• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.47-53
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• 2022

Recently, with the change of the intelligent security paradigm, study to apply various information generated from various information security systems to AI-based anomaly detection is increasing. Therefore, in this study, in order to convert log-like time series data into a vector, which is a numerical feature, the CBOW and Skip-gram inference methods of deep learning-based Word2Vec model and statistical method based on the coincidence frequency were used to transform the published ADFA system call data. In relation to this, an experiment was carried out through conversion into various embedding vectors considering the dimension of vector, the length of sequence, and the window size. In addition, the performance of the embedding methods used as well as the detection performance were compared and evaluated through GRU-based anomaly detection model using vectors generated by the embedding model as an input. Compared to the statistical model, it was confirmed that the Skip-gram maintains more stable performance without biasing a specific window size or sequence length, and is more effective in making each event of sequence data into an embedding vector.

• Information Systems Review
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• v.22 no.3
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• pp.81-97
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• 2020

Recent trends in sentiment analysis have been focused on applying single label classification approaches. However, when considering the fact that a review comment by one person is usually composed of several topics or aspects, it would be better to classify sentiments for those aspects respectively. This paper has two purposes. First, based on the fact that there are various aspects in one sentence, aspect mining is performed to classify the emotions by each aspect. Second, we apply the multiple label classification method to analyze two or more dependent variables (output values) at once. To prove our proposed approach's validity, online review comments about musical performances were garnered from domestic online platform, and the multi-label classification approach was applied to the dataset. Results were promising, and potentials of our proposed approach were discussed.

• Korean Journal of Child Studies
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• v.38 no.1
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• pp.185-203
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• 2017

Objective: The present study examined the perspectives of grandparents raising their grandchildren in an attempt to better understand grandparents' child-rearing experience while providing kinship foster care to their primary-grade grandchildren. Methods: Data were collected through individual in-depth interviews with eight grandparents who have raised one or two primary-grade (ages 8-10) grandchildren using a qualitative approach. Results: First, the participants viewed the reason for their kinship foster care as a failure for caring for their own children and accepted the present grandparent-care provision as their responsibility. Second, the participants communicated constant struggles with their own health and grandchild-care as well as positive/negative emotions associated with the care provision. Third, most of the participants did not fully understand the developmental needs of their primary-grade grandchildren. Fourth, the participants articulated concerns for their primary-grade grandchildren's learning, peer interactions, school adjustment, and extra-curricular activities. Lastly, the participants all agreed on hoping to raise grandchildren with good personality traits as members of a society and to have them fill the gap from the loss of their parents. Conclusion: Although most participants accepted the current circumstances as their obligation, they still noted difficulties in child-care provision. Given the developmental needs of grandchildren and the resource needs of grandparents, proper and continuous intervention approaches should be developed/provided.

• KIISE Transactions on Computing Practices
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• v.23 no.12
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• pp.698-702
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• 2017

Since the introduction of Deep Neural Networks to the Natural Language Processing field, two major approaches have been considered for modeling text. One method involved learning embeddings, i.e. the distributed representations containing abstract semantics of words or sentences, with the textual context. The other strategy consisted of composing the embeddings trained by the above to get embeddings of longer texts. However, most studies of the composition methods just adopt word embeddings without consideration of the optimal embedding unit and the optimal method of composition. In this paper, we conducted experiments to analyze the optimal embedding unit and the optimal composition method for modeling longer texts, such as documents. In addition, we suggest a new discourse-based composition to overcome the limitation of the sequential composition method on composing sentence embeddings.

• Journal of the Korea Convergence Society
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• v.6 no.3
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• pp.79-86
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• 2015

Recently, the tendency in the education system is toward the convergent curriculum to developing people of interdisciplinary abilities. This study was conducted to develop a clinical competence assessment program that assists health department students' clinical practice and to examine its learning effects. Study samples were composed 94 graduating student nurses who were from nursing dept of on M city. This study employed a one-group pre-post test design. knowledge, clinical competence and professional images were significantly higher in post test group. That was enhanced by clinical competence assessment program than that of the pretest group (p<.001). The results indicate that it will help students in clinical adaptability of the department of healthcare-Affiliated. Further study will be needed to identify the effect of a clinical competence and communication skills.

