• Composites Research
• /
• v.35 no.5
• /
• pp.317-321
• /
• 2022

In this study, the PIC design method with machine learning that automatically assigning different stacking sequences according to loading types was applied bumper beam. The input value and labels of the training data for applying machine learning were defined as coordinates and loading types of reference elements that are part of the total elements, respectively. In order to compare the 2D and 3D implementation method, which are methods of representing coordinate value, training data were generated, and machine learning models were trained with each method. The 2D implementation method is divided FE model into each face and generating learning data and training machine learning models accordingly. The 3D implementation method is training one machine learning model by generating training data from the entire finite element model. The hyperparameter were tuned to optimal values through the Bayesian algorithm, and the k-NN classification method showed the highest prediction rate and AUC-ROC among the tuned models. The 3D implementation method revealed higher performance than the 2D implementation method. The loading type data predicted through the machine learning model were mapped to the finite element model and comparatively verified through FE analysis. It was found that 3D implementation PIC bumper beam was superior to 2D implementation and uni-stacking sequence composite bumper.

• Journal of Korean Academy of Nursing Administration
• /
• v.5 no.3
• /
• pp.513-524
• /
• 1999

The shortage of nursing personnel was become one of the most serious problems in operating pediatric oncology nursing unit which was the first pediatric oncology nursing unit in Korea. The purpose of this study was to estimate the optimal number of nursing personnel by calculating nursing care hours. The subjects were 13 staff nurses and inpatients of pediatric oncology nursing unit at Seoul National University Hospital during the period of May 20, 1996, to June 2, 1996. The number of nurses' duty was 132, the number of patients treated was 1288 for these 2 weeks. The tools used for this study were pediatric patient classification indexes and direct & indirect care indexes. Each nurse measured the time that they spent for their activities by self record under the supervision of their nurse manager. The method used to calculate the number of nursing personnel was multiplication of the average number of nursing care hours per patient per day with the number of patients. Percentage, average, t-test, F-test were used for data analysis. The results of this study were as follows : 1) The distribution of patient class : Class I & II none, Class III 86.8%. Class IV 12.9% 2) Direct nursing care hours for a patient per shift according to patient classification: Class III : 27.64 minutes, Class IV : 54.64 minutes The average direct nursing service hours for a patient per shift(3 shift) was 31.54 minutes(94.62 m/day). The average indirect nursing service hours for each patient per duty(3 shift) is 21.3 minutes (63. 91 m/day). 3) The average nursing hours for a patient per duty was 52.80 minutes(2.64h/day). 4) The group of administering medications in direct care activities showed the highest percentage (38.9%). Checking vital signs among observation took the most time am.ong each direct care activity (6.88 minutes for a patient per duty). 5) Charting took the most time of each indirect care activity(52.53 minutes/ duty/nurse). 6) The average personal time per duty is 29.40 minutes, which 'was below 30 minutes of this hospital regulations. 7) The average nursing hours that a nurse provided for a duty was 8.60 hours, which meant that a nurse worked 1.10 hours overtime. 8) Standardizing to a 33 bed to a unit, 17 nurses were needed at the present nursing level.

• The KIPS Transactions:PartB
• /
• v.11B no.3
• /
• pp.281-288
• /
• 2004

In order to extract and analyze complex features of the behavior of animals in response to external stimuli such as toxic chemicals, we implemented an adaptive computational method to characterize changes in the behavior of chironomids in response to treatment with the insecticide, diazinon. In this paper, we propose an energy minimization model to extract the features of response behavior of chironomids under toxic treatment, which is applied on the image of velocity vectors. It is based on the improved active contour model and the variations of the energy functional, which are produced by the evolving active contour. The movement tracks of individual chironomid larvae were continuously measured in 0.25 second intervals during the survey period of 4 days before and after the treatment. Velocity on each sample track at 0.25 second intervals was collected in 15-20 minute periods and was subsequently checked to effectively reveal behavioral states of the specimens tested. Active contour was formed around each collection of velocities to gradually evolve to find the optimal boundaries of velocity collections through processes of energy minimization. The active contour which is improved by T. Chan and L. Vese is used in this paper. The energy minimization model effectively revealed characteristic patterns of behavior for the treatment versus no treatment, and identified changes in behavioral states .is the time progressed.

