• Journal of Soil and Groundwater Environment
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• v.18 no.2
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• pp.19-26
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• 2013

This study developed the classification of National Soil Quality Monitoring Network (NSQM) and its site selection criteria to meet the recently established purposes of the NSQM. The NSQM were suggested by this study to classify into the six-purposes site groups from the current classification of land uses. The six purposes site groups were 1) intensive observation sites, 2) contaminant loading sites, 3) human activity sites, 4) background sites, 5) river soil sites, and 6) sites near the groundwater quality monitoring wells. Furthermore, this study developed the site selection criteria of NSQM utilizing the accumulated NSQM data, road traffic data, chemical emission data, census, soil information, and the literature related to soil quality variation due to contaminant loads. For selecting suitable sites for NSQM, this study used road traffic, chemical emission, the distance from the contaminant sources, and population information as specific criteria. The suggested site classification and criteria were appled for the current 100 NSQM sites for evaluation. Forty sites were met to the criteria suggested by this study, but sixty sites were not met to the criteria. However, some of the sixty sites also included the obscure sites that their addresses were not apparent to find them.

• Journal of Korean Academy of Nursing
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• v.26 no.1
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• pp.177-193
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• 1996

For the purpose of integrating nursing diagnosis into the nursing curriculum, a descriptive survey research was done using the inductive method with questionnaires and a literature review. Research subjects included nurse educators, textbooks of adult nursing published in Korea, and the course outline for adult nursing used in one college of nursing. The Results show that there was common agreement on 39 nursing diagnosis which should be in cluded in the adult nursing curriculum, textbooks of adult nursing, and patient care on the medical-surgical units. The two existing nursing diagnosis classification systems(NANDA and Gordon's Human Response Patterns) show different basic frameworks and difficulties were discovered in integration of nursing diagnosis into the curriculum. To develop a conceptual framework for a nursing diagnosis classification system, diagnosis were classified into three categories ; health promotion, high risk problem, and actual problem on the basis of the framework used in adult nursing textbooks and Gordon's 11 Functional Health Patterns. Subconcepts for actual problems were classified as ; activity and rest, nutrition and elimination, perception and coordination, stress and coping. Progress in this study supports further development of a conceptual framework of nursing based on a nursing diagnosis classification system, from which improvement in nursing education and clinical practice can be expected.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.5
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• pp.277-286
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• 2019

In this paper, we propose an efficient hyperplane generation technique to classify human activity from combination of events and sequence information obtained from multiple-event sensors. By generating hyperplane efficiently, our machine learning algorithm classify with less memory and run time than the LSVM (Linear Support Vector Machine) for embedded system. Because the fact that light weight and high speed algorithm is one of the most critical issue in the IoT, the study can be applied to smart home to predict human activity and provide related services. Our approach is based on reducing numbers of hyperplanes and utilizing robust string comparing algorithm. The proposed method results in reduction of memory consumption compared to the conventional ML (Machine Learning) algorithms; 252 times to LSVM and 34,033 times to LSTM (Long Short-Term Memory), although accuracy is decreased slightly. Thus our method showed outstanding performance on accuracy per hyperplane; 240 times to LSVM and 30,520 times to LSTM. The binarized image is then divided into groups, where each groups are converted to binary number, in order to reduce the number of comparison done in runtime process. The binary numbers are then converted to string. The test data is evaluated by converting to string and measuring similarity between hyperplanes using Levenshtein algorithm, which is a robust dynamic string comparing algorithm. This technique reduces runtime and enables the proposed algorithm to become 27% faster than LSVM, and 90% faster than LSTM.