• Annals of Clinical Neurophysiology
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• v.1 no.1
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• pp.1-9
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• 1999

Since Hans Berger reported the first paper on the human electroencephalogram in 1920s, huge technological advance have made it possible to use a number of electrophysiological approaches to neurological diagnosis in clinical neurology. In majority of the neurology training hospitals they have facilities of electroencephalography(EEG), electromyography(EMG), evoked potentials(EP), polysomnography(PSG), electronystagmography(ENG) and, transcranial doppler(TCD) ete. Clinicials and electrophysiologists should understand the technologic characteristics and general applications of each electrophysiological studies to get useful informations with using them in clinics. It is generally agreed that items of these tests are selected under the clinical examination, the tests are performed by the experts, and the test results are interpretated under the clinical background. Otherwise these tests are sometimes useless and lead clinicians to misunderstand the lesion site, the nature of disease, or the disease course. In this sense the clinical utility of neurophysiological tests could be summerized in the followings. First, the abnormal functioning of the nervous system and its environments can be demonstrated when the history and neurological examinations are equivocal. Second, the presence of clinically unsuspected malfunction in the nervous system can be revealed by those tests. Finally the objective changes can be monitored over time in the patient's status. Also intraoperative monitoring technique becomes one of the important procedures when the major operations in the posterior fossa or in the spinal cord are performed. In 1996, the Korean Society for Clinical Neurophysiology(KSCN) was founded with the hope that it will provide the members with the comfortable place for discussing their clinical and academic experience, exchanging new informations, and learning new techniques of the neurophysiological tests. The KSCN could collaborate with the International Federation of Clinical Neurophysiology(IFCN) to improve the level of the clinical neurophysiologic field in Korea as will as in Asian region.1 In this paper the clinical neurophysiological tests which are commonly used in clinical neurology and which will be delt with and educated by the KSCN in the future will be discussed briefly in order of EEG, EMG, EP, PSG, TCD, ENG, and Intraoperative monitoring.

• Journal of Korean Neurosurgical Society
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• v.42 no.4
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• pp.293-299
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• 2007

Objective : Thoracoscopic spinal surgery provides minimally invasive approaches for effective vertebral decompression and reconstruction of the thoracic and thoracolumbar spine, while surgery related morbidity can be significantly lowered. This study analyzes clinical results of thoracoscopic spinal surgery performed at our institute. Methods : Twenty consecutive patients underwent video-assisted thoracosopic surgery (VATS) to treat various thoracic and thoracolumbar pathologies from April 2000 to July 2006. The lesions consisted of spinal trauma (13 cases), thoracic disc herniation (4 cases), tuberculous spondylitis (1 case), post-operative thoracolumbar kyphosis (1 case) and thoracic tumor (1 case). The level of operation included upper thoracic lesions (3 cases), midthoracic lesions (6 cases) and thoracolumbar lesions (11 cases). We classified the procedure into three groups: stand-alone thoracoscopic discectomy (3 cases), thoracoscopic fusion (11 cases) and video assisted mini-thoracotomy (6 cases). Results : Analysis on the Frankel performance scale in spinal trauma patients (13 cases), showed a total of 7 patients who had neurological impairment preoperatively : Grade D (2 cases), Grade C (2 cases), Grade B (1 case), and Grade A (2 cases). Four patients were neurologically improved postoperatively, two patients were improved from C to E, one improved from grade D to E and one improved from grade B to grade D. The preoperative Cobb's and kyphotic angle were measured in spinal trauma patients and were $18.9{\pm}4.4^{\circ}$ and $18.8{\pm}4.6^{\circ}$, respectively. Postoperatively, the angles showed statistically significant improvement, $15.1{\pm}3.7^{\circ}$ and $11.3{\pm}2.4^{\circ}$, respectively(P<0.001). Conclusion : Although VATS requires a steep learning curve, it is an effective and minimally invasive procedure which provides biomechanical stability in terms of anterior column decompression and reconstruction for anterior load bearing, and preservation of intercostal muscles and diaphragm.

