• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3764-3771
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• 2021

Based on the convolutional neural network (CNN), a novel technique is investigated for lost gamma source detection in a room. The CNN is trained with the result of a GEANT4 simulation containing a gamma source inside a meshed room. The dataset for the training process is the deposited energy in the meshes of different n-step paths. The neural network is optimized with parameters such as the number of input data and path length. Based on the proposed method, the place of the gamma source can be recognized with reasonable accuracy without human intervention. The results show that only by 5 measurements of the energy deposited in a 5-step path, (5 sequential points 50 cm apart within 1600 meshes), the gamma source location can be estimated with 94% accuracy. Also, the method is tested for the room geometry containing the interior walls. The results show 90% accuracy with the energy deposition measurement in the meshes of a 5-step path.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.793-799
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• 2021

Recently, there has been a growing interest in threat hunting presented to overcome the limitations of existing security solutions. Threat hunting is generally recognized as a technique for identifying and eliminating threats that exit inside the system. But, the definition is not clear, so there is confusion in terms with penetration testing, intrusion detection, and incident analysis. Therefore, in this paper, compare and analyze the definitions of threat hunting extracted from reports and papers to clarify their implications and compare with defense techniques.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.210-218
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• 2021

CNN (Convolutional Neural Network), a type of deep learning algorithm, is a type of artificial neural network used to analyze visual images. In deep learning, it is classified as a deep neural network and is most commonly used for visual image analysis. Accordingly, an AI autonomous driving model was constructed through real-time image processing, and a crosswalk image of a road was used as an obstacle. In this paper, we proposed a low-cost model that can actually implement autonomous driving based on the CNN model. The most well-known deep neural network technique for autonomous driving is investigated and an end-to-end model is applied. In particular, it was shown that training and self-driving on a simulated road is possible through a practical approach to realizing lane detection and keeping.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.16-26
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• 2020

Improving operator performance in complex and time-critical situations is critical to maintain plant safety and operability. These situations require quick detection, diagnosis, and mitigation actions to recover from the root cause of failure. One of the key challenges for operators in nuclear power plants is information management and following the control procedures and instructions. Nowadays Digital Twin technology can be used for analyzing and fast detection of failures and transient situations with the recommender system to provide the operator or maintenance engineer with recommended action to be carried out. Systems engineering approach (SE) is used in developing a digital twin for the CEDM system to support operator actions when there is a misalignment in the control element assembly group. Systems engineering is introduced for identifying the requirements, operational concept, and associated verification and validation steps required in the development process. The system developed by using a machine learning algorithm with a text mining technique to extract the required actions from limiting conditions for operations (LCO) or procedures that represent certain tasks.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.165-170
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• 2022

Lying is nuisance to all, and all liars knows it is nuisance but still keep on lying. Sometime people are in confusion how to escape from or how to detect the liar when they lie. In this research we are aiming to establish a dynamic platform to identify liar by using video analysis especially by calculating the ratio of changes in their appearance when they lie. The platform will be developed using a machine learning algorithm along with the dynamic classifier to classify the liar. For the experimental analysis the dataset to be processed in two dimensions (people lying and people tell truth). Both parameter of facial appearance will be stored for future identification. Similarly, there will be standard parameter to be built for true speaker and liar. We hope this standard parameter will be able to diagnosed a liar without a pre-captured data.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.599-613
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• 2022

Non-proliferative diabetic retinopathy is a representative complication of diabetic patients and is known to be a major cause of impaired vision and blindness. There has been ongoing research on automatic detection of diabetic retinopathy, however, there is also a growing need for research on an automatic severity classification system. This study proposes an automatic detection system for pathological symptoms of diabetic retinopathy such as microaneurysms, retinal hemorrhage, and hard exudate by applying the Faster R-CNN technique. An automatic severity classification system was devised by training and testing a Random Forest classifier based on the data obtained through preprocessing of detected features. An experiment of classifying 228 test fundus images with the proposed classification system showed 97.8% accuracy.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.271-280
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• 2023

FMCW (Frequency Modulated Continuous Wave) radar system is widely used in autonomous driving and navigation applications due to its high detection capabilities independent of weather conditions and environments. However, radar signals can be easily contaminated by various noises such as speckle noise, receiver saturation, and multipath reflection, which can worsen sensing performance. To handle this problem, we propose a learning-free noise removal technique for radar to enhance detection performance. The proposed method leverages adaptive thresholding to remove speckle noise and receiver saturation, and wavelet transform to detect multipath reflection. After noise removal, the radar image is reconstructed with the geometric structure of the surrounding environments. We verify that our method effectively eliminated noise and can be applied to autonomous driving by improving the accuracy of odometry and place recognition.

• Annual Conference of KIPS
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• 2011.11a
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• pp.351-354
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• 2011

This paper discusses on facial features extraction based on proposed skin color model. Different parts of face from input image are segmented based on skin color model. Moreover, this paper also discusses on concept to detect the eye and mouth position on face. A height and width ratio (${\delta}=1.1618$) based technique is also proposed to accurate detection of face region from the segmented image. Finally, we have cropped the desired part of the face. This exactly exacted face part is useful for face recognition and detection, facial feature analysis and expression analysis. Experimental results of propose method shows that the proposed method is robust and accurate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.400-403
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• 2022

This paper propose a GPU-based N-body collision detection algorithm using LBVH (Linear Bounding Volume Hierarchy) technique. This algorithm introduces a new modified Morton code scheme where the codes use an information about how much each body takes a space in the screen space. This scheme improves the GPU sorting performance of the N-Body because it culls out invisible objects in natural manner. Through the experiments, we verifies that the proposed algorithms can have at least 15% performance improvement over the existing methods

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.61.2-61.2
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• 2021

SkyMapper is the largest-aperture optical wide-field telescope in Australia and can be used for transient detection in the Southern sky. Reference images from its Southern Survey cover the sky at δ <+10 deg to a depth of I ~ 20 mag. It has been used for surveys of extragalactic transients such as supernovae, optical counterparts to gravitational-wave (GW) and fast radio bursts. We adopt an ensemble-based machine learning technique and further filtering scheme that provides high completeness ~98% and purity ~91% across a wide magnitude range. Here we present an important use-case of our robotic transient search, which is the follow-up of GW event triggers from LIGO/Virgo. We discuss the facility's performance in the case of the second binary neutron star merger GW190425. In time for the LIGO/Virgo O4 run, we will have deeper reference images for galaxies within out to ~200 Mpc distance, allowing rapid transient detection to i ~ 21 mag.