• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.59-66
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• 2019

According to the KT telecommunication statistics, people stayed inside their houses on an average of 11.9 hours a day. As well as, according to NSC statistics in the united states, people regardless of age are injured for a variety of reasons in their houses. For purposes of this research, we have investigated an abnormal event detection algorithm to classify infrequently occurring behaviors as accidents, health emergencies, etc. in their daily lives. We propose a fusion method that combines three classification algorithms with vision pattern, audio pattern, and activity pattern to detect unusual user events. The vision pattern algorithm identifies people and objects based on video data collected through home CCTV. The audio and activity pattern algorithms classify user audio and activity behaviors using the data collected from built-in sensors on their smartphones in their houses. We evaluated the proposed individual pattern algorithm and fusion method based on multiple scenarios.

• International Journal of Computer Science & Network Security
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• v.24 no.8
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• pp.61-71
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• 2024

The dynamic nature of Online Social Networks (OSNs), especially on platforms like Twitter, presents challenges in identifying and responding to community attacks, particularly within Arabic content. The proposed integrated system addresses these challenges by achieving 91% accuracy in detecting real-time community event attacks while efficiently managing computational costs. This is accomplished through the use of specialized integrated approach clustering to detect both major and minor attacks. Additionally, the system leverages clustering algorithms, temporal modules, and social network graphs to identify events, map communities, and analyze online dynamics. An extensive parameter sensitivity analysis was conducted to fine-tune the algorithm, and the system's effectiveness was validated using a benchmark dataset, demonstrating substantial improvements in event detection.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.10
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• pp.447-454
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• 2015

This paper proposes geographical name denoising by machine learning of event detection based on twitter. Recently, the increasing number of smart phone users are leading the growing user of SNS. Especially, the functions of short message (less than 140 words) and follow service make twitter has the power of conveying and diffusing the information more quickly. These characteristics and mobile optimised feature make twitter has fast information conveying speed, which can play a role of conveying disasters or events. Related research used the individuals of twitter user as the sensor of event detection to detect events that occur in reality. This research employed geographical name as the keyword by using the characteristic that an event occurs in a specific place. However, it ignored the denoising of relationship between geographical name and homograph, it became an important factor to lower the accuracy of event detection. In this paper, we used removing and forecasting, these two method to applied denoising technique. First after processing the filtering step by using noise related database building, we have determined the existence of geographical name by using the Naive Bayesian classification. Finally by using the experimental data, we earned the probability value of machine learning. On the basis of forecast technique which is proposed in this paper, the reliability of the need for denoising technique has turned out to be 89.6%.

• Smart Structures and Systems
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• v.15 no.6
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• pp.1583-1600
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• 2015

When natural disasters occur, including earthquakes, tsunamis, and debris flows, they are often accompanied by various types of damages such as the collapse of buildings, broken bridges and roads, and the destruction of natural scenery. Natural disaster detection and warning is an important issue which could help to reduce the incidence of serious damage to life and property as well as provide information for search and rescue afterwards. In this study, we propose a novel computer vision technique for debris flow detection which is feature-based that can be used to construct a debris flow event warning system. The landscape is composed of various elements, including trees, rocks, and buildings which are characterized by their features, shapes, positions, and colors. Unlike the traditional methods, our analysis relies on changes in the natural scenery which influence changes to the features. The "background module" and "monitoring module" procedures are designed and used to detect debris flows and construct an event warning system. The multi-criteria decision-making method used to construct an event warring system includes gradient information and the percentage of variation of the features. To prove the feasibility of the proposed method for detecting debris flows, some real cases of debris flows are analyzed. The natural environment is simulated and an event warning system is constructed to warn of debris flows. Debris flows are successfully detected using these two procedures, by analyzing the variation in the detected features and the matched feature. The feasibility of the event warning system is proven using the simulation method. Therefore, the feature based method is found to be useful for detecting debris flows and the event warning system is triggered when debris flows occur.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.3
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• pp.1-15
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• 1993

In this paper, we propose over-reporting avoidance scheme which avoids congestion of network traffics by adjusting managed system's over-reporting, on the OSI network management model which reports events from managed system to managing system. In case of reporting events from managed system to managing system, management traffic concentration occurs, and it causes over-loading on the managing system and congestion on the network. This scheme takes advantage of feedback from managing system to managed system. Managed system transmits event reports as much as maximum event pertime allocated to itself to managing system, and it sets it's management variables to LOCK state and stops event reports as Threshold time is reached. At the time, managing system directs event reports again by using M-set primitive with referring it's status. With this scheme, distributed processing, dynamic network adaptation, convergence of optimal operation point is possible. In addition to it, a fairness is assured. In order to detect characteristics of the Unary feedback over-reporting avoidance scheme. It is observed a control capability of the event reporting and fairness of each nodes through measuring. ThresholdTime value. It is measured a number of mean activating nodes and maintained time of LOCK state according to event reporting load, and also measured lost ratio of management packet, queuing delay in managing system, and goodput to observe effects of general packet load. Binary feedback scheme. Unary feedback overreporting avoidance scheme and raw scheme on the OSI network management system each are compared and analyzed, and finally proved that the scheme proposed in this study performs better.