• Journal of Korean Neurosurgical Society
• /
• v.64 no.5
• /
• pp.791-798
• /
• 2021

Objective : The period of mechanical ventilator (MV)-dependent respiratory failure after cervical spinal cord injury (CSCI) varies from patient to patient. This study aimed to identify predictors of MV at hospital discharge (MVDC) due to prolonged respiratory failure among patients with MV after CSCI. Methods : Two hundred forty-three patients with CSCI were admitted to our institution between May 2006 and April 2018. Their medical records and radiographic data were retrospectively reviewed. Level and completeness of injury were defined according to the American Spinal Injury Association (ASIA) standards. Respiratory failure was defined as the requirement for definitive airway and assistance of MV. We also evaluated magnetic resonance imaging characteristics of the cervical spine. These characteristics included : maximum canal compromise (MCC); intramedullary hematoma or cord transection; and integrity of the disco-ligamentous complex for assessment of the Subaxial Cervical Spine Injury Classification (SLIC) scoring. The inclusion criteria were patients with CSCI who underwent decompression surgery within 48 hours after trauma with respiratory failure during hospital stay. Patients with Glasgow coma scale 12 or lower, major fatal trauma of vital organs, or stroke caused by vertebral artery injury were excluded from the study. Results : Out of 243 patients with CSCI, 30 required MV during their hospital stay, and 27 met the inclusion criteria. Among them, 48.1% (13/27) of patients had MVDC with greater than 30 days MV or death caused by aspiration pneumonia. In total, 51.9% (14/27) of patients could be weaned from MV during 30 days or less of hospital stay (MV days : MVDC 38.23±20.79 vs. MV weaning, 13.57±8.40; p<0.001). Vital signs at hospital arrival, smoking, the American Society of Anesthesiologists classification, Associated injury with Injury Severity Score, SLIC score, and length of cord edema did not differ between the MVDC and MV weaning groups. The ASIA impairment scale, level of injury within C3 to C6, and MCC significantly affected MVDC. The MCC significantly correlated with MVDC, and the optimal cutoff value was 51.40%, with 76.9% sensitivity and 78.6% specificity. In multivariate logistic regression analysis, MCC >51.4% was a significant risk factor for MVDC (odds ratio, 7.574; p=0.039). Conclusion : As a method of predicting which patients would be able to undergo weaning from MV early, the MCC is a valid factor. If the MCC exceeds 51.4%, prognosis of respiratory function becomes poor and the probability of MVDC is increased.

• Journal of Internet Computing and Services
• /
• v.23 no.3
• /
• pp.35-45
• /
• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• The KIPS Transactions:PartB
• /
• v.16B no.4
• /
• pp.281-288
• /
• 2009

This paper proposes a novel algorithm for the efficient classification and annotation of medical images, especially X-ray images. Since X-ray images have a bright foreground against a dark background, we need to extract the different visual descriptors compare with general nature images. In this paper, a Color Structure Descriptor (CSD) based on Harris Corner Detector is only extracted from salient points, and an Edge Histogram Descriptor (EHD) used for a textual feature of image. These two feature vectors are then applied to a multi-class Support Vector Machine (SVM), respectively, to classify images into one of 20 categories. Finally, an image has the Annotation Code Array based on the pre-defined hierarchical relations of categories and priority code order, which is given the several optimal keywords by the Annotation Code Array. Our experiments show that our annotation results have better annotation performance when compared to other method.

• The Research Journal of the Costume Culture
• /
• v.14 no.5
• /
• pp.864-875
• /
• 2006

The characteristics of the demographics in Korea as it gets older are the increase of Elderly Women and continuous progress in urbanization. In this study, body shapes are classified as standard, obese, and tiny according to the previous studies based on the body shape characteristics and the body measurement of the Elderly Women. Based on the classification, we developed prototype of the panty for the Elderly Women to provide basic materials for the quality enhancement of the clothing of the increasing Elderly Women. The followings are the result of the study. 1. To categorize the body shapes of the Elderly Women focusing on the lower half, we grouped the target subjects using the nested approach by the average standard deviation and the factor analysis minimal diffusion method. Accordingly, type 1 and 2 had 36 members respectively and type 3 had 43 members. In this study, two Elderly Women subjects with standard body shape falling under the type 1 were selected as the subjects. 2. In the second trial evaluation for the prototype panty for the Elderly Women 32 items for appearance test and 3 items for functional test were evaluated. The scores in leg, sideline and hip were shown high and the balance between the parts was maintained very well. In the functional test, the panty used to be too tight for the leg curve but in the second trial it was improved, too. In each item, the second trial test showed better score than the first trial test. Conclusively, the most optimal panty prototype for the Elderly Women was proposed according to the trial test result.

