• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.225-234
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• 2012

In Wireless Sensor Networks (WSNs) where deployed sensors are not stationary, the most important demand of is to design a cost effective and reliable network. This paper proposes an energy aware network construction and routing scheme, which is based on the hierarchical approach to distribute the task in some sensors in order to prolong the network lifetime. It aims to make even the energy consumption on constitute nodes. With the node hierarchy, the sink initiates the construction by electing gateway nodes in the network and the elected gateway nodes participate to form logical clusters by electing a cluster head in each cluster. Then, the cluster heads aggregate data from the sensing sensors and transmit the data to the sink through the gateway. Our simulation result illustrates that the proposed scheme provides a basement to reduce the source of energy dissipation in network construction, and as well as in data routing.

• Journal of KIISE
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• v.44 no.12
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• pp.1290-1295
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• 2017

Document classification, a task of classifying the category of each document based on text, is one of the fundamental areas for natural language processing. Document classification may be used in various fields such as topic classification and sentiment classification. Neural network models for document classification can be divided into two categories: word-level models and character-level models that treat words and characters as basic units respectively. In this study, we propose a neural network model that combines character-level and word-level models to improve performance of document classification. The proposed model extracts the feature vector of each word by combining information obtained from a word embedding matrix and information encoded by a character-level neural network. Based on feature vectors of words, the model classifies documents with a hierarchical structure wherein recurrent neural networks with attention mechanisms are used for both the word and the sentence levels. Experiments on real life datasets demonstrate effectiveness of our proposed model.

• Journal of the Korean Institute of Gas
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• v.21 no.4
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• pp.47-61
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• 2017

The preconditions of the usefulness of an operator safety training model in large plants are the versatility and accuracy of operational procedures, obtained by detailed analysis of the various types of risks associated with the operation, and the systematic representation of knowledge. In this study, we consider the artificial intelligence planning method for the generation of operation procedures; classify them into general actions, actions and technical terms of the operator; and take into account the sharing and reuse of knowledge, defining a knowledge expression ontology. In order to expand and extend the general operations of the operation, we apply a Hierarchical Task Network (HTN). Actual boiler plant case studies are classified according to operating conditions, states and operating objectives between the units, and general emergency shutdown procedures are created to confirm the applicability of the proposed method. These results based on systematic knowledge representation can be easily applied to general plant operation procedures and operator safety training scenarios and will be used for automatic generation of safety training scenarios.

• Journal of KIISE
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• v.42 no.3
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• pp.320-327
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• 2015

Although service robots in various fields are being commercialized, most of them have problems that depend on explicit commands by users and have difficulty to generate robust reactions of the robot in the unstable condition using insufficient sensor data. To solve these problems, we modeled mirror neuron and theory of mind systems, and applied them to a robot agent to show the usefulness. In order to implement quick and intuitive response of the mirror neuron, the proposed intention-response model utilized behavior selection networks considering external stimuli and a goal, and in order to perform reactions based on the long-term action plan of theory of mind system, we planned behaviors of the sub-goal unit using a hierarchical task network planning, and controled behavior selection network modules. Experiments with various scenarios revealed that appropriate reactions were generated according to external stimuli.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.3
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• pp.166-171
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• 2023

Efficient energy management is a very important factor in sensor networks with limited resources, and cluster techniques have been studied a lot in this respect. However, a problem may occur in which energy use of the cluster header is concentrated, and when the cluster header is not evenly distributed over the entire area but concentrated in a specific area, the transmission distance of the cluster members may be large or very uneven. The transmission distance can be directly related to the problem of energy consumption. Since the energy of a specific node is quickly exhausted, the lifetime of the sensor network is shortened, and the efficiency of the entire sensor network is reduced. Thus, balanced energy consumption of sensor nodes is a very important research task. In this study, factors for balanced energy consumption by cluster headers and sensor nodes are analyzed, and a balancing distribution clustering method in which cluster headers are balanced distributed throughout the sensor network is proposed. The proposed cluster method uses multi-hop routing to reduce energy consumption of sensor nodes due to long-distance transmission. Existing multi-hop cluster studies sets up a multi-hop cluster path through a two-step process of cluster setup and routing path setup, whereas the proposed method establishes a hierarchical cluster routing path in the process of selecting cluster headers to minimize the overhead of control messages.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.71-81
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• 2010

In grid system, Task scheduling based on list scheduling models has showed low complexity and high efficiency in fully connected processor set environment. However, earlier schemes did not consider sufficiently the communication cost among tasks and the composition process of lightpath for communication in optical gird environment. In this thesis, we propose LSOG (Leveling Selection in Optical Grid) which sets task priority after forming a hierarchical directed acyclic graph (DAG) that is optimized in optical grid environment. To determine priorities of task assignment in the same level, proposed algorithm executes the task with biggest communication cost between itself and its predecessor. Then, it considers the shortest route for communication between tasks. This process improves communication cost in scheduling process through optimizing link resource usage in optical grid environment. We compared LSOG algorithm with conventional ELSA (Extended List Scheduling Algorithm) and SCP (Scheduled Critical Path) algorithm. We could see the enhancement in overall scheduling performance through increment in CCR value and smoothing network environment.

