• Journal of Biomedical Engineering Research
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• v.25 no.5
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• pp.335-342
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• 2004

This paper describes a trans-admittance scanner for breast cancer detection. A FPGA-based sinusoidal waveform generator produces a constant voltage. The voltage is applied between a hand-held electrode and a scan probe placed on the breast. The scan probe contains an 8x8 array of electrodes that are kept at the ground potential. Multi-channel precision digital ammeters using the phase-sensitive demodulation technique were developed to measure the exit current from each electrode in the array. Different regions of the breast are scanned by moving the probe on the breast. We could get trans-admittance images of resistor and saline phantoms with an anomaly inside. The images provided the information on the depth and location of the anomaly. In future studies, we need to improve the accuracy through a better calibration method. We plan to test the scanner's ability to detect a cancer lesion inside the human breast.

• Journal of KIISE:Software and Applications
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• v.35 no.12
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• pp.716-730
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• 2008

In Service-Oriented Architecture (SOA), service providers develop and deploy reusable services on the repositories, and service consumers utilize blackbox form of services through their interfaces. Services are also highly evolvable and often heterogeneous. Due to these characteristics of the service, it is hard to manage the faults if faults occur on the services. Autonomic Computing (AC) is a way of designing systems which can manage themselves without direct human intervention. Applying the key disciplines of AC to service management is appealing since key technical issues for service management can be effectively resolved by AC. In this paper, we present a theoretical model, Symptom-Cause-Actuator (SCA), to enable autonomous service fault management in SOA. We derive SCA model from our rigorous observation on how physicians treat patients. In this paper, we first define a five-phase computing model and meta-model of SCA. And, we define a schema of SCA profile, which contains instances of symptoms, causes, actuators and their dependency values in a machine readable form. Then, we present detailed algorithms for the five phases that are used to manage faults the services. To show the applicability of our approach, we demonstrate the result of our case study for the domain of 'Flight Ticket Management Services'.

• The KIPS Transactions:PartA
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• v.12A no.5 s.95
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• pp.451-460
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• 2005

We present a methodology to design energy-efficient data paths using FPGAs. Our methodology integrates domain specific modeling, coarse-grained performance evaluation, design space exploration, and low-level simulation to understand the tradeoffs between energy, latency, and area. The domain specific modeling technique defines a high-level model by identifying various components and parameters specific to a domain that affect the system-wide energy dissipation. A domain is a family of architectures and corresponding algorithms for a given application kernel. The high-level model also consists of functions for estimating energy, latency, and area that facilitate tradeoff analysis. Design space exploration(DSE) analyzes the design space defined by the domain and selects a set of designs. Low-level simulations are used for accurate performance estimation for the designs selected by the DSE and also for final design selection We illustrate our methodology using a family of architectures and algorithms for matrix multiplication. The designs identified by our methodology demonstrate tradeoffs among energy, latency, and area. We compare our designs with a vendor specified matrix multiplication kernel to demonstrate the effectiveness of our methodology. To illustrate the effectiveness of our methodology, we used average power density(E/AT), energy/(area x latency), as themetric for comparison. For various problem sizes, designs obtained using our methodology are on average $25\%$ superior with respect to the E/AT performance metric, compared with the state-of-the-art designs by Xilinx. We also discuss the implementation of our methodology using the MILAN framework.

• Journal of KIISE:Databases
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• v.29 no.5
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• pp.367-380
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• 2002

Due to recent increase in applications requiring huge amount of data such as spatial data analysis and image analysis, clustering on large databases has been actively studied. In a hierarchical clustering method, a tree representing hierarchical decomposition of the database is first created, and then, used for efficient clustering. Existing hierarchical clustering methods mainly adopted the bottom-up approach, which creates a tree from the bottom to the topmost level of the hierarchy. These bottom-up methods require at least one scan over the entire database in order to build the tree and need to search most nodes of the tree since the clustering algorithm starts from the leaf level. In this paper, we propose a novel top-down hierarchical clustering method that uses multidimensional indexes that are already maintained in most database applications. Generally, multidimensional indexes have the clustering property storing similar objects in the same (or adjacent) data pares. Using this property we can find adjacent objects without calculating distances among them. We first formally define the cluster based on the density of objects. For the definition, we propose the concept of the region contrast partition based on the density of the region. To speed up the clustering algorithm, we use the branch-and-bound algorithm. We propose the bounds and formally prove their correctness. Experimental results show that the proposed method is at least as effective in quality of clustering as BIRCH, a bottom-up hierarchical clustering method, while reducing the number of page accesses by up to 26～187 times depending on the size of the database. As a result, we believe that the proposed method significantly improves the clustering performance in large databases and is practically usable in various database applications.

