• IEIE Transactions on Smart Processing and Computing
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• 제5권4호
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• pp.256-266
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• 2016

Concurrently detected and annotated abnormal events can have a significant impact on surveillance systems. By considering the specific domain of pedestrian trajectories, this paper presents two main contributions. First, as introduced in much of the work on trajectory-based anomaly detection in the literature, only information about pedestrian paths, such as direction and speed, is considered. Differing from previous work, this paper proposes a framework that deals with additional types of trajectory-based anomalies. These abnormal events take places when a person enters prohibited areas. Those restricted regions are constructed by an online learning algorithm that uses surrounding information, including detected pedestrians and background scenes. Second, a simple data-boosting technique is introduced to overcome a lack of training data; such a problem particularly challenges all previous work, owing to the significantly low frequency of abnormal events. This technique only requires normal trajectories and fundamental information about scenes to increase the amount of training data for both normal and abnormal trajectories. With the increased amount of training data, the conventional abnormal trajectory classifier is able to achieve better prediction accuracy without falling into the over-fitting problem caused by complex learning models. Finally, the proposed framework (which annotates tracks that enter prohibited areas) and a conventional abnormal trajectory detector (using the data-boosting technique) are integrated to form a united detector. Such a detector deals with different types of anomalous trajectories in a hierarchical order. The experimental results show that all proposed detectors can effectively detect anomalous trajectories in the test phase.

• 한국인터넷방송통신학회논문지
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• 제16권6호
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• pp.1-7
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• 2016

새로운 정보통신 기술의 발전과 더불어 보안 위협도 나날이 지능화 고도화되고 있다. 본 논문은 네트워크 장치나 사물인터넷 경량 장치에서 일련의 주기를 통해 연속적으로 입력되는 시계열 데이터를 통계적 기법을 활용하여 분석하고, 분석 정보를 기반으로 장치의 이상 유무나 비정상 징후를 탐지할 수 있는 시스템을 제안한다. 제안 시스템은 과거에 입력된 데이터를 기반으로 1차 비정상 탐지를 수행하고, 시간 속성이나 그룹의 속성을 기반으로 저장되어있는 시계열 데이터를 기반으로 신뢰구간을 설정하여 2차 비정상 탐지를 수행한다. 다단계 분석은 판정 데이터의 다양성을 통해 신뢰성을 향상시키고 오탐율을 줄일 수 있다.

• 인터넷정보학회논문지
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• 제20권6호
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• pp.137-142
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• 2019

본 논문은 이상 상황을 탐지하고 모니터링하는 다양한 서비스가 존재한다. 하지만 대부분의 서비스는 화재, 가스누출에 초점을 맞추어 진행되고 있으며, 독거노인과 중증장애인들의 사망 혹은 심정지 등 위급상황에 대하여 사전 예방 및 위급상황 대응이 불가능하다. 본 연구에서는 여러 생체신호 중 가장 위중하다고 판단되는 심박 신호의 이상 상태를 탐지하기 위하여 인공지능 모델을 설계하는 과정에서 적합한 데이터 변형과 모델을 비교한다. 세부적으로는 오픈 의료 데이터 PhysioNet의 MIT-BIH Arrhythmia Database를 이용하여 심전도(ECG) 데이터를 수집하고, 수집한 데이터를 각각 다른 방법으로 데이터를 변형한 후 학습하여 기본 심전도 데이터를 이용해 학습한 인공지능 모델과 비교한다.