• Journal of Digital Convergence
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• v.11 no.7
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• pp.185-192
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• 2013

The purpose of this study is to set the relationship between each parts that forms the engine of an automobile based on the 3D visualization technology which is able to be learned according to the skill of the operator in the industry field and to recommend the auto parts using a task ontology. A visualization method was proposed by structuring the complex knowledge by signifying the link and the node in forms of a network and using SOM which can be shown in the form of 3 dimension. In addition, by using is-a Relationship-based hierarchical Taxonomy setting the relationship between each of the parts that forms the engine of an automobile, to allow a recommendation using a weighted value possible. By providing and placing the complex knowledge in the 3D space to the user for an opportunity of more realistic and intuitive navigation, when randomly selecting the automobile parts, it allows the recommendation of the parts having a close relationship with the corresponding parts for easy assembly and to know the importance of usage for the automobile parts without any special expertise.

• Child Health Nursing Research
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• v.26 no.2
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• pp.267-276
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• 2020

Purpose: Caring for a vulnerable premature baby is a challenging task, but some mothers experience growth through that process. The purpose of this study was to investigate the factors influencing post-traumatic growth in mothers with premature infants admitted to the neonatal intensive care unit. Methods: A correlational research design was used and 105 mothers of premature infants were recruited from an online community. Data were collected from January 15 to January 25, 2019. Post-traumatic growth was measured using the Korean version of the Posttraumatic Growth Inventory. Data were analyzed using descriptive statistics, the t-test, analysis of variance, the Scheffé test, Pearson correlation coefficients, and hierarchical multiple regression. Results: The final model developed in this study explained 45.5% of post-traumatic growth (F=13.66, p<.001). Resilience (β=.54, p<.001) was the strongest predictor of post-traumatic growth, followed by the age of the mother when giving birth (β=.17, p=.028) and current employment status (β=.17, p=.049). Conclusion: For mother with premature infants to grow psychologically after their experience, it may be needed to support them to develop and strengthen their resilience through either education or their own support network.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.1 s.343
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• pp.103-110
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• 2006

Internet engineering task force (IETF) has proposed hierarchical mobile IPv6 (HMIPv6) in order to reduce a frequent location registration of a mobile node in mobile IPv6 (MIPv6). All traffics toward a mobile node must be transmitted through a MAP in HMIPv6. This brings unnecessary packet latency because of the increased processing cost of packet at the MAP. At this point the processing cost of packet at the MAP is influenced by the packet arrival rate for a mobile node and the number of mobile nodes in MAP domain. In this paper, we propose that MIPv6 and HMIPv6 are adaptively selected to minimize signaling coast of network as complementing weak point of MIPv6 and HMIPv6. After suppose that the packet arrival rate for a mobile node is fixed ,with this in mind, we find the optimal number of mobile nodes compared the total cost of HMIPv6 with the total cost of MIPv6. And if Mobile Nodes that the MAP is able to manage is full in MAP domain, a mobile node entering MAP domain is provided connection by MIPv6 instead of HMIPv6. In the conclusion, the proposed method of this paper shows that the weak points of MIPv6 and HMIPv6 are removed by adaptive selecting each other.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.579-582
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• 2009

With the aid of the increasing interests of Context-aware Augmented Reality Agent (AR Agent), various researches of AR Agent have been performed to explore the possibility of the agent as novel interface and the entity responding autonomously by user's input. However, in previous works, AR Agents are lack of specific method for using various contextual information. To revolve around those problems, we propose the Behavior Generation System for Context-aware AR Agent using layered architecture. Based on Belief-Desire-Intention (BDI) model and Hierarchical Task Network (HTN) searching, the sequence of agent behavior has been selected in behavior planning layer. Then, the agent evaluates appropriateness of behaviors using previous behavior and the type of input before activation. This behavior generation system can be applied for edutainment, game, and assistant agent, which need intuitive and effective behaviors to convey information. Through this research, we expect that the Context-aware AR Agent could support for not only information delivery, but also the capability of effective communication for user.