• Smart Structures and Systems
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• v.34 no.2
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• pp.97-116
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• 2024

The standard l₁-norm regularization is recently introduced for impact force identification, but generally underestimates the peak force. Compared to l₁-norm regularization, l_p-norm (0 ≤ p < 1) regularization, with a nonconvex penalty function, has some promising properties such as enforcing sparsity. In the framework of sparse regularization, if the desired solution is sparse in the time domain or other domains, the under-determined problem with fewer measurements than candidate excitations may obtain the unique solution, i.e., the sparsest solution. Considering the joint sparse structure of impact force in temporal and spatial domains, we propose a general l_p-norm (0 ≤ p < 1) regularization methodology for simultaneous identification of the impact location and force time-history from highly incomplete measurements. Firstly, a nonconvex optimization model based on l_p-norm penalty is developed for regularizing the highly under-determined problem of impact force identification. Secondly, an iteratively reweighed l₁-norm algorithm is introduced to solve such an under-determined and unconditioned regularization model through transforming it into a series of l₁-norm regularization problems. Finally, numerical simulation and experimental validation including single-source and two-source cases of impact force identification are conducted on plate structures to evaluate the performance of l_p-norm (0 ≤ p < 1) regularization. Both numerical and experimental results demonstrate that the proposed l_p-norm regularization method, merely using a single accelerometer, can locate the actual impacts from nine fixed candidate sources and simultaneously reconstruct the impact force time-history; compared to the state-of-the-art l₁-norm regularization, l_p-norm (0 ≤ p < 1) regularization procures sufficiently sparse and more accurate estimates; although the peak relative error of the identified impact force using l_p-norm regularization has a decreasing tendency as p is approaching 0, the results of l_p-norm regularization with 0 ≤ p ≤ 1/2 have no significant differences.

• Journal of The Korean Association For Science Education
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• v.36 no.1
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• pp.177-190
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• 2016

The purpose of this study is to validate learning progressions for Earth's motion and solar system from two different perspectives of validity. One is construct validity, that is whether a hypothetical pathway derived from our study of LPs is supported by empirical evidence of children's substantive development. The other is consequential validity, which refers to the impact of LP-based adaptive instruction on children's improved learning outcomes. For this purpose, 373 fifth-grade students and 17 teachers from six elementary schools in Seoul, Kangwon province, and Gwangju participated. We designed LP-based adaptive instruction modules delving into the unit of 'Solar system and stars.' We also employed 13 ordered multiple-choice items and analyzed the transitions of children's achievement levels based on the results of pre-test and post-test. For testing construct validity, 64 % of children in the experimental group showed improvement according to the hypothetical pathways. Rasch analysis also supports this results. For testing consequential validity, the analysis of covariance between experimental and control groups revealed that the improvement of experimental group is significantly higher than the control group (F=30.819, p=0.000), and positive transitions of children's achievement level in the experimental group are more dominant than in the control group. In addition, the findings of applying Rasch model reveal that the improvement of students' ability in the experimental group is significantly higher than that of the control group (F=11.632, p=0.001).

• Journal of Korean Academy of Nursing
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• v.20 no.2
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• pp.227-248
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• 1990