• 혜화의학회지
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• 제23권1호
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• pp.167-172
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• 2014

Purpose : The purpose of this report is to record the effects of oriental treatments for the abnormal uterine bleeding Methods : This is a case report of a 16 year old female patient suffering from abnormal uterine bleeding for eight months. She was treated by Herb therapy for 3 months. During the treatments, we checked changes of symptoms. Results : After Herb therapy, abnormal uterine bleeding was disappeared and recovered the normal menstrual cycle. Conclusion : This clinical case shows that Herb therapy has potentially effective for abnormal uterine bleeding. More clinical data and studies are required for the treatment of abnormal uterine bleeding.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 춘계학술발표대회
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• pp.542-544
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• 2019

Changes in a person's health affect one's lifestyle and work activities. According to the World Health Organization (WHO), abnormal activity is growing faster in people aged 60 or more than any other age group in almost every country. This trend steadily continues and expected to increase further in the near future. Abnormal activity put these people at high risk of expected incidents since most of these people live alone. Human abnormal activity analysis is a challenging, useful and interesting problem among the researchers and its particularly crucial task in life and health care areas. In this paper, we discuss the problem of abnormal activities of old people lives alone at home. We propose Convolutional Neural Network (CNN) based model to detect the abnormal behaviors of elderlies by utilizing six simulated action data from daily life actions.

• International journal of advanced smart convergence
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• 제9권4호
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• pp.115-119
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• 2020

Recently, CCTV installations are rapidly increasing in the public and private sectors to prevent various crimes. In accordance with the increasing number of CCTVs, video-based abnormal behavior detection in control systems is one of the key technologies for safety. This is because it is difficult for the surveillance personnel who control multiple CCTVs to manually monitor all abnormal behaviors in the video. In order to solve this problem, research to recognize abnormal behavior using deep learning is being actively conducted. In this paper, we propose a model for detecting abnormal behavior based on the deep learning model that is currently widely used. Based on the abnormal behavior video data provided by AI Hub, we performed a comparative experiment to detect anomalous behavior through violence learning and fainting in videos using 2D CNN-LSTM, 3D CNN, and I3D models. We hope that the experimental results of this abnormal behavior learning model will be helpful in developing intelligent CCTV.

• Journal of Advanced Marine Engineering and Technology
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• 제30권2호
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• pp.247-252
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• 2006

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn until signal is growing to abnormal state that the signal is over or under the set point. therefore cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without any additional sensors. By analyzing the data with high correlation coefficient(CC), correlation level of interactive data can be defined. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC. FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2005년도 전기학술대회논문집
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• pp.281-286
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• 2005

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn while signal is growing to abnormal state until the signal is over or under the set point and cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without additional sensors. By analyzing this data having high correlation coefficient(CC), correlation level of interactive data can be understood. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC, FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

• Journal of information and communication convergence engineering
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• 제9권4호
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• pp.466-470
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• 2011

This paper proposes the new method based on fuzzy logic which recognizes between normal and abnormal. The object image was the Thyroid Gland cell image that was diagnosed as normal and abnormal(two types of abnormal : follicular neoplastic cell, and papillary neoplastic cell), respectively. The nuclei were successfully diagnosed as normal and abnormal. The multiple feature parameters (pre-obtained 16 feature parameters of image data) were used to extract the features of each nucleus. As a consequence of using fuzzy logic algorithm, proposed in this paper, average recognition rate of 98.25% was obtained.

• 유통과학연구
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• 제20권9호
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• pp.73-81
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• 2022

Purpose: This study aimed to examine whether key audit matters (KAMs) readability influences investor reaction. Research design, data, and methodology: The signaling theory was applied to explain the behavior of investors when they receive useful information for their decisions. Data were collected from 1,866 firm-year observations from Thai listed companies in both the Stock Exchange of Thailand (SET) and the Market for Alternative Investment (MAI) for the fiscal years of 2016-2019. The study was based on secondary data, which were collected from the SET Market Analysis and Reporting Tool (SETSMART) database and the Stock Exchange of Thailand's website (www.set.or.th). A statistical regression method was used with panel data analysis to evaluate possible associations between KAMs readability and investor reaction. The study relied on popular readability measures (Fog Index). Moreover, investor reaction was measured by absolute cumulative abnormal return and abnormal trading volume. Results: It was found that the KAMs readability has positive significance on both absolute cumulative abnormal return and abnormal trading volume. Conclusion: This study showed a significant contribution to the implication of KAMs in an emerging economy. The results reveal that more readable KAMs disclosure distributed new insights and useful information to investors and led to reducing the information gap between auditors and investors.