• Journal of KIISE:Software and Applications
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• v.35 no.12
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• pp.764-773
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• 2008

The web ontology language(OWL) has become a W3C recommendation to publish and share ontologies on the semantic web. In order to check the satisfiablity of concepts in OWL ontology, OWL reasoners have been introduced. But most reasoners simply report check results without providing a justification for any arbitrary entailment of unsatisfiable concept in OWL ontologies. In this paper, we propose dependency label based causing inconsistency axiom (CIA) detection for debugging unsatisfiable concepts in ontology. CIA is a set of axioms to occur unsatisfiable concepts. In order to detect CIA, we need to find axiom to cause inconsistency in ontology. If precise CIA is gave to ontology building tools, these ontology tools display CIA to debug unsatisfiable concepts as suitable presentation format. Our work focuses on two key aspects. First, when a inconsistency ontology is given, it detect axioms to occur unsatisfiable and identify the root of them. Second, when particular unsatisfiable concepts in an ontology are detected, it extracts them and presents to ontology designers. Therefore we introduce a tableau-based decision procedure and propose an improved method which is dependency label based causing inconsistency axiom detection. Our results are applicable to the very expressive logic SHOIN that is the basis of the Web Ontology Language.

• Journal of the Optical Society of Korea
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• v.18 no.4
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• pp.317-329
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• 2014

In vision measurement systems based on structured light, the key point of detection precision is to determine accurately the central position of the projected laser line in the image. The purpose of this research is to extract laser line centers based on a decision function generated to distinguish the real centers from candidate points with a high recognition rate. First, preprocessing of an image adopting a difference image method is conducted to realize image segmentation of the laser line. Second, the feature points in an integral pixel level are selected as the initiating light line centers by the eigenvalues of the Hessian matrix. Third, according to the light intensity distribution of a laser line obeying a Gaussian distribution in transverse section and a constant distribution in longitudinal section, a normalized model of Hessian matrix eigenvalues for the candidate centers of the laser line is presented to balance reasonably the two eigenvalues that indicate the variation tendencies of the second-order partial derivatives of the Gaussian function and constant function, respectively. The proposed model integrates a Gaussian recognition function and a sinusoidal recognition function. The Gaussian recognition function estimates the characteristic that one eigenvalue approaches zero, and enhances the sensitivity of the decision function to that characteristic, which corresponds to the longitudinal direction of the laser line. The sinusoidal recognition function evaluates the feature that the other eigenvalue is negative with a large absolute value, making the decision function more sensitive to that feature, which is related to the transverse direction of the laser line. In the proposed model the decision function is weighted for higher values to the real centers synthetically, considering the properties in the longitudinal and transverse directions of the laser line. Moreover, this method provides a decision value from 0 to 1 for arbitrary candidate centers, which yields a normalized measure for different laser lines in different images. The normalized results of pixels close to 1 are determined to be the real centers by progressive scanning of the image columns. Finally, the zero point of a second-order Taylor expansion in the eigenvector's direction is employed to refine further the extraction results of the central points at the subpixel level. The experimental results show that the method based on this normalization model accurately extracts the coordinates of laser line centers and obtains a higher recognition rate in two group experiments.

• Pediatric Infection and Vaccine
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• v.26 no.3
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• pp.199-205
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• 2019

Enteroviral infections are common in neonates. One important infection pathway is vertical transmission from an infected mother to her neonate. Here, we report the early detection and successful treatment of a vertically transmitted fulminant enteroviral infection associated with myocarditis and hepatitis. The patient had a sudden onset of high fever on the fourth day of life and developed severe, rapidly progressing symptoms of disseminated intravascular coagulopathy (DIC), hepatitis, and myocarditis accompanied by tachyarrhythmia. As it was the peak season for enteroviral infections and both the mother and the patient's 36-month-old sibling had a high fever around the time of delivery, we suspected an enteroviral infection. Thus, we initiated prompt evaluation of enteroviral infection, as well as close observation and intensive care of the neonate. We strongly recommend evaluation for the possibility of vertical enterovirus infection in neonates when the mother is suspected of having a viral infection (e.g., high fever and negative results from bacterial infectious studies) around the time of delivery and when the neonate shows some early symptoms of infectious diseases such as thrombocytopenia, DIC, hepatitis, and myocarditis. Early detection of enteroviral infections and prompt implementation of proper treatment are key to reduce the risk of complications and mortality associated with enteroviral infections in neonates.

• Korean Journal of Clinical Laboratory Science
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• v.54 no.4
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• pp.249-255
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• 2022

Alzheimer's disease (AD) represents a major public health concern and has been identified as a research priority. Clinical research evidence supports that the core cerebrospinal fluid (CSF) biomarkers for AD, including amyloid-β (Aβ42), total tau (T-tau), and phosphorylated tau (P-tau), reflect key elements of AD pathophysiology. Nevertheless, advances in the clinical identification of new indicators will be critical not only for the discovery of sensitive, specific, and reliable biomarkers of preclinical AD pathology, but also for the development of tests that facilitate the early detection and differential diagnosis of dementia and disease progression monitoring. The early detection of AD in its presymptomatic stages would represent a great opportunity for earlier therapeutic intervention. The chance of successful treatment would be increased since interventions would be performed before extensive synaptic damage and neuronal loss would have occurred. In this study, the importance of developing an early diagnostic method using cognitive decline biomarkers that can discriminate between normal, mild cognitive impairment (MCI), and AD preclinical stages has been emphasized.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.5
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• pp.7-18
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• 2017

