• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.6
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• pp.853-859
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• 2017

Terrorists always threats the global security with the possibility of using prohibited warfare, NBCs(Nuclear, Biological and Chemical Warfare). Compared to other prohibited warfares, most of biological warfare agents (BWAs) have no physical properties and time delays from spread to affect. Therefore the early detection is important to protect and decontaminate from BWAs. On the preliminary detection stage for suspicious material, most of detection kits only serve to know weather the BWAs exists or not. Due to this reason, simple field confirmation testing for suspicious substances have been used to identify materials which show negative result on detection kits. Considering the current Lab on a Chip(LOC) technologies, we suggest simple identification platform for unknown suspicious substances based on paper fluidics. We hope that our research will envision the future direction for the specific point-of-view for LOC technologies on detection strategy of BWAs.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.6-31
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• 2018

With the rapid development of the science and technology, it has been becoming more and more convenient to obtain abundant information via the diverse multimedia medium. However, the contents of the multimedia are easily altered with different editing software, and the authenticity and the integrity of multimedia content are under threat. Forensics technology is developed to solve this problem. We focus on reviewing the blind image forensics technologies for copy-move forgery in this survey. Copy-move forgery is one of the most common manners to manipulate images that usually obscure the objects by flat regions or append the objects within the same image. In this paper, two classical models of copy-move forgery are reviewed, and two frameworks of copy-move forgery detection (CMFD) methods are summarized. Then, massive CMFD methods are mainly divided into two types to retrospect the development process of CMFD technologies, including block-based and keypoint-based. Besides, the performance evaluation criterions and the datasets created for evaluating the performance of CMFD methods are also collected in this review. At last, future research directions and conclusions are given to provide beneficial advice for researchers in this field.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.6
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• pp.2395-2403
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• 2014

As the recurrence and mortality rates of bladder cancer are high, research is needed to find suitable biomarkers for early detection, evaluation of prognosis, and surveillance of drug responses. We performed a computerized search of the Medline/PubMed databases with the key words bladder cancer, biomarker, early detection, prognosis and drug response. Several markers were identified at DNA, RNA and protein levels with different sensitivities and specificities. Only a few of the potential bladder cancer biomarkers have been approved for clinical use. Efforts now should be concentrated on finding a panel of markers with acceptable sensitivity and specificity for early detection of bladder cancer.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.34D no.7
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• pp.88-96
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• 1997

As the need of automatized system has been increased recently together with the development of industrial and military technologies, the adaptive real-time target detection technologies that can be embedded on vehicles, planes, ships, robots and so on, are hgihly demanded. Accordingly, this paper proposes a novel approach to detect and segment the moving targets using the binary phase extraction joint transform correlator (BPEJTC), the advanced image subtraction filter and convex hull processing. The BPEJTC which was used as a target detection unit mainly for target tracking compensating the camera movement. The target region has been detected by processing the successful three frames using the advanced image subtraction filter, and has become more accurate by applying the developed convex hull filter. As shown by some experimental results, it is expected that the proposed approaches for compensation of the camera movement and segmentationof of target region, can be used for th emissile guiddance, aero surveillance, automatic inspectin system as well as the target detection unit of automatic target recognition system that request adaptive real-time processing.

• Journal of the Korea Safety Management & Science
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• v.11 no.3
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• pp.73-78
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• 2009

In this paper, we developed the care system for older people living alone using the RFID technologies and the living informations. The care system store living informations, extracted through a unconstrained detection method by the RFID tags and readers, into a monitering server. The unconstrained detection method improved a weakness of existing systems that detected a living informations through an infrared sensor, ultrasonic sensor, camera, consumed quantity of the tap water or gas. The result of this study will playa very important role, as a part of a composite older-welfare services. Also, in the future, accumulated living informations will be allowed for a health data of older peoples.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.760-761
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• 2019

Smart manufacturing is a process of integrating computer-related technologies in production and by doing so, achieving more efficient production management. The recent development of supercomputers has led to the broad utilization of artificial intelligence (AI) and machine learning techniques useful in predicting specific patterns. Despite the usefulness of AI and machine learning techniques in smart manufacturing processes, there are many fundamental issues with the direct deployment of these technologies related to data management. In this paper, we focus on solving the outlier detection issue in smart manufacturing applications. More specifically, we apply a state-of-the-art outlier detection technique, called Elliptic Envelope, to detect anomalies in simulation-based collected data.

• Proceedings of the KIPE Conference
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• 2010.11a
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• pp.230-231
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• 2010

In this paper, a fault detection algorithm for an UPS operation of power station is proposed. By adapting the algorithm, a grid-connected power station performs a UPS operation when faults such as sag, swell are occurred. Through a computer simulation, grid faults are simulated and the proposed fault detection algorithm using d, q axis observation method is verified.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.190-201
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• 2023

Securing transportation safety infrastructure technology for Lv.4 connected autonomous driving is very important for the spread of autonomous vehicles, and the safe operation of level 4 autonomous vehicles in adverse weather has limitations due to the development of vehicle-only technology. We developed the radar-enabled AI convergence transportation entities detection system. This system is mounted on fixed and mobile supports on the road, and provides excellent autonomous driving situation recognition/determination results by converging transportation entities information collected from various monitoring sensors such as 60GHz radar and EO/IR based on artificial intelligence. By installing such a radar-enabled AI convergence transportation entities detection system on an autonomous road, it is possible to increase driving efficiency and ensure safety in adverse weather. To secure competitive technologies in the global market, the development of four key technologies such as ① AI-enabled transportation situation recognition/determination algorithm, ② 60GHz radar development technology, ③ multi-sensor data convergence technology, and ④ AI data framework technology is required.

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08b
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• pp.1227-1230
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• 2007

This paper describes recent advances in the R&D work achieved at Fraunhofer HHI (Germany) that are believed to provide key technologies for the development of future human-machine interfaces. The paper focus on the area of vision based interaction technologies that will be one essential component in future three-dimensional display systems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.320-330
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• 2020

Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.