• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.2
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• pp.103-112
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• 2012

We demonstrate the preliminary results of ship detection application using synthetic aperture radar (SAR) and automatic identification system (AIS) together. Multi-frequency and multi-temporal SAR images such as TerraSAR-X and Cosmo-SkyMed (X-band), and Radarsat-2 (C-band) are acquired over the West Sea in South Korea. In order to compare with SAR data, we also collected an AIS data. The SAR data are pre-processed considering by the characteristics of scattering mechanism as for sea surface. We proposed the "Adaptive Threshold Algorithm" for classification ship efficiently. The analyses using the combination of the SAR and AIS data with time series will be very useful to ship detection or tracing of the ship.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1705-1711
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• 1991

On-line fault detection and diagnosis has an increasing interest in a chemical process industry, especially for a process control and automation. The chemical process needs an intelligent operation-aided workstation which can do such tasks as process monitoring, fault detection, fault diagnosis and action guidance in semiautomatic mode. These tasks can increase the performance of a process operation and give merits in economics, safety and reliability. Aiming these tasks, series of researches have been done in our lab. Main results from these researches are building appropriate knowledge representation models and a diagnosis mechanism for fault detection and diagnosis in a chemical process. The knowledge representation schemes developed in our previous research, the symptom tree model and the fault-consequence digraph, showed the effectiveness and the usefulness in a real-time application, of the process diagnosis, especially in large and complex plants. However in our previous approach, the diagnosis speed is its demerit in spite of its merits of high resolution, mainly due to using two knowledge models complementarily. In our current study, new knowledge representation scheme is developed which integrates the previous two knowledge models, the symptom tree and the fault-consequence digraph, into one. This new model is constructed using a material balance, energy balance, momentum balance and equipment constraints. Controller related constraints are included in this new model, which possesses merits of the two previous models. This new integrated model will be tested and verified by the real-time application in a BTX process or a crude unit process. The reliability and flexibility will be greatly enhanced compared to the previous model in spite of the low diagnosis speed. Nexpert Object for the expert system shell and SUN4 workstation for the hardware platform are used. TCP/IP for a communication protocol and interfacing to a dynamic simulator, SPEEDUP, for a dynamic data generation are being studied.

• Genomics & Informatics
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• v.5 no.4
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• pp.143-151
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• 2007

To understand the mechanism of transcriptional regulation, it is essential to detect promoters and regulatory elements. Various kinds of methods have been introduced to improve the prediction accuracy of regulatory elements. Since there are few experimentally validated regulatory elements, previous studies have used criteria based solely on the level of scores over background sequences. However, selecting the detection criteria for different prediction methods is not feasible. Here, we studied the calibration of thresholds to improve regulatory element prediction. We predicted a regulatory element using MATCH, which is a powerful tool for transcription factor binding site (TFBS) detection. To increase the prediction accuracy, we used a regulatory potential (RP) score measuring the similarity of patterns in alignments to those in known regulatory regions. Next, we calibrated the thresholds to find relevant scores, increasing the true positives while decreasing possible false positives. By applying various thresholds, we compared predicted regulatory elements with validated regulatory elements from the Open Regulatory Annotation (ORegAnno) database. The predicted regulators by the selected threshold were validated through enrichment analysis of muscle-specific gene sets from the Tissue-Specific Transcripts and Genes (T-STAG) database. We found 14 known muscle-specific regulators with a less than a 5% false discovery rate (FDR) in a single TFBS analysis, as well as known transcription factor combinations in our combinatorial TFBS analysis.

• Journal of Korea Multimedia Society
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• v.20 no.10
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• pp.1678-1688
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• 2017

Infiltration is one of detrimental problems occurring in nursing or medical settings. Early detection of infiltration is essential to minimize the risk of injury from infiltration. To perform a preliminary study on the point of care and automated infiltration detection system, bioelectrical impedance was investigated using bioelectrical impedance analyzer. We would like to report experimental results that allow impedance parameters to effectively distinguish infiltration. Electrodes were attached to both sides of the transparent dressing on the fusion site where IV solution was being infused. Then, impedance parameters before and after infiltration were measured as a function of time and frequency. The experimental results are as follows. After infiltration was intentionally induced by puncturing the vein wall with a needle, the resistance gradually decreased with time. That is, when an alternating current having a frequency of 20 kHz was applied to the electrodes, the resistance gradually decreased with time, reflecting the accumulation of IV solution in the extracellular fluid since the current could not pass through the cell membrane. Impedance parameters and equivalent circuit model for human cell were used to examine the mechanism of current flow before and after infiltration, which could be used for early detection of infiltration.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.6
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• pp.339-348
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• 2006

Recently, many researches on detecting and responding worms due to the fatal infrastructural damages explosively damaged by automated attack tools, particularly worms. Network service vulnerability exploiting worms have high propagation velocity, exhaust network bandwidth and even disrupt the Internet. Previous worm researches focused on signature-based approaches however these days, approaches based on behavioral features of worms are more highlighted because of their low false positive rate and the attainability of early detection. In this paper, we propose a Distributed Worm Detection Model based on packet marking. The proposed model detects Worm Cycle and Infection Chain among which the behavior features of worms. Moreover, it supports high scalability and feasibility because of its distributed reacting mechanism and low processing overhead. We virtually implement worm propagation environment and evaluate the effectiveness of detecting and responding worm propagation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.11B
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• pp.900-910
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• 2004