Recently, the number of SNS user has rapidly increased due to smart device industry development and also the amount of generated data is exponentially increasing. In the twitter, Text data generated by user is a key issue to research because it involves events, accidents, reputations of products, and brand images. Twitter has become a channel for users to receive and exchange information. An important characteristic of Twitter is its realtime. Earthquakes, floods and suicides event among the various events should be analyzed rapidly for immediately applying to events. It is necessary to collect tweets related to the event in order to analyze the events. But it is difficult to find all tweets related to the event using normal keywords. In order to solve such a mentioned above, this paper proposes A Generation and Matching Method of Normal-Transient Dictionary for realtime topic detection. Normal dictionaries consist of general keywords(event: suicide-death-loop, death, die, hang oneself, etc) related to events. Whereas transient dictionaries consist of transient keywords(event: suicide-names and information of celebrities, information of social issues) related to events. Experimental results show that matching method using two dictionary finds more tweets related to the event than a simple keyword search.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.9-19
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• 2024

In this paper, a real-time public transportation monitoring system is proposed. The proposed system was implemented by developing a public transportation app and utilizing optical sensors, pressure sensors, and an object detection algorithm. Additionally, a bus model was created to verify the system's functionality. The proposed real-time public transportation monitoring system has three key features. First, the app can monitor congestion levels within public transportation by detecting seat occupancy and the total number of passengers based on changes in optical and pressure sensor readings. Second, to prevent errors in the optical sensor that can occur when multiple passengers board or disembark simultaneously, we explored the possibility of using the YOLO object detection algorithm to verify the number of passengers through CCTV footage. Third, convenience is enhanced by displaying occupied seats in different colors on a separate screen. The system also allows users to check their current location, available public transportation options, and remaining time until arrival. Therefore, the proposed system is expected to offer greater convenience to public transportation users.

• Journal of information and communication convergence engineering
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• v.4 no.4
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• pp.141-146
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• 2006

In these days, wireless data services are becoming ubiquitous in our daily life because they offers several fundamental benefits including user mobility, rapid installation, flexibility, and scalability. Moreover, the requests for various multimedia services and the Quality of Service (QoS) support have been one of key issues in wireless data communications. Therefore the research relative to Medium Access Control (MAC) has been progressing rapidly. Especially a number of QoS-aware MAC schemes have been introduced to extend the legacy IEEE 802.11 MAC protocol which has not guaranteed any service differentiation. However, none of those schemes fulfill both QoS features and channel efficiency although these support the service differentiation based on priority. Therefore this paper studies a novel MAC scheme, referred to as Enhanced Distributed Coordination Function with Network Adaptation (EDCF-NA), for enhancements of both QoS and medium efficiency. It uses a smart factor denoted by ACK rate and Network Load Threshold (TH). In this paper, we study how the value of TH has effect on MAC performance and how the use of optimal TH pair improves the overall MAC performance in terms of the QoS, channel utilization, collision rate, and fairness. In addition, we evaluate and compare both the performance of EDCF-NA depending on several pairs of TH and the achievement of various MAC protocols through simulations by using Network Simulator-2 (NS-2).

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.23 no.8
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• pp.632-637
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• 2010

The measurement of contact wire wear in electric railways is one of the key test parameters to increase speed and maintain safety in electric railways. Wear caused by continuous interaction between pantographs and contact wires has a negative effect on current collection quality and severely damaged contact wires might cause hazardous accidents. This paper introduces a non-contact optical-based contact wire wear measuring system that will replace conventional wear detecting methods conducted by maintenance vehicles or workers. The system is implemented by high-speed cameras that can collect images of contact wires during vehicle operation, a laser used to create images profile of the contact wire surface, and a computer used to process the collected images. The proposed system is designed to assist maintenance of overhead contact lines by creating geometrically plotted images of contact wires to detect contact wire wear during operation on conventional lines or high-speed lines.

• Korean Journal of Computational Design and Engineering
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• v.17 no.2
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• pp.97-110
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• 2012

High performance sensors and modern data logging technology with real-time telemetry facilitate system fault diagnosis in a very precise manner. Fault detection, isolation and identification in fault diagnosis systems are typical steps to analyze the root cause of failures. This systematic failure analysis provides not only useful clues to rectify the abnormal behaviors of a system, but also key information to redesign the current system for retrofit. The main barriers to effective failure analysis are: (i) the gathered data (event) logs are too large in general, and further (ii) they usually contain noise and redundant data that make precise analysis difficult. This paper therefore applies suitable pre-processing techniques to data reduction and feature extraction, and then converts the reduced data log into a new format of event sequence information. Finally the event sequence information is decoded to investigate the correlation between specific event patterns and various system faults. The efficiency of the developed pre-filtering procedure is examined with a terminal box data log of a marine diesel engine.

• Korean Journal of Biological Psychiatry
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• v.19 no.4
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• pp.164-171
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• 2012

The placebo effect, a response observed during the placebo arm of a clinical trial, is produced by the psychobiological action of the placebo as well as by other potential contributors to symptom amelioration such as spontaneous improvement, regression to the mean, biases, concurrent treatments, and study design. From a psychological viewpoint, there are many mechanisms that contribute to placebo effects, including expectations, conditioning, learning, and anxiety reduction. Placebo responses are also mediated by opioid and non-opioid mechanisms including dopamine, serotonin, cholecystokinin, and immune mediators. During recent years, a trend towards increased placebo effects in clinical trials of neuropsychiatric drugs has been noted. Indeed, the placebo effects observed in clinical trials constitute an increasing problem and interfere with signal-detection analyses of potential treatments. Several potential factors including protocol/study design and conduct related factors may account for the placebo effect observed in clinical trials. This paper reviews key issues related to this problem and aims to identify potential solutions.