• Journal of The Korea Institute of Healthcare Architecture
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• v.27 no.3
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• pp.27-37
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• 2021

Purpose: The purpose of this study is to compare the changes in hospital accreditation evaluations, the changes in hospital building design guidelines, and the development of design indicators for reducing medical accidents in the state-of-the-art healthcare providers. Methods: The changes and tools were carefully investigated and compared that had been taken place and used in the building certification standards, design guidelines, and patient safety design standards to reduce accidents in the United States and the United Kingdom. Results: First, medical accidents are recognized as multiple defense layers rather than personal ones, and a public reporting and learning system is created, reporting the accidents in question publicly and suggesting ways to improve them based on the data at a time. Second, for the accreditation institute that secures the service quality of medical institutions, detailed standards for patient safety are continuously updated with focus on clinical trials. The United States is in charge of the private sector, but on the other hand the United Kingdom is in charge of the public sector. Third, the design guidelines are provided as web-based tools that complement various guidelines for patient safety, and are improved and developed as well. Fourth, detailed approaches are continuously developed and provided to secure patient safety and reduce medical accidents through appropriate research, evidence-based design and strict evaluations. Implications: When medical institutions make efforts to strength patient safety methods through valid design standards, accidents are expected to decrease, whereby hospital finances are also to be improved. A higher level of medical quality service will sure be secured through comprehensive certification evaluation.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.3
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• pp.247-271
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• 2019

Uncertainty in scientific knowledge means an uncertain state where propositions are neither true or false at present. The existing studies have analyzed the propositions written in the academic literature, and have conducted the performance evaluation based on the rule based and machine learning based approaches by using the corpus. Although they recognized that the importance of word construction, there are insufficient attempts to expand the word by analyzing the meaning of uncertainty words. On the other hand, studies for analyzing the structure of networks by using bibliometrics and text mining techniques are widely used as methods for understanding intellectual structure and relationship in various disciplines. Therefore, in this study, semantic relations were analyzed by applying Word2Vec to existing uncertainty words. In addition, WordNet, which is an English vocabulary database and thesaurus, was applied to perform a network analysis based on hypernyms, hyponyms, and synonyms relations linked to uncertainty words. The semantic and lexical relationships of uncertainty words were structurally identified. As a result, we identified the possibility of automatically expanding uncertainty words.

• Journal of the Korea Computer Graphics Society
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• v.25 no.3
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• pp.1-9
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• 2019

The image obtained from systems such as autonomous driving cars or fire-fighting robots often suffer from several degradation such as noise, motion blur, and compression artifact due to multiple factor. It is difficult to apply image recognition to these degraded images, then the image restoration is essential. However, these systems cannot recognize what kind of degradation and thus there are difficulty restoring the images. In this paper, we propose the deep neural network, which restore natural images from images degraded in several ways such as noise, blur and JPEG compression in situations where the distortion applied to images is not recognized. We adopt the channel attention modules and skip connections in the proposed method, which makes the network focus on valuable information to image restoration. The proposed method is simpler to train than other methods, and experimental results show that the proposed method outperforms existing state-of-the-art methods.

• Journal of Convergence for Information Technology
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• v.9 no.7
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• pp.100-105
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• 2019

As IoT Technology develops and Era of Hyperconnectivity comes, various kinds of customized services became available. As a next-generation display, a smart mirror accesses multimedia devices and provides various services, so it can serve as a social learning tool for the children and the old ones, as well as adults who need information. Smart Mirror must be able to identify users for individualized services. However, since the Smart Mirror is an easily accessible device, there is a possibility that information such as an individual's pattern and habit stored in the smart mirror may be exposed to the outside. Also, the other possibility of leakage of personal location information is through personal schedule or appointment stored in the smart mirror, and another possibility that privacy can be violated is through checking the health state via personal photographs. In this research, we propose a system that identify users by the information the users registered about their physique just like their face, one that provides individually customized service to users after identifying them, and one which provides minimal information and service for unauthenticated users.