• Journal of information and communication convergence engineering
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• v.15 no.4
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• pp.244-249
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• 2017

Wearable sensor-based gait analysis has been widely conducted to analyze various aspects of human ambulation abilities under the free-living condition. However, there have been few research efforts on using wearable sensors to analyze human walking on an unstable surface such as on a ship during a sea voyage. Since the motion of a ship on the unstable sea surface imposes significant differences in walking strategies, investigation is suggested to find better performing wearable sensor-based gait analysis algorithms on this unstable environment. This study aimed to compare two representative gait event algorithms including time domain and frequency domain analyses for detecting heel strike on an unstable platform. As results, although two methods did not miss any heel strike, the frequency domain analysis method perform better when comparing heel strike timing. The finding suggests that the frequency analysis is recommended to efficiently detect gait event in the unstable walking environment.

• Journal of Korea Multimedia Society
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• v.7 no.11
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• pp.1540-1549
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• 2004

In this paper, we propose an algorithm to detect semantic events in golf video using Hidden Markov Model. The purpose of this paper is to identify and classify the golf events to facilitate highlight-based video indexing and summarization. In this paper we first define 4 semantic events, and then design HMM model with states made up of each event. We also use 10 multiple visual features based on MPEG-7 visual descriptors to acquire parameters of HMM for each event. Experimental results showed that the proposed algorithm provided reasonable detection performance for identifying a variety of golf events.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.201-209
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• 2012

The point-based complex event processing handled an instantaneous event by using one time stamp in each event. However, the activity period of the event plays the important role in the field which is the same as the finance, multimedia, medicine, and meteorology. The point-based event is insufficient for expressing the complex temporal relationship in this field. In the application field of the real-time world, the event has the period. The events more than two kinds can be temporally overlapped. In addition, one event can include the other event. The relation about the events of kind of these can not be successive like the point-based event. This thesis designs and implements the method detecting the patterns of the complex event by using the interval-based events. The interval-based events can express the overlapping relation between events. Furthermore, it can include the others. By using the end point of beginning and end point of the termination, the operator of interval-based events shows the interval-based events. It expresses the sequence of the interval-based events and can detect the complex event patterns. This thesis proposes the algorithm using the active instance stack in order to raise efficiency of detection of the complex event patterns. When comprising the event sequence, this thesis applies the window push down technique in order to reduce the number of intermediate results. It raises the utility factor of the running time and memory.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.292-305
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• 2009

Wireless sensor networks have a lot of application areas such as industrial process control, machine and resource management, environment and habitat monitoring. One of the main objects of using wireless sensor networks in these areas is the event detection. To detect events at a user's request, we need a join processing between sensor data and the predicates of the events. If there are too many predicates of events compared with a node's capacity, it is impossible to store them in a node and to do an in-network join with the generated sensor data This paper proposes a predicate-merge based in-network join approach to efficiently detect multiple events, considering the limited capacity of a sensor node and many predicates of events. It reduces the number of the original predicates of events by substituting some pairs of original predicates with some merged predicates. We create an estimation model of a message transmission cost and apply it to the selection algorithm of targets for merged predicates. The experiments validate the cost estimation model and show the superior performance of the proposed approach compared with the existing approaches.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.373-385
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• 2021

To provide convenience and safety of drivers, the recent vehicles are being equipped with a number of electronic control units (ECUs). Multiple ECUs construct a network inside a vehicle to share information related to the vehicle's status; in addition, the CAN protocol is normally applied. As the modern vehicles provide highly convenient and safe services, it provides many types of attack surfaces; as a result, it makes them vulnerable to cyber attacks. The automotive IDS (Intrusion Detection System) is one of the promising techniques for securing vehicles. However, the existing methods for automotive IDS are able to analyze only periodic messages. If someone attacks on non-periodic messages, the existing methods are not able to properly detect the intrusion. In this paper, we present a method to detect intrusions including an attack using non-periodic messages. Moreover, we evaluate our method on the real vehicles, where we show that our method has 0% of FPR and 0% of FNR under our attack model.