• Journal of Chest Surgery
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• v.28 no.6
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• pp.542-548
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• 1995

The goal of this study is to develop an effective control system for cardiac output regulation based upon the preload and afterload conditions without any transducers and compliance chambers in the moving actuator type total artificial heart. Motor current waveforms during the actuator movement are used as an input to the automatic control algorithm. While the current waveform analysis is performed, the stroke length and velocity of the actuator are gradually increased up to the maximum pump output level. If the diastolic filling rate of either right or left pump begins to exceed the venous return, atrial collapse will occur. Since the diastolic suction acts as a load to the motor, this critical condition can be detected by analyzing the motor current waveforms. Every time this detection criterion is met, the control algorithm decreases the stroke velocity and length of the actuator step by step just below the critical detection level. Then, they start to increase. In this way the maximum pump output under given venous return can be achieved. Additionally the control algorithm provides some degree of afterload sensitivity. If the aortic pressure is detected to exceed 120 mmHg, the stroke length and velocity decrease in the same way as the response to the preload. Left-right pump output balance is maintained by proper adjustment of the asymmetry of the stroke angle. In the mock circulatory test, this control system worked well and there was a considerable range of stroke volume difference with adjustment of the asymmetry value. Two ovine experiments were performed. It was confirmed that the required cardiac output regulation according to the venous return could be achieved with adequate detection of diastolic function, at least in the in vivo short-term survival cases[2-3 days . We conclude that this control algorithm is a promising method to regulate cardiac output in the moving actuator type total artificial heart.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.15
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• pp.6429-6436
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• 2014

Poly-ADP-ribosylation (PAR) is a post-translational modification of mainly chromosomal proteins. It is known to be strongly involved in several molecular events, including nucleosome-remodelling and carcinogenesis. In this investigation, it was attempted to evaluate PAR level as a reliable biomarker for early detection of cancer in blood lymphocyte histones. PAR of isolated histone proteins was monitored in normal and dimethylnitrosamine (DMN)-exposed mice tissues using a novel ELISA-based immuno-probe assay developed in our laboratory. An inverse relationship was found between the level of PAR and period of DMN exposure in various histone proteins of blood lymphocytes and spleen cells. With the increase in the DMN exposure period, there was reduction in the PAR level of individual histones in both cases. It was also observed that the decrease in the level of PAR of histones resulted in progressive relaxation of genomic DNA, perhaps triggering activation of genes that are involved in initiation of transformation. The observed effect of carcinogen on the PAR of blood lymphocyte histones provided us with a handy tool for monitoring biochemical or physiological status of individuals exposed to carcinogens without obtaining biopsies of cancerous tissues, which involves several medical and ethical issues. Obtaining blood from any patient and separating blood lymphocytes are routine medical practices involving virtually no medical intervention, post-procedure medical care or trauma to a patient. Moreover, the immuno-probe assay is very simple, sensitive, reliable and cost-effective. Therefore, combined with the ease of preparation of blood lymphocytes and the simplicity of the technique, immuno-probe assay of PAR has the potential to be applied for mass screening of cancer. It appears to be a promising step in the ultimate goal of making cancer detection simple, sensitive and reliable in the near future.

• Korean Journal of Veterinary Research
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• v.50 no.1
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• pp.43-48
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• 2010

Animal disease surveillance system, defined as the continuous investigation of a given population to detect the occurrence of disease or infection for control purposes, has been key roles to assess the health status of an animal population and, more recently, in international trade of animal and animal products with regard to risk assessment. Especially, for a system aiming to determine whether or not a disease is present in a population sensitivity of the system should be maintained high enough not to miss an infected animal. Therefore, when planning the implementation of surveillance system a number of factors that affecting surveillance sensitivity should be taken into account. Of these parameters sample size is of important, and different approaches are used to calculate sample size, usually depending on the objective of surveillance systems. The purpose of this study was to evaluate the sensitivity of the current national serological surveillance programs for four selected bovine diseases assuming a specified sampling plan, to examine factors affecting the probability of detection, and to provide sample sizes required for achieving surveillance goal of detecting at least an infection in a given population. Our results showed that, for example, detecting low level of prevalence (0.2% for bovine tuberculosis) requires selection of all animals per typical Korean cattle farm (n = 17), and thus risk-based target surveillance for high risk groups can be an alternative strategy to increase sensitivity while not increasing overall sampling efforts. The minimum sample size required for detecting at least one positive animal was sharply increased as the disease prevalence is low. More importantly, high reliability of prevalence estimation was expected with increased sampling fraction even when zero-infected animal was identified. The effect of sample size is also discussed in terms of the maximum prevalence when zero-infected animals were identified and on the probability of failure to detect an infection. We suggest that for many serological surveillance systems, diagnostic performance of the testing method, sample size, prevalence, population size, and statistical confidence need to be considered to correctly interpret results of the system.

