• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.115-125
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• 2006

A concept hierarchy represents the knowledge with multi-level form, which is very useful to categorize, store and retrieve the data. Traditionally, a concept hierarchy has been built manually by domain experts. However, the manual construction of a concept hierarchy has caused many problems such as enormous development and maintenance costs and human errors such as inconsistency. This paper proposes the automatic creation of concept hierarchies using the predefined hypernym relation. To create the hierarchy automatically, we first eliminate the ambiguity of the senses of data values, and construct the hierarchy by grouping and leveling of the remaining senses. We use the WordNet explanations for multi-meaning word to eliminate the ambiguity and use the WordNet hypernym relations to create multi-level hierarchy structure.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.200-207
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• 2001

In this paper, we propose an HMM(Hidden Markov modeJ)-MLP(Multi-layer perceptron) hybrid model for recognizing legal words on the English bank check. We adopt an explicit segmentation-based word level architecture to implement an HMM engine with nonscaled and non-normalized symbol vectors. We also introduce an MLP for implicit segmentation-based word recognition. The final recognition model consists of a hybrid combination of the HMM and MLP with a new hybrid probability measure. The main contributions of this model are a novel design of the segmentation-based variable length HMMs and an efficient method of combining two heterogeneous recognition engines. ExperimenLs have been conducted using the legal word database of CENPARMI with encouraging results.

• Convergence Security Journal
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• v.13 no.6
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• pp.69-76
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• 2013

A lot of researches have been done to improve the energy efficiency of Wireless Sensor Networks. But all the current researches are based on the idea of direct communication between cluster head and sink node. Previous results assume that node can intelligently regulate signal energy according to the distance between nodes. It is difficult to implement algorithms based on this assumption. We present a multi-level routing algorithm from the sink node to all other nodes which have fixed radio wave radius. We also show the energy saving efficiency and the implementation in real WSN using the simulation result.

• Journal of the Korea Society of Computer and Information
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• v.22 no.6
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• pp.109-114
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• 2017

In this paper, I propose the effects of food therapy on subjects' health and happiness. The result has come out that food therapy enhances subjects' multi-dimensional health(medical, functional, self-evaluative) and their happiness. The participants were 230 adults over 30 years old living in Seoul and Gyeonggi district. To examine the effect of food therapy, the study sampled 220 questionnaire out of 230. By the non-equivalent control group design, the health levels were measured in three concepts: medical dimension, functional dimension and self-evaluative dimension. The results are as follows: First, participants who had taken food therapy for 3 months have been enhanced in their subjective and functional health level. Their subjective health enhanced from $2.80{\pm}.44$ to $4.07{\pm}.27$ (p=.000). And their functional health enhanced from $.92{\pm}.07$ to $.94{\pm}.07$(p=.035). Second, participants' happiness has enhanced significantly. Cognitive happiness enhanced from $3.39{\pm}.50$ to $3.84{\pm}.44$(p=.000). And emotional happiness enhanced from $2.76{\pm}.44$ to $3.07{\pm}.34$(p=.000). With this results, in case that people apply food therapy based on Yaksun, they are able to expect their enhancement of health level and happiness. Therefore study shows that Food therapy helps people to pursue their health and happiness improvement and suggest valuable information of a way to enhance quality of life.

• Nuclear Engineering and Technology
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• v.37 no.6
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• pp.557-564
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• 2005

Air-water countercurrent flow limitation at perforated plates with four holes was investigated in a vertical tank to see the effects of the plate thickness, the number of hole, and the diameter of the hole on the onset of CCFL. The thickness of plates was 1 cm and 4 cm, with a relatively large hole diameter of 5 cm. The collapsed water level formed on the perforated plate and its distribution in the upper plenum were measured. The gas flow rate in the multi-hole plate is relatively higher than one in the single tube because some of holes in the multi-hole plate provide a flow path fur liquid with less air-liquid resistance than in the single tube. The onset of CCFL occurred at nearly the same air flow rate regardless of the plate thickness. The negligible effect of the plate thickness on CCFL means that the flooding is initiated at the top of the plate rather than at its bottom. It turns out that $j_k$ and $K_k$ better fit the data than $H_k$ when hole diameter is greater than 2.86 cm. In our experimental ranges, the collapsed water levels at the onset of CCFL ranged from 7.5 cm to 10.5 cm. There was no three dimensional distribution of water level before and after the onset of CCFL.

