• Investigative Magnetic Resonance Imaging
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• v.20 no.2
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• pp.105-113
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• 2016

Purpose: Susceptibility vessel sign (SVS) on gradient echo image, which is caused by MR signal loss due to arterial thrombosis, has been reported in acute middle cerebral artery (MCA) infarction. However, the reported sensitivity and diagnostic accuracy of SVS have been variable. Susceptibility-weighted imaging (SWI) is a newly developed MR sequence. Recent studies have found that SWI may be useful in the field of cerebrovascular diseases, especially for detecting the presence of prominent veins, microbleeds and the SVS. The purpose of this study was to evaluate the diagnostic values of SWI for the detection of hyperacute MCA occlusion. Materials and Methods: Sixty-nine patients (37 males, 32 females; 46-89 years old [mean, 69.1]) with acute stroke involving the MCA territory underwent MR imaging within 6 hours after the symptom onset. MR examination included T2, FLAIR (fluid-attenuated inversion recovery), DWI, SWI, PWI (perfusion-weighted imaging), contrast-enhanced MR angiography (MRA) and contrast-enhanced T1. Of these patients, 28 patients also underwent digital subtraction angiography (DSA) within 2 hours after MR examination. Presence or absence of SVS on SWI was assessed without knowledge of clinical, DSA and other MR imaging findings. Results: On MRA or DSA, 34 patients (49.3%) showed MCA occlusion. Of these patients, SVS was detected in 30 (88.2%) on SWI. The sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of SWI were 88.2%, 97.1%, 96.8%, 89.5% and 92.8%, respectively. Conclusion: SWI was sensitive, specific and accurate for the detection of hyperacute MCA occlusion.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1141-1151
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• 2018

In an APT attack, the communication stage between infected hosts and C&C(Command and Control) server is the key stage for intrusion into the attack target. Attackers can control multiple infected hosts by the C&C Server and direct intrusion and exploitation. If the C&C Server is exposed at this stage, the attack will fail. Therefore, in recent years, the Domain Generation Algorithm (DGA) has replaced DNS in C&C Server with a short time interval for making detection difficult. In particular, it is very difficult to verify and detect all the newly registered DNS more than 5 million times a day. To solve these problems, this paper proposes a model to judge DGA-DNS detection by the morphological similarity analysis of normal DNS and DGA-DNS, and to determine the sign of APT attack through it, then we verify its validity.

• The KIPS Transactions:PartC
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• v.11C no.6 s.95
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• pp.719-724
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• 2004

Recently, since the number of internet users is increasing rapidly and, by using the public hacking tools, general network users can intrude computer systems easily, the hacking problem is getting more serious. In order to prevent the intrusion, it is needed to detect the sign in advance of intrusion in a positive prevention by detecting the various foms of hackers' intrusion trials to know the vulnerability of systems. The existing network-based anomaly detection algorithms that cope with port- scanning and the network vulnerability scans have some weakness in intrusion detection. they can not detect slow scans and coordinated scans. therefore, the new concept of algorithm is needed to detect effectively the various forms of abnormal accesses for intrusion regardless of the intrusion methods. In this paper, SPAD(Session Pattern Anomaly Detector) is presented, which detects the abnormal service patterns by comparing them with the ordinary normal service patterns.

• Journal of Korea Multimedia Society
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• v.16 no.4
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• pp.430-443
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• 2013

The recognition of current position and road surrounding of car is very important to driver for safe driving. This paper presents the recognition technique of the road traveling environment using position information hiding and viewpoint transform that monitors the information of road furniture and signature and notifies them to driver. The proposed scheme generates the road images into which the position information are hided, from car camera and GPS module and provides the road information to driver through the viewpoint transformation and the road signature detection. The driving tests with camera and GPS module verified that the position information hiding takes about 66.5ms per frame, the detection rate of road signature is about 95.83%, and the road signature detection takes about 227.45ms per frame. Therefore, we know that the proposed scheme can recognize the road traveling environment on the road video with 15 frame rate.

