• The Bulletin of The Korean Astronomical Society
• /
• v.40 no.1
• /
• pp.56.2-56.2
• /
• 2015

The KMTNet Supernovae Project utilizes the large $2^{\circ}{\times}2^{\circ}$ field of view of the three KMTNet telescopes to search and monitor supernovae, especially early ones, and other optical transients. A key component of the project is to build a data pipeline with a descent latency and an early alerting system that can handle the large volume of the data in an efficient and a prompt way, while minimizing false alarms, which casts a significant challenge to the software development. Here we present the current status of their development. The pipeline utilizes a difference image analysis technique to discover candidate transient sources after making correction of image distortion. In the early phase of the program, final selection of transient sources from candidates will mainly rely on multi-filter, multi-epoch and multi-site screening as well as human inspection, and an interactive web-based system is being developed for this purpose. Eventually, machine learning algorithms, based on the training set collected in the early phase, will be used to select true transient sources from candidates.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.12
• /
• pp.2525-2532
• /
• 2009

In this paper, a fast sphere decoder is proposed for the joint detection of phase-shift keying (PSK) signals in uncoded Vertical Bell Laboratories Layered Space Time (V-BLAST) systems. The proposed decoder, PSD, consists of preprocessing stage and search stage. The search stage of PSD relies on the depth-first branch-and-bound (BB) algorithm with "best-first" orders stored in lookup tables. Simulation results show that the PSD is able to provide the system with the maximum likelihood (ML) performance at low complexity.

• Journal of Korean Society for Quality Management
• /
• v.43 no.1
• /
• pp.31-42
• /
• 2015

Purpose: System consists of a lot of units with coherent function. In design phase, various units could be considered with the same function. In this study, we consider the alternative units with the same function and redundancy allocation to maximize system reliability in multi level system. Methods: The redundancy allocation problem with the alternative units in multi level system is formulated. Memetic algorithm(MA) is proposed to optimize the redundancy allocation problem. In addition, the performance of the proposed algorithm is explained by a numerical experiment. Results: MA showed better results than genetic algorithm(GA) and the convergence of the solutions in MA was also faster than GA. In addition, we could know from experiment that system reliability is increased and the chosen unit for redundancy allocation is changed if cost limit is increased. Conclusion: The chose unit for redundancy allocation is changed as resource constraints. It means we need to consider the alternative units in system design. In the future, we need to consider various problem related to redundancy allocation in multi level system and develop the better method to enhance search performance.

• The KIPS Transactions:PartD
• /
• v.15D no.4
• /
• pp.569-580
• /
• 2008

Web services are the current most promising technology for service oriented architecture(SOA) implementations. However, in spite of the large scale acceptance of web services, they have been relegated to internal integration projects, and the grand vision of virtual enterprises where partners can be integrated on demand is yet to be realized. The main reason is that the current standards of web services are not very suitable for the dynamic web service discovery and integration. In this paper, we present a novel SemanticBPEL solution that merges the benefit of WS-BPEL, with the advantage of OWL-S for building a semi-automatic web service composition system. In particular, this work proposes a multi-phase search method for solving dynamic discovery and integration problems of web services. The proposed method is compared with the existing keyword based retrieval method. These comparisons show that our approach outperforms the existing method.

• Convergence Security Journal
• /
• v.22 no.2
• /
• pp.105-114
• /
• 2022

This paper is presents an intelligent control system that visualizes the direction of arrival for hearing impaired using personal mobility, and aims to recognize and prevent dangerous situations caused by sound such as alarm sounds and crack sounds on roads. The position estimation method of sound source uses a machine learning classification model characterized by generalized correlated phase transformation based on time difference of arrival. In the experimental environment reproducing the road situations, four classification models learned after extracting learning data according to wind speeds 0km/h, 5.8km/h, 14.2km/h, and 26.4km/h were compared with grid search cross validation, and the Muti-Layer Perceptron(MLP) model with the best performance was applied as the optimal algorithm. When wind occurred, the proposed algorithm showed an average performance improvement of 7.6-11.5% compared to the previous studies.