• Journal of the Korean Society of Safety
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• v.29 no.3
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• pp.79-84
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• 2014

Human error is one of the major contributors to the accidents. A lot of risk assessment techniques have been developed for prevention of accidents. Nevertheless, most of them were interested in physical factors, because quantitative evaluation of human errors was difficult quantitatively. According to lack of risk assessment techniques about human errors, most of industrial risk assessment for human errors were based on data of accident analysis. In order to develop an effective countermeasure to reduce the risk caused by human errors, a systematic analysis is needed. Generally, risk assessment system is composed of 5 step(classification of work activity, identification of hazards, risk estimation, evaluation and improvement). This study aimed to develop a risk identification technique for human errors that could mainly be applied to industrial fields. In this study, Ergo-HAZOP and Comprehensive Human Error Analysis Technique were used for developing the risk identification technique. In the proposed risk identification technique, Ergo-HAZOP was used for broad-brush risk identification. More critical risks were analysed by Comprehensive Human Error Analysis Technique. In order to verify applicability, the proposed risk identification technique was applied to the work of pile head cutting. As a consequence, extensive hazards were identified and fundamental countermeasures were established. It is expected that much attention would be paid to prevent accidents by human error in industrial fields since safety personnel can easily fint out hazards of human factors if utilizing the proposed risk identification technique.

• Journal of Korean Academy of Nursing
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• v.29 no.1
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• pp.72-83
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• 1999

The purpose of this study was to classify, from collected home health care records data, nursing diagnoses according to the NANDA system and nursing interventions according to the NIC system, and to link nursing interventions to nursing diagnoses. For this study, 101 home health care records of clients seen between September, 1994 and November, 1996 at Yonsei Medical Center, Seoul, were analyzed. The results of this study are summarized as follows : 1. The most frequent nursing diagnoses were ‘Risk for infection’ and ‘Altered nutrition : Less than body requirements’, then ‘Impaired skin intergrity’ and ‘Ineffective airway clearance’ in the Exchange pattern of NANDA nine human response patterns. 2. The most frequent nursing interventions were the interventions in the Physiological : Complex domain, there were 690(50.7%) interventions among a total 1347 interventions. This results corresponds to Yom, Young Hee(1995)’s research, both Korean and U.S. nurses used the interventions in the Physiological : Complex do main most often on a daily basis. And respiratory nursing interventions were most frequent because 32.7% of the subjects were respiratory patients. 3. The next step was to link the nursing interventions to nursing diagnoses. The most frequent nursing diagnosis was ‘Risk for infection’ and 19 interventions for ‘Risk for infection’ were used 267 times. Then 14 interventions for ‘Impaired skin integrity’ were used 258 times, 12 interventions for ‘Ineffrective airway clearance’ were used 193 times, 12 interventions for ‘Altered nutrition : Less than body requirements’ were used 122 times, 10 interventions for ‘Activity intolerance’ were used 75 times, and 11 interventions for ‘Knowledge deficit’ were used 52 times. 4. The use of standardized classification in the areas of nursing diagnoses and nursing interventions facilitates clinical decision making and prompt nursing activity, and so enhances the effectiveness of nursing care.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.12 no.1
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• pp.13-21
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• 2008

Objectives: To review the history, method, parameters, system of electrogastrography(EGG) and to study its oriental medical application in Traditional Chinese Medicine. Methods: We investigate the research results through internet search engine, CNKI (China National Knowledge Infrastructure). Results: In China, EGG was studied on various regions, including multifarious regions of Traditional Chinese Medical. And also regions of Traditional Chinese Medical could divide classification of diseases, herbal medicine, acupuncture and the like. Conclusions: EGG is a noninvasive, easy and painless method that is kept recordings of the electrical activity of the stomach, therefore it has used for studies and clinics widely. EGG in Traditional Chinese Medicine has used an objective parameter that verified efficacies of oriental medicine. I would therefore like to conclude that there should be continued study and active application of EGG in the wide variety of oriental medicine.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.11
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• pp.2105-2110
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• 2008