• Journal of Intelligence and Information Systems
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• v.10 no.3
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• pp.91-103
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• 2004

Multi-agent systems for semantic web applications require efficient implementation of agent architectures without sacrificing the flexibility and the level of abstraction that agent architectures provide. In this paper, we present an agent system, called VivAce, which is implemented in Java to achieve both high efficiency and the level of abstraction provided by the BDI agent architecture. VivAce (Vivid Agent Computing Environment) has the characteristics of a vivid agent through the BDI agent model. A vivid agent is a software-controlled system whose state comprises the mental components of knowledge, perceptions, tasks, and intentions, and whose behavior is represented by means of action and reaction rules. We first identify the requirements for multi-agent systems and then present the relevant features of VivAce and experimental results.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.07a
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• pp.585-591
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• 2000

The wavelet transform is the most recent technique for processing signals with time-varying spectra. In this paper, the wavelet transform is utilized to improved the assessment and multi-resolution analysis of acoustic emission signals generating in partial discharge. This paper especially deals with the assessment of process statistical parameter using the features extracted from the wavelet coefficients of measured acoustic emission signals in case of applied voltage 20[kv]. Since the parameter assessment using all wavelet coefficients will often turn out leads to inefficient or inaccurate results, we selected that level-3 stage of multi decomposition in discrete wavelet transform. We applied FIR(Finite Impulse Response)digital filter algorithm in discrete to suppression for random noise. The white noise be included high frequency component denoised as decomposition of discrete wavelet transform level-3. We make use of the feature extraction parameter namely, maximum value of acoustic emission signal, average value, dispersion, skewness, kurtosis, etc. The effectiveness of this new method has been verified on ability a diagnosis transformer go through feature extraction in stage of acting(the early period, the last period) .

• Convergence Security Journal
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• v.23 no.1
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• pp.3-8
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• 2023

Multi-domain optical transport networks are not fundamentally interoperable and require an integrated orchestration mechanism and path provision mechanism at the entire network level. In addition, ensuring network survivability is one of the important issues. MPLS-TP (Multi-Protocol Label Switching-Transport Profile) defines various protection/recovery methods as standards, but does not mention how to calculate and select protection/recovery paths. Therefore, an algorithm that minimizes protection/recovery collisions at the optical circuit packet integrated network level and calculates and sets a path that can be rapidly protected/recovered over the entire integrated network area is required. In this paper, we proposed an algorithm that calculates and sets up a path that can be rapidly protected and restored in a T-SDN network composed of multiple ring-mesh topology.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.532-536
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• 2015

This paper presents an automated computer vision-based system to update BIM data by leveraging multi-modal visual data collected from existing buildings under inspection. Currently, visual inspections are conducted for building envelopes or mechanical systems, and auditors analyze energy-related contextual information to examine if their performance is maintained as expected by the design. By translating 3D surface thermal profiles into energy performance metrics such as actual R-values at point-level and by mapping such properties to the associated BIM elements using XML Document Object Model (DOM), the proposed method shortens the energy performance modeling gap between the architectural information in the as-designed BIM and the as-is building condition, which improve the reliability of building energy analysis. The experimental results on existing buildings show that (1) the point-level thermography-based thermal resistance measurement can be automatically matched with the associated BIM elements; and (2) their corresponding thermal properties are automatically updated in gbXML schema. This paper provides practitioners with insight to uncover the fundamentals of how multi-modal visual data can be used to improve the accuracy of building energy modeling for retrofit analysis. Open research challenges and lessons learned from real-world case studies are discussed in detail.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2458-2482
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• 2023

Medical image segmentation techniques based on convolution neural networks indulge in feature extraction triggering redundancy of parameters and unsatisfactory target localization, which outcomes in less accurate segmentation results to assist doctors in diagnosis. In this paper, we propose a multi-level semantic-rich encoding-decoding network, which consists of a Pooling-Conv-Former (PCFormer) module and a Cbam-Dilated-Transformer (CDT) module. In the PCFormer module, it is used to tackle the issue of parameter explosion in the conservative transformer and to compensate for the feature loss in the down-sampling process. In the CDT module, the Cbam attention module is adopted to highlight the feature regions by blending the intersection of attention mechanisms implicitly, and the Dilated convolution-Concat (DCC) module is designed as a parallel concatenation of multiple atrous convolution blocks to display the expanded perceptual field explicitly. In addition, MultiHead Attention-DwConv-Transformer (MDTransformer) module is utilized to evidently distinguish the target region from the background region. Extensive experiments on medical image segmentation from Glas, SIIM-ACR, ISIC and LGG demonstrated that our proposed network outperforms existing advanced methods in terms of both objective evaluation and subjective visual performance.