• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1259-1265
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• 2022

Recently, AI, big data, and IoT technologies are being used in various solutions such as fire detection and gas or dangerous substance detection for safety accident prevention. According to the status of occupational accidents published by the Ministry of Employment and Labor in 2021, the accident rate, the number of injured, and the number of deaths have increased compared to 2020. In this paper, referring to the dataset construction guidelines provided by the National Intelligence Service Agency(NIA), the dataset is directly collected from the field and learned with YOLOv4 to propose a collision risk object detection system through object detection. The accuracy of the dangerous situation rule violation was 88% indoors and 92% outdoors. Through this system, it is thought that it will be possible to analyze safety accidents that occur in industrial sites in advance and use them to intelligent platforms research.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.109-115
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• 2020

As the maritime targets that threaten allies become lower, smaller, and faster, the need for analysis and modeling of clutter according to sea state increases. Clutter according to the sea state has a great influence on radar performance, such as lowering the probability of detection of low-altitude small maritime targets. In this paper, to analyze the detection performance of a multi function radar for a ship, a sea clutter model suitable for the radar operating environment is selected from several sea clutter models, and analysis of low-altitude, small target detection under a clutter is performed. By using the actual data of the already mounted radar for maritime target detection, four known clutter models have been implemented for each sea state and compared with the actual data. Through this, by selecting a clutter model that best reflects the actual radar environment, reliability of the clutter model is improved. Subsequently, the selected model is used to detect the detectable distance to the low-altitude small target.

• Journal of the Korea Computer Industry Society
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• v.4 no.12
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• pp.965-974
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• 2003

Today's network consists of a large number of routers and servers running a variety of applications. In this paper, we have designed and constructed the general simulation environment of network security model composed of multiple IDSs agent and a firewall agent which coordinate by CNP (Contract Net Protocol). The CNP, the methodology for efficient integration of computer systems on heterogeneous environment such as distributed systems, is essentially a collection of agents, which cooperate to resolve a problem. Command console in the CNP is a manager who controls the execution of agents or a contractee, who performs intrusion detection. In the knowledge-based network security model, each model of simulation environment is hierarchically designed by DEVS (Discrete Event system Specification) formalism. The purpose of this simulation is the application of rete pattern-matching algorithm speeding up the inference cycle phases of the intrusion detection expert system. we evaluate the characteristics and performance of CNP architecture with rete pattern-matching algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.2
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• pp.111-122
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• 2015

This study purposed on introducing a new automated solution for detecting railway tracks and reconstructing track models from the mobile laser scanning data. The proposed solution completes following procedures; the study initiated with detecting a potential railway region, called Region Of Interest (ROI), and approximating the orientation of railway track trajectory with the raw data. At next, the knowledge-based detection of railway tracks was performed for localizing track candidates in the first strip. In here, a strip -referring the local track search region- is generated in the orthogonal direction to the orientation of track trajectory. Lastly, an initial track model generated over the candidate points, which were detected by GMM-EM (Gaussian Mixture Model-Expectation & Maximization) -based clustering strip- wisely grows to capture all track points of interest and thus converted into geometric track model in the tracking by detection framework. Therefore, the proposed railway track tracking process includes following key features; it is able to reduce the complexity in detecting track points by using a hypothetical track model. Also, it enhances the efficiency of track modeling process by simultaneously capturing track points and modeling tracks that resulted in the minimization of data processing time and cost. The proposed method was developed using the C++ program language and was evaluated by the LiDAR data, which was acquired from MMS over an urban railway track area with a complex railway scene as well.

