• The Journal of the Korea institute of electronic communication sciences
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• v.2 no.2
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• pp.106-112
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• 2007

Recently the crowd environment in the department of the animation is necessary to the digital industry. The goal of researching a proper crowd animation is to design character animation that is defined by the reality of scenes, performance of system and interaction with users to show the crowd vividly and effectively in cyber underwater. It is important to set up the crowd behavior patterns to represent for moving crowd naturally in cyber space. In the paper, we expressed the behavior patterns for flocks of fish in cyber underwater, and compared with the number of mesh, the number of fish, the number of frame, elapsed time, and resolution and analyzed them with the fish behavior simulating system.

• The Journal of the Korea institute of electronic communication sciences
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• v.2 no.3
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• pp.157-162
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• 2007

The goal of researching a proper crowd animation is to design system that is satisfied with the reality of scenes, performance of system, and interaction with users to show the crowd vividly and effectively in virtual underwater world. In this paper, we smartly expressed the behavior patterns for flocks of fish in virtual underwater and we made up for the weak points in spending time and cost to produce crowd animation. We compared with the number of mesh, the number of fish, the number of frame, elapsed time, and resolution and analyzes them with the fish behavior simulating system. We developed a virtual underwater simulator using this system.

• Journal of the Korea Society of Computer and Information
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• v.9 no.3
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• pp.87-93
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• 2004

In this paper we present a hybrid model that enables dynamic regrouping based on emotion in determining the behavioral pattern of crowds in order to enhance the reality of crowd simulation in virtual environments such as games. Emotion determination rules are defined and they are used for dynamic human regrouping to simulate the movement of characters through crowds realistically. Our experiments show more natural simulation of crowd behaviors as results of this research.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.196-204
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• 2017

Behavior of intelligent video surveillance system is recognized by analyzing the pattern of the object of interest by using the frame information of video inputted from the camera and analyzes the behavior. Detection of object's certain behaviors in the crowd has become a critical problem because in the event of terror strikes. Recognition of object's certain behaviors is an important but difficult problem in the area of computer vision. As the realization of big data utilizing machine learning, data mining techniques, the amount of video through the CCTV, Smart-phone and Drone's video has increased dramatically. In this paper, we propose a multiple-sliding window method to recognize the cumulative change as one piece in order to improve the accuracy of the recognition. The experimental results demonstrated the method was robust and efficient learning units in the classification of certain behaviors.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.3
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• pp.8-15
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• 2021

Despite advances in technology, crushing accidents still occur during emergency evacuations of crowded public spaces. To prevent crushing accidents, it is necessary to understand the flow of pedestrians during evacuation scenarios through experiments. Since experiments with humans can generate real accidents, we performed experiments on rodents to approximate human behavior. To trigger an emergency evacuation response, we applied electrical stimulation to the feet of the rodents. Although electrical stimulation has been applied to mice in many experiments, studies on the intensity and pattern of electric stimulation required to evoke a rapid evacuation response in mice is still lacking. In this study, we experimentally investigated how the evacuation flow of mice changes according to the amplitude, event, and duration of electric stimulation.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.