• The Journal of Korea Robotics Society
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• v.10 no.1
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• pp.33-41
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• 2015

Recent developments in robotics and intelligent vehicle area, bring interests of people in an autonomous driving ability and advanced driving assistance system. Especially fully automatic parking ability is one of the key issues of intelligent vehicles, and accurate parked vehicles detection is essential for this issue. In previous researches, many types of sensors are used for detecting vehicles, 2D LiDAR is popular since it offers accurate range information without preprocessing. The L shape feature is most popular 2D feature for vehicle detection, however it has an ambiguity on different objects such as building, bushes and this occurs misdetection problem. Therefore we propose the accurate vehicle detection method by using a 3D complete vehicle model in 3D point clouds acquired from front inclined 2D LiDAR. The proposed method is decomposed into two steps: vehicle candidate extraction, vehicle detection. By combination of L shape feature and point clouds segmentation, we extract the objects which are highly related to vehicles and apply 3D model to detect vehicles accurately. The method guarantees high detection performance and gives plentiful information for autonomous parking. To evaluate the method, we use various parking situation in complex urban scene data. Experimental results shows the qualitative and quantitative performance efficiently.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.1
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• pp.35-43
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• 2018

MANET composed of only wireless nodes is increasingly utilized in various fields. However, it is exposed to many security vulnerabilities because it doesn't have any infrastructure and transmits data by using multi-hop method. Therefore, MANET should be applied the intrusion detection technique that can detect efficiently malicious nodes and decrease impacts of various attacks. In this paper, we propose a distributed intrusion detection technique that can detect the various attacks while improving the efficiency of attack detection and reducing the false positive rate. The proposed technique uses the cluster structure to manage the information in the center and monitor the traffic of their neighbor nodes directly in all nodes. We use three parameters for attack detection. We also applied an efficient authentication technique using only key exchange without the help of CA in order to provide integrity when exchanging information between cluster heads. This makes it possible to free the forgery of information about trust information of the nodes and attack nodes. The superiority of the proposed technique can be confirmed through comparative experiments with existing intrusion detection techniques.

• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.198-206
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• 2019

A new methodology to analyze the nuclear material accountability for pyroprocessing system is developed. The $Pu-to-^{244}Cm$ ratio quantification is one of the methods for Pu accountancy in pyroprocessing. However, an uncertainty in the $Pu-to-^{244}Cm$ ratio due to the non-uniform composition in used fuel assemblies can affect the accountancy of Pu. A random variable, LOPu, is developed to analyze the probability of detection for Pu diversion of hypothetical scenarios at a pyroprocessing facility considering the uncertainty in $Pu-to-^{244}Cm$ ratio estimation. The analysis is carried out by the hypothesis testing and the event tree method. The probability of detection for diversion of 8 kg Pu is found to be less than 95% if a large size granule consisting of small size particles gets sampled for measurements. To increase the probability of detection more than 95%, first, a new Material Balance Area (MBA) structure consisting of more number of Key Measurement Points (KMPs) is designed. This multiple KMP-measurement for the MBA shows the probability of detection for 8 kg Pu diversion is greater than 96%. Increasing the granule sample number from one to ten also shows the probability of detection is greater than 95% in the most ranges for granule and powder sizes.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.428-442
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• 2022

A reliable automatic passenger counting (APC) system is a key point in transportation related to the efficient scheduling and management of transport routes. In this study, we introduce a lightweight head detection network using deep learning applicable to an embedded system. Currently, object detection algorithms using deep learning have been found to be successful. However, these algorithms essentially need a graphics processing unit (GPU) to make them performable in real-time. So, we modify a Tiny-YOLOv3 network using certain techniques to speed up the proposed network and to make it more accurate in a non-GPU environment. Finally, we introduce an APC system, which is performable in real-time on embedded systems, using the proposed head detection algorithm. We implement and test the proposed APC system on a Samsung ARTIK 710 board. The experimental results on three public head datasets reflect the detection accuracy and efficiency of the proposed head detection network against Tiny-YOLOv3. Moreover, to test the proposed APC system, we measured the accuracy and recognition speed by repeating 50 instances of entering and 50 instances of exiting. These experimental results showed 99% accuracy and a 0.041-second recognition speed despite the fact that only the CPU was used.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2781-2784
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• 2007

Early detection of a disease is important to tackle treatment issues in a better manner. Several diagnostic techniques are in use, these days; for such purpose and tremendous research is going on to develop newer and newer methods. However, more work is required to be done to develop cheap and reliable early detection techniques. Micro-fluidic chips are also playing key role to deliver new devices for better health care. The present study focuses on a review of recent developments in the interrogation of different techniques and present state-of-the-art of microfluidic sensor for better, quick, easy, rapid, early, inexpensive and portable POCT (Point of Care testing device) device for a particular study, in this case, bone disease called osteoporosis. Some simulations of the microchip are also made to enable feasibility of the development of a blood-chip-based system. The proposed device will assist in early detection of diseases in an effective and successful manner.

