• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.77-86
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• 2019

As the number of single-person households increases, it is not easy to ask for help alone if a single-person household is severely injured in the home. This paper detects abnormal event when members of a single household in the home are seriously injured. It proposes an vision detection algorithm that analyzes and recognizes patterns through videos that are collected based on home CCTV. And proposes audio detection algorithms that analyze and recognize patterns of sound that occur in households based on Smartphones. If only each algorithm is used, shortcomings exist and it is difficult to detect situations such as serious injuries in a wide area. So I propose a fusion method that effectively combines the two algorithms. The performance of the detection algorithm and the precise detection performance of the proposed fusion method were evaluated, respectively.

• Journal of KIISE
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• v.41 no.12
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• pp.1066-1074
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• 2014

In this paper, we present a visual analytics system that uses serial-correlation to detect an abnormal event in spatio-temporal data. Our approach extracts the topic-model from spatio-temporal tweets and then filters the abnormal event candidates using a seasonal-trend decomposition procedure based on Loess smoothing (STL). We re-extract the topic from the candidates, and then, we apply STL to the second candidate. Finally, we analyze the serial-correlation between the first candidates and the second candidate in order to detect abnormal events. We have used a visual analytic approach to detect the abnormal events, and therefore, the users can intuitively analyze abnormal event trends and cyclical patterns. For the case study, we have verified our visual analytics system by analyzing information related to two different events: the 'Gyeongju Mauna Resort collapse' and the 'Jindo-ferry sinking'.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.2
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• pp.159-178
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• 1995

Supervisory control is an approach based on formal language. it is used to model and control discrete event systems in which each discrete event process is represented as an automation. A supervisor is a generator that switches control patterns in such a way that a given discrete evenet process behaves in obedience to various constraints. A flexible manufacturing cell (FMC) is one of discrete evenet systems. Functions necessary for the operation of an FMC are characterized by operational components and informational compoments. The operational components can be modeled using the finite state machines and the informational components can be modeled using the abstract formalism which describes supporting operations of the cell controller. In this paper, we addressed function required for FMC control specification, software engineering aspects on FMC control based on supervisory control, a concept of event queue for resolving synchronization problem, and complexity reduction. Based on the mathematical model of an FMC. we synthesized the controller by integrating a supervisor for FMC with control specification that specifies event-driven operation of the cell controller. The proposed control scheme is stable mathematically so that the system always behaves on a controlled way even under the existence of uncontrollable events. Furthermore, using an event queue concept, we can solve a synchronization problem caused by the violation of instantaneity assumption of supervisory control theory in real life situation. And also, we can propotype a control software rapidly due to the modularity of the proposed control scheme.

• Journal of The Korean Association For Science Education
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• v.26 no.6
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• pp.723-731
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• 2006

In this study, twenty-eight 7th graders were interviewed to explore their emotional responses and perceived teaching-learning strategies for effective conceptual change by the types of cognitive responses to a discrepant event. The results revealed that cognitive conflict was more induced by a discrepant event when its reliability and validity were emphasized. The students' cognitive responses to a discrepant event, the existence of alternative hypotheses, and their clearness influenced the patterns of emotional responses such as interest and anxiety. Many students perceived that emotional responses would have positive influences on concept learning processes. In the cases of the students exhibiting cognitive responses such as belief decrease, peripheral belief change, and belief change, opinions about teaching-learning strategies for effective conceptual change were different depending on whether they had alternative hypotheses or not. Educational implications are discussed.