• The Korean Journal of Cytopathology
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• v.5 no.1
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• pp.15-22
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• 1994

Cancer of the cervix is the most common malignancy in women in developing countries and the second most common cancer in women throughout the world with approximately 500,000 new cases each year. Prevention of this large number of premature deaths among women is, therefore, a goal worthy of urgent and serious consideration. Due to its high diagnostic disagreement among pathologists and large quantity of specimens, it is necessary to develop an automatic screening system measuring morphologic and densitometric features of the samples. Many research works have been published but most of them used Feulgen stained specimens which are not a usual staining method used in clinics. In this thesis, an automatic cancerous nucleus detection method essential to a screening system with papanicolaou stained specimens called Pap-smear is proposed which employs image processing techniques. It uses edge information to segment objects and morphologic as well as densitometric information to distinguish cancerous nuclei from dirts or normal nuclei. It has produced useful results in our study.

• International Journal of Fluid Machinery and Systems
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• v.8 no.4
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• pp.264-273
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• 2015

This presentation describes an experimental approach for the detection of cavitation in hydraulic machines by use of ultrasonic signal analysis. Instead of using the high frequency pulses (typically 1MHz) only for transit time measurement different other signal characteristics are extracted from the individual signals and its correlation function with reference signals in order to gain knowledge of the water conditions. As the pulse repetition rate is high (typically 100Hz), statistical parameters can be extracted of the signals. The idea is to find patterns in the parameters by a classifier that can distinguish between the different water states. This classification scheme has been applied to different cavitation sections: a sphere in a water flow in circular tube at the HSLU in Lucerne, a NACA profile in a cavitation tunnel and two Francis model test turbines all at LMH in Lausanne. From the signal raw data several statistical parameters in the time and frequency domain as well as from the correlation function with reference signals have been determined. As classifiers two methods were used: neural feed forward networks and decision trees. For both classification methods realizations with lowest complexity as possible are of special interest. It is shown that two to three signal characteristics, two from the signal itself and one from the correlation function are in many cases sufficient for the detection capability. The final goal is to combine these results with operating point, vibration, acoustic emission and dynamic pressure information such that a distinction between dangerous and not dangerous cavitation is possible.

• Journal of Life Science
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• v.25 no.3
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• pp.357-367
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• 2015

With the ongoing development of next-generation sequencing (NGS) platforms and advancements in the latest bioinformatics tools at an unprecedented pace, the ultimate goal of sequencing the human genome for less than $1,000 can be feasible in the near future. The rapid technological advances in NGS have brought about increasing demands for statistical methods and bioinformatics tools for the analysis and management of NGS data. Even in the early stages of the commercial availability of NGS platforms, a large number of applications or tools already existed for analyzing, interpreting, and visualizing NGS data. However, the availability of this plethora of NGS data presents a significant challenge for storage, analyses, and data management. Intrinsically, the analysis of NGS data includes the alignment of sequence reads to a reference, base-calling, and/or polymorphism detection, de novo assembly from paired or unpaired reads, structural variant detection, and genome browsing. While the NGS technologies have allowed a massive increase in available raw sequence data, a number of new informatics challenges and difficulties must be addressed to improve the current state and fulfill the promise of genome research. This review aims to provide an overview of major NGS technologies and bioinformatics tools for NGS data analyses.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.262-267
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• 2008