Route failure is mainly caused by mobility of mobile host in ad hoc networks. Route failure, which may lead to sudden packet losses and delays, is losing the route from source to destination. In this situation, TCP assumes that congestion has occurred within the network and also initiates the congestion control procedures. Congestion control algorithm provides the means for the source to deal with lost packets. TCP performance in ad hoc environments will be degraded as TCP source cannot distinguish congestion from route failure. In this paper, we propose TCP-P as pre-detection approach to deal with route failure. TCP-P freezes TCP through pre-detection of route failure. Route failure information of the proposed mechanism is obtained not by routing protocol but by MAC protocol. The intermediated node, obtaining route failure information by its MAC layer, relays the information to TCP source and lets TCP source stop the congestion control algorithm. Results reveal that TCP-P responding with proactive manner outperforms other approaches in terms of communication throughput under the presence of node mobility.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8C
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• pp.748-757
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• 2002

Cyber-terror trials are increased in nowadays and these attacks are commonly using security vulnerability and information gathering method by variable services grew by the continuous development of Internet Technology. IDS's application environment is affected by this increasing Cyber Terror. General Network based IDS detects intrusion by signature based Intrusion Detection module about inflowing packet through network devices. Up to now security in network is commonly secure host, an regional issue adopted in special security system but these system is vulnerable intrusion about the attack in globally connected Internet systems. Security mechanism should be produced to expand the security in whole networks. In this paper, we analyzer the DARPA's program and study Infusion Detection related Technology. We design policy security framework for policy enforcing in whole network and look at the modules's function. Enforcement of security policy is acted by Intrusion Detection system on gateway system which is located in network packet's inflow point. Additional security policy is operated on-line. We can design and execute central security policy in managed domain in this method.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.17
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• pp.7997-8002
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• 2015

Gene amplification is an important mechanism in the development and progression of cancer. Currently, gene amplification status is generally determined by in situ hybridization (ISH). Multiplex ligation-dependent probe amplification (MLPA) is a PCR-based method that allows copy number detection of up to 50 nucleic acid sequences in one reaction. The aim of the present study was to compare results for HER2, CCND1, MYC and ESR1 gene amplification detected by MLPA with fluorescent in situ hybridization (FISH) and chromogenic in situ hybridization (CISH) as clinically approved methods. Tissue samples of 170 invasive breast cancers were collected. All were ER positive. Tissue samples had previously been tested for HER2 using immunohistochemistry. Amplification of the selected genes were assessed using MLPA, FISH and CISH and results were compared. HER2 MLPA and ISH results were also compared with HER2 immunohistochemistry (IHC) which detects protein overexpression. Amplification of HER2, CCND1, MYC and ESR1 by MLPA were found in 9%, 19%, 20% and 2% of samples, respectively. Amplification of HER2, CCND1, MYC and ESR1 by FISH was noted in 7%, 16%, 16% and 1% of samples, respectively. A high level of concordance was found between MLPA/FISH (HER2: 88%, CCND1: 88%, MYC: 86%, ESR1: 92%) and MLPA/CISH (HER2: 84%). Of all IHC 3+ cases, 91% were amplified by MLPA. In IHC 2+ group, 31% were MLPA amplified. In IHC 1+ group, 2% were MLPA amplified. None of the IHC 0 cases were amplified by MLPA. Our results indicate that there is a good correlation between MLPA, IHC and ISH results. Therefore, MLPA can serve as an alternative to ISH for detection of gene amplification.

• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.140-146
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• 2020

Since the current crime prevention systems have a standard mechanism that victims request for help by themselves or ask for help from a third party nearby, it is difficult to obtain appropriate help in situations where a prompt response is not possible. In this study, we proposed and developed an automatic rescue request model and system using Deep Learning and OpenCV. This study is based on the prerequisite that immediate and precise threat detection is essential to ensure the user's safety. We validated and verified that the system identified by more than 99% of the object's accuracy to ensure the user's safety, and it took only three seconds to complete all necessary algorithms. We plan to collect various types of threats and a large amount of data to reinforce the system's capabilities so that the system can recognize and deal with all dangerous situations, including various threats and unpredictable cases.

• Korean Journal of Cognitive Science
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• v.17 no.1
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• pp.1-13
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• 2006

We applied fMRI to examine brain activation at intentionality detection (ID) task. The main purpose of this study was to explore whether brain activation regions involved in intentionality detection (known as the basic mechanism of theory of mind) differ or not, according to prior instruction. Left uncus, superior temporal gyrus and right inferior occipital gyrus, supramarginal gyrus, inferior parietal lobule, thalamus (medial dorsal nucleus), and precuneus were activated with prior instruction. In contrast, ID task with no instruction activated merely inferior parietal lobule and superior parietal lobule. Common activated area between the two instruction conditions was inferiordparietal lobule. Our results suggest thar prior instruction activated ID-related brain regions more explicitly. furtherdinvestigations would be loused on spontaneity of intentionality detector and characteristic of participants.