• Proceedings of the Safety Management and Science Conference
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• 2011.04a
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• pp.95-99
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• 2011

The research analyzes structure models of Repeated Measurement Design (RMD) and Hierarchical Design (HD). The experimental unit of RMD model is living organisms, such as human. In contrast, HD is used when all the factors are random. The HD models are derived from R:B:A, R:C:B:A and R:C:($A{\times}B$).

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.3
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• pp.192-200
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• 2012

In order for humans and robots to interact in daily life, robots need to understand human speech and link it to their actions. This paper proposes a hierarchical behavior model for robot action control using natural language commands. The model, which consists of episodes, primitive actions and atomic functions, uses a sentential cognitive system that includes multiple modules for perception, action, reasoning and memory. Human speech commands are translated to sentences with a natural language processor that are syntactically parsed. A semantic parsing procedure was applied to human speech by analyzing the verbs and phrases of the sentences and linking them to the cognitive information. The cognitive system performed according to the hierarchical behavior model, which consists of episodes, primitive actions and atomic functions, which are implemented in the system. In the experiments, a possible episode, "Water the pot," was tested and its feasibility was evaluated.

• Proceedings of the Korean Society for the Gifted Conference
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• 2005.05a
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• pp.173-180
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• 2005

The purpose of this study was to identify common and domain-specific cognitive characteristics of gifted students based on a hierarchical structural model of human abilities. This study is based on the premise that abilities identified by tests can appear as observable characteristics in test or school situations. Abilities proposed by major models of intelligence were reviewed in terms of their power to explain cognitive characteristics of gifted students. However, due to the lack of their explanatory power and disagreement on common and domain-specific cognitive abilities, a new hierarchical structural model was conceptualized in a unique way based on interrelationships between abilities proposed by the models. The newly established model hypothesizes a cognitive mechanism that accounts for how domain-specific knowledge is formed, as well as which abilities are common and domain-specific, how they are related functionally, and how they account for common and domain-specific cognitive characteristics of gifted students. The cognitive mechanism has important implications for our understanding of the chronically controversial concepts, 'intelligence' and 'knowledge.' Clearer definitions of what intelligence is (g or multiple), what knowledge is, and how knowledge develops ('genetic or environmental,' 'rationalistic or empiricist') may result from this model.

• Korean Journal of Child Studies
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• v.27 no.3
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• pp.169-187
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• 2006

A hierarchical linear model(HLM) provides advantages over existing traditional statistical methods (e.g., ordinary least squares regression, repeated measures analysis of variance, etc.) for analyzing multilevel/longitudinal data or diary methods. HLM can gauge a more precise estimation of lower-level effects within higher-level units, as well as describe each individual's growth trajectory across time with improved estimation. This article 1) provides scholars who study children and families with an overview of HLM (i.e., statistical assumptions, advantages/disadvantages, etc.), 2) provides an empirical study to illustrate the application of HLM, and 3) discusses the application of HLM to the study of children and families. In addition, this article provided useful information on available articles and websites to enhance the reader's understanding of HLM.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.135-146
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• 1997

This paper describes fault diagnosis method in complex system with hierachical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. Fault diagnosis system can forecast faults in a system and decide from current machine state signal information. Comparing with other diagnosis system for single fault, the developed system deals with multiple fault diagnosis comprising Hierarchical Neural Network(HNN). HNN consists of four level neural network, first level for item fault symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. UNIX IPC(Inter Process Communication) is used for implementing HNN wiht multitasking and message transfer between processes in SUN workstation with X-Windows(Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural newtork operate as a separate process in HNN. The message queue take charge of information exdhange and cooperation between each neural network.

• International Journal of Advanced Culture Technology
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• v.2 no.1
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• pp.1-13
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• 2014

In next-generation wireless networks, provisioning of IP-based network architecture and seamless transmission services are very important issues for mobile nodes. For this reason, a mobility management mechanism to support global roaming is highly regarded. These technologies bring a broader life by using a global roaming account through the connection of multiple devices or technology to mobile users; they also provide real-time multimedia services. This paper presents a comprehensive performance analysis of fast handover for hierarchical mobile IPv6 (F-HMIPv6), hierarchical mobile IPv6 (HMIPv6), Proxy Mobile IPv6 (PMIPv6), and fast Proxy Mobile IPv6 (FPMIPv6) using the fluid-flow model and random-walk model. As a result, the location update cost of the PMIPv6 and FPMIPv6 is better than that of HMIPv6 and F-HMIPv6. These results suggest that the network-based mobility management technology is superior to the hierarchical mobility management technology in the mobility environment.

