• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.11a
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• pp.17-18
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• 2019

해상교통관제 업무의 최적화를 위하여 요구되는 인적요소 분야 중 관제사의 상황인식(SA: Situation Awareness)와 관제 업무부하(Workload)와의 관계성을 확인하는 것이 해상교통 분야에서는 중요한 실정이다. 이 연구에서는 관제사의 상황인식과 업무부하를 상황인식평가기술(SART)과 다차원 작업부하 지표(NASA-TLX)를 실제적으로 측정하고, 측정 결과를 비교함으로서 개념들에 대한 이해와 시스템적으로 관리할 수 있는 방법을 제시함으로써 해상교통관제사 전문성 제고방안에 기여하고자 한다.

• Proceedings of the KIEE Conference
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• summer
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• pp.1406-1408
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• 2004

Recently, the load increasement and regenerative system of electrified railway system make a difficult to distinguish between the load current and fault current and it need a new intelligent protection system. In this paper, we proposed intelligent algorithm for discriminating normal and abnormal situation instead of the system being operated abnormally.

• The Journal of Korean Physical Therapy
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• v.12 no.1
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• pp.33-40
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• 2000

This study was carried out to compare the relation between vestibular function and balance skills in normal with heating-impaired children. The subjects were 20 normal children (8-10 years) and 20 hearing-impaired children (8-10 years). The SCPNT was used to assess vestibular function, then, functional reach test and backward walking test were usee to compare dynamic balance skills of normal and hearing-impaired children according to existence of visual input. The results were as follows : 1. In SCPNT, normal and hearing-impaired children showed statistical significance in all left-sided and right-sided rotations(p<.01), and the vestibular function responses of healing-impaired children were normal $20\%$, abnormal $45\%$, absent $35\%$. 2, To compare dynamic balance skills between normal and healing-impaired according to eye open and eye close, functional reach test did not show statistical significance in eye open situation(p>.05), but showed statistical significance in eye close situation(p<.05). 3. Backward walking test showed statistical significance in eye open and eye close situation(p<.01).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.12
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• pp.1619-1625
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• 2018

Portable electronic devices secondary batteries can cause fire and explosion due to micro-current change in addition to the situation of short-circuit inrush current, safety can not be secured with a general operation limited current fuse. Therefore, in secondary battery, it is necessary for the protector to satisfy both the limit current type operation in the open-short-circuit inrush current and the current detection operation characteristic in the micro current change situation and for this operation, a fuse for the current detection type secondary battery protection circuit can be applied. The purpose of this study is to design a protection device that operates stably in the hazardous situation of small capacity secondary battery for portable electronic devices through the design of low melting fuse elements alloy of sensing type fuse and secures stability in abnormal current state. As a result of the experiment, I-T and V-T operation characteristics are satisfied in a the design of the alloy of the current sensing type self-contained low melting point fuse and the resistance of the heating resistor. It is confirmed that it can prevent accidents of short circuit over-current and micro current change of secondary battery.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.17-26
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• 2021

Recently, COVID-19 has spread and time to stay at home has been increasing in accordance with quarantine guidelines of the government such as recommendations to refrain from going out. As a result, the number of single-person households staying at home is also increasingsingle-person households are less likely to be notified to the outside world in times of emergency than multi-person households. This study collects various situations occurring in the home with lidar, image, and voice sensors and analyzes the data according to the sensors through their respective algorithms. Using this method, we analyzed abnormal patterns such as emergency situations and conducted research to detect abnormal signs in humans. Artificial intelligence algorithms that detect abnormalities in people by each sensor were studied and the accuracy of anomaly detection was measured according to the sensor. Furthermore, this work proposes a fusion method that complements the pros and cons between sensors by experimenting with the detectability of sensors for various situations.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.11-19
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• 2021