Embedding the compact wearable units to monitor the health status of a person has been analysed as a convenient solution for the home health care. This paper presents a method to detect fall from the other activities of daily living and also to classify those activities. This kind of ambulatory monitoring of the elderly and people with limited mobility can not only provide their general health status but also alarms whenever an emergency such as fall or gait has been occurred and a help is needed. A timely assistance in such a situation can reduce the loss of life. This work shows a detailed analysis of the data received from a chest worn sensor unit embedding a 3-axis accelerometer and depicts which features are important for the classification of human activities. How to arrange and reduce the features to a new feature set so that it can be classified using a simple classifier and also improving the classification resolution. Principal component analysis (PCA) has been used for modifying the feature set and afterwards for reducing the size of the same. Finally a Neural network classifier has been used to analyse the classification accuracies. The accuracy for detection of fall events was found to be 86%. The overall accuracy for the classification of Activities or daily living (ADL) and fall was around 94%.

• Transactions on Electrical and Electronic Materials
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• v.8 no.6
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• pp.293-298
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• 2007

The aged are faced with increasing risk for falls. The aged have more fragile bones than others. When falls occur, it is important to detect this emergency state because such events often lead to more serious illness or even death. A implementation of PDA system, for detection of emergency situation, was developed using 3-axis accelerometer in this paper as follows. The signals were acquired from the 3-axis accelerometer, and then transmitted to the PDA through a Bluetooth module. This system can classify human activity, and also detect an emergency state like falls. When the fall occurs, the system generates the alarm on the PDA. If a subject does not respond to the alarm, the system determines whether the current situation is an emergency state or not, and then sends some information to the emergency center in the case of an urgent situation. Three different studies were conducted on 12 experimental subjects, with results indicating a good accuracy. The first study was performed to detect the posture change of human daily activity. The second study was performed to detect the correct direction of fall. The third study was conducted to check the classification of the daily physical activity. Each test lasted at least 1 min. in the third study. The output of the acceleration signal was compared and evaluated by changing various postures after attaching a 3-axis accelerometer module on the chest. The newly developed system has some important features such as portability, convenience and low cost. One of the main advantages of this system is that it is available at home healthcare environment. Another important feature lies in its low cost of manufacture. The implemented system can detect the fall accurately, so it will be widely used in emergency situations.

• Journal of the Korea Convergence Society
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• v.11 no.5
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• pp.9-17
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• 2020

Human activity recognition(HAR) using smartphones is a hot research topic in computational intelligence. Smartphones are equipped with a variety of sensors. Fusing the data of these sensors could enable applications to recognize a large number of activities. However, these devices have fewer resources because of the limited number of sensors available, and feature selection and classification methods are required to achieve optimal performance and efficient feature extraction. This paper proposes a smartphone-based HAR scheme according to these requirements. The proposed method in this paper extracts time-domain features from acceleration sensors, gyro sensors, and barometer sensors, and recognizes activities with high accuracy by applying KDA and SVM. This approach selects the most relevant feature of each sensor for each activity. Our comparison results shows that the proposed system outperforms previous smartphone-based HAR systems.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.354-359
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• 2009

Land cover types of Hustai National Park (HNP) in Mongolia, a hotspot area with rare species, were classified and their temporal changes were evaluated using Landsat MSS TM/ETM data between 1994 and 2000. Maximum likelihood classification analysis showed an overall accuracy of 88.0% and 85.0% for the 1994 and 2000 images, respectively. Kappa coefficients associated with the classification were resulted to 0.85 for 1994 and 0.82 for 2000 image. Land cover types revealed significant temporal changes in the classification maps between 1994 and 2000. The area has increased considerably by $166.5km^2$ for mountain steppe. By contrast, agricultural areas and degraded areas affected by human being activity were decreased by $46.1km^2$ and $194.8km^2$ over the six year span, respectively. These areas were replaced by mountain steppe area. Specifically, forest area was noticeably fragmented, accompanied by the decrease of $\sim400$ ha. The forest area revealed a pattern with systematic gain and loss associated with the specific phenomenon called as forest free-south slope. We discussed the potential environmental conditions responsible for the systematic pattern and addressed other biological impacts by outbreaks of forest pests and ungulates.