• Journal of Information Processing Systems
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• v.17 no.5
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• pp.867-878
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• 2021

In context awareness and user intention tasks, dataset construction is expensive because specific domain data are required. Although pretraining with a large corpus can effectively resolve the issue of lack of data, it ignores domain knowledge. Herein, we concentrate on data domain knowledge while addressing data scarcity and accordingly propose a multi-channel long short-term memory (LSTM). Because multi-channel LSTM integrates pretrained vectors such as task and general knowledge, it effectively prevents catastrophic forgetting between vectors of task and general knowledge to represent the context as a set of features. To evaluate the proposed model with reference to the baseline model, which is a single-channel LSTM, we performed two tasks: voice phishing with context awareness and movie review sentiment classification. The results verified that multi-channel LSTM outperforms single-channel LSTM in both tasks. We further experimented on different multi-channel LSTMs depending on the domain and data size of general knowledge in the model and confirmed that the effect of multi-channel LSTM integrating the two types of knowledge from downstream task data and raw data to overcome the lack of data.

• Research in Community and Public Health Nursing
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• v.27 no.1
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• pp.21-30
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• 2016

Purpose: This research was conducted to identify long-term care service needs in the baby boom generation. Methods: Data were collected from September 3 to October 9, 2012 targeting 196 baby boomers residing in Seoul and Gyeonggi-do with the measurement of long-term care service needs having five domains. Collected data were analyzed using the SPSS 20.0 program. Results: Demand for long term care service in those who preferred nursing homes was highest as $4.40{\pm}0.69$ in the safe environment domain. In case of those preferring home care services, demand was highest as $4.37{\pm}0.56$ in the social interaction domain. People who preferred nursing homes had higher needs in personal health care and improvement of the service quality domains. Those who preferred home care services showed diverse needs according to their characteristics. Conclusion: The baby boom generation had high needs in all the domains of long-term care services and such needs were diverse according to their characteristics. However, long-term care services had limitations that they provided standardized and uniformed services only. Therefore, health care services and improved quality services should be provided in a way of meeting the users' needs, and tailored services should be provided in consideration of the users' characteristics.

• Bulletin of the Korean Mathematical Society
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• v.44 no.3
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• pp.547-567
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• 2007

We obtain spatial-temporal decay rates of weak solutions of incompressible flows in exterior domains. When a domain has a boundary, the pressure term yields difficulties since we do not have enough information on the pressure term near the boundary. For our calculations we provide an idea which does not require any pressure information. We also estimated the spatial and temporal asymptotic behavior for strong solutions.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.375-382
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• 2024

Large Language Models (LLMs) still encounter challenges in comprehending domain-specific expressions within construction documents. Analogous to humans acquiring unfamiliar expressions from dictionaries, language models could assimilate domain-specific expressions through the use of a thesaurus. Numerous prior studies have developed construction thesauri; however, a practical issue arises in effectively leveraging these resources for instructing language models. Given that the thesaurus primarily outlines relationships between terms without indicating their relative importance, language models may struggle in discerning which terms to retain or replace. This research aims to establish a robust framework for guiding language models using the information from the thesaurus. For instance, a term would be associated with a list of similar terms while also being included in the lists of other related terms. The relative significance among terms could be ascertained by employing similarity scores normalized according to relevance ranks. Consequently, a term exhibiting a positive margin of normalized similarity scores (termed a pivot term) could semantically replace other related terms, thereby enabling LLMs to comprehend domain-specific terms through these pivotal terms. The outcome of this research presents a practical methodology for utilizing domain-specific thesauri to train LLMs and analyze construction documents. Ongoing evaluation involves validating the accuracy of the thesaurus-applied LLM (e.g., S-BERT) in identifying similarities within construction specification provisions. This outcome holds potential for the construction industry by enhancing LLMs' understanding of construction documents and subsequently improving text mining performance and project management efficiency.

