng frequency records were adjusted based on years in power and the number of articles, creating an interest index for comparative rankings across reigns. Analysis revealed higher interest in ginseng during the reigns of King Jeongjo and King Yeongjo in the 18th century, King Sunjo in the 19th century, King Sejong in the 15th century, King Sukjong in the 17th century, and King Gojong in the 19th century. Examining the temporal emergence and changes in ginseng during the Joseon era, general ginseng types like insam and sansam had the highest frequency in the 15th century. It appears that Korea adeptly utilized ceremonial goods in diplomatic relations with China and Japan, meeting the demand for ginseng from their royal and aristocratic societies. Processed ginseng varieties such as hongsam and posam, along with traded and taxed ginseng, showed peak frequency in the 18th century. This coincided with increased cultivation, allowing a higher supply and fostering the development of ginseng processing technologies like hongsam.

• The Journal of the Acoustical Society of Korea
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• v.25 no.5
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• pp.239-245
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• 2006

Time-reversal processing (TRP) has been shown as an effective way to focus in both time and space. The temporal focusing properties have been used extensively in underwater acoustics communications. Recently. adaptive time-reversal processing (ATRP) was applied to the simultaneous multiple focusing in an ocean waveguide. In this study. multiple focusing with ATRP is extended to the underwater acoustic communication algorithm for multiple receiving locations. The developed algorithm is applied to the underwater acoustic communication to show, via simulation and real data, that the simultaneous self-equalization at multiple receiving locations is achieved.

• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.235-243
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• 2023

Due to the revision of the River Act and the enactment of the Act on the Investigation, Planning, and Management of Water Resources, a regular bed change survey has become mandatory and a system is being prepared such that local governments can manage water resources in a planned manner. Since the topography of a bed cannot be measured directly, it is indirectly measured via contact-type depth measurements such as level survey or using an echo sounder, which features a low spatial resolution and does not allow continuous surveying owing to constraints in data acquisition. Therefore, a depth measurement method using remote sensing-LiDAR or hyperspectral imaging-has recently been developed, which allows a wider area survey than the contact-type method as it acquires hyperspectral images from a lightweight hyperspectral sensor mounted on a frequently operating drone and by applying the optimal bandwidth ratio search algorithm to estimate the depth. In the existing hyperspectral remote sensing technique, specific physical quantities are analyzed after matching the hyperspectral image acquired by the drone's path to the image of a surface unit. Previous studies focus primarily on the application of this technology to measure the bathymetry of sandy rivers, whereas bed materials are rarely evaluated. In this study, the existing hyperspectral image-based water depth estimation technique is applied to rivers with vegetation, whereas spatio-temporal hyperspectral imaging and cross-sectional hyperspectral imaging are performed for two cases in the same area before and after vegetation is removed. The result shows that the water depth estimation in the absence of vegetation is more accurate, and in the presence of vegetation, the water depth is estimated by recognizing the height of vegetation as the bottom. In addition, highly accurate water depth estimation is achieved not only in conventional cross-sectional hyperspectral imaging, but also in spatio-temporal hyperspectral imaging. As such, the possibility of monitoring bed fluctuations (water depth fluctuation) using spatio-temporal hyperspectral imaging is confirmed.