• Journal of KIISE:Databases
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• v.37 no.3
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• pp.146-164
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• 2010

Due to an increasing volume of XML data, it is considered prudent to store XML data on an industry-strength database system instead of relying on a domain specific application or a file system. For shredded XML data stored in relational tables, however, it may not be straightforward to apply existing algorithms for twig query processing, since most of the algorithms require XML data to be accessed in a form of streams of elements grouped by their tags and sorted in a particular order. In order to support XML query processing within the common framework of relational database systems, we first propose several bitmap indexes and their strategies for supporting holistic twig joining on XML data stored in relational tables. Since bitmap indexes are well supported in most of the commercial and open-source database systems, the proposed bitmapped indexes and twig query processing strategies can be incorporated into relational query processing framework with more ease. The proposed query processing strategies are efficient in terms of both time and space, because the compressed bitmap indexes stay compressed during data access. In addition, we propose a hybrid index which computes twig query solutions with only bit-vectors, without accessing labeled XML elements stored in the relational tables.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.91-98
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• 2008

We present an efficient and robust technique to estimate the velocity of moving targets from a single SAR image. In SAR images, azimuth image shift is a well blown phenomenon, which is observed in moving targets having slant-range velocity. Most methods estimated the velocity of moving targets from the distance difference between the road and moving targets or between ship and the ship wake. However, the methods could not be always applied to moving targets because it is difficult to find the road and the ship wake. We propose a method for estimating the velocity of moving targets from azimuth differentials of range-compressed image. This method is based on a phenomenon that Doppler center frequency shift of moving target causes a phase difference in azimuth differential values. The phase difference is linearly distorted by Doppler rate due to the geometry of SAR image. The linear distortion is eliminated from phase removal procedure, and then the constant phase difference is estimated. Finally, range velocity estimates for moving targets are retrieved from the constant phase difference. This technique was tested using an ENVISAT ASAR image in which several unknown ships are presented. In the case of a isolated target, the result was nearly coincident with the result from conventional method. However, in the case of a target which is located near non-target material, the difference of the result between from our algorithm and from conventional method was more than 1m/s.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.531-540
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• 2021

As cyber attacks and crimes increase exponentially and hacking attacks become more intelligent and advanced, hacking attack methods and routes are evolving unpredictably and in real time. In order to reinforce the enemy's responsiveness, this study aims to propose a method for developing an artificial intelligence-based security control platform by building a next-generation security system using artificial intelligence to respond by self-learning, monitoring abnormal signs and blocking attacks.The artificial intelligence-based security control platform should be developed as the basis for data collection, data analysis, next-generation security system operation, and security system management. Big data base and control system, data collection step through external threat information, data analysis step of pre-processing and formalizing the collected data to perform positive/false detection and abnormal behavior analysis through deep learning-based algorithm, and analyzed data Through the operation of a security system of prevention, control, response, analysis, and organic circulation structure, the next generation security system to increase the scope and speed of handling new threats and to reinforce the identification of normal and abnormal behaviors, and management of the security threat response system, Harmful IP management, detection policy management, security business legal system management. Through this, we are trying to find a way to comprehensively analyze vast amounts of data and to respond preemptively in a short time.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.887-901
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• 2022

Estimation of the parameters for individual rainfall-rainfall events can lead to a different set of parameters for each event which result in lack of parameter identifiability. Moreover, it becomes even more ambiguous to determine a representative set of parameters for the watershed due to enhanced variability exceeding the range of model parameters. To reduce the variability of estimated parameters, this study proposed a parameter optimization framework with the simultaneous use of multiple rainfall-runoff events based on NSE as an objective function. It was found that the proposed optimization framework could effectively estimate the representative set of parameters pertained to their physical range over the entire watershed. It is found that the difference in NSE value of optimization when it performed individual and multiple rainfall events, is 0.08. Furthermore, In terms of estimating the observed floods, the representative parameters showed a more improved (or similar) performance compared to the results obtained from the single-event optimization process on an NSE basis.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1120-1128
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• 2022

In the event of a marine accident, the longer the exposure time to the sea increases, the faster the chance of survival decreases. However, because the search area of the sea is extremely wide compared to that of land, marine object detection technology based on the sensor mounted on a satellite or an aircraft must be applied rather than ship for an efficient search. The purpose of this study was to rapidly detect an object in the ocean using a hyperspectral image sensor mounted on an aircraft. The image captured by this sensor has a spatial resolution of 8,241 × 1,024, and is a large-capacity data comprising 127 spectra and a resolution of 0.7 m per pixel. In this study, a marine object detection model was developed that combines a seawater identification algorithm using DBSCAN and a density-based land removal algorithm to rapidly analyze large data. When the developed detection model was applied to the hyperspectral image, the performance of analyzing a sea area of about 5 km² within 100 s was confirmed. In addition, to evaluate the detection accuracy of the developed model, hyperspectral images of the Mokpo, Gunsan, and Yeosu regions were taken using an aircraft. As a result, ships in the experimental image could be detected with an accuracy of 90 %. The technology developed in this study is expected to be utilized as important information to support the search and rescue activities of small ships and human life.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.100-103
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• 2023

Recently, development of maritime autonomoust surface ships and eco-friendly ships, production and evaluation research considering various marine environments is needed in the field of optimal routes as the demand for accurate and detailed real-time marine environment prediction information expands. An algorithm that can calculate the optimal route while reducing the risk of the marine environment and uncertainty in energy consumption in smart ships was developed in 2 stages. In the first stage, a profile was created by combining marine environmental information with ship location and status information within the Automatic Ship Identification System(AIS). In the second stage, a model was developed that could define the marine environment energy map using the configured profile results, A regression equation was generated by applying Random Forest among machine learning techniques to reflect about 600,000 data. The Random Forest coefficient of determination (R²) was 0.89, showing very high reliability. The Dijikstra shortest path algorithm was applied to the marine environment prediction at June 1 to 3, 2021, and to calculate the optimal safety route and express it on the map. The route calculated by the random forest regression model was streamlined, and the route was derived considering the state of the marine environment prediction information. The concept of route calculation based on real-time marine environment prediction information in this study is expected to be able to calculate a realistic and safe route that reflects the movement tendency of ships, and to be expanded to a range of economic, safety, and eco-friendliness evaluation models in the future.