• Korean Journal of Construction Engineering and Management
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• v.12 no.6
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• pp.130-141
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• 2011

The intelligent excavating system(IES), the development in South Korea of which has been underway since 2006, aims for the full-scale automation of the excavation process that includes a series of tasks such as movement, excavation and loading. The core elements to ensure the quality and safety of the automated excavation equipment include 3D modeling of terrain that surrounds the excavating robot and the technology for detecting objects accurately(i.e., for detecting the location of nearby loading trucks and humans as well as of obstacles positioned on the movement paths). Therefore the purpose of this research is to ensure the quality and safety of automated excavation detecting the objects surrounding the excavating robot via a 3D laser scanning system. In this paper, an algorithm for estimating the location, height, width, and shape of objects in the 3D-realized terrain that surrounds the location of the excavator was proposed. The performance of the algorithm was verified via tests in an actual earthwork field.

• Journal of IKEEE
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• v.24 no.2
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• pp.475-485
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• 2020

Recently, a crowdsensing system that provides a new sensing service with real-time sensing data provided from a user's device including a sensor without installing a separate sensor has attracted attention. In the crowdsensing system, meaningless data may be provided due to a user's operation error or communication problem, or false data may be provided to obtain compensation. Therefore, the detection and removal of the abnormal data determines the quality of the crowdsensing service. The proposed methods in the past to detect these anomalies are not efficient for the fast-changing environment of crowdsensing. This paper proposes an anomaly data detection method by extracting the characteristics of continuously and rapidly changing sensing data environment by using machine learning technology and modeling it with an appropriate algorithm. We show the performance and feasibility of the proposed system using deep learning binary classification model of supervised learning and autoencoder model of unsupervised learning.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.2
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• pp.115-125
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• 2003

As Web services are generally open for external uses and not filtered by Firewall, these result in attacker's target. Web attacks which exploit vulnerable web-applications and malicious users' requests cause economical and social problems. In this paper, we are modelling general web service usages based on user-web-session and detect anomal usages with Bayesian estimation method. Finally we propose SAD(Session Anomaly Detection) for detection unknown web attacks. To evaluate SAD, we made an experiment on attack simulation with web vulnerability scanner, whisker. The results show that the detection rate of SAD is over 90%, which is influenced by several features such as size of window or training set, detection filter method and web topology.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.1
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• pp.70-78
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• 2019

In this paper, modeling of a high-resolution multi-function radar is proposed to simulate radar performance in a war game simulator, called AddSIM. To incorporate the multi-function radar model into the AddSIM, the modeling must comprise a component-based structure consisting of physics, logics, and information blocks. Therefore, we assign the RF hardware of a RADAR as the physic block, a controller as the logics block, and the RF specifications of the RADAR as the information block. Detailed modeling of the physics and logics blocks are addressed, and data structure is also presented on an engineering level. On a multi-target engaged scenario, the performance of the multi-function radar is numerically analyzed and its validation is examined.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.3
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• pp.37-46
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• 2012

In RADAR and SONAR detection systems, noise environment can be classified into homogeneous and heterogeneous environment. Especially heterogeneous environments are modelled as target masking and clutter edge. Since the variability-index (VI) CFAR, a composed CFAR algorithm, dynamically selects one of the mean-level algorithms based on the VI and the MR (mean ratio) test, it is robust to various environments. However, the VI CFAR still suffers from lowered detection probabilities in heterogeneous environments. To overcome these problems, we propose an improved VI CFAR processor where TM (trimmed mean) CFAR and a sub-windowing technique are introduced to minimize the degradation of the detection probabilities appeared in heterogeneous environments. Computer simulation results show that the proposed method has the better performance in terms of detection probability and false alarm probability compared to the VI CFAR and single CFAR algorithms.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.5
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• pp.25-30
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• 2011

This paper present a method for abnormal traffic behavior, or trajectory, detection in static traffic surveillance camera with user-defined trajectories. The method computes the abnormality of moving object with a trajectory of the object and user-defined trajectories. Because of using user-define based information, the presented method have more accurate and faster performance than models need a learning about normal behaviors. The method also have adaptation process of assigned rule, so it can handle scene variation for more robust performance. The experimental results show that our method can detect abnormal traffic behaviors in various situation.