• The Research Journal of the Costume Culture
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• v.25 no.3
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• pp.327-339
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• 2017

A consumer is an individual entity with various motivations. This study is intended to incorporate a hierarchical structure of motivation to understand self-determined motivation for purchasing secondhand merchandise at thrift stores. A conceptual model adopted from Cadwallader et al. (2010)'s comprehensive model of motivation used in a marketing context was developed to investigate motivational process in secondhand merchandise shopping. The conceptual model includes the three levels of motivational structure-the global, contextual (environmental concern and frugality), and situational motivation. A series of the causal relationships among the three levels of self-determined motivations and buying intention to shop at thrift stores were hypothesized. A total of 219 respondents from two different northeastern state universities in the U.S. completed a self-administered survey. The results indicated that secondhand merchandise shopping is well explained in the hierarchical structure of self-determined motivation where the global motivation had a positive impact on the contextual motivations regarding environmental concern and frugality. Of the two contextual motivations, only environmental concern had a positive impact on situational motivation for shopping at thrift stores. Finally, the situational motivation positively influenced the intention to shop at thrift stores. The results of this model suggest that the hierarchical structure of self-determined motivation would be a very useful framework to understand consumer behavior for apparel shopping. Also, further research can be done to identify other contextual motivational factors to understand consumer motivation for shopping at thrift stores.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.11C
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• pp.1551-1563
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• 2004

This paper proposes a hierarchical approach to shape model ASM using wavelet transform. Local structure model fitting in the ASM plays an important role in model-based pose and shape analysis. The proposed algorithm can robustly find good solutions in complex images by using wavelet decomposition. we also proposed effective method that estimates and corrects object's movement by using Wavelet transform-based hierarchical motion estimation scheme for ASM-based, real-time video tracking. The proposed algorithm has been tested for various sequences containing human motion to demonstrate the improved performance of the proposed object tracking.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1614-1632
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• 2017

Bag of visual words is a popular model in human action recognition, but usually suffers from loss of spatial and temporal configuration information of local features, and large quantization error in its feature coding procedure. In this paper, to overcome the two deficiencies, we combine sparse coding with spatio-temporal pyramid for human action recognition, and regard this method as the baseline. More importantly, which is also the focus of this paper, we find that there is a hierarchical structure in feature vector constructed by the baseline method. To exploit the hierarchical structure information for better recognition accuracy, we propose a tree regularized classifier to convey the hierarchical structure information. The main contributions of this paper can be summarized as: first, we introduce a tree regularized classifier to encode the hierarchical structure information in feature vector for human action recognition. Second, we present an optimization algorithm to learn the parameters of the proposed classifier. Third, the performance of the proposed classifier is evaluated on YouTube, Hollywood2, and UCF50 datasets, the experimental results show that the proposed tree regularized classifier obtains better performance than SVM and other popular classifiers, and achieves promising results on the three datasets.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.431-434
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• 2001

Knowledge discovery in databases(KDD) is the process for extracting valid, novel, potentially useful and understandable knowledge form real data. There are many academic and industrial activities with new technologies and application areas. Particularly, data mining is the core step in the KDD process, consisting of many algorithms to perform clustering, pattern recognition and rule induction functions. The main goal of these algorithms is prediction and description. Prediction means the assessment of unknown variables. Description is concerned with providing understandable results in a compatible format to human users. We introduce an efficient data mining algorithm considering predictive and descriptive capability. Reasonable pattern is derived from real world data by a revised neural network model and a proposed fuzzy rule extraction technique is applied to obtain understandable knowledge. The proposed neural network model is a hierarchical self-organizing system. The rule base is compatible to decision makers perception because the generated fuzzy rule set reflects the human information process. Results from real world application are analyzed to evaluate the system\`s performance.