The study was conceived in relation to a concern over the growing gap between the needs of chronic patients and the availability of care from the current health care system in Korea. Patients with agonizing chronic pain, discomfort, despair and disability are left with helplessly unprepared families with little help from the acute care oriented health care system after discharge from hospital. There is a great need for the development of an alternative means of quality care that is economically feasible and culturally adaptible to our society. Thus, the study was designed to demonstrate the effectiveness of home heath care as an alternative to bridge the existing gap between the patients' needs and the current practice of health care. The study specifically purports to test the effects of home care on health expenditure, readmission, job retention, compliance to health care regime, general conditions, complications, and self-care knowledge and practices. The study was guided by the operations research method advocated by the Primary Health Care Operations Research Institute(PRICOR) which constitutes 3 stages of research : namely, problem analysis solution development, and solution validation. The first step in the operations research was field preparation to develop the necessary consensus and cooperation. This was done through the formation of a consulting body at the hospital and a steering committee among the researchers. For the stage of problem analysis, the Annual Report of Seoul National University Hospital and the patients records for last 5 years were reviewed and selective patient interviews were conducted to find out the magnitude of chronic health problems and areas of unmect health care needs to finally decide on the kinds of health problems to study. On the basis of problem analysis, the solution development stage was devoted to home care program development asa solution alternative. Assessment tools, teaching guidelines and care protocols were developed and tested for their validity. The final stage was the stage of experimentation and evaluation. Patients with liver diseases, hemiplegic and diabetic conditions were selected as study samples. Discharge evaluation, follow up home care, measurement and evaluation were carried out according to the protocols of care and measurement plan for each patient for the period of 6 months after discharge. The study was carried out for the period from Jan. 1987 to Dec. 1989. The following are the results of the study presented according to the hypotheses set forth for the study ; 1. Total expenditures for the period of study were not reduced for the experimental group, however, since the cost per hospital visit is about 4 times as great as the cost per home visit, the effect of cost saving by home care will become a reality as home care replaces part of the hospital visits. 2. The effect on the rate of readmission and job retention was found to be statistically nonsignificant though the number of readmission was less among the experimental group receiving home care. 3. The effect on compliance to the health care regime was found to be statistically significant at the 5% level for hepatopathic and diabetic patients. 4. Education on diet, rest and excise, and medication through home care had an effect on improved liver function test scores, prevention of complications and self - care knowledge in hepatopathic patients at a statistically significant level. 5. In hemiplegic patient, home care had an effect on increased grasping power at a significant level. However. there was no significant difference between the experimental and control groups in the level of compliane, prevention of complications or in self-care practices. 6. In diabetic patients, there was no difference between the experimental and control groups in scores of laboratory tests, appearance of complications, and self-care knowledge or self -care practices. The above findings indicate that a home care program instituted for such short term as 6 months period could not totally demonstrate its effectiveness at a statistically significant level by quantitative analysis however, what was shown in part in this analysis, and in the continuous consultation sought by those who had been in the experimental group, is that home health care has a great potential in retarding or preventing pathological progress, facilitating rehabilitative and productive life, and improving quality of life by adding comfort, confidence and strength to patients and their families. For the further studies of this kind with chronic patients it is recommended that a sample of newly diagnosed patients be followed up for a longer period of time with more frequent observations to demonstrate a more dear- cut picture of the effectiveness of home care.

• Horticultural Science & Technology
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• v.33 no.5
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• pp.647-657
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• 2015

To investigate a method for calculation of the heating load for environmental designs of horticultural facilities, measurements of total heating load, infiltration rate, and floor heat flux in a large-scale plastic greenhouse were analyzed comparatively with the calculation results. Effects of ground heat exchange and infiltration loss on the greenhouse heating load were examined. The ranges of the indoor and outdoor temperatures were $13.3{\pm}1.2^{\circ}C$ and $-9.4{\sim}+7.2^{\circ}C$ respectively during the experimental period. It was confirmed that the outdoor temperatures were valid in the range of the design temperatures for the greenhouse heating design in Korea. Average infiltration rate of the experimental greenhouse measured by a gas tracer method was $0.245h^{-1}$. Applying a constant ventilation heat transfer coefficient to the covering area of the greenhouse was found to have a methodological problem in the case of various sizes of greenhouses. Thus, it was considered that the method of using the volume and the infiltration rate of greenhouses was reasonable for the infiltration loss. Floor heat flux measured in the center of the greenhouse tended to increase toward negative slightly according to the differences between indoor and outdoor temperature. By contrast, floor heat flux measured at the side of the greenhouse tended to increase greatly into plus according to the temperature differences. Based on the measured results, a new calculation method for ground heat exchange was developed by adopting the concept of heat loss through the perimeter of greenhouses. The developed method coincided closely with the experimental result. Average transmission heat loss was shown to be directly proportional to the differences between indoor and outdoor temperature, but the average overall heat transfer coefficient tended to decrease. Thus, in calculating the transmission heat loss, the overall heat transfer coefficient must be selected based on design conditions. The overall heat transfer coefficient of the experimental greenhouse averaged $2.73W{\cdot}m^{-2}{\cdot}C^{-1}$, which represents a 60% heat savings rate compared with plastic greenhouses with a single covering. The total heating load included, transmission heat loss of 84.7~95.4%, infiltration loss of 4.4~9.5%, and ground heat exchange of -0.2~+6.3%. The transmission heat loss accounted for larger proportions in groups with low differences between indoor and outdoor temperature, whereas infiltration heat loss played the larger role in groups with high temperature differences. Ground heat exchange could either heighten or lessen the heating load, depending on the difference between indoor and outdoor temperature. Therefore, the selection of a reference temperature difference is important. Since infiltration loss takes on greater importance than ground heat exchange, measures for lessening the infiltration loss are required to conserve energy.

