• Women's Health Nursing
• /
• v.17 no.1
• /
• pp.10-20
• /
• 2011

Purpose: The purpose of this study was to examine the effects of integrated menopause management program derived theoretical framework of King (1981)'s goal attainment theory model for middle aged women. Methods: This research was a nonequivalent control group non-synchronized design. The subjects of this study were 37 middle aged women in Busan and experiencing menopause; 17 for the experimental group and 20 for the control group. Experimental group was educated for 1 hour group interchange activity and five minutes individual interchange activity, once a week during 8 weeks. Measurement for comparison were taken two times, at baseline, 8wks. The effects were evaluated with menopause symptom, menopause knowledge, menopause attitude and menopause management. Results: The experimental group was significantly lower than control group on menopause symptom (F=5.936, p=.010) and higher than control group on menopause knowledge (F=12.031, p=.001) and menopause management (F=5.861, p=.010) after integrated menopause management program. However integrated menopause management program did not make significant differences on menopause attitude (F=0.105, p=.374). Conclusion: Results indicate that integrated menopause management program could be an effective intervention decreasing menopause symptom and for increasing menopause knowledge, menopause management in middle aged women.

• Korean Journal of Remote Sensing
• /
• v.22 no.5
• /
• pp.367-372
• /
• 2006

Cloud detection algorithm is being developed as primary one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-IR and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithms and preliminary test results of both algorithms.

• Structural Engineering and Mechanics
• /
• v.46 no.4
• /
• pp.459-486
• /
• 2013

A novel damage classification method based on wavelet packet transform and statistical analysis is developed in this study for structural health monitoring. The response signal of a structure under an impact load is normalized and then decomposed into wavelet packet components. Energies of these wavelet packet components are then calculated to obtain the energy distribution. Statistical similarity comparison based on an F-test is used to classify the structure from changes in the wavelet packet energy distribution. A statistical indicator is developed to describe the damage extent of the structure. This approach is applied to the test results from simply supported reinforced concrete beams in the laboratory. Cases with single and two damages are created from static loading, and accelerations of the structure from under impact loads are analyzed. Results show that the method can be used with no reference baseline measurement and model for the damage monitoring and assessment of the structure with alarms at a specified significance level.

• Journal of applied mathematics & informatics
• /
• v.39 no.5_6
• /
• pp.785-804
• /
• 2021

The proposal of new families has been worked out by many authors over recent years. Many ways to generate new families have been developed as the methods of addition, linear combination, composition and, one of the newer, the T-X family of distributions. Using this latter method, Korkmaz et al. (2018) proposed a new class called Weibull Marshall-Olkin-G (WMO-G) family. In the present work, we propose a new distribution, based on the WMO-G family, using the Lomax distribution as baseline, called Weibull Marshall-Olkin Lomax (WMOL) distribution. The hazard rate function of this distribution can be increasing, decreasing, bathtub-shaped, decreasing-increasing-decreasing and unimodal. Some properties of the proposed model are developed. Besides that, we consider method of maximum likelihood for estimating the unknown parameters of the WMOL distribution. We provide a simulation study in order to verify the asymptotic properties of the maximum likelihood estimates. The applicability of the new distribution to modeling real life data is proved by two real data sets.

• Ocean Systems Engineering
• /
• v.8 no.4
• /
• pp.441-459
• /
• 2018

A coupled dynamic analysis of a semisubmersible-type FOWT has been carried out in time domain under the combined action of irregular wave and turbulent wind represented respectively by JONSWAP spectrum and Kaimal spectrum. To account for the turbine-floater motion coupling in a more realistic way, the wind turbulence has been incorporated into the calculation of aerodynamic loads. The platform model was referred from the DeepCwind project and the turbine considered here was the NREL 5MW Baseline. To account for the operationality of the turbine, two different environmental conditions (operational and survival) have been considered and the aerodynamic effect of turbine-rotation on actual responses of the FOWT has been studied. Higher mean offsets in surge and pitch responses were obtained under the operational condition as compared to the survival condition. The mooring line tensions were also observed to be sensitive to the rotation of turbine due to the turbulence of wind and overestimated responses were found when the constant wind was considered in the analysis. Additionally, a special analysis case of sudden shutdown of the turbine has also been considered to study the swift modification of responses and tension in the mooring cables.

• Steel and Composite Structures
• /
• v.31 no.4
• /
• pp.341-359
• /
• 2019

Non-monolithic concrete structural connections are commonly used both in new constructions and retrofitted structures where anchors are used for connections. Often, flaws are present in anchor system due to poor workmanship and deterioration; and methods available to check the quality of the composite system afterward are very limited. In case of presence of flaw, load transfer mechanism inside the anchor system is severely disturbed, and the load carrying capacity drops drastically. This raises the question of safety of the entire structural system. The present study proposes a wave propagation technique to assess the integrity of the anchor system. A chemical anchor (embedded in concrete) composite system comprising of three materials viz., steel (anchor), polymer (adhesive) and concrete (base) is considered for carrying out the wave propagation studies. Piezoelectric transducers (PZTs) affixed to the anchor head is used for actuation and the PZTs affixed to the surrounding concrete surface of the concrete-anchor system are used for sensing the propagated wave through the anchor interface to concrete. Experimentally validated finite element model is used to investigate three types of composite chemical anchor systems. Studies on the influence of geometry, material properties of the medium and their distribution, and the flaw types on the wave signals are carried out. Temporal energy of through time domain differentiation is found as a promising technique for identifying the flaws in the multi-layered composite system. The present study shows a unique procedure for monitoring of inaccessible but crucial locations of structures by using wave signals without baseline information.