An abnormal object refers to a person, an object, or a mechanical device that performs abnormal and unusual behavior and needs observation or supervision. In order to detect this through artificial intelligence algorithm without continuous human intervention, a method of observing the specificity of temporal features using optical flow technique is widely used. In this study, an abnormal situation is identified by learning an algorithm that translates an input image frame to an optical flow image using a Generative Adversarial Network (GAN). In particular, we propose a technique that improves the pre-processing process to exclude unnecessary outliers and the post-processing process to increase the accuracy of identification in the test dataset after learning to improve the performance of the model's abnormal behavior identification. UCSD Pedestrian and UMN Unusual Crowd Activity were used as training datasets to detect abnormal behavior. For the proposed method, the frame-level AUC 0.9450 and EER 0.1317 were shown in the UCSD Ped2 dataset, which shows performance improvement compared to the models in the previous studies.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.39-48
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• 2022

Cyberattacks around the world continue to increase, and their damage extends beyond government facilities and affects civilians. These issues emphasized the importance of developing a system that can identify and detect cyber anomalies early. As above, in order to effectively identify cyber anomalies, several studies have been conducted to learn BGP (Border Gateway Protocol) data through a machine learning model and identify them as anomalies. However, BGP data is unbalanced data in which abnormal data is less than normal data. This causes the model to have a learning biased result, reducing the reliability of the result. In addition, there is a limit in that security personnel cannot recognize the cyber situation as a typical result of machine learning in an actual cyber situation. Therefore, in this paper, we investigate BGP (Border Gateway Protocol) that keeps network records around the world and solve the problem of unbalanced data by using SMOTE. After that, assuming a cyber range situation, an autoencoder classifies cyber anomalies and visualizes the classified data. By learning the pattern of normal data, the performance of classifying abnormal data with 92.4% accuracy was derived, and the auxiliary index also showed 90% performance, ensuring reliability of the results. In addition, it is expected to be able to effectively defend against cyber attacks because it is possible to effectively recognize the situation by visualizing the congested cyber space.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.5
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• pp.53-59
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• 2018

Due to the recent developments in computer vision technology, complex actions can be recognized with reasonable accuracy in smart cities. In contrast, violence recognition such as events related to fight and knife, has gained less attention. The capability of visual surveillance can be used for detecting fight in streets or in prison centers. In this paper, we proposed a deep learning-based violence recognition method for surveillance cameras. A convolutional neural network (CNN) model is trained and fine-tuned on available benchmark datasets of fights and knives for violence recognition. When an abnormal event is detected, an alarm can be sent to the nearest police station to take immediate action. Moreover, when the probabilities of fight and knife classes are predicted very low, this situation is considered as normal situation. The experimental results of the proposed method outperformed other state-of-the-art CNN models with high margin by achieving maximum 99.21% accuracy.

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.2
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• pp.21-25
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• 2005

Human performance of system control under excessive mental-workload may differ from stable situation. In this study, design guidelines of secondary control system were introduced to enhance performance of safety control system. Under urgent situation, the first performance criterion is not a reaction time but safe control reaction that prevents system disaster. Therefore it is important to find out the facts that are mainly related system safety. Experimental results show performance of primary task didn't reflect whole system influence within a limited short reaction time. In this situation, the secondary task is more sensitive to system influence that varied with some factors of urgent status. Therefore, when a system proceeds to abnormal and unsafe status, and even more the reaction time is limited within a very short time to control the system, the estimation of human performance is more sensitive using secondary task performance then primary task performance. Those results mean it is required to develop various secondary tasks to design safety control systems preventing disaster, And also require many studies of estimation methods human performances especially when system status varies dangerous and/or unsafe situation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.216-223
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• 2023

As the technology and level of autonomous vehicles advance and they drive in more diverse road environments, an intuitive and efficient interaction system is needed to resolve and respond to the situations the vehicle faces. The development of driving technology from the perspective of autonomous driving has the ultimate goal of responding to situations involving humans or more. In particular, in complex road environments where mutual concessions must be made, the role of a system that can respond flexibly through efficient communication methods to understand each other's situation between vehicles or between pedestrians and vehicles is important. In order to resolve the status of the vehicle or the situation being faced, the provision and method of information must be intuitive and the efficient operation of an autonomous vehicle through interaction with intention is required. In this paper, we explain the vehicle structure and functions that can display information about the situation in which the autonomous vehicle driving in a living lab can drive stably and efficiently in a diverse and complex environment.