• Journal of the Korean Society for Marine Environment & Energy
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• v.15 no.3
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• pp.208-218
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• 2012

Numerical simulation technique has been developed to calculate microwave backscattering from water surface. The simulation plays a role of a substitute for experiments. Validation of the simulation was shown by comparing with experimental results. Water area observations by microwave radar have been simulated to evaluate algorithms and systems. Furthermore, the simulation can be used to understand microwave scattering mechanism on the water surface. The simulation has applied to the various methods for water area observations, and the utilizations of the simulation are introduced in this paper. In the case of fixed radar, we show following examples, 1. Radar image with a pulse Doppler radar, 2. Effect of microwave irradiation width and 3. River observation (Water level observation). In addition, another application (4.Synthetic aperture radar image) is also described. The details of the applications are as follows. 1. Radar image with a pulse Doppler radar: A new system for the sea surface observation is suggested by the simulation. A pulse Doppler radar is assumed to obtain radar images that display amplitude and frequency modulation of backscattered microwaves. The simulation results show that the radar images of the frequency modulation is useful to measure sea surface waves. 2. Effect of microwave irradiation width: It is reported (Rheem[2008]) that microwave irradiation width on the sea surface affects Doppler spectra measured by a CW (Continuous wave) Doppler radar. Therefore the relation between the microwave irradiation width and the Doppler spectra is evaluated numerically. We have shown the suitable condition for wave height estimation by a Doppler radar. 3. River observation (Water level observation): We have also evaluated algorithms to estimate water current and water level of river. The same algorithms to estimate sea surface current and sea surface level are applied to the river observation. The simulation is conducted to confirm the accuracy of the river observation by using a pulse Doppler radar. 4. Synthetic aperture radar (SAR) image: SAR images are helpful to observe the global sea surface. However, imaging mechanisms are complicated and validation of analytical algorithms by SAR images is quite difficult. In order to deal with the problems, SAR images in oceanic scenes are simulated.

• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.773-780
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• 2020

With the development of the 4th industrial, research is being conducted to prevent diseases and reduce damage in various fields of science and technology such as medicine, health, and bio. As a result, artificial intelligence technology has been introduced and researched for image analysis of radiological examinations. In this paper, we will directly apply a deep learning model for classification and detection of pneumonia using chest X-ray images, and evaluate whether the deep learning model of the Inception series is a useful model for detecting pneumonia. As the experimental material, a chest X-ray image data set provided and shared free of charge by Kaggle was used, and out of the total 3,470 chest X-ray image data, it was classified into 1,870 training data sets, 1,100 validation data sets, and 500 test data sets. I did. As a result of the experiment, the result of metric evaluation of the Inception V3 deep learning model was 94.80% for accuracy, 97.24% for precision, 94.00% for recall, and 95.59 for F1 score. In addition, the accuracy of the final epoch for Inception V3 deep learning modeling was 94.91% for learning modeling and 89.68% for verification modeling for pneumonia detection and classification of chest X-ray images. For the evaluation of the loss function value, the learning modeling was 1.127% and the validation modeling was 4.603%. As a result, it was evaluated that the Inception V3 deep learning model is a very excellent deep learning model in extracting and classifying features of chest image data, and its learning state is also very good. As a result of matrix accuracy evaluation for test modeling, the accuracy of 96% for normal chest X-ray image data and 97% for pneumonia chest X-ray image data was proven. The deep learning model of the Inception series is considered to be a useful deep learning model for classification of chest diseases, and it is expected that it can also play an auxiliary role of human resources, so it is considered that it will be a solution to the problem of insufficient medical personnel. In the future, this study is expected to be presented as basic data for similar studies in the case of similar studies on the diagnosis of pneumonia using deep learning.