• The Bulletin of The Korean Astronomical Society
• /
• v.40 no.1
• /
• pp.59.2-59.2
• /
• 2015

We present the results of simultaneous observations of the H2O and SiO masers emitted from the circumstellar envelopes of the late-type stars. These observations have been carried out at the K and Q bands using KVN since 2014 August and were scheduled to test the feasibility of multi-frequency phase referencing analysis on the maser lines. In order to increase the accuracy of group delay solution in the fringe search on the continuum source, the IF channels were randomly distributed within the available bandwidth of 500 MHz in each band. The positions of all maser spots are relatively described with respect to the position of the reference continuum source through the source frequency phase referencing technique, and this provides the astrometric position accuracy. Therefore, the relative locations of the H2O maser spots with respect to the SiO maser spots are determined from our observations, and the capability of the simultaneous multi-band observation of KVN is proved to be powerful to study the maser pumping mechanism around the late-type stars.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.35 no.1
• /
• pp.181-187
• /
• 2012

Various computer-based simulation tools such as 3D-CAD and CAE systems are widely used to design automotive body structure at the early phase of design. Designers must search the optimal solution that satisfies a number of performance requirements by using their tools and a trial-and-error approach. In the previous three reports, a set-based design approach has been proposed for achieving design flexibility and robustness while capturing designer's preference, and its effectiveness has been illustrated with a simple side-door impact beam design problem and real vehicle side-door structure design. This report presents the development of integrated 3D-CAD and CAE system, and the applicability of our proposal for obtaining the multi-objective satisfactory design solutions by applying to an automotive front-side frame.

• Smart Structures and Systems
• /
• v.33 no.1
• /
• pp.65-91
• /
• 2024

The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMA-MLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R²) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.

• Journal of Intelligence and Information Systems
• /
• v.15 no.2
• /
• pp.49-59
• /
• 2009

Contents-based image retrieval methods are in general more objective and effective than text-based image retrieval algorithms since they use color and texture in search and avoid annotating all images for search. SIM(Self-organizing Image browsing Map) is one of contents-based image retrieval algorithms that uses only browsable mapping results obtained by SOM(Self Organizing Map). However, SOM may have an error in selecting the right BMU in learning phase if there are similar nodes with distorted color information due to the intensity of light or objects' movements in the image. Such images may be mapped into other grouping nodes thus the search rate could be decreased by this effect. In this paper, we propose an improved SIM that uses HSV color model in extracting image features with color quantization. In order to avoid unexpected learning error mentioned above, our SOM consists of two layers. In learning phase, SOM layer 1 has the color feature vectors as input. After learning SOM Layer 1, the connection weights of this layer become the input of SOM Layer 2 and re-learning occurs. With this multi-layered SOM learning, we can avoid mapping errors among similar nodes of different color information. In search, we put the query image vector into SOM layer 2 and select nodes of SOM layer 1 that connects with chosen BMU of SOM layer 2. In experiment, we verified that the proposed SIM was better than the original SIM and avoid mapping error effectively.

• The Bulletin of The Korean Astronomical Society
• /
• v.39 no.2
• /
• pp.115-115
• /
• 2014

First, We will briefly introduce early science results of AGN observations with KVN and KaVA. KaVA is the combined array of the Korean VLBI network (KVN) and VLBI Exploration of Radio Astronomy (VERA). These include KaVA monitoring of M87, Sgr A* and a few bright blazars and KVN Search for circular polarized Blazars. Furthermore, we will present our future plan of monitoring observation of Sgr A* and M87 with KaVA and Low Radio Power AGN multi frequency polarization survey with KVN. Because of the largeness of their centralsuper-massive black holes, we select them as top-priority sources of our key science program (KSP). The main science goals of the KaVA KSP are (1) mapping the velocity field of the M87 jet and testing magnetically-driven-jet paradigm, and (2) obtaining tightest constraints on physical properties of radio emitting region in Sgr A. High sensitivity achieved through simultaneous multifrequency phase referencing technique of KVN will allow us to explore Low Radio Power AGN cores which build majority of AGNs and therefore are important for undestanding the evolution of AGNs and of their hosts.