• Proceedings of the KIPE Conference
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• 2016.07a
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• pp.351-352
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• 2016

Since anti-islanding scheme of grid-connected inverter is a key function of standards compliance, unintentional islanding results in safety hazards, reliability, and many other issues. Therefore, many anti-islanding schemes have been researched, however, existing anti-islanding schemes show poor power quality and non-detection zone issues. Besides, most of them have problems which deteriorate performance of islanding detection under parallel-operation. Therefore, this paper proposes a new anti-islanding scheme that has both negligible power quality degradation, no non-detection zone and precise islanding detection under parallel-operation. Finally, both simulation and experimental results validate the proposed scheme.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2005.11a
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• pp.421-424
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• 2005

X-ray is currently most effective method in detecting small malignant breast tumors but has the several problems due to suppressing breast, ionizing radiation and not detecting small cancer. In this paper, a new method is proposed by using dielectric characteristics of pathological tissues and time delay of backscattered response. We have developed a detection algorithm and verified it by numerical simulation and measurement for a prototype system. For a prototype system, we have fabricated experimental model(artificial breast with a cancer) and UWB(ultra-wideband) antenna. The results of the measurement simulation show an excellent detection capability of a cancer tissue. It is found that a good UWB antenna is a key element of such detection system. Further study is ongoing to develop a commercial system.

• The Plant Pathology Journal
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• v.25 no.4
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• pp.328-332
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• 2009

Diseases caused by Pepper yellow leaf curl virus infection is considered to be emerging plant diseases in Indonesia in the last five years. One key factor for disease management is the availability of accurate detection of the virus in plants. Polyclonal antibody for Pepper yellow leaf curl Indonesia virus-Bogor (PYLCIV-Bgr) was produced for detection of the virus using I-ELISA and DIBA methods. The antibody was able to detect PYLCIV-Bgr from infected plants up to dilution 1/16,384 and cross reaction was not observed with Cucumber mosaic virus (CMV), Tobacco mosaic virus (TMV), and Chilli veinal mottle virus (ChiVMV). Positive reaction was readily detected in membrane containing Begomovirus samples from Yogyakarta (Kaliurang and Kulonprogo) and West Java (Bogor and Segunung). Infection of PYLCIV-Bgr in chillipepper, tomato, and Ageratum conyzoides was also confirmed using polyclonal antibody for PYLCIV-Bgr in DIBA. Polyclonal antibody for PYLCIV-Bgr is suggested to be included in disease management approach due to its good detection level.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.225-227
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• 1995

An object detection algorithm using a modified IDM(Image Differential Method) is proposed for detecting an object in a level crossing area. The conventional object detection method using LASER light has the deadzone that it cannot detect small objects, while the object detection method using image data in a level crossing area can detect such small objects. But the image data in a level crossing area can be changeable easily because the data is outdoor and sensitive to such surrounding environments as the change of the sun beam, the shadow of cars, and so on. So we resolve these problems by adding the normalization and the process for shadow of the image data in a level crossing area to the basic IDM(Image Differential Method).

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.144-152
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• 2017

Among the technologies for implementing autonomous vehicles, advanced driver assistance system is a key technology to support driver's safe driving. In the technology using the vision sensor having a high utility, various preprocessing methods are used prior to feature extraction for lane detection. However, in the existing methods, the unnecessary lane candidates such as cars, lawns, and road separator in the road area are false positive. In addition, there are cases where the lane candidate itself can not be extracted in the area under the overpass, the lane within the dark shadow, the center lane of yellow, and weak lane. In this paper, we propose an efficient preprocessing method using k-means clustering for image division and the HSV color model. When the proposed preprocessing method is applied, the true positive region is maximally maintained during the lane detection and many false positive regions are removed.