• Journal of Food Hygiene and Safety
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• v.32 no.5
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• pp.411-417
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• 2017

This study was conducted to establish the analysis method for the contents of choline in infant formulas and follow-up formulas by ion chromatograph (IC). To optimize the method, we compared several conditions for extraction, purification and instrumental measurement using spiked samples and certified reference material (CRM; NIST SRM 1849a) as test materials. IC method for choline was established using Ion Pac CG column and 18 mM $H_2SO_4$ mobile phase. The parameters of validation were specificity, linearity, LOD, LOQ, recovery, accuracy, precision and repeatability. The specificity was confirmed by the retention time and the linearity, $R_2$ was over 0.999 in range of 0.5~10 mg/L. The detection limit and quantification limit were 0.14, 0.43 mg/L. The accuracy and precision of this method using CRM were 95%, 2.1% respectively. Optimized methods were applied in sample analysis to verify the reliability. All the tested products were acceptable contents of choline compared with component specification for nutrition labeling. The standard operating procedures were prepared for choline to provide experimental information and to strengthen the management of nutrient in infant formula and follow-up formula.

• Korean Journal of Environmental Agriculture
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• v.31 no.4
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• pp.318-327
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• 2012

BACKGROUND: For Non-Point Source(NPS) loads reduction, pollutant loads need to be quantified for major farming methods. The objective of this study was to evaluate impacts of farming methods on NPS pollutant loads from a paddy rice field during the growing season. METHODS AND RESULTS: The height of drainage outlet, amount of fertilizer, irrigation water quality were considered as farming factors for scenarios development. The control was derived from conventional farming methods and four different scenarios were developed based combination of farming factors. A field scale model, CREAMS-PADDY(Chemicals, Runoff, and Erosion from Agricultural Management Systems for PADDY), was used to calculate pollutant nutrient loads. The data collected from an experimental plot located downstream of the Idong reservoir were used for model calibration and validation. The simulation results agreed well with observed values during the calibration and validation periods. The calibrated model was used to evaluate farming scenarios in terms of NPS loads. Pollutant loads for T-N, T-P were reduced by 5~62%, 8~37% with increasing the height of drainage outlet from 100 mm of 100 mm, respectively. When amount of fertilizer was changed from standard to conventional, T-N, T-P pollutant loads were reduced by 0~22%, 0~24%. Irrigation water quality below water criteria IV of reservoir increased T-N of 9~65%, T-P of 9~47% in comparison with conventional. CONCLUSION(S): The results indicated that applying increased the height of drainage after midsummer drainage, standard fertilization level during non-rainy seasons, irrigation water quality below water criteria IV of reservoir were effective farming methods to reduce NPS pollutant loads from paddy in Korea.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.159-172
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• 2010