• Journal of The Korean Association of Information Education
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• v.23 no.4
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• pp.303-314
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• 2019

Since 2019, the informatics education is being conducted for elementary school 5th, 6th grade students through the curriculum revised 2015. But, informatics education is implemented from the lower grades of elementary school in many countries. The purpose of this study was to suggest the direction in the choice of programming language considering characteristics for lower grades student. In order to achieve the goal, evaluation criteria were developed considering the development characteristics of lower grades and necessary elements of educational programming language. The results of analyzing the usability of the two languages based on the criterion are as follows. First, Viscuit can be used to consider the expressive power of students with lower school age or to learn algorithms without learning about programming concepts. Second, ScratchJr is easy to learn the concept of algorithm and programming. This study is meaningful in that has presented implications considering the developmental state of the students in preparation for rhe programming education.

• Journal of IKEEE
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• v.23 no.3
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• pp.883-892
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• 2019

This study is the method which is adapted to control ankle joint movement for resolving the problem of gait imbalance in intervals where gait environments are changed and slope walking, as applying terrain-adaptive technique to intelligent above-knee prosthesis. In this development of above-knee prosthesis, to classify the gait modes is essential. For distinguishing the stance phases and the swing phase depending on roads, a machine learning which combines decision tree and random forest from knee angle data and inertial sensor data, is proposed and adapted. By using this method, the ankle movement state of the prosthesis is controlled. This study verifies whether the problem is resolved through butterfly diagram.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2304-2320
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• 2021

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.

• 한국사회정책
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• v.19 no.3
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• pp.49-83
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• 2012

Currently in Germany, there is talk of 'state of emergency care', which is addressed to the problems of quality assurance in aged care and shortage of aged care workers. In order to solve this problem in the aging German society, the federal government has set itself the goal of providing high qualified care givers through a systematic and on a high level of professional training opportunities. Various projects and measures have been carried out to improve the reputation of the primarily care profession in society and for the purpose of attracting especially young trainees for the aged care professions. The present work considers training and qualifications in the long-term care sector in Germany; it points out both the characteristics of aged care education and the learning content in the aged care education and attempts to highlight what roles and perception of tasks contained therein.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.545-552
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• 2012

The rapid development of information technology is making large changes in our lives today. Also the infrastructure and services are combinding with information technology which predicts another huge change in our environment. However, the development of information technology brings various types of side effects and these side effects not only cause financial loss but also can develop into a nationwide crisis. Therefore, the detection and quick reaction towards these side effects is critical and much research is being done. Intrusion detection systems can be an example of such research. However, intrusion detection systems mostly tend to focus on judging whether particular traffic or files are malicious or not. Also it is difficult for intrusion detection systems to detect newly developed malicious codes. Therefore, this paper proposes a method which determines whether the present network model is normal or abnormal by comparing it with past network situations.

• The Korean Journal of Applied Statistics
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• v.34 no.1
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• pp.99-114
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• 2021

With the Fourth Industrial Revolution based on new technology, the semiconductor manufacturing industry researches various analysis methods such as detecting process abnormalities and predicting yield based on equipment sensor data generated in the manufacturing process. The semiconductor manufacturing process consists of hundreds of processes and thousands of measurement processes associated with them, each of which has properties that cannot be defined by chemical or physical equations. In the individual measurement process, the actual measurement ratio does not exceed 0.1% to 5% of the target product, and it cannot be kept constant for each measurement point. For this reason, efforts are being made to determine whether to manage by using equipment sensor data that can indirectly determine the normal state of each step of the process. In this study, the Functional Data Analysis (FDA) was proposed to define a process abnormality detection process based on equipment sensor data and compensate for the disadvantages of the currently applied statistics-based diagnosis method. Anomaly detection accuracy was compared using machine learning on actual field case data, and its effectiveness was verified.