• Korea Trade Review
• /
• v.45 no.2
• /
• pp.211-228
• /
• 2020

The purpose of this study aims to analyze the comparative effects of luxury consumption behavior for Chinese consumers. Many research have been conducted in luxury consumption behavior based on perspective of culture, brand, and purchase motives. However, previous studies seem somewhat limited in fully explaining luxury consumption behavior due to less understanding of consumers' psychological trait. In order to fill this gap, this study adopts narcissism (overt narcissism and covert narcissism) to explain consumers' psychological trait. Based on specific psychological trait, consumers would lead to different luxury purchasing behavior depending on purchase motives. Especially, overt narcissism would show high tendency of self-esteem, arrogance, which means that it is closely related to need for uniqueness. Conversely, covert narcissism would show high sensitivity to others, which indicate that it is involved with need for approval. Also, each narcissism would result in different behavior for luxury purchasing based on generation difference (20-30s, vs. 40-50s). The result of this study shows that overt narcissism, covert narcissism, need for uniqueness, need for approval have significant influence on luxury consumption behavior. Especially, overt narcissism has interaction effect with need for uniqueness and young generation (in the 20s, 30s) for luxury purchasing behavior. On the other hand, it was found that covert narcissism has no interaction effect with other variables.

• Child Health Nursing Research
• /
• v.12 no.1
• /
• pp.15-24
• /
• 2006

Purpose: This study was done to define nursing behavior in neonatal intensive care units so as to estimate resource-based relative value-. Method: Participating in this study were 292 nurses in neonatal intensive care units. The study surveyed physical and mental labor, stress and time involved in nursing work. Tool used in this study was a nursing labor per relative value tool. For analyzes, the relative value of each nursing behavior was calculated, where the mean value of the three components, labor intensity and component-by-component explanatory power were in percentage terms. Results: 1. Nursing behaviors in neonatal intensive care unit were classified and defined at three levels: 5 main domains, 17 mid-domains, and 42 small domains. 2. The per component explanatory power of intensity involved in nursing labor showed physical effort to be 32.45%, mental 32.86%, and stress 34.69%. 3. The reliability of nursing labor factors was very strong, Cronbach's alpha value of 0.96. Conclusion: In this research, which is a first in defining nursing behavior in neonatal intensive care units, individual nursing behavior were broken down using resource-based relative value for nursing cost, and each nursing behavior was successfully translated to a numerical value.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.6 no.4
• /
• pp.314-320
• /
• 2006

Navigation in environments that are densely cluttered with obstacles is still a challenge for Autonomous Ground Vehicles (AGVs), especially when the configuration of obstacles is not known a priori. Reactive local navigation schemes that tightly couple the robot actions to the sensor information have proved to be effective in these environments, and because of the environmental uncertainties, STSF(Space and Time Sensor Fusion)-based fuzzy behavior systems have been proposed. Realization of autonomous behavior in mobile robots, using STSF control based on spatial data fusion, requires formulation of rules which are collectively responsible for necessary levels of intelligence. This collection of rules can be conveniently decomposed and efficiently implemented as a hierarchy of fuzzy-behaviors. This paper describes how this can be done using a behavior-based architecture. The approach is motivated by ethological models which suggest hierarchical organizations of behavior. Experimental results show that the proposed method can smoothly and effectively guide a robot through cluttered environments such as dense forests.

• Journal of the Korea Society of Computer and Information
• /
• v.20 no.1
• /
• pp.29-37
• /
• 2015

Nowadays, the user-centric application based web 2.0 has replaced the web 1.0. The users gain and provide information by interactive network applications. As a result, traditional approaches that only extract and analyze users' local document operating behavior and network browsing behavior to build the users' preference profile cannot fully reflect their interests. Therefore this paper proposed a preference analysis and indicating approach based on the users' communication information from MicroBlog, such as reading, forwarding and @ behavior, and using the improved PersonalRank method to analyze the importance of a user to other users in the network and based on the users' communication behavior to update the weight of the items in the user preference. Simulation result shows that our proposed method outperforms the ontology model, TREC model, and the category model in terms of 11SPR value.