• Journal of Home Health Care Nursing
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• v.24 no.2
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• pp.181-188
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• 2017

Purpose: The aim of this study was to compare between performance and requirements of visiting nursing care in long-term care insurance using the OMAHA system. Methods: The subjects were 72 nurses who had worked in a visiting nursing care center in long-term care insurance. Data were collected from December 5, 2016 to January 31, 2017 using self-recorded questionnaires. The collected data were analyzed using descriptive statistics and paired t-tests. Results: Four dimensions of the OMAHA system showed statistically significant differences between performance and requirements of visiting nursing care in long-term care insurance. The requirements of visiting nursing care were higher than was performance on all 40 items of the OMAHA system. The greatest difference was in environmental domain and then the psychosocial domain. Conclusion: Based on the results, we found that the environmental and psychosocial domains were the largest gap areas. Therefore, with the reality of elderly people living alone and the increase in elderly couples, active intervention connected with the community is needed in residential areas. Further, we suggest that the OMAHA system can be utilized as an integrated conceptual framework for developing and enhancing visiting nursing care in long-term care insurance.

• Journal of computational fluids engineering
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• v.22 no.1
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• pp.51-58
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• 2017

A penalized VIC method offers an efficient hybrid particle-mesh algorithm to simulate an incompressible viscous flow passing a solid body in an infinite domain. In this manner, the computational domain needs to be restricted to a relatively small region to reduce computational cost which would be very high in case of using a large domain. In this paper, we present how to dispose of far-field particles to avoid an unnecessarily large computational domain. The present approach constraints expansion of the domain and thus prevents the incremental computational cost. To validate the numerical approach, a flow around an impulsively started sphere was simulated for Reynolds numbers of 100 and 1000.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.271-280
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• 2011

Heart Rate Variability (HRV) analysis is a convenient tool to assess Myocardial Ischemia (MI). The analysis methods of HRV can be divided into time domain and frequency domain analysis. This paper uses wavelet transform as frequency domain analysis in contrast to time domain analysis in short term HRV analysis. ST-T and normal episodes are collected from the European ST-T database and the MIT-BIH Normal Sinus Rhythm database, respectively. An episode can be divided into several segments, each of which is formed by 32 successive RR intervals. Eighteen HRV features are extracted from each segment by the time and frequency domain analysis. To diagnose MI, the Neural Network with Weighted Fuzzy Membership functions (NEWFM) is used with the extracted 18 features. The results show that the average accuracy from time and frequency domain features is 75.29% and 80.93%, respectively.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.2
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• pp.111-117
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• 2008

This paper presents a new continuation-based quasi-steady-state(CQSS) time-domain simulation algorithm incorporating a multiplicative aggregated load model for power systems. The authors' previous paper introduced a CQSS algorithm, which has the robust convergent characteristic near the singularity point due to the application of a continuation method. The previous CQSS algorithm implemented the load restoration in power systems using the exponent-based load recovery model that is derived from the additive dynamic load model. However, the reformulated exponent-based model causes the inappropriate variation of short-term load characteristics when switching actions occur, during time-domain simulation. This paper depicts how to incorporate a multiplicative load restoration model, which does not have the problem of deforming short-term load characteristics, into the time simulation algorithm, and shows an illustrative example with a 39-bus test system.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.84-89
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• 2006

Keeping pace with the speed of development of science & technology domain, the domain terms continuously repeat the step of creation and extinction. This study tries to define term life cycle and analyze extracted terms from a large corpus with the viewpoint. We chose 'ETNEWS' corpus which includes about 17 million Eojeols for 12 years because it is easy to inspect the transition of term life cycle and the corpus represents computer, IT, and electrotechnology domains. This study acquired several useful conclusions including the relation between specificity and life of terms. We expect that term life cycle will contribute to analyze the competition of similar technologies and determine which term be registered into general dictionary.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.15-19
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• 2012

A Domain Dictionary generation algorithm based on pseudo feedback model is presented in this paper. This algorithm can increase the precision of domain dictionary generation algorithm. The generation of Domain Dictionary is regarded as a domain term retrieval process: Assume that top N strings in the original retrieval result set are relevant to C, append these strings into the dictionary, retrieval again. Iterate the process until a predefined number of domain terms have been generated. Experiments upon corpus show that the precision of pseudo feedback model based algorithm is much higher than existing algorithms.