The recommender system is one of the possible solutions to assist customers in finding the items they would like to purchase. To date, a variety of recommendation techniques have been developed. One of the most successful recommendation techniques is Collaborative Filtering (CF) that has been used in a number of different applications such as recommending Web pages, movies, music, articles and products. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. Broadly, there are memory-based CF algorithms, model-based CF algorithms, and hybrid CF algorithms which combine CF with content-based techniques or other recommender systems. While many researchers have focused their efforts in improving CF performance, the theoretical justification of CF algorithms is lacking. That is, we do not know many things about how CF is done. Furthermore, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting the performances of CF algorithms in advance is practically important and needed. In this study, we propose an efficient approach to predict the performance of CF. Social Network Analysis (SNA) and Artificial Neural Network (ANN) are applied to develop our prediction model. CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. SNA facilitates an exploration of the topological properties of the network structure that are implicit in data for CF recommendations. An ANN model is developed through an analysis of network topology, such as network density, inclusiveness, clustering coefficient, network centralization, and Krackhardt's efficiency. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Inclusiveness refers to the number of nodes which are included within the various connected parts of the social network. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. Krackhardt's efficiency characterizes how dense the social network is beyond that barely needed to keep the social group even indirectly connected to one another. We use these social network measures as input variables of the ANN model. As an output variable, we use the recommendation accuracy measured by F1-measure. In order to evaluate the effectiveness of the ANN model, sales transaction data from H department store, one of the well-known department stores in Korea, was used. Total 396 experimental samples were gathered, and we used 40%, 40%, and 20% of them, for training, test, and validation, respectively. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. The input variable measuring process consists of following three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used Net Miner 3 and UCINET 6.0 for SNA, and Clementine 11.1 for ANN modeling. The experiments reported that the ANN model has 92.61% estimated accuracy and 0.0049 RMSE. Thus, we can know that our prediction model helps decide whether CF is useful for a given application with certain data characteristics.

• Korean Journal of Microbiology
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• v.52 no.2
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• pp.140-147
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• 2016

A process for manufacturing virally-safe porcine bone hydroxyapatite (HA) has been developed to serve as advanced xenograft material for dental applications. Porcine bone pieces were defatted with successive treatments of 30% hydrogen peroxide and 80% ethyl alcohol. The defatted porcine bone pieces were heat-treated in an oxygen atmosphere box furnace at $1,300^{\circ}C$ to remove collagen and organic compounds. The bone pieces were ground with a grinder and then the bone powder was sterilized by gamma irradiation. Morphological characteristics such as SEM (Scanning Electron Microscopy) and TEM (Transmission Electron Microscopy) images of the resulting porcine bone HA (THE Graft$^{(R)}$) were similar to those of a commercial bovine bone HA (Bio-Oss$^{(R)}$). In order to evaluate the efficacy of $1,300^{\circ}C$ heat treatment and gamma irradiation at a dose of 25 kGy for the inactivation of porcine viruses during the manufacture of porcine bone HA, a variety of experimental porcine viruses including transmissible gastroenteritis virus (TGEV), pseudorabies virus (PRV), porcine rotavirus (PRoV), and porcine parvovirus (PPV) were chosen. TGEV, PRV, PRoV, and PPV were completely inactivated to undetectable levels during the $1,300^{\circ}C$ heat treatment. The mean log reduction factors achieved were $${\geq_-}4.65$$ for TGEV, $${\geq_-}5.81$$ for PRV, $${\geq_-}6.28$$ for PRoV, and $${\geq_-}5.21$$ for PPV. Gamma irradiation was also very effective at inactivating the viruses. TGEV, PRV, PRoV, and PPV were completely inactivated to undetectable levels during the gamma irradiation. The mean log reduction factors achieved were $${\geq_-}4.65$$ for TGEV, $${\geq_-}5.87$$ for PRV, $${\geq_-}6.05$$ for PRoV, and $${\geq_-}4.89$$ for PPV. The cumulative log reduction factors achieved using the two different virus inactivation processes were $${\geq_-}9.30$$ for TGEV, $${\geq_-}11.68$$ for PRV, $${\geq_-}12.33$$ for PRoV, and $${\geq_-}10.10$$ for PPV. These results indicate that the manufacturing process for porcine bone HA from porcine-bone material has sufficient virus-reducing capacity to achieve a high margin of virus safety.