• Journal of the Korean Society of Physical Medicine
• /
• v.12 no.2
• /
• pp.67-74
• /
• 2017

PURPOSE: The purpose of this study was to investigate the difference and relationship between sensory processing abilities, gross motor and fine motor capabilities in children with cerebral palsy. METHODS: 104 children with cerebral palsy participated in the study. Sensory processing abilities of the subjects were measured by Short Sensory Profile (SSP). Gross and fine motor abilities were each measured using the Gross Motor Function Classification System (GMFCS) and Manual Ability Classification System (MACS), respectively. RESULTS: There were significant correlations between SSP level and GMFCS (R=.72, p<.00) or MACS (R=.77, p<.00) levels. Significant differences were showed each gross motor (p=.01) and fine motor level (p=.00) among sensory processing level of children. In addition, sub-items of sensory processing as Tactile sensitivity, Movement sensitivity, Auditory filtering and Low energy/Weak were significantly were showed significant correlations gross motor and fine motor level (p=.01). Also, multiple regression result was showed that as MACS level and GMFCS level were higher, the SSP total score was higher all of participants (adjusted $R^2=.62$). CONCLUSION: Sensory processing abilities of children with cerebral palsy were related with gross motor and fine motor capabilities. Also gross motor and fine motor capabilities are as higher, the sensory processing skill was well of cerebral palsy.

• 대한공업교육학회지
• /
• v.39 no.1
• /
• pp.143-163
• /
• 2014

This study developed a National Competency Standard(NCS) regarding the casino operations management based on the manual for developing National Competency Standard of 13 years and a revised classification system chart of the National Competency Standard. For the research method, this study developed a national competency standard of relevant jobs after going through review, consultation, modification, supplementation, and reporting procedures 10 times with development experts of the National Competency Standard, the industrial setting professionals, education and training experts, qualification(job analysis) experts, facilitators, and a working group of job verification committee based on the phased range and DACUM procedure of study. The major development results of this study are as follows. First, this study selected and defined duties based on the revised classification system chart of the National Competency Standard, then drew and developed total 8 ability units based on the applicable duties. Secondly, based on the developed ability units, total 27 ability unit factors were deduced and developed. Thirdly, a standard system by ability unit factor was developed based on the level of the national competency standard and revised classification system chart, then this study deduced and developed a supra-domain of the standard system by competency units using the standard tranquility value by these competency unit factors. Based on such development contents and guidelines for the national competency standard ability unit classification number, this study deduced and developed category numbers by relevant competency unit. Fourthly, total 27 relevant performance standard by competency units and knowledge, skill, and attitude were deduced and developed. Fifthly, this study deduced and developed a scope of application, work situation, evaluation guide, core competency, and development history in reference to the total 8 relevant competency units based on the duties.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.10
• /
• pp.1010-1014
• /
• 2010

Article-related product recommender system is an emerging e-commerce service which recommends items based on association in contexts between items and articles. Current services recommend based on the similarity between tags of articles and items, which is deficient not only due to the high cost in manual tagging but also low accuracies in recommendation. As a component of novel article-related item recommender system, we propose a new method for tagging item images based on pre-defined categories. We suggest a hypernetwork-based algorithm for learning association between images, which is represented by visual words, and categories of products. Learned hypernetwork are used to assign multiple tags to unlabeled item images. We show the ability of our method with a product set of real-world online shopping-mall including 1,251 product images with 10 categories. Experimental results not only show that the proposed method has competitive tagging performance compared with other classifiers but also present that the proposed multi-tagging method based on hypernetworks improves the accuracy of tagging.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.117-127
• /
• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.41 no.4
• /
• pp.55-62
• /
• 2004

In this paper, we have developed a portable acceleration measurement system to measure acceleration signals during walking and a gait analysis algorithm which can evaluate gait regularity and symmetry and estimate gait parameters automatically. Portable acceleration measurement system consists of a biaxial accelerometer, amplifiers, lowpass filter with cut-off frequency of 16Hz, one-chip microcontroller, EEPROM and RF(TX/RX) module. The algerian includes FFT analysis, filter processing and detection of main peaks. In order to develop the algorithm, eight hemiplegic patients for training set and the other eight hemiplegic patients for test set are participated in the experiment. Acceleration signals during 10m walking were measured at 60 samples/sec from a biaxial accelerometer mounted between L3 and L4 intervertebral area. The algorithm, detected foot contacts and classified right/left steps, and then calculated gait parameters based on these informations. Compared with video data and analysis by manual, algorithm showed good performance in detection of foot contacts and classification of right/left steps in test set perfectly. In the future, with improving the reliability and ability of the algerian so that calculate more gait Parameters accurately, this system and algerian could be used to evaluate improvement of walking ability in hemiplegic patients in clinical practice.