Recently, landslides frequently occur on natural slope and/or man-made cut slope during periods of intense rainfall. With a rapidly increasing population on or near steep terrain, landslides have become one of the most significant natural hazards. Thus, it is necessary to protect people from landslides and to minimize the damage of houses, roads and other facilities. To accomplish this goal, many landslide monitoring systems have been developed throughout the world. In this paper, a simple landslide detection system that enables people to escape the endangered area is introduced. The system is focused on the debris flows which happen frequently during periods of intense rainfall. The system is based on the wireless sensor network (WSN) that is composed of wireless sensor nodes, gateway, and remote server system. Wireless sensor nodes and gateway are deployed by commercially available Microstrain G-Link products. Five wireless sensor nodes and one gateway are installed at the test slope for detecting ground movement. The acceleration and inclination data of test slope can be obtained, which provides a potential to detect landslide. In addition, thresholds to determine whether the test slope is stable or not are suggested by a series of numerical simulations, using geotechnical analysis software package. It is obtained that the alarm should be issued if the x-direction displacement of sensor node is greater than 20mili-meters and the inclination of sensor node is greater than 3 degrees. It is expected that the landslide detection method using wireless senor network can provide early warning where landslides are prone to occur.

• Journal of KIISE:Software and Applications
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• v.33 no.6
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• pp.564-573
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• 2006

Temporal information plays an important role in natural language processing (NLP) applications such as information extraction, discourse analysis, automatic summarization, and question-answering. In the topic detection and tracking (TDT) area, the temporal information often used is the publication date of a message, which is readily available but limited in its usefulness. We developed a relatively simple NLP method of extracting temporal information from Korean news articles, with the goal of improving performance of TDT tasks. To extract temporal information, we make use of finite state automata and a lexicon containing time-revealing vocabulary. Extracted information is converted into a canonicalized representation of a time point or a time duration. We first evaluated the extraction and canonicalization methods for their accuracy and investigated on the extent to which temporal information extracted as such can help TDT tasks. The experimental results show that time information extracted from text indeed helps improve both precision and recall significantly.

• Journal of the Korea Society for Simulation
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• v.28 no.1
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• pp.23-39
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• 2019

The use of drone-bots is demanded in times regarding the reduction of military force, the spread of the life-oriented thought, and the use of innovative technology in the defense through the fourth industrial revolution. Especially, the drone's surveillance and reconnaissance are expected to play a big role in the future battlefield. However, there are not many cases in which the concept of operation is studied scientifically. In this study, We propose search algorithms for reconnaissance drone through simulation analysis. In the simulation, the drone and target move linearly in continuous space, and the target is moving adopting the Random-walk concept to reflect the uncertainty of the battlefield. The research investigates the effectiveness of existing search methods such as Parallel and Spiral Search. We analyze the probabilistic analysis for detector radius and the speed on the detection probability. In particular, the new detection algorithms those can be used when an enemy moves toward a specific goal, PS (Probability Search) and HS (Hamiltonian Search), are introduced. The results of this study will have applicability on planning the path for the reconnaissance operations using drone-bots.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1044-1058
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• 2021

Tempered electronic contents have multiplied in last few years, thanks to the emergence of sophisticated artificial intelligence(AI) algorithms. Deepfakes (fake footage, photos, speech, and videos) can be a frightening and destructive phenomenon that has the capacity to distort the facts and hamper reputation by presenting a fake reality. Evidence of ownership or authentication of digital material is crucial for combating the fabricated content influx we are facing today. Current solutions lack the capacity to track digital media's history and provenance. Due to the rise of misrepresentation created by technologies like deepfake, detection algorithms are required to verify the integrity of digital content. Many real-world scenarios have been claimed to benefit from blockchain's authentication capabilities. Despite the scattered efforts surrounding such remedies, relatively little research has been undertaken to discover where blockchain technology can be used to tackle the deepfake problem. Latest blockchain based innovations such as Smart Contract, Hyperledger fabric can play a vital role against the manipulation of digital content. The goal of this paper is to summarize and discuss the ongoing researches related to blockchain's capabilities to protect digital content authentication. We have also suggested a blockchain (smart contract) dependent framework that can keep the data integrity of original content and thus prevent deepfake. This study also aims at discussing how blockchain technology can be used more effectively in deepfake prevention as well as highlight the current state of deepfake video detection research, including the generating process, various detection algorithms, and existing benchmarks.