• Journal of the Society of Disaster Information
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• v.16 no.1
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• pp.87-95
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• 2020

Purpose: In this paper, we propose an algorithm for detecting respiratory rate and heart beat of a driver of car by exploiting Doppler radar, and verifying the feasibility of the study through experiments. Method: In this paper, we propose a weighted peak detection technique using peak frequency values. The tests are performed in stop-state and driving-state, and the experiment result is analyzed by two proposed algorithms. Result: The results showed more than 95% and 96% accuracy of respiratory and heart rate, respectively. It also showed more than 72% and 84% accuracy of those even for driving experiments. Conclusion: The proposed detection scheme for vital signs can be used for the safety of the driver as well as for prevention of a large size of car accidents.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.51-59
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• 2024

In this paper, we propose a novel traffic safety system designed to reduce pedestrian traffic accidents and enhance safety on university campuses. The system involves real-time detection of vehicle speeds in designated areas and the interaction between vehicles and pedestrians at crosswalks. Utilizing the YOLOv5s model and Deep SORT method, the system performs speed measurement and object tracking within specified zones. Second, a condition-based output system is developed for crosswalk areas using the YOLOv5s object detection model to differentiate between pedestrians and vehicles. The functionality of the system was validated in real-time operation. Our system is cost-effective, allowing installation using ordinary smartphones or surveillance cameras. It is anticipated that the system, applicable not only on university campuses but also in similar problem areas, will serve as a solution to enhance safety for both vehicles and pedestrians.

• Korean Journal of Veterinary Service
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• v.29 no.2
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• pp.89-96
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• 2006

Bovine viral leukosis is a viral disease of cattle characterized by the development of tumors in the lymphatic tissue. A female Holstein, 3-year-old, was submitted for diagnosis at the Diagnostic laboratory, Chonbuk National University. Clinical sign of the affected animal showed emaciation, enlargement of superficial lymph node and mild diarrhea. Remarkable lesions were enlargement of many internal lymph nodes. Histopathology revealed excessive neoplastic lymphoid cells characteristic of BVL infection. Subsequently, serums from all cattle were collected and serological examination was done where a 85% seropositive rate was detected using ELISA test. ELISA method showed a comparatively 75% higher detection rate than the agar gel immunodiffusion (AGID) test (85% vs 40%). Serologically positive cattle were variably detected in all ages from under 1 year to over 6 year of age. Hematological examination consistently showed leukocytosis and a differential lymphocytosis of seropositive cattle. Detailed comparative pathological and serological data diagnosed the presence of bovine viral leukosis.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.8
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• pp.1392-1399
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• 2016

Blackout effect occurs when a driver misadapts to the changed lighting conditions upon entering a tunnel. This could lead to a decrease in visibility especially in the daylight, depending on the difference in the degree of brightness between inside and outside the tunnel. To alleviate such a problem, we decrease windshield transparency before the driver arrives at the tunnel entrance. Controlled amount of light inside the car can allow the drivers to adjust to the dark prior to entering. The windshield transparency coefficient is to be determined by the arrival time at the tunnel and difference in the level of brightness between inside and outside the tunnel. Navigation, road sign detection, and tunnel entrance detection provide the arrival time. We also designed an opto-electronic conversion function to estimate the level of brightness. The black-hole phenomenon alleviation method is verified by field experiments using an automobile camera and a navigation. The result shows that the adjusted windshield transparency is able to provide an environment with a comfortable level of brightness with which the drivers can enter tunnels without the visibility problem.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.64-72
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• 2024

In recent times cyber attackers can use Artificial Intelligence (AI) to boost the sophistication and scope of attacks. On the defense side, AI is used to enhance defense plans, to boost the robustness, flexibility, and efficiency of defense systems, which means adapting to environmental changes to reduce impacts. With increased developments in the field of information and communication technologies, various exploits occur as a danger sign to cyber security and these exploitations are changing rapidly. Cyber criminals use new, sophisticated tactics to boost their attack speed and size. Consequently, there is a need for more flexible, adaptable and strong cyber defense systems that can identify a wide range of threats in real-time. In recent years, the adoption of AI approaches has increased and maintained a vital role in the detection and prevention of cyber threats. In this paper, an Ensemble Deep Restricted Boltzmann Machine (EDRBM) is developed for the classification of cybersecurity threats in case of a large-scale network environment. The EDRBM acts as a classification model that enables the classification of malicious flowsets from the largescale network. The simulation is conducted to test the efficacy of the proposed EDRBM under various malware attacks. The simulation results show that the proposed method achieves higher classification rate in classifying the malware in the flowsets i.e., malicious flowsets than other methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.2
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• pp.105-114
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• 2013

Expansion and growth of body information monitoring service based on WBAN technology speeds up technological evolution in bio-signal detection and measurement, real time monitoring of vital sign and telemedicine control. It is essential for taking action against such technological evolution that newest technology trend and standardization issue should be included in designing and materializing body-information monitoring system strategically to secure preceding technology and to preoccupy market. This paper investigates and analyzes technological trend & issues, and suggests task to take action technologically.