• The Research Journal of the Costume Culture
• /
• v.15 no.5
• /
• pp.737-748
• /
• 2007

Korea is now entering into aging society next to Japan in Asia, which is considered as unusual in the semi-developed countries. More than 50 year-old consumer market is anticipated to grow to 28.7% of the total in 2010 from 20.4% in 2000. In particular, the silver market is estimated to be formed in full range in 2010 when the generation born in 50s and 60s after the Korean War start to retire. In this study, body shapes are classified as standard, obese, and tiny according to the previous studies based on the body shape characteristics and the body measurement of the Elderly Women. Based on the classification, we developed pattern of the panty for the obese Elderly Women to provide basic materials for the quality enhancement of the clothing of the increasing Elderly Women. The followings are the result of the study 1. To categorize the body shapes of the Elderly Women focusing on the lower half, we grouped the target subjects using the nested approach by the average standard deviation and the factor analysis minimal diffusion method. Accordingly, type 1 and 2 had 36 members respectively and type 3 had 43 members. In this study, two Elderly Women subjects with standard body shape falling under the type 1 were selected as the subjects. 2. In the second trial evaluation for the panty pattern for the Elderly Women 32 items for appearance test and 3 items for functional test were evaluated. The scores in leg, sideline and hip were shown high and the balance between the parts was maintained very well. In the functional test, the panty used to be too tight for the leg curve but in the second trial it was improved, too. In each item, the second trial test showed better score than the first trial test. Conclusively, the most optimal panty prototype for the Elderly Women was proposed according to the trial test result.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.5
• /
• pp.423-432
• /
• 2018

Surface water mapping has been widely used in various remote sensing applications. Water indices have been commonly used to distinguish water bodies from land; however, determining the optimal threshold and discriminating water bodies from similar objects such as shadows and snow is difficult. Deep learning algorithms have greatly advanced image segmentation and classification. In particular, FCN (Fully Convolutional Network) is state-of-the-art in per-pixel image segmentation and are used in most benchmarks such as PASCAL VOC2012 and Microsoft COCO (Common Objects in Context). However, these data sets are designed for daily scenarios and a few studies have conducted on applications of FCN using large scale remotely sensed data set. This paper aims to fine-tune the pre-trained FCN network using the CRMS (Coastwide Reference Monitoring System) data set for surface water mapping. The CRMS provides color infrared aerial photos and ground truth maps for the monitoring and restoration of wetlands in Louisiana, USA. To effectively learn the characteristics of surface water, we used pre-trained the DeepWaterMap network, which classifies water, land, snow, ice, clouds, and shadows using Landsat satellite images. Furthermore, the DeepWaterMap network was fine-tuned for the CRMS data set using two classes: water and land. The fine-tuned network finally classifies surface water without any additional learning process. The experimental results show that the proposed method enables high-quality surface mapping from CRMS data set and show the suitability of pre-trained FCN networks using remote sensing data for surface water mapping.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.5
• /
• pp.1921-1929
• /
• 2015

The smart substation is the heart of a transmission system, which is particularly emphasized as the most significant composition of smart grids in China. In order to assess the functionality performance of substation technologies, this paper presents methods used to identify the most promising solutions for smart substation design and to evaluate the technical levels of available technologies. The multi-index optimization model is presented to address the issue of smart substation planning. A mathematical model of the planning decision problem is established with multiple objectives consisting of economic, reliability, and green key indices, and many kinds of concerns including physical and environmentally friendly operations are formulated as a set of constraints. With respect to the assessment of the technical level regarding integration of advanced technologies into a substation, a modified grey whitenization weight function is adopted to structure a novel grey clustering method. The proposed grey clustering approach is used to overcome the difficulty of insufficient quantitative assessment capacity for traditional methods. The evaluation of technical effects provides the classification definition for the development phase and the maturity level of the smart substation. The effectiveness of the proposed approaches in planning decision-making and evaluation of construction efforts is demonstrated with case studies involving the actual smart substation projects of Wenchongkou substation in China Southern Power Grid (CSG) and Mengzi substation in State Grid Corporation of China (SGCC).