• The Journal of the Acoustical Society of Korea
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• v.37 no.1
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• pp.60-65
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• 2018

In this paper, a covariance matrix and modulogram are proposed for realizing amphibian sound classification using CNN (Convolutional Neural Network). First of all, a database is established by collecting amphibians sounds including endangered species in natural environment. In order to apply the database to CNN, it is necessary to standardize acoustic signals with different lengths. To standardize the acoustic signals, covariance matrix that gives distribution information and modulogram that contains the information about change over time are extracted and used as input to CNN. The experiment is conducted by varying the number of a convolutional layer and a fully-connected layer. For performance assessment, several conventional methods are considered representing various feature extraction and classification approaches. From the results, it is confirmed that convolutional layer has a greater impact on performance than the fully-connected layer. Also, the performance based on CNN shows attaining the highest recognition rate with 99.07 % among the considered methods.

• Safety and Health at Work
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• v.11 no.2
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• pp.127-135
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• 2020

A safe work environment is crucial in high-risk industries, such as construction refurbishment. Safety incidents caused by uncertainty and unexpected events in construction refurbishment systems are difficult to control using conventional safety management techniques. Resilience engineering (RE) is proposed as an alternative to traditional safety management approaches. It presents a successful safety management methodology designed to deal with uncertainty in high-risk work environments. Despite the fact that RE resides in the safety domain, there is no common set of RE indicators to measure and assess resilient in the work environment. The main aim of this research is to explore RE indicators that have been identified as important in developing and assessing the resilient work environment in high-risk industries, particularly in construction refurbishment. Indicators have been attained through a systematic literature review of research and scholarly articles published between the years 2004 and 2019. The literature review explored RE indicators in various industries. Descriptive analysis and co-occurrence-based network visualization were used for data analysis. The findings revealed 28 RE indicators in 11 different high-risk industries. The results show that the four commonly used indicators were: top-management commitment, awareness, learning, and flexibility, all of which have a strong relationship with RE. The findings of this study are useful for stakeholders when making decisions concerning the most important RE indicators in the context of their research or practice as this would avoid the ambiguity and disparity in the identification of RE indicators.

• Proceedings of the IEEK Conference
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• summer
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• pp.410-415
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• 2004

Employing knowledge of adaptive possibilities of agents in multi-agents system, we have explored new aspects of distributed modular systems for building ubiquitous heterogeneous robotic systems using intelligent building blocks (I-BLOCKS) [1] as reconfigurable modules. This paper describes early technological approaches related to technical design, experimental developments and evaluation of adaptive processing and information interaction among I-BLOCKS allowing users to easily develop modular robotic systems. The processing technology presented in this paper is embedded inside each $DUPLO^1$ brick by microprocessor as well as selected sensors and actuators in addition. Behaviors of an I-BLOCKS modular structure are defined by the internal processing functionality of each I-Block in such structure and communication capacities between I-BLOCKS. Users of the I-BLOCKS system can easily do 'programming by building' and thereby create specific functionalities of a modular robotic structure of intelligent artefacts without the need to learn and use traditional programming language. From investigating different effects of modern artificial intelligence, I-BLOCKS we have developed might possibly contain potential possibilities for developing modular robotic system with different types of morphology, functionality and behavior. To assess these potential I-BLOCKS possibilities, the paper presents a limited range of different experimental scenarios in which I-BLOCKS have been used to set-up reconfigurable modular robots. The paper also reports briefly about earlier experiments of I-BLOCKS created on users' natural inspiration by a just defined concept of modular artefacts.