• Journal of Information Processing Systems
• /
• v.20 no.2
• /
• pp.159-172
• /
• 2024

The number of healthcare products available for pets has increased in recent times, which has prompted active research into wearable devices for pets. However, the data collected through such devices are limited by outliers and missing values owing to the anomalous and irregular characteristics of pets. Hence, we propose pet behavior recognition based on a hybrid one-dimensional convolutional neural network (CNN) and long short- term memory (LSTM) model using pet wearable devices. An Arduino-based pet wearable device was first fabricated to collect data for behavior recognition, where gyroscope and accelerometer values were collected using the device. Then, data augmentation was performed after replacing any missing values and outliers via preprocessing. At this time, the behaviors were classified into five types. To prevent bias from specific actions in the data augmentation, the number of datasets was compared and balanced, and CNN-LSTM-based deep learning was performed. The five subdivided behaviors and overall performance were then evaluated, and the overall accuracy of behavior recognition was found to be about 88.76%.

• Journal of Korea Game Society
• /
• v.2 no.2
• /
• pp.37-45
• /
• 2002

Computer games could be regarded as simulation of the real world. Control problems of software agents have long been studied in the field of Artificial Intelligence (AI), resulting in giving a birth to the behavior-based approach. three main approaches might be categorized out of the history of AI study. First, Cognitivists propose that intelligence could be represented and manipulated in terms of symbols. Second, Connectionists claim that symbols could not be isolated but they are embedded in the body structure. Third, the behavior-based approach is an approach to AI which suggests that intelligence is dynamic property that exists nowhere but emerges in the relationship of an agent and the world including observers while the agent performs behavior. This paper explains and compares the three approaches to AI, then discusses the plausibility of the behavior-based approach and problems. Finally, this paper proposes application of behavior-based approach to computer games in terms of agent control.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.6
• /
• pp.2188-2203
• /
• 2021

With the rapid development of mobile Internet, smart phones have been widely popularized, among which Android platform dominates. Due to it is open source, malware on the Android platform is rampant. In order to improve the efficiency of malware detection, this paper proposes deep learning Android malicious detection system based on behavior features. First of all, the detection system adopts the static analysis method to extract different types of behavior features from Android applications, and extract sensitive behavior features through Term frequency-inverse Document Frequency algorithm for each extracted behavior feature to construct detection features through unified abstract expression. Secondly, Long Short-Term Memory neural network model is established to select and learn from the extracted attributes and the learned attributes are used to detect Android malicious applications, Analysis and further optimization of the application behavior parameters, so as to build a deep learning Android malicious detection method based on feature analysis. We use different types of features to evaluate our method and compare it with various machine learning-based methods. Study shows that it outperforms most existing machine learning based approaches and detects 95.31% of the malware.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.51 no.9
• /
• pp.402-412
• /
• 2002

The navigation algorithms enable autonomous mobile robots to reach given target points without collision against obstacles. To achieve safe navigations in unknown environments, this paper presents an effective navigation algorithm for the autonomous mobile robots with ultrasonic sensors. The proposed navigation algorithm consists of an obstacle-avoidance behavior, a target-reaching behavior and a fuzzy-based decision maker. In the obstacle-avoidance behavior and the target-reaching behavior, artificial immune networks are used to select a proper steering angle, make the autonomous mobile robot avoid obstacles and approach a given target point. The fuzzy-based decision maker combines the steering angles from the target-reaching behavior and the obstacle-avoidance behavior in order to steer the autonomous mobile robot appropriately. Simulational and experimental results show that the proposed navigation algorithm is very effective in unknown environments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.281-282
• /
• 2019

In recent years welfare of animals is the biggest challenge because animals, especially dogs are widely recognized as pet as well as they are using as service animals. So, for the wellbeing of the dog it is necessary to perform objective assessment to track their behavior in everyday life. In this paper, we have proposed an automatic behavior assessment system for dogs based on a neck worn and tail worn accelerometer and gyroscope platform, and data analysis techniques that recognize typical dog activities. We evaluate the system based on the analysis of 8 behavior traits in 3 dogs, incorporating 2 breeds of various sizes. Our proposed framework able to reproduce the manual assessment that is based on the video recording which is treated as gold standard that exhibits the real-life use case of automated dog behavior analysis.

• Journal of the Korean Home Economics Association
• /
• v.37 no.4
• /
• pp.1-16
• /
• 1999

The objectives of this study are examine the variables effecting management behavior of home-band worker through empirical study. The data used for statistical analysis is 285 home-based worker. The statistical methods used for data analysis were frequencies, mean, t-test, multiple regression, factor analysis and path analysis. The major findings can be summarized as fellows. Home-based workers' various characteristics were statistically significant variable to management behavior. Home-based work income were higher for older women, no employ experience in out of home, lower age of children, business owner, lower time flexibility. In cause-effect model analysis that affects life satisfaction was related to work management, home management behavior and income. Based on the finds of the study, it was found that home-based work can be good alternative to induce married women to labour market if some problems are covered.