• Journal of Construction Engineering and Project Management
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• v.4 no.2
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• pp.31-40
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• 2014

The management of vehicular supply of "perishable" construction material, such as concrete mixes, faces a series of uncertainties such as weather, daily traffic patterns and accidents. Presented in this paper is a logistics control model for managing a hauling fleet with interrelated processes at both ends and queue capacities. Discrete event simulation is used to model the complex interactions of production units and the randomness of the real world. Two alternative strategies for ready mix concrete delivery, with and without an off-site waiting queue, are studied to compare supply performance. Secondly, the paper discusses the effect of an agent-based GPS tracking system providing real-time travel data that lessens the uncertainty of trucking time. The results show that the combination of GPS information with off-site queuing reduces productivity loss and process wastes of concrete placement as well as the idleness of supply trucks when crew or pump experience an unexpected stoppage.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.5
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• pp.913-919
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• 2011

In this paper, we present a new efficient event detection algorithm for sensor networks with faults. We focus on multi-attributed events, which are sets of data points that correspond to interesting or unusual patterns in the underlying phenomenon that the network monitors. Conventional algorithms cannot detect some events because they treat only their own sensor readings which can be affected easily by environmental or physical problem. Our approach exploits spatio-temporal correlation of sensor readings. Sensor nodes exchange a fault-tolerant code encoded their own readings with neighbors, organize virtual sensor readings which have spatio-temporal correlation, and determine a result for multi-attributed events from them. In the result, our proposed algorithm provides improvement of detecting multi-attributed events and reduces the number of false-negatives due to negative environmental effects.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.71-77
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• 2011

CEP(Complex Event Processing) is a technique to find complex event pattern in a massive information system. Based on CEP technique, an abuse pattern monitoring method has been developed to provide an real-time detection. In the method, the events occurred by game-play are observed to be against the rules using CEP. User abuse patterns are pre-registered in CEP engine. And CEP engine monitors user abuse after aggregating the game data transferred by game logging server. This article provides the performance analysis results of the abuse pattern monitoring method using real game DB. We results that the method proposed in previous study is effective to monitor abusing users.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.308-317
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• 2008

The objects real time rendered in the 3D cyber space can interact with each others according to the events which are happened when satisfying some conditions. But to representing the behaviors with these interactions, too many event conditions are considered because each behavior pattern and event must be corresponded in a one-to-one ratio. It leads to problems which increase the system complexity. So, in this paper, we try to physical method based on elasticity force for representing more realistic behaviors of AI fish and apply to the deformable multi-detection sensor, so we suggest the new method which can create the various behavior patterns responding to one evasion event.

• Korean Journal of Applied Biomechanics
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• v.17 no.3
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• pp.115-124
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• 2007

The purpose of this study was to analyze the kinematics variables of upper limb joint during forehand stroke by swings patterns. Eight high school tennis players were chosen for the study, who have never been injured for last six months, in Busan. They performed horizontal swing and vertical swing that it was done each five consecutive trial in the condition of square, semi-open and open stance. It was filmed by 6 video camera and used with 3-dimensional motion analyzer system. The following kinematic variables were analyzed in relation to angle of joint(shoulder, elbow and wrist joint). The conclusion were as follow: 1. The angle of right shoulder joint represented all event that both swing were shown similar pattern in swing type and stance pattern. 2. All event in the angle of elbow joint had consistent with that except E2, horizontal and vertical swings in square stance. 3. All event in the angle of wrist joint was show to similar pattern except E2, horizontal and vertical swing in open stance.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.2_1
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• pp.217-223
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• 2023

With the rapid advancement of technologies, need for different research fields where this technology can be used is also increasing. One of the most researched topic in computer vision is object detection, which has widely been implemented in various fields which include healthcare, video surveillance and education. The main goal of object detection is to identify and categorize all the objects in a target environment. Specifically, methods of object detection consist of a variety of significant techniq ues, such as image processing and patterns recognition. Anomaly detection is a part of object detection, anomalies can be found various scenarios for example crowded places such as subway stations. An abnormal event can be assumed as a variation from the conventional scene. Since the abnormal event does not occur frequently, the distribution of normal and abnormal events is thoroughly imbalanced. In terms of public safety, abnormal events should be avoided and therefore immediate action need to be taken. When abnormal events occur in certain places, real time detection is required to prevent and protect the safety of the people. To solve the above problems, we propose a modified YOLOv5 object detection algorithm by implementing dilated convolutional layers which achieved 97% mAP50 compared to other five different models of YOLOv5. In addition to this, we also created a simple mobile application to avail the abnormal event detection on mobile phones.