• The Korean Journal of Health Service Management
• /
• v.12 no.4
• /
• pp.31-41
• /
• 2018

Objectives: This study was designed to evaluate whether employment status is associated with the experience of unmet care needs. Methods: This study utilized the Korea Health Panel data from 2012 to 2016. A total of 4,083 workers were selected as baseline subjects in 2012 and were followed for four years. This study used the GLIMMIX procedure under the marginal model while adjusting for covariates. Results: A total of 12.4% of 4,083 people said they had failed at least once to have a treatment or checkup despite the needs. Those more likely to experience unmet care needs were women and people of lower income level, with worse health conditions and chronic disease. Precarious workers, the self-employed, and the unemployed were more likely to experience unmet care needs caused by economic burden than permanent workers (Odds Ratio: 2.14, 2.07, 2.74, respectively). Conclusions: This disparity means that precarious workers and the unemployed are more likely to face barriers in obtaining needed health services. Given their insecure employment status, meeting their needs for health care is an important consideration.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.4
• /
• pp.1275-1292
• /
• 2021

As a fine-grained classification problem, aspect-level sentiment classification predicts the sentiment polarity for different aspects in context. To address this issue, researchers have widely used attention mechanisms to abstract the relationship between context and aspects. Still, it is difficult to effectively obtain a more profound semantic representation, and the strong correlation between local context features and the aspect-based sentiment is rarely considered. In this paper, a hybrid attention capsule network for aspect-level sentiment classification (ABASCap) was proposed. In this model, the multi-head self-attention was improved, and a context mask mechanism based on adjustable context window was proposed, so as to effectively obtain the internal association between aspects and context. Moreover, the dynamic routing algorithm and activation function in capsule network were optimized to meet the task requirements. Finally, sufficient experiments were conducted on three benchmark datasets in different domains. Compared with other baseline models, ABASCap achieved better classification results, and outperformed the state-of-the-art methods in this task after incorporating pre-training BERT.

• Genomics & Informatics
• /
• v.19 no.3
• /
• pp.22.1-22.5
• /
• 2021

Automatic document classification for highly interrelated classes is a demanding task that becomes more challenging when there is little labeled data for training. Such is the case of the coronavirus disease 2019 (COVID-19) clinical repository-a repository of classified and translated academic articles related to COVID-19 and relevant to the clinical practice-where a 3-way classification scheme is being applied to COVID-19 literature. During the 7th Biomedical Linked Annotation Hackathon (BLAH7) hackathon, we performed experiments to explore the use of named-entity-recognition (NER) to improve the classification. We processed the literature with OntoGene's Biomedical Entity Recogniser (OGER) and used the resulting identified Named Entities (NE) and their links to major biological databases as extra input features for the classifier. We compared the results with a baseline model without the OGER extracted features. In these proof-of-concept experiments, we observed a clear gain on COVID-19 literature classification. In particular, NE's origin was useful to classify document types and NE's type for clinical specialties. Due to the limitations of the small dataset, we can only conclude that our results suggests that NER would benefit this classification task. In order to accurately estimate this benefit, further experiments with a larger dataset would be needed.

• Journal of Korea Multimedia Society
• /
• v.25 no.10
• /
• pp.1535-1545
• /
• 2022

Sign language has completely different meaning depending on the direction of the hand or the change of facial expression even with the same gesture. In this respect, it is crucial to capture the spatial-temporal structure information of each movement. However, sign language translation studies based on Sign2Gloss2Text only convey comprehensive spatial-temporal information about the entire sign language movement. Consequently, detailed information (facial expression, gestures, and etc.) of each movement that is important for sign language translation is not emphasized. Accordingly, in this paper, we propose Spatial-temporal Keypoints Centered Sign2Gloss2Text Translation, named STKC-Sign2 Gloss2Text, to supplement the sequential and semantic information of keypoints which are the core of recognizing and translating sign language. STKC-Sign2Gloss2Text consists of two steps, Spatial Keypoints Embedding, which extracts 121 major keypoints from each image, and Temporal Keypoints Embedding, which emphasizes sequential information using Bi-GRU for extracted keypoints of sign language. The proposed model outperformed all Bilingual Evaluation Understudy(BLEU) scores in Development(DEV) and Testing(TEST) than Sign2Gloss2Text as the baseline, and in particular, it proved the effectiveness of the proposed methodology by achieving 23.19, an improvement of 1.87 based on TEST BLEU-4.